Global aerodynamic design optimization based on data dimensionality reduction

In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However, for complex problems in which large number of design variables are needed, the computational cost becomes prohibitive, and thus original global optimization strategies are required. To address this need, data dimensionality reduction method is combined with global optimization methods, thus forming a new global optimization system, aiming to improve the efficiency of conventional global optimization. The new optimization system involves applying Proper Orthogonal Decomposition (POD) in dimensionality reduction of design space while maintaining the generality of original design space. Besides, an acceleration approach for samples calculation in surrogate modeling is applied to reduce the computational time while providing sufficient accuracy. The optimizations of a transonic airfoil RAE2822 and the transonic wing ONERA M6 are performed to demonstrate the effectiveness of the proposed new optimization system. In both cases, we manage to reduce the number of design variables from 20 to 10 and from 42 to 20 respectively. The new design optimization system converges faster and it takes 1/3 of the total time of traditional optimization to converge to a better design, thus significantly reducing the overall optimization time and improving the efficiency of conventional global design optimization method.     

航空航天智能材料与智能结构研究进展

智能材料作为新兴多功能材料,能够实现结构功能化、功能多样化。智能结构是在结构中集成智能材料作为传感器和驱动器,使结构除了具有承载、传力、连接等功能外,还具有自感知、自诊断、自驱动、自修复等能力,以更好地适应外界环境的变化,可显著提升航空航天架构的性能。目前智能材料与智能结构已成为航空航天架构减重增效研究的重点。根据国内外智能材料和结构的研究进展,综述了压电材料、铁磁材料、形状记忆材料、智能复合材料等智能材料的发展;讨论了智能结构的研究及应用前景,包括自诊断智能结构、自修复智能结构和减振降噪智能结构;最后,指出了智能材料与结构当前面临的一些挑战性问题,展望了其在航空航天领域的应用前景。     

Optimization of entry-vehicle shapes during conceptual design

During the conceptual design of a re-entry vehicle, the vehicle shape and geometry can be varied and its impact on performance can be evaluated. In this study, the shape optimization of two classes of vehicles has been studied: a capsule and a winged vehicle. Their aerodynamic characteristics were analyzed using local-inclination methods, automatically selected per vehicle segment. Entry trajectories down to Mach 3 were calculated assuming trimmed conditions. For the winged vehicle, which has both a body flap and elevons, a guidance algorithm to track a reference heat-rate was used. Multi-objective particle swarm optimization was used to optimize the shape using objectives related to mass, volume and range. The optimizations show a large variation in vehicle performance over the explored parameter space. Areas of very strong non-linearity are observed in the direct neighborhood of the two-dimensional Pareto fronts. This indicates the need for robust exploration of the influence of vehicle shapes on system performance during engineering trade-offs, which are performed during conceptual design. A number of important aspects of the influence of vehicle behavior on the Pareto fronts are observed and discussed. There is a nearly complete convergence to narrow-wing solutions for the winged vehicle. Also, it is found that imposing pitch-stability for the winged vehicle at all angles of attack results in vehicle shapes which require upward control surface deflections during the majority of the entry.     

Aerodynamic optimization design for high pressure turbines based on the adjoint approach

A first study on the continuous adjoint formulation for aerodynamic optimization design of high pressure turbines based on S2surface governed by the Euler equations with source terms is presented.The objective function is defined as an integral function along the boundaries,and the adjoint equations and the boundary conditions are derived by introducing the adjoint variable vectors.The gradient expression of the objective function then includes only the terms related to physical shape variations.The numerical solution of the adjoint equation is conducted by a finitedifference method with the Jameson spatial scheme employing the first and the third order dissipative fluxes.A gradient-based aerodynamic optimization system is established by integrating the blade stagger angles,the stacking lines and the passage perturbation parameterization with the quasi-Newton method of Broyden–Fletcher–Goldfarb–Shanno(BFGS).The application of the continuous adjoint method is validated through a single stage high pressure turbine optimization case.The adiabatic efficiency increases from 0.8875 to 0.8931,whilst the mass flow rate and the pressure ratio remain almost unchanged.The optimization design is shown to reduce the passage vortex loss as well as the mixing loss due to the cooling air injection.     

