Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization

An efficient method employing a Principal Component Analysis (PCA)-Deep Belief Network (DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study. In order to reduce the number of design variables for aerodynamic optimizations, the PCA technique is implemented to the geometric parameters obtained by parameterization method. For the purpose of predicting aerodynamic parameters, the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output, and it is trained using the k-step contrastive divergence algorithm. The established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils, and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate models. Then the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization (PSO) framework, and applied to the robust aerodynamic design optimizations of Natural Laminar Flow (NLF) airfoil and transonic wing. The optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic wing. By employing the PCA-DBN-based surrogate model, the developed efficient method improves the optimization efficiency obviously.     

A novel surrogate modeling strategy of the mechanical properties of 3D braided composites

In this paper, a surrogate-based modeling methodology is developed and presented to predict the elastic properties of three dimensional (3D) four-directional braided composites. Using this approach, the prediction process becomes feasible with only a limited number of training points. The surrogate models constructed using Finite Element (FE) method and Diffuse Approximation, reduce the computational time and cost for preparing experimental samples. In the FE model, multiscale method is applied to couple the computations of elastic properties at microscale and mesoscale. Subsequently, a parametric study is performed to analyze the effects of the three design parameters on the elastic properties. Satisfactory results are obtained via the surrogate-based modeling predictions, which are compared with the experimental measurements. Moreover, the predictions obtained from surrogate models concur well with the FE predictions. This study orients a new direction for predicting the mechanical properties based on surrogate models which can effectively reduce the sample preparation cost and computational efforts.     

Sensitivity analysis of flowfield modeling parameters upon the flow structure and aerodynamics of an opposing jet over a hypersonic blunt body

Implementation of an opposing jet in design of a hypersonic blunt body significantly modifies the external flowfield and yields a considerable reduction in the aerodynamic drag. This study aims to investigate the effects of flowfield modeling parameters of injection and freestream on the flow structure and aerodynamics of a blunt body with an opposing jet in hypersonic flow. Reynolds-Averaged Navier-Stokes (RANS) equations with a Shear Stress Transport (SST) turbulence model are employed to simulate the intricate jet flow interaction. Through utilizing a Non-Intrusive Polynomial Chaos (NIPC) method to construct surrogates, a functional relation is established between input modeling parameters and output flowfield and aerodynamic quantities in concern. Sobol indices in sensitivity analysis are introduced to represent the relative contribution of each parameter. It is found that variations in modeling parameters produce large variations in the flow structure and aerodynamics. The jet-to-freestream total-pressure ratio, jet Mach number, and freestream Mach number are the major contributors to variation in surface pressure, demonstrating an evident location-dependent behavior. The penetration length of injection, reattachment angle of the shear layer, and aerodynamic drag are also most sensitive to the three crucial parameters above. In comparison, the contributions of freestream temperature, freestream density, and jet total temperature are nearly negligible.     

超燃冲压发动机可调尾喷管多目标优化设计

针对超燃冲压发动机宽马赫数、攻角范围内高性能工作要求,建立了基于试验设计方法和代理模型的可调尾喷管多目标优化设计方法,获得了尾喷管结构随马赫数和攻角变化的调节规律.以推力系数、升力系数和力矩系数为优化目标,以三次型线尾喷管为对象,采用遗传算法优化设计,得到Pareto最优解集;以一组Pareto最优解为基准在不同马赫数和攻角下进行尾喷管变结构设计优化,拟合得到尾喷管结构随马赫数和攻角的变化曲线.仿真结果显示了理论分析的正确性,并发现:变结构设计实现了尾喷管大范围高性能工作;尾喷管性能和几何参数,飞行状态参数均高度非线性,任一个改变都会影响其性能;采用试验设计方法和代理模型,能大大缩小优化设计时间,简化设计过程.     

RP-3航空煤油模拟替代燃料的着火延迟特性

为了获得一种新的国产RP-3航空煤油模拟替代燃料的着火延迟特性，在化学激波管中完成了初始压力分别为0.1MPa与0.3MPa，当量比分别为0.5，1.0与1.5，着火温度为1000~1700K条件下该模拟替代燃料着火延迟时间的试验测试，分析了初始压力与当量比对该模拟替代燃料着火延迟时间的影响规律，并与相应工况条件下RP-3航空煤油的着火延迟特性进行了对比分析。结果表明，不同工况条件下，该模拟替代燃料的着火延迟时间的对数与着火温度的倒数呈线性关系；其着火延迟时间随当量比的降低或着火温度、初始压力的升高逐渐缩短；相同工况条件下，该模拟替代燃料的着火延迟时间与RP-3航空煤油的着火延迟时间吻合较好。     

