Rotor design optimization using a multidisciplinary approach

A multidisciplinary optimization tool for helicopter rotor blade design has been developed. It uses a comprehensive analysis program, CAMRAD/JA, capable of performing analyses in all involved disciplines in a consistent and efficient manner, together with CONMIN's method of feasible directions. Design variables, constraints, and objective functions have been chosen to address actual design requirements in a realistic manner. The optimization procedure setup provides the flexibility to take full advantage of the comprehensive nature of the analysis code, allowing optimization driven by aerodynamic, aeroelastic, and flight mechanics design requirements. The optimization tool is applied to the McDonnell Douglas Helicopter Company AH-64A, a modern, high performance helicopter. Results are presented for combined hover/forward flight performance optimization, fuselage vibration reduction, and combined performance/vibration optimization. Blade aerodynamic and structural properties are used as design variables. The optimized designs show significant improvements and demonstrate that a practical and efficient optimization tool has been developed.Paper AIAA 91-0477, presented at the 29th Aerospace Sciences Meeting, January 7–10, 1991, Reno, Nevada     

Implementation of set-based design in multidisciplinary design optimization

Set-based design is a design approach where feasible regions for the design variables are determined from different disciplines, with the goal of locating and working with the areas of feasible overlap. During the process the constraints are adjusted in order to accommodate conflicting requirements between disciplines. The main objective of set-based design is to narrow the design space, while delaying the pursuit of a single point design as much as possible. This process avoids finalizing decisions early and allows for flexibility in dealing with requirement creep. This paper presents the development and application of a new multidisciplinary design optimization (MDO) algorithm inspired by the principles of set-based design. The new MDO algorithm was developed with the core concept of describing the design using sets to incorporate features of set-based design and achieve greater flexibility than with a single-point optimization. The MDO algorithm was applied to a ship design problem and the ship design application demonstrated the value of utilizing set-based design as a space-reducing technique before approaching the problem with a point-based optimization. Furthermore, incorporating flexibility in the constraints allowed the optimization to handle a problem with very strict constraints in a rational manner and minimize the necessary constraint violation.     

Multiparametric optimization for multidisciplinary engineering design

The area of Multiparametric Optimization (MPO) solves problems that contain unknown problem data represented by parameters. The solutions map parameter values to optimal design and objective function values. In this paper, for the first time, MPO techniques are applied to improve and advance Multidisciplinary Design Optimization (MDO) to solve engineering problems with parameters. A multiparametric subgradient algorithm is proposed and applied to two MDO methods: Analytical Target Cascading (ATC) and Network Target Coordination (NTC). Numerical results on test problems show the proposed parametric ATC and NTC methods effectively solve parametric MDO problems and provide useful insights to designers. In addition, a novel Two-Stage ATC method is proposed to solve nonparametric MDO problems. In this new approach elements of the subproblems are treated as parameters and optimal design functions are constructed for each one. When the ATC loop is engaged, steps involving the lengthy optimization of subproblems are replaced with simple function evaluations.     

Development of a simulator for radiographic image optimization

A software package, incorporating two computational patient phantoms, has been developed for optimizing X-ray radiographic imaging. A tomographic phantom, visible photographic Man tomographic phantom (VIP-Man), constructed from Visible Human anatomical color images is used to simulate the scattered portion of an X-ray system using the Electron Gamma Shower National Research Council (EGSnrc) Monte Carlo code. The primary portion of an X-ray image is simulated using the projection ray-tracing method through the Visible Human CT data set. To produce a realistic image, the software simulates quantum noise, blurring effects, lesions, detector absorption efficiency, and other imaging artifacts. The primary and scattered portions of an X-ray chest image are combined to form a final image for future observer studies and image quality analysis. Absorbed doses in organs and tissues of the segmented VIP-Man phantom were also obtained from the Monte Carlo simulations. This paper presents methods of the simulator and preliminary results.     

