Implementation and Validation of an Improved Collaborative Optimization Method

2010 ◽  
Vol 143-144 ◽  
pp. 1445-1449
Author(s):  
Ming Hong Han
Keyword(s):  
Optimal Solution ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
System Level ◽  
Engineering System ◽  
L1 Norm ◽  
Object Function ◽  
Subsystem Level ◽  
Complex Engineering ◽  
Complex Engineering System

Collaborative optimization (CO) is the most widely used Multidisciplinary design optimization (MDO) method for the design of complex engineering system. But some serious computational difficulties are found in its application. Reasons that cause computational difficulties in original CO were analyzed and a new improved collaborative optimization method (ICO) was presented. The L1 norm was used to improve subsystem consistency constraint and to avoid discontinuities in subsystem object function derivatives. Penalty function was added to system-level object function to convert constrained optimization into unconstrained optimization. A quick-start strategy was used to make the best use of optimal solution of system-level optimization in subsystem-level optimization. Experimental results show that the robustness, reliability and computing efficiency of ICO are higher than CO.

Setting Performance Targets Based on Subsystem Pareto Frontiers in Multilevel Optimization

2006 ◽  
Author(s):  
Huibin Liu ◽  
Christopher Hoyle ◽  
Xiaolei Yin ◽  
Wei Chen
Keyword(s):  
Design Process ◽  
Pareto Frontier ◽  
System Level ◽  
Engineering System ◽  
Multilevel Optimization ◽  
Performance Targets ◽  
Target Values ◽  
Target Cascading ◽  
Complex Engineering ◽  
Complex Engineering System

The design of a complex engineering system typically involves tradeoffs among multiple design criteria or disciplinary performance to achieve the optimal design. The design process is usually an iterative procedure with individual discipline sub-systems designed concurrently to meet target values assigned from the system level. One of the most challenging issues is the large number of iterations in this design process, especially when uncertainty is taken into account. To improve the design concurrency while maintaining preferred tradeoffs at the system level, a new method is developed that identifies proper targets based on disciplinary design capability information while optimizing the design goal at the system level. The design capability of a discipline or criterion is represented by the achievable area bounded by its Pareto frontier. Using target values obtained from this method using Pareto information, the number of design iterations can be reduced in both deterministic and probabilistic design scenarios compared to existing approaches, such as Analytical Target Cascading (ATC). To demonstrate applications and benefits of the developed method this approach is applied to the design of a two-bar truss structure.

Using Rio-Paris Flight 447 Crash to Assess Human Error and Failure Propagation Analysis Early in Design

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Lukman Irshad ◽  
H. Onan Demirel ◽  
Irem Y. Tumer ◽  
Guillaume Brat
Keyword(s):  
Human Error ◽  
Prediction Algorithm ◽  
Engineering System ◽  
Human Errors ◽  
System Failures ◽  
Propagation Analysis ◽  
Failure Propagation ◽  
Complex Engineering ◽  
Component Failures ◽  
Complex Engineering System

Abstract While a majority of system vulnerabilities such as performance losses and accidents are attributed to human errors, a closer inspection would reveal that often times the accumulation of unforeseen events that include both component failures and human errors contribute to such system failures. Human error and functional failure reasoning (HEFFR) is a framework to identify potential human errors, functional failures, and their propagation paths early in design so that systems can be designed to be less prone to vulnerabilities. In this paper, the application of HEFFR within the complex engineering system domain is demonstrated through the modeling of the Air France 447 crash. Then, the failure prediction algorithm is validated by comparing the outputs from HEFFR and what happened in the actual crash. Also, two additional fault scenarios are executed within HEFFR and in a commercially available flight simulator separately, and the outcomes are compared as a supplementary validation.

Optimization Method Based on a Mix Strategy

2013 ◽  
Vol 816-817 ◽  
pp. 1154-1157
Author(s):  
Xu Yin ◽  
Ai Min Ji
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Optimization Method ◽  
Local Solution ◽  
Collaborative Optimization ◽  
Design Variables ◽  
Gradient Optimization ◽  
Local Optimal Solution ◽  
Optimal Values ◽  
Low Efficiency

To solve problems that exist in optimal design such as falling into local optimal solution easily and low efficiency in collaborative optimization, a new mix strategy optimization method combined design of experiments (DOE) with gradient optimization (GO) was proposed. In order to reduce the effect on the result of optimization made by the designers decision, DOE for preliminary analysis of the function model was used, and the optimal values obtained in DOE stage was taken as the initial values of design variables in GO stage in the new optimization method. The reducer MDO problem was taken as a example to confirm the global degree, efficiency, and accuracy of the method. The results show the optimization method could not only avoid falling into local solution, but also have an obvious superiority in treating the complex collaborative optimization problems.

