Performance Evaluation of AES Algorithm on Various Development Platforms
计量经济学 伍德里奇 第一章
The main challenge of an impact evaluation is the construction of a suitable counterfactual situation.
An ideal experiment can be conducted to obtain the causal effect of fertilizer amount on yield when the levels of fertilizer are assigned to plots independently of other plot features that affect yield.
.12
.1
.08
unemployment rate
.06
.04
1976 1980
1985
1990
1995
2000
2005
Note: Shaded areas are times of recession following the definition of Elsby et al. (2009).
2010
Dandan Zhang (NSD)
Sep.-Dec. 2014 1 / 37
1. Introduction
Course Structure
1. Introduction (We4 Chapter 1) 2. Mathematical Foundations,Probability Theory (We4 Appendix B & C) 3. The Bivariate Linear Regression Model (We4 Chapter 2) 4. The Multivariate Linear Regression Model (We4 Chapter 3) 5. Inference (We4 Chapter 4) 6. Further Issues (We4 Chapter 6) 7. Multiple Regression Analysis with Qualitative Information (We4 Chapter 7) 8. Heteroscedasticity (We4 Chapter 8) 9. Specification and Data Issues (We4 Chapter 9) 10. Instrument variables (We4 Chapter 15) 11. Panel Data (We4 Chapter 14)
指南:通过校准提高总设备效果(OEE)说明书
Guide to improvingOverall EquipmentEffectiveness (OEE)through calibrationApplication NoteCalibration is an indispensable tool for makingmeasureable improvements in process perfor-mance. An effective calibration program helpsreduce significant wastes of time spent on:•U nplanned maintenance•S crapped product•R eworkMeasuring the impact of these wastes on pro-cess performance is facilitated using OEE (OverallEquipment Effectiveness).OEE is a “best practice” metric for evaluating the efficiency of a manufacturing process. It is often a key performance indicator in an organiza-tion implementing a lean production system. OEE identifies the most common sources of manufac-turing productivity losses and places them into one of three groups:•A vailability•P erformance•Q ualityOEE is calculated from the formulaTo maximize efficiency, the OEE value should ideally be as close to 100% as possible. Actual values range from 0% to 100%, with a bench-mark value typically around 85% that varies by industry.AvailabilityA plant represents a considerable investment, and stakeholders expect it to be managed effectively and efficiently. The amount of time equipment is available for operation is the Planned Production Time. It includes scheduled maintenance and other factors that might lead to a planned shutdown. For example, most plants schedule downtime for regular maintenance, including cali-bration of sensors and instrumentation. Planned maintenance reduces the impact of a shutdown on a business. However, even with good plan-ning, Planned Production Time can be lost when downtime occurs unexpectedly. The time left after subtracting unplanned downtime from Planned Production Time is the remaining Operating Time. The ratio of Operating Time to Planned Production Time is the OEE metric of Availability.One of the reasons for lost production time is unplanned maintenance. This can occur whena sensor or transmitter drifts out of specifica-tion during a production run. The problem may be detected by a Supervisory Control and Data Acquisition (SCADA) system alarm or further down the line after considerable troubleshooting, whenquality defects are observed during inspection.OEE = (Availability)×(Performance)×(Quality)Critical instruments require special attentionIn a process facility, instruments are used to moni-tor and control processes, and some instruments are more important than others. In many casesa high degree of confidence in an instrument’s performance is required: for example, consider those instruments that have a direct impact on product quality and throughput, or those that help ensure the safety of personnel, customers, the community or the environment. In addition, cer-tain instruments or systems may be critical during emergency response activities. In a well-managed facility, regular maintenance at predetermined intervals ensures these instruments continually conform to their specifications, so that costly or even disastrous surprises are avoided. Calibration intervals need to be monitoredTo avoid unplanned downtime and other surprises, calibrations should occur at regularly scheduled intervals. Calibration verifies the functionality of the instrument and can reset the drift that all mea-surement equipment experiences over time. Longer intervals between calibrations are desir-able to help maximize equipment operating time. However, if an instrument is found out of tolerance at the time of calibration, the calibration interval is usually reduced (for example, a policy may require that the instrument be calibrated twice as often when found out of tolerance to mitigate the risk of future recalls due to an out-of-tolerance condition).To ensure that the period between calibra-tions is as long as possible, while also ensuringthat instruments remain in tolerance between calibrations, wise managers make certain that the equipment used to calibrate their instruments isthe best that they can get. They know that a moreaccurate calibration maximizes Operating Time.Up-timeLower quality calibrationHigher quality calibrationIncreased up-timeAccuracy improvementMaximum allowable errorOut of toleranceFigure 1. Sensors andtransmitters that driftout of specificationduring a production runcan cause unplanneddowntime, safetyissues, and losses inproduct quality.Figure 2. Minimizing error maximizes up-time. Measuring instrument error tends to increase (drift) over time. This error is corrected through calibration. Less error in the calibration means fewer surprises and a longer period of time before the next calibration is required.PerformanceWhen a process is operating, it may run slower than planned. For example, some operators may not be efficient, accidents may happen, or the equipment may be worn or poorly maintained. These factors combine to slow the process down and contribute to a reduced Net Operating Time. The ratio of Net Operating Time to Operating Time is the OEE metric of Performance.The key to maximizing performance is to reduce the many short stops that make less efficient use of Operating Time.Net Operating Time is usually calculated this way: Or equivalentlyIdeal Cycle Time is the ideal amount of time it should take to produce one unit such as a piece or a volume of product. Calculating Net Operating Time this way ensures the amount produced is measured and not just how much time was spent producing.QualityQuality takes into account the products which do not meet quality standards, including pieces that might require rework. Time spent reworking or pro-ducing a rejected product is lost time. Time left after quality-related losses is the Fully Productive Time.Quality is affected when critical measurements are made by instruments that are operating outside of their designed specifications. Calibration helps to keep critical process variables within the param-eters required by the process. When calibrations are not performed properly, occur too infrequently, or if calibration standards lack the required accu-racy, then quality may be impacted. When quality problems are detected an unplanned shutdown may follow, which has an impact on Availability and further reduces OEE.ExampleHere is how we might calculate OEE for a hypo-thetical shift with the following data:AvailabilityTo calculate the Availability we need to know the Planned Production Time and the Operating Time.Operating timePerformanceThe calculation of Performance is based on a standard production cycle time, the number of units produced and the operating time calculated above.Net Operating Time = (Total Units Produced)×(Ideal Cycle Time)QualityThe Quality calculation is the ratio of Fully Pro-ductive Time to Net Operating Time, but you get the same answer if you take the ratio of GoodOEE (Overall Equipment Effectiveness) is calcu-lated by taking the product of the three metrics calculated above:Improving OEEIf the process is not as effective as it should be, then what can be done to make it more effec-tive? For example, if the benchmark metric of 85% mentioned in the beginning is achieved, then the metrics of Availability, Performance, and Quality will each probably be in the mid 90’s. The above example includes room for improvement in each of the metrics. Here are some things to consider to improve OEE through calibration:1. Schedule maintenance at a time when it will be least disruptive and ensure that calibration is part of the maintenance program, especially for critical instruments.2. Follow best practices when calibrating and use the best calibration equipment available to prevent unscheduled troubleshooting and cali-bration due to nonconforming instrumentation.3. Reduce planned downtime by carefully manag-ing calibration intervals. This can be achieved by using high-quality instruments, monitoring their performance, and following best practices to maintain them.4. Strive to maintain a 4:1 test accuracy ratio (TAR) to minimize the risk of incorrectly evaluating the tolerance status of the instru-ments being calibrated. A 4:1 TAR means that the accuracy of the calibration standard is four times better than the accuracy of the instru-ment it is calibrating. Incorrectly identifying a nonconforming instrument as “in tolerance” may lead to quality and other potential prob-lems. Incorrectly identifying the instrument as “out of tolerance” leads to increased downtime, more maintenance costs, and shorter calibra-tion intervals.5. Automate calibration with software to minimize operator time, ensure best practices and speed up the process. In some cases automation can be achieved without software. For example: a) A Fluke Calibration 1586A Super-DAQ Preci-sion Temperature Scanner can automate anddocument a temperature calibration involv-ing a bath, dry-well, or furnace.Fluke Calibration 1586A Super-DAQ Precision Temperature Scanner automates the calibration of thermocouples in a Fluke Calibration 9190A Ultra-Cool Field Metrology Well.OEE = (Availability)×(Performance)×(Quality)= 85.7 % × 83.3 % × 88.0 % = 62.8 %Fluke Calibration PO Box 9090,Everett, WA 98206 U.S.A.Fluke Europe B.V.PO Box 1186, 5602 BD Eindhoven, The NetherlandsFor more information call:In the U.S.A. (877) 355-3225 or Fax (425) 446-5116In Europe/M-East/Africa +31 (0) 40 2675 200 or Fax +31 (0) 40 2675 222 In Canada (800)-36-Fluke or Fax (905) 890-6866From other countries +1 (425) 446-5500 or Fax +1 (425) 446-5116 Web access: ©2015 Fluke Calibration. Specifications subject to change without notice. Printed in U.S.A. 12/2015 6006652a-enModification of this document is not permitted without written permission from Fluke Calibration.Fluke Calibration. Precision, performance, confidence.™b) A Fluke 754 Documenting Process Calibra-tor connected to a Fluke dry-well using the Hart Drywell Cable automates anddocuments the calibration of a temperature sensor and transmitter. DPC/Track software is required with the 754 to download the information to a PC and manage calibra-tion data.6. Reduce planned downtime by carefully manag-ing calibration intervals. This can be achieved by using high-quality instruments, monitoring their performance, and following best practices to maintain them.7. Reduce rework during production with a prop-erly tuned control system that produces product conforming to its design specifications.ConclusionCalibration is an important part of improving the Overall Equipment Effectiveness of processes that use instrumentation to control the quality of both the process and product. An effective calibration program will help reduce three significant wastes of time spent on:•unplanned maintenance •s crap •r eworkSuch a calibration program will use the best calibration equipment available, and follow best calibration practices including automation where possible. This will ensure that critical measure-ment equipment is not the cause of an unplanned shutdown or quality issue.Fluke 754 Documenting Process Calibrator calibrating a temperature sensor and transmitter with the help of a Fluke Calibration 9142 Field Metrology Well.。
纯化水系统性能确认方案中英文对照
XXX LTD.标题:WS-01纯化水系统性能确认方案TITLE: PROTOCAL FOR PERFORMANCE QUALIFICATION OF PURIFIEDWATERGENERATION,STORAGE ANDDISTRIBUTION SYSTEM 011.0 目的PURPOSE:方案编码Protocol No.:生效日期Effective Date: 页码:1 / 17此验证方案旨在为原料药二车间和制剂〔II〕车间的纯化水系统供给性能确认程序。
The purpose of this protocol is to provide the procedure for the performance qualification of Purified water generation ,storage and distribution system for workshop 2 and workshop 10 as described in the change control.证明纯化水制水系统,存储系统和输送系统能够连续稳定的供给符合标准要求的纯化水并确定它的牢靠性,同时供给证明文件。
To provide documented evidence that the Purified water generation system, Storage and Distribution System is capable to continuously supply the Purified Water with the specified quality attributes in consistent manner and thereby establishing its dependability.在如期完成纯化水系统WS-01 的安装确认和运行确认后,供给纯化水系统存储系统和输送系统性能确认的原理机制。
斯伦贝谢-测井岩性识别技术与应用(1)共32页
地层对比
对比深度以补心海 拔深度对齐。第一 道为ECS 计算的铁 元素的含量;第二 道为ECS 计算的钙 元素的含量;第三 道为ECS 计算的岩 性剖面。图中可以 明显看出,白垩系 与侏罗系以一套砂 岩、泥质砂岩为界 ,在钙曲线上表现 为上高下低,是一 个明显的界面。头 屯河组和西山窑组 的界面在铁曲线上 表现为上低下高, 在钙曲线上表现为 上高下低,特征非 常明显,头屯河组 以砂岩、泥质砂岩 结束。
采集NPLC-B
伽马谱
Maximum Tool Dia
3-3/8 in.
Pressure, Temperature
20 kpsi, 175 oC
剥谱处T理ool length, Weight
元素产额 8 ft, 128 lb
Power
50 W
闭合氧环分析
干元素比重
Si, Ca, Fe, S, Ti, Gd
沉积分析
铁元素的变化与沉积的关系
沉积岩中铁的来源主要为母岩的风化、剥蚀产物,其主要以胶体溶液 搬运,在化学和生物化学作用下沉积下来。湖泊是其较重要的沉积场所, 尤其是湖岸沼泽地带更为富集。我国“沼铁矿”常与煤系地层共生。选择 每口井各层系泥岩段铁值的变化做交会图 。
为什么选泥岩段? 1、微量元素含量高。 2、泥岩中的元素是母岩化学风化的产物选择性沉积的结果,所以, 可以利用元素的特征推测沉积环境。 3、砂岩元素的组成主要反映岩石的岩屑、矿物的成分,一定程度上 可反映母岩的性质和搬运距离,而不反映沉积环境对元素聚散的影响。
岩性识别
碳酸盐岩
岩心分析数据表明: XX13~XX20米层段碳 酸盐岩含量最高达75% ;粘土类型以伊蒙间层 为主,个别段含有少量 高岭石和绿泥石。
材料分析测试方法-13-2(AES)
《材料分析测试方法》
3. 俄歇电子的产额:
俄歇电子的产额相当于俄歇跃迁的几率,与俄歇 谱峰的强度相对应,是元素定量分析的依据。
每个K电子空穴的产额
俄歇电子产额
特征X射线产额
原子序数
《材料分析测试方法》
俄歇电子的产额:
在低原子序数元素中,俄歇过程占主导,而且 变化不大。 对于高原子序数元素,X射线发射则成为优先 过程。
经验公式:
E
Z
1 Z1 Z Z 1 Z E E E ( E E E E ) 2
Z Z Z
(电子束缚能之差)
(修正项)
《材料分析测试方法》
例:计算Ni的KL1L2俄歇电子能量
1 Cu Ni Ni E E E E (E L2 E L2 E Cu E L1 ) L1 2 已知:
《材料分析测试方法》
主要俄歇电子能量图
《材料分析测试方法》
4.AES定量分析
依据:微分谱峰上峰-峰值
方法:纯元素标样法
相对灵敏度因子法
《材料分析测试方法》
定量分析——纯元素标样法
在相同条件下,测量i元素的俄歇峰强度 I i, WXY , S 及标样的同一俄歇峰强度 I i, WXY 。
(所取WXY俄歇峰一般为主峰) 则试样中i元素的浓度Ci为: C i
此方法不需要纯元素标样,精度低,实用性强。
《材料分析测试方法》
定量分析——举例
在304不锈钢断口表面的微分谱(Ep=3keV)。
《材料分析测试方法》
表面元素含量计算:
IFe,703= 10.1 ICr,529= 4.29 SNi,848= 0.27
ISA标准目录美国仪器系统和自动化协会
ISA标准目录美国仪器、系统和自动化协会– US Instruments, systems associationISA Standards listISA 5.1 2009.09.08 Instrumentation Symbols and IdentificationISA 5.2 1976.01.01 Binary Logic Diagrams for Process Operations - Formerly ANSI/ISA 5.2-1976 (R1992)ISA 5.3 1983.01.01 Graphic Symbols for Distributed Control/Shared Display Instrumentation, Logic and Computer Systems - Formerly ISA - S5.3 - 1983ISA 5.4 1991.01.01 Instrument Loop Diagrams - Formerly ANSI/ISA 5.4-1991ISA 5.5 1985.01.01 Graphic Symbols for Process Displays - Formerly ISA S5.5 - 1985ISA 5.06.01 2007.10.29 Functional Requirements Documentation for Control Software ApplicationsISA 7.0.01 1996.01.01 Quality Standard for Instrument Air - Formerly ANSI/ISA S7.0.01-1996ISA 12.00.02 2009.05.01 Certificate Standard for AEx Equipment for Hazardous (Classified) LocationsISA 12.01.01 2009.03.27 Definitions and Information Pertaining to Electrical Equipment in Hazardous (Classified) LocationsISA 12.02.04 2006.01.01 Fieldbus Intrinsically Safe Concept (FISCO) and Fieldbus Non-Incendive Concept (FNICO)ISA 12.04.01 2004.01.01 Electrical Apparatus for Explosive Gas Atmospheres 鈥?Part 2 Pressurized Enclosures "p" - IEC 60079-2 MODISA 12.10 1988.01.01 Area Classification in Hazardous (Classified) Dust Locations - Formerly ISA - S12.10-1988ISA 12.10.03 2006.01.01 Electrical Apparatus for Use in Zone 21 and Zone 22 Hazardous (Classified) Locations - Protection by Enclosures "tD"ISA 12.10.05 2004.01.01 Electrical Apparatus for Use in Zone 20, Zone 21 and Zone 22 Hazardous (Classified) Locations - Classification of Zone 20, Zone 21 and Zone 22 Hazardous (Classified) Locations - IEC 61241-10 ModISA 12.10.06 2006.01.01 Electrical Apparatus for Use in Zone 21 and Zone 22 Hazardous (Classified) Locations - Protection by Pressurization "pD"ISA 12.10.07 2006.01.01 Electrical Apparatus for Use in Zone 20, Zone 21 and Zone 22 Hazardous (Classified) Locations - Protection by Encapsulation "mD"ISA 12.12.01 2007.04.12 Nonincendive Electrical Equipment for Use in Class I and II, Division 2 and Class III, Divisions 1 and 2 Hazardous (Classified) LocationsISA 12.13.01 2003.01.01 Performance Requirements for Combustible Gas Detectors - IEC 61779-1 through 5 ModISA 12.13.04 2007.03.07 Performance Requirements for Open Path Combustible Gas DetectorsISA 12.20.01 2009.05.04 General Requirements for Electrical Ignition Systems for Internal Combustion Engines in Class I, Division 2 or Zone 2, Hazardous (Classified) LocationsISA 12.27.01 2003.01.01 Requirements for Process Sealing Between Electrical Systems and Flammable or Combustible Process FluidsISA 18.1 1979.01.01 Annunciator Sequences and Specifications - Formerly ANSI/ISA - S18.1-1979ISA 18.2 2009.06.23 Management of