Engineering Commons^™

Zirconium Carbide Based Materials For Extreme Aerospace Environments, Nathaniel Hyman Blatt

Doctoral Dissertations

This research focuses on the processing and properties of zirconium carbide-based materials to promote their use in extreme environment aerospace applications, including nuclear thermal propulsion and hyper sonics. Several carbide systems including ZrC, ZrC-Mo cermets, (Zr, Nb)C, and a high entropy carbide were developed. The ZrC-Mo cermet was studied extensively to understand the effect of starting carbide grain size on the final microstructure, composition, elastic moduli, hardness, fracture toughness, room and elevated temperature flexural strength, thermal diffusivity, electrical resistivity, thermal expansion coefficient, and thermal conductivity. It was shown that heat transport in the cermets was dominated by the ZrC phase …

Machine Learning Based Automation Of Pcb Pdn Design And Optimization, Haran Manoharan

Doctoral Dissertations

The rapid increase in power density and stringent power-integrity requirements in modern System-on-Chip (SoC) platforms have made Power Delivery Network (PDN) design an increasingly complex, multi-stage challenge. Critical decisions must be made both during pre-layout planning, such as stackup configuration, power-plane geometry, and early decoupling capacitor (decap) budgeting, and during post-layout refinements. Traditional heuristic and evolutionary optimization techniques struggle with scalability, require extensive manual iteration, leading to long runtimes and limited adaptability across varying board configurations. To address these challenges, this work proposes a unified reinforcement-learning-driven framework for automated PDN synthesis and decap optimization that spans both pre-layout and post-layout …

Incremental Cluster Validity Indices And Their Role In Interpreting Lifelong Learning Systems, Niklas Max Melton

Doctoral Dissertations

Clustering and supervised learning are often treated as distinct paradigms, yet both rely on structure in feature space. This dissertation investigates the relationship between cluster validity indices (CVIs) and supervised learning in real-time and lifelong learning settings where data arrive incrementally and cannot be revisited. Across four studies, it develops methods for online cluster validation, uses supervised learning to improve their interpretability, and applies these ideas to evaluating performance degradation in continual learning.

The first study extends incremental cluster validity indices (iCVIs), enabling widely used validation metrics to operate in streaming environments. Experiments on synthetic and real-world datasets show systematic …

Advancing Coal Rib Support Design Through The Integration Of Field Studies And Numerical Simulations, Alper Kirmaci

Doctoral Dissertations

Coal rib stability remains a major safety concern in U.S. underground coal mines, with rib failure-related injuries and fatalities still occurring. A key challenge is the lack of a standardized methodology for designing rib support systems that can address varying geological conditions. As a result, many mines rely on trial-and-error or traditional practices, leading to inconsistent designs. This research aims to develop a systematic methodology for rib support design to improve coal rib stability in U.S. mining operations.

The study consists of: i) field monitoring in active room-and-pillar coal mines, ii) in-situ pull-out tests on coal ribs, iii) numerical model …

Critical Parameters Controlling Oxide Scale Formation And Hydro-Descaling Efficiency During Steelmaking, Tochukwu Princewill Ojiako

Doctoral Dissertations

In modern steelmaking, cast slabs are exposed to high-temperature oxidizing environments during secondary cooling, reheating, and hot rolling, resulting in the formation of multilayer oxide scales on the steel surface. These scales interact with mold-flux residues originating from the casting process (CC). The morphology, chemistry, and adhesion of oxide scale strongly influence its removability during high-pressure hydraulic descaling and ultimately determine the surface quality of hot-rolled products. However, the mechanistic relationship between oxide scale evolution, scale-steel interfacial structure, and hydraulic descaling performance remains poorly understood.

