Announcements

The Graduate School has stipulated that starting on August 30, 2021, all thesis and dissertation defenses must occur fully in person unless an exemption has been granted by the Graduate School. The defending student and committee must be physically present in the examination room during the entire defense and during the committee’s private deliberations following the examination. If either the student or any committee member wishes to request remote participation, they will need to first obtain a waiver from the Graduate School (using the link provided later in this document). 

For students planning to complete their degree program this semester, we are sending some additional guidance for the defense. The steps to follow are below.

1. Please email the Graduate Office (megrad@umd.edu) two weeks prior to your defense with the information below. If a student or committee member is requesting a waiver for remote participation, the graduate office should be notified at least three weeks prior to the defense: 

• List of committee members
• Abstract
• Day/Time
• Room/Location 
  • Notify the Graduate Office if you need help reserving a room

2. All thesis and dissertation committee members, including members external to UMD, will be able to sign the Report of the Examining Committee electronically in Adobe Sign. A request to initiate the electronic Report of Examining Committee (REC) form must be made at least 10 business days before the scheduled defense. The committee chair will submit a request for the electronic REC here. In order to complete the request:

• The committee chair (or designee) must have the student’s information, including name and UID, as well as all committee member names and email addresses.
  
• The email address provided for each committee member will serve as authentication when accessing the electronic REC. Special members who do not have a UMD login will no longer have any issues signing the form electronically.

3. The Graduate Office will still be sending out the Middle States Assessment Form and Electronic Publication Form by email to the committee chair prior to the defense.

4. It is very important to note that it will be extremely difficult to find and obtain approval for an emergency, last-minute replacement faculty member for any committee.  It is recommended that you send multiple reminders to all committee members starting at least three days prior to the defense date and ask them to re-confirm their attendance.

5. In light of the ongoing COVID-19 pandemic, the Graduate School will consider exemptions to the remote defense policy for the Fall 2021 semester. These exemptions will include unusual circumstances such as:

• Students or advisors residing abroad who are unable to return to campus due to travel restrictions and/or visa processing issues.
  
• Students or committee members with health conditions that prohibit an in-person defense, in compliance with the University of Maryland Disability and Accessibility Policy and Procedures and University guidelines for Fall 2021.

To request a remote participation of a committee member during the Fall 2021 semester, you can complete this form starting on August 1: go.umd.edu/gs-remote-def. Please allow ten business days for a resolution that is not an emergency, and note that a remote defense cannot occur without prior approval. 

Remote Defense Request / Procedures 

Ahead of the defense, we encourage you to review the policy on remote participation in a thesis defense or a dissertation defense.  When you make a request, you will be asked to acknowledge these policies.

In light of the ongoing COVID-19 pandemic, the Graduate School will consider exemptions to the in-person defense policy on an individual basis. Remote participation by the student or committee chair, or Dean’s Representative will be permitted in exceptional and compelling circumstances such as:

• Students or advisors residing abroad who are unable to return to campus due to travel restrictions and/or visa processing issues.
  
• Students or committee members with health conditions that prohibit an in-person defense, in compliance with the University of Maryland Disability and Accessibility Policy and Procedures and University guidelines for Fall 2021.

To request the remote participation of one or all participants, please complete this form go.umd.edu/remotedrequest or visit our website for more details.

• Please allow ten business days for a resolution that is not an emergency, and note that remote participation cannot occur without prior approval from Graduate School. 

Title: NEXT GENERATION HEAT PUMP SYSTEM EVALUATION METHODOLOGIES

Author: Hanlong Wan

Date/Time: 9/2/21 | 2:00PM-4:00PM

Location/Room: EGR4164B Martin Hall (CEEE’s conference room).