Alarm Systems for the Process IndustriesISA 20 1981.01.01 Specification Forms for Process Measurement and Control Instruments, Primary Elements and Control Valves - Formerly ISA - S20-1981ISA 37.1 1975.01.01 Electrical Transducer Nomenclature and Terminology - Formerly ANSI MC 6.1-1975; Formerly ISA - S37.1-1975 (R1982)ISA 37.3 1982.01.01 Specifications and Tests for Strain Gage Pressure Transducers - Formerly ISA - S37.3-1982 (R1995)ISA 37.5 1982.01.01 Specifications and Tests for Strain Gage Linear Accelerator Transducers - Formerly ISA - S37.5-1982 (R1995)ISA 37.6 1982.01.01 Specifications and Tests for Potentiometric Pressure Transducers - Formerly ISA - S37.6-1982 (R1995)ISA 37.8 1982.01.01 Specifications and Tests for Strain Gage Force Transducers - Formerly ISA - S37.8-1982 (R1995)ISA 37.10 1982.01.01 Specifications and Tests for Piezoelectric Pressure and Sound Pressure Transducers - Formerly ISA - S37.10-1982 (R1995)ISA 37.12 1982.01.01 Specifications and Tests for Potentiometric Displacement Transducers - Formerly ISA - S37.12-1982 (R1995)ISA 37.16.01 2002.11.21 A Guide for the Dynamic Calibration of Pressure TransducersISA 50.00.01 1975.01.01 Compatibility of Analog Signals for Electronic Industrial Process Instruments - Formerly ANSI/ISA 50.1-1982 (R1992); Formerly ANSI/ISA鈥?0.1鈥?975 (R1992) Per ANSI had to do back to 1975 doc.ISA 51.1 1979.01.01 Process Instrumentation Terminology - Formerly ANSI/ISA S51.1 - 1979 (R1993)ISA 67.01.01 2002.09.16 Transducer and Transmitter Installation for Nuclear Safety ApplicationsISA 67.02.01 1999.11.15 Nuclear Safety-Related Instrument-Sensing Line Piping and Tubing Standard for Use in Nuclear Power Plants - Formerly ANSI/ISA - 67.02.01 - 1999ISA 67.03 1982.01.01 Light Water Reactor Coolant Pressure Boundary Leak Detection - Formerly ISA S67.03 - 1982ISA 67.04.01 2006.05.16 Setpoints for Nuclear Safety-Related InstrumentationISA 67.06.01 2002.01.01 Performance Monitoring for Nuclear Safety-Related Instrument Channels in Nuclear Power PlantsISA 67.14.01 2000.02.15 Qualifications and Certification of Instrumentation and Control Technicians in Nuclear Facilities - Formerly ANSI/ISA - S67.14.01 - 2000ISA 71.01 1985.01.01 Environmental Conditions for Process Measurement and Control Systems: Temperature and Humidity - Formerly ISA S71.01 - 1985ISA 71.02 1991.06.01 Environmental Conditions for Process Measurement and Control Systems: Power - Formerly ISA S71.02 - 1991ISA 71.03 1995.01.12 Environmental Conditions for Process Measurement and Control Systems: Mechanical Influences - Formerly ANSI/ISA S71.03 - 1995ISA 71.04 1985.01.01 Environmental Conditions for Process Measurement and Control Systems: Airborne Contaminants - Formerly ISA - S71.04 - 1985ISA 75.01.01 2007.11.07 Flow Equations for Sizing Control ValvesISA 75.02.01 2008.01.01 Control Valve Capacity Test ProceduresISA 75.03 1992.01.01 Face-to-Face Dimensions for Integral Flanged Globe-Style Control Valve Bodies (ANSI Classes 125, 150, 250, 300, and 600) - Formerly ISA S75.03 - 1992; Formerly ISA S4.0.01ISA 75.05.01 2000.01.01 Control Valve Terminology - Replaces 75.05-1983ISA 75.07 1997.08.31 Laboratory Measurement of Aerodynamic Noise Generated by Control Valves - Formerly ISA - S75.07 - 1997ISA 75.08 1999.08.31 Installed Face-To-Face Dimensions for Flanged Clamp or Pinch Valves - Formerly ANSI/ISA - S75.08 - 1999ISA 75.08.01 2002.01.01 FAce-to-Face Dimensions for Integral Flanged Globe-Style Control Valve Bodies (Classes 125, 150, 250, 300, and 600)ISA 75.08.02 2003.01.01 Face-to-Face Dimensions for Flangeless Control Valves (Classes 150, 300, and 600)ISA 75.08.03 2001.01.01 Face-to-Face Dimensions for Socket Weld-End and Screwed-End Globe-Style Control Valves (Classes 150, 300, 600, 900, 1500, and 2500) ISA 75.14 1993.01.01 Face-To-Face Dimensions for Buttweld-End Globe-Style Control Valves (ANSI Class 4500)ISA 75.08.04 2001.01.01 Face-To-Face Dimensions for Buttweld-End Globe-Style Control Valves (Class 4500)ISA 75.08.05 2002.01.01 Face-to-Face Dimensions for Buttweld-End Globe-Style Control Valves (Class 150, 300, 600, 900, 1500, and 2500)ISA 75.08.06 2002.01.01 Face-to-Face Dimensions for Flanged Globe-Style Control Valve Bodies (Classes 900, 1500, and 2500) - Formerly ISA 75.16ISA 75.08.07 2001.01.01 Face-to-Face Dimensions for Separable Flanged Globe-Style Control Valves (Classes 150, 300, and 600)ISA 75.08.08 1999.08.31 Face-to-Centerline Dimensions for Flanged Globe-Style Angle Control Valve Bodies (ANSI Classes 150, 300, and 600)ISA 75.08.09 2004.01.01 Face-to-Face Dimensions for Sliding Stem Flangeless Control Valves (Classes 150, 300, and 600)ISA 75.10.01 2008.10.28 General Requirements for Clamp or Pinch ValvesISA 75.11 1985.01.01 Inherent Flow Characteristic and Rangeability of Control Valves - Formerly ISA - S75.11 - 1985 (R1997)ISA 75.11.01 1985.01.01 Inherent Flow Characteristic and Rangeability of Control Valves - Formerly ISA - S75.11 - 1985 (R1997)ISA 75.13.01 1996.01.01 Method of Evaluating the Performance of Positioners with Analog Input Signals and Pneumatic Output - Second PrintingISA 75.15 1994.01.01 Face-to-Face Dimensions for Buttweld-End Globe-Style Control Valves (ANSI Classes 150, 300, 600, 900, 1500, and 2500) - Formerly ANSI/ISAS75.15-1994ISA 75.16 1994.08.24 Face-to-Face Dimensions for Flanged Globe-Style Control Valve Bodies (ANSI Classes 900, 1500, and 2500) - Formerly ANSI/ISA S75.16 - 1994 ISA 75.17 1989.01.01 Control Valve Aerodynamic Noise Prediction - Formerly ANSI/ISA S75.17 - 1989ISA 75.19.01 2007.01.01 Hydrostatic Testing of Control ValvesISA 75.25.01 2000.01.01 Test Procedure for Control Valve Response Measurement from Step InputsISA 75.26.01 2006.01.01 Control Valve Diagnostic Data Acquisition and ReportingISA 76.00.02 2002.06.13 Modular Component Interfaces for Surface-Mount Fluid Distribution Components - Part 1: Elastomeric SealsISA 77.13.01 1999.12.15 Fossil Fuel Power Plant Steam Turbine Bypass SystemISA 77.20 1993.01.01 Fossil Fuel Power Plant Simulators - Functional RequirementsISA 77.41.01 2005.08.02 Fossil Fuel Power Plant Boiler Combustion Controls - Formerly ISA-S77.41 - 1992ISA 77.42.01 1999.01.01 Fossil Fuel Power Plant Feedwater Control System - Drum Type - Formerly ANSI/ISA-S77.42.01-1999ISA 77.43 1994.01.01 Fossil Fuel Power Plant Unit/Plant Demand Development Drum Type - Formerly ANSI/ISA S77.43 - 1994ISA 77.43.01 1994.01.01 Fossil Fuel Power Plant Unit/Plant Demand Development 鈥?Drum TypeISA 77.44.01 2007.01.01 Fossil Fuel Power Plant - Steam Temperature ControlsISA 77.44.02 2001.01.01 Fossil Fuel Power Plant Steam Temperature Control System Once-Through TypeISA 77.70 1994.01.01 Fossil Fuel Power Plant Instrument Piping InstallationISA 82.02.01 2004.07.12 Safety Requirements for Electrical Equipment for Measurement, Control, and Laboratory Use 鈥?Part 1: General Requirements Approved 12 July 2004 ANSI/ISA鈥?1010-1 (82.02.01) CSA C22.2 No. 1010.1 ANSI/UL 61010-1 AMERICAN NATIONAL STANDARD ISA The Instrumentation, Systems, and Automation Society 鈥?TM Formerly ANSI/ISA-82.02.01-1999 (IEC 61010-1 Mod) Updated with Annex DV US 22 July 2005 - Formerly ANSI/ISA-82.02.01-1999; (IEC 61010-1 Mod); Second Printing: 07/22/2005;ISA 82.02.04 1996.01.01 Safety Requirements for Electrical Equipment for Measurement, Control, and Laboratory Use - Formerly ANSI/ISA S82.02.04 - 1996; (IEC 61010-2-032); Identical to IEC 61010-2-032ISA 82.03 1988.01.01 Safety Standard for Electrical and Electronic Test, Measuring, Controlling, and Related Equipment - Formerly ISA S82.03 - 1988; Partial Revision and Redesignation of ANSI C39.5-1974ISA 84.00.01 P1 2004.09.02 Functional Safety: Safety Instrumented Systems for the Process Industry Sector - Part 1: Framework, Definitions, System, Hardware and Software Requirements - IEC 61511-1 ModISA 84.00.01 P2 2004.09.02 Functional Safety: Safety Instrumented Systems for the Process Industry Sector - Part 2: Guidelines for the Application ofANSI/ISA-84.00.01-2004 Part 1 (IEC 61511-1 Mod) - Informative - IEC 61511-2 ModISA 84.00.01 P3 2004.09.02 Functional Safety: Safety Instrumented Systems for the Process Industry Sector - Part 3: Guidance for the Determination of the Required Safety Integrity Levels - Informative - IEC 61511-3 ModISA 88.00.02 2001.02.07 Batch Control Part 2: Data Structures and Guidelines for LanguagesISA 88.00.03 2003.01.01 Batch Control Part 3: General and Site Recipe Models and RepresentationISA 88.00.04 2006.01.01 Batch Control Part 4: Batch Production RecordsISA 88.01 1995.02.28 Batch Control Part 1: Models and TerminologyISA 91.00.01 2001.01.01 Identification of Emergency Shutdown Systems and Controls That are Critical to Maintaining Safety in Process Industries - Reaffirmation and Redesignation of ANSI/ISA - S91.01 - 1995ISA 92.0.01, PART I 1998.01.01 Performance Requirements for Toxic Gas-Detection Instruments: Hydrogen Sulfide - Formerly ANSI/ISA-S92.0.01, Part 1-1998; Replaces ISA-S12.15 Part 1-1990ISA 92.02.01 PART I 1998.01.01 Performance Requirements for Carbon Monoxide Detection Instruments (50-1000 ppm Full Scale)ISA 92.03.01 1998.01.01 Performance Requirements for Ammonia Detection Instruments (25-500 ppm) - Formerly ISA-S92.03.01-1998ISA 92.04.01 PART I 2007.01.01 Performance Requirements for Instruments Used to Detect Oxygen-Deficient/Oxygen-Enriched AtmospheresISA 92.06.01 1998.01.01 Performance Requirements for Chlorine Detection Instruments (0.5-30 ppm Full Scale) - Formerly ISA-S92.06.01-1998ISA 93.00.01 1999.01.01 Standard Method for the Evaluation of External Leakage of Manual and Automated On-Off Valves - Formerly ANSI/ISAS-93.00.01-1999ISA 95.00.01 2000.07.15 Enterprise-Control System Integration Part 1: Models and Terminolgy - 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SupersedesANSI/ISA-12.00.01-2002 (IEC 60079-0 Ed 3 Mod)ISA 60079-1 2009.04.10 Explosive Atmospheres - Part 1: Equipment Protection by Flameproof Enclosures 鈥渄鈥?,Active"ISA 60079-5 2009.07.24 Explosive Atmospheres 鈥?Part 5: Equipment Protection by Powder Filling 鈥渜鈥?,Active"ISA 60079-6 2009.07.24 Explosive Atmospheres 鈥?Part 6: Equipment Protection by Oil Immersion 鈥渙鈥?,Active"ISA 60079-7 2002.12.02 Electrical Apparatus for Use in Class I, Zone 1 Hazardous (Classified) Locations Type of Protection Increased Safety "e" - SupersedesANSI/ISA-12.16.01-1998; IEC 60079-7 Mod; Second Printing 07/15/2005ISA 60079-11 2002.01.01 Electrical Apparatus for Use in Class I, Zones 0, 1, & 2 Hazardous (Classified) Locations - Intrinsic Safety "i" - Supersedes ISA-12.02.01-1999; IEC 60079-11 Mod; Second Printing: 07/15/2005ISA 60079-15 2009.07.17 Electrical Apparatus for Use in Class I, Zone 2 Hazardous (Classified) Locations: Type of Protection "n"ISA 60079-18 2009.07.31 Electrical Apparatus for Use in Class I, Zone 1 Hazardous (Classified) Locations: Type of Protection - Encapsulation 鈥渕鈥?,Active"ISA 60079-26 2008.01.01 Electrical Apparatus for Use in Class I, Zone 0 Hazardous (Classified) Locations - 12.00.03ISA 60079-27 2007.01.29 Fieldbus Intrinsically Safe Concept (FISCO) and Fieldbus Non-Incendive Concept (FNICO)ISA 61010-031 2007.03.28 Safety Requirements for Electrical Equipment for Measurement, Control, and Laboratory Use 鈥?Part 031: Safety requirements for hand-held probe assemblies for electrical measurement and test - 82.02.02ISA 61241-0 (12.10.02) 2006.01.01 Electrical Apparatus for Use in Zone 20, Zone 21 and Zone 22 Hazardous (Classified) Locations - General RequirementsISA 61241-1 (12.10.03) 2006.01.01 Electrical Apparatus for Use in Zone 21 and Zone 22 Hazardous (Classified) Locations 鈭?Protection by Enclosures 鈥渢D鈥?,Active"ISA 61241-2 (12.10.06) 2006.01.01 Electrical Apparatus for Use in Zone 21 and Zone 22 Hazardous (Classified) Locations 鈥?Protection by Pressurization 鈥減D 鈥?,Active"ISA 61241-11 (12.10.04) 2006.01.01 Electrical Apparatus for Use in Zone 20, Zone 21 and Zone 22 Hazardous (Classified) Locations 鈭?Protection by Intrinsic Safety 鈥渋D鈥?,Active"ISA 61241-18 (12.10.07) 2006.06.27 Electrical Apparatus for Use in Zone 20, Zone 21 and Zone 22 Hazardous (Classified) Locations Protection by Encapsulation 鈥渕D 鈥?,Active"ISA 61804-3 2007.03.30 Function Blocks (FB) For Process Control - Part 2: Electronic Device Description Language (EDDL)ISA MC96.1 1982.08.12 Temperature Measurement ThermocouplesISA RP2.1 1978.01.01 Manometer TablesISA RP12.2.02 1996.05.15 Recommendations for the Preparation, Content, and Organization of Intrinsic Safety Control DrawingsISA RP12.4 1996.01.01 Pressurized EnclosuresISA RP12.06.01 2003.01.01 Recommended Practice for Wiring Methods for Hazardous (Classified) Locations Instrumentation Part 1: Intrinsic SafetyISA RP12.12.03 2002.05.10 Recommended Practice for Portable Electronic Products Suitable for Use in Class I and II, Division 2, Class I Zone 2 and Class III, Division 1 and 2 Hazardous (Classified) LocationsISA RP12.13.02 2003.01.01 Recommended Practice for the Installation, Operation, and Maintenance of Combustible Gas Detection Instruments - IEC 61779-6 MODISA RP31.1 1977.04.30 Specification, Installation, and Calibration of Turbine FlowmetersISA RP37.2 1982.01.01 Guide for Specifications and Tests for Piezoelectric Acceleration Transducers for Aerospace TestingISA RP42.00.01 2001.11.12 Nomenclature for Instrument Tube FittingsISA RP60.1 1990.10.05 Control Center FacilitiesISA RP60.2 1995.01.01 Control Center Design Guide and TerminologyISA RP60.3 1985.06.30 Human Engineering for Control CentersISA RP60.4 1990.06.04 Documentation for Control CentersISA RP60.6 1984.02.28 Nameplates, Labels and Tags for Control CentersISA RP60.8 1978.06.28 Electrical Guide for Control CentersISA RP60.9 1981.05.31 Piping Guide for Control CentersISA RP60.11 1991.01.01 Crating, Shipping and Handling for Control CentersISA RP67.04.02 2000.01.01 Methodologies for the Determination of Setpoints for Nuclear Safety-Related Instrumentation - Equivalent to ISA - RP67.04, Part II - 1994 ISA RP74.01 1984.03.30 Application and Installation of Continuous-Belt Weighbridge ScalesISA RP75.21 1989.01.01 Process Data Presentation for Control ValvesISA RP75.23 1995.06.02 Considerations for Evaluating Control Valve CavitationISA RP76.0.01 1998.01.01 Analyzer System Inspection and AcceptanceISA RP77.60.02 2000.07.25 Fossil Fuel Power Plant Human-Machine Interface: AlarmsISA RP77.60.05 2001.11.12 Fossil Fuel Power Plant Human Machine Interface: Task AnalysisISA RP92.0.02 PT II 1998.01.01 Installation, Operation, and Maintenance of Toxic Gas-Detection Instruments: Hydrogen Sulfide - Replaces ISA-RP12.15, Part II-1990ISA RP92.02.02 PART II 1998.01.01 Installation, Operation, and Maintenance of Carbon Monoxide Detection Instruments (50-1000 ppm Full Scale)ISA RP92.03.02 1999.01.01 Installation, Operation, and Maintenance of Ammonia Detection Instruments (25-500 ppm Full Scale)ISA RP92.04.02 PART II 1996.05.15 Installation, Operation, and Maintenance of Instruments Used to Detect Oxygen-Deficient/Oxygen-Enriched AtmospheresISA RP92.06.02 1999.01.01 Installation, Operation, and Maintenance of Chlorine Detection Instruments (0.5-30 ppm Full Scale)ISA S50.02 PART 4 1997.01.01 Fieldbus Standard for Use in Industrial Control Systems, Part 4: Data Link Protocol SpecificationISA S82.01 1994.01.01 Safety Standard for Electrical and Electronic Test, Measuring, Controlling and Related Equipment - 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Reprinted 2006ISA TR50.02, PART 9 2000.01.01 Fieldbus Standard for Use in Industrial Control Systems: User Layer Technical ReportISA TR50.02, PARTS 3&4 2000.04.15 Fieldbus Standard for Use in Industrial Control Systems, Parts 3 & 4: Technical Report for Fieldbus Data Link Layer - TutorialISA TR52.00.01 2006.01.01 Recommended Environments for Standards LaboratoriesISA TR67.04.08 1996.03.21 Setpoints for Sequenced ActionsISA TR67.04.09 2005.01.01 Graded Approaches To Setpoint DeterminationISA TR75.04.01 1998.01.01 Control Valve Position StabilityISA TR75.25.02 2000.01.01 Control Valve Response Measurement from Step InputsISA TR77.42.02 2009.04.21 Fossil Fuel Power Plant Compensated Differential Pressure Based Drum Level MeasurementISA TR77.60.04 1996.05.24 Fossil Fuel Power Plant Human-Machine Interface - Human-Machine Interface 鈥?Electronic Screen DisplaysISA TR77.81.05 1995.05.31 Standard Software Interfaces for CEMS Relative Accuracy Test Audit DataISA TR84.00.02 PART 1 2002.06.17 Safety Instrumented Functions (SIF) - Safety Integrity Level (SIL) Evaluation Techniques Part 1: IntroductionISA TR84.00.02 PART 2 2002.06.17 Safety Instrumented Functions (SIF) - Safety Integrity Level (SIL) Evaluation Techniques Part 2: Determining the SIL of a SIF via Simplified EquationsISA TR84.00.02 PART 3 2002.06.17 Safety Instrumented Functions (SIF) - Safety Integrity Level (SIL) Evaluation Techniques Part 3: Determining the SIL of a SIF via Fault Tree AnalysisISA TR84.00.02 PART 4 2002.06.17 Safety Instrumented Functions (SIF) - Safety Integrity Level (SIL) Evaluation Techniques Part 4: Determining the SIL of a SIF via Markov AnalysisISA TR84.00.02 PART 5 2002.06.17 Safety Instrumented Functions (SIF) - Safety Integrity Level (SIL) Evaluation Techniques Part 5: Determining the PFD of SIS Logic Solvers via Markov AnalysisISA TR84.00.03 2002.06.17 Guidance for Testing of Process Sector Safety Instrumented Functions (SIF) Implemented as or within Safety Instrumented Systems (SIS) ISA TR84.00.04 PART 1 2005.01.01 Guidelines for the Implementation of ANSI/ISA-84.00.01-2004 (IEC 61511 Mod)ISA TR84.00.04 PART 2 2005.01.01 Example Implementation of ANSI/ISA-84.00.01-2004 (IEC 61511 Mod)ISA TR88.00.02 2008.08.01 Machine and Unit States: An Implementation Example of ISA-88ISA TR88.0.03 1996.12.20 Possible Recipe Procedure Presentation FormatsISA TR91.00.02 2003.01.02 Criticality Classification Guideline for InstrumentationISA TR92.06.03 1999.01.01 Feasibility of Chlorine Detection Instrument TestingISA TR96.05.01 2008.05.04 Partial Stroke Testing of Automated Block ValvesISA TR98.00.02 2006.07.28 Skill Standards for Control System TechniciansISA TR99.00.01 2007.10.29 Security Technologies for Manufacturing and Control SystemsISA TR100.00.01 2006.01.01 The Automation Engineer鈥檚Guide to Wireless Technology Part 1 鈥?The Physics of Radio, a Tutorial。
