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The purpose this paper is to delineate why there exists a trade-off between biomechanical realism and algorithmic efficiency for human motion simulation models, and to illustrate how empirical human movement data and findings can be integrated with novel modeling techniques to overcome such a realism-efficiency tradeoff. We first review three major classes of biomechanical models for human motion simulation. The review of these models is woven together by a common fundamental problem of redundancy—kinematic and/or muscle redundancy. We describe how this problem is resolved in each class of models, and unveil how the trade-off arises, that is, how the computational demand associated with solving the problem is amplified as a model evolves from small scale to large scale, or from less realism to more realism.

Ignition location is one of the important factors that affect the thermal efficiency, exhaust emissions and knock sensitivity in premixed-charge ignition engines. However, the ignition initiation locations of pre-chamber generated turbulent jet ignition, which is a promising ignition enhancement method, are not clearly understood due to the complex physics behind it. Motivated by this, the ignition initiation location of a transient turbulent jet in a constant volume combustor is analyzed by the use of computational fluid dynamics (CFD) simulations. In the CFD simulations of this work, commercial codes KIVA-3 V release 2 and an in-house-developed chemical solver with a detailed mechanism for H2/air mixtures are used. Comparisons are performed between simulated and experimental ignition initiation locations, and they agree well with one another. A detailed parametric study of the influence of in-cylinder temperature on the ignition initiation location is also performed.

A prediction model for hand prehensile movements was developed and validated. The model is based on a new approach that blends forward dynamics and a simple parametric control scheme. In the development phase, model parameters were first estimated using a set of hand grasping movement data, and then statistically analyzed. In the validation phase, the model was applied to novel conditions created by varying the subject group and size of the object grasped. The model performance was evaluated by the prediction errors under various novel conditions as compared to the benchmark values with no extrapolation. Analyses of the model parameters led to insights into human movement production and control. The resulting model also offers computational simplicity and efficiency, a much desired attribute for digital applications.

Service loading histories have the same general character for an individual route and the magnitudes vary from driver to driver. Both the magnitude and character of the loading history change from route to route and a linear scaling of one loading history does not characterize the variability of usage over a wide range of operating conditions. In this paper a technique for measuring and extrapolating cumulative exceedance diagrams to quantify the distribution of service loading in a vehicle is described. Monte Carlo simulations are coupled with the local stress strain approach for fatigue to obtain distributions of service loading. Fatigue life estimates based on the original loading histories are compared to those obtained from statistical descriptions of exceedance diagrams.