高超声速飞行器气动外形设计课件1

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高超声速飞行器的气动性能研究

高超声速飞行器的气动性能研究在现代航空航天领域，高超声速飞行器的发展正引起广泛关注。

高超声速飞行器具备极高的飞行速度，能够在极短时间内抵达远距离目标，这使其在军事、民用等多个领域都具有巨大的应用潜力。

然而，要实现高超声速飞行器的高效、稳定飞行，对其气动性能的深入研究至关重要。

高超声速飞行器在飞行时面临着极其复杂的气动环境。

当飞行器的速度超过 5 倍音速时，空气的物理性质会发生显著变化。

此时，空气的可压缩性变得极为突出，传统的空气动力学理论和方法已不再适用。

在高超声速条件下，气流会产生强烈的激波，这些激波与飞行器表面相互作用，导致巨大的气动阻力和强烈的热效应。

此外，飞行器表面的边界层也会出现复杂的流动现象，如分离、再附等，进一步影响飞行器的气动性能。

为了研究高超声速飞行器的气动性能，研究人员采用了多种实验和数值模拟方法。

风洞实验是其中一种重要的手段。

通过在风洞中模拟高超声速飞行条件，研究人员可以测量飞行器模型表面的压力、温度和气流速度等参数，从而获取飞行器的气动特性。

然而，风洞实验也存在一些局限性，例如实验成本高、模型尺寸受限以及难以完全模拟真实飞行环境等。

数值模拟方法则为高超声速飞行器的气动性能研究提供了另一种有效的途径。

基于计算流体动力学（CFD）的数值模拟能够对飞行器周围的流场进行详细的计算和分析。

通过建立精确的数学模型和采用高效的数值算法，研究人员可以预测飞行器在不同飞行条件下的气动性能。

然而，数值模拟也面临着一些挑战，如计算网格的生成、湍流模型的选择以及计算资源的需求等。

在高超声速飞行器的气动外形设计中，减小气动阻力是一个关键目标。

常见的气动外形设计策略包括采用尖锐的前缘和后缘、优化飞行器的细长比以及设计合理的翼身融合结构等。

尖锐的前缘和后缘能够减少激波的强度和阻力，细长的外形有助于降低摩擦阻力，而翼身融合结构则可以改善飞行器的升阻比。

此外，高超声速飞行器的热防护也是一个重要问题。

由于强烈的气动加热，飞行器表面的温度会急剧升高，这对飞行器的结构强度和材料性能提出了极高的要求。

[美国高超音速项目PPT]EN-AVT-150-10-PPT

Acknowledgements
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高超声速飞行器的气动设计分析

高超声速飞行器的气动设计分析在现代航空航天领域，高超声速飞行器的发展正成为一个备受关注的焦点。

高超声速飞行器具有飞行速度快、突防能力强等显著优势，但其面临的技术挑战也极为艰巨，其中气动设计是关键环节之一。

高超声速飞行器的飞行速度通常在 5 倍音速以上，这使得其所处的气动环境与传统飞行器有很大的不同。

在这样的高速条件下，空气的压缩性和粘性效应变得极为显著。

空气不再是可以被简单视为理想气体的介质，而是呈现出复杂的物理和化学变化。

首先，来谈谈高超声速飞行器的外形设计。

为了降低阻力，其外形通常采用流线型，以减少空气的阻力和激波的产生。

尖锐的头部设计可以有效地减小激波阻力，就像针尖更容易刺破空气一样。

而机身的细长比例有助于保持飞行的稳定性和降低阻力。

同时，翼身融合的设计理念也被广泛应用，使得飞行器在高超声速飞行时能够更好地适应气流的变化。

在气动加热方面，高超声速飞行带来的强烈摩擦会导致飞行器表面温度急剧升高。

这不仅对飞行器的结构材料提出了极高的要求，也对气动设计产生了重要影响。

通过合理的外形设计，可以控制热流的分布，减少局部高温区域的出现。

例如，采用适当的前缘钝化处理，可以在一定程度上降低热流的集中。

再来说说高超声速飞行器的进气道设计。

进气道的作用是将高速气流引入发动机，为燃烧提供足够的氧气。

在高超声速条件下，进气道的设计需要考虑复杂的激波系和气流分离现象。

常见的进气道类型包括冲压式进气道和超燃冲压式进气道。

冲压式进气道在较低超声速时表现较好，而超燃冲压式进气道则更适用于高超声速飞行。

其设计需要精确计算气流的压缩、减速和混合过程，以确保发动机能够高效工作。

高超声速飞行器的控制面设计也与传统飞行器有所不同。

由于飞行速度快，控制面的响应时间和效能都会受到影响。

因此，需要采用更先进的控制策略和更高效的控制面布局。

例如，采用矢量推力技术可以增加飞行器的机动性和控制能力。

在气动设计过程中，数值模拟和实验研究是两个重要的手段。

高超声速飞行器的气动性能与挑战研究与分析

高超声速飞行器的气动性能与挑战研究与分析在当今科技飞速发展的时代，高超声速飞行器成为了航空航天领域的研究热点。

高超声速飞行器具有极高的飞行速度和复杂的气动特性，这给其设计和应用带来了诸多挑战。

本文将对高超声速飞行器的气动性能以及所面临的挑战进行深入研究与分析。

高超声速飞行器的飞行速度通常在 5 倍音速以上，这种高速飞行使得空气的流动特性发生了显著变化。

在高超声速条件下，空气不再被视为不可压缩的流体，而是呈现出强烈的压缩性和粘性效应。

这导致了飞行器表面的气动加热现象极为严重，飞行器周围的激波结构也变得异常复杂。

从气动性能的角度来看，高超声速飞行器具有独特的优势。

首先，高速度带来了快速到达目的地的能力，大大缩短了飞行时间。

其次，高超声速飞行能够突破传统飞行器的限制，实现更高效的任务执行，例如快速侦察、远程打击等。

然而，要实现这些优势，必须解决一系列的技术难题。

气动加热是高超声速飞行器面临的首要挑战之一。

当飞行器以高超声速飞行时，与空气的剧烈摩擦会产生大量的热量，使得飞行器表面温度急剧升高。

这不仅对飞行器的结构材料提出了极高的要求，还可能影响飞行器的外形和气动性能。

为了应对气动加热问题，科研人员需要研发新型的耐高温材料，同时优化飞行器的外形设计，以减少热量的产生和传递。

激波的产生和控制也是一个关键问题。

高超声速飞行器周围的激波会导致巨大的阻力，影响飞行器的飞行效率和性能。

此外，激波与边界层的相互作用还可能引发流动分离，进一步增加阻力并降低飞行器的稳定性。

为了减小激波阻力，需要对飞行器的外形进行精心设计，采用先进的流动控制技术，如等离子体控制、主动吹气等。

高超声速飞行器的气动性能还受到飞行姿态和控制面的影响。

在高速飞行条件下，飞行器的姿态变化会引起气动力和力矩的快速变化，这对飞行器的控制系统提出了很高的要求。

控制面的效率和响应速度也需要进行优化，以确保飞行器能够在复杂的飞行环境中保持稳定和可控。

此外，高超声速飞行器的气动性能研究还需要依靠先进的实验技术和数值模拟方法。

飞行器飞行原理ppt课件

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2.3 飞机飞行原理
可重复使用的放热材料
用于像航天飞机类似的可重复使用的航天器的防热。根据航天器表面不同温度的区域，采用相应的可重复使用的防热材料。
例如：机身头部、机翼前缘温度最高，采用增强碳碳复合材料，温度可耐受1593度；机身、机翼下表面前部和垂尾前缘温度高，可采用防热隔热陶瓷材料；机身、机翼上表面前部和垂尾前缘气动加热不是特别严重处，可采用防热隔热的陶瓷瓦材料；机身中后部两侧和有效载荷舱门处，温度相对较低（约350度），可采用柔性的表面隔热材料；对于温度最高的区域，采用热管冷却和强制循环冷却和发汗冷却等。
材料来制造飞机的重要受力构件和蒙皮； 2. 用隔热层来保护机内设备和人员； 3. 采用冷却液冷却结构内表面。
美国SR-71的机体结构的93%采用钛合金越过热障，达到3.3倍音速。
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2.3 飞机飞行原理
航天器的防热方法：
材料：石墨、陶瓷等。高温下的热解和相变：固液，固气，液气。应用：烧蚀法适用于不重复使用的飞船、卫星等。
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2.3 飞机飞行原理
B. 超声速飞机的机翼平面形状和布局形式
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2.3 飞机飞行原理
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2.3 飞机飞行原理
F-14 Tomcat 舰载机
米格-23
B-1 Lancer轰炸机
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2.3 飞机飞行原理
边条涡
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2.3 飞机飞行原理
超声速飞机的气动外形
鸭翼产生的脱体漩涡
机翼升力
鸭翼升力机翼升力
流体黏性和温度有关，气体温度升高，黏性增大。液体相反。
4. 可压缩性
当气体的压强改变时，其密度和体积也改变，为气体可压缩性。 5. 声速

