Reliability based multidisciplinary design optimization of cooling turbine blade considering uncertainty data statistics

Considering the coupling among aerodynamic, heat transfer and strength, a reliability based multidisciplinary design optimization method for cooling turbine blade is introduced. Multidisciplinary analysis of cooling turbine blade is carried out by sequential conjugated heat transfer analysis and strength analysis with temperature and pressure interpolation. Uncertainty data including the blade wall, rib thickness, elasticity Modulus and rotation speed is collected. Data statistics display the probability models of uncertainty data follow three-parameter Weibull distribution. The thickness of blade wall, thickness and height of ribs are chosen as design variables. Kriging surrogate model is introduced to reduce time-consuming multidisciplinary reliability analysis in RBMDO loop. The reliability based multidisciplinary design optimization of a cooling turbine blade is carried out. Optimization results shows that the RBMDO method proposed in this work improves the performance of cooling turbine blade availably.

     

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     

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.     

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。     

A graph theoretic approach to problem formulation for multidisciplinary design analysis and optimization

The formulation of multidisciplinary design, analysis, and optimization (MDAO) problems has become increasingly complex as the number of analysis tools and design variables included in typical studies has grown. This growth in the scale and scope of MDAO problems has been motivated by the need to incorporate additional disciplines and to expand the parametric design space to enable the exploration of unconventional design concepts. In this context, given a large set of disciplinary analysis tools, the problem of determining a feasible data flow between tools to produce a specified set of system-level outputs is combinatorially challenging. The difficulty is compounded in multi-fidelity problems, which are of increasing interest to the MDAO community. In this paper, we propose an approach for addressing this problem based on the formalism of graph theory. The approach begins by constructing the maximal connectivity graph (MCG) describing all possible interconnections between a set of analysis tools. Graph operations are then conducted to reduce the MCG to a fundamental problem graph (FPG) that describes the connectivity of analysis tools needed to solve a specified system-level design problem. The FPG does not predispose a particular solution procedure; any relevant MDO solution architecture could be selected to implement the optimization. Finally, the solution architecture can be represented in a problem solution graph (PSG). The graph approach is applied to an example problem based on a commercial aircraft MDAO study.     

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.     

An integrated multidisciplinary particle swarm optimization approach to conceptual ship design

A particle swarm optimization (PSO) solver is developed based on theoretical information available from the literature. The implementation is validated by utilizing the PSO optimizer as a driver for a single discipline optimization and for a multicriterion optimization and comparing the results to a commercially available gradient based optimization algorithm, previously published results, and a simple sequential Monte Carlo model. A typical conceptual ship design statement from the literature is employed for developing the single discipline and the multicriterion benchmark optimization statements. In the main new effort presented in this paper, an approach is developed for integrating the PSO algorithm as a driver at both the top and the discipline levels of a multidisciplinary design optimization (MDO) framework which is based on the Target Cascading (TC) method. The integrated MDO/PSO algorithm is employed for analyzing a multidiscipline optimization statement reflecting the conceptual ship design problem from the literature. Results are compared to MDO analyses performed when a gradient based optimizer comprised the optimization driver at all levels. The results, the strengths, and the weaknesses of the integrated MDO/PSO algorithm are discussed as related to conceptual ship design.     

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.     

A data mining approach considering missing values for the optimization of semiconductor-manufacturing processes

Due to the rapid development of information technologies, abundant data have become readily available. Data mining techniques have been used for process optimization in many manufacturing processes in automotive, LCD, semiconductor, and steel production, among others. However, a large amount of missing values occurs in the data set due to several causes (e.g., data discarded by gross measurement errors, measurement machine breakdown, routine maintenance, sampling inspection, and sensor failure), which frequently complicate the application of data mining to the data set. This study proposes a new procedure for optimizing processes called missing values-Patient Rule Induction Method (m-PRIM), which handles the missing-values problem systematically and yields considerable process improvement, even if a significant portion of the data set has missing values. A case study in a semiconductor manufacturing process is conducted to illustrate the proposed procedure.     