This dissertation investigates oxide scale formation, modification, and removal in low-carbon thin-slab steels produced by …

Dynamic Modeling Of The Earth's Trapped Proton Environment, Xiaojing Xu, Steve R. Blattnig, Francis F. Badavi, Martha S. Clowdsley, Edward J. Semones

Physics Faculty Publications

Context: Reliable prediction of space radiation exposure is critical for safeguarding spacecraft systems and ensuring astronaut health during missions. Accurate radiation risk assessment for space mission requires advanced models of the Earth’s trapped proton environment. These models must reflect temporal variations driven by geomagnetic field evolution and solar cycle modulation. Existing static models, such as AP8 and IRENE-AP9, are not designed to fully capture these evolving conditions. Aims: This paper presents a dynamic modeling method for the prediction of trapped proton fluxes, which incorporate time-dependent variations due to geomagnetic field evolution and solar cycle fluctuations. Methods: The …

Forcing A Molecule To Switch: Quantifying Mechanical Control At The Atomic Scale, A. M. Shashika D. Wijerathna, Markus Zirnheld, Michael L. Hildebrand, Myles Perry, Marjorie Cenese, Yuan Zhang

Physics Faculty Publications

Mechanically induced conformational switching at the single-molecule level represents a fundamental mechanism for molecular functionality, yet quantitative characterization of the underlying force and energy landscape remains limited. Here, we study individual TBrPP-Co(II) molecules on Au(111) using qPlus atomic force microscopy. By reconstructing interaction potentials from 3D Δf(x,y,z) data, we determine a threshold force of ∼96 ± 8 pN and a tip-induced switching interaction energy of ∼38 ± 4 meV associate with the conformational transition. The isolated tip-molecule force follows a power law (exponent ∼6), indicating dominance of long-range van der Waals interactions. …

Computational Methods For Identification Of Molecular Signatures, Weijun Yi

Graduate Theses, Dissertations, and Problem Reports (ETD)

This work develops computational methods for identifying molecular signatures from high-throughput genomic data and for modeling long non-coding RNA (lncRNA) sub-cellular localization. The response of multiple myeloma to CB-6644, a selective RUVBL1/2 complex inhibitor with potential anti-tumor activity, is analyzed to identify drug-responsive pathways and molecular signatures. Conventional gene set enrichment analysis (GSEA) often excludes low-expression genes. Here, phenotype comparison is reformulated as a supervised machine learning problem: genes most informative for discrimination are first selected using a machine learning approach, and GSEA is then applied to these machine-learning derived gene sets. This framework improves detection of CB-6644-associated pathways. For …

Bridging Physics-Based Modeling And Machine Learning To Predict Material Behavior: Applications In Fatigue Crack Growth And Dielectric Property Characterization, Ansan Pokharel

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation integrates physics-based modeling with machine learning (ML) to predict how materials behave under complex thermal and mechanical conditions. A key innovation of this work is the use of finite element analysis (FEA) to supplement experimental data. This approach creates more diverse and representative synthetic datasets, helping to reduce the limitations and biases that arise when training ML models solely on experimental measurements. The research focuses on two applications: improving the prediction of fatigue properties in superalloys and estimating temperature-dependent, high-frequency dielectric properties relevant to microwave-based chemical processing.

In the first study, low-cycle fatigue experiments were performed on the …

Development Of Hydrogen Emissions Quantification Systems To Expand Understanding Of Leaks And Losses Associated With The Burgeoning Hydrogen Transportation Sector, Christopher Loomis

Graduate Theses, Dissertations, and Problem Reports (ETD)

To combat global warming, alternative fuels are being investigated. Hydrogen is at the forefront of this movement, advertised as having zero emissions due to the products of the energy reaction being only water. However, studies over recent decades indicate that hydrogen in the atmosphere acts as a form of greenhouse gas (GHG) by increasing the lifespan of methane in the atmosphere. Models are attempting to estimate the possible effects of a transition to a hydrogen-fueled economy but lack empirical data to provide confidence to these estimated values. There is little data that quantifies hydrogen from anthropogenic sources.