高超声速飞行器

Design and Implementation of Neuro-Fuzzy Vector Control for Wind-Driven Doubly-Fed InductionGeneratorHany M.Jabr,Student Member,IEEE,Dongyun Lu,Student Member,IEEE,andNarayan C.Kar,Senior Member,IEEEAbstract—Wound-rotor induction generators have numerous advantages in wind power generation over other types of gener-ators.One scheme is realized when a converter cascade is used between the slip-ring terminals and the utility grid to control the rotor power.This conﬁguration is called the doubly-fed induction generator(DFIG).In this paper,a vector control scheme is devel-oped to control the rotor side voltage source converter that allows independent control of the generated active and reactive power as well as the rotor speed to track the maximum wind power point.A neuro-fuzzy gain tuner is proposed to control the DFIG.The input for each neuro-fuzzy system is the error value of generator speed,active or reactive power.The choice of only one input to the system simpliﬁes the design.Experimental investigations have also been conducted on a laboratory DFIG to verify the calculated results.Index Terms—Doubly-fed induction generator(DFIG),neuro-fuzzy,vector control,wind power generation.I.N OMENCLATURE,Real and reactive power.,Stator voltage and current.,-and-axis components of the stator voltage.,-and-axis components of the rotor voltage.,-and-axis components of the stator current.,-and-axis components of the rotor current.,-and-axis components of the magnetizingcurrent.,-and-axis components of the statorﬂuxlinkage.,-and-axis components of the rotorﬂuxlinkage.,Stator and rotor winding resistances.,Stator and rotor leakage reactances.Manuscript received October14,2010;revised March04,2011;accepted May01,2011.Date of publication June23,2011;date of current version September21,2011.The authors are with the Department of Electrical and Computer Engi-neering,University of Windsor,Windsor,ON,N9B3P4,Canada(e-mail: moham1k@uwindsor.ca;lu1y@uwindsor.ca;nkar@uwindsor.ca).Color versions of one or more of theﬁgures in this paper are available online at .Digital Object Identiﬁer10.1109/TSTE.2011.2160374Magnetizing reactance.Stator reactance.Supply frequency.Rotor speed.,Proportional and integral gains.Mechanical power delivered by the turbine.Active power delivered by the rotor to theconverter.Reactive power delivered by the rotor to theconverter.Line-side active power.Line-side reactive power.Line-side converter current.Rotor-side converter current.Number of poles.Active power through the transmission line.Reactive power through the transmission line.Capacitance.II.I NTRODUCTIONT HE use of doubly-fed induction generators(DFIGs)is receiving increasing attention for grid-connected wind power generation where the terminal voltage and frequency are determined by the grid itself[1]–[8].One conﬁguration is realized by using back-to-back converters in the rotor circuit and employing vector control.This allows the wind turbine to operate over a wide range of wind speed and,thus,maximizes annual energy production.The750-kW and1.5-MW turbines and the3.6-MW prototypes for offshore applications from GE Wind Energy Systems employ vector control of the DFIG rotor currents which provides fast dynamic adjustment of electro-magnetic torque in the machine[9].Fuzzy logic has been successfully applied to control wind-driven DFIGs in different aspects.In[1],fuzzy logic was used to control both the active,and reactive power generation.In[2]and [3],a fuzzy logic gain tuner was used to control the generator speed to maximize the total power generation as well as to con-trol the active and reactive power generation through the control1949-3029/$26.00©2011IEEEof the rotor side currents as demonstrated in Appendix A.The error signal of the controlled variable was the single variable used as an input to the fuzzy system.In the above-mentioned applications,the design of the fuzzy inference system was com-pletely based on the knowledge and experience of the designer, and on methods for tuning the membership functions(MFs) so as to minimize the output error.To overcome problems in the design and tuning processes of previous fuzzy controllers,a neuro-fuzzy based vector control technique isﬁrst proposed by the authors to effectively tune the MFs of the fuzzy logic con-troller while allowing independent control of the DFIG speed, active,and reactive power.The proposed neuro-fuzzy vector controller utilizes six neuro-fuzzy gain tuners.Each of the pa-rameters,generator speed,active,and reactive power,has two gain tuners.The input for each neuro-fuzzy gain tuner is chosen to be the error signal of the controlled parameter.The choice of only one input to the system simpliﬁes the design.In this research,the two-axis(direct and quadrature axes)dy-namic machine model is chosen to model the wind-driven DFIG due to the dynamic nature of the application.Since the machine performance signiﬁcantly depends on the saturation conditions, both mainﬂux and leakageﬂux saturations have been consid-ered in the induction machine modeling[9].The machine model is then used to build and simulate a neuro-fuzzy vector con-trolled wind-driven DFIG system.The controllers used in vector control are a set of standard PI controllers with neuro-fuzzy gain schedulers.In these controllers,both proportional and integral gains are scheduled based upon the value of the error signal of the speed,active,or reactive power as discussed above.The neuro-fuzzy systems are designed and trained to provide the best dynamic performance while tracking the wind turbine’s max-imum power point curve.Experimental investigations have also been conducted on a2-kW laboratory DFIG to verify the calcu-lated results.III.P ROPORTIONAL AND I NTEGRAL G AIN T UNERSA.Conventional PI,Adaptive,and Fuzzy Gain Tuners Conventional vector controllers[10]and[11]utilize a PI con-troller withﬁxed proportional and integral gains,and, determined by the zero/pole placement.Such controllers give a predetermined system response and cannot be changed easily. As the system becomes highly nonlinear,more advanced control schemes are required.In[7],an adaptive controller is proposed by the authors that can schedule both and depending on the value of the error.Different characteristics such as linear, exponential,piece-wise linear,and fourth-order functions,rep-resenting the variation in and as a function of the absolute value of the error are used.The coefﬁcients were selected such that,for the proportional gain,a fast system response with less overshoot and small settling time is obtained.While for the inte-gral gain,it is required to reduce the overshoot and to eliminate the steady state error.It has been found that the performance of the system using the exponential characteristic produces the best system response with less overshoot,less settling time,and steady-state error.In[2],a fuzzy algorithm for tuning these two gains of the PI controller is proposed to produce good controlperformance Fig.1.Neuro-fuzzy gain scheduler for vector control of wind-driven DFIG. when parameter variations take place and/or when disturbances are present.This approach uses fuzzy rules to generate propor-tional and integral gains.The design of these rules is based on a qualitative knowledge,deduced from extensive simulation tests of a conventional PI controller of the system for different values of and,for operating conditions.B.Proposed Neuro-Fuzzy Gain TunerIn the neuro-fuzzy system,a learning method similar to that of neural network is used to train and adjust the parameters of the membership functions.Neuro-adaptive learning techniques provide a method for the fuzzy modeling procedure to learn in-formation about a data set.Then,the parameters of membership functions that best allow the associated fuzzy inference system to track the given input/output data are computed as described in Appendix B.The vector control technique is implemented in Fig.1.As shown in thisﬁgure,the wind speed is measured in order to determine the set values for both the maximum DFIG output power and the corresponding generator speed in order to track the maximum power curve.These set values are then used to calculate the error signal which is the set value minus its corre-sponding measured actual value.The abso-lute value of the error signal is used to calculate the scheduled proportional and integral gains using the neuro-fuzzy controller for each of the speed,active and reactive power controllers.To apply the vector control to the DFIG system,six neuro-fuzzy gain tuners are trained ofﬂine.Two for each of active power,re-active power,and speed controllers.One unit is responsible for tuning the proportional gain and the other for tuning the integral gain.The developed neuro-fuzzy system is aﬁrst-order Sugeno type which has a single input with ten Gaussian distribution membership functions.It has ten if–then rules.A simple struc-ture of the developed neuro-fuzzy system is shown in Fig.2 where the input is the error signal of the controlled variable of speed,active,or reactive power.The training is performed using the hybrid back-propagation algorithm.The training data used are collected from extensive simulations of the vector controllerFig.2.Simple structure of a single unit of the neuro-fuzzy gainscheduler.Fig.3.Training error for the speed neuro-fuzzy gaintuner.Fig.4.Input membership functions for the proportional gain tuners.(a)Active power controller.(b)Speed controller.system with various PI gains so that the trained tuner can tune the PI gains online based on the knowledge of the different PI controllers under different operating conditions.The number of training epochs is set to 45with an error tolerance of .The number of epochs is chosen to be the highest number after which there is no signi ﬁcant reduction in the training error.Fig.3shows the error while training at each epoch for the neuro-fuzzy gain tuner of the speed controller.After the training process,the input membership functions for the neuro-fuzzy proportional gain tuner of both active power and speed are shown in Fig.4.The output membership functions are chosen to be linear;the parameters of the ten linear output membership functions for the speed controller and active and reactive power controllers are listed in Tables I and II,respectively.TABLE IPARAMETERSOF THEL INEAR O UTPUT M EMBERSHIP F UNCTIONS OFTHES PEED C ONTROLLERTABLE IIP ARAMETERS OF THE L INEAROUTPUT M EMBERSHIP F UNCTIONS OFTHEA CTIVE AND R EACTIVE P OWER C ONTROLLERSFig.5.Wind-driven DFIG system con ﬁguration.The proportional and integral gains are inputs to the standard PI controller part of the vector controller to generate the control signals and .Then and along with the stator and rotor angles are used to generate signals for the back-to-back converter.The angle and are calculated as in Appendix A.These angles along with and help evaluate a three-phase stator voltage signal that is sent to a PWM controller to generate switching pulses for the back-to-back converters.IV .S IMULATION R ESULTS FOR S UBSYNCHRONOUS ANDS UPER -S YNCHRONOUS O PERATIONS OF THE DFIG The system considered in this paper is a grid connected wind-driven DFIG with the rotor circuit connected to the grid through back-to-back PWM voltage source converters in a con ﬁguration shown in Fig.5.While the rotor-side converter controls the rotor speed and the active and reactive power output through -and -axis com-ponents of the rotor voltage,and ,by using the neuro-Fig.6.DFIG coupled to the dc motor in the experimental setup.TABLE IIIP ARAMETERS OF THE DFIG U SED IN THE INVESTIGATIONSfuzzy-based vector control strategy outlined previously,the grid side converter is controlled to maintain a constant voltage level across the coupling capacitor as demonstrated in Appendix C.A transformer is usually used in the rotor circuit due to the dif-ferent voltage levels between the stator and the rotor.Also,a ﬁlter is utilized to minimize the harmonics injected into the grid due to the switching of the power electronic devices.A.DFIG Used in the InvestigationsThe 2-kW DFIG used for the investigations in this paper is driven by a laboratory dc motor,as shown in Fig.6.The pa-rameters of this machine are presented in Table III.The DFIG parameters have been determined by conducting dc,no-load,and locked-rotor tests on the machine.These tests are explained in the IEEE standard of Test Procedure for Poly-Phase Induc-tion Motors and Generators [12]and produce unsaturated ma-chine parameters.In order to obtain a more realistic representa-tion of the machine,saturation in the magnetic circuit along the main and leakage ﬂux paths should be included in the machine model.To determine the saturation characteristics of the magne-tizing,stator and rotor leakage inductances,two unconventional tests are carried out.These test procedures are explained in de-tail in [13].The no-load generator test at synchronous speed is carried out to determine the main ﬂux saturation characteris-tics in Fig.7(a).The terminal voltage–armature current curve with the machine unloaded and unexcited,and the open-circuit characteristics are determined twice;one on the stator and the other on the rotor,to determine the stator and rotor leakage in-ductance saturation characteristics in Fig.7(b)and (c),respec-tively.This main ﬂux path saturation has been represented in the generator model by modifying the unsaturated magnetizing inductance corresponding to the magnetizing current using Fig.7(a).In order to take the leakage ﬂux saturation into ac-count,the unsaturated stator and rotor leakage inductances in the machine model have been modi ﬁed employing the stator and rotor leakage inductance saturation characteristics in Fig.7(b)and (c),respectively.B.Maximum Power Point TrackingThe output power changes as a function of the wind speed as well as the generator speed as shown in Fig.8.To track the max-imum