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Acoustic Design Optimization With Isogeometric Analysis And Differential Evolution, Garrett W. Dodgen Dec 2019

Acoustic Design Optimization With Isogeometric Analysis And Differential Evolution, Garrett W. Dodgen

Mechanical Engineering Theses

The objective of this study is to utilize shape optimization to enhance the performance of devices relying on acoustic wave propagation. Particularly, the shape of a horn speaker and an acoustic energy harvester were optimized to enhance their performance at targeted frequencies. High order Isogeometric Analysis (IGA) was performed to estimate the acoustic pressure with minimum geometry and pollution errors [1]. The analysis platform was then combined with Differential Evolution (DE) to optimize the geometry of the horn speaker and energy harvester at a given frequency. These cases effectively demonstrate two applications of Isogeomtric shape optimization for devices relying on ...


Gearbox Baffle Optimization, Megan Arduin Dec 2019

Gearbox Baffle Optimization, Megan Arduin

Master's Theses

Current literature reveals there is limited consensus on the placement of baffles within a gearbox to reduce churning losses. Thus, there is a need for a process to identify baffle clearances that result in maximum and minimum churning losses. There are two types of baffles: axial and radial. While both axial and radial baffles cause reductions in churning losses to various degrees, the focus is on the effect of radial baffles. The effect of a board (rectangular plate) baffle location on the churning losses of a single gear gearbox are evaluated using computational fluid dynamics (CFD) implemented in Ansys. Several ...


A Reinforcement Learning Approach To Spacecraft Trajectory Optimization, Daniel S. Kolosa Dec 2019

A Reinforcement Learning Approach To Spacecraft Trajectory Optimization, Daniel S. Kolosa

Dissertations

This dissertation explores a novel method of solving low-thrust spacecraft targeting problems using reinforcement learning. A reinforcement learning algorithm based on Deep Deterministic Policy Gradients was developed to solve low-thrust trajectory optimization problems. The algorithm consists of two neural networks, an actor network and a critic network. The actor approximates a thrust magnitude given the current spacecraft state expressed as a set of orbital elements. The critic network evaluates the action taken by the actor based on the state and action taken. Three different types of trajectory problems were solved, a generalized orbit change maneuver, a semimajor axis change maneuver ...


Takeoff And Performance Tradeoffs Of Retrofit Distributed Electric Propulsion For Urban Transport, Kevin Moore, Andrew Ning Aug 2019

Takeoff And Performance Tradeoffs Of Retrofit Distributed Electric Propulsion For Urban Transport, Kevin Moore, Andrew Ning

Faculty Publications

While vertical takeoff and landing aircraft have shown promise for urban air transport, distributed electric propulsion on existing aircraft may offer immediately implementable alternatives. Distributed electric propulsion could potentially decrease takeoff distances enough to enable thousands of potential inter-city runways. This conceptual study explores the effects of a retrofit of open-bladed electric propulsion units. To model and explore the design space we use blade element momentum method, vortex lattice method, linear-beam finite element analysis, classical laminate theory, composite failure, empirically-based blade noise modeling, motor and motor-controller mass models, and gradient-based optimization. With liftoff time of seconds and the safe total ...


Unsupervised-Learning Assisted Artificial Neural Network For Optimization, Varun Kote Jul 2019

Unsupervised-Learning Assisted Artificial Neural Network For Optimization, Varun Kote

Mechanical & Aerospace Engineering Theses & Dissertations

Innovations in computer technology made way for Computational Fluid Dynamics (CFD) into engineering, which supported the development of new designs by reducing the cost and time by lowering the dependency on experimentation. There is a further need to make the process of development more efficient. One such technology is Artificial Intelligence. In this thesis, we explore the application of Artificial Intelligence (AI) in CFD and how it can improve the process of development.

AI is used as a buzz word for the mechanism which can learn by itself and make the decision accordingly. Machine learning (ML) is a subset of ...


Gaussian Process Regression Applied To Marine Energy Turbulent Source Tuning Via Metamodel Machine Learning Optimization, Sterling Olson Apr 2019

Gaussian Process Regression Applied To Marine Energy Turbulent Source Tuning Via Metamodel Machine Learning Optimization, Sterling Olson

Mechanical Engineering ETDs

Converting energy from the currents found within tidal channels, open ocean, rivers, and canals is a promising yet untapped source of renewable energy. In order to permit current energy converters for installation in the environment, the CECs must be shown to non-negatively impact the environment. While developing these model increased utility may be gained if researchers may optimize mechanical power while constraining environmental effects. Surrogate models have garnered interest as optimization tools because they maximize the utility of expensive information by building predictive models in place of computational or experimentally expensive model runs. Marine hydrokinetic current energy converters require large-domain ...


Characterization And Optimization Of A Propeller Test Stand, Colin Bruce Leighton Benjamin Apr 2019

Characterization And Optimization Of A Propeller Test Stand, Colin Bruce Leighton Benjamin

Mechanical & Aerospace Engineering Theses & Dissertations

In recent history, there has been a rapid rise in the use of drones, and they are expanding in popularity each year. The widespread use and future capabilities of these unmanned aerial vehicles (UAVs) will call for increased study and classification of propellers to maximize their performance. As a result, it is necessary to have continuity in the development, maximization, and optimization of propeller test stand’s capability to collect accurate and precise measurements. It is of significant advantage to have the capability of accurately characterizing a propeller based on its thrust and torque. In this study, a propeller test ...


