An efficient aerodynamic shape optimization of blended wing body UAV using multi-fidelity models

This paper presents a novel optimization technique for an efficient multi-fidelity model building approach to reduce computational costs for handling aerodynamic shape optimization based on high-fidelity simulation models. The wing aerodynamic shape optimization problem is solved by dividing optimization into three steps—modeling 3D(high-fidelity) and 2D(lowfidelity) models, building global meta-models from prominent instead of all variables, and determining robust optimizing shape associated with tuning local meta-models. The adaptive robust design optimization aims to modify the shape optimization process. The sufficient infilling strategy—known as adaptive uniform infilling strategy—determines search space dimensions based on the last optimization results or initial point. Following this, 3D model simulations are used to tune local meta-models. Finally, the global optimization gradient-based method—Adaptive Filter Sequential Quadratic Programing(AFSQP) is utilized to search the neighborhood for a probable optimum point. The effectiveness of the proposed method is investigated by applying it, along with conventional optimization approach-based meta-models, to a Blended Wing Body(BWB) Unmanned Aerial Vehicle(UAV). The drag coefficient is defined as the objective function, which is subjected to minimum lift coefficient bounds and stability constraints. The simulation results indicate improvement in meta-model accuracy and reduction in computational time of the method introduced in this paper.     

Variable-fidelity optimization with design space reduction

Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task owing to the curse of dimensionality. This paper presents a new algorithm to reduce the size of a design space to a smaller region of interest allowing a more accurate surrogate model to be generated. The framework requires a set of models of different physical or numerical fidelities. The low-fidelity (LF) model provides physics-based approximation of the high-fidelity (HF) model at a fraction of the computational cost. It is also instrumental in identifying the small region of interest in the design space that encloses the high-fidelity optimum. A surrogate model is then constructed to match the low-fidelity model to the high-fidelity model in the identified region of interest. The optimization process is managed by an update strategy to prevent convergence to false optima. The algorithm is applied on mathematical problems and a two-dimen-sional aerodynamic shape optimization problem in a variable-fidelity context. Results obtained are in excellent agreement with high-fidelity results, even with lower-fidelity flow solvers, while showing up to 39% time savings.     

航天器轨迹优化的通用数值方法

给出了航天器轨迹优化的一种通用数值仿真方法，该方法是由静态参数优化和动态参数优化构成，其中，静态参数优化采用可变误差多面体算法，动态参数优化采用基于最优控制理论的共轭梯度方法。     

航空电子设备冷却轴流风扇优化设计

基于风扇参数化设计和数值模拟程序,分析风扇各主要结构参数对其气动性能的影响,为进一步的优化设计提供了条件.采用并行计算流体力学(CFD)方法,基于组合优化策略(正交实验设计、响应面模型、遗传算法和混合整型算法)以多工作点流量系数平均值最大为目标函数对风扇进行多参数结构优化.8025型轴流风扇的优化结果比初步设计方案的性能提高14.5%.      

适用于参数可调结构的非定常气动力降阶建模方法

基于计算流体力学(CFD)的非定常气动力降阶模型(ROM)可以极大提高气动弹性分析效率,然而现有的ROM只能针对固定参数结构,即只适合于固定模态振型,这使得现有ROM在气动弹性优化设计和不确定性分析等结构变参问题中应用受限。针对该问题,在文献[20]基础上提出了一种新的适用于任意模态振型的非定常气动力建模方法。首先将待设计/分析的结构进行参数化抽样和模态分析,之后通过主成分分析(PCA)得到若干基振型,再将这些基振型线性叠加即可拟合抽样空间内任何参数下结构的前若干阶振型。当结构参数改动时,仅仅是叠加系数发生变化。算例表明,仅用很少的基振型就能达到理想的拟合精度。经典的气动力降阶方法可用于基振型坐标下的气动力降阶,进一步变换可得到适用于不同结构的ROM,这意味着,结构参数可以在抽样空间内任意调节改动,而ROM却是通用的。该方法能广泛用于气动弹性优化设计和不确定性分析工作,可提高颤振分析精度和效率。     

基于分层置信规则库的惯导系统性能评估方法

为解决惯导系统（INS）性能评估所面临的高价值样本缺失、评估指标多、系统复杂等问题,提出一种基于分层置信规则库（Hierarchical BRB）的惯导系统性能评估方法。将专家知识与监测数据进行有效融合,提高了惯导系统的性能评估精度。首先,针对惯导系统结构构建分层BRB模型,同时将系统内部器件产生组合误差考虑在模型中。其次,为降低专家知识不确定性对初始模型评估精度的影响,采用基于投影算子的协方差矩阵自适应优化策略（P-CMA-ES）构建优化模型,通过监测数据对模型参数进行微调。最后,以某型捷联惯导系统的性能评估为例,验证了所提方法的有效性。     

离心压气机通流/造型/CFD参数化优化设计集成反问题方法

在自主开发的离心压气机通流/造型反问题设计程序的基础上, 发展了一套离心压气机的通流/造型/CFD于优化设计程序系统下集成的反问题方法, 建立了离心压气机的参数化优化设计平台.采用“试验设计+响应曲面模型+近似优化”的快速优化算法, 对一个10 daN推力级微型涡喷发动机的离心压气机进行了多准则气动优化.结果表明, 这种过程集成的快速优化方法是很有效的.      