power point,a lookup table is generated based on Fig.8for wind speeds less than 12m/s and saved into the neuro-fuzzy vector controller.Wind speeds higher than 12m/s are beyond the scope of this research.When the measured wind speed changes,the lookup table will be searched to ﬁnd the set values for both the maximum output power and the generator speed corresponding to the maximum power point.Although measuring the wind speed may have some drawbacks,it is the most accurate and easy way to change the generator speed in order to maximize the power generation.Major wind turbine manufacturers such as Vestas and Nordex have ultrasonic wind sensors in their V90-3.0MW model [14]and N90/2500kW model [15],respectively.The speed infor-mation from the sensor can be used for maximum power point tracking.C.Sub-and Super-Synchronous Operations of the DFIGThe performance of the system employing the proposed neuro-fuzzygaintunerisexaminedunderdifferentoperatingconditions,as shown in Figs.9and 10.Two cases are considered in this paper.The ﬁrst case investigates the subsynchronous operation where the wind speed changes from 7to 8m/s at s.According to Fig.8,for the maximum power generation of 0.96kW at a wind speedof8m/s,thegeneratorsetspeedincreasesfrom1200to1400rpm.The second case investigates the super-synchronous opera-tion where the wind speed changes from 9to 11m/s at s.The generator set speed increases from 1600to 1900rpm according to the maximum power point curve in Fig.8,where the corre-sponding power output is 1.86kW at a wind speed of 11m/s.For both cases,theproposed neuro-fuzzygain scheduler isemployed.The speed response,stator current,rotor line voltage,and rotor current for subsynchronous operation are shown in Fig.9,while,for the super-synchronous operation,they are shown in Fig.10.V .E XPERIMENTAL D ETERMINATION OF THE DFIG P ERFORMANCE U SING THE P ROPOSED C ONTROLLERS The main objective is to validate the simulation results ob-tained in the previous section as well as investigate the perfor-mance of the DFIG when using different controllers.The types of controllers considered are:adaptive gain scheduler [7],fuzzy logic [2],[3],and neuro-fuzzy.The performance of the DFIG system using the above-mentioned controllers is compared to that of the conventional PI controller with constant gains.While the system stability analysis employing these controllers is not the focus of this paper,the controllers were developed with system stability in mind and it was observed that the system was stable during all experiments.As frequent and rapid changes of the controller gains may lead to instability,there is a limit as to how often and how fast the controller gains can be changed.The conventional PI controller has a proportional gain of 45and an integral gain of 22.5.The DFIG used in this experiment is coupled to a dc motor (Fig.6).The dc motor can be used as a prime-mover in wind turbine applications [10],[11]to adjust speed and deliver the required torque.Fig.7.Saturation characteristics of the DFIG.(a)Magnetizing inductance.(b)Stator leakage inductance.(c)Rotor leakageinductance.Fig.8.Typical wind turbine power curves for different wind speeds showing maximum power point curve.Numerous cases were considered and,for illustration pur-poses,two were chosen and will be demonstrated.For the ease of comparison,these two cases are chosen to be same as the two described in the simulation section.The ﬁrst case investi-gates the subsynchronous operation of the DFIG with different controllers while the second case investigates the super-syn-chronous operation.A.Hardware ImplementationA practical experimental setup is built using the DFIG system in Fig.6.A power electronics back-to-back voltage source con-verter system is developed that is connected between the rotor circuit and the grid,and a microcontroller is programmed to per-form the controller tasks and calculate the gains depending on the control scheme,whether it is a conventional PI,adaptive,fuzzy,or neuro-fuzzy.The Microchip PIC18F4431microcon-troller was chosen for its high computational performance at an economical price,with the addition of high endurance enhanced ﬂash program memory and a high-speed 10-bit A/D converter.On top of these features,the PIC18F4431introduces design en-hancements that make this microcontroller a logical choice for many high performance motor control applications.LTV-827opto-couplers are used for electrical isolation.Knowing that the working voltage is 208V and the current is a maximum of 8A with a switching frequency of 5kHz,the best choice was MOSFET.The chosen MOSFET is from International Recti-ﬁer (IRF)with a part number IFR740.It is an -channel power MOSFET having a rated voltage of 400V and rated current of 10A.In the case of IRF740,the total gate charge is 63nC with a turn-on time of 41ns which gives a gate current of 1.53 A.Fig.9.Calculated responses for subsynchronous operation.(a)Speed response.(b)Stator current.(c)Rotor line voltage.(d)Rotor current.The selected gate driver is IRS2186from IRF.It is capable of providing 4A for the MOSFET gate.Fig.10.Calculated responses for super-synchronous operation.(a)Speed re-sponse.(b)Stator current.(c)Rotor line voltage.(d)Rotor current.After selecting the components,the next stage is to build the circuit and fabricate the printed circuit board(PCB).Al-tium Designer was used to perform this task for its powerful capabilities in designing the routes and massive component li-brary.All stator and rotor currents are experimentally measured and recorded using a Tektronix TDS1002digital storage oscil-loscope.The sampling frequency is1.25kHz.B.Subsynchronous Operation of the DFIGIn this case,the generator speed changes from1200to 1400rpm.The resulting speed change for each controlleris Fig.11.Measured responses for subsynchronous operation.(a)Speed re-sponse.(b)Stator current.(c)Rotor line voltage.(d)Rotor phase voltage.(e)Rotor current.illustrated in Fig.11(a).It can be seen from thisﬁgure and Table IV that the conventional PI controller with constant gain has a rise time of2.8s with2%overshoot and1.5%steady-state error while the adaptive PI gain scheduler employing an ex-ponential characteristic has a rise time of2.5s and a settling time of6s with1%overshoot and no steady-state error.On theTABLE IVC ONTROLLERS ’P ERFORMANCE FOR S UBSYNCHRONOUS OPERATIONother hand,the fuzzy PI gain scheduler has a rise time of 2s and a settling time of 3s with no overshoot and no steady-state error while the neuro-fuzzy PI gain scheduler has a rise time of 1.5s and a settling time of 2.8s with no overshoot and no steady-state error.For the same case,the change in speed increased the stator active power production from 0.78to 0.96kW while the reac-tive power produced is kept constant at 0.80kV AR.This rise in power production increased the stator current as shown in Fig.11(b).At the rotor side,the increase in speed required an increase in the rotor current and rotor line voltage which are shown in Fig.11(c)–(e),respectively.In order to increase the active power generation from 0.78to 0.96kW,an increase in the -axis component of the rotor current is required.How-ever,there is no change in the -axis component of the rotor current since the reactive power generation has been kept constant.This increase in has resulted in an increase in the rotor current.C.Super-Synchronous Operation of the DFIGIn the second case,the generator speed changes from 1600to 1900rpm.The resulting speed change for each controller is illustrated in Fig.12(a).It can be seen from this ﬁgure and Table V that the conventional PI controller with constant gain has a rise time of 3.8s and a settling time of 4.5s with no over-shoot and no steady-state error.The adaptive PI gain scheduler with an exponential characteristic also yielded similar results.On the other hand,the fuzzy PI gain scheduler has a rise time of 2.5s and a settling time of 4.5s with 0.6%overshoot and no steady-state error while the neuro-fuzzy PI gain scheduler has a rise time of 2.8s and a settling time of 4.5s with no over-shoot and steady-state error.Although the fuzzy PI gain sched-uler has a shorter rise time than that of the neuro-fuzzy PI gain scheduler in this case,it has the largest overshoot among the compared gain scheduler.For this case,the change in speed in-creased the stator active power production from 1.32to 1.86kW while the reactive power production is kept constant at 1kV AR.This increase in power production increased the stator current as shown in Fig.12(b).At the rotor side,the increase in speed required an increase in the rotor current and rotor line voltage which are shown in Fig.12(c)–(e),parison of the ResultsIt can be seen from Figs.9and 10that the speed and stator and rotor quantities calculated by employing the neuro-fuzzy controller in Section IV-C are in good agreement with the exper-imentally measured ones shown in Figs.11to 12.This indicates the accuracy of the proposed control system.The neuro-fuzzy PI gain scheduler enables proportional and integral gains within the vector control scheme to be changed depending on the op-erating conditions.It can also be seen that using theproposedFig.12.Measured responses for super-synchronous operation.(a)Speed response.(b)Stator current.(c)Rotor line voltage.(d)Rotor phase voltage.(e)Rotor current.neuro-fuzzy controller,the dynamic response can be improved and more precise control is achieved in comparison to PI,adap-tive,and fuzzy logic controllers.The proposed neuro-fuzzy PI gain scheduler achieved faster system response with almost no overshoot,shorter settling time and no steady-state error.TABLE VC ONTROLLERS ’P ERFORMANCE FOR S UPER -S YNCHRONOUS OPERATIONFor DFIG operation in the entire speed range,it is well un-derstood that the speed and frequencies of induction machines are such that the stator frequency is equal to the rotor electrical speed combined with the rotor frequency.It can be seen from Figs.10and 12that the relation holds true for the super-syn-chronous mode of operation.Referring to Figs.9(a)and (b)and 11(a)and (b),for subsynchronous mode,it is seen that the rotor speed settles to the new set point of 1400rpm approximately at 4s and the stator current has constant frequency of 60Hz during the entire event;however,Figs.9(c)and (d)and 11(c)and (d)show that the frequency of the rotor quantities decrease even after the speed has settled.Current research is underway to iden-tify and resolve this apparent discrepancy.VI.C ONCLUSIONThis paper presents a control method to maximize power gen-eration of a wind-driven DFIG considering the effect of satura-tion in both main and leakage ﬂux paths.This is achieved by ap-plying vector control techniques with a neuro-fuzzy gain sched-uler.The overall DFIG system performance using the proposed neuro-fuzzy gain tuner is compared to that using the conven-tional PI controllers.The generator speed response as well as the stator and rotor currents and the rotor voltages in response to a sudden change in the wind speed are presented.The main ﬁndings of the paper can be summarized in the following points:1)Traditional vector control schemes that employ a conven-tional PI controller with ﬁxed proportional and integral gains give a predetermined response and cannot be changed.However,the proposed neuro-fuzzy PI gain scheduler enables proportional and integral gains within the vector control scheme to be changed depending on the operating conditions.2)It is demonstrated that,using the proposed controller,the system response can be improved and more precise control is achieved.3)The proposed neuro-fuzzy PI gain scheduler achieves faster system response with almost no overshoot,shorter settling time,and no steady-state error.A PPENDIXA)Vector Control:The vector control technique allows decoupled or independent control of both active and reactive power.This section reviews the basic vector control strategy in the case of DFIG.The stator ﬂux oriented rotor current control,with decoupled control of active and reactive power,is adapted in this paper.The control schemes for the DFIG are expected to track a prescribed maximum power curve for maximum powercapturing and to be able to control the reactive power genera-tion.The total active and reactive power generated can be ob-tained from the stator voltage and the -and -axis components of the stator current and can be expressed as(A1)The ﬁeld oriented control is based on the -axis modeling,where the reference frame rotates synchronously with respect to the stator ﬂux linkage.The direct axis of the reference frame overlaps the axis of stator ﬂux making the -axis component of stator ﬂux .In such a case,the following expression is obtained:(A2)Using (A2)and the active power equation in (A1),the equation of the active power can be rewritten as follows:(A3)The -axis component of stator current can be written as [16](A4)Using (A4)and the reactive power in (A1),the equation of the reactive power can be rewritten as follows:(A5)Therefore,it can be seen from (A3)and (A5)that the -axis component of the rotor current can be controlled to regulate the stator reactive power while the -axis component of the rotor current can be controlled to regulate the stator active power.As a result,the control of the stator active power via and the control of the stator reactive power via are essentially decoupled and,thus,a separate decoupler is not necessary to implement ﬁeld orientation control for the slip power recovery.The calculation of the stator and rotor angles requires infor-mation pertaining to the stator current,rotor speed and machine parameters.The stator ﬂux angle can be calculated from the fol-lowing equation:(A6)The rotor angle can be calculated as(A7)B)ANFIS Architecture:Currently,several neuro-fuzzy networks exist in the literature.Most notable is the adaptive network-based fuzzy inference system (ANFIS)developed by。

