Defenses

Author: He (Andy) Yun

Title of dissertation:

Manufacturability and Reliability of Additively Manufactured Planar Transformer Windings using Silver-based Paste

Date, time, & location of defense:

Monday, 4/10, from 1 – 3 PM, at 1131B Martin Hall

Committee members:

Prof. F. Patrick McCluskey (Advisor & Chair)

Prof. Aris Christou

Prof. Abhijit Dasgupta

Prof. Hugh Bruck

Prof. Alireza Khaligh (Dean’s Representative)

Abstract:

This dissertation is primarily concerned with the integration of additive manufacturing (AM) techniques into planar magnetics to achieve more efficient designs for power modules, which are in high demand. The two main focuses of this dissertation are: (1) the use of a paste-based AM technique called syringe-printing to create planar transformer windings without the need for pressure, using silver-based paste. The dissertation will address manufacturing considerations such as trace width, gaps, and heights that are printable, as well as the impact of electrical resistivity on the sintering process for the syringe-printed silver-based windings; and (2) the evaluation of the reliability of the syringe-printed silver-based windings, which will involve assessing adhesion performance between the metal/ceramic interface, conducting accelerated life tests (including thermal aging and thermal cycling tests), and identifying failure modes, failure sites, failure mechanisms, and developing degradation/failure models.

In order to achieve the desired printing geometry in terms of width and gaps between segments, printing settings were studied parametrically by fitting targeted values with actual values. A low-temperature sintering profile was optimized, with a dwell time of 8 hours at 350°C resulting in a resistivity as low as 4.39E-8 ohm.m, which was approximately 2.5 times higher than bulk silver. To improve bonding prior to syringe-printing the silver-based windings, it was suggested that an adhesive layer consisting of titanium (Ti) and silver (Ag) be deposited onto the alumina substrate. A degradation model was developed for thermal aging tests. Two batches of single layer 7-turn syringe-printed windings were subjected to thermal cycling tests, and the corresponding failure modes and mechanisms were investigated. The failure data was used to combine with the strain-energy density extracted from the finite element simulation to develop the fatigue model, with the Coffin-Mason model being fitted for comparison. A more conservative model was recommended for real-world applications. Finally, the silver-based paste was syringe-printed onto a cooler with limited footprint area, which served as the primary and secondary planar transformer board, and was successfully used in a 10 kW DC-DC full-bridge power converter with 97% efficiency. Corresponding thermal and electrical performance were discussed.

Author: Kendyl Waddell

Title: NEAR-LIMIT SPHERICAL DIFFUSION FLAMES AND COOL DIFFUSION FLAMES

Committee Members:
Dr. Peter Sunderland, Chair
Dr. Christopher Cadou, Dean’s Representative
Dr. Fernando Raffan-Montoya
Dr. Stanislav Stoliarov
Dr. Arnaud Trouve

Date and time: April 10, 2023 at 1:00 pm

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

Zoom link:https://umd.zoom.us/j/7068085301?pwd=MEpuemxyOStaN0h2MkZ6Sno1ZG1vUT09

Abstract: To combat the rising threats of climate change, current combustion technologies must evolve to become cleaner and more efficient. This requires a better understanding of the fundamental properties of combustion. One way to gain this is through microgravity experiments, where the lack of buoyancy reduces flames to their most basic components, simplifying modeling efforts. The low-temperature combustion of warm and cool flames, which has applications in advanced engine technologies and implications in terrestrial and spacecraft fire safety, is favored in microgravity. In this work, microgravity spherical diffusion flames are generated aboard the International Space Station using a spherical porous burner. A transient numerical model with detailed chemistry, transport, and radiation is used to simulate the flames. This incorporates the UCSD mechanism with 57 species and 270 reactions. Hot, warm, and cool diffusion flames are all studied. Experimental flame temperature was measured using thin-filament pyrometry, which was calibrated using a blackbody furnace. The measured temperatures agreed reasonably well with numerical simulations for a wide range of conditions, and were in the range of 950-1600 K, with an estimated uncertainty of ± 100 K. The temperatures of the porous spherical burner were measured by a thermocouple embedded in its surface. These measured temperatures, combined with numerical simulations of the gas phase, yield insight into the complex heat transfer processes that occur in and near the porous sphere.