Component-oriented decomposition for multidisciplinary design optimization in building design

The potential of Multidisciplinary Design Optimization (MDO) is not sufficiently exploited in current building design practice. I argue that this field of engineering requires a special setup of the optimization model that considers the uniqueness of buildings, and allows the designer to interact with the optimization in order to assess qualities of aesthetics, expression, and building function. For this reason, the approach applies a performance optimization based on resource consumption extended by preference criteria. Furthermore, building design-specific components serve for the decomposition and an interactive way of working. The component scheme follows the Industry Foundation Classes (IFC) as a common Building Information Model (BIM) standard in order to allow a seamless integration into an interactive CAD working process in the future. A representative case study dealing with a frame-based hall design serves to illustrate these considerations. An N-Square diagram or Design Structure Matrix (DSM) represents the system of components with the disciplinary dependencies and workflow of the analysis. The application of a Multiobjective Genetic Algorithm (MOGA) leads to demonstrable results.     

一种多学科设计优化工作流验证方法

在多学科设计优化集成系统中,设计过程和优化求解算法均通过可视化工作流实现,工作流有效性验证对提高设计效率和提高系统的用户体验具有重要意义。当前验证方法主要针对办公自动和企业管理系统中的工作流验证问题,多学科设计优化集成系统中的工作流验证问题研究较少。在分析前期工作验证技术的基础上,针对以循环结构为特征的优化环,提出一种基于图论方法的,名为浓缩环(concentration-loop)的验证算法。结合发射平台数字化设计系统的设计与实现,对该算法进行了验证。     

Designing a family of reconfigurable vehicles using multilevel multidisciplinary design optimization

Continuous advancements in technology have resulted in customers expecting enhanced performance across multiple operating conditions. In this paper, the desire to meet a variety of objectives after the system has been deployed is accomplished through the design of reconfigurable systems. However, permitting a system to adapt increases both complexity and cost. If this increase is too large, only a subset of design variables can be made adaptable. A multilevel multidisciplinary design optimization (MDO) approach is presented to determine the core architecture for a family of three reconfigurable vehicles when accommodating a changing number of adaptable design variables. To illustrate this approach, a case study involving a three-driver racing team is introduced. A common architecture is determined for the three vehicle variants, resulting in lap-time performance increases of 2.08%, 3.27%, and 3.67% when compared to the static, optimized baseline vehicle. The results of this study demonstrate the effectiveness of combining reconfigurability with product platforming and MDO. This paper is based on AIAA paper AIAA-2007-1880 that was reviewed for, and presented at, the 48th AIAA/ASMR/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference.     

A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with the general process of satellite system design optimization in conceptual design phase, a multistage-multilevel MDO procedure is proposed in this paper by integrating multiple-discipline-feasible (MDF) and concurrent subspace optimization (CSSO), termed as MDF-CSSO. In the first stage, the approximation surrogates of high-fidelity disciplinary models are built by disciplinary specialists independently, based on which the single level optimization procedure MDF is used to quickly identify the promising region and roughly locate the optimum of the MDO problem. In the second stage, the disciplinary specialists are employed to further investigate and improve the baseline design obtained in the first stage with high-fidelity disciplinary models. CSSO is used to organize the concurrent disciplinary optimization and system coordination so as to allow disciplinary autonomy. To enhance the reliability and robustness of the design under uncertainties, the probabilistic version of MDF-CSSO (PMDF-CSSO) is developed to solve uncertainty-based optimization problems. The effectiveness of the proposed methods is verified with one MDO benchmark test and one practical satellite conceptual design optimization problem, followed by conclusion remarks and future research prospects.     

卫星有效载荷的多目标多学科设计优化研究

在卫星有效载荷系统研究中,实施多目标多学科优化的可行性设计。首先,分析了开展卫星有效载荷多学科设计优化的关键技术。建立了包含天线、转发器、数据传输、可靠性、成本和质量的多学科分析模型。然后,应用多目标遗传算法对某卫星有效载荷的可靠性和成本进行多目标设计优化,获得最优解集。最后,运用多学科协同优化结合遗传算法进行可靠性单目标设计优化。研究结果表明:有效载荷的多目标多学科设计优化全面考虑了多个学科之间的关系,设计人员可按需选择其满意的优化结果,大幅提高设计效率;协同优化方法有助于实现学科自治、并行设计,提高设计的灵活性和缩短设计周期。     