Homomorphic Evaluation of the AES Circuit
Homomorphic Evaluation of the AES CircuitCraig Gentry IBM ResearchShai HaleviIBM ResearchNigel P.SmartUniversity of Bristol February16,2012AbstractWe describe a working implementation of leveled homomorphic encryption(without bootstrapping) that can evaluate the AES-128circuit.Our current implementation takes about a week to evaluate anentire AES encryption operation,using NTL(over GMP)as our underlying software platform,andrunning on a large-memory ing SIMD techniques,we can process close to100blocks ineach evaluation,yielding an amortized rate of roughly2hours per block.For this implementation we developed both AES-specific optimizations as well as several“generic”tools for FHE evaluation.These last tools include(among others)a different variant of the Brakerski-Vaikuntanathan key-switching technique that does not require reducing the norm of the ciphertext vector,and a method of implementing the Brakerski-Gentry-Vaikuntanathan modulus-switching transformationon ciphertexts in CRT representation.Keywords.AES,Fully Homomorphic Encryption,ImplementationThefirst and second authors are sponsored by DARPA under agreement number FA8750-11-C-0096.The ernment is authorized to reproduce and distribute reprints for Governmental purposes notwithstand-ing any copyright notation thereon.The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements,either expressed or implied,of DARPA or the ernment.Distribution Statement“A”(Approved for Public Release, Distribution Unlimited).The third author is sponsored by DARPA and AFRL under agreement number FA8750-11-2-0079.The same disclaimers as above apply.He is also supported by the European Commission through the ICT Programme under Contract ICT-2007-216676ECRYPT II and via an ERC Advanced Grant ERC-2010-AdG-267188-CRIPTO,by EPSRC via grant COED–EP/I03126X,and by a Royal Society Wolfson Merit Award.The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements,either expressed or implied,of the European Commission or EPSRC.Contents1Introduction1 2Background22.1Notations and Mathematical Background (2)2.2BGV-type Cryptosystems (3)2.3Computing on Packed Ciphertexts (5)3General-Purpose Optimizations63.1A New Variant of Key Switching (6)3.2Modulus Switching in Evaluation Representation (7)3.3Dynamic Noise Management (8)3.4Randomized Multiplication by Constants (8)4Homomorphic Evaluation of AES94.1Homomorphic Evaluation of the Basic Operations (9)4.2Implementing The Permutations (11)4.3Performance Details (12)References12 A More Details13A.1Plaintext Slots (14)A.2Canonical Embedding Norm (14)A.3Double CRT Representation (15)A.4Sampling From A q (15)A.5Canonical embedding norm of random polynomials (16)B The Basic Scheme16B.1Our Moduli Chain (16)B.2Modulus Switching (17)B.3Key Switching (18)B.4Key-Generation,Encryption,and Decryption (19)B.5Homomorphic Operations (20)C Security Analysis and Parameter Settings21C.1Lower-Bounding the Dimension (22)C.1.1LWE with Sparse Key (23)C.2The Modulus Size (24)C.3Putting It Together (26)D Further AES Implementation Methods27E Scale(c,q t,q t−1)in dble-CRT Representation281IntroductionIn his breakthrough result[11],Gentry demonstrated that fully-homomorphic encryption was theoretically possible,assuming the hardness of some problems in integer lattices.Since then,many different improve-ments have been made,proposing new variants,improving efficiency,suggesting other hardness assump-tions,etc.Some of these works were accompanied by implementation[20,12,7,21,16],but all the imple-mentations so far were either“proofs of concept”that can compute only one basic operation at a time(at great cost),or special-purpose implementations limited to evaluating very simple functions.In this work we report on thefirst implementation powerful enough to support an“interesting real world circuit”.Specifi-cally,we implemented a variant of the leveled FHE-without-bootstrapping scheme of Brakerski,Gentry,and Vaikuntanathan[4](BGV),with support for deep enough circuits so that we can evaluate an entire AES-128 encryption operation.Why AES?We chose to shoot for an evaluation of AES since it seems like a natural benchmark:AES is widely deployed and used extensively in security-aware applications(so it is“practically relevant”to imple-ment it),and the AES circuit is nontrivial on one hand,but on the other hand not astronomical.Moreover the AES circuit has a regular(and quite“algebraic”)structure,which is amenable to parallelism and optimiza-tions.Indeed,for these same reasons AES is often used as a benchmark for implementations of protocols for secure multi-party computation(MPC),for example[19,8,14,15].Using the same yardstick to measure FHE and MPC protocols is quite natural,since these techniques target similar application domains and in some cases both techniques can be used to solve the same problem.Beyond being a natural benchmark,homomorphic evaluation of AES decryption also has interesting applications:When data is encrypted under AES and we want to compute on that data,then homomorphic AES decryption would transform this AES-encrypted data into an FHE-encrypted data,and then we could perform whatever computation we wanted.(Such applications were alluded to in[16,21]).Our Contributions.Our implementation is based on a variant of the ring-LWE scheme of BGV[4,6,5], using the techniques of Smart and Vercauteren(SV)[21]and Gentry,Halevi and Smart(GHS)[13],and we introduce many new optimizations.Some of our optimizations are specific to AES,these are described in Section4.Most of our optimization,however,are more general-purpose and can be used for homomorphic evaluation of other circuits,these are described in Section3.Many of our general-purpose optimizations are aimed at reducing the number of FFTs and CRTs that we need to perform,by reducing the number of times that we need to convert polynomials between coef-ficient and evaluation representations.Since the cryptosystem is defined over a polynomial ring,many of the operations involve various manipulation of integer polynomials,such as modular multiplications and additions and Frobenius maps.Most of these operations can be performed more efficiently in evaluation representation,when a polynomial is represented by the vector of values that it assumes in all the roots of the ring polynomial(for example polynomial multiplication is just point-wise multiplication of the evalu-ation values).On the other hand some operations in BGV-type cryptosystems(such as key switching and modulus switching)seem to require coefficient representation,where a polynomial is represented by listing all its coefficients.1Hence a“naive implementation”of FHE would need to convert the polynomials back and forth between the two representations,and these conversions turn out to be the most time-consuming part of the execution.In our implementation we keep ciphertexts in evaluation representation at all times, converting to coefficient representation only when needed for some operation,and then converting back.1The need for coefficient representation ultimately stems from the fact that the noise in the ciphertexts is small in coefficient representation but not in evaluation representation.1We describe variants of key switching and modulus switching that can be implemented while keeping almost all the polynomials in evaluation representation.Our key-switching variant has another advantage, in that it significantly reduces the size of the key-switching matrices in the public key.This is particularly important since the main limiting factor for evaluating deep circuits turns out to be the ability to keep the key-switching matrices in memory.Other optimizations that we present are meant to reduce the number of modulus switching and key switching operations that we need to do.This is done by tweaking some operations(such as multiplication by constant)to get a slower noise increase,by“batching”some operations before applying key switching,and by attaching to each ciphertext an estimate of the“noisiness”of this ciphertext,in order to support better noise bookkeeping.Our Implementation.Our implementation was based on the NTL C++library running over GMP,we utilized a machine which consisted of a processing unit of Intel Xeon CPUs running at2.0GHz with18MB cache,and most importantly with256GB of RAM.2Memory was our main limiting factor in the implemen-tation.With this machine it took us just under eight days to compute a single block AES encryption using an implementation choice which minimizes the amount of memory required;this is roughly two orders of magnitude faster than what could be done with the Gentry-Halevi implementation[12].The computation was performed on ciphertexts that could hold1512plaintext slots each;where each slot holds an element of F28.This means that we can compute 1512/16 =94AES operations in parallel,which gives an amortize time per block of roughly two hours.We note that there are a multitude of optimizations that one can perform on our basic implementation. Most importantly,we believe that by using the“bootstrapping as optimization”technique from BGV[4]we can speedup the AES performance by an additional order of magnitude.Also,there are great gains to be had by making better use of parallelism:Unfortunately,the NTL library(which serves as our underlying software platform)is not thread safe,which severely limits our ability to utilize the multi-core functionality of modern processors(our test machine has24cores).We expect that by utilizing many threads we can speed up some of our(higher memory)AES variants by as much as a16x factor;just by letting each thread compute a different S-box lookup.Organization.In Section2we review the main features of BGV-type cryptosystems[5,4],and briefly survey the techniques for homomorphic computation on packed ciphertexts from SV and GHS[21,13]. Then in Section3we describe our“general-purpose”optimizations on a high level,with additional details provided in Appendices A and B.A brief overview of AES and a high-level description(and performance numbers)of one of our AES-specific implementations is provided in Section4,with details of alternative implementations being provided in Appendix D.2Background2.1Notations and Mathematical BackgroundFor an integer q we identify the ring Z/q Z with the interval(−q/2,q/2]∩Z,and we use[z]q to denote the reduction of the integer z modulo q into that interval.Our implementation utilizes polynomial rings defined by cyclotomic polynomials,A=Z[X]/Φm(X).The ring A is the ring of integers of a the m th cyclotomic numberfield Q(ζm).We let A q def=A/q A=Z[X]/(Φm(X),q)for the(possibly composite)integer q,and we identify A q with the set of integer polynomials of degree uptoφ(m)−1reduced modulo q.2This machine was BlueCrystal Phase2;and the authors would like to thank the University of Bristol’s Advanced Computing Research Centre(https:///)for access to this facility2Coefficient vs.Evaluation Representation.Let m,q be two integers such that Z /q Z contains a primitive m -th root of unity,and denote one such primitive m -th root of unity by ζ∈Z /q Z .Recallthat the m ’th cyclotomic polynomial splits into linear terms modulo q ,Φm (X )= i ∈(Z /m Z )∗(X −ζi )(mod q ).For an element a ∈A q ,we consider two ways of representing it:Viewing a as a degree-(φ(m )−1)poly-nomial,a (X )= i<φ(m )a i X i ,we can just list all the coefficients in order a = a 0,a 1,...,a φ(m )−1 ∈(Z /q Z )φ(m ).We call a the coefficient representation of a .For the other representation we consider the values that the polynomial a (X )assumes on all primitive m -th roots of unity modulo q ,b i =a (ζi )mod q for i ∈(Z /m Z )∗.The b i ’s in order also yield a vector b ∈(Z /q Z )φ(m ),which we call the evaluation representation of a .Clearly these two representations are related via b =V m ·a ,where V m is the Van-dermonde matrix over the primitive m -th roots of unity modulo q .We remark that for all i we have the equality (a mod (X −ζi ))=a (ζi )=b i ,hence the evaluation representation of a is just a polynomial Chinese-Remaindering representation.In both evaluation and coefficient representations,an element a ∈A q is represented by a φ(m )-vector of integers in Z /q Z .If q is a composite then each of these integers can itself be represented either using the standard binary encoding of integers or using Chinese-Remaindering relative to the factors of q .We usually use the standard binary encoding for the coefficient representation and Chinese-Remaindering for the evaluation representation.(Hence the latter representation is really a double CRT representation,relative to both the polynomial factors of Φm (X )and the integer factors of q .)2.2BGV-type CryptosystemsOur implementation uses a variant of the BGV cryptosystem due to Gentry,Halevi and Smart,specifically the one described in [13,Appendix D](in the full version).In this cryptosystem both ciphertexts and secret keys are vectors over the polynomial ring A ,and the native plaintext space is the space of binary polynomials A 2.(More generally it could be A p for some fixed p ≥2,but in our case we will always use A 2.)At any point during the homomorphic evaluation there is some “current integer modulus q ”and “current secret key s ”,that change from time to time.A ciphertext c is decrypted using the current secret key s by taking inner product over A q (with q the current modulus)and then reducing the result modulo 2in coefficient representation .Namely,the decryption formula isa ←[[ c ,s mod Φm (X )]q noise ]2.