Defenses

Author: Harsimranjit Singh

Date/Time/Location of defense: 07/20/2023, 10:00 am to 12:00 pm, Rm. 2164 in Martin Hall
Zoom link: https://umd.zoom.us/j/4473163587?pwd=ZDN6aDhQZGlWalJncDlsSEJIc1dxUT09

Committee Members:

-Dr. Michael Ohadi, Chair

-Dr. Bao Yang

-Dr. Patrick McCluskey

-Dr. Amir Riaz

-Dr. Ratnesh Tiwari

-Dr. Christopher Cadou, Dean’s Representative

Title: THERMAL MANAGEMENT OF HIGH-HEAT FLUX ELECTRONICS WITH INTERLACED FILM EVAPORATION AND ENHANCED FLUID DELIVERY SYSTEM (iFEEDS)

Abstract:

Author: Changsu Kim

Date/Time/Location of Defense: 7/21/2023 at 12:00 pm in EGR-2162

Committee:

-Professor Bongtae Han (Chair)

-Professor Patrick McCluskey

-Professor Peter Sandborn

-Professor Michael Osterman

-Professor Sung W. Lee (Dean’s Representative)

Title: Measurements of Effective Cure Shrinkage of Epoxy Molding Compound and Induced In-line Warpage during Molding Process

Abstract: Cure shrinkage accumulated only after the gel point is known as effective cure shrinkage (ECS), which produces residual stresses inside molded components.  The ECS of an epoxy-based molding compound (EMC) is measured by an embedded fiber Bragg grating (FBG) sensor.  Under a typical molding condition, a high mold pressure inherently produces large friction between EMC and mold inner surfaces, which hinders EMC from contracting freely during curing.  A two-stage curing process is developed to cope with the problem.  In the first stage, an FBG sensor is embedded in EMC by a molding process, and the FBG-EMC assembly is separated from the mold at room temperature.  The molded specimen is heated to a cure temperature rapidly in the second stage using a constraint-free curing fixture.  The ECS of an EMC with a filler content of 88 wt% is measured by the proposed method, and its value is 0.077%.  The measured ECS can be used to predict the warpage caused by molding processes.  The validity of the prediction can be verified only by measuring the warpage during molding.  A point-based measurement technique utilizing uniquely-generated multiple beams and binarization-based beam tracing method is developed to cope with the challenges associated with the warpage measurement during molding.  The proposed method is implemented successfully to measure the warpage of a bimaterial disk that consists of aluminum and EMC as a function of time during molding process.  Measurements are repeated to establish the measurement accuracy of the proposed method.

Author: Lingxi Kong

Defense date/time/location: July 18th, 2023 at 2:30pm in EGR-2164

Committee Members:

-Prof. Michael G. Pecht (Chair)

-Prof. Lourdes G. Salamanca-Riba (Dean’s Representative)

-Prof. Hosam K. Fathy

-Prof. F. Patrick McCluskey

-Prof. Stanislav I. Stoliarov

-Prof. Michael H. Azarian

Title of Dissertation: “In-situ Investigation of Lithium Dendrite Growth and Its Interactions with a Polymer Separator in a Lithium Metal Cell”

Abstract: Lithium dendrites are metallic structures that initiate and grow inside a lithium battery during charging. Lithium dendrite growth can negatively affect battery cycle life and safety. Observing the dendrite growth process and revealing its interaction with other components is necessary to improve battery safety. This study uses a transparent optical cell to directly observe the dendrite growth process, explore the lithium dendrite growth modes under various current densities, evaluate the interactions between the dendrite and separator, and explore the effect of electrolyte additives on dendrite growth behavior. The dendrite growth under different current densities showed the transition of dendrite morphologies from a dense structure to a porous structure. The examination of the dendrite-separator interaction regions showed that dendrites can deform and penetrate the separator. We show that additives can enhance the uniformity of lithium dendrite distribution compared with the dendrite formed in the electrolyte without additives.

Author: Pattanun Chanpiwat

Date, time, & location:

• June 20, 2023, between 10:00-12:00 EDT (UTC-4h)
• 100% online (Zoom link: https://umd.zoom.us/j/6321422042)

Committee members:

• Professor Steven A. Gabriel, Chair/Advisor, Department of Mechanical Engineering
• Professor Qingbin Cui, Dean’s Representative, Department of Civil and
  Environmental Engineering
• Professor Hosam K. Fathy, Dept. of Mechanical Engineering
• Associate Professor Mark D. Fuge, Department of Mechanical Engineering
• Associate Professor Fabricio Oliveira, Department of Mathematics and
  Systems Analysis, Aalto University, Finland
• Dr. Maxwell Brown, U.S. National Renewable Energy Laboratory

Title: Three Essays on Optimization, Machine Learning, and Game Theory in Energy

Abstract:

This dissertation comprises three main essays that share a common theme: developing methods to promote sustainable and renewable energy from both the supply and demand sides, from an application perspective.

