Matrix-free aerostructural optimization of aircraft wings

In structural optimization subject to failure constraints, computing the gradients of a large number of functions with respect to a large number of design variables may not be computationally practical. Often, the number of constraints in these optimization problems is reduced using constraint aggregation at the expense of a higher mass of the optimal structural design. This work presents results of structural and coupled aerodynamic and structural design optimization of aircraft wings using a novel matrix-free augmented Lagrangian optimizer. By using a matrix-free optimizer, the computation of the full constraint Jacobian at each iteration is replaced by the computation of a small number of Jacobian-vector products. The low cost of the Jacobian-vector products allows optimization problems with thousands of failure constraints to be solved directly, mitigating the effects of constraint aggregation. The results indicate that the matrix-free optimizer reduces the computational work of solving the optimization problem by an order of magnitude compared to a traditional sequential quadratic programming optimizer. Furthermore, the use of a matrix-free optimizer makes the solution of large multidisciplinary design problems, in which gradient information must be obtained through iterative methods, computationally tractable.     

A computerized optimization framework for the morphological matrix applied to aircraft conceptual design

This paper presents a formal mathematical framework for the use of the morphological matrix in a computerized conceptual design framework. Within the presented framework, the matrix is quantified so that each solution principle is associated with a set of characteristics such as weight, cost, performance, etc. Selection of individual solutions is modeled with decision variables and an optimization problem is formulated. The applications are the conceptual design of subsystems for an Unmanned Aerial Vehicle and an aircraft fuel transfer system. Both the system models and the mathematical framework are implemented in MS Excel.     

A parallel computing framework for performing structural-acoustic optimization with stochastic forcing

Structural and Multidisciplinary Optimization - Structural-acoustic optimization procedures can be used to find the optimal design for reduced noise or vibration in many real-world scenarios....     

A hyper-heuristic approach to aircraft structural design optimization

The conceptual design of an aircraft is a challenging problem in which optimization can be of great importance to the quality of design generated. Mass optimization of the structural design of an aircraft aims to produce an airframe of minimal mass whilst maintaining satisfactory strength under various loading conditions due to flight and ground manoeuvres. Hyper-heuristic optimization is an evolving field of research wherein the optimization process is continuously adapted in order to provide greater improvements in the quality of the solution generated. The relative infancy of hyper-heuristic optimization has resulted in limited application within the field of aerospace design. This paper describes a framework for the mass optimization of the structural layout of an aircraft at the conceptual level of design employing a novel hyper-heuristic approach. This hyper-heuristic approach encourages solution space exploration, thus reducing the likelihood of premature convergence, and improves the feasibility of and convergence upon the best solution found. A case study is presented to illustrate the effects of hyper-heuristics on the problem for a large commercial aircraft. Resulting solutions were generated of considerably lighter mass than the baseline aircraft. A further improvement in solution quality was found with the use of the hyper-heuristics compared to that obtained without, albeit with a penalty on computation time.     

A multidisciplinary design optimization approach for high temperature aircraft engine components

Rapid turn-around time for investigating new design concepts is a primary force driving design productivity initiatives across the industry. An integration framework focusing on the collaborative nature of rapid design automation at the preliminary and detailed design stage would ensure higher quality designs from the beginning of the product design cycle. As a result, producing reliable, robust optimum designs from the preliminary design phase would enable companies to reduce the overal design cycle time.The focus of the present work is to study the applicability of a Multidisciplinary Design Optimization (MDO) method called Concurrent SubSpace Optimization (CSSO) for the design and optimization of large scale real-life engineering systems. This work can be divided into three parts. The first part is the introduction and development of a benchmark MDO problem that simulates the design and optimization of high temperature engine components (e.g. turbines, compressors etc.). The design problem addressed herein is a stepped beam problem that couples multiple analysis codes using NASTRAN, PATRAN (The MacNeal Schwendler Corporation 1997a,b) and Response Surface Approximations (RSA). The second part focuses on the effectiveness of the polynomial based response surface approximations for capturing the temperature in a thin walled high temperature component. Specifically, quadratic response surface approximations are being investigated for their suitability. The third and the final part provides details of the generic implementation of CSSO within iSIGHT (Engenious Software Inc. 1997) and the results of testing this implementation in application to the benchmark problem mentioned above.     