高超音速飞行器的气动设计分析

高超音速飞行器的气动设计分析在当今航空航天领域，高超音速飞行器的发展正成为各国竞相追逐的焦点。

高超音速飞行器具有速度极快、飞行环境极端等特点，这使得其气动设计成为了一个极具挑战性的课题。

高超音速飞行器在飞行时，面临着极其复杂的气动力和气动热问题。

首先，高速飞行带来的强烈空气压缩会产生巨大的激波，这不仅增加了飞行阻力，还会导致飞行器表面温度急剧升高。

其次，高超音速气流的黏性效应也变得尤为显著，气流在飞行器表面的边界层内会发生复杂的流动现象，如分离、再附等，这对飞行器的稳定性和操控性产生重要影响。

为了应对这些挑战，设计师们在高超音速飞行器的气动外形设计上采取了多种创新策略。

其中，尖锐的头部设计是常见的选择。

尖锐的头部可以减小激波的强度，从而降低阻力和气动加热。

此外，细长的机身和扁平的翼面也有助于减少空气阻力，并提高飞行器的升阻比。

在飞行器的表面处理方面，采用耐高温、低摩擦的特殊材料至关重要。

这些材料能够在高温高速的气流冲刷下保持良好的性能，减少热传递和摩擦阻力。

同时，通过优化飞行器表面的粗糙度和纹理，可以进一步改善气流的附着和流动特性，降低气动阻力。

高超音速飞行器的气动布局也是设计中的关键环节。

常见的布局包括乘波体布局和轴对称布局等。

乘波体布局利用激波产生升力，具有较高的升阻比和良好的气动性能。

轴对称布局则在结构强度和稳定性方面具有一定优势。

在设计过程中，数值模拟和风洞试验是不可或缺的手段。

数值模拟可以通过建立复杂的数学模型，对飞行器在高超音速流场中的气动特性进行预测和分析。

然而，由于高超音速流动的复杂性，数值模拟往往存在一定的误差，因此风洞试验仍然是验证和优化设计的重要方法。

风洞试验能够真实地模拟高超音速气流环境，获取飞行器的气动力、压力分布和热流等关键数据。

通过对试验结果的分析和对比，可以不断改进和优化飞行器的气动设计。

此外，多学科优化设计也是提高高超音速飞行器性能的重要途径。

将气动设计与结构设计、热防护设计等多个学科进行综合考虑，通过优化算法寻找最优的设计方案，能够在满足各种性能要求的前提下，实现飞行器的整体性能提升。

基于气动图方法设计高速飞行器方案

基于气动图方法设计高速飞行器方案高速飞行器方案设计基于气动图方法高速飞行器是一种具有极高速度的飞行器，其设计和研发对于实现超音速、高超音速飞行和应用于未来高速交通工具、宇宙探索等领域具有重要意义。

