CDS&E: Theoretical Foundation and Computational Tools for Complex Nonlinear Stochastic Dynamical Engineering Systems - A New Paradigm

University Of Arizona, Tucson AZ

Investigators

Abstract

Engineered structures excited by dynamic loadings, including natural events like earthquakes, cause enormous damage in terms of loss of life, property damage, and lost economic activity. Because of the unpredictability of these events, the excitation time histories cannot be known in advance. The cost of designing structures against such excitation is enormous, and there is considerable room for improvement in the current state of knowledge in predicting the response of structures under these conditions. This study will provide a theoretical foundation and computational tools to analyze structures, and thus to improve the efficiency and overall safety of engineering designs. The impact of the study is expected to be multi-disciplinary in nature. However, the immediate major beneficiary will be the engineering profession interested in designing seismic damage-tolerant and risk-consistent structures. Since the underlying risk cannot completely be eliminated, it needs to be managed and the study will provide the necessary tools. This multi-disciplinary approach will help broaden the participation of underrepresented groups in research and will impact engineering education. A multi-disciplinary transformational theoretical concept will address the knowledge gap mentioned above. Instead of using the classical random vibration approach, the uncertain dynamic loadings will be applied in the time domain and will explicitly incorporate information on major sources of nonlinearity, improved modeling and several recently introduced energy dissipation features, and associated uncertainties, as the stochastic system goes through several phases just before failure. Some of the basic challenges are the understanding of input uncertainties, propagating them in large nonlinear dynamic systems from the parameter to the system level satisfying the underlying physics, obtaining reliable probabilistic response characteristics/metrics/statistics, and validating them using limited data. The research objectives will be achieved by taking into account sensitivity analysis, model reduction techniques, intelligent sampling schemes, and several advanced factorial schemes producing a beneficial compounding effect to obtain efficiency without sacrificing accuracy. They will be implemented in a multi-scale environment exploiting state-of-the-art computational power. The formulation will extract stochastic dynamical behavior using only tens of intelligent analyses instead of thousands of simulation-based analyses. Essentially, this is intelligent multiple deterministic analyses. The overall effort will effectively integrate education, outreach, research, and training.