Previous work has found that ethylene microgravity spherical diffusion flames extinguish near 1130 K at atmospheric pressure, regardless of the level of reactant dilution. The chemical kinetics associated with this consistent extinction temperature are explored using the transient numerical model. Species concentrations, reaction rates, and heat release rates are examined. Upon ignition, the peak temperature is above 2000 K, but this decreases until extinction due to radiative losses. This allows the kinetics to be studied over a wide range of temperatures for the same fuel and oxidizer. At high temperature, the dominant kinetics are similar to those reported for typical normal-gravity hydrocarbon diffusion flames. There are well defined zones of pyrolysis and oxidation, and negligible reactant leakage through the reaction zone. As the flame cools, there is increased reactant leakage leading to higher O, OH, and HO₂ concentrations in the fuel-rich regions. The pyrolysis and oxidation zones overlap, and most reactions occur in a narrow region near the peak temperature. Reactions involving HO₂ become more significant and warm flame chemistry appears. At atmospheric pressure, this low-temperature chemistry delays extinction, but does not produce enough heat to prevent it.

As ambient pressure is increased, low-temperature chemistry is enhanced, allowing the flame to extend into the warm flame and cool flame regimes. Experimental results show that increasing the pressure from 1 bar to 3 bar decreased the ethylene extinction temperature by almost 60 K. Numerical simulations showed similar behavior, as well as the emergence of cool flame behavior when the pressure was increased to 50 bar. This allows the kinetics of spherical warm and cool diffusion flames and the role of increased HO₂ participation to be examined.There are few options for studying cool diffusion flames experimentally that do not require expensive facilities that are unavailable to the average researcher. A method is presented for observing cool diffusion flames inexpensively using a pool of liquid n-heptane and parallel plates heated to produce a stably stratified stagnation flow. The flames were imaged with a color camera and an intensified camera. Measurements included gas phase temperatures, fuel evaporation rates, and formaldehyde yields. These are the first observations of cool flames burning near the surfaces of fuel pools. The measured peak temperatures were between 705 – 760 K and were 70 K above the temperature of the surrounding air. Autoignition first occurred at 550 K.

Author: Andres Alfredo Ruiz-Tagle Palazuelos

Title: Exploiting Causal Reasoning to Improve the Quantitative Risk Assessment of Engineering Systems Through Interventions and Counterfactuals

Date and Time: 04/10/2023 at 11:30 AM

Location:  Clark Memorial Conference Room 1117 in A. James Clark Hall

Zoom invite:  https://umd.zoom.us/j/2803971669?pwd=SXZPazNZamczZ0xrMEVxV3MxTC9sQT09

Committee members:

• Dr. Katrina Groth, Chair/Advisor
• Dr. Enrique Lopez Droguett
• Dr. Michelle Bensi
• Dr. Jeffrey Herrmann
• Dr. Michel Cukier
• Dr. Gregory Baecher, Dean’s Representative

Abstract:

The main strength of quantitative risk assessment (QRA) is to enable risk management by providing causal insights into the risk of an engineering system or process. Bayesian Networks (BNs) have become popular in QRA because they offer an improved causal structure that represents analysts’ knowledge of a system and enable reasoning under uncertainty. Currently, the use of BNs for risk-informed decisions is based solely on associative reasoning, answering questions of the form “If we observe X=x, how likely is it to observe Y=y?” However, risk management in the industry relies on understanding how a system could change in response to external influences (e.g., interventions and decisions) and identifying the causes and mechanisms that could explain the outcome of past events (e.g., accident investigations and lessons learned). This dissertation shows that associative reasoning alone is insufficient to provide these insights, and it provides a framework for obtaining more complex causal insight using BNs with intervention and counterfactual reasoning.  

