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Computational speed is a major limiting factor for performing design sensitivity, and optimization studies of total knee replacements. Much of this limitation arises from extensive geometry calculations required by contact analyses. This study presents a novel surrogate contact modeling approach to address this limitation. The approach involves fitting contact forces from a computationally expensive contact model (e.g., a finite element model) as a function of the relative pose between the contacting bodies. Because contact forces are much more sensitive to displacements in some directions than others, standard surrogate sampling and modeling techniques do not work well, necessitating the development of special techniques for contact problems. We present a computational evaluation and practical application of the approach using dynamic wear simulation of a total knee replacement constrained to planar motion in a Stanmore machine. The sample points needed for surrogate model fitting were generated by an elastic foundation (EF) contact model. For the computational evaluation, we performed nine different dynamic wear simulations with both the surrogate contact model and the EF contact model. In all cases, the surrogate contact model accurately reproduced the contact force, motion, and wear volume results from the EF model, with computation time being reduced from 13 min to 13 s. For the practical application, we performed a series of Monte Carlo analyses to determine the sensitivity of predicted wear volume to Stanmore machine setup issues. Wear volume was highly sensitive to small variations in motion and load inputs, especially femoral flexion angle, but not to small variations in component placements. Computational speed was reduced from an estimated 230 h to 4 h per analysis. Surrogate contact modeling can significantly improve the computational speed of dynamic contact and wear simulations of total knee replacements and is appropriate for use in design sensitivity and optimization studies. [DOI: 10.1115/1.3005152]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.paerosci.2005.02.001 Byline: Nestor V. Queipo (a), Raphael T. Haftka (a), Wei Shyy (a), Tushar Goel (a), Rajkumar Vaidyanathan (a), P. Kevin Tucker (b) Abstract: A major challenge to the successful full-scale development of modern aerospace systems is to address competing objectives such as improved performance, reduced costs, and enhanced safety. Accurate, high-fidelity models are typically time consuming and computationally expensive. Furthermore, informed decisions should be made with an understanding of the impact (global sensitivity) of the design variables on the different objectives. In this context, the so-called surrogate-based approach for analysis and optimization can play a very valuable role. The surrogates are constructed using data drawn from high-fidelity models, and provide fast approximations of the objectives and constraints at new design points, thereby making sensitivity and optimization studies feasible. This paper provides a comprehensive discussion of the fundamental issues that arise in surrogate-based analysis and optimization (SBAO), highlighting concepts, methods, techniques, as well as practical implications. The issues addressed include the selection of the loss function and regularization criteria for constructing the surrogates, design of experiments, surrogate selection and construction, sensitivity analysis, convergence, and optimization. The multi-objective optimal design of a liquid rocket injector is presented to highlight the state of the art and to help guide future efforts. Author Affiliation: (a) Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA (b) NASA Marshall Space Flight Center, Huntsville, AL, USA

A solution methodology is proposed for multiobjective optimal placement of convectively cooled electronic components on printed wiring boards. The approach features a genetic algorithm, a multiobjective optimization strategy and a flow and heat transfer solver. Comparisons with a case with known optimal solutions demonstrate the methodology's potential in generating solutions to electronic component placement problems.

This paper presents a solution methodology for the optimal placement of convectively and conductively air-cooled electronic components on planar printed wiring boards considering thermal and electrical/cost design objectives. The methodology combines the use of a heat transfer solver for the prediction of the temperature distribution among the electronic components and a genetic algorithm for the adaptive search of optimal or near optimal solutions and a multiobjective optimization strategy (Pareto optimization and multiattribute utility analysis). After proper validation of the elements of the solution methodology (heat transfer solver/genetic algorithm) in isolation, the methodology under consideration is tested using a placement problem (case study) that considers as optimization criteria the minimization of an estimate of the failure rate of the system of components due to thermal overheating (via an Arrhenius relation) and the minimization of the total wiring length given some interconnectivity requirements. Results corresponding to the case study are presented and discussed for both Pareto optimization and multiattribute utility analysis. [S1043-7398(00)00801-X]