基于局部并行搜索的分布式约束优化算法框架

针对当前局部搜索算法在求解大规模、高密度的分布式约束优化问题（DCOP）时,求解困难且难以跳出局部最优取得进一步优化等问题,提出一种基于局部并行搜索的分布式约束优化算法框架（LPOS）,算法中agent通过自身的取值并行地搜索局部所有邻居取值来进一步扩大对解空间的搜索,从而避免算法过早陷入局部最优。为了保证算法的收敛性与稳定性,设计了一种自适应平衡因子K来平衡算法对解的开发和继承能力,并在理论层面证明了并行搜索优化算法可以扩大对解空间的搜索,自适应平衡因子K可以实现平衡目的。综合实验结果表明,基于该算法框架的算法在求解低密度和高密度DCOP时性能都优于目前最新的算法。特别是在求解高密度DCOP中有显著的提升。     

Concurrent wing and high-lift system aerostructural optimization

A method is presented for concurrent aerostructural optimization of wing planform, airfoil and high lift devices. The optimization is defined to minimize the aircraft fuel consumption for cruise, while satisfying the field performance requirements. A coupled adjoint aerostructural tool, that couples a quasi-three-dimensional aerodynamic analysis method with a finite beam element structural analysis is used for this optimization. The Pressure Difference Rule is implemented in the quasi-three-dimensional analysis and is coupled to the aerostructural analysis tool in order to compute the maximum lift coefficient of an elastic wing. The proposed method is able to compute the maximum wing lift coefficient with reasonable accuracy compared to high-fidelity CFD tools that require much higher computational cost. The coupled aerostructural system is solved using the Newton method. The sensitivities of the outputs of the developed tool with respect to the input variables are computed through combined use of the chain rule of differentiation, automatic differentiation and coupled-adjoint method. The results of a sequential optimization, where the wing shape and high lift device shape are optimized sequentially, is compared to the results of simultaneous wing and high lift device optimization.     

Spark并行计算框架的内存优化

以Spark为代表的集群并行计算框架在大数据、云计算浪潮中广泛应用,其运行性能优化是应用的关键。为提高运行性能,分析了Spark框架执行流程、内存管理机制,结合Spark和JVM两个层面内存管理的特点,提出3条优化策略：(1)通过序列化和压缩方式减少缓存数据大小,使得GC消耗降低,提升性能;(2)在一定范围内减少运行内存大小,用重算代替缓存,可以提升性能;(3)配置适当的JVM新生代和老生代的比例、Spark计算与缓存空间比例等内存分配参数,能够较大程度地提升性能。实验结果表明,序列化和压缩能够减少缓存占用空间42%;提交运行内存由1 000 MB减少到800 MB时,性能增加21%;优化内存配比,性能比默认参数有10%～30%的提升。     

新涡桨飞机机头结构优化设计

在新涡桨支线飞机机头结构初始方案设计阶段,采用MSC Nastran对机头初始设计方案进行总体结构分析和尺寸优化,为机头结构选型和详细设计提供参考.     

Coupled adjoint aerostructural wing optimization using quasi-three-dimensional aerodynamic analysis

This paper presents a method for wing aerostructural analysis and optimization, which needs much lower computational costs, while computes the wing drag and structural deformation with a level of accuracy comparable to the higher fidelity CFD and FEM tools. A quasi-three-dimensional aerodynamic solver is developed and connected to a finite beam element model for wing aerostructural optimization. In a quasi-three-dimensional approach an inviscid incompressible vortex lattice method is coupled with a viscous compressible airfoil analysis code for drag prediction of a three dimensional wing. The accuracy of the proposed method for wing drag prediction is validated by comparing its results with the results of a higher fidelity CFD analysis. The wing structural deformation as well as the stress distribution in the wingbox structure is computed using a finite beam element model. The Newton method is used to solve the coupled system. The sensitivities of the outputs, for example the wing drag, with respect to the inputs, for example the wing geometry, is computed by a combined use of the coupled adjoint method, automatic differentiation and the chain rule of differentiation. A gradient based optimization is performed using the proposed tool for minimizing the fuel weight of an A320 class aircraft. The optimization resulted in more than 10 % reduction in the aircraft fuel weight by optimizing the wing planform and airfoils shape as well as the wing internal structure.     

High-fidelity aerostructural optimization with integrated geometry parameterization and mesh movement

This paper extends an integrated geometry parameterization and mesh movement strategy for aerodynamic shape optimization to high-fidelity aerostructural optimization based on steady analysis. This approach provides an analytical geometry representation while enabling efficient mesh movement even for very large shape changes, thus facilitating efficient and robust aerostructural optimization. The geometry parameterization methodology uses B-spline surface patches to describe the undeflected design and flying shapes with a compact yet flexible set of parameters. The geometries represented are therefore independent of the mesh used for the flow analysis, which is an important advantage to this approach. The geometry parameterization is integrated with an efficient and robust grid movement algorithm which operates on a set of B-spline volumes that parameterize and control the flow grid. A simple technique is introduced to translate the shape changes described by the geometry parameterization to the internal structure. A three-field formulation of the discrete aerostructural residual is adopted, coupling the mesh movement equations with the discretized three-dimensional inviscid flow equations, as well as a linear structural analysis. Gradients needed for optimization are computed with a three-field coupled adjoint approach. Capabilities of the framework are demonstrated via a number of applications involving substantial geometric changes.     