The first essay (Chapter 2) addresses demand response (DR) scheduling using dynamic programming (DP) and customer classification. The goal is to analyze and cluster residential households into homogeneous groups based on their electricity load. This allows retail electric providers (REPs) to reduce energy use and financial risks during peak demand periods. Compared to a business-as-usual heuristic, the proposed approach has an average 2.3% improvement in profitability and runs approximately 70 times faster by avoiding the need to run the DR dynamic programming separately for each household.

The second essay in Chapter 3 analyzes the integration of renewable energy sources and battery storage in energy systems. It develops a stochastic mixed complementarity problem (MCP) for analyzing oligopolistic generation with battery storage, taking into account both conventional and variable renewable energy supplies. This contribution is novel because it considers multi-stage stochastic MCPs with recourse decisions. The sensitivity analysis shows that increasing battery capacity can reduce price volatility and variance of power generation. However, it has a small impact on carbon emissions reduction. Using a stochastic MCP approach can increase power producers’ profits by almost 20 percent, as proposed by the value of stochastic equilibrium solutions. Higher battery storage capacity reduces the uncertainty of the system in all cases related to average delivered prices. However, investing in enlarging battery storage has diminishing returns to producers’ profits at a certain point restricted by market limitations such as demand and supply or pricing structure.

The third essay (Chapter 4) proposes a new version of the stochastic dual dynamic programming (SDDP) algorithm that considers uncertainties in the electricity market, such as electricity prices, residential photovoltaic (PV) generation, and loads. The SDDP model optimizes the scheduling of battery storage usage for sequential decision-making over a planning horizon by considering predicted uncertainty scenarios and their associated probabilities. After examining the effects of battery storage on SDDP models, the results show that using battery storage in the SDDP model improves the average objective function values (i.e., costs) by approximately 32% compared to a model without it. The results also indicate that the mean objective function values at the end of the first stage of the proposed SDDP model with battery storage and the deterministic LP model equivalent (with perfect foresight) with battery storage differ by less than 30%.

The models and insights developed in this dissertation are valuable for facilitating energy policy-making in our rapidly evolving industry. Furthermore, these contributions can advance computational techniques for solving energy-policy problems.

Author: Lingzhe Wang
Date and time: June 23th, 2023, at 2:00 PM

Location: EGR-2162, DeWALT meeting

Zoom link: https://umd.zoom.us/j/2731945012
Zoom meeting ID: 273 194 5012

Committee:

Professor Jelena Srebric, Chair/Advisor

Professor Hosam Fathy

Professor Jin-Oh Hahn

Professor Bao Yang

Professor Donald Milton, Dean’s Representative

Title: Occupant-oriented Indoor Environmental Controls in Public Spaces

Abstract: The indoor environment has significant impacts on the health and comfort of building occupants. In addition, occupant behavior can affect building energy consumption. It is essential to consider actual occupant needs when controlling indoor environmental systems. To provide a healthy, comfortable, and energy-efficient indoor environment, the present dissertation presents a comprehensive research framework for occupant-oriented indoor environmental controls by conducting (i) air quality characterization in occupant breathing zone, (ii) data-driven thermal comfort identification, and (iii) simultaneous air quality, thermal comfort, and building energy controls.
For air quality characterization in occupant breathing zone, the present dissertation characterized aerosol plumes associated with the risk of airborne virus transmission to investigate the occupant requirements for air quality controls. The study considered both the aerosol plume source strength and convective transport capability by conducting experiments with 18 human subjects. The source strength was characterized by the source aerosol emission rate, and the convective transport capability was characterized by the plume influence distance. The performances of multiple mitigation strategies were tested. The findings show that controlling the air quality in the breathing zone is crucial for protecting occupants from getting infected by airborne infectious microorganisms.