Intervention and counterfactual reasoning must be implemented along with BNs to provide more complex insights about the risk of a system. Intervention reasoning answers queries of the form “How does doing X=x change the likelihood of observing Y=y?” and can be used to inform the causal effect of interventions and decisions on the risk and reliability of a system. Counterfactual reasoning answers queries of the form “Had X been X=x’ in an event, instead of the observed X=x, could Y have been Y=y’, instead of the observed Y=y?” and can be used to learn from past events and improve safety management activities. BNs present a unique opportunity as a risk modeling approach that incorporates the complex causal dependencies present in a system’s variables and allows reasoning under uncertainty. Therefore, exploiting the causal reasoning capabilities of BNs in QRAs can be highly beneficial to improve modern risk analysis.

The goal of this work is to define how to exploit the causal reasoning capabilities of BNs to support intervention and counterfactual reasoning in the QRA of complex systems and processes. To achieve this goal, this research first establishes the mathematical background and methods required to model interventions and counterfactuals within a BN approach. Then, we demonstrate the proposed methods with two case studies concerning the risk of third-party excavation damage to natural gas pipelines in the U.S. The first case study showed that the intervention reasoning methods developed in this work produce unbiased causal insights into the effectiveness of implementing new excavation practices. The second case study showed how the counterfactual reasoning methods developed in this work can expand on the lessons learned from an accident investigation on the Sun Prairie 2018 gas explosion by providing new insights into the effectiveness of current damage prevention practices. Finally, associative, intervention, and counterfactual reasoning methods with BNs were integrated into a single model and used to assess the risk of a highly complex challenge for the future of clean energy: excavation damages to natural gas pipelines transporting hydrogen. The impact of this research is a first-of-its-kind approach and a novel set of QRA methods that provide expanded causal insights for understanding failures and accidents in complex engineering systems and processes.

Author: Bhargav Sai Chava

Title: Atomistic Exploration of Water and Salt Confined in Sub-Nanometer and Nanometer Wide Boron-Nitride Nanotubes

Advisory Committee:

Dr. Siddhartha Das, Chair/Advisor

Dr. Pratyush Tiwary

Dr. Amir Riaz

Dr. Po-Yen Chen

Dr. Yifei Mo, Dean’s Representative

Day/Time: April 6, 3:00 pm

Location: EGR 2162 (DeWALT Conference Room)

Author: Vincent Paglioni
Date: April 6, 2023
Time: 3:00pm
Location: EGR 2164 (DeWalt Conference Room)
Zoom (public) with link: https://umd.zoom.us/j/6552181941?pwd=a3g3T2k1anJndk8vV0ZMbnI3ZW9HUT09
Passcode for meeting is “VPDefense”

Committee Members:

• Prof. Katrina M. Groth (Chair, Advisor)
• Prof. Gregory Baecher (Dean’s Representative)
• Prof. Michelle Bensi
• Dr. Elizabeth Fleming (Special Member)
• Prof. Mohammad Modarres
• Prof. Yunfei Zhao

Title of Dissertation: Improving the Foundational Knowledge of Dependency in Human Reliability Analysis

Author: Alec Van Slooten

Date: Monday, April 3rd, 2023, at 2:00 pm

Location: EGR-2164

Committee Members:

• Dr. Mark Fuge / Advisor
• Dr. Shapour Azarm
• Dr. Johan Larsson

Title of Thesis: Investigation of Latent Spaces for Airfoil Design and Optimization

Abstract: Airfoil optimization is critical to the design of turbine blades and aerial vehicle wings, among other aerodynamic applications. This design process is often constrained by the computational time required to perform CFD simulations on different design options, or the availability of adjoint solvers. A common method to mitigate some of this computational expense in non-gradient optimization is to perform dimensionality reduction on the data and optimize the design within this smaller subspace. Although learning these low-dimensional airfoil manifolds often facilitates aerodynamic optimization, these subspaces are often still computationally expensive to explore. Moreover, the complex data organization of many current nonlinear models make it difficult to reduce dimensionality without model retraining. Inducing orderings of latent components restructures the data, reduces dimensionality reduction information loss, and shows promise in providing near-optimal representations in various dimensions while only requiring the model to be trained once. Exploring the response of airfoil manifolds to data and model selection and inducing latent component orderings have potential to expedite airfoil design and optimization processes.

