Announcements

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:

Title: Modeling Syndromic Surveillance and Outbreaks in Subpopulations

Date: Monday, Dec. 16, 2019

Time: 12:00pm

Location: Martin Hall  EGR-2164

Committee Members:

Professor Jeffrey Herrmann

Professor Robert Gold
Assistant Professor Allison Reilly

Professor Linda Schmidt

Assistant Professor Monifa Vaughn-Cooke

Abstract:

This research is motivated by the need to assist resource limited communities by enhancing the use of syndromic surveillance (SyS) systems and data. Public health agencies and academic researchers have developed and implemented SyS systems as a pattern recognition tool to detect a potential disease outbreak using pre-diagnostic data. SyS systems collect data from multiple types of sources: absenteeism records, over the counter medicine sales, chief complaints, web queries, and more. It could be expensive, however, to gather data from every available source; subsequently, gathering information about only some subpopulations may be a desirable option. This raises questions about the differences between subpopulation behavior and which subpopulations’ data would give the earliest, most accurate warning of a disease outbreak.

To investigate the feasibility of using subpopulation data, this research will gather and organize SyS data by subpopulation (separated by population characteristics such as age or location) and identify how well the SyS data correlates to the real world disease progression. This research will study SyS how reports of Influenza-like-illness (ILI) in subpopulations represent the disease behavior. The first step of the research process is to understand how SyS is used in environments with varying levels of resources and what gaps are present in SyS modeling techniques. Various modeling techniques and applications are assessed, specifically the Susceptible Infected Recovered “SIR” model and associated modifications of that model. Through data analysis, well correlated subpopulations will be identified and compared to actual disease behavior and SyS data sets.  A model referred to as ModSySIR will be presented that uses real world community data ideal for ease of use and implementation in a resource limited community. The highest level research objective is to provide a potential data analysis method and modeling approach to inform decision making for health departments using SyS systems that rely on fewer resources.

Title: Phonon mediated thermal transport in transition-metal dichalcogenides

Date: Tuesday, Dec. 3rd, 2019

Time: 11:00am

Location: Martin Hall  EGR-2164

Committee Members:

Professor Peter Chung (Chair)

Professor Bao Yang

Professor Agis Iliadis (Dean’s Representative, Electrical and Computer Science Department)

Professor Abhijit Dasgupta

Professor Patrick McCluskey

Dr. Sina Najmaei (Special Member for the committee, ARL research scientist)

Title: New Methodology for Predicting Ultimate Capacity of One-Sided Composite Patch Repaired Cracked Aluminum Plate

Date: Wednesday, Dec 11^th, 2019

Time: 10:00am

Location: Martin Hall, EGR-2164

Committee:

Professor Hugh Bruck (Chair)

Professor Peter Chung

Professor Abhijit Dasgupta

Professor Teng Li

Professor Sung Lee (Dean’s Representative, Aerospace Department)

Abstract

Composite patch repairs are an alternative to traditional weld repair methods to address cracking in aluminum plates. Analytical and numerical design methods use linear elastic fracture mechanics (LEFM) that do not account for elastic-plastic crack tip behavior demonstrated in static tests of one-sided patch repaired ductile panels. This research used digital image correlation (DIC) and three-dimensional finite element analysis (FEA) with first order elements to study crack tip effects due to the one-sided composite patch applied to center crack tension (CCT) specimens loaded monotonically to failure. The measurable effects on crack tip behavior due to the composite patch were ultimate tensile load increase of more than 100% and a total achieved crack opening displacement (COD) increase of 20% over the unpatched behavior. Crack tip fracture behavior was found to be an intrinsic property of the aluminum and directly related to the COD independent of the one-sided composite patch. Increased capacity was related to accumulation of large-strain free surface area and through thickness volume ahead of the crack tip. Test data and numerical predictions correlated with measured load, strain, displacement fields, and J-integral behavior. Correlation of displacement fields with HRR and K fields established a state of small scale yielding prior to failure. Data and predictions indicated critical COD occurs when unpatched and patched large strain area is equivalent, which occurs before crack tip behavior transitions from small scale to large scale yielding and crack growth. Identifying a critical COD for both small and large scale one-sided patch repaired cracked ductile panels results in a predicted failure closer to the ultimate tensile load and 80% greater than predicted with LEFM methods.

Observations and predictions demonstrated in this research resulted in three scientific contributions: (1) development of criteria to determine crack growth in cracked ductile panels repaired with a one-sided composite patch using a critical COD, (2) development of a three-dimensional FEA to study development of the plastic zone and evolution of the large-strain region ahead of the crack tip, and (3) development of a numerical methodology to predict ultimate tensile load capacity of cracked ductile panels repaired with a one-sided composite patch