Uncertainty analysis and design optimization of hybrid rocket motor powered vehicle for suborbital flight

In this paper, we propose an uncertainty analysis and design optimization method and its applications on a hybrid rocket motor(HRM) powered vehicle. The multidisciplinary design model of the rocket system is established and the design uncertainties are quantified. The sensitivity analysis of the uncertainties shows that the uncertainty generated from the error of fuel regression rate model has the most significant effect on the system performances. Then the differences between deterministic design optimization(DDO) and uncertainty-based design optimization(UDO) are discussed. Two newly formed uncertainty analysis methods, including the Kriging-based Monte Carlo simulation(KMCS) and Kriging-based Taylor series approximation(KTSA), are carried out using a global approximation Kriging modeling method. Based on the system design model and the results of design uncertainty analysis, the design optimization of an HRM powered vehicle for suborbital flight is implemented using three design optimization methods: DDO, KMCS and KTSA. The comparisons indicate that the two UDO methods can enhance the design reliability and robustness. The researches and methods proposed in this paper can provide a better way for the general design of HRM powered vehicles.In this paper,we propose an uncertainty analysis and design optimization method and its applications on a hybrid rocket motor(HRM)powered vehicle.The multidisciplinary design model of the rocket system is established and the design uncertainties are quantified.The sensitivity analysis of the uncertainties shows that the uncertainty generated from the error of fuel regression rate model has the most significant effect on the system performances.Then the differences between deterministic design optimization(DDO)and uncertainty-based design optimization(UDO)are discussed.Two newly formed uncertainty analysis methods,including the Kriging-based Monte Carlo simulation(KMCS)and Kriging-based Taylor series approximation(KTSA),are carried out using a global approximation Kriging modeling method.Based on the system design model and the results of design uncertainty analysis,the design optimization of an HRM powered vehicle for suborbital flight is implemented using three design optimization methods:DDO,KMCS and KTSA.The comparisons indicate that the two UDO methods can enhance the design reliability and robustness.The researches and methods proposed in this paper can provide a better way for the general design of HRM powered vehicles.     

Effect of Variable Selection on Multidisciplinary Design Optimization： a Flight Vehicle Example

Different multidisciplinary design optimization (MDO) problems are formulated and compared. Two MDO formulations are applied to a sounding rocket in order to optimize the performance of the rocket. In the MDO of the referred vehicle, three disciplines have been considered,which are trajectory, propulsion and aerodynamics. A special design structure matrix is developed to assist data exchange between disciplines. This design process uses response surface method (RSM) for multidisciplinary optimization of the rocket. The RSM is applied to the design in two categories: the propulsion model and the system level. In the propulsion model, RSM deter-mines an approximate mathematical model of the engine output parameters as a function of design variables. In the system level, RSM fits a surface of objective function versus design variables. In the first MDO problem formulation, two design variables are selected to form propulsion discipline. In the second one, three new design variables from geometry are added and finally, an optimization method is applied to the response surface in the system level in order to find the best result. Application of the first developed multidisciplinary design optimization procedure increased accessible altitude (performance index) of the referred sounding rocket by twenty five percents and the second one twenty nine.     

Reliability design optimization of composite structures based on PSO together with FEA

The present work aims to develop a method for reliability-based optimum design of composite structures. A procedure combining particle swarm optimization (PSO) and finite element analysis (FEA) has been proposed. Numerical examples for the reliability design optimization (RDO) of a laminate and a composite cylindrical shell are worked out to demonstrate the effectiveness of the method. Then a design for composite pressure vessels is studied. The advantages and necessity of RDO over the conventional equi-strength design are addressed. Examples show that the proposed method has good stability and is efficient in dealing with the probabilistic optimal design of composite structures. It may serve as an effective tool to optimize other complicated structures with uncertainties.     

Hyper Heuristic Approach for Design and Optimization of Satellite Launch Vehicle

Satellite launch vehicle lies at the cross-road of multiple challenging technologies and its design and optimization present a typical example of multidisciplinary design and optimization(MDO) process.The complexity of problem demands highly effi-cient and effective algorithm that can optimize the design.Hyper heuristic approach(HHA) based on meta-heuristics is applied to the optimization of air launched satellite launch vehicle(ASLV).A non-learning random function(NLRF) is proposed to con-trol low-level meta-heuristics(LLMHs) that increases certainty of global solution,an essential ingredient required in product conceptual design phase of aerospace systems.Comprehensive empirical study is performed to evaluate the performance advan-tages of proposed approach over popular non-gradient based optimization methods.Design of ASLV encompasses aerodynamics,propulsion,structure,stages layout,mass distribution,and trajectory modules connected by multidisciplinary feasible design approach.This approach formulates explicit system-level goals and then forwards the design optimization process entirely over to optimizer.This distinctive approach for launch vehicle system design relieves engineers from tedious,iterative task and en-ables them to improve their component level models.Mass is an impetus on vehicle performance and cost,and so it is considered as the core of vehicle design process.Therefore,gross launch mass is to be minimized in HHA.     

A fully automated framework for helicopter rotor blades design and analysis including aerodynamics,structure, and manufacturing

This study describes an integrated framework in which basic aerospace engineering aspects (performance, aerodynamics, and structure) and practical aspects (configuration visualiza-tion and manufacturing) are coupled and considered in one fully automated design optimization of rotor blades. A number of codes are developed to robustly perform estimation of helicopter config-uration from sizing, performance analysis, trim analysis, to rotor blades configuration representa-tion. These codes are then integrated with a two-dimensional airfoil analysis tool to fully design rotor blades configuration including rotor planform and airfoil shape for optimal aerodynamics in both hover and forward flights. A modular structure design methodology is developed for real-istic composite rotor blades with a sophisticated cross-sectional geometry. A D-spar cross-sectional structure is chosen as a baseline. The framework is able to analyze all realistic inner configurations including thicknesses of D-spar, skin, web, number and ply angles of layers of each composite part, and materials. A number of codes and commercial software (ANSYS, Gridgen, VABS, PreVABS, etc.) are implemented to automate the structural analysis from aerodynamic data processing to sec-tional properties and stress analysis. An integrated model for manufacturing cost estimation of composite rotor blades developed at the Aerodynamic Analysis and Design Laboratory (AADL), Aerospace Information Engineering Department, Konkuk University is integrated into the framework to provide a rapid and dynamic feedback to configuration design. The integration of three modules has constructed a framework where the size of a helicopter, aerodynamic performance analysis, structure analysis, and manufacturing cost estimation could be quickly investigated. All aspects of a rotor blade including planform, airfoil shape, and inner structure are considered in a multidisciplinary design optimization without an exception of critical configuration.     

Interactive Multi-objective Optimization Design for the Pylon Structure of an Airplane

The pylon structure of an airplane is very complex, and its high-fidelity analysis is quite time-consuming. If posterior preference optimization algorithm is used to solve this problem, the huge time consumption will be unacceptable in engineering practice due to the large amount of evaluation needed for the algorithm. So, a new interactive optimization algorithm-interactive multi-objective particle swarm optimization （IMOPSO） is presented. IMOPSO is efficient, simple and operable. The decision-maker can expediently determine the accurate preference in IMOPSO. IMOPSO is used to perform the pylon structure optimization design of an airplane, and a satisfactory design is achieved after only 12 generations of IMOPSO evolutions. Compared with original design, the maximum displacement of the satisfactory design is reduced, and the mass of the satisfactory design is decreased for 22%.     

Stochastic structural optimization using particle swarm optimization, surrogate models and Bayesian statistics

This paper focuses on a method to solve structural optimization problems using particle swarm optimization (PSO), surrogate models and Bayesian statistics. PSO is a random/stochastic search algorithm designed to find the global optimum. However, PSO needs many evaluations compared to gradient-based optimization. This means PSO increases the analysis costs of structural optimization. One of the methods to reduce computing costs in stochastic optimization is to use approximation techniques. In this work, surrogate models are used, including the response surface method (RSM) and Kriging. When surrogate models are used, there are some errors between exact values and approximated values. These errors decrease the reliability of the optimum values and discard the realistic approximation of using surrogate models. In this paper, Bayesian statistics is used to obtain more reliable results. To verify and confirm the efficiency of the proposed method using surrogate models and Bayesian statistics for stochastic structural optimization, two numerical examples are optimized, and the optimization of a hub sleeve is demonstrated as a practical problem.     