Advisory Committee:
Prof. Reinhard K. Radermacher, Chair
Prof. Peter Sunderland, Dean’s Representative
Research Prof. Yunho Hwang
Prof. Nikhil Chopra
Prof. Jelena Srebric
Prof. Bao Yang

Abstract:
Energy consumption of Heat Pump (HP) systems plays a significant role in the world residential building energy sector. The conventional HP system evaluation method focused on the energy efficiency during a given time scale (e.g., hourly, seasonally, or annually). Nevertheless, these evaluation methods or test metrics are unable to properly reflect the thermodynamic characteristics of the system (e.g., the start-up process). In addition, previous researchers typically conducted HP field tests within one year period. Only limited studies conducted the system performance over multiple years. Furthermore, the climate is changing faster than previously predicted beyond the irreversible and catastrophic tipping point. HP systems are the main contributor to global warming but also can be a part of the solution. A holistic evaluation of the HP system’s global warming impact during its life cycle needs to account for the direct refrigerant greenhouse gas (GHG) emissions, indirect fossil fuel GHG emissions and embodied equipment emissions. This dissertation leverages machine learning, deep learning, data digging, and Life Cycle Analysis (LCA) approaches to develop next generation HP system evaluation methodologies with three thrusts: 1) field test data analysis, 2) data-driven modeling, and 3) Enhanced Life Cycle Climate Performance (En-LCCP) analysis. This study found that first, time-average performance metrics can save time in extensive data calculation, while quasi-steady-state performance metrics can elucidate some details of the studied system. Second, deep-learning-based algorithms have higher accuracy than conventional modeling approaches and can be used to analyze the system’s dynamic performance. However, the complicated structure of the networks, numerous parameters needing to be optimized, and longer training time are the main drawbacks of these methods. Third, this dissertation improved current environmental impact evaluation method considering ambient conditions variation, local grid source structure, and next-generation low-GWP refrigerants, which led the results closer to reality and provided alternative methods for limited-data cases. Future work could be studying the uncertainty within the deep learning networks and a general process for modeling settings. People may develop a multi-objective optimization model for HP system design considering both the LCCP and cost.

Title: Analysis and Optimization of Input Trajectories for Parameter Identifiability in Multi-Compartment Dynamic System Models

Author: Mahsa Doosthosseini

Date/Time: 08/17/2021  12:30 pm-2:30 pm

Examining Committee:
Dr. Hosam K. Fathy  (Chair/Advisor)
Dr. Alisa Morss Clyne (Dean’s representative)
Dr. Joseph S. Friedberg
Dr. Jin-Oh Hahn
Dr. Simona Onori
Dr. Monifa Vaughn-Cooke

Zoom Link: https://umd.zoom.us/j/6260893257

Abstract:
This dissertation examines the interconnected problems of (i) analyzing and (ii) optimizing the impact of a multi-compartment dynamic system’s input history on the identifiability of its parameters. Identifiability refers to the feasibility and accuracy with which a system’s parameters can be uniquely estimated from input-output test data. The shape of a system’s input history versus time often affects identifiability. This makes it possible to optimize this input shape for identifiability, in a manner analogous to the use of a cardiac stress test to better diagnose patients with heart disease.

The research in this dissertation makes four contributions to the literature, motivated by the following four practical research questions. First, is it possible to characterize CO2 gas transport dynamics in a laboratory animal where the peritoneal perfusion of a perfluorocarbon (PFC) is used as a potential treatment for hypercarbia? Second, how does the shaping of chemotherapeutic treatment affect the accuracy with which drug resistance dynamics can be estimated in a partially drug-resistant cancerous tumor? Third, can the dynamic cycling of a lithium-sulfur (Li-S) battery be tailored to maximize the accuracy with which its parameters are estimated? Finally, can Pontryagin methods from optimal control theory yield fundamental insights into the structure of the ambient temperature cycling trajectory that maximizes the identifiability of a lithium-ion battery model’s thermal parameters?