(1)The polynomial [ c ,s mod Φm (X )]q is called the “noise”in the ciphertext c .Informally,c is a valid ciphertext with respect to secret key s and modulus q if this noise has “sufficiently small norm”relative to q .The meaning of “sufficiently small norm”is whatever is needed to ensure that the noise does not wrap around q when performing homomorphic operations,in our implementation we keep the norm of the noise always below some pre-set bound (which is determined in Appendix C.2).The specific norm that we use to evaluate the magnitude of the noise is the “canonical embedding norm reduced mod q ”,as described in [13,Appendix D](in the full version).This is useful to get smaller parameters,but for the purpose of presentation the reader can think of the norm as the Euclidean norm of the noise in coefficient representation.More details are given in the Appendices.We refer to the norm of the noise as the noise magnitude .The central feature of BGV-type cryptosystems is that the current secret key and modulus evolve as we apply operations to ciphertexts.We apply five different operations to ciphertexts during homomorphic evaluation.Three of them —addition,multiplication,and automorphism —are “semantic operations”that we use to evolve the plaintext data which is encrypted under those ciphertexts.The other two operations3—key-switching and modulus-switching —are used for “maintenance”:These operations do not change the plaintext at all,they only change the current key or modulus (respectively),and they are mainly used to control the complexity of the evaluation.Below we briefly describe each of these five operations on a high level.For the sake of self-containment,we also describe key generation and encryption in Appendix B.More detailed description can be found in [13,Appendix D].Addition.Homomorphic addition of two ciphertext vectors with respect to the same secret key and mod-ulus q is done just by adding the vectors over A q .If the two arguments were encrypting the plaintext polynomials a 1,a 2∈A 2then the sum will be an encryption of a 1+a 2∈A 2.This operation has no effect on the current modulus or key,and the norm of the noise is at most the sum of norms from the noise in the two arguments.Multiplication.Homomorphic multiplication is done via tensor product over A q .In principle,if the two arguments have dimension n over A q then the product ciphertext has dimension n 2,each entry in the output computed as the product of one entry from the first argument and one entry from the second.3This operation does not change the current modulus,but it changes the current key:If the two input ciphertexts are valid with respect to the dimension-n secret key vector s ,encrypting the plaintext polynomi-als a 1,a 2∈A 2,then the output is valid with respect to the dimension-n 2secret key s which is the tensor product of s with itself,and it encrypt the polynomial a 1·a 2∈A 2.The norm of the noise in the product ciphertext can be bounded in terms of the product of norms of the noise in the two arguments.The specific bound depends on the norm in use,for our choice of norm function the norm of the product is no larger than the product of the norms of the two arguments.Key Switching.The public key of BGV-type cryptosystems includes additional components to enable converting a valid ciphertext with respect to one key into a valid ciphertext encrypting the same plaintext with respect to another key.For example,this is used to convert the product ciphertext which is valid with respect to a high-dimension key back to a ciphertext with respect to the original low-dimension key.To allow conversion from dimension-n key s to dimension-n key s (both with respect to the same modulus q ),we include in the public key a matrix W =W [s →s ]over A q ,where the i ’th column of W is roughly an encryption of the i ’th entry of s with respect to s (and the current modulus).Then given a valid ciphertext c with respect to s ,we roughly compute c =W ·c to get a valid ciphertext with respect to s .In some more detail,the BGV key switching transformation first ensures that the norm of the ciphertext c itself is sufficiently low with respect to q .In [4]this was done by working with the binary encoding of c ,and one of our main optimization in this work is a different method for achieving the same goal (cf.Section 3.1).Then,if the i ’th entry in s is s i ∈A (with norm smaller than q ),then the i ’th column of W [s →s ]is an n -vector w i such that [ w i ,s mod Φm (X )]q =2e i +s i for a low-norm polynomial e i ∈A .Denoting e =(e 1,...,e n ),this means that we have s W =s +2e over A q .For any ciphertext vector c ,setting c =W ·c ∈A q we get the equation[ c ,s mod Φm (X )]q =[s W c mod Φm (X )]q =[ c ,s +2 c ,e mod Φm (X )]qSince c ,e ,and [ c ,s mod Φm (X )]q all have low norm relative to q ,then the addition on the right-hand side does not cause a wrap around q ,hence we get [[ c ,s mod Φm (X )]q ]2=[[ c ,s mod Φm (X )]q ]2,as needed.The key-switching operation changes the current secret key from s to s ,and does not change the current modulus.The norm of the noise is increased by at most an additive factor of 2 c ,e .3It was shown in [6]that over polynomial rings this operation can be implemented while increasing the dimension only to 2n −1rather than to n 2.4Modulus Switching.The modulus switching operation is intended to reduce the norm of the noise,to compensate for the noise increase that results from all the other operations.To convert a ciphertext c with respect to secret key s and modulus q into a ciphertext c encrypting the same thing with respect to the same secret key but modulus q ,we roughly just scale c by a factor q /q (thus getting a fractional ciphertext),then round appropriately to get back an integer ciphertext.Specifically c is a ciphertext vector satisfying(a)c =c (mod 2),and (b)the “rounding error term”τdef =c −(q /q )c has low norm.Converting cto c is easy in coefficient representation,and one of our optimizations is a method for doing the same in evaluation representation (cf.Section 3.2)This operation leaves the current key s unchanged,changes the current modulus from q to q ,and the norm of the noise is changed as n ≤(q /q ) n + τ·s .Note that if the key s has low norm and q is sufficiently smaller than q ,then the noise magnitude decreases by this operation.A BGV-type cryptosystem has a chain of moduli,q 0<q 1···<q L −1,where fresh ciphertexts are with respect to the largest modulus q L −1.During homomorphic evaluation every time the (estimated)noise grows too large we apply modulus switching from q i to q i −1in order to decrease it back.Eventually we get ciphertexts with respect to the smallest modulus q 0,and we cannot compute on them anymore (except by using bootstrapping).Automorphisms.In addition to adding and multiplying polynomials,another useful operation is convert-ing the polynomial a (X )∈A to a (i )(X )def =a (X i )mod Φm (X ).Denoting by κi the transformationκi :a →a (i ),it is a standard fact that the set of transformations {κi :i ∈(Z /m Z )∗}forms a group under composition (which is the Galois group G al (Q (ζm )/Q )),and this group is isomorphic to (Z /m Z )∗.In [4,13]it was shown that applying the transformations κi to the plaintext polynomials is very useful,some more examples of its use can be found in our Section 4.Denoting by c (i ),s (i )the vector obtained by applying κi to each entry in c ,s ,respectively,it was shown in [4,13]that if s is a valid ciphertext encrypting a with respect to key s and modulus q ,then c (i )is a valid ciphertext encrypting a (i )with respect to key s (i )and the same modulus q .Moreover the norm of noise remains the same under this operation.We remark that we can apply key-switching to c (i )in order to get an encryption of a (i )with respect to the original key s .2.3Computing on Packed CiphertextsSmart and Vercauteren observed [20,21]that the plaintext space A 2can be viewed as a vector of “plaintext slots”,by an application the polynomial Chinese Remainder Theorem.Specifically,if the ring polynomial Φm (X )factors modulo 2into a product of irreducible factors Φm (X )= −1j =0F j (X )(mod 2),then a plaintext polynomial a (X )∈A 2can be viewed as encoding different small polynomials,a j =a mod F j .Just like for integer Chinese Remaindering,addition and multiplication in A 2correspond to element-wise addition and multiplication of the vectors of slots.The effect of the automorphisms is a little more involved.When i is a power of two then the transforma-tions κi :a →a (i )is just applied to each slot separately.When i is not a power of two the transformation κi has the effect of roughly shifting the values between the different slots.For example,for some parameters we could get a cyclic shift of the vector of slots:If a encodes the vector (a 0,a 1,...,a −1),then κi (a )(for some i )could encode the vector (a −1,a 0,...,a −2).This was used in [13]to devise efficient procedures for applying arbitrary permutations to the plaintext slots.We note that the values in the plaintext slots are not just bits,rather they are polynomials modulo the irreducible F j ’s,so they can be used to represents elements in extension fields GF (2d ).In particular,in some of our AES implementations we used the plaintext slots to hold elements of GF (28),and encrypt one5byte of the AES state in each slot.Then we can use an adaption of the techniques from [13]to permute the slots when performing the AES row-shift and column-mix.3General-Purpose OptimizationsBelow we summarize our optimizations that are not tied directly to the AES circuit and can be used also in homomorphic evaluation of other circuits.Underlying many of these optimizations is our choice of keeping ciphertext and key-switching matrices in evaluation (double-CRT)representation.Our chain of moduli is defined via a set of primes of roughly the same size,p 0,...,p L −1,all chosen such that Z /p i Z has a m ’th roots of unity.(In other words,m |p i −1for all i .)For i =0,...,L −1we then define our i ’th modulus as q i = i j =0p i .The primes p 0and p L −1are special (p 0is chosen to ensure decryption works,and p L −1is chosen to control noise immediately after encryption),however all other primes p i are of size 217≤p i ≤220if L <100,see Appendix C.In the t -th level of the scheme we have ciphertexts consisting of elements in A q t (i.e.,polynomialsmodulo (Φm (X ),q t )).We represent an element c ∈A q t by a φ(m )×(t +1)“matrix”of its evaluationsat the primitive m -th roots of unity modulo the primes p 0,...,p t .Computing this representation from the coefficient representation of c involves reducing c modulo the p i ’s and then t +1invocations of the FFT algorithm,modulo each of the p i (picking only the FFT coefficients corresponding to (Z /m Z )∗).To convert back to coefficient representation we invoke the inverse FFT algorithm t +1times,each time padding the φ(m )-vector of evaluation point with m −φ(m )zeros (for the evaluations at the non-primitive roots of unity).This yields the coefficients of t +1polynomials modulo (X m −1,p i )for i =0,...,t ,we then reduce each of these polynomials modulo (Φm (X ),p i )and apply Chinese Remainder interpolation.We stress that we try to perform these transformations as rarely as we can.3.1A New Variant of Key SwitchingAs described in Section 2,the key-switching transformation introduces an additive factor of 2 c ,e in the noise,where c is the input ciphertext and e is the noise component in the key-switching matrix.To keep the noise magnitude below the modulus q ,it seems that we need to ensure that the ciphertext c itself has low norm.In BGV [4]this was done by representing c as a fixed linear combination of small vectors,i.e.c = i 2i c i with c i the vector of i ’th bits in c .Considering the high-dimension ciphertextc ∗=(c 0|c 1|c 2|···)and secret key s ∗=(s |2s |4s |···),we note that we have c ∗,s ∗ = c ,s ,and c ∗has low norm (since it consists of 0-1polynomials).BGV therefore included in the public key the matrix W =W [s ∗→s ](rather than W [s →s ]),and had the key-switching transformation computes c ∗from c and sets c =W ·c ∗.When implementing key-switching,there are two drawbacks to the above approach.First,this increases the dimension (and hence the size)of the key switching matrix.This drawback is fatal when evaluating deep circuits,since having enough memory to keep the key-switching matrices turns out to be the limiting factor in our ability to evaluate these deep circuits.Another drawback is it seems that this key-switching procedure requires that we first convert c to coefficient representation in order to compute the c i ’s,then convert each of the c i ’s back to evaluation representation before multiplying by the key-switching matrix.In level t of the circuit,this seem to require Ω(t log q t )FFTs.In this work we propose a different variant:Rather than manipulating c to decrease its norm,we instead temporarily increase the modulus q .To that end we recall that for a valid ciphertext c ,encrypting plaintext a with respect to s and q ,we have the equality c ,s =2e +a over A q ,for a low-norm polynomial e .6This equality,we note,implies that for every odd integer p we have the equality c ,p s =2e +a ,holding over A pq ,for the “low-norm”polynomial e (namely e =p ·e +p −12a ).Clearly,when considered relativeto secret key p s and modulus pq ,the noise in c is p times larger than it was relative to s and q .However,since the modulus is also p times larger,we maintain that the noise has norm sufficiently smaller than the modulus.In other words,c is still a valid ciphertext that encrypts the same plaintext a with respect to secret key p s and modulus pq .By taking p large enough,we can ensure that the norm of c (which is independent of p )is sufficiently small relative to the modulus pq .We therefore include in the public key a matrix W =W [p s →s ]modulo pq for a large enough odd integer p .(Specifically we need p ≈q √m .)Given a ciphertext c ,valid with respect to s and q ,we apply the key-switching transformation simply by setting c =W ·c over A pq .The additive noise term c ,e that we get is now small enough relative to our large modulus pq ,thus the resulting ciphertext c is valid with respect to s and pq .We can now switch the modulus back to q (using our modulus switching routine),hence getting a valid ciphertext with respect to s and q .We note that even though we no longer break c into its binary encoding,it seems that we still need to recover it in coefficient representation in order to compute the evaluations of c mod p .However,since we do not increase the dimension of the ciphertext vector,this procedure requires only O (t )FFTs in level t (vs.O (t log q t )=O (t 2)for the original BGV variant).Also,the size of the key-switching matrix is reduced by roughly the same factor of log q t .Our new variant comes with a price tag,however:We use key-switching matrices relative to a larger modulus,but still need the noise term in this matrix to be small.This means that the LWE problem under-lying this key-switching matrix has larger ratio of modulus/noise,implying that we need a larger dimension to get the same level of security than with the original BGV variant.In fact,since our modulus is more than squared (from q to pq with p >q ),the dimension is increased by more than a factor of two.This translates to more than doubling of the key-switching matrix,partly negating the size and running time advantage that we get from this variant.We comment that a hybrid of the two approaches could also be used:we can decrease the norm of c only somewhat by breaking it into digits (as opposed to binary bits as in [4]),and then increase the modulus somewhat until it is large enough relative to the smaller norm of c .We speculate that the optimal setting in terms of runtime is found around p ≈√q ,but so far did not try to explore this tradeoff.3.2Modulus Switching in Evaluation RepresentationGiven an element c ∈A q t in evaluation (double-CRT)representation relative to q t = t j =0p j ,we wantto modulus-switch to q t −1–i.e.,scale down by a factor of p t ;we call this operation Scale (c,q t ,q t −1)The output should be c ∈A ,represented via the same double-CRT format (with respect to p 0,...,p t −1),such that (a)c ≡c (mod 2),and (b)the “rounding error term”τ=c −(c/p t )has a very low norm.As p t is odd,we can equivalently require that the element c †def=p t ·c satisfy(i)c †is divisible by p t ,(ii)c †≡c (mod 2),and(iii)c †−c (which is equal to p t ·τ)has low norm.Rather than computing c directly,we will first compute c †and then set c ←c †/p t .Observe that once we compute c †in double-CRT format,it is easy to output also c in double-CRT format:given the evaluations for c †modulo p j (j <t ),simply multiply them by p −1t mod p j .The algorithm to output c †in double-CRT format is as follows:7。
Modeling hurricane waves and storm surge using integrally-coupled,
Modeling hurricane waves and storm surge using integrally-coupled,scalable computationsJ.C.Dietrich a ,⁎,M.Zijlema b ,J.J.Westerink a ,L.H.Holthuijsen b ,C.Dawson c ,R.A.Luettich Jr.d ,R.E.Jensen e ,J.M.Smith e ,G.S.Stelling b ,G.W.Stone faDepartment of Civil Engineering and Geological Sciences,University of Notre Dame,156Fitzpatrick Hall,Notre Dame,IN 46556,United States bFaculty of Civil Engineering and Geosciences,Delft University of Technology,Stevinweg 1,2628CN,Delft,The Netherlands cInstitute for Computational Engineering and Sciences,University of Texas at Austin,201East 24Street,Austin,TX 78712,United States dInstitute of Marine Sciences,University of North Carolina at Chapel Hill,3431Arendell Street,Morehead City,NC 28557,United States eCoastal and Hydraulics Laboratory,U.S.Army Engineer Research and Development Center,3909Halls Ferry Road,Vicksburg,MS 39180,United States fCoastal Studies Institute,Louisiana State University,Old Geology Building,Room 331,Baton Rouge,LA 70803,United Statesa b s t r a c ta r t i c l e i n f o Article history:Received 26March 2010Received in revised form 9July 2010Accepted 9August 2010Keywords:ADCIRC SWANHurricanes WavesStorm surgeThe unstructured-mesh SWAN spectral wave model and the ADCIRC shallow-water circulation model have been integrated into a tightly-coupled SWAN +ADCIRC model.The model components are applied to an identical,unstructured mesh;share parallel computing infrastructure;and run sequentially in time.Wind speeds,water levels,currents and radiation stress gradients are vertex-based,and therefore can be passed through memory or cache to each model component.Parallel simulations based on domain decomposition utilize identical sub-meshes,and the communication is highly localized.Inter-model communication is intra-core,while intra-model communication is inter-core but is local and ef ficient because it is solely on adjacent sub-mesh edges.The resulting integrated SWAN +ADCIRC system is highly scalable and allows for localized increases in resolution without the complexity or cost of nested meshes or global interpolation between heterogeneous meshes.Hurricane waves and storm surge are validated for Hurricanes Katrina and Rita,demonstrating the importance of inclusion of the wave-circulation interactions,and ef ficient performance is demonstrated to 3062computational cores.