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Title: DEEP LEARNING IN ENGINEERING MECHANICS: WAVE PROPAGATION AND DYNAMICS IMPLEMENTATIONS
Creator: Finol Berrueta, David
Date: 2019
Description: With the advent of Artiﬁcial Intelligence research in the 1960s, the need for intelligent systems that are able to truly comprehend the...
Show moreWith the advent of Artiﬁcial Intelligence research in the 1960s, the need for intelligent systems that are able to truly comprehend the physical world around them became relevant. Signiﬁcant milestones in the realm of machine learning and, in particular, deep learning during the past decade have led to advanced data-driven models that are able to approximate complex functions from pure observations. When it comes to the application of physics-based scenarios, the vast majority of these models rely on statistical and optimization constructs, leaving minimal room in their development for the physics-driven frameworks that more traditional engineering and science ﬁelds have been developing for centuries. On the other hand, the more traditional engineering ﬁelds, such as mechanics, have evolved on a diﬀerent set of modeling tools that are mostly based on physics driven assumptions and equations, typically aided by statistical tools for uncertainty handling. Deep learning models can provide signiﬁcant implementation advantages in commercial systems over traditional engineering modeling tools in the current economies of scale, but they tend to lack the strong reliability their counterparts naturally allow. The work presented in this thesis is aimed at assessing the potential of deep learning tools, such as Convolutional Neural Networks and Long Short-Term Memory Networks, as data-driven models in engineering mechanics, with a major focus on vibration problems. In particular, two implementation cases are presented: a data driven surrogate model to a Phononic eigenvalue problem, and a physics-learning model in rigid-body dynamics scenario. Through the applications presented, this work that shows select deep learning architectures can appropriately approximate complex functions found in engineering mechanics from a system’s time history or state and generalize to set expectations outside training domains. In spatio-temporal systems, it is also that shown local learning windows along space and time can provide improved model reliability in their approximation and generalization performance
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Title: AN EXPERIMENTAL INVESTIGATION OF THE DYNAMICS OF AN INVERTED SERRATED FLAG
Creator: MURUGESAN PAZHANI, KAUSHIK
Date: 2018
Description: An experimental investigation of the role of leading-edge triangular serrations was conducted to understand the role of free leading edge in...
Show moreAn experimental investigation of the role of leading-edge triangular serrations was conducted to understand the role of free leading edge in large amplitude flapping of an inverted flag. The serrations are in the form of triangles arranged spanwise along the leading edge of the flag model. High – speed camera imaging experiment was conducted in open – loop wind tunnel at air – speeds ranging from 3.3m/s to 6.5m/s. For this velocity range, the non – dimensional bending stiffness (the ratio of bending force to the fluid inertial forces) ranges from 0.285 to 0.073. Flow visualization experiment using PIV technique was conducted for baseline flag and two serrated flags at flow velocity 4.8m/s (bending stiffness – 0.13). At a critical value of the velocity or bending stiffness, the flag oscillations transition from low amplitude asymmetric oscillations to symmetric high amplitude oscillations. This critical velocity is higher for the serrated flags indicating a reduction in the instantaneous lift force. The critical velocity was found to increase as serration height increased for a fixed number of serrations. The serrations create leading edge counter rotating eddy structures that interact with the primary tip vortex formation and breakdown process leading to changes in critical velocity, amplitude and frequency. The flapping amplitude and frequency were found to decrease as serration height increased for a fixed number of serrations. The “shallow” serrations have no effect of serrations while “tall” serrations decrease the non – dimensional flapping frequency and amplitude. The phase averaged velocity results show serrations delay leading edge vortex formations, and flow separation. This leads to decrease in pressure difference causing the serrated flag to deform less than baseline flag. Leading edge vortex formed in serrated flags were observed to be deformed compared to baseline flag leading edge vortex. Vortex deformation is due to serration induced three-dimensional flow effects. Serrated flags exhibit elongated vortical structures from flag tip instead of periodic vortex shedding in rebound phase. Streamlines used for qualitative analysis also shows, serrated flags lack periodic vortex formation and shedding during rebound phase. Using qualitative evidence from streamline plots and vorticity contour plots (elongated vortex structures) it could be stated due to change in leading edge geometry, serrated flags demonstrate a non – VIV flapping.