在设计高速飞行器方案时，我们可以采用气动图方法来优化飞行器的设计，以满足其高速性能要求和安全稳定性。

气动图方法是一种基于流体力学原理的飞行器设计方法，它通过研究飞行器在不同空气动力条件下的受力特性，包括升力、阻力、气动力矩等，来优化飞行器的外形和气动特性。

以下是基于气动图方法设计高速飞行器方案的步骤和内容需求。

首先，需要进行飞行器的初步设计。

针对高速飞行器的应用场景和性能要求，如超音速巡航、高超音速飞行、高机动性能等，我们需要确定飞行器的整体布局和主要参数。

包括飞行器的机翼形状、机身外形、尾翼、进气道等。

这些参数将直接影响飞行器的气动性能和稳定性。

其次，进行气动图分析和实验。

根据初步设计确定的飞行器参数，我们可以利用数值模拟软件或实验设备来进行气动图分析。

通过模拟和实验，我们可以获取飞行器在不同工况下的升力系数、阻力系数、气动力矩等数据。

这些数据将作为设计优化的依据。

接下来，进行气动图优化设计。

根据气动图分析得到的数据，我们可以进行飞行器方案的优化设计。

例如，通过调整机翼形状和展弦比、改变机身外形、优化进气道设计等，来降低飞行器的阻力，提高升力系数。

此外，还可以通过合理布置尾翼和加装辅助设备来提高飞行器的稳定性和操控性能。

同时，还需要进行飞行器结构和材料的优化设计。

高速飞行器在高速飞行时会受到巨大的气动力和惯性载荷，要求飞行器的结构和材料具有良好的强度和刚度。

基于气动图方法的设计还需要考虑飞行器结构的轻量化和抗疲劳设计。

可以采用复合材料、钛合金等先进材料，并进行结构优化设计，以满足高速飞行器对结构强度和重量的要求。

最后，进行模拟和实验验证。

完成飞行器方案的设计之后，需要进行模拟和实验验证。

通过数值模拟和风洞试验，我们可以验证飞行器在不同工况下的气动性能和稳定性。

高超声速飞行器的气动设计与分析

高超声速飞行器的气动设计与分析在现代航空航天领域，高超声速飞行器的发展备受瞩目。

高超声速飞行器通常指飞行速度超过 5 倍音速的飞行器，其独特的性能和应用前景使其成为各国研究的重点。

而气动设计与分析在高超声速飞行器的研发中起着至关重要的作用。

高超声速飞行器面临着极为复杂和恶劣的气动环境。

在高超声速条件下，空气的物理特性发生了显著变化，例如空气的粘性和热传导效应变得更加突出。

这就导致了飞行器表面的气动加热现象非常严重，可能会对飞行器的结构强度和材料性能产生巨大的挑战。

为了应对这些挑战，高超声速飞行器的气动外形设计需要精心考虑。

首先，尖锐的头部设计是常见的选择。

尖锐的头部能够有效地减小激波阻力，提高飞行器的飞行效率。

比如，采用细长的尖锥形状可以减少头部的空气压缩和热量积聚。

另外，飞行器的机身形状也至关重要。

细长的机身有助于减少空气阻力，并且能够在高超声速飞行时保持较好的稳定性。

同时，机身的表面需要尽量光滑，以降低摩擦阻力和热交换。

机翼和尾翼的设计也是关键的环节。

在高超声速条件下，传统的机翼和尾翼设计可能不再适用。

一些新型的设计概念，如乘波体布局，逐渐受到关注。

乘波体是一种利用激波产生升力的设计理念，能够在高超声速飞行中提供有效的升力和控制能力。

在气动设计过程中，数值模拟是一种非常重要的分析手段。

通过建立复杂的数学模型和运用强大的计算能力，可以对高超声速飞行器周围的流场进行精确的模拟和分析。

这有助于设计人员更好地理解飞行器的气动特性，发现潜在的问题，并对设计进行优化。

然而，数值模拟也存在一定的局限性。

例如，模型的准确性和计算精度可能会受到多种因素的影响，如边界条件的设定、网格的划分等。

因此，实验研究仍然是不可或缺的。

风洞实验是高超声速飞行器气动研究中常用的实验方法之一。

在风洞中，可以模拟高超声速的气流条件，对飞行器模型进行测试。

通过测量各种气动参数，如压力、温度、速度等，可以直接获取飞行器的气动性能数据。

高超声速飞行器气动热计算【13页】

高超声速飞行器气动热计算
工作内容
物面压力和外缘参
数计算
零攻角气动热计算
变攻角气动热计算
具体实例球头锥体热流
计算
两个独立变量压力熵
物面压力计算
修正牛顿算
驻点： Fay—Riddell公式 Kemp—Riddell公式
Scala公式 Lees公式修正Lees公式非驻点： Less钝体层流公式
等熵
变熵
湍流层流湍流层流
球锥的热流分布计算
球锥的热流分布计算
球锥的热流分布计算
球锥的热流分布计算
球锥外形示意图
球锥的热流分布计算
球锥的热流分布计算
球锥的热流分布计算
有攻角热环境工程方法
轴对称比拟法等价锥法
实验数据关联法
轴向/周向热流结果分析
画图比较
物面压力计算
修正牛顿理论计算表面压力基本思想是：流体质点与物面相碰后，垂直于物面法向动量将损失掉，流体将沿着物面切向运动，切向动量没有损失。
外缘参数计算
零攻角驻点热流计算
非驻点热流计算
非驻点热流计算

