Machine Learning from Computer Simulations with Applications in Rail Vehicle Dynamics and System Identification

dc.contributor.authorTaheri, Mehdien
dc.contributor.committeechairAhmadian, Mehdien
dc.contributor.committeememberHajj, Muhammad R.en
dc.contributor.committeememberWest, Robert L.en
dc.contributor.committeememberMirzaeifar, Rezaen
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2017-12-24T07:00:57Zen
dc.date.available2017-12-24T07:00:57Zen
dc.date.issued2016-07-01en
dc.description.abstractThe application of stochastic modeling for learning the behavior of multibody dynamics models is investigated. The stochastic modeling technique is also known as Kriging or random function approach. Post-processing data from a simulation run is used to train the stochastic model that estimates the relationship between model inputs, such as the suspension relative displacement and velocity, and the output, for example, sum of suspension forces. Computational efficiency of Multibody Dynamics (MBD) models can be improved by replacing their computationally-intensive subsystems with stochastic predictions. The stochastic modeling technique is able to learn the behavior of a physical system and integrate its behavior in MBS models, resulting in improved real-time simulations and reduced computational effort in models with repeated substructures (for example, modeling a train with a large number of rail vehicles). Since the sampling plan greatly influences the overall accuracy and efficiency of the stochastic predictions, various sampling plans are investigated, and a space-filling Latin Hypercube sampling plan based on the traveling salesman problem (TPS) is suggested for efficiently representing the entire parameter space. The simulation results confirm the expected increased modeling efficiency, although further research is needed for improving the accuracy of the predictions. The prediction accuracy is expected to improve through employing a sampling strategy that considers the discrete nature of the training data and uses infill criteria that considers the shape of the output function and detects sample spaces with high prediction errors. It is recommended that future efforts consider quantifying the computation efficiency of the proposed learning behavior by overcoming the inefficiencies associated with transferring data between multiple software packages, which proved to be a limiting factor in this study. These limitations can be overcome by using the user subroutine functionality of SIMPACK and adding the stochastic modeling technique to its force library.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:7739en
dc.identifier.urihttp://hdl.handle.net/10919/81417en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectStochastic modelingen
dc.subjectmeta-modelsen
dc.subjectSurrogate modelsen
dc.subjectThree-piece trucken
dc.subjectGlobal optimizationen
dc.subjectLatin Hypercube sampling planen
dc.titleMachine Learning from Computer Simulations with Applications in Rail Vehicle Dynamics and System Identificationen
dc.typeDissertationen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en
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