©2010Elsevier B.V.All rights reserved.1.IntroductionA broad energy spectrum exists in oceans,with wave periods ranging from seconds to months.Short waves,such as wind-driven waves and swell,have periods that range from 0.5to 25s.Longer waves,such as seiches,tsunamis,storm surges and tides,have periods that range from minutes to months.These short and long waves are well-separated in the energy spectrum and have well-de fined spatial scales.This separation leads to distinct modeling approaches,depending on whether the associated scales can be resolved.For oceanic scales,short-wave models cannot resolve spatially or temporally the individual wind-driven waves or swell,and thus they treat the wave field as an energy spectrum and apply the conservation of wave action density to account for wave –current interactions.Long-wave models apply forms of conservation of massand momentum,in two or three spatial dimensions,to resolve the circulation associated with processes such as tsunamis,storm surges or tides.Although wind-driven waves and circulation are separated in the spectrum,they can interact.Water levels and currents affect the propagation of waves and the location of wave-breaking zones.Wave transformation generates radiation stress gradients that drive set-up and currents.Wind-driven waves affect the vertical momentum mixing and bottom friction,which in turn affect the circulation.Water levels can be increased by 5–20%in regions across a broad continental shelf,and by as much as 35%in regions of steep slope (Funakoshi et al.,2008;Dietrich et al.,2010).Thus,in many coastal applications,waves and circulation processes should be coupled.Wave and circulation models have been limited by their spectral,spatial and temporal resolution.This limitation can be overcome by nesting structured meshes,to enhance resolution in speci fic regions by employing meshes with progressively finer scales.In a wave application,nesting also allows the use of models with different physics and numerics.Relatively fine nearshore wave models,such as STWAVE and SWAN,can be nested inside relatively coarse deep-water wave models,such as WAM and WaveWatch III (WAMDI Group,1988;Komen et al.,1994;Booij et al.,1999;Smith et al.,2001;Coastal Engineering 58(2011)45–65⁎Corresponding author.Tel.:+15746313864.E-mail addresses:dietrich.15@ (J.C.Dietrich),m.zijlema@tudelft.nl(M.Zijlema),jjw@ (J.J.Westerink),l.h.holthuijsen@tudelft.nl (L.H.Holthuijsen),clint@ (C.Dawson),rick_luettich@ (R.A.Luettich),robert.e.jensen@ (R.E.Jensen),jane.m.smith@ (J.M.Smith),g.s.stelling@tudelft.nl (G.S.Stelling),gagreg@ (G.W.Stone).0378-3839/$–see front matter ©2010Elsevier B.V.All rights reserved.doi:10.1016/j.coastaleng.2010.08.001Contents lists available at ScienceDirectCoastal Engineeringj o u r n a l h o m e p a g e :w ww.e l s ev i e r.c o m /l o c a t e /c o a s ta l e n gThompson et al.,2004;Gunther,2005;Tolman,2009).The nearshore wave models may not be ef ficient if applied to large domains,and the deep-water wave models may not contain the necessary physics or resolution for nearshore wave simulation.Until recently,wave models required nesting in order to vary resolution from basin to shelf to nearshore applications.These structured wave models can be coupled to structured circulation models that run on the same nested meshes (Kim et al.,2008).Unstructured circulation models have emerged to provide localized resolution of gradients in geometry,bathymetry/topogra-phy,and flow processes.Resolution varies over a range of scales within the same mesh from deep water to the continental shelf to the channels,marshes and floodplains near shore (Westerink et al.,2008).Unstructured meshes allow for localized resolution where solution gradients are large and correspondingly coarser resolution where solution gradients are small,thus minimizing the computa-tional cost relative to structured meshes with similar minimum mesh spacings.The coupling of wave and circulation models has been imple-mented typically with heterogeneous meshes.A coupling application may have one unstructured circulation mesh and several structured wave meshes,and the models may pass information via external files (Funakoshi et al.,2008;Dietrich et al.,2010;Weaver and Slinn,2004;Ebersole et al.,2007;Chen et al.,2008;Pandoe and Edge,2008;Bunya et al.,2010).This ‘loose ’coupling is disadvantageous because it requires intra-model interpolation at the boundaries of the nested,structured wave meshes and inter-model interpolation between the wave and circulation meshes.This interpolation creates problems with respect to both accuracy and ef ficiency.Overlapping nested or adjacent wave meshes often have different solutions,and inter-mesh interpolation can smooth or enhance the integrated wave forcing.Furthermore,even if a component model is locally conservative,its interpolated solution will not necessarily be conservative.Finally,inter-model interpolation must be performed at all vertices of the meshes.This interpolation is problematic in a parallel computing environment,where the communication between sub-meshes is inter-model and semi-global.The sub-meshes must communicate on an area basis (i.e.,the information at all vertices on a sub-mesh mustbe shared).Global communication is costly and can prevent models from being scalable in high-performance computing environments.An emerging practice is to couple models through a generic framework,such as the Earth System Modeling Framework (ESMF)(Hill et al.,2004;Collins et al.,2005),the Open Modeling Interface (OpenMI)Environment (Moore and Tindall,2005;Gregersen et al.,2005)or the Modeling Coupling Toolkit (MCT)(Warner et al.,2008).These frameworks manage when and how the individual models are run,interpolate information between models if necessary,and make transparent the coupling to developers and users.However,these frameworks do not eliminate the fundamental problems of coupling when using heterogeneous meshes.Boundary conditions must be interpolated between nested,structured wave meshes,and water levels,currents and wave properties must be interpolatedbetweenFig.1.Schematic of parallel communication between models and cores.Dashed lines indicate communication for all vertices within a sub-mesh,and are inter-model and intra-core.Solid lines indicate communication for the edge-layer-based nodes between sub-meshes,and are intra-model andinter-core.Fig.2.ADCIRC SL15model domain with bathymetry (m).46J.C.Dietrich et al./Coastal Engineering 58(2011)45–65the unstructured circulation and structured wave meshes.This interpolation is costly,destroys the scalability of the coupled model,and thus limits the resolution that can be employed and the corresponding physics that can be simulated.The recent introduction of unstructured wave models makes nesting unnecessary.Resolution can be enhanced nearshore and relaxed in deep water,allowing the model to simulate ef ficiently the wave evolution.SWAN has been used extensively to simulate waves in shallow water (Booij et al.,1999;Ris et al.,1999;Gorman and Neilson,1999;Rogers et al.,2003),and it has been converted recently to run on unstructured meshes (Zijlema et al.,2010).This version of SWAN employs the unstructured-mesh analog to the solution technique from the structured version.It retains the physics and numerics of SWAN,but it runs on unstructured meshes,and it is both accurate and ef ficient in the nearshore and in deep water.In this paper,we describe a ‘tight ’coupling of the SWAN wave model and the ADCIRC circulation model.SWAN and ADCIRC are run on the same unstructured mesh.This identical,homogeneous mesh allows the physics of wave-circulation interactions to be resolved correctly in both models.The unstructured mesh can be applied on a large domain to follow seamlessly all energy from deep to shallow water.There is no nesting or overlapping of structured wave meshes,and there is no inter-model interpolation.Variables and forces reside at identical,vertex-based rmation can be passed without interpolation,thus reducing signi ficantly the communication costs.In parallel computing applications,identical sub-meshes and communication infrastructure are used for both SWAN and ADCIRC,which run as the same program on the same computational core.All inter-model communication on a sub-mesh is done through local memory or munication between sub-meshes is rmation is passed only to the edges of neighboring sub-meshes,and thus the coupled model does not require global communication over areas.Domain decomposition places neighbor-ing sub-meshes on neighboring cores,so communication costs are minimized.The coupled model is highly scalable andintegratesFig.3.ADCIRC SL15bathymetry and topography (m),relative to NAVD88(2004.65),for southernLouisiana.Fig.4.ADCIRC SL15mesh resolution (m)in southern Louisiana.47J.C.Dietrich et al./Coastal Engineering 58(2011)45–65seamlessly the physics and numerics from ocean to shelf tofloodplain. Large domains and high levels of local resolution can be employed for both models,allowing the accurate depiction of the generation, propagation and dissipation of waves and surge.The resulting SWAN +ADCIRC model is suited ideally to simulate waves and circulation and their propagation from deep water to complicated nearshore systems.In the sections that follow,the component SWAN and ADCIRC models are described,and the mechanics of their tight coupling is introduced.The coupled model is then validated through its application to hindcasts of Hurricanes Katrina and Rita.Finally,a benchmarking study shows SWAN+ADCIRC is highly scalable.2.Methods2.1.SWAN modelSWAN predicts the evolution in geographical space⇀x and time t of the wave action density spectrum N(→x,t,σ,θ),withσthe relative frequency andθthe wave direction,as governed by the action balance equation(Booij et al.,1999):∂N+∇⇀x⋅⇀c g+⇀UNh i+∂cθN+∂cσN=S tot:ð1ÞThe terms on the left-hand side represent,respectively,the change of wave action in time,the propagation of wave action in⇀x-space (with∇⇀x the gradient operator in geographic space,⇀c g the wave group velocity and⇀U the ambient current vector),depth-and current-induced refraction and approximate diffraction(with propagation velocity or turning rate cθ),and the shifting ofσdue to variations in mean current and depth(with propagation velocity or shifting rate cσ).The source term,S tot,represents wave growth by wind;action lost due to whitecapping,surf breaking and bottom friction;and action exchanged between spectral components in deep and shallow water due to nonlinear effects.The associated SWAN parameterizations are given by Booij et al.(1999),with all subsequent modificationsas Fig.5.Example of the METIS domain decomposition of the ADCIRC SL15mesh on1014computational cores.Colors indicate local sub-meshes and shared boundary layers.Table1Geographic location by type and number shown in Figs.6and7.Rivers and channels1Calcasieu Shipping Channel 2Atchafalaya River3Mississippi River4Southwest PassBays,lakes and sounds5Sabine Lake6Calcasieu Lake7White Lake8Vermilion Bay9Terrebonne Bay10Timbalier Bay11Lake Pontchartrain12Lake Borgne13Gulf of MexicoIslands14Grand Isle15Chandeleur IslandsPlaces16Galveston,TX17Tiger and Trinity Shoals 18New Orleans,LA Fig.6.Schematic of the Gulf of Mexico with locations of the12NDBC buoy stations used for the deep-water validation of SWAN during both Katrina and Rita.The hurricane tracks are also shown.48J.C.Dietrich et al./Coastal Engineering58(2011)45–65present in version40.72,including the phase-decoupled refraction–diffraction(Holthuijsen et al.,2003),although diffraction is not enabled in the present simulations.The unstructured-mesh version of SWAN implements an analog to the four-direction Gauss–Seidel iteration technique employed in the structured version,and it maintains SWAN's unconditional stability (Zijlema,2010).SWAN computes the wave action density spectrum N (⇀x,t,σ,θ)at the vertices of an unstructured triangular mesh,and it orders the mesh vertices so it can sweep through them and update the action density using information from neighboring vertices.It then sweeps through the mesh in opposite directions until the wave energy has propagated sufficiently through geographical space in all direc-tions.It should be noted that,as a spectral model,SWAN does not attempt to represent physical processes at scales less than a wave length even in regions with veryfine-scale mesh resolution.Phase-resolving wave models should be employed at these scales if sub-wave length scaleflow features need to be resolved.However,this fine-scale mesh resolution may be necessary for other reasons,such as representing the complex bathymetry and topography of the region, or to improve the numerical properties of the computed solution.2.2.ADCIRC modelADCIRC is a continuous-Galerkin,finite-element,shallow-water model that solves for water levels and currents at a range of scales (Westerink et al.,2008;Luettich and Westerink,2004;Atkinson et al., 2004;Dawson et al.,2006).Water levels are obtained through solution of the Generalized Wave Continuity Equation(GWCE):∂2ζ∂t2+τ0∂ζ∂t+∂˜J x∂x+∂˜J y∂y−UH∂τ0∂x−VH∂τ0∂y=0;ð2Þwhere:˜J x =−Q x∂U∂x−Q y∂U∂y+fQ y−g∂ζ2∂x−gH∂∂xP s−αη+τsx;wind+τsx;waves−τbxρ0+M x−D xðÞ+U∂ζ∂t+τ0Q x−gH∂ζ∂x;ð3Þ˜J y =−Q x∂V−Q y∂V−fQ x−g∂ζ2−gH∂P s−αη+τsy;wind+τsy;waves−τbyρ0+M y−D y+V∂ζ+τ0Q y−gH∂ζ;ð4Þand the currents are obtained from the vertically-integrated momen-tum equations:∂U∂t+U ∂U∂x+V∂U∂y−fV=−g∂∂xζ+P sgρ0−αη+τsx;winds+τsx;waves−τbxρ0H+M x−D xH;ð5Þand:∂V∂t+U ∂V∂x+V∂V∂y+fU=−g∂∂yζ+P sgρ0−αη+τsy;winds+τsy;waves−τbyρ0H+M y−D yH;ð6Þwhere H=ζ+h is the total water depth;ζis the deviation of the water surface from the mean;h is the bathymetric depth;U and V are depth-integrated currents in the x-and y-directions,respectively;Q x=UH and Q y=VH arefluxes per unit width;f is the Coriolis parameter;g is the gravitational acceleration;P s is the atmospheric pressure at the surface;ρ0is the reference density of water;ηis the Newtonian equilibrium tidal potential andαis the effective earth elasticity factor;τs,winds andτs,waves are surface stresses due to winds and waves,respectively;τb is the bottom stress;M are lateral stress gradients;D are momentum dispersion terms;andτ0is a numerical parameter that optimizes the phase propagation properties(Atkinson et al.,2004;Kolar et al.,1994). ADCIRC computes water levelsζand currents U and V on an unstructured,triangular mesh by applying a linear Lagrange interpola-tion and solving for three degrees of freedom at every mesh vertex.2.3.Sharing informationSWAN is driven by wind speeds,water levels and currents computed at the vertices by ADCIRC.Marine winds can be input to ADCIRC in a variety of formats,and these winds are adjusted directionally to account for surface roughness(Bunya et al.,2010).ADCIRC interpolates spatially and temporally to project these winds to the computational vertices, and then it passes them to SWAN.The water levels and ambient currents are computed in ADCIRC before being passed to SWAN,where they are used to recalculate the water depth and all related wave processes (wave propagation,depth-induced breaking,etc.).The ADCIRC model is driven partly by radiation stress gradients that are computed using information from SWAN.These gradientsτs,waves are computed by:τsx;waves=−∂S xx−∂S xy;ð7Þand:τsy;waves=−∂S xy∂x−∂S yy∂y;ð8Þwhere S xx,S xy and S yy are the wave radiation stresses(Longuet–Higgins and Stewart,1964;Battjes,1972):S xx=ρ0g∬n cos2θ+n−12σNdσdθ;ð9ÞS xy=ρ0g∬n sinθcosθσNðÞdσdθ;ð10Þand:S yy=ρ0g∬n sin2θ+n−12σNdσdθ;ð11ÞFig.7.Schematic of southern Louisiana with numbered markers of the locations listed in Table1.Locations of the two CSI nearshore wave gauges and the hurricane tracks are also shown.49J.C.Dietrich et al./Coastal Engineering58(2011)45–65where n is the ratio of group velocity to phase velocity.The radiation stresses are computed at the mesh vertices using Eqs.(9)–(11).Then they are interpolated into the space of continuous,piecewise linear functions and differentiated to obtain the gradients in Eqs.