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Title: ELECTROCHEMICAL BEHAVIOR OF ADDITIVELY MANUFACTURED NON-SPHERICAL TI-6AL-4V POWDER IN 3.5 WT. % NACL SOLUTION
Creator: Bagi, Sourabh Dilip
Date: 2021
Description: In laser powder bed fusion (LPBF), also known as selective laser melting (SLM), the feedstock powder and processing parameters affect the...
Show moreIn laser powder bed fusion (LPBF), also known as selective laser melting (SLM), the feedstock powder and processing parameters affect the properties of additively manufactured parts. Limited research has been conducted on non-spherical Ti6Al4V feedstock powder prepared by Hydride-Dehydride process. Significant progress in metal powder additive manufacturing (AM) requires the inter-linking of multiple variables, which includes starting materials, process settings, and post-treatment to achieve desired resultant properties. Owing to the rapid emergence of metal 3D-printing, process-property relationships, and appropriate post-treatment conditions have not been as extensively characterized as for conventional materials, thus requiring significant attention. Over the years, spherical powders were used in powder bed AM machines and there have been various concerns related to powder as well as processing parameters leading to defects formation, poor part quality, and unsatisfactory performance. It is critical to keep the cost of manufacturing low for large-scale production which results in significant interest in low-cost powder, making it vital to understand the effect of microstructural defects on corrosion behavior. Recently, economical powder attracted attention in AM, thus, making it is necessary to understand the role of possible microstructural defects on corrosion behavior. In powder bed additive manufacturing, feedstock and processing affect final microstructure and properties of the 3D printed parts. While numerous studies have evaluated 3D-printing of spherical powder, very limited research has examined the processing of the non-spherical feedstock. In this research, parts are manufactured by SLM of hydride-dehydride (HDH) Ti6Al4V powder. heat treatment and hot isostatic pressing are applied on SLM parts. The microstructures, potentiodynamic curves, and electrochemical impedance spectroscopy are characterized for SLM processed, heat treated, and hot isostatically pressed HDH Ti6Al4V specimens. Results indicate although the as-built specimen has anisotropic microstructure (i.e., lamellar α + acicular α’ + β phases), the heat treatment and hot isostatic pressing result in homogenized grain structures and enhanced corrosion behavior. Results indicate that type of constituent phase, grain size, and morphology directly determine corrosion resistance. This research is beneficial for the manufacturing of low-cost titanium alloys. In the current research, we evaluate non-spherical powder processing by hydride-dehydride (HDH) method and selective laser melted in powder bed AM machine followed by heat treatment and hot isostatic pressing to alter microstructure and electrochemical behavior. If successful, the usage of non-spherical morphology in conjunction with the newer powder dispensing method of double smoothing will enable remarkable improvements in the quality and performance of additively manufactured products. This method will also cut down costs associated with a greener powder production method and enhance the fabrication rate. It is a well-established fact that corrosion behavior is drastically affected by heterogeneous microstructure and defects. Thus, it is paramount to conduct a systematic study on the role of processing parameters and post process heat treatment, which can enhance our understanding of possible defect formation in micro and macro scale and their impact on electrochemical behavior.
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Title: Computationally Efficient Predictive Control Strategies for Autonomous Vehicles
Creator: Bhattacharyya, Viranjan
Date: 2021
Description: This thesis aims at developing computationally efficient (hence real-time applicable) control strategies for autonomous vehicles in the...
Show moreThis thesis aims at developing computationally efficient (hence real-time applicable) control strategies for autonomous vehicles in the presence of uncertainty, while incorporating high fidelity vehicle dynamics. The motivation for the control strategies is to ensure safety and improve energy efficiency of the vehicles. In this research, an effort has been made to develop control strategies to strike a balance between these competing factors. The specific contributions are: development of a new hierarchical control framework that can guarantee avoidance of red-light idling in the presence of uncertainty in preceding vehicle information/prediction in connected environment (hence improves system mobility); exploitation of a data-driven modeling approach for identifying a linear predictor for the nonlinear vehicle dynamics, which facilitates formulation of a convex equivalent problem of the original non-convex problem (hence facilitates computational tractability); introduction of a novel vehicle dynamics-aware fast game-theoretic planner for behavior and motion planning of vehicles in uncertain and unconnected environments. This thesis explores both the possible directions of future autonomous vehicles: connected and unconnected autonomous vehicles. In particular, the first problem relates to longitudinal fuel efficient driving (eco-driving) in a connected urban environment, where the connected and automated vehicles (CAVs) aim at the improvement of fuel efficiency and reduction of red-light idling (stop and go motion). The CAVs also focus on ensuring collision avoidance with the preceding vehicles despite the prediction uncertainty in future trajectory of preceding vehicles. This problem assumes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, and is a longitudinal control problem. The next problem considers the uncertainty in prediction of future states of neighbouring vehicles in an unconnected environment and involves both lateral and longitudinal control. Following previous research, the interactive nature of driving is modeled using game-theory and a computationally efficient game-theoretic planner is introduced. Simulation results show the efficacy of the proposed methods in terms of computational tractability and fuel-efficiency.