A novel approach of testability modeling and analysis for PHM systems based on failure evolution mechanism

Prognostics and health management (PHM) significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability (DFT) developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test (UUT) in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis (FMEMECA), and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.     

A novel testability model for health management of heading attitude system

Prognostics and health management (PHM) is very important to guarantee the reliability and safety of aerospace systems, and sensing and test are the precondition of PHM. Integrating design for testability into early design stage of system early design stage is deemed as a fundamental way to improve PHM performance, and testability model is the base of testability analysis and design. This paper discusses a hierarchical model-based approach to testability modeling and analysis for heading attitude system health management. Quantified directed graph, of which the nodes represent components and tests and the directed edges represent fault propagation paths, is used to describe fault-test dependency, and quantitative testability information is assigned to nodes and directed edges. The fault dependencies between nodes can be obtained by functional fault analysis methodology that captures the physical architecture and material flows such as energy, heat, data, and so on. By incorporating physics of failure models into component, the dynamic process of a failing or degrading component can be projected onto system behavior, i.e., system symptoms. Then, the analysis of extended failure modes, mechanisms and effects is utilized to construct fault evolution-test dependency. Using this integrated model, the designers and system analysts can assess the test suite’s fault detectability, fault isolability and fault predictability. And heading attitude system application results show that the proposed model can support testability analysis and design for PHM very well.     

基于双循环的离心叶轮多学科可靠性优化设计

 针对传统多学科设计优化方法中未能考虑不确定因素的问题,开展基于可靠性的多学科设计优化(RBMDO)方法的研究。以离心式压气机叶轮为对象,综合考虑工作状况和材料参数等随机性因素的影响,利用改进的一次二阶矩(AFOSM)法进行可靠性分析,通过双循环策略将多学科可行优化方法与可靠性分析相结合,合理引入近似技术,建立了基于可靠性的多学科设计优化系统。对某离心叶轮进行多学科可靠性优化设计的算例表明,在满足所有可靠性指标的前提下,该方法可实现离心叶轮综合性能的提高,并有效地缩减设计周期。     

基于单学科可行法的多学科可靠性设计优化

 近年来对多学科系统中不确定性的研究逐渐增多。针对该问题的计算复杂性,利用序列优化及可靠性评估（SORA）框架,提出用单学科可行（IDF）方法来求解多学科可靠性设计优化（RBMDO）问题。此方法将可靠性分析和多学科设计优化分离开来,分为确定性多学科设计优化和可靠性分析两个问题顺序执行,以提高计算效率。可靠性分析和优化的过程都采用多学科设计优化中高效的方法——IDF方法。最后通过例子验证此方法的有效性,算例结果表明,采用基于IDF的方法,学科1和学科2所用的函数计算次数分别减少了28.9%和25.0%。     

通用飞行器气动优化设计数字化集成平台——DIPasda

未来航空工业的发展，需要解决多学科综合设计的关键问题，为新型高性能飞行器的设计提供有力的设计方法和设计工具。DIPasda作为复杂外形设计的通用飞行器多学科优化设计平台，研制目的主要是提供一套新型通用、鲁棒、高效的优化设计架构，应用于通用飞行器工业设计环境，改善传统设计耗时低效的状况，提高新型飞行器设计的效率和精度。DIPasda平台系统包含了优化设计过程中所需用到的各类方法，主要包括数值优化算法、几何模型参数化方法、代理模型方法、高精度的学科分析工具等。通过详细介绍平台的系统架构、主要的功能模块、伴随优化设计和多目标优化设计流程，展现了DIPasda平台系统架构设计的灵活性和功能模块的完备性。最后通过优化算例展示了系统的综合优化设计能力。     