To address this …

Effect Of Graphene Oxide On The Upconversion Photoluminescence Behavior Of Er/Yb Co-Doped Pvdf-Go Composite Nanofibers, Saptasree Bose, Jack Ryan Summers, Bhupendra B. Srivastava, Karen Lozano, Victoria Padilla-Gainza

Mechanical Engineering Faculty Publications

Upconversion photoluminescence (UCPL) materials, particularly rare-earth (RE) doped nanoparticles, have garnered significant attention due to their ability to convert near-infrared (NIR) excitation into visible emission, offering benefits such as high photostability, long lifetimes, low autofluorescence, and deep tissue penetration. Among various platforms, polymer-based one-dimensional (1D) nanofibers with in-situ lanthanide doping remain relatively unexplored, despite their superior mechanical flexibility, processability, and potential for improved luminescence performance. In this study, we report the fabrication and UCPL quenching behavior of Er3+/Yb3+ co-doped polyvinylidene difluoride (PVDF) nanofibers incorporated with graphene oxide (GO), synthesized for the first time using the scalable Forcespinning® technique. PVDF, a …

Wideband Acoustic Immittance: Suitable Quantities For Assessing Middle-Ear Status, Kren Monrad Nørgaard, Jingxin Jessica Feng, Susan E. Voss

Engineering: Faculty Publications

This paper investigates the physical significance of ear-canal wave quantities—such as absorbance—that are commonly measured in wideband-acoustic-immittance tests and evaluates whether these quantities uniquely characterize middle-ear function. This is explored using simulations of lumped-element and transmission-line ear canals, along with measurements in three-dimensional-printed uniform and anatomical ear canals of varying geometry, terminated by the same middle-ear simulator. The results demonstrate the following points: (1) Wave quantities computed using estimated or geometrical cross-sectional areas do not uniquely characterize the middle ear because they describe the wave interaction between ear canal and middle ear and are therefore confounded by variations in ear-canal …

Security-Oriented Voice Authentication Using Machine Learning, Ifeoluwa Stella Elegbe

College of Graduate Studies: Theses & Dissertations

This study develops and evaluates a machine learning and deep learning-based voice authentication system for secure identity verification. As traditional authentication methods such as passwords, PINs, and security tokens continue to face challenges, including identity theft, forgetting, and unauthorized access, voice biometrics offers a more secure, convenient, and user-friendly alternative, especially for remote, hands-free, and accessibility-focused applications. The study adopts a closed-set speaker identification framework, where the system determines the most likely speaker from a predefined group of enrolled users. A structured methodology is implemented, beginning with audio preprocessing and feature extraction. Key acoustic features, including Mel-Frequency Cepstral Coefficients (MFCCs), …

Feasibility Of Quantifying The Dosage Of Zycotherm In Its Modified Asphalt Binders, Susmita Acharjee

College of Graduate Studies: Theses & Dissertations

The quantification of low-percentage additives in asphalt binders is challenging due to their minimal concentration and the complex composition of the binder matrix. Zycotherm, a nano-organosilane anti-stripping additive, is typically used in very small dosages (0.05%–0.20%), making its detection and quantification particularly difficult. This study investigated the feasibility of estimating Zycotherm dosage in asphalt binders by examining chemical and rheological and moisture responses. Two asphalt binder grades, PG 64-22 and PG 76-22, were modified with Zycotherm at dosages of 0.05%, 0.10%, 0.15%, and 0.20% by weight of binder, along with corresponding base binders. Laboratory experiments were conducted using Fourier Transform …

Explainable Ai-Driven Predictive Maintenance Curriculum For Smart Manufacturing, Abdullah Al Mamun, Murat Kuzlu, Katherine Smith, Vukica Jovanovic, Dalya Ismael, Adel El-Shahat, Angela Sicaja, Md. Hedayetul Islam Chy

Engineering Technology Faculty Publications

Nowadays, Industry 4.0 has transformed manufacturing industries into a data-rich system driven by IoT, automation, and Artificial Intelligence (AI). Within this context, Predictive Maintenance (PdM) provides a proactive strategy that leverages heterogeneous sensor data such as vibration, acoustic, electrical, and visual signals along with historical performance and advanced analytics to forecast equipment failures before they occur. Usually, AI-driven PdM (AI-PdM) enhances this capability by integrating AI-based sensor analytics to automate fault prediction and optimize system reliability. However, traditional AI-PdM often functions as a “black box,” providing limited interpretability of its decision-making process and posing challenges for trust, validation, and human …