This thesis first investigates airfoil manifolds by testing the performance of linear and nonlinear dimensionality reduction models, examining if optimized geometries occupy lower dimensional manifolds than non-optimized geometries, and by testing if the learned representation can be improved by using target optimization conditions as data set features. We find that Autoencoders, although often suffering from stability issues, have increased performance over linear methods such as PCA in low dimensional representations of airfoil databases. We also find that the use of optimized geometry and the addition of performance parameters have little effect on the intrinsic dimensionality of the data. This thesis then explores a recently proposed approach for inducing latent space orderings called Rotation Augmented Gradient (RAG) [1]. We extend their algorithm to nonlinear models to evaluate its efficacy at creating easily-navigable latent spaces with reduced training, increased stability and increased optimization performance. We show that this approach is not guaranteed to ameliorate design space preconditioning for gradient-free optimization, as measured by Bayesian Optimization (BO) convergence speed on a Reynold-Averaged Navier Stokes (RANS) solver. In spite of this, our extension of the RAG algorithm to nonlinear models has potential to expedite dimensional analyses in cases with near-zero gradients and long training times by eliminating the need to retrain the model for different dimensional subspaces.

[1] Bao, X., Lucas, J., Sachdeva, S., and Grosse, R. B., 2020, “Regularized linear autoencoders recover the principal components, eventually,” Advances in Neural Information Processing Systems, 33, pp. 6971–6981.

Author: Andres Alfredo Ruiz-Tagle Palazuelos

Title: Exploiting Causal Reasoning to Improve the Quantitative Risk Assessment of Engineering Systems Through Interventions and Counterfactuals

Date and Time: 04/10/2023 at 11:30 AM

Location:  Clark Memorial Conference, Room 1117, in Clark Hall

Zoom invite:  https://umd.zoom.us/j/2803971669?pwd=SXZPazNZamczZ0xrMEVxV3MxTC9sQT09

Committee members:

• Dr. Katrina Groth, Chair/Advisor
• Dr. Enrique Lopez Droguett, Special member
• Dr. Michelle Bensi
• Dr. Jeffrey Herrmann
• Dr. Michel Cukier
• Dr. Gregory Baecher, Dean’s representative

Abstract:

The main strength of quantitative risk assessment (QRA) is to enable risk management by providing causal insights into the risk of an engineering system or process. Bayesian Networks (BNs) have become popular in QRA because they offer an improved causal structure that represents analysts’ knowledge of a system and enable reasoning under uncertainty. Currently, the use of BNs for risk-informed decisions is based solely on associative reasoning, answering questions of the form “If we observe X=x, how likely is it to observe Y=y?” However, risk management in the industry relies on understanding how a system could change in response to external influences (e.g., interventions and decisions) and identifying the causes and mechanisms that could explain the outcome of past events (e.g., accident investigations and lessons learned). This dissertation shows that associative reasoning alone is insufficient to provide these insights, and it provides a framework for obtaining more complex causal insight using BNs with intervention and counterfactual reasoning.  

Intervention and counterfactual reasoning must be implemented along with BNs to provide more complex insights about the risk of a system. Intervention reasoning answers queries of the form “How does doing X=x change the likelihood of observing Y=y?” and can be used to inform the causal effect of interventions and decisions on the risk and reliability of a system. Counterfactual reasoning answers queries of the form “Had X been X=x’ in an event, instead of the observed X=x, could Y have been Y=y’, instead of the observed Y=y?” and can be used to learn from past events and improve safety management activities. BNs present a unique opportunity as a risk modeling approach that incorporates the complex causal dependencies present in a system’s variables and allows reasoning under uncertainty. Therefore, exploiting the causal reasoning capabilities of BNs in QRAs can be highly beneficial to improve modern risk analysis.