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Title: Modeling and Control Methods for Boundary Constrained Soft Robots
Creator: Zhou, Qiyuan
Date: 2021
Description: Soft and deformable robots have been an active field of research in the past few years. However, they are limited in that they cannot apply...
Show moreSoft and deformable robots have been an active field of research in the past few years. However, they are limited in that they cannot apply much force to an environment due to the limitations of the flexible materials from which they are made of. To help overcome this limitation, a new architecture named the Jamming and Morphing Enabled Bot Array (JAMoEBA) system was conceived. This system consists of a flexible outer membrane which encloses an interior composed of a granular medium. Active sub-units along the flexible outer membrane allow for actuation and locomotion of the system. The granular material coupled with the flexible outer membrane allows the robot to maintain the characteristics typically associated with soft robots (continuum, compliant, configurable). At the same time, the granular material is also able to undergo a solid phase transition with the application of pressure to the flexible outer membrane and allow the system to behave more like a rigid robot if needed. This allows for the robot system to exploit the desirable characteristics of both soft and rigid robots in its tasks.The purpose of this thesis is to offer a discussion and demonstration of various simulation methods for the physically accurate modeling of the JAMoEBA constrained boundary robotic system and to show some of the control methods which have been investigated within the selected modeling framework. Simulation methods based on Lennard-Jones (L-J) potentials, non-smooth contact dynamics (NSCD), as well as the discrete element methods based on complementarity (DEM-C) and penalty (DEM-P) conditions as implemented in the open source physics library Project Chrono are considered. Comparisons are made in the areas of physical accuracy, computational efficiency, and feature availability in the consideration of the best simulation method for the JAMoEBA system. Investigations of control strategies such as leader-follower and heuristics based approaches are carried out using the selected simulation method. Finally, a framework for self contained localization which relies on measurements from onboard sensors and linear Kalman filtering is tested within the simulation framework, and the effectiveness of approximating the shape of the JAMoEBA system using elliptical Fourier descriptors is shown.The main contributions made in this thesis are in the areas of suitable modeling methods, controls strategies, and localization techniques for the novel boundary constrained JAMoEBA soft robot architecture. The work done serves as a solid foundation for the future study of this novel soft robotic architecture due to the demonstration of successful methods for modeling, control, and localization of the system. The work presented is not meant to be a comprehensive or deep dive into any one specific area, but rather a jumping off point for future areas of research.
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Title: Modeling and Optimization of Power Plant Cooling Tower Systems Using Physics-Based and Neural-Network-Based Models
Creator: Salomon, Basile Clément Paul
Date: 2023
Description: Condensers and cooling towers are commonly used in steam power plants to condense the steam exiting the turbine and to recycle the condensed...
Show moreCondensers and cooling towers are commonly used in steam power plants to condense the steam exiting the turbine and to recycle the condensed-water into the boiler in a closed-loop system. These condensers typically use cooling water drawn from a water body (lake, river etc) to condense the steam. Cooling towers are used to lower the temperature of the warm water exiting the condenser. Since the steam condensation temperature plays an important role in the power plant efficiency, cool- ing tower performance which is limited by the wet-bulb temperature of the ambient air has been extensively studied. This work investigates the modeling of an enhanced cooling tower technology using a new pre-cooling and dehumidifying system (PDHS). This new system, based on a reversed Brayton cycle, is made out of a compressor, an air-cooled heat exchanger (HX), a heat and mass exchanger (HMX) and an expander. The goal of this PDHS concept is to pre-cool the air entering the cooling tower in order to improve its performance. In this work, a systems model has been developed. Thermodynamic models have been used for the compressor, the air-cooled heat exchanger and the expander. For the remaining components, i.e. the heat and mass exchanger, the cooling tower and the condenser, physics-based models have been developed and tested. Once tested and validated, each model can be integrated into the integrated PDHS-cooling tower-condenser system. Two different configurations of the PDHS have been considered in this thesis. In the open water loop configuration, the water in the HMX is obtained from the municipal water supply (or an alternate water source) and is released back to the source after exiting the HMX. In the closed water loop configuration, the water used to cool down the air in the HMX is being recirculated and cooled in the power plant cooling tower. The physics-based model of the PDHS developed in this work has been validated using results from an empirical model of the PDHS by GTI Energy. This first case study also shows how the PDHS can be used to save water in the cooling tower (CT). Indeed, when using the PDHS, a 37% reduction in the cooling tower evaporation rate can be observed when comparing to the baseline. This decrease in the CT evaporation rate is the main source of make-up water savings. Moreover, the water harvested by condensation in the PDHS can be redirected towards the CT, bringing another source of water savings. These two combined lead to an overall 46% decrease of the make-up water usage in the cooling tower. Another case study has been conducted on a 500 MW condenser unit. It shows that, under summer ambient conditions i.e. Ta,db = 35°C and φ = 47%, the PDHS can help the condenser restore its designed cooling load of 453 MW. Finally, using the physics-based model to create a dataset, an artificial neural network model of the PDHS has been developed to constitute a black box for the PDHS that would be able to predict with sufficient accuracy the condenser and HMX loads, the air conditions at the inlet of the CT and water temperature at both ends of the condenser and CT given the ambient air condition, the compressor pressure ratio and the water split between the condenser and the heat and mass exchanger.