For data-driven thermal comfort identification, the present dissertation developed data-driven models to predict actual occupant thermal comfort based on physiological variables. By incorporating multiple HRV indices along with wrist temperatures, the performance of the models was significantly improved, achieving more than four times the accuracy compared to models based solely on wrist temperatures. This highlights the crucial role of HRV as physiological variables in accurately predicting thermal comfort. With the F1 score, the performance evaluation index of the developed machine learning thermal comfort model, exceeded the value of 0.90, this investigation provides a reliable thermal comfort prediction method, which could be used in actual building occupant-oriented controls.

For simultaneous air quality, thermal comfort, and building energy controls, this dissertation developed a wearable micro air cleaner and deployed the extremum seeking control. The wearable micro air cleaner achieved 60% – 70% protective efficiency for both nasal and mouth breathing. Importantly, unlike current mitigation methods such as masks, this device allows users to be thermal comfortable when the indoor air temperature is above 25 °C. Additionally, this dissertation implemented the extremum seeking control to balance the trade-offs between individual thermal comfort preferences and building energy consumption in real-time. This control method successfully achieved energy savings of up to 22% compared to a constant temperature setpoint of 24 °C. The developed framework for simultaneous air quality, thermal comfort, and building energy controls holds great potential in providing building occupants with a healthy, comfortable, and energy-efficient indoor environment.

Author: Mohamed Mohsen Ahmed

Title: Development of a Lagrangian-Eulerian Modeling Framework to Describe Thermal Degradation of Porous Fuel Particles in Simulations of Wildland Fire Behavior at Flame Scale

Date and time: May 26, 2023, at 9:00 AM

Location: Fire and Risk Alliance Conference Room, 3106 J.M. Patterson Building

Zoom link: https://umd.zoom.us/j/7685207098

Zoom meeting ID: 768 520 7098

Committee Members:

Dr. Arnaud Trouvé, Chair/Advisor

Dr. James Baeder, Dean’s Representative

Dr. Mark Finney

Dr. Johan Larsson

Dr. Stanislav Stoliarov

Dr. Peter Sunderland

The dynamics of wildland fires involve multi-physics phenomena occurring at multiple scales ranging from sub-millimeter scale representative of small vegetation particles, to several kilometers representative of meteorological scales. The objective of this research is to develop an advanced physics-based computational tool for detailed modeling of the coupling between the solid-phase and the gas-phase processes that control the dynamics of flame spread in wildland fire problems. This work focuses on a modelling approach that resolves processes occurring at flame and vegetation scales, i.e., the formation of flammable vapors from the porous biomass vegetation due to pyrolysis, the subsequent combustion of these fuel vapors with ambient air, the establishment of a turbulent flow because of heat release and buoyant acceleration, and the thermal feedback to the solid biomass through radiative and convective heat transfer. A modeling capability called PBRFoam is developed in this dissertation based on the general-purpose Computational Fluid Dynamics (CFD) library OpenFOAM and an in-house Lagrangian Particle Burning Rate (PBR) model that treats drying, thermal pyrolysis, oxidative pyrolysis and char oxidation using a one-dimensional porous medium formulation. This modeling capability allows description of fire spread in vegetation fuel beds comprised of mono- or poly- dispersed porous particles including thermal degradation processes occurring during both flaming and smoldering combustion.

The modeling capability is calibrated for cardboard and pine wood using available micro- and bench-scale experimental data obtained. Then it is applied to simulate the fire spread across the idealized fuel beds made of laser-cut cardboard sticks that have been studied experimentally at the Missoula Fire Sciences Laboratory. The simulations are conducted with prescribed particle and environmental properties (i.e., fuel bed height, fuel bed packing, particle size, moisture content, and wind velocity) that match the experimental conditions. The model is first validated against experimental measurements and observations such as the rate of spread of the fire and the flame residence time. The modeling capability is then used to provide insights into local as well as global behavior at individual particle level and at the fuel bed level with variations of the fuel packing.

The modelling capability is also applied to simulations of fire spread across idealized vegetation beds corresponding to mixed-size cylindrical-shaped sticks of pine wood under prescribed wind conditions. Depending on the particle size distribution, the simulations feature complete fuel consumption with successful transition from flaming to smoldering combustion or partial fuel consumption with no or limited smoldering. These simulations show the existence of either a mixed mode of heat transfer through convection and radiation for small particles or a radiation dominant heat transfer mode for larger particles. The results are interpreted using a novel diagnostic called the Pseudo Incident Heat Flux (PIHF) and 2-D maps that characterize single particle response as a function of the PIHF and the flame residence time.

Author: Jacob Welch

Date/Time/Location: April 24^th, 1:30-3PM, ERF 1207.

