Defenses

Title: Droplet and Bubble Measurements in Turbulent Free-Surface Flows

Defense Date and Time: Friday, April 17 starting at 10:00 am.

Important Note about Connectivity: If any prospective audience member is having issues connecting to the Zoom Meeting via the link provided, please email Professor James Duncan (duncan@umd.edu), who can send you a personal invitation link.

Committee Members:

Professor James H. Duncan, Chair/Advisor

Professor Kenneth Kiger

Associate Professor Johan Larsson

Emeritus Professor Peter Bernard

Associate Professor Anya Jones, Dean’s Representative

Abstract:

In this dissertation, the generation and dynamics of drops and bubbles in breaking waves and turbulent free-surface boundary layer shear flows, respectively, are studied in laboratory scale experiments. In the drop experiment, breaking waves are generated by a programmable wavemaker and measurements of breaker profile evolution and spatio-temporal distribution of drops are reported. The drop and breaking profile measurements are used synergistically to relate drop production to breaker characteristics and sub-processes in two related studies. In the first study, spray generation mechanisms by a weak plunging breaker are explored. Three distinct time zones of drop production are found, first when the jet impacts the free surface, second when the large air bubbles trapped by the plunging jet impact reach the surface and pop, and third when smaller bubbles reach the surface later in the breaking process and pop. In the second study, drop production by three plunging breakers is correlated to mean wave characteristics such as surface features, plunging jet impact velocity, and wave crest speed. The number of drops produced per breaking event is found to increase with breaker intensity. The relative importance of breaker intensity on breaking sub-processes, identified in the first study, is reported.

In the bubble experiment, air entrainment in a turbulent free-surface boundary layer shear flow is studied in a laboratory-scale experimental facility. The boundary layer is created by a horizontally moving surface-piercing stainless steel belt that travels in a loop between two rollers. One length of the belt between the two vertically oriented rollers is exposed to water (with a free surface). The belt accelerates suddenly from rest until reaching constant speed and creates a temporally evolving free-surface boundary layer analogous to the spatially evolving boundary layer that would exist along a surface-piercing towed flat plate. Air entrainment mechanisms and bubble statistics like bubble size, number, and speed, are reported and qualitatively compared to direct numerical simulations of a similar problem conducted by a different research group.

Title: “The Dynamic Characterization of Impact-mitigating Materials Using Electromagnetic Velocity Gauges”

Date: Friday, April 10th, 2020

Committee Members:

Title: “Modeling And Simulation Of Novel Medical Response Systems For Out-Of-Hospital Cardiac Arrest”
Date: Monday, April 20th, 2020

Committee Members:

Title: “Effects of Rest Time and Temperature on Graphite – LiCoO2 Battery Degradation”  

Date: Friday, April 17th
Time: 11:00am
Link: https://umd.zoom.us/j/380744735

Committee Members:

• Professor Michael Pecht, Chair/Advisor
• Professor Abhijit Dasgupta
• Professor Peter Sandbord
• Professor Mark Fuge
• Professor Eric Wachsman, Dean’s Representative

Abstract:

Lithium-ion batteries are used as energy storage devices in a variety of applications ranging from small portable electronics to high-energy/high-power electric vehicles. These batteries degrade and lose their capacity, defined as the amount of charge the battery holds, as a result of charge–discharge operations and various degradation mechanisms. Degradation of lithium-ion batteries is affected by many operational and environmental conditions, including temperature, discharge and charge current, and depth of discharge. Another factor, which has not been given due attention, is the rest period after full charge during the cycling operation of the batteries.  This study investigates the effects of rest period after charge operation on the degradation behavior of graphite–LiCoO₂ pouch batteries under three different ambient temperatures. Battery degradation has been quantified in terms of the capacity fade and shifts in the peaks of the differential voltage curves which also provide inferences about the individual electrode degradation.  Relation between rest time and battery state of charge has been established to explain the capacity fade trends of batteries. A capacity fade trend modeling analysis has been conducted and validated using experimental data of graphite-LiCoO₂ pouch batteries from multiple sources with inactive material variations. The degradation mechanisms have been investigated using differential voltage analysis (DVA), X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDX) techniques. Applicability of rest time as an accelerating stress factor for Li-ion battery testing has also been discussed.   

Title: “Experimental Investigation into the Heat Transfer Mechanism of Oscillating Heat Pipes using Temperature Sensitive Paints.”

Date: Wednesday, April 08th
Time: 11:00am

Committee Members:
Professor Jungho Kim, Chair
Professor Reinhardt Radermacher
Professor Bao Yang

Title: DYNAMICS AND HAZARDS OF CASCADING FAILURE IN CELL ARRAYS: ANALYSIS, PASSIVE MITIGATION, AND ACTIVE SUPPRESSION

Defense date and time: Friday- Feb 28, 2020 at 10 am

Location: A. James Clark Bldg.  (AJC 2132)

Committee members:

Title: Deep Adversarial Approaches in Reliability 

Date: Tuesday, December 17th

Time: 1pm

Location: AV Williams 2460 ECE Conference Room 

Committee Members:

Professor Mohammad Modarres, Chair

Associate Professor Enrique Lopez Droguett

Assistant Professor Mark Fuge

Assistant Professor Katrina Groth

Professor Balakumar Balachandran

Professor Mohamad Al-Sheikhly (Dean’s Representative)

Abstract:

Reliability engineering has long been proposed with the problem of predicting failures using all the available data.  As modeling techniques have become more sophisticated, so too have the data sources from which reliability engineers can draw conclusions.  The Internet of Things (IoT) and cheap sensing technologies have ushered in a new expansive set of multi-dimensional big machinery data in which previous reliability engineering modeling techniques remain ill-equipped to handle.  Therefore, the objective of this dissertation is to develop and advance reliability engineering research by proposing four comprehensive deep learning methodologies to handle these big machinery data sets. In this dissertation, a supervised fault diagnostic deep learning approach with applications to the rolling element bearings incorporating a deep convolutional neural network on time-frequency images was developed. A semi-supervised generative adversarial networks-based approach to fault diagnostics using the same time-frequency images was proposed.  The time-frequency images were used again in the development of an unsupervised generative adversarial network-based methodology for fault diagnostics.  Finally, to advance the studies of remaining useful life prediction, a mathematical formulation and subsequent methodology to combine variational autoencoders and generative adversarial networks within a state-space modeling framework to achieve both unsupervised and semi-supervised remaining useful life estimation was proposed.

All four proposed contributions showed state of the art results for both fault diagnostics and remaining useful life estimation. While this research utilized publicly available rolling element bearings and turbofan engine data sets, this research is intended to be a comprehensive approach such that it can be applied to a data set of the engineer’s chosen field. This research highlights the potential for deep learning-based approaches within reliability engineering problems.

Title: EIT BASED PIEZORESISTIVE TACTILE SENSORS: A SIMULATION STUDY

ABSTRACT: