Modeling and Simulation of Random Processes and Fields in Civil Engineering and Engineering Mechanics

Modeling and Simulation of Random Processes and Fields in Civil Engineering and Engineering Mechanics Brett A. Benowitz This thesis covers several topics within computational modeling and simulation of problems arising in Civil Engineering and Applied Mechanics. There are two distinct parts. Part 1 covers work in modeling and analyzing heterogeneous materials using the eXtended Finite Element Method (XFEM) with arbitrarily shaped inclusions. A novel enrichment function, which can model arbitrarily shaped inclusions within the framework of XFEM, is proposed. The internal boundary of an arbitrarily shaped inclusion is first discretized, and a numerical enrichment function is constructed “on the fly” using spline interpolation. This thesis considers a piecewise cubic spline, which is constructed from seven localized discrete boundary points. The enrichment function is then determined by solving numerically a nonlinear equation, which determines the distance from any point to the spline curve. Parametric convergence studies are carried out to show the accuracy of this approach, compared to pointwise and linear segmentation of points, for the construction of the enrichment function in the case of simple inclusions and arbitrarily shaped inclusions in linear elasticity. Moreover, the viability of this approach is illustrated on a Neo-Hookean hyperelastic material with a hole undergoing large deformation. In this case, the enrichment is able to adapt to the deformation and effectively capture the correct response without remeshing. Part 2 then moves on to research work in simulation of random processes and fields. Novel algorithms for simulating random processes and fields such as earthquakes, wind fields, and properties of functionally graded materials are discussed. Specifically, a methodology is presented to determine the Evolutionary Spectrum (ES) for non-stationary processes from a prescribed or measured non-stationary Auto-Correlation Function (ACF). Previously, the existence of such an inversion was unknown, let alone possible to compute or estimate. The classic integral expression suggested by Priestley, providing the ACF from the ES, is not invertible in a unique way so that the ES could be determined from a given ACF. However, the benefits of an efficient inversion from ACF to ES are vast. Consider for example various problems involving simulation of non-stationary processes or non-homogeneous fields, including non-stationary seismic ground motions as well as non-homogeneous material properties such as those of functionally graded materials. In such cases, it is sometimes more convenient to estimate the ACF from measured data, rather than the ES. However, efficient simulation depends on knowing the ES. Even more important, simulation of non-Gaussian and nonstationary processes depends on this inversion, when following a spectral representation based approach. This work first examines the existence and uniqueness of such an inversion from the ACF to the ES under a set of special conditions and assumptions (since such an inversion is clearly not unique in the most general form). It then moves on to efficient methodologies of computing the inverse, including some established optimization techniques, as well as proposing a novel methodology. Its application within the framework of translation models for simulation of non-Gaussian, non-stationary processes is developed and discussed. Numerical examples are provided demonstrating the capabilities of the methodology. Additionally in Part 2, a methodology is presented for efficient and accurate simulation of wind velocities along long span structures at a virtually infinite number of points. Currently, the standard approach is to model wind velocities as a multivariate stochastic process, characterized by a Cross-Spectral Density Matrix (CSDM). In other words, the wind velocities are modeled as discrete components of a vector process. To simulate sample functions of the vector process, the Spectral Representation Method (SRM) is used. The SRM involves a Cholesky decomposition of the CSDM. However, it is a well known issue that as the length of the structure, and consequently the size of the vector process, increases, this Cholesky decomposition breaks down (from the numerical point of view). To avoid this issue, current research efforts in the literature center around approximate techniques to simplify the decomposition. Alternatively, this thesis proposes the use of the frequency-wavenumber (F-K) spectrum to model the wind velocities as a stochastic “wave,” continuous in both space and time. This allows the wind velocities to be modeled at a virtually infinite number of points along the length of the structure. In this work, the relationship between the CSDM and the F-K spectrum is first examined, as well as simulation techniques for both. The F-K spectrum for wind velocities is then derived. Numerical examples are then carried out demonstrating that the simulated wave samples exhibit the desired spectral and coherence characteristics. The efficiency of this method, specifically through the use of the Fast Fourier Transform, is demonstrated.