Title: Impact of uncertainty quantification on design: an engine optimisation case study

Authors: Michael Kokkolaras, Zissimos P. Mourelatos, Panos Y. Papalambros

Addresses: Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA. ' Department of Mechanical Engineering, Oakland University, Rochester, MI, USA. ' Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA

Abstract: The method for solving design optimisation problems when some or all design variables and/or parameters are not deterministic depends on how we quantify uncertainty. Probabilistic design methods can be employed when sufficient information is available. In reality, however, we often do not have enough knowledge and/or data to conduct statistical inference. The amount of available information about the uncertain quantities may be limited to ranges of values. Possibility theory may then be employed to reformulate and solve the optimal design problem. In this paper, we use both probability and possibility theories to determine optimal values of engine characteristics for a hydraulic-hybrid powertrain of a medium-sized truck while accounting for the most significant modelling uncertainties. A worst-case optimisation using interval analysis is considered as a special case of possibilistic design. We contrast the two sets of results, draw some conclusions and discuss features of the two approaches.

Keywords: design optimisation; uncertainty quantification; possibility theory; probability theory; optimal design; engine optimisation; case study; hydraulic hybrid powertrain; modelling uncertainties; truck engine design; vehicle design.

DOI: 10.1504/IJRS.2006.010786

International Journal of Reliability and Safety, 2006 Vol.1 No.1/2, pp.225 - 237

Available online: 31 Aug 2006 *

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