A gradient-based transformation method in multidisciplinary design optimization

Multidisciplinary design optimization (MDO) has become essential for solving the complex engineering design problems. The most common approach is to “divide and conquer” the MDO problem, that is, to decompose the complex problem into several sub-problems and to collect the local solutions to give a new design point for the original problem. In 1990s, researchers have developed some decomposition strategies to find or synthesize the optimal model of the optimization structure in order to evenly distribute the computational workloads to multiple processors. Several MDO methods, such as Collaborative Optimization (CO) and Analytical Target Cascading (ATC), were then developed to solve the decomposed sub-problems and coordinate the coupling variables among them to find the optimal solution. However, both the synthesis of the decomposition structure and the coordination of the coupling variables require additional function evaluations, in terms of evaluating the functional dependency between each sub-problem and determining the proper weighting coefficients between each coupling functions respectively. In this paper, a new divide-and-conquer strategy, Gradient-based Transformation Method (GTM), is proposed to overcome the challenges in structure synthesis and variable coordination. The proposed method first decomposes the MDO problem into several sub-systems and distributes one constraint from the original problem to each sub-system without evaluating the dependency between each sub-system. Each sub-system is then transformed to the single-variate coordinate along the gradient direction of the constraint. The total function evaluations equal the number of constraints times the number of variables plus one in every iteration. Due to the monotonicity characteristics of the transformed sub-problems, they are efficiently solved by Monotonicity Analyses without any additional function evaluations. Two coordination principles are proposed to determine the significances of the responses based on the feasibility and activity conditions of every sub-problem and to find the new design point at the average point of the most significant responses. The coordination principles are capable of finding the optimal solution in the convex feasible space bounded by the linearized sub-system constraints without additional function evaluations. The optimization processes continue until the convergence criterion is satisfied. The numerical examples show that the proposed methodology is capable of effectively and efficiently finding the optimal solutions of MDO problems.     

A survey of multidisciplinary design optimization methods in launch vehicle design

Optimal design of launch vehicles is a complex problem which requires the use of specific techniques called Multidisciplinary Design Optimization (MDO) methods. MDO methodologies are applied in various domains and are an interesting strategy to solve such an optimization problem. This paper surveys the different MDO methods and their applications to launch vehicle design. The paper is focused on the analysis of the launch vehicle design problem and brings out the advantages and the drawbacks of the main MDO methods in this specific problem. Some characteristics such as the robustness, the calculation costs, the flexibility, the convergence speed or the implementation difficulty are considered in order to determine the methods which are the most appropriate in the launch vehicle design framework. From this analysis, several ways of improvement of the MDO methods are proposed to take into account the specificities of the launch vehicle design problem in order to improve the efficiency of the optimization process.     

Distributed computing for multidisciplinary design optimization using Java

The programming language Java (recently referred to as the computer language of the Web) offers substantial possibilities for the type of complex engineering problems typically encountered in multidisciplinary design optimization (MDO) problems. In order to demonstrate the potential uses of Java for MDO problems, this paper presents the development of the Web Interface for complex engineering design (WICkED) software, which simulates the convergence of a decomposed complex system in a distributed computing environment and computes the sensitivity derivatives of the system with respect to the independent input variables using the GSE method or the finite difference method. In this application, one computer is designated as the server and sends out required inputs to a number of client subsystems over the Internet. A number of client computers can connect to the server and then receive the inputs necessary to calculate the solution to their model. As the code necessary to solve the model already exists at the client, only the inputs have to be sent over the network. When the client has solved the calculation, it returns the results to the server which processes the result to produce new inputs.WICkED is written entirely in the Java programming language which allows server and clients to exist on completely different computer types and in heterogeneous, distributed networks. A number of parametric studies on the behaviour of complex systems in a distributed environment are performed and the results are reported in this paper. This research serves to identify potential problems as well as advantages in using Java for MDO applications.     