变形飞行器建模及控制方法研究综述

针对变形飞行器的发展现状、气动建模、动力学建模以及控制方法研究进行了综述。首先阐述了变形飞行器的定义及变形需求,再从四个方面梳理了变形飞行器的技术优势。针对不同的机翼变形方式,对飞行器各种变形技术的特点及发展现状进行了详细的分析与总结;将变形飞行器与固定外形飞行器的气动机理进行了对比分析,并对变形飞行器的动力学建模方法进行了梳理,总结了各种方法的特点;结合变形飞行器自身特性分析了飞行控制的特点及难点,并详细阐述了各类变形飞行器的控制研究进展。最后总结分析了变形飞行器的发展趋势：深化基础理论与技术研究,从材料、结构及控制等领域全方面朝着智能化方向发展,最终实现变形飞行器的工程化、自主化、智能化应用。     

高超声速飞行器气动热耗散、输运及转换技术研究进展

针对高超声速飞行器面临的严重气动热集中、累积和时变问题,从气动热耗散、输运和转换三个方面,分别论述了被动热耗散材料、结构和主动热耗散技术,基于高导热材料、热管和工质对流的热输运技术,以及再生转换和热电转换技术的研究现状及其在高超声速飞行器上的应用案例,提出了上述技术在面临高超声速气动热时存在的问题。最后,针对高超声速飞行器气动热耗散、输运和转换技术的发展趋势进行了展望。     

静子叶栅安装角异常非定常流场数值研究

为了研究静子叶栅安装角异常对叶栅通道流场的影响,利用商业软件NUMECA对二维叶栅某个叶片安装角增加5°不同攻角进行非定常流场数值模拟,得到了因安装角异常导致叶栅通道堵塞时对叶栅通道内的流场结构及叶片载荷的影响。结果表明,安装角调节5°时,在正攻角下,随着攻角的增大叶栅通道内流动不断恶化,吸力面出现大面积的附面层分离;负攻角时,气流比较平稳,但是漩涡脱落频率高达3500Hz。分析发现,安装角异常时攻角对漩涡尺度和脱落频率影响较大,其中叶片动态气动力脉动量迅速增大,可能是导致发动机叶片疲劳破坏的原因之一。     

聚氨酯改性环氧树脂形状记忆材料

以甲基四氢邻苯二甲酸酐(METHPA)为固化剂,利用自制的聚氨酯预聚体(PU)改性环氧树脂(TDE-85)制备形状记忆聚合物。研究了形状记忆环氧树脂的力学性能、Tg和形状记忆性能。研究表明,形状记忆环氧树脂的韧性得到明显的提高,Tg得到了相应的下降,形状记忆性能良好,形状回复率皆在96%以上,形状固定率约达100%。     

An artificial neural network approach for aerodynamic performance retention in airframe noise reduction design of a 3D swept wing model

With the progress of high-bypass turbofan and the innovation of silencing nacelle in engine noise reduction, airframe noise has now become another important sound source besides the engine noise. Thus, reducing airframe noise makes a great contribution to the overall noise reduction of a civil aircraft. However, reducing airframe noise often leads to aerodynamic perfor-mance loss in the meantime. In this case, an approach based on artificial neural network is intro-duced. An established database serves as a basis and the training sample of a back propagation (BP) artificial neural network, which uses confidence coefficient reasoning method for optimization later on. Then the most satisfactory configuration is selected for validating computations through the trained BP network. On the basis of the artificial neural network approach, an optimization pro-cess of slat cove filler (SCF) for high lift devices (HLD) on the Trap Wing is presented. Aerody-namic performance of both the baseline and optimized configurations is investigated through unsteady detached eddy simulations (DES), and a hybrid method, which combines unsteady DES method with acoustic analogy theory, is employed to validate the noise reduction effect. The numerical results indicate not merely a significant airframe noise reduction effect but also excel-lent aerodynamic performance retention simultaneously.