Data-Driven Prediction Of Compressive Strength In Pofa-Modified Concrete: A Comparative Study Of Lr, Nlr, And Ann Models, Shuaaib A. Mohammed, Hawdin Ismael Ibrahim, Diar Fatah Abdulrahman Askar, Hersh F Mahmood, Mehdi Khodaei, Soran Abdrahman Ahmad

Mansoura Engineering Journal

Production of concrete results in high CO₂ emissions, high energy demand, and consumption of various raw materials; however, it is one of the most widely used materials in construction to date. Therefore, researchers aim to minimize the impact of concrete production through various methods, such as introducing sustainable sources of materials that can be used in concrete. For instance, agricultural waste materials can be used as partial cement replacement. This study presents a comprehensive data-driven analysis of the effects of POFA on the properties of normal-strength concrete. By synthesizing a database of 196 experimental datasets from existing literature, three …

Recycling Of Plastic Into Fibers Using A Novel Machine Design And Analysis Of Fiber Characteristics, Mahmoud Khamees, Nasser Ayoub, Sabreen Addullah Abdelwahab, M. M. Maghawry

Mansoura Engineering Journal

This study presents a modified extrusion-based recycling system with an improved nozzle design and controlled fiber production capability. The machine was conceptualized and fabricated to efficiently process waste plastic and extrude it into fibers of varying diameters. The study focuses on evaluating how fiber diameter influences critical material properties. Microstructural analysis by scanning electron microscopy (SEM) was conducted to investigate the morphology and fiber diameters, while tensile testing provided insights into mechanical performance, including strength and elongation. Moisture absorption tests were also performed to assess durability and environmental resistance. The results demonstrate clear correlations between fiber diameter and the examined …

Federated And Explainable Spiking Neural Networks For Fair And Privacy-Preserving Nail Disease Diagnostics, Ch Pavani Reddy, Krishnanaik Vankdoth

Mansoura Engineering Journal

Automated nail disease diagnostics provide a non-invasive pathway for identifying underlying systemic health conditions; however, conventional centralized deep learning approaches often raise concerns related to privacy, fairness, and interpretability. Although the original NeuroNail-SNN framework demonstrated an energy-efficient and edge-ready diagnostic solution, its broader clinical adoption remained limited by unresolved trust, transparency, and ethical considerations. In this study, we propose the Federated and Explainable NeuroNail-SNN, which extends the original spiking neural architecture by integrating federated learning (FL), explainable artificial intelligence (XAI), fairness evaluation, and uncertainty quantification within a unified framework. Federated learning enables decentralized model training across hospitals and mobile clinics …

Event-Related Potential Variation In Response Accuracy: A Comparative Analysis Using Mne-Python, Akash Rajak, Sachin Umrao

Mansoura Engineering Journal

Event-Related Potentials (ERPs) are extensively employed in the examination of neural responses linked with cognitive processing and changes in brain activity. In this work, we conducted a comparative analysis of ERPs on a publicly available EEG dataset (122 subjects, alcoholic and control groups) with the help of the MNE-Python framework. The 64 electrodes for EEG were placed according to the international 10 - 20 system, and EEG was sampled at a 256 Hz sampling rate over 1-second epochs. The preprocessing pipeline consisted of 1 - 40 Hz bandpass filtering, noise reduction, spectral computation, and ERP waveform analysis. Temporal neural activity …

Optimisation Of Stepped Basin Solar Still With Response Surface Methodology And Machine Learning Methods, M. Balamurugan, V. Santhanam, B. Deepanraj, S. P Manikandan, M. Yuvaperiyasamy, N. Senthilkumar

Mansoura Engineering Journal

This research focuses on enhancing the distillate output of a stepped basin solar still by optimizing key operational variables using Response Surface Methodology (RSM) and advanced machine learning techniques. As water scarcity remains a critical global challenge, solar stills offer a viable, sustainable solution for desalination. These systems offer an energy-efficient approach to converting saline water intofreshwater, providing an alternative to conventional desalination methods. The primary objective was to investigate how variations in solar radiation, step height, and water inlet temperature influence the productivity of the stepped basin solar still. The solar radiation ranged from 600 to 1000 W/m², the …

Improving Efficiency In Noma Schemes Having Inter-User Interference Using Mechanism Design, Zory Marantz

