MODELING AND SIMULATION UNCERTAINTY IN MULTIDISCIPLINARY DESIGN OPTIMIZATION

This paper is intended to contribute to the ongoing discussion of selected concepts related to the topic of technical risk or uncertainty in the model-based design of physical artifacts. The paper focuses on the use of analytic models and numerical simulation in the multidisciplinary design optimization process. It considers how issues of physical process variability, information uncertainty and the use of models and simulations influence the design decision process. This paper only qualitatively addresses these issues but the goal is to provide a focus for discussion of concepts associated with information uncertainty as applied to model-based multidisciplinary design and optimization.

[1]  Erik K. Antonsson,et al.  PRELIMINARY VEHICLE STRUCTURE DESIGN: AN INDUSTRIAL APPLICATION OF IMPRECISION IN ENGINEERING DESIGN , 1998 .

[2]  Stephen M. Batill,et al.  Experimental uncertainty and drag measurements in the national transonic facility , 1994 .

[3]  Robert L. Mullen,et al.  Development of interval based methods for fuzziness in continuum-mechanics , 1995, Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society.

[4]  Hans-Jürgen Zimmermann,et al.  An application-oriented view of modeling uncertainty , 2000, Eur. J. Oper. Res..

[5]  P. R. Bevington,et al.  Data Reduction and Error Analysis for the Physical Sciences , 1969 .

[6]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[7]  Dimitri N. Mavris,et al.  Robust Design Simulation: A Probabilistic Approach to Multidisciplinary Design , 1999 .

[8]  R. Iman,et al.  A distribution-free approach to inducing rank correlation among input variables , 1982 .

[9]  Kathryn B. Laskey.  Model uncertainty: theory and practical implications , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[10]  A. E. Sepulveda,et al.  Approximation of System Reliabilities Using a Shooting Monte Carlo Approach , 1997 .

[11]  S. Brown,et al.  Approximation of system reliability using a shooting Monte Carlo approach , 1994 .

[12]  Farrokh Mistree,et al.  Statistical Approximations for Multidisciplinary Design Optimization: The Problem of Size , 1999 .

[13]  Simon French,et al.  Uncertainty and Imprecision: Modelling and Analysis , 1995 .

[14]  Xiaoping Du,et al.  AN EFFICIENT APPROACH TO PROBABILISTIC UNCERTAINTY ANALYSIS IN SIMULATION-BASED MULTIDISCIPLINARY DESIGN , 2000 .

[15]  E. Antonsson,et al.  Representing imprecision in engineering design: Comparing fuzzy and probability calculus , 1990 .

[16]  S. Isukapalli,et al.  Stochastic Response Surface Methods (SRSMs) for Uncertainty Propagation: Application to Environmental and Biological Systems , 1998, Risk analysis : an official publication of the Society for Risk Analysis.

[17]  Hugh W. Coleman,et al.  Experimentation and Uncertainty Analysis for Engineers , 1989 .

[18]  Palle Thoft-Christensen,et al.  Reliability and optimization of structural systems '90 : proceedings of the 3rd IFIP WG 7.5 Conference, Berkeley, California, USA, March 26-28, 1990 , 1991 .

[19]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[20]  Erik K. Antonsson,et al.  Imprecision in Engineering Design , 1995 .

[21]  David R. Oakley,et al.  Multidisciplinary Stochastic Optimization , 1995 .

[22]  John E. Renaud,et al.  AN INVESTIGATION OF MULTIDISCIPLINARY DESIGN SUBJECT TO UNCERTAINTY , 1998 .

[23]  J. Høybye,et al.  Model Error Propagation and Data Collection Design. An Application in Water Quality Modelling , 1998 .

[24]  Farrokh Mistree,et al.  A procedure for robust design: Minimizing variations caused by noise factors and control factors , 1996 .

[25]  S. J. Kline,et al.  Describing Uncertainties in Single-Sample Experiments , 1953 .

[26]  George A. Hazelrigg,et al.  A Framework for Decision-Based Engineering Design , 1998 .

[27]  Gunnar Abrahamson,et al.  Terminology for model credibility , 1980 .