湍流燃烧机理和调控的活性子空间分析方法

高效、低排放等需求促使发动机燃烧趋于近极限燃烧组织,亟需在稳定可控燃烧方面取得突破。湍流燃烧机理复杂,影响湍流燃烧数值模拟预测的物理化学和初始/边界条件参数众多。但是在该高维映射关系中,预测目标量往往仅在输入参数空间中的少数方向上梯度显著,称之为活跃方向。当活跃方向与空间基的方向不一致时,采用传统的全局敏感性方法难以高效地分析出主控参数以及后续的湍流燃烧机理。而活性子方法可以通过梯度的协方差矩阵特征分解得到上述活跃方向。本文综述了活性子空间方法理论及在湍流燃烧模拟中的应用：即探究海量输入参数空间中的活跃方向,构造低维活性子空间和低维响应面,从而高效地量化模拟不确定性、表征主控物理过程,从而揭示湍流燃烧机理。最后,进一步探讨了基于活性子空间分析方法的湍流燃烧调控。     

Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids

A variable-fidelity method can remarkably improve the efficiency of a design optimization based on a high-fidelity and expensive numerical simulation, with assistance of lower-fidelity and cheaper simulation(s). However, most existing works only incorporate “two” levels of fidelity, and thus efficiency improvement is very limited. In order to reduce the number of high-fidelity simulations as many as possible, there is a strong need to extend it to three or more fidelities. This article proposes a novel variable-fidelity optimization approach with application to aerodynamic design. Its key ingredient is the theory and algorithm of a Multi-level Hierarchical Kriging (MHK), which is referred to as a surrogate model that can incorporate simulation data with arbitrary levels of fidelity. The high-fidelity model is defined as a CFD simulation using a fine grid and the lower-fidelity models are defined as the same CFD model but with coarser grids, which are determined through a grid convergence study. First, sampling shapes are selected for each level of fidelity via technique of Design of Experiments (DoE). Then, CFD simulations are conducted and the output data of varying fidelity is used to build initial MHK models for objective (e.g. C_D) and constraint (e.g. C_L, C_m) functions. Next, new samples are selected through infill-sampling criteria and the surrogate models are repetitively updated until a global optimum is found. The proposed method is validated by analytical test cases and applied to aerodynamic shape optimization of a NACA0012 airfoil and an ONERA M6 wing in transonic flows. The results confirm that the proposed method can significantly improve the optimization efficiency and apparently outperforms the existing single-fidelity or two-level-fidelity method.     

Towards intelligent design optimization: Progress and challenge of design optimization theories and technologies for plastic forming

Plastic forming is one of enabling and fundamental technologies in advanced manufacturing chains. Design optimization is a critical way to improve the performance of the forming system, exploit the advantages of high productivity, high product quality, low production cost and short time to market and develop precise, accurate, green, and intelligent (smart) plastic forming technology. However, plastic forming is quite complicated, relating to multi-physics field coupling, multi-factor influence, multi-defect constraint, and triple nonlinear, etc., and the design optimization for plastic forming involves multi-objective, multi-parameter, multi-constraint, nonlinear, high-dimensionality, non-continuity, time-varying, and uncertainty, etc. Therefore, how to achieve accurate and efficient design optimization of products, equipment, tools/dies, and processing as well as materials characterization has always been the research frontier and focus in the field of engineering and manufacturing. In recent years, with the rapid development of computing science, data science and internet of things (IoT), the theories and technologies of design optimization have attracted more and more attention, and developed rapidly in forming process. Accordingly, this paper first introduced the framework of design optimization for plastic forming. Then, focusing on the key problems of design optimization, such as numerical model and optimization algorithm, this paper summarized the research progress on the development and application of the theories and technologies about design optimization in forming process, including deterministic and uncertain optimization. Moreover, the applicability of various modeling methods and optimization algorithms was elaborated in solving the design optimization problems of plastic forming. Finally, considering the development trends of forming technology, this paper discusses some challenges of design optimization that may need to be solved and faced in forming process.