The goal of this work is to define how to exploit the causal reasoning capabilities of BNs to support intervention and counterfactual reasoning in the QRA of complex systems and processes. To achieve this goal, this research first establishes the mathematical background and methods required to model interventions and counterfactuals within a BN approach. Then, we demonstrate the proposed methods with two case studies concerning the risk of third-party excavation damage to natural gas pipelines in the U.S. The first case study showed that the intervention reasoning methods developed in this work produce unbiased causal insights into the effectiveness of implementing new excavation practices. The second case study showed how the counterfactual reasoning methods developed in this work can expand on the lessons learned from an accident investigation on the Sun Prairie 2018 gas explosion by providing new insights into the effectiveness of current damage prevention practices. Finally, associative, intervention, and counterfactual reasoning methods with BNs were integrated into a single model and used to assess the risk of a highly complex challenge for the future of clean energy: excavation damages to natural gas pipelines transporting hydrogen. The impact of this research is a first-of-its-kind approach and a novel set of QRA methods that provide expanded causal insights for understanding failures and accidents in complex engineering systems and processes.

Author: Chien-Ming Huang

Title of dissertation: Durability distribution analysis of lead-free solder joints for printed circuit board applications

The date, time, & location: Friday, April 7th, 2023, at 01:00 pm in EGR-2164

List of all your committee members:

Dr. Jeffrey W. Herrmann, Chair and Advisor
Dr. Abhijit Dasgupta
Dr. Shapour Azarm
Dr. Katrina Groth
Dr. Aristos Christou, Dean’s Representative

Abstract:

Fatigue models for predicting the cycles to failure of solder joints under temperature cycling situations have been discussed and developed for decades. However, most models were developed according to specific solder materials, components, and printed circuit boards in each research. There is no study to cover and compare the fatigue models of solder joints through these different conditions. Therefore, the accuracy of the durability prediction of solder interconnections by using any of the available fatigue models can be unknown. On the other hand, current energy-based fatigue models for predicting the cycles to failure of the solder joint under thermo-mechanical loadings can only provide point estimates of the characteristic life or mean life. Nevertheless, the prediction of the fatigue life should be distributions with the uncertainties. Unfortunately, no study has been found that discusses the uncertainty of the cycles to failure, especially for the solder joints under temperature cycling. Therefore, the uncertainty propagation analysis of the cycles to failure is necessary to better estimate the distribution of the fatigue life of the solder joint.

The first part of this dissertation discusses nine existing energy-based fatigue models for different solder materials and components, and then analyzes major divergences between these studies. Moreover, the constants of the fatigue models are compared according to the divergences. Finite element simulation tool is applied to demonstrate the contributions of the factors on strain energy density and the variation on the predictions by applying these fatigue models. The results lead to conclusions with the advantages and limitations of using these available fatigue models for durability prediction of solder interconnections. These results provide insights that can help product designers understand and exploit the predictions of fatigue life while designing a printed circuit board and estimating its durability.

The second part of this dissertation identifies 11 uncertain input variables, which can propagate the uncertainties, via basic mechanics theory. The eigenvector dimension reduction method and FEA simulation tool are employed to determine the distribution of the system response, which is the strain energy density. Then, the distribution of strain energy density is converted to the distribution of characteristic life (in cycles) by choosing the appropriate fatigue model. Finally, the distribution of cumulative density function of the fatigue life of the solder joint is determined by taking the interval of characteristic life and specific shape parameters. In the end, the uncertainty of the fatigue life of the solder joint can be well estimated if the shape parameter information is available.