Title of Thesis: Spectral Methods of Modeling and Calculating Fatigue in Electronic Interconnects

List of Committee Members:

Abhijit Dasgupta (Chair)

Michael Osterman

Avik Dutt

Abstract:

The purpose of this thesis is to provide a predictive estimate of fatigue damage accumulation in PWA interconnects using purely frequency-domain (spectral) information such as the PSD of the input excitation.  This method is used to predict fatigue damage accumulation rate in the critical interconnects, for broad-band random vibration excitation on a PWA with LQFP components.  Results are compared with those obtained from a direct time-domain approach.  The modeling is achieved using a two-stage global-local modeling process in Abaqus, where the dominant mode shapes of the dynamic global model are applied to a 3D quasi-static local model, using multi-point constraint equations.  The transfer function from PWB strain to interconnect strain is then estimated for each of the dominant modes.  The PWB response PSD from the global model is then used in conjunction with these strain transfer functions, to estimate the PSD of the strain response in the critical lead/solder for each dominant mode, and summed over each mode, and then used to reconstruct a pseudo-time history of lead strain.  This pseudo time history is  then used to perform a Rainflow cycle count. From this point established methods are able to be used to estimate fatigue damage accumulation using either the cyclic count or with competing methods such as Rayleigh method or Dirlik method.  Results are compared to corresponding fatigue damage estimates from a time-domain analysis method.

Author: Drew Hohenhaus

Title: Prediction and Closed-Loop Control of Blood Pressure for Hemorrhage Resuscitation

Date/Time: April 17th at 11:00 am

Location: EGR 2162

Committee:

Dr. Jin-Oh Hahn, Chair
Dr. Hosam Fathy
Dr. Yancy Diaz-Mercado

Abstract:

Hemorrhage is responsible for a large percentage of mortality worldwide and the majority of fatalities on the battlefield. Resuscitation procedures for hemorrhage trauma patients are critical for their recovery. Currently, during resuscitation physicians manually monitor blood pressure and use intuition to determine when fluid should be administered and how much. Due to many factors such as exhaustion, distraction, and inexperience, this method of resuscitation has often been reported as fallible. This thesis proposes two methods to assist in automating hemorrhage resuscitation. The first is a blood pressure prediction algorithm for decision support systems. The algorithm individualizes itself to different subjects using extended Kalman filtering, to account for high inter-subject variability, before accurately forecasting future blood pressure. The second method is an observer-based feedback controller which regulates blood pressure from a hypotensive state back to a healthy setpoint. The controller was designed using linear matrix inequality techniques to ensure it was absolutely stable, which let a portion of the hemodynamic plant model remain unspecified and allowed for performance over a range of physiologies. Both strategies were evaluated in-silico on a cohort of 100 virtual patients generated from an experimental dataset. The prediction algorithm showed superior accuracy to conventional assumptions. The controller tracked the given setpoint with an accuracy and performance comparable to more complex adaptive methods while outperforming empirical controllers. Further work, with respect to the prediction algorithm, includes developing it into a full decision-support system and incorporating disturbance rejecting components to account for common issues such as rebleed. The controller’s performance deteriorates for low setpoints, suggesting further study is required to increase its situational flexibility.

Author: Mohamed Mohsen Ahmed

Title of Dissertation: Development of a Lagrangian-Eulerian Modeling Framework to Describe Thermal

Degradation of Porous Fuel Particles in Simulations of Wildland Fire Behavior at Flame Scale

Date and time: April 14, 2023, at 8:30 AM

Location: Fire and Risk Alliance Conference Room, 3106 J.M. Patterson Building

Zoom link: https://umd.zoom.us/j/7685207098 | Zoom meeting ID: 768 520 7098

Committee Members:

• Professor Arnaud Trouvé, Chair/Advisor
• Professor James Baeder, Dean’s Representative
• Dr. Mark Finney
• Professor Johan Larsson
• Professor Stanislav Stoliarov
• Professor Peter Sunderland

Abstract:

Wildland fire is a multi-scale problem in which different length-scales are believed to play a role in fire behavior. These length-scales range from sub-millimeter representative of small vegetation particles, to several kilometers’ representative of meteorological scales. Computational Fluid Dynamics (CFD) models have the potential to describe wildland fire behavior at different scales. Our objective in the present study is to develop a computational tool to better describe the coupling between solid phase and gas phase processes that control the dynamics of flame spread in wildland fire problems. We focus on a modelling approach that resolves processes occurring at flame and vegetation scales, i.e., the formation of flammable vapors from the biomass vegetation due to pyrolysis, the subsequent combustion of these fuel vapors with ambient air, the establishment of a turbulent flow because of heat release and buoyant acceleration, and the thermal feedback to the solid biomass through radiative and convective heat transfer. The modelling capability is based on a general-purpose Computational Fluid Dynamics (CFD) library called OpenFOAM and an inhouse Lagrangian Particle Burning Rate (PBR) model that treats drying, thermal pyrolysis, oxidative pyrolysis and char oxidation using a one-dimensional porous medium formulation that allows descriptions of thermal degradation processes occurring during both flaming and smoldering combustion. We also introduce a novel diagnostic called Pseudo Incident Heat Flux (PIHF) to characterize the particle external heat loading.

The modelling capability is calibrated for cardboard and pine wood using available micro-and bench-scale experimental data obtained. The model is applied to simulations of the fire spread across idealized fuel beds made of laser-cut cardboard sticks that have been studied experimentally at the Missoula Fire Sciences Laboratory. The simulations are conducted at prescribed particle and environmental properties (i.e., fuel bed height, fuel bed packing, particle size, moisture content, and wind velocity) that match the experimental conditions. The model is first validated against experimental measurements and observations such as the rate of spread of the fire and the flame residence time. The modeling capability is then used to provide insights into local as well as global behavior at individual particle level and at the fuel bed level with the fuel packing. The modelling capability is also applied to simulations of fire spread across idealized vegetation beds corresponding to thin, mono-disperse or bi-disperse, cylindrical-shaped sticks of pine wood under prescribed wind conditions. Depending on the particle size distribution, the simulations feature complete fuel consumption with successful transition from flaming to smoldering combustion or partial fuel consumption with no or limited smoldering. These simulations show the existence of either a mixed mode of heat transfer through convection and radiation for small particles or a radiation dominant heat transfer mode for larger particles. The results are interpreted using maps that characterize single particle burning behavior as a function of intensity and duration of the thermal loading process.

Author: Eesh Kamrah

Date: Monday, April 10th, 2023 at 1:30 pm

Location: EGR-4164B

Committee Members:

• Dr. Mark Fuge / Chair
• Dr. Shapour Azarm
• Dr. Nikhil Chopra

Title of Thesis: ‘A STUDY TO EVALUATE WHEN TO NOT USE DIVERSE EXAMPLES

Abstract:

Design researchers have struggled to provide quantifiable forecasts on whether diversity helps or hinders the design search process. [1] This thesis studies this problem by answering the following question “how and when do diverse initial stimuli lead to better quality designs?” It does so by presenting a design research study on a modular ND test problem and Delta Design Game [2].

During our investigation some methods were developed as they couldn’t be found in popular literature. For example, a sampling method that can sample both relatively less and highly diverse initial data; we addressed this by developing a fast DPP rank based diversity method. Next, we could not find a modular test function that has parameters that would allow us to control the function’s ruggedness/complexity, we addressed this by creating a modular test function based on an existing concept that can be used to generate test functions of similar and varying complexity.

The thesis is thus laid out in a manner so that initially we address the methods that are necessary to understand the study. This begins with Chapter 2, where we look at how the diversity data sampling method can be used to generate diverse and less-diverse examples. In Chapter 3, we look at the different test problems that have been developed that are used in the study. Once familiar with this, the latter part of the thesis shows the results that provide evidence that initializing an optimizer with diverse examples is not always beneficial. We also identify the conditions in which the less diverse initial examples perform better. In the last chapter we identify the limitations of the provided results and how further experiments can be designed to investigate the identified limitations.

[1] Fu, Katherine, Joel Chan, Jonathan Cagan, Kenneth Kotovsky, Christian Schunn, and Kristin Wood. “The Meaning of ‘Near’ and ‘Far’: The Impact of Structuring Design Databases and the Effect of Distance of Analogy on Design Output.” Journal of Mechanical Design 135, no. 2 (January 7, 2013). https://doi.org/10.1115/1.4023158.

[2] Nepf, Heidi, Herbert Einstein, and Louis Buicciarelli. “Delta Game | Introduction to Civil and Environmental Engineering Design I | Civil and Environmental Engineering | MIT OpenCourseWare.” MIT – Course Information. INTRODUCTION TO CIVIL AND ENVIRONMENTAL ENGINEERING DESIGN I, Fall 2016. https://ocw.mit.edu/courses/1-101-introduction-to-civil-and-environmental-engineering-design-i-fall-2006/pages/delta-game/.