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 22 for “"Dynamic Mode Decomposition (DMD)"”.
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Optimization Methods for Dynamic Mode Decomposition of Nonlinear Partial Differential Equations
Reduced-order models have long been used to understand the behavior of nonlinear partial differential equations. Naturally, reduced-order modeling techniques come at the price of either computational accuracy or computation time. Optimization techniques are studied to improve either or both of …
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Wavelet-based Dynamic Mode Decomposition in the Context of Extended Dynamic Mode Decomposition and Koopman Theory
Koopman theory is widely used for data-driven modeling of nonlinear dynamical systems. One of the well-known algorithms that stem from this approach is the Extended Dynamic Mode Decomposition (EDMD), a data-driven algorithm for uncontrolled systems. In this thesis, we will start by discussing the …
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Intelligent Monitoring of Powerline Vibrations - Sparse Sensing and Predictive Modeling Approach
… and fail to provide real-time insights into the dynamic behavior of conductors. This thesis presents a data-driven framework for real-time monitoring and state estimation of vibration profiles in transmission lines, offering a scalable and intelligent alternative to conventional methods.The …
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Unsteady Metric Based Grid Adaptation using Koopman Expansion
… to high-speed applications, demanding precise modelling to characterize their unsteady features accurately. The simulation of unsteady supersonic and hypersonic flows is inherently computationally expensive, requiring a highly refined mesh to capture these unsteady effects. While anisotropic …
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Mathematical Modeling and Dynamic Recovery of Power Systems
Power networks are sophisticated dynamical systems whose stable operation is essential to modern society. We study the swing equation for networks and its linearization (LSEN) as a tool for modeling power systems. Nowadays, phasor measurement units (PMUs) are used across power networks to measure …
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DMD and POD Modal Analysis for Store Separation
… time in this process is the implementation of a mode based reduced order model (ROM) for modeling store separation. The objective of this study was to first identify the leading modes that can best be used to model a store separating from an aircraft. To obtain these modes, two algorithms were …
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Dynamic Estimation of Large-Scale Flow Events in Open Cavity Flows
<p>Flow over an open cavity has a dynamically complex flowfield, generating pressure fluctuations that can amplify shear stress and cause structural damage to the stores within it and the aircraft as a whole. These pressure loads are linked to large-scale resonant shear layer phenomena. …
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Reduced order modeling for stochastic prediction and data assimilation onboard autonomous platforms at sea
… and updating forecasts of the evolving dynamics using their observations. Due to the operational constraints such as onboard power, memory, bandwidth, and space limitations, efficient adaptive reduced order models (ROMs) are needed for onboard predictions. In the first part, several …
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Multi-scale Coherent Structure Extraction and Visualization for Flow Analysis
… phenomena and help improve our capability of modeling complex turbulence flows, such as those often seen in combustion, chemical reaction, and heat transfer. However, due to their multi-scale nature and non-unified characterizations, extraction and separation of coherent structures remain a …
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Reduced Order Modeling for Stochastic Prediction and Data Assimilation Onboard Autonomous Platforms At Sea
… and updating forecasts of the evolving dynamics using their observations. Due to the operational constraints such as onboard power, memory, bandwidth, and space limitations, efficient adaptive reduced order models (ROMs) are needed for onboard predictions. In the first part, several …
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Data Analysis of an Unsteady Cavitating Flow on a Venturi-type Profile
The instability modes and non-linear behavior of a cavitating flow have been studied based on the experimental data obtained from planar Particle Image Velocimetry (PIV). Three data-driven techniques, Proper Orthogonal Decomposition (POD), Dynamic Mode Decomposition (DMD), and Clustered-based …
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Applications of Data-Driven Learning Models in Fluid Mechanics: Solid-Fluid Multiphase Systems and Bat Flight
… this domain: (1) deep learning-based drag force modeling for particulate suspensions, (2) reduced-order modeling (ROM) for flow predictions in randomly arranged solid arrays, and (3) data-driven analysis of bat flight kinematics. First, we address the challenge of accurately predicting individual …
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Techniques for determining hidden properties of large-scale power systems
… the knowledge in power system equivalent modeling, and dynamic mode estimation. Work related to these respective topics is presented herein in two parts -- (i) Network Based Methods, and (ii) Measurement Based Methods. The first part focuses on the problem of creating limit preserving …
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Analyses of flame response to acoustic forcing in a rocket combustor
… test case for investigating the flame response. Modelling and complementary data analysis methods are developed and applied to model the chamber flow field, identify and predict the excited acoustic disturbance, identify the flame response using optical data, and to predict the flame response …
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Dynamic mode decomposition with application to optimal control
High-dimensional fluid dynamics systems are central to a variety of modern engineering challenges. Direct numerical simulation of these processes is possible, but the high computational cost of these simulations is always an important consideration. Optimal control of these high-dimensional fluid …
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Adaptive Stochastic Reduced-Order Modeling for Autonomous Ocean Platforms
… platforms, efficient adaptive reduced-order models (ROMs) are needed. In this thesis, we first review existing approaches and then develop a new adaptive Dynamic Mode Decomposition (DMD)-based, data-driven, reduced-order model framework that provides onboard forecasting and data assimilation …
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An experimental investigation of transverse jets in supersonic crossflow
… approximately double. Spectral Proper Orthogonal Decomposition (SPOD) and Dynamic Mode Decomposition (DMD) were applied to the schlieren data and coherent structures were extracted. The SPOD results show that the modal energy peak frequencies are consistent with the shear layer vortex shedding …
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Swirl-stabilized lean-premixed flame combustion dynamics: An experimental investigation of flame stabilization, flame dynamics and combustion instability control strategies
Though modern low-emission combustion strategies have been successful in abating the emission of pollutants in aircraft engines and power generation gas turbines, combustion instability remains one of the foremost technical challenges in the development of next generation lean premixed combustor …
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A Data Driven Modeling Approach for Store Distributed Load and Trajectory Prediction
… desire to incorporate computational fluid dynamics (CFD) into the early stages of the store separation analysis. A viable method for achieving this objective is available through data-driven surrogate modeling of store distributed loads. This dissertation investigates the practicality of …
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Large eddy simulations of premixed turbulent flame dynamics : combustion modeling, validation and analysis
… conditions are some of the key requirements of modem-day combustors. To achieve these objectives, lean premixed flames are generally preferred as they achieve efficient and clean combustion. A drawback of lean premixed combustion, however, is that the flames are more prone to dynamics. The …
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