Title: MODELING OF ADVANCED HEAT PUMP CYCLES AND AERODYNAMIC DESIGN OF A SMALL-SCALE CENTRIFUGAL COMPRESSOR FOR ELECTRIC VEHICLES

Date: March 31st 9am-11am

Location: EGR-4164B

Join Zoom Meeting
https://umd.zoom.us/j/2112804847?pwd=Q0lhdHp2NTZuNG40OTBodW10RjdjQT09

Meeting ID: 211 280 4847
Passcode: 65hXXk

Committee members:
Dr. Amir Riaz
Dr. Bao Yang
Dr. Jelena Srebric
Dr. Peter B. Sunderland, Dean’s Representative
Dr. Reinhard Radermacher, Chair
Dr. Yunho Hwang

Abstract:
Unlike conventional vehicles powered by internal combustion engines, electric vehicles do not have enough waste heat to provide sufficient heating to the cabin. Thus, an additional heating system, such as a heat pump, is needed. However, its performance decreases significantly when the ambient temperature is low. The new kangaroo heat pump cycle (KC) is proposed to increase the heating capacity in low temperature climates. It is an enhanced flash tank-based vapor injection heat pump cycle (FT-VIC). A sub-cycle is added to the system to increase the refrigerant inlet quality entering the flash tank, which leads to a higher refrigerant mass flow rate and heating capacity. Because KC has higher heating capacity, the heating needed from the electric heater can be reduced, thus reducing the energy consumption and increasing the driving distance. In this study, thermodynamic models were developed for the basic heat pump cycle, FT-VIC, and KC. And their annual energy consumption and life cycle climate performance (LCCP) were evaluated based on the SAE J2766 standard. Results show that KC did provide more heating capacity, but its energy consumption also increases a lot. In terms of energy saving, KC is only superior in extremely cold climates. At -15°C, KC saves 13.8% energy compared to BC, and saves 2.7% energy compared to FT-VIC. In addition, KC also has higher LCCP than other cycles. Transient models were also developed to assess their performance in urban driving conditions. KC heated up the cabin faster than other cycles but also consumed more energy. In addition, a small-scale centrifugal compressor was designed for an electric vehicle. Results show that despite its smaller size and high efficiency at the design point, it has poor efficiency at the off-design point and a narrow operating range. Future study on methods for improving its operating range is needed.

Author: Nikhil Oberoi

Title: MULTI-FIDELITY PARAMETRIC SENSITIVITY FOR LARGE EDDY SIMULATION

Committee members:

Professor Johan Larsson, Chair/Advisor
Professor Christoph Brehm, Dean’s representative
Professor Kenneth Kiger
Professor Arnaud Trouvé
Professor Jacob Wenegrat

Location: EGR-2164

Date and time: March 28, 2.00 pm

Abstract: Designing engineering systems involving fluid flow under uncertainty or for optimality often requires performing many computational fluid dynamics (CFD) calculations. For low-fidelity turbulence modeling simulations such as Reynolds-averaged Navier-Stokes (RANS), such a framework has been established and is in use. However, for high-fidelity turbulence resolving simulations such as large eddy simulations (LES), the relatively high computational cost of even a single calculation hinders the development of such a framework. The overarching goal of this work is to aid LES in becoming a usable engineering design tool.

In this thesis, a computationally affordable approach to estimate parametric sensitivities of engineering relevant quantities of interest in an LES is explored. The method is based on defining a RANS problem that is constrained to reproduce the LES mean flow field. The proposed method is described and assessed for a shock/boundary layer interaction problem, where the shock angle and wall temperature are considered variable or uncertain. In the current work, a proof-of-concept of the proposed method is demonstrated. The method offers qualitative improvements to the sensitivity prediction of certain flow features as compared to standalone RANS simulations, while using a fraction of the LES cost. Different cost functions to infer auxiliary RANS variables are also examined and their influence on the sensitivity estimation is assessed. Overall, the results serve as an important proof-of-concept of the method and suggests the most promising path for future developments.