《高速空气动力学》课件

材料选择与耐热性
燃烧室内部的材料需要具备出色的耐高温性能和抗烧蚀能力，以确保发动机的可靠性和寿命。
05
高速空气动力学的发展趋势和展望
高速空气动力学面临的主要挑战
高马赫数流动的复杂性
随着飞行速度的增加，空气流动的特性变得更加复杂，包括湍流、激波、边界层分离等现象，这给数值模拟和实验测量带来了极大的挑战。
研究高超声速飞行中的热力学效应和化学反应，对于理解高超声速飞行中的空气动力学问题具有重要意义。
数值模拟与实验验证
提高数值模拟的精度和稳定性，以及加强实验验证，是未来研究的重点方向之一。
THANKS
感谢观看
高超声速飞行
随着科技的发展，高超声速飞行已成为可能，这将对航空航天领域产生重大影响。研究高超声速飞行中的空气动力学问题，如热力学效应、化学反应等，是未来的重要研究方向。
数值模拟与实验验证相结合
随着计算能力的提升，数值模拟已成为研究高速空气动力学的重要手段。未来将更加注重数值模拟与实验验证相结合，以提高研究的准确性和可靠性。
激波
由于流体速度的突然变化，导致压力和密度急剧增加的现象
。
膨胀波
由于流体速度的减小，导致压力和密度降低的现象。
形成机制
流体的压缩性和粘性是激波和膨胀波形成的关键因素。
传播特性
激波和膨胀波在流体中以声速传播。
高速流动的边界层理论
边界层
流体的一个薄层，其中流体的速度从零变化到流体的自由流速。
件和目标。
风洞实验方法
风洞实验通常包括模型制作、安装、气流调整、数据采集与分析等步骤。这些步骤对于获得准确
可靠的实验结果至关重要。
飞行试验技术

高超声速气动构型

高超声速气动构型高超声速气动构型，说起来听着有点高大上，但其实它就是一种研究飞机、导弹、航天器等飞行器在超高速飞行时如何保持稳定、不出问题的技术。

哎，别看名字有点儿复杂，实际上它是为了让这些飞行器飞得更快、更稳、甚至更远。

想象一下，飞机速度快得像闪电，眼睛都看不清它的影子——这就是高超声速飞行。

你会想，飞得这么快，那些空气啥的应该早把它打成筛子了吧？可不，科学家就是要搞明白，飞得这么快，怎么让飞机不被空气“压垮”，甚至还能飞得更稳更远！咱们常说“鱼与熊掌不可兼得”，但高超声速的设计就告诉我们，可能两者都能兼得——既要快，又要稳，甚至还要省油。

想想看，如果速度太快，飞行器就会遇到强烈的空气阻力，温度也会飙升，这种情况下，飞行器的表面温度可能直接达到几千度，这就像是你把冰淇淋放在太阳底下，几秒钟就融化了。

但如果设计得巧妙，能在超高速下控制空气流动，让飞行器保持良好的气动性能，那就能实现一个“高速度、高效率、高稳定”的完美飞行。

说到气动构型，我们可以把它想成是飞机的“外形设计”，这可不是随便捏个样子就行的，得像精心打磨的雕塑一样，每一处都得考虑周到。

特别是那些在高超声速下飞行的飞行器，光是外形的流线型设计就决定了它能不能顺利通过空气的阻碍，飞得又快又远。

你看啊，飞机的前端如果设计得过于尖锐，空气就会在前端快速堆积，形成极高的压力，飞机很容易受力不均，飞行起来就像是坐过山车一样，晃得你头晕眼花。

如果前端设计得过于圆滑，空气流动又会出现紊流，飞行器同样会失去稳定性。

怎么才算完美的气动构型呢？最关键的就是“超高速度下的气流控制”这一点。

你可以想象一下，高超声速飞行器就像是一只在空中划过的箭，飞行器的每一部分都要与空气巧妙地配合。

最常见的设计方法之一就是“冲击波控制”，这个东西听起来很抽象，其实它就像是一个隐形的盾牌，能够把空气的阻力尽量降到最低，同时避免温度过高。

为了做到这一点，设计师们会在飞行器的表面设计一些特殊的曲线和凹槽，这样可以把飞行时产生的空气冲击波引导得更顺畅，减少它们对飞行器造成的负担。

飞机机电设备维修《高速飞机气动外形的特点》

高速飞机气动外形的特点亚音速飞机的飞行马赫数一定要小于飞机的临界马赫数。

所以，为了提高亚音速飞机的飞行速度，就必须提高飞机的临界马赫数，使飞机的飞行速度尽量向音速靠近，这种飞机就称为高亚音速飞机。

对于要进行超音速飞行的飞机，在气动外形设计上要改善飞机的跨音速空气动力特性，减小波阻，使之能很快通过跨音速区域进入超音速飞行。

所以，高速飞机气动外形变化的主要目的就是提高临界马赫数、改善飞机的跨音速空气动力特性和减小波阻。

1采用薄翼型高速飞机的机翼应采用相对厚度比较小〔即比较扁平的〕、最大厚度点位置向后移，X大约为50%的薄翼型。

c从式〔2-5〕可以知道，飞机的升力与升力系数C L和飞行速度的平方成正比。

低亚音速飞机的飞行速度比较小，为了得到足够的升力，一般采用相对厚度、相对弯度比较大，最大厚度点靠前，X大约30%的翼型，如图2-42所示，这种翼型可c以使气流很快加速到最大速度，在低速飞行时得到比较大的升力系数C L。

图3-42 低速翼型对于高速飞机来说，飞行速度大，为了得到足够的升力并不需要大的升力系数C L，而是要提高临界马赫数和减小波阻。

翼型的相对厚度越小，上翼面的气流加速就越缓慢，速度增量就越小，可以有效地提高飞机的临界马赫数和飞机的最大平飞速度。

另外，进入跨音速飞行后，产生的激波波阻会随着翼型相对厚度的增加而增大，所以，采用薄翼型对减小跨音速飞行的波阻也是非常有利的。

在前面讲到的为了保持层流附面层而采用的层流翼型〔见图3-21〕，前缘半径比较小，最大厚度的位置靠后，X约为c40%~50%，上翼面气流加速比较缓慢，压力分布比较平坦，对提高临界马赫数也有作用。

所以层流翼型比较适合高亚音速飞行，是高亚音速飞机采用教多大的翼型。

对提高临界马赫数有效并在跨音速区域中有较好空气动力特性的翼型是超临界翼型。

这种翼型有较大的前缘半径，上翼面比较平坦，后部略向下弯〔见图3-43〔b〕〕。

因为上翼面比较平坦，气流加速比较缓慢，所以他的临界马赫数比较大。

高超声速飞机气动外形概念设计

高超声速飞机气动外形概念设计
刘济民;颜仙荣;张朝阳;沈伋
【期刊名称】《航空科学技术》
【年(卷),期】2022(33)7
【摘要】本文对高超声速情报、监视及侦察(ISR)飞机概念外形进行了初步设计。

在乘波前体、中部机身、高超声速机翼以及机身尾部设计的基础上,建立了高超声速ISR平台一体化基准外形,对基准外形在设计状态和非设计状态下的气动性能进行了分析,并对概念方案满足设计需求情况进行了验证。

结果表明,高超声速ISR平台气动外形在设计状态下的升阻比为4.8822,升阻特性满足设计需求,当升力等于2.0×10^(5)N时,阻力小于4.2×10^(4)N。

