Dissertation defense schedule

ROLE OF ADDITIVE MANUFACTURING AND ADVANCED MATERIALS IN ENABLING NEXT ERA ELECTRIC MOTORS FOR AEROSPACE AND TRACTION APPLICATIONS

The rapid transformation towards more electrified propulsion systems in ground and aerospace applications has intensified the need to advance the current State-Of-The-Art (SOTA) electrical machines for improved power density, efficiency, and materials sustainability. Achieving these demanding characteristics and pushing the boundary of the SOTA require attempting new design methodologies to leverage advanced materials and innovative manufacturing techniques.

Additive Manufacturing (AM) has emerged as a promising enabler to contribute to such advancements of electrical machines in their active or passive parts. AM offers a wide range of materials with opening the design space for customized and sophisticated topologies, enabling integration of subsystems including complex direct cooling approaches and Power Electronics Unit (PEU), and reducing materials waste. However, the adoption of AM in electric machines requires a thorough understanding of its trade-off in performance, manufacturability, and build quality to best exploitation of its advantages. In addition, development in various engineering aspects is driven by materials affecting performance, sustainability, and associated technology cost.

Permanent Magnet (PM) traction electric motors have enabled improved power/torque densities with higher efficiencies and thus dominate Ground Electric Vehicles (GEVs) applications. However, the preferred type of PM (Dy-NdFeB) contains critical Rare-Earth (RE) elements that are susceptible to price volatility and sustainability concerns. Therefore, the development of new materials, especially new PM materials to reduce the reliance on critical elements is a key enabler for the continued efforts to deploy GEVs. Notable among these materials is second generation Iron Nitride (FeN) that is currently under development by Niron Magnetics with an energy product of 24-36 MGOe. FeN has attracted increasing attention for its high remanent flux density exceeding 1.3 T and RE-free composition, making it a potential candidate to replace or reduce the utilization of conventional RE-PMs.

This dissertation builds on two major U.S. Department of Energy–funded efforts that contribute to the advancement of SOTA in sustainable electric propulsion. The first, the ARPA-E ASCEND project (in collaboration with Raytheon Technologies, NREL, Florida State University, and Ampaire), aims to demonstrate a 250-225 kW class aerospace motor with an exceptionally high Specific Power (SP) above 12 kW/kg (at a system level) and efficiency exceeding 93% throughout the cruising flight condition. The proposed motor features hollow AM-ALuminum (AM-AL) windings with integrated Heat Pipes (HPs), integrated to dual AM-Condenser Chambers using PEEK aerospace grades to realize direct cooling and lightweight construction. The

contributions of this dissertation include the analysis and optimization of the motor electromagnetic design, design topology trade-off studies, and design of novel AM heat exchanger, enabling the motor to take advantage of directly integrated stator cooling through the adoption of AM. The AM-AL windings enable the tight integration to the PEU in modular approach improving overall system SP. The design in this dissertation has been optimized and evaluated in terms electromagnetic performance, effectiveness of its Thermal Management System (TMS) with CFD and thermal analyses on the novel TMS, including prototyping the proposed design with testing the full system. The motor has been fabricated with total mass of 32.4 kg including the integration of its TMS, which leads to a system SP of 7-7.7 kW/kg (225-250 kW). The motor entered the testing phase and has been tested up to 65% of its power capability. Moreover, the motor operated at 140 kW at a current density of 17.5 Arms/mm2, while testing is in progress to operate at the maximum power of 225 kW.

The second research funding effort, the DOE EERE-VTO Program, targets the development of rare-earth-free electric powertrain for GEVs through collaboration with Niron Magnetics, General Motors, NREL, and Virginia Tech. This dissertation explores blending FeN PMs and high-performance Ferrite PMs within Interior Permanent Magnet (IPM) motors operating up to 16,000 rpm and at 800V, furthering the recent industry trend of increasing speed and voltage. The contribution of this dissertation is the demonstration of the proposed design for this funding effort, addressing demagnetization concerns with FeN magnets and delivering competitive electromagnetic performance. Of note in this work is the development of very detailed electromagnetic and mechanical parametrizations for performing multi-physics optimization covering electromagnetic performance and rotor stress mechanical analysis.

By integrating these two directions, advanced manufacturing and advanced materials, advanced electric propulsion systems can be realized. The outcome highlights the use of both directions in electric machines can redefine performance limits for both aerospace and ground transportation applications, contributing to achieving global electrification goals for energy efficiency and sustainable energy conversion technologies.