Publications and Research

Modern wireless systems utilize non-orthogonal multiple access to increase their rate capacities; however, the efficiency of the individual utility defined in bits per Joule has yet to be considered. Multiple variations of non-orthogonal multiple access have the interference of the signal-to-interference-plus-noise ratio as a function of the received power from multiple other users due to code implementations that are non-orthogonal or non-ideal cancellation in successive-interference-cancellation methods. Game theoretic concepts are used to improve user bits-per-Joule performance. Previous solutions increment transmit power and are not based on closed form systematic methods. The mechanism design presented here led to a non-cooperative Nash …

Pafex: Compiler-Based Floating-Point Exception Detection For Gpu Kernels With Papispecific Software-Defined Events, Samin Islam, Shirley V. Moore, Christoph Q. Lauter

Graduate Student Papers (CS)

As high-performance computing becomes progressively heterogeneous, the dependence upon vendor specific tools for numerical correctness has become an impediment to portability. Although modern GPUs comply with the IEEE 754 standard, the lack of practical native hardware support to raise and handle exceptions (special values like ±∞ or NaN) is a well-known architectural limitation. To embed portable numerical correctness across heterogeneous systems, we propose an architecture agnostic prototype based on LLVM-compiler infrastructure. This framework detects floating-point exceptions in GPU kernels at the Intermediate Representation (IR) level, instrumenting both device code and host code, strictly complying with the 2019 IEEE 754 standard. …

Toward Neurosymbolic Reinforcement Learning Via Editable Specifications, Vedant Khandelwal, Hong Yung Yip, Amit Sheth

Publications

Reinforcement learning systems are commonly adapted to new settings by retraining or fine-tuning policies. This default is costly, difficult to audit, and poorly aligned with structured requirement changes such as revised safety rules, new operational constraints, or updated user preferences. We argue for an alternative abstraction: adaptation via edits to an external, human-readable specification that the agent consults at execution time. We propose conditioning decision-making on an editable knowledge graph encoding (i) rules capturing action applicability and high-level effects, (ii) hard constraints defining feasibility, and (iii) soft preferences shaping tradeoffs among feasible behaviors. Requirement changes become graph edits, not policy …

Estimating Sediment Properties Using A New Source Level Function For Wind-Driven Underwater Sound Derived From Long-Term Archival Data, S Bruce Martin, Martin Siderius

Electrical and Computer Engineering Faculty Publications and Presentations

Wind-driven breaking waves generate the background sound throughout the ocean. An accurate source level for wind-driven breaking waves is needed for estimating the ambient sound levels needed for sound exposure modeling, environmental assessments, and assessing the detection performance of sonars. Previous models applied a constant roll-off of sound levels at -16 dB/decade at all wind speeds, and these models' source levels were flat at frequencies below ∼1000 Hz due to a lack of measurements. Here, we analyzed 16 long-term archival datasets with limited anthropogenic sound sources to estimate the wind-driven source level down to 100 Hz. We estimated the site-specific …

Renewable And Affordable Energy For Apartments. Inquiry Into Renewable And Affordable Energy For Apartments, Nishadi Ruwandima Weerasinghe Mudiyanselage, Asma Aziz

Research outputs 2022 to 2026

Recent literature identifies a persistent disparity between detached housing and apartments in accessing renewable and affordable electricity. While rooftop solar PV deployment in Australia  has expanded rapidly since 2017, access to these technologies in multi-unit dwellings (MUDs) remains limited, resulting in higher electricity costs for apartment residents compared with standalone houses. This inequity is increasingly significant given rising electricity prices and the growing share of Australians living in apartments.