In addressing the above practical research questions, this dissertation navigates a progression of four fundamental topics in the field of multi-compartment dynamic system parameter identification and identifiability. Specifically, the dissertation’s examination of peritoneal CO2 gas transport dynamics highlights and motivates the importance of analyzing multi-compartment dynamic system identifiability. The subsequent examination of the identifiability of drug resistance dynamics in cancerous tumors highlights the degree to which input shaping can negatively affect parameter identifiability. In contrast, the examination of parameter identifiability for Li-S batteries highlights the potential of input shaping to improve identifiability significantly for multi-compartment systems. Finally, the dissertation’s examination of thermal battery parameter identifiability highlights the degree to which the fundamental tool of Pontryagin analysis can help gain insight into optimal input shaping for identifiability. In summary, the work in this dissertation explores a progression of fundamental topics in the area of dynamic system parameter identifiability while highlighting the broad applicability of this area to different practical domains.

Title: DEGRADATION ANALYSIS OF LITHIUM-ION BATTERIES WITH KNEE POINTS

Author: Weiping Diao 

Day/Time: 08/26/2021 10:00 AM – 12:00 PM EST

Examining Committee: 
Dr. Michael Pecht, Chair
Dr. Chunsheng Wang, Dean’s Representative
Dr. Michael Azarian
Dr. Hosam Fathy
Dr. Paul Albertus
Dr. Stanislav I. Stoliarov

Abstract: 

Commercialization of lithium-ion batteries has enabled applications ranging from portable consumer devices to high-power electric vehicles to become commonplace. The capacity, which has been used to determine if lithium-ion batteries have reached the end of life, decreases over usage (cycling) and storage (rest). An increase in the capacity fade rate after some charge-discharge cycles is often observed in lithium-ion batteries. The phenomenon has been described as a knee point and can lead to a shorter life than expected. 

Although the general degradation modes, mechanisms, and effects on lithium-ion batteries are known, the dominant degradation modes and mechanisms for the knee point phenomenon have yet to be determined. Understanding why and when the knee point will appear on the capacity fade curves is valuable to battery manufacturers and device companies to predict or mitigate the knee point. This study presents the degradation behavior with knee point identification algorithms, accelerated testing and capacity modeling methods to assess the degradation and predict the knee point, and experimental analysis which identify the dominant degradation modes and mechanisms. 

Title: BAYESIAN METHODOLOGY FOR RELIABILITY GROWTH PLANNING, PROJECTION AND TRACKING FOR DISCRETE-USE SYSTEMS UTILIZING MULTI-SOURCE DATA

Author: Paul Nation

Day/Time: Monday, August 23rd from 1:00 pm to 3:00 pm

Examining Committee:
Professor Mohammed Modarres, Chair and Advisor
Professor Aris Christou
Assistant Professor Katrina Groth
Professor Jeffrey Hermann
Doctor Martin Wayne, Special Advisor
Professor Gregory Baecher, Dean’s Representative

Abstract:

This research aims to present a Bayesian model for reliability growth planning of discrete-use systems suitable for use throughout all stages of system development. Traditional discrete-use models for reliability growth utilize test data from individual test events at the current stage of development. They often neglect the inclusion of historical information from previous tests, testing similar systems or elicitation of expert opinion. Examining and using data attained from prior bench analyses, sub-system tests or user trial events often fails to occur or is conducted poorly. Additionally, no current approach permits the probabilistic treatment of the initial system reliability at the commencement of the test program in conjunction with the management variables that may change throughout the execution of the test plan.

This research contributes to the literature in several ways. Firstly, a new Bayesian model is developed from first principles which considers the uncertainty surrounding discrete-use systems under delayed and arbitrary corrective action regimes to address failure modes. This differs from current models that fail to address the randomized times that corrective actions to observed failure modes may be implemented depending on the selected management strategy. Some current models only utilize the first observed failure on a test, meaning a significant loss of information transpires as subsequent failures are ignored. Additionally, the proposed strategy permits a probabilistic assessment of the test program, accounting for uncertainty in several management variables.

