Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 72 for “"Physics-based models"”.
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Local tool wear profiles prediction using physics-based models
Thesis (Ph. D.)--Michigan State University. Mechanical Engineering, 2009
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Physics-based models of hysteresis in multiphase flow in porous media
We propose a novel probabilistic framework based on pore-scale probabilistic events to derive a theory of hysteresis in multiphase flow in porous media. In particular, we define the pore-space accessivity to contrast the serial and parallel arrangement of different-radius pore slices, and the …
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Understanding sewer infiltration and inflow using impulse response functions derived from physics-based models
… investigated at the residential lot scale using physics-based models. The typical flow response of each I&I source is calculated and these flow responses, called Impulse Response Functions (IRFs), are evaluated. I&I estimation using the three IRFs, calibrated using a genetic algorithm (GA), was …
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Reduction of Dynamic Nonlinear Models of Magnetic Devices
… and the technique results in easy-to-simulate, physics-based models. The mathematical framework for FEM-MOR is set up in the context of the analysis of the magnetic devices. This work provides a foundation for modeling more complicated magnetic devices, such as multiphase coupled inductors, …
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Spectroscopic measurements and modeling of carbonaceous particle combustion in a shock tube
… integrates new shock-tube diagnostics with physics-based models to quantify CNP combustion across free-molecular to transitional heat-transfer regimes. These advances deliver actionable constraints for multiphase detonation models by linking measured optical signatures to underlying particle …
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Data-Driven Modeling and Real-Time Optimal Control of Continuous Manufacturing Processes
… system dynamics more accurately than traditional physics-based models. It further examines using a high-fidelity digital twin, constructed from experimental data via linear system identification and nonlinear deep learning (NARX) approaches, to optimize PID controller parameters through …
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Rapid Prediction of Tsunamis and Storm Surges Using Machine Learning
… and efficient tsunami and storm surge prediction models are needed. However, existing physics-based numerical models have the disadvantage of being difficult to satisfy both accuracy and efficiency at the same time. In this dissertation, several surrogate models are developed using statistical and …
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Data assimilation into physics-based thermoacoustic models using Bayesian neural network ensembles
… tests, leading to costly re-designs. A physics-informed, data-driven model of a flame would allow for important quantities, such as the fluctuating heat release rate of the combustion process, to be estimated for a given burner geometry. This in turn would enable different geometries to …
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Development and assessment of a physics-based model for subcooled flow boiling with application to CFD
… and, more recently, in electronics cooling. Physics-based models that describe boiling heat transfer, when coupled with Computational Fluid Dynamics (CFD), can be an invaluable tool to increase the performance of such systems. Existing modeling approaches do not incorporate all relevant heat …
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Mathematical and Computational Foundations to Enable Predictive Digital Twins at Scale
… are developed to enable asset-specific physics-based models to be incorporated into a digital twin. A central element of the proposed approach is a library of component-based reduced-order models derived from high-fidelity simulations of the asset in various states. The component-based …
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A digital approach to the management of brownfields
… oil wells. Building on the output of validated physics-based models, this thesis investigates a range of analytic methods which may be used to determine a probable depth of gas lift injection of wells without pressure gauges, and finds that the Random Forest method coupled with a k-means …
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Calibration of empirical, semi-empirical, and physics-based material models for the prediction of creep and tensile behaviour of Alloy 617
Material models are powerful analytical tools for predicting material behaviour under various loading conditions. These models vary in complexity, progressing from empirical models that rely solely on fitting experimental data, to semi-empirical models that incorporate simplified physical concepts, …
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Physics-guided Machine Learning Approaches for Applications in Geothermal Energy Prediction
… geothermal energy mapping, scientists have used physics-based models and bottom-hole temperature measurements from oil and gas wells to generate heat flow and temperature-at-depth maps. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models …
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Using machine learning methods to aid scientists in laboratory environments
… method can be used to learn the dynamics of physics-based models and exploit the knowledge gained to achieve a given objective with measurable confidence. We also demonstrate how the agent's behaviour changes when the frequency of certain measurements is limited.
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Forecasting and Modelling Space Weather with Deep Learning Methods
… systems. Lastly, thermospheric density models are trained that can significantly outperform existing physics-based models.
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Multi-fidelity Design with Optimization Guided Incremental Decisions
… markets. However, traditional design processes based on empirical methods and expert knowledge may be inadequate for these novel configurations. This thesis introduces a design strategy that utilizes physics-based models and Multi-Disciplinary Analysis and Optimization (MDAO) as a framework to …
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TCAD-Informed Surrogate Models of Semiconductor Devices
… conducted over the last half-century to develop models of semiconductor devices for use in circuit analysis and simulation. Such models typically fall into one of two categories: “Cheap” analytical models that can be solved quickly but introduce significant error, and “expensive” physics-based …
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An investigation of space suit mobility with applications to EVA operations
… to bend the joints of a space suit, developing models of the mechanics of space suit joints based on experimental data, and utilizing these models to estimate a human factors performance metric, the work envelope for space suited EVA work. A detailed space suit joint torque-angle database is …
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Multi-scale structural health monitoring using wireless smart sensors
… Multi-metric monitoring, in combination with physics-based models, has great potential to enhance SHM methods; however, the efficacy of the multi-metric SHM has not been illustrated using WSS networks to date, due primarily to limited hardware capabilities of currently available smart sensors …
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Nanoscale interface mechanics with application to magnetic storage
… this goal entails the understanding of the physics at the head-disk interface and being able to reliably predict system performance in terms of flyability and contact. This dissertation presents continuum, physics-based models of the head-disk interface that were validated through …
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