A parallel computing framework for big data

Big data has received great attention in research and application. However, most of the current efforts focus on system and application to handle the challenges of “volume” and “velocity”, and not much has been done on the theoretical foundation and to handle the challenge of “variety”. Based on metric-space indexing and computationalcomplexity theory, we propose a parallel computing framework for big data. This framework consists of three components, i.e., universal representation of big data by abstracting various data types into metric space, partitioning of big data based on pair-wise distances in metric space, and parallel computing of big data with the NC-class computing theory.     

面向分级设计优化的飞行器参数化建模方法

针对飞行器气动隐身外形综合设计优化问题,提出合适的面向分级设计优化流程,建立适应该流程的渐进分层参数化建模方法;用基于敏度分析的参数影响程度分析方法筛选复杂设计变量;采用多学科设计优化（Multidisplinary Design Optimization,MDO）理论和差分进化算法进行飞行器气动隐身外形的综合设计优化.将该方法用于某飞行器外形设计优化,结果表明：该方法合理可行,可为飞行器外形多学科设计优化提供一定参考.     

飞机结构总体优化设计方法

优化设计可以改善结构的应力分布,合理布置材料,从而提高材料的利用率.通过对全机结构逐层分解,确定优化设计变量、优化区域以及响应约束,并应用MSC Nastran中的可行方向法和敏度分析方法,对MA700飞机的机翼进行优化设计方法研究,考虑稳定性因素,初步解决长桁尺寸确定的问题,为后续优化设计工作积累经验.     

A web services-based multidisciplinary design optimization framework for complex engineering systems with uncertainties

With the increased complexity of complex engineering systems (CES), more and more disciplines, coupled relationships, work processes, design data, design knowledge and uncertainties are involved. Currently, the MDO is facing unprecedented challenges especially in dealing with the CES by different specialists dispersed geographically on heterogeneous platforms with different analysis tools. The product design data integration and data sharing among the participants and the workflow optimization hamper the development and applications of MDO in enterprises seriously. Therefore, a multi-hierarchical integrated product design data model (MiPDM) supporting the MDO in web environment and a web services-based MDO framework considering aleatory and epistemic uncertainties are proposed in this paper. With the enabling technologies including web services, ontology, workflow, agent, XML, and evidence theory, the proposed framework enables the designers geographically dispersed to work collaboratively in the MDO environment. The ontology-based workflow enables the logical reasoning of MDO to be processed dynamically. Finally, a proof-of-concept prototype system is developed based on Java 2 Platform Enterprise Edition (J2EE) and an example of supersonic business jet is demonstrated to verify the web services-based MDO framework.     

A framework for self-tuning optimization algorithm

The performance of any algorithm will largely depend on the setting of its algorithm-dependent parameters. The optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems. However, such parameter tuning itself is a tough optimization problem. In this paper, we present a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself. Using the firefly algorithm as an example, we show that this framework works well. It is also found that different parameters may have different sensitivities and thus require different degrees of tuning. Parameters with high sensitivities require fine-tuning to achieve optimality.     

A distributed parallel programming framework

This paper presents Visper, a novel object-oriented framework that identifies and enhances common services and programming primitives, and implements a generic set of classes applicable to multiple programming models in a distributed environment. Groups of objects, which can be programmed in a uniform and transparent manner, and agent-based distributed system management, are also featured in Visper. A prototype system is designed and implemented in Java, with a number of visual utilities that facilitate program development and portability. As a use case, Visper integrates parallel programming in an MPI-like message-passing paradigm at a high level with services such as checkpointing and fault tolerance at a lower level. The paper reports a range of performance evaluation on the prototype and compares it to related works     

A design framework for metaheuristics

This paper is concerned with taking an engineering approach towards the application of metaheuristic problem solving methods, i.e., heuristics that aim to solve a wide variety of problems. How can a practitioner solve a problem using metaheuristic methods? What choices do they have, and how are these choices influenced by the problem at hand? Are there sensible universal choices which can be made, or are these choices always problem-dependent? The aim of this paper is to address questions such as these in the context of a (soft) engineering design framework for the application of metaheuristics. The aim of this framework is to make explicit the choices which a practitioner needs to make in applying these techniques, and to give some guidelines for how metaheuristics might be tuned to problems by considering different problem- and solution-types.