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 uncertainty-based approach to discrete-time fault estimation observer design for nonuniformly sampled systems

This paper considers the fault estimation problem of nonuniformly sampled system in which sensor sampling is performed at aperiodic interval. After being discretized at sampling instant, the nonuniformly sampled system is modeled as an equivalent polytopic system with norm bounded uncertainties. A discrete-time time-varying fault estimation observer with multiple design freedom is then constructed, and a sufficient condition given in linear matrix inequality (LMI) is provided to obtain the constant filter gain and ensure not only the asymptotic stability of fault estimation error but also the robustness of uncertainties. Compared with the existing observer designed based on continuous-time delay approach, the proposed one has a better estimation accuracy and less conservatism and is easy for digital implementation. A numerical simulation and a quadruple-tank benchmark are used to demonstrate the effectiveness and superiority of the proposed method.     

Conservative multi-objective optimization considering design robustness and tolerance: a quality engineering design approach

Methods of multi-objective optimization are proposed to account for tolerance of design variable and variation in problem parameter. The post-optimization effort is initiated from deterministic Pareto-optimal solutions that were obtained from NSGA-II. The successive process to determine search directions and step sizes toward conservative multi-objective solutions was conducted by design of experiments to determine the worst design that had the highest constraint violation. The signal-to-noise (S/N) ratio was also employed to represent the robustness of constrained objective functions under parameter variation. Structural optimization was explored to accommodate both design tolerance and parameter variation and further apply S/N ratio in conservative multi-objective optimization.     

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.     

Reliability measure approach for confidence-based design optimization under insufficient input data

Structural and Multidisciplinary Optimization - In most of the reliability-based design optimization (RBDO) researches, accurate input statistical model has been assumed to concentrate on the...     

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.     

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.     

An efficient strategy for reliability-based multidisciplinary design optimization using BLISS

This paper presents an efficient reliability-based multidisciplinary design optimization (RBMDO) strategy. The conventional RBMDO has tri-level loops: the first level is an optimization in the deterministic space, the second one is a reliability analysis in the probabilistic space, and the third one is the multidisciplinary analysis. Since it is computationally inefficient when high-fidelity simulation methods are involved, an efficient strategy is proposed. The strategy [named probabilistic bi-level integrated system synthesis (ProBLISS)] utilizes a single-level reliability-based design optimization (RBDO) approach, in which the reliability analysis and optimization are conducted in a sequential manner by approximating limit state functions. The single-level RBDO is associated with the BLISS formulation to solve RBMDO problems. Since both the single-level RBDO and BLISS are mainly driven by approximate models, the accuracy of models can be a critical issue for convergence. The convergence of the strategy is guaranteed by employing the trust region–sequential quadratic programming framework, which validates approximation models in the trust region radius. Two multidisciplinary problems are tested to verify the strategy. ProBLISS significantly reduces the computational cost and shows stable convergence while maintaining accuracy.     

微机械陀螺的多学科设计优化建模的研究

以梳状音叉式振动微机械陀螺为例,将多学科设计优化（MDO）方法应用到微机械陀螺的优化设计中。将微机械陀螺复杂系统分解为归属不同学科的多个子系统,阐明了在多学科设计优化中各子系统之间的相互关系。建立了微机械陀螺的结构设计、机械性能、电学性能子系统和系统级的多学科设计优化模型。用已研制的MMCDO多学科混合协同设计优化算法计算,得到了满意的优化设计结果。     

Development of a multidisciplinary design optimization test simulator

Many of the method development efforts in the field of multidisciplinary design optimization (MDO) attempt to simplify the design of a large, complex system by dividing the system into a series of smaller, simpler, and coupled subsystems. A representative and efficient means of determining the feasibility and robustness of MDO methods is crucial. This paper describes the construct and applications of a test simulator, CASCADE (Complex Application Simulator for the Creation of Analytical Design Equations), that is capable of randomly generating and then converging a system of coupled analytical equations, of user-specified size (Hulme and bloebaum 1996). CASCADE-generated systems can be used for test sequencing and system reduction strategies, convergence strategies, optimization techniques, MDO methods, and distributed computing techniques (via Parallel Virtual Machine), among others.