A product platform is a set of common components, modules or parts from which a stream of derivative products can be created. Product platform design requires selection of the shared parts and assessment of the potential sacrifices in individual product performance that result from parts sharing. A multicriteria optimization problem can be formulated to study such decisions in a quantitative manner at the product performance level. Studying the Pareto sets that correspond to various derivative products leads to a systematic methodology for design decision making. Design of a nail gun platform is used to illustrate the concepts presented. �DOI: 10.1115/1.1355775�

A technique which uses trained neural nets to model the compressor in the context of a turbocharged diesel engine simulation is introduced. This technique replaces the usual interpolation of compressor maps with the evaluation of a smooth mathematical function. Following presentation of the methodology, the proposed neural net technique is validated against data from a truck type, 6-cylinder 14-liter diesel engine. Furthermore, with the introduction of an additional parameter, the proposed neural net can be trained to simulate an entire family of compressors. As a demonstration, a family of compressors of different sizes is represented with a single neural net model which is subsequently used for matching calculations with intercooled and nonintercooled engine configurations at different speeds. This novel approach readily allows for evaluation of various options within a wide range of possible compressor configurations prior to prototype production. It can also be used to represent the variable geometry machine regardless of the method used to vary compressor characteristics. Hence, it is a powerful design tool for selection of the best compressor for a given diesel engine system and for broader system optimization studies.

The DIRECT algorithm of Jones et al. is a simple and effective Lipschitzian-based global optimization algorithm which does not need the evaluation of gradients. However, the local convergence rate is relatively slow when compared to popular quasi-Newton techniques. By incorporating the judicious use of gradients, modifications are proposed that result in a new algorithm. This new algorithm still covers effectively the design space and eliminates regions which are non-optimal but has the benefit or a fast local convergence rate.A comparison is made using six standard test problems showing that utilization of gradients is beneficial not only for convergence but also during the first few iterations.

Large-scale design optimization problems frequently require the exploitation of structure in order to obtain efficient and reliable solutions. Successful algorithms for general nonlinear programming problems with theoretical underpinnings do not usually accommodate any additional structure within the problem. In this article modifications are made to a trust region algorithm to take advantage of hierarchical structure without compromising the theoretical properties of the original algorithm. 1. Start with some initial point x 1 , an iteration counter k = 1, and any other necessary parameters.