(7)and (8),which are constant on each element.These element-based gradients are projected to the vertices by taking an area-weighted average of the gradients on the elements adjacent to each vertex.2.4.Coupling procedureADCIRC and SWAN run in series on the same local mesh and core.The two models “leap frog ”through time,each being forced with information from the other model.Because of the sweeping method used by SWAN to update the wave information at the computational vertices,it can takemuchFig.8.Hurricane Katrina signi ficant wave height contours (m)and wind speed vectors (m s −1)at 12-h intervals in the Gulf of Mexico.The six panels correspond to the following times:(a)2200UTC 26August 2005,(b)1000UTC 27August 2005,(c)2200UTC 27August 2005,(d)1000UTC 28August 2005,(e)2200UTC 28August 2005and (f)1000UTC 29August 2005.50J.C.Dietrich et al./Coastal Engineering 58(2011)45–65larger time steps than ADCIRC,which is diffusion-and also Courant-time-step limited due to its semi-explicit formulation and its wetting-and-drying algorithm.For that reason,the coupling interval is taken to be the same as the SWAN time step.On each coupling interval,ADCIRC is run first,because we assume that,in the nearshore and the coastal floodplain,wave properties are more dependent on circulation.At the beginning of a coupling interval,ADCIRC can access the radiation stress gradients computed by SWAN at times corresponding to the beginning and end of the previous interval.ADCIRC uses that information to extrapolate the gradients at all of its time steps in the current interval.These extrapolated gradients are used to force the ADCIRC solution as described previously.Once the ADCIRC stage is finished,SWAN is run for one time step,to bring it to the same moment in time as ADCIRC.SWAN can access the wind speeds,water levels and currents computed at the mesh vertices by ADCIRC,at times corresponding to the beginning and end of the current interval.SWAN applies the mean of those values to force its solution on its time step.In this way,the radiation stress gradients used by ADCIRC are always extrapolated forward in time,while the wind speeds,water levels and currents used by SWAN are always averaged over each of its time steps.2.5.Parallel coupling frameworkThe METIS domain-decomposition algorithm is applied to distribute the global mesh over a number of computational cores (Karypis and Kumar,1999).The decomposition minimizes inter-core communication by creating local sub-meshes with small ratios of the number of vertices within the domain to the number of shared vertices at sub-mesh interfaces.The decomposition also balances the computational load by creating local sub-meshes with a similar number of vertices;the local meshes decrease in geographical area as their average mesh size is decreased.A schematic of the communication is shown in Fig.1.Each local core has a sub-mesh that shares a layer of boundary elements with the sub-meshes on its neighbor cores.To update the information at these boundaries in either model,information is passed at the shared vertices on each sub-mesh.This communication is local between adjacent sub-meshes.Furthermore,only a small fraction of the vertices on any sub-mesh are shared.Thus the parallel,inter-core communication is localized and ef ficient.SWAN and ADCIRC utilize the same local rmation is stored at the vertices in both models,so it can be passed through local memory or cache,without the need for any network-based,inter-core communication.In contrast to loose coupling paradigms,in which the model components run on different sub-meshes and different cores,SWAN+ADCIRC does not destroy its scalability by interpolating semi-globally.The inter-model communication is intra-core.3.Hindcasts of Katrina and Rita 3.1.Parameters of hindcastsSWAN+ADCIRC will utilize the SL15mesh that has been validated for applications in southern Louisiana (Dietrich et al.,2010;BunyaFig.9.Hurricane Katrina winds and waves at 1000UTC 29August 2005in southeastern Louisiana.The panels are:(a)wind contours and vectors (m s −1),shown with a 10min averaging period and at 10m elevation;(b)signi ficant wave height contours (m)and wind vectors (m s −1);(c)mean wave period contours (s)and wind vectors (m s −1);and (d)radiation stress gradient contours (m2s −2)and wind vectors (m s −1).51J.C.Dietrich et al./Coastal Engineering 58(2011)45–65et al.,2010).The complex bathymetry/topography and mesh resolution are shown in Figs.2–4.This mesh incorporates local resolution down to 50m,but also extends to the Gulf of Mexico and the western North Atlantic Ocean.It includes a continental shelf that narrows near the protruding delta of the Mississippi River,suf ficient resolution of the wave-transformation zones near the delta and over the barrier islands,and intricate representation of the various natural and man-made geographic features that collect and focus storm surge in this region.The SL15mesh contains 2,409,635vertices and 4,721,496triangular elements.An example of the METIS domain decomposition of the SL15mesh on 1014cores is shown in Fig.5.Local sub-meshes are shown in separate colors,and the cores communicate via the layers of overlapping elements that connect these local meshes.Each parallel core utilizes the same unstructured local sub-mesh for both SWAN and ADCIRC.Notable geographic locations are summarized in Table 1and shown in Figs.6and 7.SWAN+ADCIRC has been validated via hindcasts of Katrina and Rita,which utilize optimized wind fields developed with an Interactive Objective Kinematic Analysis (IOKA)System (Cox et al.,1995;Cardone et al.,2007).The Katrina wind fields also have an inner core that is data-assimilated from NOAA's Hurricane Research Division Wind Analysis System (H*WIND)(Powell et al.,1996,1998).The wind speeds are referenced to 10m in height,peak 30min averaged “sustained ”wind speed,and marine exposure.They contain snapshots at 15min intervals on a regular 0.05°grid.The wind fields are read by ADCIRC,and then each local core interpolates onto its local sub-mesh.With the lone exception of the source of its radiation stress gradients,ADCIRC uses the same parameters as discussed in Bunya et al.(2010).The water levels are adjusted for the regional difference between LMSL and NAVD88(2004.65)and the seasonal fluctuation in sea level in the Gulf of Mexico.Bottom friction is parameterized using a Manning's n formulation,with spatially-variable values based on land classi fication.The Mississippi and Atchafalaya Rivers are forced with flow rates that are representative of the conditions during the storms.In addition,seven tidal constituents are forced on the open boundary in the Atlantic Ocean.ADCIRC applies a wind drag coef ficient due to Garratt (1977)with a cap of C d ≤0.0035.The SWAN time step and the coupling interval are 600s.The SWAN frequencies range from 0.031to 0.548Hz and are discretized into 30bins on a logarithmic scale (Δσ/σ≈0.1).The wave directions are discretized into 36sectors,each sector representing 10°.The present simulations use the SWAN default for wind input based on Snyder et al.(1981)and the modi fied whitecapping expression of Rogers et al.(2003),which yields less dissipation in lower frequency components and better prediction of the wave periods compared to the default formulation of Hasselmann (1974).Quadruplet nonlinear interactions are computed with the Discrete Interaction Approxima-tion (Hasselmann et al.,1985).For the shallow-water source terms,depth-induced breaking is computed with a spectral version oftheFig.10.Hurricane Katrina water levels and currents at 1000UTC 29August 2005in southeastern Louisiana.The panels are:(a)water level contours (m)and wind vectors (m s −1);(b)wave-driven set-up contours (m)and wind vectors (m s −1);(c)current contours (m s −1)and wind vectors (m s −1);and (d)wave-driven current contours (m s −1)and wind vectors (m s −1).52J.C.Dietrich et al./Coastal Engineering 58(2011)45–65model due to Battjes and Janssen (1978)with the breaking index γ=0.73,bottom friction is based on the JONSWAP formulation (Hasselmann et al.,1973)with friction coef ficient C b =0.067m 2s −3,and the triad nonlinear interactions are computed with the Lumped Triad Approximation of Eldeberky (1996).Although the resolution in the SL15mesh is well-suited to simulate waves and surge along the coastlines of Louisiana,Mississippi and Alabama,its relatively coarse resolution in the Caribbean Sea and Atlantic Ocean can create spurious wave refraction over one spatial element.Thus,wave refraction is enabled only in the computational sub-meshes in which the resolution of the bathymetry is suf ficient,speci fically in the northern Gulf of Mexico.SWAN applies a wind drag coef ficient due to Wu (1982)with a cap of C d ≤0.0035.In the validation sections that follow,the SWAN wave quantities will be compared to the measured data and also to the solution from a loose coupling to structured versions of WAM and STWAVE.WAM was run on a regular 0.05°mesh with coverage of the entire Gulf of Mexico,while STWAVE was run on four or five nested sub-meshes with resolution of 200m and coverage of southern Louisiana,Mississippi and Alabama.The details of this loose coupling can be found in Bunya et al.(2010)and Dietrich et al.(2010).For the validation herein,wave parameters from WAM and STWAVE were integrated to 0.41Hz,while parameters from SWAN were integrated to 0.55Hz.3.2.Hurricane KatrinaKatrina is a good validation case because of its size and scope.It was a large hurricane,with waves of 16.5m measured off the continental shelf and storm surge of 8.8m measured along the Mississippi coastline.But it also generated waves and storm surge over multiple scales and impacted the complex topography and levee protection system of southeastern Louisiana.To simulate the evolution of this hurricane,the coupled model must describe the system in rich detail and integrate seamlessly all of its components.3.2.1.Evolution of waves in deep waterBecause SWAN has not been used traditionally in deep water,we examine the behavior of its solution as Katrina moved through the Gulf of Mexico.Fig.8depicts the computed signi ficant wave heights at 12h intervals as Katrina enters the Gulf,generates waves throughout the majority of the basin,and then makes landfall in southern Louisiana.In its early stages,Katrina generated signi ficant wave heights of 6–9m in the eastern half of the Gulf.However,as the storm strengthened on 28August 2005,the signi ficant wave heights increased to a peak of about 22m at 2200UTC,and waves of at least 3m were generated throughout most of the Gulf.The impact of the hurricane on waves was widespread anddramatic.Fig.11.Signi ficant wave heights (m)during Hurricane Katrina at 12NDBC buoys.The measured data is shown with black dots,the modeled SWAN results are shown with black lines,and the modeled WAM results are shown with gray lines.53J.C.Dietrich et al./Coastal Engineering 58(2011)45–65。
ICP—AES法测定Beta沸石中常量和微量组分
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沸石传统的分析方法是化学滴定法 , 即将沸石
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AESS标准
Specification: SEAC/RCSCA has prepared a generic specification that includes many common fabricatio help communicate a designer’s expectations to the fabricator. The specification includes a number of editor’s notes to provide guidance. The headings in the specification are coordinated with the line times from the cost matrix and sample board. The intent of the specification is to provide a consistent mechanism to define appearance quality requirements that were selected with the sample board and budgeted with the cost matrix. The primary scope of the project was to offer a common language to address the appearance issues of structural steel used in exposed locations.
M.2135 Guidelines for evaluation of radio interface technologies for IMT-Advanced
This Report provides guidelines for both the procedure and the criteria (technical, spectrum and service) to be used in evaluating the proposed IMT-Advanced radio interface technologies (RITs) or Sets of RITs (SRITs) for a number of test environments and deployment scenarios for evaluation. These test environments are chosen to simulate closely the more stringent radio operating environments. The evaluation procedure is designed in such a way that the overall performance of the candidate RIT/SRITs may be fairly and equally assessed on a technical basis. It ensures that the overall IMT-Advanced objectives are met.
All rights reserved. No part of this publication may be reproduced, by any means whatsoever, without written permission of ITU.
Rep. ITU-R M.2135-1
1
performance evaluation理工英语4
performance evaluation理工英语4Performance evaluation is an essential tool in the workplace for assessing and improving an employee's performance. It is a process that involves setting clear goals and expectations, monitoring progress, providing feedback, and identifying areas for development. This article will discuss the importance of performance evaluation, the key components of an effective evaluation process, and best practices for conducting evaluations.Importance of Performance Evaluation:Performance evaluation plays a critical role in creating a high-performance culture within an organization. It helps employees understand what is expected of them, provides feedback on their performance, and identifies opportunities for growth and development. By monitoring and evaluating employee performance, organizations can identify areas for improvement, address performance issues, and recognize and reward top performers.Key Components of an Effective Evaluation Process:1. Goal Setting: The first step in the performance evaluation process is to establish clear, measurable goals and expectationsfor each employee. Goals should be specific, challenging, and achievable, and should align with the organization's overall objectives.2. Ongoing Feedback: Regular feedback is essential for helping employees understand how their performance is being evaluated and identify areas for improvement. Managers should provide both positive feedback to reinforce good performance and constructive feedback to address areas that need improvement.3. Performance Appraisal: At the end of a performance evaluation period, managers should conduct a formal performance appraisal to review the employee's performance against the established goals and expectations. This appraisal should include a discussion of strengths, areas for improvement, and development opportunities.4. Development Planning: Following the performance appraisal, managers and employees should collaborate on a development plan that outlines specific actions and resources needed to support the employee's professional growth and development.Best Practices for Conducting Performance Evaluations:1. Be Transparent: Communicate the evaluation process and criteria to employees, so they understand how their performance will be assessed.2. Provide Timely Feedback: Offer feedback on a regular basis, so employees have the opportunity to address performance issues and make improvements.3. Focus on Behavior: Evaluate employees based on their behaviors and actions, rather than personal characteristics or traits.4. Use Multiple Data Sources: Gather feedback from multiple sources, such as peers, subordinates, and customers, to gain a comprehensive view of an employee's performance.5. Document Performance: Keep detailed records of employee performance throughout the evaluation period to support performance ratings and feedback.In conclusion, performance evaluation is a vital process for assessing and improving employee performance. By setting clear goals, providing ongoing feedback, conducting performance appraisals, and developing action plans, organizations can help employees reach their full potential and contribute to the success of the organization. By following best practices forconducting evaluations, organizations can create a culture of continuous improvement and excellence.。
Performance of a Solar Chimney Under Egyptian Weather Conditions
pt P Pr q’’ r, x R Ra Rex ReD Rch Rcoll S t T U u,w
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50
A. Mostafa; M. F. Sedrak; Adel M. Abdel Dayem / Energy Science and Technology Vol.1 No.1 2011
needed. Thus solar radiation causes a constant updraft in the chimney. The energy is converted into mechanical energy by pressure-staged wind turbines at the base of the chimney, and into electrical energy by conventional generators. The productivity of solar chimney power plant depends on many factors. They are grouped in basic geometrical parameters such as collector radius, chimney height, chimney radius, canopy (absorber) height, which is the height of collector cover from ground, climate conditions, physical and mechanical properties of the different components, and turbine characteristics. The first large-scale 200 MW solar chimney power plant in the world was commissioned at 1982 in Manzanares – Spain with a great potential of such power plant. From that date many small-scale plants were installed, the most of research work in that area is a numerical simulation due to high initial cost of such plants. The small-scale plants cannot give enough power to rotate a practical plant. Economic appraisals based on experience and knowledge gathered so far have shown that even solar chimneys rated at 100 and 200 MW are capable of generating energy at costs comparable to those of conventional power plants [1]. Accordingly Mullett [2] developed a numerical analysis for the solar chimney of solar chimney at Manzanares, Spain. He showed that, the overall efficiency was directly related to the height of the chimney and is shown to be about l% for a height of 1000 m. In addition Pasumarthi et al. [3] validated a numerical simulation of Manzanares plant within 20% in the exit velocity, and within 9.5% of the electric power output was attained. On that way, Bilgen et al. [4] showed that solar chimney power plants at high latitudes may had satisfactory thermal performance and produce as much as 85% of the same plants in southern locations with horizontal collector field. The overall thermal performance of these plants was a little less than 0.5%. A numerical simulation was performed by Tingzhen et al. [5] to analyze the characteristics of heat transfer and air flow in the solar chimney power plant system. They showed that the relative static pressure decreased while the velocity increased significantly inside the system with the increase of solar radiation. Moreover, Gannon et al. [6], Fluri et al.
