Doctor of Philosophy (PhD)
First Committee Member
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Evaluating the materials properties under different loading conditions is critical in various industries. Compared to quasi-static loading, predicting the behavior of structures under dynamic loads is more challenging. In this work, we will address multiple problems with strain rates varying from quasi-static to hypervelocity conditions. Computer simulation is increasingly used in the design and evaluation phases to improve the efficiency, cost-effectiveness, and flexibility. However, verification and validation of each simulation is necessary. Experiments are performed in all topics and the computational models are validated by comparing with the experiments. One of the most common types of connections in structures is the bolted joints. As bolted structures exhibit multiple nonlinearities such as material behavior, initial bolt tightening, and friction, it is necessary to evaluate how bolted connections affect the shock propagation under the impact conditions. In this work, computational approaches are developed for predicting shock transmission through a bolted joint structure subjected to drop-weight impact loading. While the coatings have been shown to enhance the performance of various components, the mechanisms of these enhancements are not well understood. One of the problems regarding the use of nanocomposite materials in structures is the lack of specific formulations for obtaining the material properties due to the limited ability to conduct material characterization tests, similar to what can be done with bulk materials. In addition, conventional analytical techniques are mostly not applicable for composite materials. The numerical techniques have shown promising results in identifying the material properties of the composites, once combined with the experimental studies. In this work, a multi-scale simulation approach is developed to determine the material model and characteristics of a Ti/SiC Metal Matrix Nanocomposite (MMNC) coating: a meso scale model and a micro scale finite element model. The material model obtained from the meso-scale study will be used in the micro-scale simulation of the micro-indentation testing under various loads. The indentation results and the MMNC coating material model parameters will be evaluated by comparison with the experimental results. As the literature failed to identify the mechanical characteristics of this type of coating under high strain rates, the effect of this coating on enhancing the hypervelocity impact resistance of titanium substrate was studied. The experiments were conducted using a two-stage light gas gun with projectile velocities ranging from 3.7 to 5.4 km/s. A Smoothed Particle Hydrodynamics (SPH) computational approach was developed to supplement the experimental studies in evaluating the appropriate material model for the MMNC coating. The resulting SPH model can be used for parametric studies. Two-stage light gas guns accelerate projectiles at high velocities of multiple kilometers per second. These guns are used in many applications including experiments to understand the response of spacecraft and satellite structural components when impacted by orbital debris. Obtaining a specific projectile velocity can be challenging due to many uncertainties. Therefore, most gas guns depend on the expertise of operators to estimate the mass of projectile, mass of main charge, the propellent gas type, the piston mass, and the pump tube (PT) pressure. This uncertainty leads to multiple and costly gas gun experiments to achieve a certain projectile velocity. This study aims to account for the uncertainties associated with two-stage gas guns. A dataset of 211 tests conducted at the UNLV two-stage gas gun with 0.22-inch caliber was used. Feature selection was performed and the most critical features were selected to train the Machine Learning models. The projectile velocities from each observation (experiment) were used as the dependent variable (target) to produce training samples. Different regression techniques were evaluated. The predicted projectile velocities were then tested using the unused experimental dataset to verify the effectiveness of the models and to select the most accurate one. Among the tested models, the Random Forest model showed the best performance with R-Squared value above 93 %. The results showed that combining the experimental studies and Machine Learning can predict projectile velocities, saving time and cost of experimentation.
Computational Modeling; Impact Behavior; Machine Learning; Material Characterization; Mechanical Properties; Metal Matrix Nanocomposite
University of Nevada, Las Vegas
Shojaeishahmirzadi, Pouya, "Modeling the Mechanical Behavior and Shock Propagation of Metallic and Nanocomposite Materials" (2021). UNLV Theses, Dissertations, Professional Papers, and Capstones. 4315.
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