The second contribution seeks to extend the Bayesian discrete-use system model by considering aspects of developmental, acceptance and operational testing to allow the formulation of a holistic reliability growth plan framework that extends over the entire system lifecycle. The proposed approach considers the posterior distribution from each phase of reliability growth testing as the prior for the following growth test event. The same methodology is then employed using the posterior from the final phase of reliability growth testing as the prior for acceptance testing. It then follows that the acceptance testing posterior distribution forms the prior for subsequent operational testing through a Bayesian learning method. The approach reduces unrealistic and unattainable reliability demonstration testing that may result from a purely statistical analysis. The proposed methodology also permits planning for combined developmental and acceptance test activities within a financially constrained context.

Finally, the research seeks to define an approach to effectively communicate developmental system reliability growth plans and risks to decision-makers. Reliability professionals, like many of their other specialist science peers, are fantastic communicators – with other reliability practitioners. However, when reliability professionals move beyond their world to make an impact, they often face the same challenge scientists from every discipline face – the difficulties of clearly communicating science to their audience. The research presents approaches that utilize the vital communication, influence and emotional intelligence skills that are necessary for motivating decision-makers and colleagues who can assist in supporting and implementing reliability engineering efforts.

Title: EXPERIMENTAL CHARACTERIZATION AND MODELING OF FLAME HEAT FEEDBACK AND OXIDATIVE PYROLYSIS FOR SIMULATION OF BENCH SCALE FIRE TESTS

Author: Conor McCoy

Date/Time: Thursday, August 5th | 9:30am

Examining Committee:
Professor Stanislav I. Stoliarov, Chair
Professor Mohamad Al-Sheikhly, Dean’s Representative
Dr. Richard E. Lyon
Professor Arnaud Trouvé
Professor Bao Yang

Abstract: Two important bench scale fire tests, the cone calorimeter test and UL-94V, were characterized experimentally to allow for predictions using a numerical pyrolysis solver, ThermaKin2Ds with pyrolysis parameter sets. Flame heat feedback was measured in cone calorimeter tests for several polymers to develop a generalized flame model. Flame heat flux was measured in the center and near one side and was found to be 11–23 kW m^-2 and 32–49 kW m^-2, respectively. Based on the difference in measured heat flux, a center zone and a side zone were defined and separate models developed. The final model was an area-weighted combination of the center and side zone simulations. Heat release rate data were predicted well by the final model. Ignition times for low irradiation were not predicted well initially but a correction was made to account for the effect of oxygen. The UL-94V test required characterization of the flame heat feedback but also of the burner flame (temperature, heat flux, and oxygen content). UL-94V tests were performed using polymers of different flammability ratings to evaluate the model; some samples had insulated sides to investigate edge effects. Additional UL-94V tests performed with an embedded heat flux gauge served to measure polymer flame heat feedback. All UL-94V tests were recorded on video using a 900-nm narrow-band filter to focus on emissions from soot for tracking flame length over time. Flame heat fluxes of insulated PMMA samples confirmed a previously developed wall flame submodel, while non-insulated PMMA samples had significantly greater heat fluxes; the wall flame submodel was scaled accordingly. Burner flame oxygen content was measured to be about 5 vol% and was found to enhance decomposition of two materials; oxidation submodels were then developed accordingly. Overall, the model predicted flame spread on insulated UL-94V samples reasonably well but significantly underpredicted the results on non-insulated samples. Discrepancies were attributed to burning and spread on the edges which were not modeled explicitly. Finally, given the importance of oxidation on predictions of ignition time, oxidative pyrolysis was studied both in mg-scale and gram-scale pyrolysis experiments. Kinetic parameters were first developed based on inverse analysis of TGA tests in atmospheres of varied oxygen content. Two models were developed: a surface reaction model and a volumetric model. Mass flux data from gram-scale gasification tests were used to evaluate the models. The anaerobic model gave the best predictions of mass flux for 15 kW m^-2 gasification tests but the oxidative models gave better predictions for the 25 kW m^-2 gasification tests. The volumetric model gives better predictions unless mass transport of oxygen is considered in which case, the surface model gives better predictions.