离子色谱仪检定及整机性能不确定度评定
化学分析计量CHEMICAL ANALYSIS AND METERAGE第30卷,第1期2021年1月V ol. 30,No. 1Jan. 202167doi :10.3969/j.issn.1008–6145.2021.01.014离子色谱仪检定及整机性能不确定度评定王树加1,黎佩珊2,陈晓丽1,陈佩丽1,苏秋成1(1.中国科学院广州能源研究所,广州 510640; 2.广州特种承压设备检测研究院,广州 510100)摘要 介绍电导检测器离子色谱检定方法。
采用氯离子标准物质,使用泵流量设定值误差、流量稳定性、基线噪声、基线漂移、最小检测浓度、线性相关性、整机性能定性定量重复性等参数对离子色谱仪进行检定。
同时用5 μg /mL 氯离子为检测离子,结合JJF 1059.1–2012 《测量不确定度评定与表示》对整机性能不确定度来源进行考察。
结果表明:在置信区间为95%时(包含因子k =2),氯离子的质量浓度为(4.94±0.35)μg /mL 。
经不确定度分析,标准工作曲线拟合引入的不确定度最大,其次是样品的重复随机测试过程。
关键词 离子色谱仪;检定;整机性能;不确定度中图分类号:O657.7 文献标识码:A 文章编号:1008–6145(2021)01–0067–05Verification of ion chromatograph and evaluation of uncertainty of overall performanceWang Shujia 1, Li Peishan 2, Chen Xiaoli 1, Chen Peili 1, Su Qiucheng 1(1. Guangzhou Institute of Energy Conversion , Chinese Academy of Sciences, Guangzhou 510640, China; 2. Guangzhou Special Pressure Equipment Inspection and Research Institute, Guangzhou 510100, China)Abstract The verification method of ion chromatograph was introduced. Chloride ion was used as the standard material, the pump flow setting error value, flow stability, baseline noise, baseline drift, minimum detection concentration, linear correlation, qualitative and quantitative repeatability of the whole machine were used for verification ion chromatograph. At the same time, 5 μg /mL chloride ion was used as the detection ion, according to JJF 1059.1–2012 Evaluation and Expression of Uncertainly in Measurement, the source of uncertainty of the overall performance in the measurement process was evaluated. The result showed that when the confidence interval was 95% (the contains factor k was 2), the standard quality control sample of chloride ion was (4.94±0.35) μg /mL. After uncertainty analysis, the uncertainty introduced by the standard working curve fitting was the largest, followed by the repeated random testing process of samples.Keywords ion chromatograph; verification; overall performance; uncertainty离子色谱是高效液相色谱的一种,主要应用于阴阳离子的分析检测,可以同时测定多种离子[1],利用待测组分的保留时间进行定性[2],以色谱峰高或峰面积进行样品的定量[3]。
03-AESSIMS俄歇电子能谱 材料研究方法
即:样品表面原子受到电子束的轰击而电离,在电离原子的退激发过程中会释放出俄歇电子,这种电子具有对应于元素种类的固有能量,如:KLL俄歇电子能量E KLL= E K−2E LE K、E L分别为K、L能级的结合能数据收取电子探测器屏蔽罩溅射离子枪电子枪扫描电源样品(A)(B)(C)微量纯银的三种AES图谱Al AlSi 24.1 18.6 Al 8.4 6.1 Mg 0.7 2.2 Ca 1.8 6.3 B 3.0 4.1 F 1.8 0.4 O 61.1 61.8断口表面距表面较难测出Na各有特征峰Mo 2C石墨SiC硅片上的镍铬合金的深度分布(层厚确定)氧化膜镍铬硅片Au-Ni-Cu体系的深度分布以俄歇信号峰高作纵坐标以原子浓度作纵坐标浸泡前SBF 液中浸泡1h 后表面层组份变化不大X侵蚀后表面Na、Ca减少,富硅材料的化学稳定性内部(原始表面)钠钙玻璃受水侵蚀后表面的AES图谱CaOSi SFU 未处理的玻璃瓶二氟乙烷处理的玻璃瓶处理的玻璃瓶(耐久性最好)离子探针装置X溅射过程中能量和动量转换离子能量分析质量分析器源二次离子一次离子离子检测深度剖面分析图二次离子像SIMS原理示意图SIMS装置的构成SIMS测定Li2O-Al2O3-SiO2玻璃表面结构Depth profile curve of three-layered coating “A”on stainless steel substrate Sputtering times (s)0 200 400 600 800 1000 1200 1400 1600C o u nts 10610510410310102oxidized steel layeralumina layer Alumina/steel interphaseAl +10Fe +10Cr+10Ni +10CsFe +40CsNi +20CsCr +N a /S i-SiO2抗碱涂层玻璃纤维表面的BaO-TiO2uncoated fiber coated fiber 未涂层的E-玻璃纤维增强水泥材料在50°C 水中放置28d 后的SEM 照片×1000×1000×300×300×1000×1000×1000涂层的E-玻璃纤维增强水泥材料在50ºC 水中放置60d 后的SEM 照片100°C 1N NaOH 中1.5hSNMS spectrum of a 40BaO-40TiO 2-20SiO 2coatingtime (s)r e l a t i v e i n t e n s i t y (a .u .)侵蚀前SNMS spectrum of a 40BaO-40TiO 2-20SiO 2coatingtime (s)侵蚀前12000s=96nmSNMS spectrum of a 40BaO-40TiO 2-20SiO 2coating after corroded in 1N NaOH solution at 60°C after 144 h.time (s)Sputtering rate: 1s~0.008 nmr e l a t i v e i n t e n s i t y (a .u .)侵蚀后SNMS spectrum of the triple TiO 2/40BaO-40TiO 2-20SiO coatings on a silicate glass slide after corroded in 2N NaOH solution at 80°C after 72 h.time (s)Sputtering rate: 1s~0.008 nm多层膜的耐久性内部Al2O3concentration distributions of SiO2coated float glasses, annealed at 500 °C for different timesDifferent oxide concentration distributions of 4.6 mol%Na2O –95.4 mol% SiO2coated float glass, annealed at 500 °C for1020 min, also with a measurement of the major constituent SiO2)2(.2),(Dt x x erfc c c t x c Au o −−=涂在Ag上的果糖的SIMS谱氧化镁纯镁涂在Ag上的腺嘌呤的SIMS谱涂在Ag上的苯哌啶醋酸甲脂与柯卡因混合物的SIMS谱。
E2096
3.3.4RFT system ,n —the electronic instrumentation,probes,and all associated components and cables required forperforming RFT.3.3.5RFT system reference standard ,n —a reference stan-dard with specified artificial flaws,used to set up and standard-ize a remote field system and to indicate flaw detectionsensitivity.3.3.6sample rate —the rate at which data is digitized fordisplay and recording,in data points per second.3.3.7strip chart ,n —a diagram that plots coordinates ex-tracted from points on a phase-amplitude diagram versus timeor axial position (Fig.1c).3.3.8zero point ,n —a point on the phase-amplitude diagramrepresenting zero detector output voltage.3.3.8.1Discussion —Data on the phase-amplitude diagramare plotted with respect to the zero point.The zero point isseparate from the nominal point unless the detector is config-ured for zero output in nominal tube.The angle of a flawindication is measured about the nominal point.3.4Acronyms:3.4.1RFT ,n —remote field testing4.Summary of Practice4.1The RFT data is collected by passing a probe through each tube.The electromagnetic field transmitted from the exciter to the detector is affected by discontinuities;by the dimensions and electromagnetic properties of the tube;and by objects in and around the tube that are ferromagnetic or conductive.System sensitivity is verified using the RFT system reference standard.System sensitivity and settings are checked and recorded prior to and at regular intervals during the examination.Data and system settings are recorded in a manner that allows archiving and later recall of all data and system settings for each tube.Interpretation and evaluation are carried out using one or more flaw characterization standards.The supplier generates a final report detailing the results of the examination.5.Significance and Use 5.1The purpose of RFT is to evaluate the condition of the tubing.The evaluation results may be used to assess the likelihood of tube failure during service,a task which is not covered by this practice.5.2Principle of Probe Operation —In a basic RFT probe,the electromagnetic field emitted by an exciter travels outwards through the tube wall,axially along the outside of tube,andFIG.1A and B:Typical Phase-Amplitude Diagrams Used in RFT;C:Generic Strip Chart WithFlawback through the tube wall to a detector 5(Fig.2a).5.2.1Flaw indications are created when (1)in thin-walledareas,the field arrives at the detector with less attenuation andless time delay,(2)discontinuities interrupt the lines ofmagnetic flux,which are aligned mainly axially,or (3)discon-tinuities interrupt the eddy currents,which flow mainly cir-cumferentially.A discontinuity at any point on the through-transmission path can create a perturbation;thus RFT hasapproximately equal sensitivity to flaws on the inner and outerwalls of the tube.55.3Probe Configuration —The detector is typically placedtwo to three tube diameters from the exciter,in a locationwhere the remote field dominates the direct-coupling field.5Other probe configurations or designs may be used to optimizeflaw detection,as described in 9.3.5.4Comparison with Conventional Eddy-Current Testing —Conventional eddy-current test coils are typically configured tosense the field from the tube wall in the immediate vicinity ofthe emitting element,whereas RFT probes are typically de-signed to detect changes in the remote field. 6.Basis of Application 6.1Personnel Qualification :6.1.1Personnel performing examinations to this practice shall be qualified as specified in the contractual agreement.6.1.2Recommendations for qualification as an RFT system operator (Level I)are as follows:6.1.2.1Forty hours of RFT (Level I)classroom training.6.1.2.2Written and practical examinations similar to those described by ASNT SNT-TC-1A or Can CGSB 48.9712-95.6.1.2.3Two hundred and fifty hours of field experience under the supervision of a qualified RFT Level II,50%of which should involve RFT instrumentation setup and opera-tion.6.1.3Recommendations for qualification as an RFT data analyst (Level II)are as follows:6.1.3.1Forty hours of RFT (Level II)classroom training.6.1.3.2Written and practical examinations similar to those described by ASNT SNT-TC-1A or Can CGSB 48.9712-95.6.1.3.3Fifteen hundred hours of field experience under thesupervision of a qualified RFT Level II or higher,25%ofwhich should involve RFT data analysis.N OTE 1—At the time of approval of this practice,no nationally or 5Schmidt,T.R.,“The Remote Field Eddy Current Inspection Technique,”Materials Evaluation ,V ol.42,No.2,Feb.1984,pp.225-230.N OTE 1—Arrows indicate flow of electromagnetic energy from exciter to detector.Energy flow is perpendicular to lines of magnetic flux.FIG.2RFTProbesinternationally recognized guideline for personnel qualification in RFT was available.N OTE2—Eddy-current training provides some useful background to RFT training.Previous Level II eddy-current certification may count towards50%of training and experience hours for RFT Level I,provided that the remaining experience hours are entirely involved in RFT instrumentation setup and operation.6.2Qualification of Nondestructive Testing Agencies—If specified in the contractual agreement,NDT agencies shall be qualified and evaluated as described in Practice E543,with reference to sections on electromagnetic testing.The appli-cable edition of Practice E543shall be specified in the contractual agreement.7.Job Scope and Requirements7.1The following items may require agreement between the using parties and should be specified in the purchase document or elsewhere:7.1.1Location and type of tubed component to be exam-ined,design specifications,degradation history,previous non-destructive examination results,maintenance history,process conditions,and specific types offlaws that are required to be detected,if known.7.1.2The maximum window of opportunity for work. (Detection of smallflaws may require a slower probe pull speed,which will affect productivity.)7.1.3Size,material grade and type,and configuration of tubes to be examined.7.1.4A tube numbering or identification system.7.1.5Extent of examination,for example:complete or partial coverage,which tubes and to what length,whether straight sections only,and the minimum radius of bends that can be examined.7.1.6Means of access to tubes,and areas where access may be restricted.7.1.7Type of RFT instrument and probe;and description of reference standards used,including such details as dimensions and material.7.1.8Required operator qualifications and certification. 7.1.9Required tube cleanliness.7.1.10Environmental conditions,equipment,and prepara-tions that are the responsibility of the purchaser;common sources of noise that may interfere with the examination.N OTE3—Nearby welding activities may be a major source of interfer-ence.7.1.11Complementary methods or techniques(including possible tube removal)that may be used to obtain additional information.7.1.12Acceptance criteria to be used in evaluatingflaw indications.7.1.13Disposition of examination records and reference standards.7.1.14Format and outline contents of the examination report.8.Interferences8.1This section describes items and conditions which may compromise RFT.8.2Material Properties:8.2.1Variations in the material properties of ferromagnetic tubes are a potential source of inaccuracy.Impurities,segrega-tion,manufacturing process,grain size,stress history,present stress patterns,temperature history,present temperature,mag-netic history,and other factors will affect the electromagnetic response measured during RFT.The conductivity and perme-ability of tubes with the same grade of material are often measurably different.It is common tofind that some of the tubes to be examined are newer tubes with different material properties.8.2.2Permeability variations may occur at locations where there was uneven temperature or stress during tube manufac-ture,near welds,at bends,where there were uneven heat transfer conditions during service,at areas where there is cold working(such as that created by an integralfinning process), and in other locations.Indications from permeability variations may be mistaken for,or obscureflaw indications.Effects may be less severe in tubes that were stress-relieved during manu-facture.8.2.3Residual stress,with accompanying permeability variations,may be present when discontinuities are machined into a reference standard,or during the integralfinning process.8.2.4The RFT is affected by residual magnetism in the tubing,including residual magnetism created during a previous examination using another magnetic method.Tubes with sig-nificant residual magnetism should be demagnetized prior to RFT.8.3Ferromagnetic and Conductive Objects:8.3.1Objects near the tube that are ferromagnetic or con-ductive may reduce the sensitivity and accuracy offlaw characterization in their immediate vicinity.Such objects may in some cases be mistaken forflaws.Knowledge of the mechanical layout of the component to be examined is recom-mended.Examples of ferromagnetic or conductive objects include:tube support plates,baffle plates,end plates,tube sheets,anti-vibration bars,neighboring tubes,impingement plates,loose parts,and attachments clamped or welded to a tube.N OTE4—Interference from ferromagnetic or conductive objects can be of practical use when RFT is used to confirm the position of an object installed on a tube or to detect where objects have become detached and have fallen against a tube.8.3.2Neighboring Tubes:8.3.2.1In areas where there is nonconstant tube spacing (bowing)or where tubes cross close to each other,there are indications which may be mistaken forflaws.8.3.2.2Neighboring or adjacent tubes,according to their number and position,create an offset in the phase.This phenomenon is known as the bundle effect and is a minor source of inaccuracy when absolute readings in nominal tube are required.8.3.2.3In cases where multiple RFT probes are used simul-taneously in the same heat exchanger,care should be taken to ensure adequate spacing between different probes.8.3.3Conductive or magnetic debris in or on a tube that may create false indications or obscureflaw indications should be removed.8.4Tube Geometry Effects:8.4.1Due to geometrical effects(as well as to the effects of permeability variations described in8.2.2),localized changes in tube diameter such as dents,bulges,expansions,and bends create indications which may obscure or distortflaw indica-tions.8.4.2Reductions in the internal diameter may require a smaller diameter probe that is able to pass through the restriction.In the unrestricted sections,flaw sensitivity is likely to be limited by the smaller probefill factor.8.4.3RFT End Effect—Thefield from the exciter is able to propagate around the end of a tube when there is no shielding from a tube sheet or vessel shell.Aflaw indication may be obscured or distorted if theflaw or any active probe element is within approximately three tube diameters of the tube end. 8.5Instrumentation:8.5.1The operator should be aware of indicators of noise, saturation,or signal distortion particular to the instrument being used.Special consideration should be given to the following concerns:8.5.1.1In a given tube,an RFT system has a frequency where theflaw sensitivity is as high as practical without undue influence from noise.8.5.1.2Saturation of electronic components is a potential problem in RFT because signal amplitude increases rapidly with decreasing tube wall thickness.Data acquired under saturation conditions is not acceptable.8.5.2Instrument-induced Phase Offset—During the ampli-fication andfiltering processes,instruments may introduce a frequency-dependent time delay which appears as a constant phase offset.The instrument phase offset may be a source of error when phase values measured at different frequencies are compared.9.RFT System9.1Instrumentation—The electronic instrumentation shall be capable of creating exciter signals of one or more frequen-cies appropriate to the tube material.The apparatus shall be capable of phase and amplitude analysis of detector outputs at each frequency,independent of other frequencies in use simul-taneously.The instrument shall display data in real time.The instrument shall be capable of recording data and system settings in a manner that allows archiving and later recall of all data and system settings for each tube.9.2Driving Mechanism—A mechanical means of traversing the probe through the tube at approximately constant speed may be used.9.3Probes—The probes should be of the largest diameter practical for the tubes being examined,leaving clearance for debris,dents,changes in tube diameter,and other obstructions. The probes should be of an appropriate configuration and size for the tube being examined and for theflaw type or types to be detected.Probe centering is recommended.9.3.1Absolute Detectors—Absolute detectors(Fig.2c)are commonly used to characterize and locate large-volume and gradual metal loss.9.3.2Differential Detectors—Differential detectors(Fig.2c) are commonly used to characterize and locate large-volume and gradual metal loss,and also tend to maximize the response from small volumeflaws and abrupt changes along the tube length.9.3.3Array Detector—Array detectors use a configuration of multiple sensing elements(Fig.2c).Each element is sensitive to a discrete section of the tube circumference.The elements may be oriented with their axes aligned axially or radially with respect to the tube.N OTE5—The detector’s response represents an average of responses to allflaws within its sensing area.9.3.4Exciter and Detector Configurations—Probes may have multiple exciters and detectors in a variety of configura-tions(see,for example,Fig.2b).These configurations may reduce interference from support plates and other conductive objects.9.4Data Displays:9.4.1The data display should include a phase-amplitude diagram(Fig.1a and1b).9.4.2Strip Charts—Coordinates that may be displayed on strip charts include:horizontal position,vertical position, angular position,or radial position.Angular position may represent phase.Angular position and the logarithm of radial position for an absolute detector may be linearly related to overall wall thickness.10.RFT Tube Standards10.1The RFT tube standards should be of the same nominal dimensions,material type,and grade as the tubes to be examined.In the case where a tube standard identical to the tubes to be examined is not available,a demonstration of examination equivalency is recommended.Allowable differ-ences in material and dimensional variations are specified in 11.6.2.10.2The RFT system reference standard shall not be used forflaw characterization unless the artificialflaws can be demonstrated to be similar to theflaws detected.10.3Typical Artificial Flaws in Flaw Characterization Standards:10.3.1Through,Round-Bottomed,and Flat-Bottomed Holes—Holes of different depths are used for pit characteriza-tion,and may be machined individually or in groups.Drill and milling tools of different diameters can be used to produce differentflaw volumes for a given depth of metal loss(Fig.3a).10.3.2Circumferential Grooves—A circumferential groove is an area of metal loss whose depth at any axial location is uniform around the tube circumference.Short grooves,with a maximum axial length of less than one half a tube diameter, may be used to simulate small-volume metal loss.Grooves with an axial length of several tube diameters may be used to simulate uniform wall loss(Fig.3b).10.3.3One-Sided Flaws—Metal loss is referred to as one-sided if it is predominantly on one side of a tube.Outside diameter long,flatflaws typically simulate tube-to-tube wear. Circumferentially tapered one-sidedflaws typically simulate tube wear at support plates.Flaws tapered in both axial and circumferential directions typically simulate steam erosion adjacent to the tube support(Fig.3c).10.4RFT System Reference Standards—Flaw depths are specified by giving the deepest point of theflaw as apercentageof the measured average wall thickness.Flaw depths shall bemeasured and accurate to within 620%of the depth specifiedor 60.003in.