GOALI: Computational Multibody Dynamics: Addressing Modeling and Simulation Limitations in Problems with Friction and Contact

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This Grant Opportunity for Academic Liaison with Industry (GOALI) Program project brings together a research group from the University of Wisconsin-Madison and a team of engineers from Caterpillar in a joint effort that aims at investigating and experimentally validating modeling and numerical solution methods capable of predicting through computer simulation the time evolution of large dynamic systems. The proposed research, development, and validation effort will increase the role that computer simulation plays in investigating processes such as transporting, mixing, and compacting discrete material (powders, grains, gravel, rocks). Although these processes are relevant in a broad spectrum of industries: e.g., manufacturing, construction, farming, food and drug, their investigation through computer modeling and simulation provides limited benefit today unless the problem size and/or duration of the analysis are drastically curtailed, often below practically useful limits. Better understanding the dynamics of large multibody systems is important since after water, discrete media are manipulated in industry the most. Moreover, about 50% of all traded products worldwide are in granular form and their manufacturing, transport, processing, packing and mixing pose challenging problems in which the outcomes of this research is anticipated to provide valuable insights. The granular systems to be investigated have millions to billions of components whose collective behavior is dominated by frictional contact interactions between individual elements. The research effort will concentrate on (1) investigating two classes of solutions methods; i.e., Krylov-subspace and Primal-Dual Interior Point methods, to address current numerical solution limitations that stem from the multiscale aspect of practical applications; (2) investigate augmented Lagrangian formulations to understand whether they can enhance the modeling role that complementarity approaches play in multibody dynamics; and (3) compare two classes of solution methods; i.e., penalty-based and complementarity-based, in terms of accuracy, robustness and scalability in order to compile a factual body of evidence that summarizes the advantages and disadvantages of each approach.