Announcements

Author: Gilad Nave

Date: September 13^th, 2023 at 1:00pm

Zoom Link: https://umd.zoom.us/j/5095422567

Location: EGR-2164, Martin Hall

Committee Members:

Dr. Francis Patrick McCluskey, Chair

Dr. Mohammad Al-Sheikhly, Dean’s Representative

Dr. Hugh Bruck

Dr. Diganta Das

Dr. Abhijit Dasgupta

Dr. Peter Sandborn

Title: Electrical and Structural Formation of Transient Liquid Phase Sinter (TLPS) Materials During Early Processing Stage

Abstract:

The growing demands of electrification are driving research into new electronic materials. These electronic materials must have high electrical conductivity, withstand harsh environments and high temperatures and demonstrate reliable solutions as part of complete electronic packaging solutions. This dissertation focuses on characterizing the initial stage of the manufacturing process of Transient Liquid Phase Sinter (TLPS) alloys in a paste form as candidates for Pb-free high-temperature and high-power electronic materials.

The main objective of this dissertation work is to investigate the factors and decouple the multiple cross effects occurring during the first stage of TLPS processing in order to improve the understanding of material evolution. The work proposes, develops, and conducts in-situ electrical resistivity tests to directly measure material properties and analyze the dynamics at different stages of the material’s evolution. The research explores various factors, including alloying elements, organic binders, and heating rates, to understand their effects on the development of electrical performance in electronic materials. More specifically, the work examines the performance of Ag-In, Ag-Sn and Cu-Sn TLPS paste systems. Additionally, packing density and changes in cross-section are investigated using imaging techniques and image processing to gain insights into the early formation of the material’s structural backbone. An Arrhenius relationship together with Linear Mixed Models (LMM) techniques are used to extract the activation energies involved with each of the processing stages. The study then develops procedures to model different states of the TLPS microstructures at different heating stages based on experimentally observed data. Using these models, the study uses Finite Element Method (FEM) analysis to verify the experimental results and gain a better understanding and visualization into the involved mechanisms. This investigation not only sheds light on the material’s behavior but also has implications for robust additive manufacturing (AM) applications.

Author: Lokesh Sangepu

Date: Friday, July 28th, 2023, at 11:00 AM

Zoom Link:https://umd.zoom.us/meeting/register/tJYocuyprjwtHNdboK5kJ-r6O3ua_2WlLi3_

Location: EGR-2164, Martin Hall

Committee Members:

• Dr. Diganta Das, Chair
• Prof. Francis Patrick McCluskey
• Prof. Peter Sandborn

Thesis Title: PART SELECTION AND MANAGEMENT BASED ON RELIABILITY ASSESSMENT FOR DIE-LEVEL FAILURE MECHANISMS

Abstract:

Electronic part manufacturers often communicate part reliability information using metrics such as mean time between failures (MTBF) or failure per billion hours (FIT). However, these metrics, which rely on constant failure rate assumptions, do not adequately account for damage accumulation or wear-out phenomena leading to limitations in making informed decisions regarding the part selection and management for specific applications. This thesis addresses these limitations by proposing a physics-of-failure approach for developing a part selection methodology based on time-to-failure estimation of electronic parts.

The thesis contributes to the field by providing a comprehensive and physics-based approach to perform part selection and management. By moving beyond constant failure rate assumptions and considering wear-out phenomena, it offers a more accurate estimation of time to failure for electronic parts. The thesis begins by providing the challenges associated with manufacturers’ avoidance of sharing critical information, highlighting the impact on product quality, reliability, safety, and customer satisfaction. It describes that the insufficient information manufacturers provide hampers decision-making processes, necessitating an alternative approach for part selection.

The thesis focuses on four die-level failure mechanisms and investigates the extent to which industry-published documents discuss these mechanisms and their applicability to failure models. By understanding the specific failure mechanisms, the thesis aims to assist in selecting an appropriate failure model concerning the part and identify the required parameters for estimating the part’s time to failure. A methodology is developed to perform part selection utilizing the estimated time to failure. An application is created using MATLAB GUI to facilitate practical implementation, enabling designers, engineers, and procurement teams to make informed decisions when selecting electronic parts for specific applications. The methodology considers the susceptibility of parts to die-level failure mechanisms and identifies components with superior reliability performance. This approach enables informed decision-making, enhances product reliability, and improves customer satisfaction. The research findings and methodology presented in this thesis provide valuable insights for users to improve the reliability and performance of electronic systems through effective part selection.

Author: Eesh Kamrah

Date: Tuesday, July 25th, 2023, at 11:00 am

Location: EGR-2164

Committee Members:

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

Title of Thesis: EFFECTS OF DIVERSE INITIALIZATION ON BAYESIAN OPTIMIZERS.

Abstract: Design researchers have struggled to produce quantitative predictions for exactly why and when diversity might help or hinder design search efforts.

This thesis addresses that problem by studying one ubiquitously used search strategy – Bayesian Optimization (BO) – on a 2D test problem with modifiable convexity and difficulty.
Specifically, we test how providing diverse versus non-diverse initial samples to BO affects its performance during search and introduce a fast ranked-DPP method for computing diverse sets, which we need to detect sets of highly diverse or non-diverse initial samples.

We initially found, to our surprise, that diversity did not appear to affect BO, neither helping nor hurting the optimizer’s convergence. However, follow-on experiments illuminated a key trade-off. Non-diverse initial samples hastened posterior convergence for the underlying model hyper-parameters a Model Building advantage. In contrast, diverse initial samples accelerated exploring the function itself a Space Exploration advantage. Both advantages help BO, but in different ways, and the initial sample diversity directly modulates how BO trades those advantages. Indeed, we show that fixing the BO hyper-parameters removes the Model Building advantage, causing diverse initial samples to always outperform models trained with non-diverse samples.
These findings shed light on why, at least for BO-type optimizers, the use of diversity has mixed effects and cautions against the ubiquitous use of space-filling initializations in BO.
To the extent that humans use explore-exploit search strategies similar to BO, our results provide a testable conjecture for why and when diversity may affect human-subject or design team experiments.

The thesis is organized as follows: Chapter 2 provides an overview of existing studies that explore the impact of different initial stimuli. In Chapter 3, we explain the methodology used in the subsequent experiments. Chapter 4 presents the results of our initial study on the diverse initialization of BO (Bayesian Optimization) applied to the wildcat wells function. In Chapter 5, we analyze the conditions under which less diverse initial examples perform better and expand on these findings in Chapter 6 by considering additional ND continuous functions. The final chapter discusses the limitations of our findings and proposes potential areas for future research.

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.