Optimization Method for Globally Solving a Kind of Multiplicative Problems with Coefficients

2011 ◽  
Vol 467-469 ◽  
pp. 526-530 ◽  
Author(s):  
Hong Wei Jiao ◽  
Jing Ben Yin ◽  
Yun Rui Guo
Keyword(s):  
Original Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Optimization Method ◽  
Linearization Method ◽  
Equivalent Transformation ◽  
Engineering System ◽  
Linear Programming Problems ◽  
Global Optimal ◽  
Multiplicative Problems

Multiplicative problems are a kind of difficult global optimization problems known to be NP-hard. At the same time, these problems have some important applications in engineering, system, finance, economics, and other fields. In this paper, an optimization method is proposed to globally solve a class of multiplicative problems with coefficients. Firstly, by utilizing equivalent transformation and linearization method, a linear relaxation programming problem is established. Secondly, by using branch and bound technique, a determined algorithm is proposed for solving equivalent problem. Finally, the proposed algorithm is convergent to the global optimal solution of original problem by means of the subsequent solutions of a series of linear programming problems.

scholarly journals Interaction Prediction Optimization in Multidisciplinary Design Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Debiao Meng ◽  
Xiaoling Zhang ◽  
Hong-Zhong Huang ◽  
Zhonglai Wang ◽  
Huanwei Xu
Keyword(s):  
Interaction Design ◽  
Large Scale ◽  
Optimization Problems ◽  
Collaborative Optimization ◽  
System Level ◽  
Design Parameters ◽  
Large Scale Systems ◽  
Interaction Prediction ◽  
Design Variables ◽  
Subsystem Level

The distributed strategy of Collaborative Optimization (CO) is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In this paper, one large-scale systems control strategy, the interaction prediction method (IPM), is introduced to enhance CO. IPM is utilized for controlling subsystems and coordinating the produce process in large-scale systems originally. We combine the strategy of IPM with CO and propose the Interaction Prediction Optimization (IPO) method to solve MDO problems. As a hierarchical strategy, there are a system level and a subsystem level in IPO. The interaction design variables (including shared design variables and linking design variables) are operated at the system level and assigned to the subsystem level as design parameters. Each discipline objective is considered and optimized at the subsystem level simultaneously. The values of design variables are transported between system level and subsystem level. The compatibility constraints are replaced with the enhanced compatibility constraints to reduce the dimension of design variables in compatibility constraints. Two examples are presented to show the potential application of IPO for MDO.

Using Robust Design Techniques to Model the Effects of Multiple Decision Makers in a Design Process

1998 ◽  
Author(s):  
Kurt Hacker ◽  
Kemper Lewis
Keyword(s):  
Robust Design ◽  
Interaction Analysis ◽  
System Modeling ◽  
Decision Makers ◽  
Engineering System ◽  
Entire System ◽  
Multiple Decision Makers ◽  
Complex Engineering ◽  
Passenger Aircraft ◽  
Complex Engineering System

Abstract In this paper we introduce a methodology to reduce the effects of uncertainty in the design of a complex engineering system involving multiple decision makers. We focus on the uncertainty that is created when a disciplinary designer or design team must try and predict or model the behavior of other disciplinary subsystems. The design of a complex system is performed by many different designers and teams, each of which only have control over a small portion of the entire system. Modeling the interaction among these decision makers and reducing the uncertainty caused by the lack of global control is the focus of this paper. We use well developed concepts from the field of game theory to describe the interactions taking place, and concepts from robust design to reduce the effects of one decision-maker on another. Response Surface Methodology (RSM) is also used to reduce the complexity of the interaction analysis while preserving behavior of the systems. The design of a passenger aircraft is used to illustrate the approach, and some encouraging results are discussed.

scholarly journals Locating Sensors in Complex Engineering Systems for Fault Isolation Using Population-Based Incremental Learning

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 310
Author(s):  
Jinxin Wang ◽  
Zhongwei Wang ◽  
Xiuzhen Ma ◽  
Guojin Feng ◽  
Chi Zhang
Keyword(s):  
Directed Graph ◽  
Incremental Learning ◽  
Population Based ◽  
Fault Isolation ◽  
Engineering System ◽  
Fault Diagnostics ◽  
Sensor Location ◽  
Full Life Cycle ◽  
Complex Engineering ◽  
Complex Engineering System

Fault diagnostics aims to locate the origin of an abnormity if it presents and therefore maximize the system performance during its full life-cycle. Many studies have been devoted to the feature extraction and isolation mechanisms of various faults. However, limited efforts have been spent on the optimization of sensor location in a complex engineering system, which is expected to be a critical step for the successful application of fault diagnostics. In this paper, a novel sensor location approach is proposed for the purpose of fault isolation using population-based incremental learning (PBIL). A directed graph is used to model the fault propagation of a complex engineering system. The multidimensional causal relationships of faults and symptoms were obtained via traversing the directed path in the directed graph. To locate the minimal quantity of sensors for desired fault isolatability, the problem of sensor location was firstly formulated as an optimization problem and then handled using PBIL. Two classical cases, including a diesel engine and a fluid catalytic cracking unit (FCCU), were taken as examples to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can minimize the quantity of sensors while keeping the capacity of fault isolation unchanged.

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