高超声速ISR平台基准构型在设计状态下的气动性能比较稳定,在研究的非设计范围内,气动系数随飞行马赫数和高度的变化都不大,具有在广域宽速范围内工作的能力。

【总页数】8页(P15-22)
【作者】刘济民;颜仙荣;张朝阳;沈伋
【作者单位】海军研究院
【正文语种】中文
【中图分类】V211.3
【相关文献】
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2.高超声速轴对称再入机动飞行器气动外形设计与布局研究
3.一种空天飞机外形高超声速气动特性实验研究
4.
临近空间高超声速飞行气动外形设计初探5.临近空间高超声速飞行气动外形设计初探
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2）阻力明显的增加，CFD数值模拟的收敛性变差； 3）激波类型：�� 激波和弓形激波。
Source : Fundamentals of Aerodynamics, Anderson
第一章引论
21
1.1 高超声速原理和流动特征
根据马赫数划分的机制
④ 超音速流动（1.2<Ma<5） --- 理想气体流动特点： 1）流动对下游是否有物体并不预知，直至遇到激波； 2）激波前后的流动都是超音速；
第一章引论
9
Motivation
为什么要进行气动设计？（提供良好的控制作用）
Source: Website
第一章引论
10
Motivation
为什么要进行气动设计？（提供良好的控制作用）
Source: Website
第一章引论
11
Motivation
为什么要进行气动设计？（提供隐身性能）
Source: Website
Source : Fundamentals of Aerodynamics, Anderson
3）对于楔角大的情况，��∞ = 3 就可以出现高超音速流动；楔角小时需要5马赫。
24
第一章引论
1.1 高超声速原理和流动特征
高超声速流动主要特征
薄激波层和小密度比
大的熵梯度和旋度
平均速度第一章引论
3
Motivation
为什么要进行气动设计？
Source: Website
第一章引论
4
Motivation
为什么要进行气动设计？
Source: Website
第一章引论
5
Motivation
为什么要进行气动设计？
Source: Website
第一章引论
6
Motivation
�� (��° → ��°)
26
第一章引论
1.1 高超声速原理和流动特征
薄激波层和小密度比
原理：
2 �� + 1 ��,1 ��2 = 2 ��1 2 + �� − 1 ��,1
��2 2�� 2 =1+ ��,1 −1 ��1 �� + 1 ��2 ��2 ��1 = ��1 ��1 ��2
Source : Fundamentals of Aerodynamics, Anderson
3）与亚音速流动相比，其物理特性和数学特性都存在很大差异。
22
第一章引论
1.1 高超声速原理和流动特征
根据马赫数划分的机制
⑤ 高超音速流动（5<Ma） --- 真实气体流动特点： 1）激波角变小； 2）波后流动出现严重的耗散甚至于出现气体的电离；
��(��° → ��°)
�� = ��° �� = ��
�� = ��°
Source : Fundamentals of Aerodynamics, Anderson
�� − �� − ��
��0,2 ∆�� = ��2 − ��1 = −�� ln ��0,1
Source : Fundamentals of Aerodynamics, Anderson
克罗柯（Crocco）定理：
�� × �� × �� = −��∆��
Source: website 32
第一章引论
1.2 高超飞行器空气动力学发展
第二阶段：
Delta Clipper LH-20 PLS 样机 80年代～90年代
可重复使用的航天飞机 Source: website
第一章引论
33
1.2 高超飞行器空气动力学发展
第三阶段：
X-33 X-51 X-43
�� ↑ ��
Source : Fundamentals of Aerodynamics, Anderson
第一章引论
27
1.1 高超声速原理和流动特征
大的熵梯度和旋度
原理：
∆�� = ��2 − ��1 = 2 2�� 2 + �� − 1 �� 1 2 �� ln 1 + ��1 −1 2 �� + 1 �� + 1 ��1 2�� 2 −�� ln 1 + ��1 −1 �� + 1
导弹 Vergeltung 2 （1944）
Source: wiki and website 1945， 1959
第一章引论
31
1.2 高超飞行器空气动力学发展
第二阶段：
联盟号 Soyuz
东方号 Vistok
上升号 Voskhod
双子星座号 Gemini
阿波罗号 Appollo
水星号 Mercury
1）与飞行试验比，花销少，灵活性大，便于控制，易于获得数据 2）由于地面低雷诺数，高测试马赫数和试验地面模拟装臵干扰具有一定的局限性 0～8M，由试验数据推导试验 3）由于存在化学反应非平衡流动，只能进行局部测试 1）与地面试验比，结果更真实，但是花销大飞行数据用于验证开发的模型自由 2）试验准备期长，因此不能大量的重复试验数值求解工具。大于13M 飞 3）难于考虑各种因素的影响，是综合性检验时由验证过的程序求解
第一章引论
例如：
90km左右高速飞行
（a）采用速度滑移边界条件和壁面温度跳跃边界条件；（b）间断假设失效，需要采用RankineHugoniot激波关系式进行修正。
30
1.2 高超飞行器空气动力学发展
第一阶段：
东方号飞船首次绕地球低轨道飞行 Vistok 1（1961）第一颗人造地球卫星 Sputnik 1（1957）
强粘性效应（粘性干扰）
高温效应（真实气体效应）
低密度效应
第一章引论
25
1.1 高超声速原理和流动特征
薄激波层和小密度比
原理： �� 给定， �� ↑ ， �� ↓ ，导致激波层变薄
示例： �� = 15° �� = 36 �� ≈ 18°
为什么要进行气动设计？
Source: Website
第一章引论
7
Motivation
为什么要进行气动设计？（提供良好气动特性）
试验得到翼型系列
H.F. Phillips 的翼型专利
层流翼型第一章引论
高升力翼型
8
Motivation
为什么要进行气动设计？（提供良好气动特性）
Source: Website
Source : Fundamentals of Aerodynamics, Anderson
2）全流场区域内都是低于音速的流动。
第一章引论
20
1.1 高超声速原理和流动特征
根据马赫数划分的机制
③ 跨音速流动（0.8<Ma<1.2） --- 理想气体流动特点： 1）绕流出现局部超音速区；
第一章引论
正激波前后总压比随来流马赫数的变化曲线
28
1.1 高超声速原理和流动特征
强粘性效应（粘性干扰）
原理：
高超声速边界层内 �� ↑，从而 �� ↑，粘性效应增强。边界层内压力沿法向不变，但是由于�� ↑ ，�� ↓，根据质量守恒，可知边界层的 �� ↑。又由于激波层变薄，可能整个激波层都存在粘性。
15
参考书目
• 高超声速飞行器空气动力学，黄志澄 • 高超声速飞行器技术，蔡国飚等 • Selected Aerothermodynamic Design Problems of Hypersonic Flight Vehicles , E. H. Hirschel (高超声速飞行器气动热力学设计问题精选) • Fundamentals of Aerodynamics, J. D. Anderson • Hypersonic and High-Temperature Gas Dynamics, J. D. Anderson • Computational Fluid Dynamics, J. D. Anderson • Compressible Fluid Flow, J. D. Anderson • 飞行器优化设计，段德高
高超飞行器气动一体化设计 Nhomakorabea
前体/进气道设计后体/尾喷管设计隔离段设计燃烧室设计高超声速气动力工程预测方法高超声速CFD求解技术高超声速气动热工程预测方法
高超声速气动分析方法

牛顿方法切楔/切锥方法激波-膨胀波方法
飞行器气动优化设计

优化基本理论非线性优化方法介绍约束优化问题
��
另一种定义： �� <5%
��
Source : Website
流动特点：流体密度被认为是常量。
第一章引论
19
1.1 高超声速原理和流动特征