A Novel Hybrid Robust Transfer Learning-Based Adaptive Fractional-Order Super-Twisting Sliding Mode Controller Enhanced For Speed Regulation Of Brushless Dc Motor, Seyyed Morteza Ghamari, Asma Aziz, Daryoush Habibi

Research outputs 2022 to 2026

Brushless DC (BLDC) motors are widely used in applications that are highly-efficient, reliable, and compact, such as electric vehicles, robotics, and medical devices. However, the inherent nonlinearities and load sensitivity of BLDC motors require a robust and adaptive control strategy to ensure satisfactory performance under various operating conditions. Sliding mode control (SMC) has been widely used for the BLDC drives. However, because of its simplicity and robustness, the control effectiveness of the control is limited by the sensitivity to the disturbances and the chattering phenomenon. To remedy this, super-twisting (ST) technique has been proposed to achieve smoother response and better …

Mobility Of Hydrophilic And Hydrophobic Nanoparticles In Carbonate Reservoirs: Application To Subsurface Projects, Zain Ul Abedin Arain, Sarmad Al-Anssari, Muhammad Ali, Faisal Ur Rahman Awan, Alireza Keshavarz, Jitendra Sangwai, Mohammad Sarmadivaleh, Stefan Iglauer

Research outputs 2022 to 2026

Nanoparticles (NPs) have been suggested for subsurface projects, including enhanced oil recovery (EOR), carbon capture and storage (CCS), and hydrogen storage, due to their small size, high surface energy, controlled surface properties, and significant effects on the underground formation properties. Silica is an excellent NP that efficiently modifies the surface properties of subsurface formations for incremental oil recovery and to derisk carbon and hydrogen leaks. However, the retention of injected NPs in a narrow area near the injection inlet can limit a project’s feasibility. In the present study, hydrophobic (hybrid) NPs were produced via silanization of silica NPs, and the …

A Review On Underwater Beamforming: Techniques, Challenges, And Future Directions, Ruba Zaheer, Quoc Viet Phung, Iftekhar Ahmad, Asma Aziz, Daryoush Habibi, Yue Rong, Walid K. Hasan

Research outputs 2022 to 2026

This paper comprehensively reviews recent advancements in Underwater Beamforming (UWB) systems, highlighting its pivotal role in underwater communication, sensing, and environmental monitoring. It explores the various beamforming applications, ranging from maritime surveillance to marine life monitoring, and indicates its significance in enhancing signal clarity, spatial resolution, and noise suppression in underwater acoustic environments. The unique challenges posed by the underwater environment that introduce complexities into the beamforming process such as non-stationary noise interference, severe signal attenuation, multipath propagation, and dynamic environmental variability are thoroughly discussed. The review systematically discusses and examines conventional, adaptive, and learning-based beamforming techniques, analyzing their strengths, …

Enhanced Strength And Corrosion Resistance Of Ti-13nb-12ta-10zr-4sn Alloy By Aging Treatment, Yuhua Li, Rong Zhao, Qian Zhang, Haojie Wang, Yujing Liu, Lai Chang Zhang

Research outputs 2022 to 2026

This work investigates the effect of aging treatment on the mechanical properties and corrosion resistance of Ti-13Nb-12Ta-10Zr-4Sn (TNTZS) alloy prepared by vacuum arc melting. The as-cast TNTZS alloy was solution-treated and aged at 450°C (HT450) and 550°C (HT550). Microstructural characterization revealed significant changes in the phase proportion (in vol%), which transformed from 64% β, 19% α, and 17% α″ in as-cast condition to 54% β, 13% α, and 33% α″ for HT450 and 55% β, 25% α, and 20% α″ for HT550, respectively. The tensile strength and yield strength increased from 608 and 424 MPa in as-cast condition to 1192 …

Super-Resolution Learning Inspired Spectral-Spatial Correlation Network For Hyperspectral Target Detection, Jiaping Zhong, Yunsong Li, Jianxin Li, Yanzi Shi, Weiying Xie, Paolo Gamba

Research outputs 2022 to 2026

Hyperspectral target detection (HTD) aims at extracting targets from complex backgrounds while overcoming noise interference. Existing deep learning models for HTD usually suffer from low spatial resolution and unitary representation, especially in space-borne platforms. Super-resolution, as a critical technology to enhance the spatial details, could effectively address the aforementioned issue. To make super-resolution absolutely pose positive effects on target detection, this paper proposes an end-to-end novel super-resolution learning inspired spectral-spatial correlation network for hyperspectral target detection (SR-HTD) from the perspective of spatial and spectral regularization to achieve high-precision detection. Specifically, we designed a Spatial Correlation Aggregation (SCA) module inspired by …