Optimal Power Flow Control Of Networked Dc Microgrids, Eddy H. Trinklein Jan 2019

Optimal Power Flow Control Of Networked Dc Microgrids, Eddy H. Trinklein

Dissertations, Master's Theses and Master's Reports

The US military is moving toward the electrification of many weapon systems and platforms. Advanced weapon systems such as high energy radar, electro-magnetic kinetic weapons and directed energy pose significant integration challenges due to their pulsed power electrical load profile. Additionally, the weapons platforms, including ships, aircraft, and vehicles can be studied as a mobile microgrids with multiple generation sources, loads, and energy storage. There is also a desire to extend the mission profile and capabilities of these systems. Common goals are to increase fuel efficiency, maintaining system stability, and reduce energy storage size as typically required to enable pulsed ...


Optimization Of Multi-Injection Diesel Combustion Through Direct Application Of Abc And Pso Variant Algorithms, Ryan Michael Ogren Jan 2019

Optimization Of Multi-Injection Diesel Combustion Through Direct Application Of Abc And Pso Variant Algorithms, Ryan Michael Ogren

Graduate Theses and Dissertations

In this study a modified artificial bee colony algorithm and the cooperative-swarm variant of particle swarm optimization were applied to minimize diesel engine emissions and fuel consumption in the laboratory at medium load conditions. Tests were conducted using No. 2 diesel fuel in a four-cylinder, production diesel engine with series turbochargers and a high-pressure exhaust gas recirculation loop. Emissions were recorded at steady-state conditions and input into custom scripts in Matlab.

Both triple-injection strategies, consisting of a pilot-main-post injection scheme, and quadruple-injection strategies, using two pilots, were investigated for a high exhaust gas recirculation rate of 38%. A two-factor design ...


Design And Process Of 3d-Printed Parts Using Composite Theory, Jordan Garcia Jan 2019

Design And Process Of 3d-Printed Parts Using Composite Theory, Jordan Garcia

Theses and Dissertations--Mechanical Engineering

3D printing is a revolutionary manufacturing method that allows the productions of engineering parts almost directly from modeling software on a computer. With 3D printing technology, future manufacturing could become vastly efficient. However, it is observed that the procedures used in 3D printing differ substantially among the printers and from those used in conventional manufacturing. In this thesis, the mechanical properties of engineering products fabricated by 3D printing were comprehensively evaluated and then compared with those made by conventional manufacturing. Three open-source 3D printers, i.e., the Flash Forge Dreamer, the Tevo Tornado, and the Prusa, were used to fabricate ...


Optimization And Control Of Arrays Of Wave Energy Converters, Jianyang Lyu Jan 2019

Optimization And Control Of Arrays Of Wave Energy Converters, Jianyang Lyu

Dissertations, Master's Theses and Master's Reports

Wave Energy Converter Array is a practical approach to harvest ocean wave energy. To leverage the potential of the WEC array in terms of energy extraction, it is essential to have a properly designed array configuration and control system. This thesis explores the optimal configuration of Wave Energy Converters (WECs) arrays and their optimal control. The optimization of the WEC array allows both dimensions of individual WECs as well as the array layout to varying. In the first optimization problem, cylindrical buoys are assumed in the array where their radii and drafts are optimization parameters. Genetic Algorithms are used for ...


Machine Learning Assisted Optimization With Applications To Diesel Engine Optimization With The Particle Swarm Optimization Algorithm, Aaron M. Bertram Jan 2019

Machine Learning Assisted Optimization With Applications To Diesel Engine Optimization With The Particle Swarm Optimization Algorithm, Aaron M. Bertram

Graduate Theses and Dissertations

A novel approach to incorporating Machine Learning into optimization routines is presented. An approach which combines the benefits of ML, optimization, and meta-model searching is developed and tested on a multi-modal test problem; a modified Rastragin's function. An enhanced Particle Swarm Optimization method was derived from the initial testing. Optimization of a diesel engine was carried out using the modified algorithm demonstrating an improvement of 83% compared with the unmodified PSO algorithm. Additionally, an approach to enhancing the training of ML models by leveraging Virtual Sensing as an alternative to standard multi-layer neural networks is presented. Substantial gains were ...


Performance Enhancement Of Human Motion Based Piezoelectric Energy Harvesters, Iman Izadgoshasb Jan 2019

Performance Enhancement Of Human Motion Based Piezoelectric Energy Harvesters, Iman Izadgoshasb

Theses

Harvesting electricity from human motions using piezoelectric materials is attracting the attention of many researchers in recent years. These harvesters can potentially power portable electronic devices without the need of external power sources.

The aim of this thesis was to improve the efficiency of piezoelectric energy harvesting from human motions. To achieve this, optimising orientation of piezoelectric cantilever beam investigated; the new mechanism consisting of double pendulum system was studied and finally the new shape design of cantilever was proposed to generate multi resonance peaks. These achievements may help to improve the efficiency of piezoelectric energy harvesters in the future.


Time-Dependent Reliability Methodologies With Saddlepoint Approximation, Zhangli Hu Jan 2019

Time-Dependent Reliability Methodologies With Saddlepoint Approximation, Zhangli Hu

Doctoral Dissertations

"Engineers always encounter time-dependent uncertainties that ubiquitously exist, such as the random deterioration of material properties and time-variant loads. Therefore the reliability of engineering systems becomes time-dependent. It is crucial to predict the time-dependent reliability in the design stage, given possible catastrophic consequences of a failure. Although extensive research has been conducted on reliability analysis, estimating the reliability accurately and efficiently is still challenging. The objective of this work is to develop accurate and efficient reliability methodologies for engineering design. The basic idea is the integration of traditional reliability methods with saddlepoint approximation (SPA), which can accurately approximate the tail ...