Ethical Leadership and Workplace Social Capital: A Quantitative Correlational Study of Nonprofit Organizations in Western Wisconsin

The purpose of this quantitative correlational study was to examine the relationship between ethical leadership and workplace social capital within nonprofit organizations in Western Wisconsin. Grounded in ethical leadership and social capital theories, the study explored the relationship between ethical leadership and overall workplace social capital, as well as the dimensions of bridging, bonding, and linking social capital. Data were collected through an online survey using the Ethical Leadership Scale (ELS) and the Workplace Social Capital Scale (WSCS). The final sample included 100 nonprofit stakeholders representing a variety of organizational roles. Spearman’s rho correlation analyses revealed statistically significant
positive relationships between ethical leadership and overall workplace social capital (ρ = .767, p < .001), bridging social capital (ρ = .613, p < .001), bonding social capital (ρ = .655, p < .001), and linking social capital (ρ = .731, p < .001). Ethical leadership was positively associated with workplace social capital in nonprofit organizations. These findings contribute to the growing body of literature by providing empirical evidence of the relationship between ethical leadership and workplace social capital and support ethical leadership as a relational leadership practice associated with stronger workplace social capital in nonprofit organizations.

"L'art pour I'art"?: The Infused-Acquired Duplex Ordo of Art as an Intellectual Virtue

This dissertation, utilizing a Thomistic analysis, investigates the intellectual virtue of ars (art) in relation to the coexistence of the infused and acquired virtues within the cognitive structure of the Christian artist. In other words, this work analyzes the relationship between acquired ars and infused ars, with their distinct ends and interrelated configuration, as grounded in the perfection of human nature through the supernatural reality of grace. The basic formulation of this interrelationship is as follows: infused ars as directed to the production of sacred artwork builds upon acquired ars as directed to the production of secular artwork, but infused ars completes and fulfills acquired ars as meritoriously directing its proximate end to the human person’s supernatural end. As a whole, this project – by focusing on the intellectual virtue of ars – unites Thomistic virtue ethics and aesthetics in a heuristic synthesis.

This analysis is divided into six chapters. First, I articulate the historical crisis in art and aesthetics that has contributed to the internal alienation between the categories of artist, artisan, and beauty. Second, I organize a developed Thomistic aesthetic that unites St. Thomas Aquinas’s understanding of ars as an intellectual virtue proportional to the practical intellect, a careful reflection on the Thomistic characterization of beauty, and a synthesis of ars and beauty in light of the empirical fact of historical development in art. Third, I examine the unity within the Christian artist’s rationality by utilizing Alasdair MacIntyre’s conception of tradition-dependence and Bernard Lonergan’s theology of conversion. Fourth, I develop this synthesis by drawing attention to the external social/cultural conditions needed for ars to flourish, focusing on the dialectical structure of artwork and the internal movement between traditions in liturgical history. Fifth, I revisit the general debate concerning the coexistence of the acquired and infused virtues in the same Christian subject. Sixth, I synthesize the distinct dimensions of the infused-acquired duplex ordo of ars by illustrating the manner in which (i) infused ars builds upon preexisting acquired ars and (ii) acquired ars is directed by infused ars to a final supernatural end.

INTEGRATING AI AND EDUCATION DATA FOR PRIVACY-PRESERVING PREDICTION OF STUDENT SUCCESS

Student retention remains a persistent challenge in higher education; this dissertation addresses it through three interconnected studies that advance methods for predicting and supporting at-risk students while enabling privacy-preserving collaboration among institutions.

The first study surveys transformer-based text classification for educational applications across six dimensions: data modality, model size, input length, accuracy, computational cost, and safety (privacy, bias, and explainability). Although roughly 60 to 80 percent of organizational data pairs text with tabular fields, multimodal research has focused on text-image and text-video methods, a significant gap for educational applications.

The second study develops a cluster-then-classify methodology integrating categorical and continuous student data. Using records from 3,089 undergraduates, K-Prototypes clustering identifies five subtypes among non-returning students, and XGBoost and Gradient Boosting classifiers detect departure and assign subtype. Departure proves only weakly predictable from administrative and academic-performance data (non-returning F1 of 0.43), while subtypes are highly separable among students who do leave (macro F1 of 0.93 to 0.94); the binding constraint is detection rather than subtype assignment. Inverse-frequency class weighting outperforms synthetic oversampling, and a fairness audit on Pell eligibility finds that the most accurate model exhibits the smallest equalized-odds disparity.

The third study presents a privacy-preserving Remote Data Science framework: researchers from three universities of varying sizes develop classifiers on synthetic data, and the data owner executes them on one institution's private records with differential privacy applied to results. The framework achieves consistent performance (macro F1 of 0.690 to 0.695) under strict FERPA compliance, and its dual-server design suits smaller institutions with limited technical resources.

Together, these studies contribute methodological advances in educational data mining, a reproducible typology of undergraduate departure, and a practical path to inter-institutional analytics with responsible data practices.