Mechanical Engineering

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https://hdl.handle.net/10323/11894

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Browsing Mechanical Engineering by Author "Gu, Randy J"

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    A Simulation-Based Fatigue Life Estimation Method for Nonlinear Systems under Non-Gaussian Loads
    (2023-01-01) Mande, Onkar K; Mourelatos, Zissimos P.; Gu, Randy J; Monroe, Ryan; Drignei, Dorin
    In the fields of durability and stochastic structural dynamics, it is customary to focus on linear structures subjected to Gaussian excitations. However, real-world engineering systems often exhibit nonlinear behavior and are exposed to non-Gaussian loads. Calculating fatigue life for such nonlinear systems under non-Gaussian loading presents many challenges such as complex nonlinear dynamics, multifaceted statistical characteristics, and time-dependent effects resulting in a very high computational effort. To overcome these hurdles, this research uses non-Gaussian Karhunen-Loeve expansion (NG-KLE) to not only predict the expected fatigue life but also obtain the Probability Density Function (PDF) of fatigue life. It integrates a sub-domain-based technique to significantly reduce the computational demands while preserving accuracy, by efficiently obtaining long time trajectories of random processes. This development is very useful for excitation signals that far exceed the process correlation length. The NG-KLE method serves as the main tool for characterizing the excitation process by estimating its non-Gaussian marginal distribution and autocorrelation function. A Karhunen-Loeve (KL) expansion is executed only for the first subdomain, and then extended to subsequent subdomains by establishing correlations between the KL expansion coefficients of adjacent subdomains. This innovative approach is adapted to non-Gaussian (NG) excitation, allowing for efficient characterization of both the input and output random processes using NG-KLE, enabling the generation of very long synthetic output random stress process samples. The fatigue life corresponding to each output stress trajectory contributes to the estimation of the PDF of fatigue life. The proposed generalized fatigue life estimation approach accommodates both Gaussian and non-Gaussian processes for both narrow and wide band signals. To demonstrate its effectiveness, we use a duffing oscillator system and a practical example involving a truck assembly modeled by the Finite Element Method (FEM).
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    Development of a Hybrid Finite Element Method for Solving Inverse Engineering Problems
    (2024-01-01) Wang, Xiyun; Gu, Randy J; Gu, Randy J; Yang, Lianxiang; Horvath, Tamas; Chang, Yin-Ping
    The ill-posed boundary value problems, such as contact problems, adhesive joint analysis, and damage identification, are of foremost importance in the design and manufacturing of machines. The analysis of these problems has attracted considerable attention from engineers and researchers in various industries. This dissertation presents a novel hybrid finite element method for solving ill-posed boundary value problems through inverse engineering, with a focus on accurately determining contact stress, identifying damage, and analyzing adhesive joints in mechanical engineering. A significant aspect of this method involves integrating empirical measurements with numerical simulations to enhance both the accuracy and reliability of finite element analyses under insufficient boundary conditions. A constrained optimization framework is also employed in this study. The method is evaluated through seven case studies, which include assessments of plate bending, rigid contact problems, Hertzian contact problems, damage identification tasks, single lap joint, and T-peel joint evaluations. A novel constraint equation based on the gradient of the loading function is introduced as well. These case studies highlight the method’s comprehensive applicability and effectiveness across a range of complex engineering challenges. The dissertation outlines future work that aims to expand the methods application to three-dimensional problems, improve the optimization algorithms, and explore further applications. This work lays a foundation for advancing more complex and reliable modeling techniques in mechanical engineering, with significant implications for both research and industrial applications.