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.
International Journal of Reliability and Safety, 2006 Vol.1 No.1/2, pp.225 - 237
Available online: 31 Aug 2006 *Full-text access for editors Access for subscribers Purchase this article Comment on this article