CAREER: Higher-Order Methods for Nonlinear Stochastic Structural Dynamics

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This Faculty Early Career Development (CAREER) grant is to study the influence of real, observable dynamic loads that are randomly varying in nature (e.g. wind velocity and pressure) on structures that exhibit complex behavior. This study will enable more accurate structural modeling that will inform improved design practices for high performance structures that operate for longer service lives at lower cost. The research will be applicable to structures of all sorts where efficiency is critical and loads are highly unpredictable, ranging from civil structures to aircraft, automobiles, ships, and spacecraft. This award supports fundamental research to simulate the nature of these randomly varying loads in greater detail than has been previously possible. Leveraging these studies, the response of different classes of structures subjected to such environments will be investigated with the ultimate aim of optimizing performance. The award will also support the development of new undergraduate research modules, which will be made publicly available, designed to introduce students to careers in research and teach basic research skills that are useful beyond academia. Many physical phenomena are characterized by complex stochastic processes that exhibit strongly non-Gaussian and nonlinear features. This project will build new methods for characterizing and synthesizing these higher-order stochastic processes that will be used as excitation for various nonlinear structural systems. The nonlinear structural systems studied herein - including hyperelastic, elastic-plastic, and chaotic systems - are especially sensitive to the nature of the stochastic excitation and may exhibit drastically different structural behavior based on seemingly minor changes in the stochastic excitation. Currently, the sensitivity of nonlinear structural response to various features of the stochastic excitation is not well understood. This research will extensively study this sensitivity for different classes of nonlinear structural systems and develop new topology optimization model to enhance structural performance.