火箭协同优化中的气动代理模型研究

随着运载火箭研发模式转变，快速迭代和协同优化设计成为主要发展方向，这就要求作为小回路论证中重要一环的气动特性计算能够实现在线输出数据，亟需研究一种快速计算气动特性的代理模型，代替耗时的CFD计算和风洞试验参与到总体优化设计中。综合比较多种快速计算途径，选择高斯基Kriging插值和BP神经网络两种方法构建代理模型。使用脚本控制的Cart3D软件生成数值试验样本，样本点精度与Fluent软件计算误差小于14%。通过样本点训练、内参优化和加点策略，最终获得相对误差小于10%的代理模型，能够实现给定外形参数在线秒级输出气动数据，极大地推动了气动计算在总体论证中的作用。     

Aeroacoustic and aerodynamic optimization of propeller blades

It is of great significance to develop a high-efficiency and low-noise propeller optimization method for new-generation propeller aircraft design. Coupled with free form deformation method, dynamic mesh interpolation technology, optimization algorithm, surrogate model, aerodynamic calculation and aeroacoustic prediction model module, the integrated aerodynamic and aeroacoustic design method of propeller is built. The optimization design for the six-blade propeller is carried out. The non-reduction in efficiency, thrust coefficient and the minimum of aerodynamic noise is treated as the optimization design objective. The spatial vorticity distribution of the propeller before and after the design is also analyzed by using unsteady computational fluid dynamics method. The results show that the optimized propeller can effectively reduce the aerodynamic noise level. The maximum total sound pressure level can be reduced by 5 dB without reducing its aerodynamic performance. The developed method has good application potential in low-noise optimization design of propeller and other rotating machinery.     

升力体外形设计的代理模型优化方法

面向飞行器概念设计提出了一种将类型函数/形状函数变换技术与代理模型技术及全局优化算法相结合,实现升力体外形多目标优化的设计策略.类型函数/形状函数变换技术能够通过调整少量尺寸参数或类型函数/形状函数控制参数衍生出多种设计构型.选用最优拉丁超立方抽样完成实验设计,将径向基函数作为近似方法构建代理模型,并通过序贯采样后验策略改善代理模型拟合优度.在Ma 6,迎角20°设计点处搭建了升力体简化外形的气动分析代理模型.应用NSGA -Ⅱ多目标遗传算法实现了以升阻比和容积利用率最大化为目标的折衷优化,得到包含一系列改进设计方案的Pareto -解集,其中,各构型目标响应相对实际分析值误差均在6.1％以内,表明该方法既可以通过代理模型大幅提升设计效率,降低应用全局优化算法的计算复杂度,又能够得到满足概念设计精度需要的设计方案,有利于概念设计中定量高效地实现多个设计目标的折衷优化,明确飞行器外形修改方向.     

损失及落后角代理模型在多级轴流压气机特性预测中的应用

为了提高轴流压气机设计能力,进而提高发动机的特性,需要掌握一种能够较好预测轴流压气机的压比、效率等特性的方法。结合运用三元流动理论和传统损失落后角模型计算出的压气机流场数据,利用正则化径向基函数神经网络取代经验公式搭建了一种新的损失及落后角模型,计算了E3十级高压压气机的特性;并分别研究了不进行正则化和进行正则化对损失及落后角预测的影响与其对压气机效率和压比特性预测的影响。结果表明,在多级压气机中,在训练样本区分转静子、区分转速、区分工况条件下,使用正则化的径向基神经网络代理模型在大部分情况下能够较好地预测损失、落后角及多级压气机整体特性,但是对沿叶高分布的损失及落后角预测能力还有待提高。     

考虑性能及成本的固体火箭发动机多学科设计优化

为综合考虑固体火箭发动机的燃烧室、药柱、内弹道、喷管及成本等学科影响,梳理学科间耦合关系,并建立了以总冲最大、成本最小为优化目标的固体火箭发动机多学科设计优化（MDO）模型。为降低MDO问题的计算成本,提出一种基于Kriging代理模型的多目标自适应优化方法（KRG-MAOM）。优化过程中,分别对目标与约束构建Kriging模型,并采用多目标优化算法求解,在伪Pareto解中综合考虑支配关系与分布特性选取新增样本点,引导优化快速收敛。算例结果表明,KRG-MAOM算法在全局收敛性与优化效率方面具有显著优势。最后,采用KRG-MAOM算法求解该MDO问题,得到可行的Pareto解集方案,与初始方案相比,同性能情况下成本节省约3.36%;同成本情况下性能提升约10.93%,从而验证MDO模型合理性与KRG-MAOM算法有效性。