(60.08mm),whichever is smaller.All otherflaw dimensions (such as length and diameter)shall be accurateto within 60.010in.(60.25mm)of the dimension specified.Angles shall be accurate to within 65°.10.5Artificial Flaws for RFT System Reference Standards :10.5.1The RFT system reference standard has specificartificial flaws.It is used to set up and standardize a remotefield system and to indicate flaw detection sensitivity.Unlessotherwise specified by the purchaser,the artificial flaws for theRFT system reference standard are as follows:10.5.1.1Through-Hole —A through-hole (Fig.4,Flaw A)whose diameter is equal to the tube wall thickness multipliedby a specified factor.For tubes of outside diameter less than1.000in.(25.40mm),the factor is 1.For tubes of outsidediameter greater than or equal to 1.000in.,the factor is 1.5.10.5.1.2Flat-Milled Flaw —A flat-milled flaw (Fig.4,FlawB)of a depth of 50%and axial length one half the tubenominal outside diameter.The flat should be side-milled usinga milling tool of a diameter of 0.250in.(6.35mm)to createrounded corners.10.5.1.3Short Circumferential Groove —A short circumfer-ential groove (Fig.4,Flaw C)of a depth of 20%and axiallength of 0.625in.(15.88mm).Edges shall be angled at 105°as indicated in the insert in Fig.4.10.5.1.4Wear Scar —A simulated wear scar from a tube support plate (Fig.4,Flaw D),consisting of a circumferentially tapered groove,40%deep,extending over 180°of the tube circumference.Axial length measured at the bottom surface of the flaw shall be 0.625in.(15.88mm).Edges shall be angled at 105°as indicated in the insert in Fig.4.10.5.1.5Tapered Flaw —A tapered flaw simulating near-tube-support erosion (Fig.4,Flaw E)consisting of a groove,60%deep,tapered circumferentially,and in both directions axially.The steep side of the flaw shall be angled at 65°to the tube axis.The shallow side of the flaw shall be axially tapered so that it extends an axial distance of four tube diameters from the deepest point.The circumferential extent at the maximum point shall be 90°.10.5.1.6Long Circumferential Groove —A long circumfer-ential groove (Fig.4,Flaw F)of a depth of 20%and recommended axial length of two tube diameters.Length is optional according to application.Edges shall be angled at 105°,as indicated in the insert in Fig.4.10.6Simulated Support Structures :10.6.1The RFT tube standards may have simulated support structures to represent heat exchanger bundle conditions.10.6.2Support Plates —Support plates may be simulated by drilling a single hole through a solid flat plate with a clearance of up to 0.015in.(0.38mm)beyond the outside diameter of the RFT tube standard.To prevent the field frompropagatingN OTE 1—Not to scale.FIG.3Typical Artificial Discontinuities Used for Flaw Characterization ReferenceStandardsaround the plate,the minimum distance from the edge of thetube hole to the edge of the plate should be greater than twotube diameters,unless a smaller dimension can be demon-strated to be adequate.For example,the simulated tube supportplate for a 1-in.diameter tube should be at least a 5-in.(127.00-mm)square or a 5-in.diameter circle.The accuracy ofthe support plate simulation may be increased if the simulatedplate is of the same thickness and material as the support platesin the component to be examined.10.7Manufacture and Care of RFT Tube Standards :10.7.1Drawings —For each RFT tube standard,there shallbe a drawing that includes the as-built measured flaw dimen-sions,material type and grade,and the serial number of theactual RFT tube standard.10.7.2Serial Number —Each RFT tube standard shall beidentified with a unique serial number and stored so that it canbe obtained and used for reference when required.10.7.3Flaw Spacing —Artificial flaws should be positionedaxially to avoid overlapping of indications and interferencefrom end effects.10.7.4Machining personnel shall use proper machiningpractices to avoid excessive cold-working,over-heating,andundue stress and permeability variations.10.7.5Tubes should be stored and shipped so as to preventmechanical damage.11.Procedure11.1If necessary,clean the inside of the tubes to remove obstructions and heavy ferromagnetic or conductive debris.11.2Instrument Settings :11.2.1Operating Frequency —Using the appropriate RFT system reference standard,the procedures in 11.2.1.1or 11.2.1.2are intended to help the user select an operating frequency.Demonstrably equivalent methods may be used.If the RFT system is not capable of operating at the frequency described by this practice,the supplier shall declare to the purchaser that conditions of reduced sensitivity may exist.11.2.1.1Using the RFT system reference standard,and referring to the phase-amplitude diagram,set the frequency to obtain a difference of 50to 120°between the angles of indication for the reference through-hole (Flaw A in Fig.4)and a 20%circumferential groove of a axial length of 0.125in.(3.18mm)(as permitted for Flaw F in Fig.4).11.2.1.2If phase is measured and displayed,set the fre-quency so that a circumferential groove with an axial length of two tube diameters (as permitted for Flaw F in Fig.4)creates a phase shift of between 18and 22°in the absolute detector output.11.2.2Secondary Frequencies —To detect and characterize some damage mechanisms,it may be necessary to use second-ary frequencies to provide additional information.11.2.3Pull Speed —Determine a pull speed appropriate to the frequency,sample rate,and required sensitivity to flaws.11.2.4Set other instrument settings as appropriate to achieve the minimum required sensitivity toflaws.N OTE 1—Not to scale.See 10.5for tolerances and details.FIG.4Manufacturing Reference for RFT System ReferenceStandardN OTE6—Factors which influence sensitivity toflaws include,but are not limited to:operating frequency,instrument noise,instrumentfiltering, digital sample rate,probe speed,coil configuration,fill factor,probe travel noise,and interferences described in Section8.11.3Ensure that the system yields the minimum required sensitivity to allflaws on the RFT system reference standard at the examination pull speed.For aflaw to be considered detectable,its indication should exceed the ambient noise by a factor of at least3,unless otherwise specified by the purchaser. An exception may be made when the purchaser requires only a large-volume metal loss examination,in which case,sensi-tivity should be demonstrated for specified large-volumeflaws on the RFT system reference standard.11.4Acquire and record data from the RFT system refer-ence standard andflaw characterization standards at the se-lected examination pull speed.11.5Acquire and record data from the tubes to be examined. Maintain as uniform a probe speed as possible throughout the examination to produce repeatable indications.11.5.1Record data and system settings in a manner that allows archiving and later recall of all data and system settings for each tube.Throughout the examination,data shall be permanently recorded,unless otherwise specified by the pur-chaser.11.5.2For maintaining system consistency throughout the examination,monitor typical RFT responses from support plates and tube ends,or monitor the absolute phase in the nominal tube.If conditions change,appropriate adjustments need to be made in accordance with11.6.11.6Compensation for Material and Dimensional Differ-ences:11.6.1To compensate for differences in dimensional and material properties,the system may be re-normalized where appropriate by adjusting frequency or gain,or both.To re-normalize,adjust the settings so that one of the following values remains equal in the reference standard and in a nominal examined tube:11.6.1.1The amplitude and angular position of a support plate indication on the phase-amplitude diagram,or11.6.1.2The angular difference between a support plate indication and the tube-exit indication on the phase-amplitude diagram,or11.6.1.3The absolute phase in the nominal tube.N OTE7—For an alternate method of compensating for differences in dimensional and material properties,see11.12.11.6.2The frequencies used in the reference standards and in the tubes to be examined should not differ by more than a factor of two.If the factor exceeds this value,the reference standard should be considered inappropriate and replaced with one that more accurately represents the material to be tested.11.6.3After frequency and gain adjustments have been made,apply appropriate compensations to the examination sample rate and pull speed.11.7Compensation for Ferromagnetic or Conductive Ob-jects:11.7.1Techniques that may improve RFT results near inter-fering ferromagnetic or conductive objects include:11.7.1.1Comparison of baseline or previous examination data with the current examination data.11.7.1.2Comparison of indications from known objects with and without metal loss.(Obtain a reference indication from a typical object on or near the nominal tube or from a simulated object on a reference standard.)11.7.1.3The use of special probe coil configurations. 11.7.1.4Processing of multiple-frequency signals to sup-press nonrelevant indications.11.7.1.5The use of a complementary method or technique (see11.12).11.8System Check—At regular intervals,carry out a system check using the RFT system reference standard to demonstrate system sensitivity and operating parameters to the satisfaction of the purchaser.Carry out a system check prior to starting the examination,after anyfield compensation adjustments in accordance with11.6,at the beginning and end of each work shift,when equipment function is in doubt,after a change of personnel,after a change of any essential system components, and overall at a minimum of every four hours.If theflaw responses from the RFT system reference standard have changed substantially,the tubes examined since the last system check shall be reexamined.11.9Interpret the data(identify indications).11.10Note areas of limited sensitivity,using indications from the RFT system reference standard as an indicator offlaw detectability.11.11Using aflaw characterization standard,evaluate rel-evant indications in accordance with acceptance criteria speci-fied by the purchaser.11.11.1A common parameter used as aflaw depth indicator is the angle of an indication on the phase-amplitude diagram. Different angle-depth calibration curves may be used according toflaw volume,as indicated by the amplitude of the indication on the phase-amplitude diagram.11.12If desired,examine selected areas using an appropri-ate complementary method or technique to obtain more infor-mation,adjusting results where appropriate.11.13Compile and present a report to the purchaser.12.Report12.1The following items may be included in the examina-tion report.All the following information should be archived, whether or not it is required in the report.12.1.1Owner,location,type,and serial number of compo-nent examined.12.1.2Size,material type and grade,and configuration of tubes examined.12.1.3Tube numbering system.12.1.4Extent of examination,for example,areas of interest, complete or partial coverage,which tubes,and to what length.12.1.5Personnel performing the examination and their qualifications.12.1.6Models,types,and serial numbers of the components of the RFT system used,including probe and extension length.12.1.7For the initial data acquisition from the RFT system reference standard,a complete list of all relevant instrument settings and parameters used,such as operating frequencies, probe drive voltages,gains,types of mixed or processed channels,and probe speed.The list shall enable settings tobe。
aes能谱测试流程
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作文自动批阅技术 批阅流程
作文自动批阅技术批阅流程English Answer:Automated Essay Scoring Technology: The Grading Process.Automated essay scoring (AES) technology has emerged as a valuable tool for educators and assessment professionals, offering numerous advantages over traditional human grading. AES employs sophisticated algorithms and natural language processing (NLP) techniques to analyze essays and assign grades or scores. The grading process typically involves a series of steps:1. Text Preprocessing: The essay is first preprocessedto clean and tokenize the text. This includes removing punctuation, stop words, and any non-essential characters. The resulting text is then converted into a format that the algorithm can understand.2. Feature Extraction: The preprocessed text isanalyzed to extract a set of features that are relevant to essay scoring. These features may include word frequency, sentence length, part-of-speech tagging, and more. The features are then used to build a model that predicts the essay score.3. Model Training: A machine learning algorithm is used to train the model on a large dataset of essays with known scores. The algorithm learns to identify patterns and relationships in the features that are associated with different score levels.4. Model Evaluation: The trained model is then evaluated on a separate dataset of unseen essays. This process ensures that the model is accurate and reliable in scoring essays from different sources and topics.5. Scoring: Once the model has been trained and evaluated, it can be used to score new essays. The essay is preprocessed and analyzed to extract the same features as those used in the training dataset. The model then predicts the score based on the extracted features.Advantages of AES:AES offers several key advantages over traditional human grading:Efficiency: AES can grade essays much faster than human graders, saving time and resources.Objectivity: AES algorithms are consistent and unbiased, eliminating the subjectivity and potential biases that can arise in human grading.Scalability: AES technology can be deployed to grade large volumes of essays, making it suitable for large-scale assessments.Data-driven Insights: AES can provide rich data and insights into essay performance, helping educators identify areas for improvement.Challenges of AES:While AES offers numerous benefits, there are also some challenges to consider:Complexity: Developing and deploying AES systems can be complex and requires specialized expertise.Contextual Understanding: AES systems may struggle to fully understand the contextual meaning and nuances of essays, which can impact the accuracy of the scores.Bias: AES systems can be biased if the training data is biased, leading to unfair or inaccurate scoring.Conclusion:Automated essay scoring technology is a rapidly evolving field with the potential to revolutionize the way essays are graded. While there are still challenges to overcome, the advantages of AES make it a valuable tool for educators and assessment professionals. As technology continues to improve, it is likely that AES will play anincreasingly important role in the assessment of writing skills.Chinese Answer:作文自动批阅技术,批阅流程。
AES简介
3.AES的算法(编译过程) 3.AES的算法(编译过程) 的算法
加密的步骤: 加密的步骤: • 计算子密钥 • 求第一轮加密结果 初始轮密钥加: ①初始轮密钥加:B1=B0+K0 进行字节变换, ②对B1进行字节变换,行移位,列混 进行字节变换 行移位, 合运算得到B1' 合运算得到 ③得到第一轮加密结果 C1=B2=B1'+K1 • 用迭代的方法经 轮加密 用迭代的方法经10轮
把B,K表示成矩阵
32 43 B0 = f6 ad 31 e0 5a 31 37 30 98 07 8d a 2 34 88
2b 28 ab 09 7e ae f 7 ef K 0 = (W0 ,W1 ,W2 , W3 ) = 15 d 2 15 4 f 16 a6 88 3c
得到第一轮子密钥K1 得到第一轮子密钥
a0 fa K = W,W,W,W) 1 (4 5 6 7 = fe 17
88 23 2a 54 a3 6c 2c 39 76 b 39 05 1
初始轮密钥加பைடு நூலகம்
B1 = B0 ⊕ K 0 32 88 43 5a = f6 30 ad 8d 19 3d = e3 be a0 f4 e2 2b 31 31 98 a2 9a c6 8d 2a e 0 2b 37 7 e ⊕ 07 15 34 16 e9 f 8 48 08 28 ae d2 a6 ab f7 15 88 09 ef 4f 3c
5.AES的发展前景 5.AES的发展前景
谢谢观看!
2012年 2012年5月6日
b8 b 4 5d e5 28 06 26 4c
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its convenience, flexibility and roaming. A user is not tied down to a LAN and can move around with relative ease while staying connected. WLANs are also easy to install. An entire network can be put together in a matter of hours rather than days without digging, drilling holes or altering construction. Finally, WLAN may be installed where rewiring is impractical. Wireless systems can be installed in different environments and users can communicate with the existing wired network through access points or wireless adapters. Although WLANs solve some problems that exist in traditional wired LANs, they also introduce new security and power management issues [1]. The risks that WLAN services present can only be mitigated rather than completely eliminated. Although there is no single solution for perfect V/LAN security, it is a strong belief that WLAN security can be enhanced to an acceptable level by a proper combination of countermeasures. As interest in wireless grew, IEEE expanded the plethora of standards under 802.11 by developing specific substandards with different specifications for bandwidth, frequency and transmission technologies [3]. 802.1 Ii is an emerging standard and in the long term, necessitates a framework for using AES for its encryption services. With the ever-expanding market for palm computers, and the inevitable networking of palm devices, it appears that encryption on limited processors may warrant closer investigation. The primary objective of this paper is to study and compare the outcome obtained from implementing AES algorithm on various platforms. In next section the algorithm is described, followed by a section on design methodology and considerations needed for implementing the algorithm. Section IV presents the design and implementation results.
Performance Evaluation of AES Algorithm on Various Development Platforms
Chirag Parikh, M.S. Department of Electrical and Computer Engineering University of Texas at San Antonio One UTSA circle San Antonio, TX 78249 cparikh2000(
Abstract
Parimal Patel, Ph.D. DepaБайду номын сангаасtment of Electrical and Computer Engineering University of Texas at San Antonio One UTSA circle San Antonio, TX 78249 parimal.patel(
authenticity of data and messages, and protecting systems from network based attacks [2]. In the past, encryption algorithms such as DES had been sufficient to handle most security needs, but it became apparent that the time of DES had quickly approached its end. With the limitations of DES's 56-bit key and the advent of faster computers, DES could no longer be considered a secure algorithm. In recent years, Triple-DES which uses 168-bit key, has taken the place of DES in an attempt to create stronger security without compromising currently accepted encryption standards. For many purposes, however, Triple-DES is simply too slow [5]. With the eminent demise of DES, the National Institute of Standards and Technology (NIST) had issued a request for submissions of stronger encryption algorithms to replace the aging DES. These algorithms had to conform to several strict requirements: it must be a block cipher with longer key length, larger block size, fast in computation and greater flexibility. After several rounds of submissions and eliminations, the NIST had narrowed the applicant pool down to five finalists out of which Advanced Encryption Standard (AES) algorithm, also known as Rijndael algorithm, was selected. While such algorithms are relatively simple and efficient to implement on high-performance processors found in current desktop and laptop computers, such may not be the case for the smaller processors found in current palm computing devices or embedded systems. Handheld computing devices of today are as powerful as the old desktop PCs costing as little as one tenth. This has been made possible by advances in CPU, memory and integration technologies. Unfortunately, battery technology has not kept pace with this development. The slow improvement in battery lifetime shows that energy consumption is and will be one of the most important factors in designing embedded systems and portable devices participating in wireless networking [1]. Wireless Local Area Network (WLAN) solutions are being increasingly adopted by various industry segments both locally and globally. Wireless LAN products can provide LAN users with access to real-time information anywhere in their organization. This mobility supports productivity and service opportunities not possible with wired networks. The most appealing aspect of WLAN is