基于可靠性的涡轮叶片双循环多学科设计优化

提出一种适用于涡轮叶片复杂结构的基于可靠性多学科设计优化方法.使用多学科可行性优化方法解耦.根据实验设计结果,通过Kriging模型建立多学科分析过程的近似模型,并在计算过程中不断更新近似模型.将Hasofer-Lind Rackwitz-Fiessler(HL-RF)可靠性计算方法和优化算法以双循环可靠性优化方法相结合,实现基于可靠性的多学科设计优化模型.实例分析表明,在保证结构的可靠度要求条件下,设计结果满足性能最优.验证了基于可靠性多学科设计优化方法在工程实践中的应用是可行的.      

基于分解策略的涡轮叶片可靠性及多学科设计优化

将反一阶可靠性分析方法与多学科可行方法相结合,提出了一种适用于涡轮叶片复杂结构的可靠性及多学科设计优化方法.在优化过程中使用Kriging近似模型并不断提高模型精度,解决了多学科可行方法反复调用仿真程序进行多学科分析,计算量较大的问题.该方法将可靠性分析与多学科优化过程分离,提高了优化计算效率.以某型涡轮叶片的设计优化为例,对该方法进行了验证并与传统双循环方法进行了对比.结果表明,优化结果满足可靠性的要求,与双循环方法相比优化效率提高63.8%,证明了该方法在工程应用中的可行性和有效性.      

基于单循环方法的涡轮叶片可靠性及多学科设计优化

为了得到适用于涡轮叶片复杂结构并同时考虑可靠性的多学科设计优化方法,将基于单循环方法的可靠性分析（SLBRA）与并行子空间设计优化方法 （CSSO）相结合,提出了一种基于可靠性的多学科设计优化（RBMDO）方法。在优化过程中使用Kriging近似模型并不断提高模型精度。该方法在计算最可能失效点（MPP）的过程中避免了优化迭代,提高了计算效率。以涡轮叶片的设计优化为例,对该方法进行了验证并与传统双循环方法进行了对比。结果表明,优化结果满足可靠性的要求,与双循环方法相比优化效率明显提高,证明了该方法在工程应用中的可行性和有效性。     

多学科设计优化在非常规布局飞机总体设计中的应用

以飞翼布局飞机总体设计为例,展示如何将多学科设计优化(MDO)方法有效地应用于非常规布局飞机总体设计.基于二级优化方法,提出一种飞机总体MDO实施流程.该流程包括系统级优化、子系统级优化(或评估)和多学科模型生成器3个部分.系统级优化的任务是优化全局设计变量,使系统目标最优.子系统级优化涉及的学科包括气动、隐身、结构、...     

涡轮叶片多学科可靠性及稳健设计优化

为了得到一种适用于涡轮叶片复杂结构并同时考虑可靠性及稳健性的多学科设计优化方法,将6sig-ma可靠性及稳健设计优化方法与多学科可行方法(MDF)相结合,采用二阶Taylor展开法进行可靠性及稳健性分析,实现了涡轮叶片多学科6sigma可靠性及稳健设计优化。使用Kriging近似模型并不断提高模型精度,解决了多学科可行方法计算量较大的问题。实例分析表明,与确定性多学科设计优化相比,采用该方法得到的涡轮叶片可靠性及稳健性均有大幅度提高,同时设计目标最优,满足工程应用的要求,验证了该方法在工程应用中的可行性。     

分布式协同进化MDO算法及其在导弹设计中应用

 针对现有基于梯度的多学科设计优化 (MDO)算法不适用于具有离散和整数设计变量、设计空间非凸或不连通的多学科设计优化问题,以及倾向于收敛到接近初始点的局部最优点的缺点,为充分发挥进化算法的优越性,根据协同进化与 MDO在本质上的相似性,采用分布式协同进化机制进行 MDO算法研究。提出了分布式协同进化 MDO算法,并将该算法应用于导弹的气动 /发动机 /控制一体化优化设计。