An efficient metamodel-based multi-objective multidisciplinary design optimization framework

This paper presents an efficient metamodel-based multi-objective multidisciplinary design optimization (MDO) architecture for solving multi-objective high fidelity MDO problems. One of the important features of the proposed method is the development of an efficient surrogate model-based multi-objective particle swarm optimization (EMOPSO) algorithm, which is integrated with a computationally efficient metamodel-based MDO architecture. The proposed EMOPSO algorithm is based on sorted Pareto front crowding distance, utilizing star topology. In addition, a constraint-handling mechanism in non-domination appointment and fuzzy logic is also introduced to overcome feasibility complexity and rapid identification of optimum design point on the Pareto front. The proposed algorithm is implemented on a metamodel-based collaborative optimization architecture. The proposed method is evaluated and compared with existing multi-objective optimization algorithms such as multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II), using a number of well-known benchmark problems. One of the important results observed is that the proposed EMOPSO algorithm provides high diversity with fast convergence speed as compared to other algorithms. The proposed method is also applied to a multi-objective collaborative optimization of unmanned aerial vehicle wing based on high fidelity models involving structures and aerodynamics disciplines. The results obtained show that the proposed method provides an effective way of solving multi-objective multidisciplinary design optimization problem using high fidelity models.     

多学科设计优化中的智能算法比较*

对多学科设计优化领域中涉及的几种智能优化算法的特点进行了总结.在此基础上提出了时间、精度、解决问题的个数等几个比较指标,首次将精度作为比较指标,并且创新性地提出一个针对工程问题的有效比较指标,即短时寻优能力.通过对所选取的系列案例运行得到一系列对工程问题有指导意义的结论,手机实例验证了所得到的结论的正确性.     

Integrating linear physical programming within collaborative optimization for multiobjective multidisciplinary design optimization

Multidisciplinary design optimization (MDO) is a concurrent engineering design tool for large-scale, complex systems design that can be affected through the optimal design of several smaller functional units or subsystems. Due to the multiobjective nature of most MDO problems, recent work has focused on formulating the MDO problem to resolve tradeoffs between multiple, conflicting objectives. In this paper, we describe the novel integration of linear physical programming within the collaborative optimization framework, which enables designers to formulate multiple system-level objectives in terms of physically meaningful parameters. The proposed formulation extends our previous multiobjective formulation of collaborative optimization, which uses goal programming at the system and subsystem levels to enable multiple objectives to be considered at both levels during optimization. The proposed framework is demonstrated using a racecar design example that consists of two subsystem level analyses — force and aerodynamics — and incorporates two system-level objectives: (1) minimize lap time and (2) maximize normalized weight distribution. The aerodynamics subsystem also seeks to minimize rearwheel downforce as a secondary objective. The racecar design example is presented in detail to provide a benchmark problem for other researchers. It is solved using the proposed formulation and compared against a traditional formulation without collaborative optimization or linear physical programming. The proposed framework capitalizes on the disciplinary organization encountered during large-scale systems design.     

Sequential optimization and reliability assessment for multidisciplinary systems design

With higher reliability and safety requirements, reliability-based design has been increasingly applied in multidisciplinary design optimization (MDO). A direct integration of reliability-based design and MDO may present tremendous implementation and numerical difficulties. In this work, a methodology of sequential optimization and reliability assessment for MDO is proposed to improve the efficiency of reliability-based MDO. The central idea is to decouple the reliability analysis from MDO with sequential cycles of reliability analysis and deterministic MDO. The reliability analysis is based on the first-order reliability method (FORM). In the proposed method, the reliability analysis and the deterministic MDO use two MDO strategies, the multidisciplinary feasible approach and the individual disciplinary feasible approach. The effectiveness of the proposed method is illustrated with two example problems.     

Utilization of a dependency-tracking language to reduce computational time during multidisciplinary design optimization

Reduction in computational time was desired and achieved in optimizing a multidisciplinary missile design system. The time reduction was realized using an object-oriented, dependency-tracking, demand-driven language called the adaptive modeling language (AML). The features of this language allowing for reduction in computational time are referred to as dependency-tracking and demand-driven computations. The dependency-tracking feature keeps track of the relationship amongst properties and objects within the hierarchy. This feature ensures that only necessary computations be carried out and it also ensures that computations that have previously been performed not be carried out again so long as the input to these computations have not changed. The timesaving features of this language make it an attractive choice when performing optimizations. A computational reduction in time of between 33 and 44% was achieved in the case when the language was used in conjunction with design of experiment and response surface models. The missile design system, interactive missile design and the optimization interface are coded in AML. The efficiency of the language was studied in conjunction with design of experiment, response surface analysis, and gradient-based optimization. The advancement of the missile design software by integrating optimization functionality is also discussed.