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Title: Design and Fabrication of Battery-Operated Radiator Control (BORC) Utilizing 3D Printing Strategies
Creator: Riley, Christopher W.
Date: 2023
Description: The scope of this work aims to serve as a continuation of prior research focused on the “development and evaluation of an automatic steam...
Show moreThe scope of this work aims to serve as a continuation of prior research focused on the “development and evaluation of an automatic steam radiator control system or retrofitting legacy heating systems in existing buildings” (Syed Ali et al., 2020) by describing and testing the mechanical components of the developed controller in full detail. Other aspects of radiator efficiency are also explored. Primarily, this work aims to elaborate on the importance of material selection and mechanical properties of the design process. It also proposes initiative-taking solutions for the building’s energy recovery by monitoring the initial set up and focusing on certain details such as cardinal direction, thermal breaks, etc. These legacy systems are generally problematic when attempting to calculate energy efficiency, as a majority of radiator controls tend to be manual. Though there are comparable products within the European market, they cater to hot water systems and not steam, and in some instances require an internet bridge for operation (Tahersima et al., 2010). Since this is an extension of our earlier project, I will refer to it as Battery Operated Radiator Control (BORC) and the previous version as BERG’s Automated Radiator Control (ARC).
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Title: Data-Driven Methods for Soft Robot Control and Turbulent Flow Models
Creator: Lopez, Esteban Fernando
Date: 2022
Description: The world today has seen an exponential increase in its usage of computers for communication and measurement. Thanks to recent technologies,...
Show moreThe world today has seen an exponential increase in its usage of computers for communication and measurement. Thanks to recent technologies, we are now able to collect more data than ever before. This has dawned a new age of data-driven methods which can describe systems and behaviors with increasing accuracy. Whereas before we relied on the expertise of a few professionals with domain-specific knowledge developed over years of rigorous study, we are now able to rely on collected data to reveal patterns, develop novel ideas, and offer solutions to the world’s engineering problems. No domain is safe. Within the engineering realm, data-driven methods have seen vast usage in the areas of control and system identification. In this thesis we explore two areas of data-driven methods, namely reinforcement learning and data-driven causality. Reinforcement learning is a method by which an agent learns to increase its selection of ideal actions and behaviors which result in an increasing reward. This method was applied to a soft-robotic concept called the JAMoEBA to solve various tasks of interest in the robotics community, specifically tunnel navigation, obstacle field navigation, and object manipulation. A validation study was conducted to show the complications that arise when applying reinforcement learning to such a complex system. Nevertheless, it was shown that reinforcement learning is capable of solving three key tasks (static tunnel navigation, obstacle field navigation, and object manipulation) using specific simulation and learning hyperparameters. Data-driven causality encompasses a range of metrics and methods which attempt to uncover causal relationships between variables in a system. Several information theoretic causal metrics were developed and applied to nine mode turbulent flow data set which represents the Moehlis model. It was shown that careful consideration into the method used was required to identify significant causal relationships. Causal relationships were shown to converge over several hundred realizations of the turbulent model. Furthermore, these results match the expected causal relationships given known information of self-sustaining processes in turbulence, validating the method’s ability to identify causal relationships in turbulence.
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