Reliability Optimization Involving Mixed Continuous-Discrete Uncertainties

2005 ◽  
Author(s):  
Subroto Gunawan ◽  
Panos Y. Papalambros
Keyword(s):  
Engineering Design ◽  
Optimization Method ◽  
Spatial Distance ◽  
Reliability Optimization ◽  
Discrete Variables ◽  
Design Problems ◽  
Engineering Design Problems

Engineering design problems frequently involve a mix of both continuous and discrete uncertainties. However, most methods in the literature deal with either continuous or discrete uncertainties, but not both. In particular, no method has yet addressed uncertainty for categorically discrete variables or parameters. This article develops an efficient optimization method for problems involving mixed continuous-discrete uncertainties. The method reduces the number of function evaluations performed by systematically filtering the discrete factorials used for estimating reliability based on their importance. This importance is assessed using the spatial distance from the feasible boundary and the probability of the discrete components. The method is demonstrated in examples and is shown to be very efficient with only small errors.

Reliability Optimization With Mixed Continuous-Discrete Random Variables and Parameters

2006 ◽  
Vol 129 (2) ◽  
pp. 158-165 ◽  
Author(s):  
Subroto Gunawan ◽  
Panos Y. Papalambros
Keyword(s):  
Engineering Design ◽  
Random Variables ◽  
Optimization Method ◽  
Spatial Distance ◽  
Reliability Optimization ◽  
Design Problems ◽  
Discrete Random Variables ◽  
Engineering Design Problems ◽  
Random Components ◽  
Discrete Type

Engineering design problems frequently involve a mix of both continuous and discrete random variables and parameters. However, most methods in the literature deal with only the continuous or the discrete type, but not both. In particular, no method has yet addressed problems for which the random components (variables and∕or parameters) are categorically discrete. This paper develops an efficient optimization method for problems involving mixed continuous-discrete random variables and parameters. The method reduces the number of function evaluations performed by systematically filtering the discrete combinations used for estimating reliability based on their importance. This importance is assessed using the spatial distance from the feasible boundary and the probability of the discrete components. The method is demonstrated in examples and is shown to be very efficient with only small errors.

Multi-Objective Optimal Design of Automotive Engine Using Genetic Algorithm

1998 ◽  
Author(s):  
Kikuo Fujita ◽  
Noriyasu Hirokawa ◽  
Shinsuke Akagi ◽  
Shinji Kitamura ◽  
Hideaki Yokohata
Keyword(s):  
Genetic Algorithm ◽  
Engineering Design ◽  
Design Problem ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Total Power ◽  
Design Problems ◽  
Automotive Engine ◽  
Multi Objective ◽  
Engineering Design Problems

Abstract A genetic algorithm based optimization method is proposed for a multi-objective design problem of an automotive engine, that includes several difficulties in practical engineering optimization problems. While various optimization techniques have been applied to engineering design problems, a class of realistic engineering design problems face on a mixture of different optimization difficulties, such as the rugged nature of system response, the numbers of design variables and objectives, etc. In order to overcome such a situation, this paper proposes a genetic algorithm based multi-objective optimization method, that introduces Pareto-optimality based fitness function, similarity based selection and direct real number crossover. This optimization method is also applied to the design problem of an automotive engine with the design criteria on a total power train. The computational examples show the ability of the proposed method for finding a relevant set of Pareto optima.

A Unified Approach to Solving Graph Based Design Problems

2007 ◽  
Author(s):  
Matthew I. Campbell ◽  
Sandeep Nair ◽  
Jay Patel
Keyword(s):  
Engineering Design ◽  
Graph Transformation ◽  
Electric Circuit ◽  
Optimization Method ◽  
Graph Grammar ◽  
Science Literature ◽  
Design Problems ◽  
Novel Approach ◽  
Engineering Design Problems ◽  
Graph Transformation Systems

This paper proposes a new perspective of using graph transformation systems as a way of organizing and solving engineering design problems. Using this novel technique the synthesis of optimal solutions in the form of graph topologies for design problems is made possible. Though the concept of graph grammars has existed for several decades in computer science literature, researchers in the field of design have now begun to realize the merit of using them to harness both the knowledge and heuristics of a particular problem domain. This paper examines the fundamental challenges in applying graph transformations in a design context. The paper also presents the first topology optimization method that has been developed specifically for domains representable by a graph grammar schema. This novel approach could also be used in several problems such as network problems (especially in determining the placement of hubs), electric circuit design, neural networks, sheet metal, and product architecture. The abstraction afforded by graphs also enables us to tackle multi-disciplinary problems found throughout engineering design. A few engineering examples are shown in this paper in order to illustrate the power of the approach in automating the design process.

Robust optimization based on a polynomial expansion of chaos constructed with integration point rules

2008 ◽  
Vol 223 (5) ◽  
pp. 1263-1272 ◽  
Author(s):  
D L Wei ◽  
Z S Cui ◽  
J Chen
Keyword(s):  
Robust Optimization ◽  
Engineering Design ◽  
Uncertainty Propagation ◽  
Probabilistic Approach ◽  
Main Idea ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Computationally Efficient ◽  
Design Problems ◽  
Engineering Design Problems

Robust optimization is a probabilistic approach to engineering design under uncertainty. The main idea is to select designs insensitive to changes in given parameters. Robust optimization using numerical simulations for black-box problems has received increasing interest. However, when the simulation programmes are computationally expensive, robust optimization is difficult to implement due to the intensive computational demand of uncertainty propagation. Based on polynomial chaos expansion (PCE), an efficient robust optimization method is presented. The PCE is constructed with points of monomial cubature rules (MCRs) to approximate the original model. As the number of points of MCRs is small and all of the points are sampled, the robust optimization procedure is computationally efficient and stable. Two engineering design problems are employed to demonstrate the availability of the proposed method.

Standards, Codes of Practice and Expert Systems

1988 ◽  
Vol 21 (1) ◽  
pp. 5-9 ◽  
Author(s):  
E G McCluskey ◽  
S Thompson ◽  
D M G McSherry
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Engineering Design ◽  
Performance Criteria ◽  
Design Problems ◽  
Codes Of Practice ◽  
Engineering Design Problems ◽  
And Performance

Many engineering design problems require reference to standards or codes of practice to ensure that acceptable safety and performance criteria are met. Extracting relevant data from such documents can, however, be a problem for the unfamiliar user. The use of expert systems to guide the retrieval of information from standards and codes of practice is proposed as a means of alleviating this problem. Following a brief introduction to expert system techniques, a tool developed by the authors for building expert system guides to standards and codes of practice is described. The steps involved in encoding the knowledge contained in an arbitrarily chosen standard are illustrated. Finally, a typical consultation illustrates the use of the expert system guide to the standard.

Biologically Inspired Design: A Macrocognitive Account

2010 ◽  
Author(s):  
Swaroop S. Vattam ◽  
Michael Helms ◽  
Ashok K. Goel
Keyword(s):  
Biological Sciences ◽  
Information Processing ◽  
Engineering Design ◽  
Design Patterns ◽  
Information Processing Model ◽  
Biologically Inspired ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Other Information ◽  
Biologically Inspired Design

Biologically inspired engineering design is an approach to design that espouses the adaptation of functions and mechanisms in biological sciences to solve engineering design problems. We have conducted an in situ study of designers engaged in biologically inspired design. Based on this study we develop here a macrocognitive information-processing model of biologically inspired design. We also compare and contrast the model with other information-processing models of analogical design such as TRIZ, case-based design, and design patterns.

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