Engineering Commons^™

Strength Comparison For Fully Dense Zirconium Diboride Ceramics Tested By Different Methods, Darko Kosanović, Suzana Filipović, Isaak Trajković, Nina Obradović, Paul M. Brune, Gregory E. Hilmas, William G. Fahrenholtz

Materials Science and Engineering Faculty Research & Creative Works

The strength of zirconium diboride ceramics was tested by three different methods, 3-point flexure, 4-point flexure, and compression. Nearly full-density ceramics were obtained by hot pressing commercial ZrB₂ powder with the addition of.5 wt.% carbon as a sintering aid. The thermal properties and hardness were studied for ZrB₂ milled with ZrB₂ and WC media. Based on phase purity and higher thermal conductivity, ZrB₂ ceramics prepared from powders milled with ZrB₂ media were selected for mechanical property studies. The strength in 3-point flexure was 546 ± 55 MPa. The flexure strength was 476 ± 41 MPa …

Experimental Investigation Of A Viscoelastic Liner To Reduce Under Helmet Overpressures And Shock Wave Reflections, Cody J.H. Thomas, Fatih Dogan, Catherine E. Johnson

Materials Science and Engineering Faculty Research & Creative Works

Introduction: Shock wave overpressure exposures can result in blast-induced traumatic brain injury (bTBI) in warfighters. Although combat helmets provide protection against blunt impacts, the protection against blast waves is limited due to the observed high overpressures occurring underneath the helmet. One route to enhance these helmets is by incorporating viscoelastic materials into the helmet designs, reducing pressures imposed on the head. This study aims to further investigate this mitigation technique against under-helmet overpressures by adding a viscoelastic liner to the inside of a combat helmet. Methods: The liner's effectiveness was evaluated by exposing it to free-field blasts of Composition C-4 …

Effect Of Ph And Hydroxyapatite-Like Layer Formation On The Antibacterial Properties Of Borophosphate Bioactive Glass Incorporated Poly(Methyl Methacrylate) Bone Cement, Kara A. Hageman, Rebekah L. Blatt, William A. Kuenne, Richard K. Brow, Terence E. Mciff

Materials Science and Engineering Faculty Research & Creative Works

Infection is a leading cause of total joint arthroplasty failure. Current preventative measures incorporate antibiotics into the poly (methyl methacrylate) (PMMA) bone cement that anchors the implant into the natural bone. With bacterial resistance to antibiotics on the rise, the development of alternative antibacterial materials is crucial to mitigate infection. Borate bioactive glass, 13–93-B3, has been studied previously for use in orthopedic applications due to its ability to be incorporated into bone cements and other scaffolds, convert into hydroxyapatite (HA)-like layer, and enhance the osseointegration and antibacterial properties of the material. The purpose of this study is to better understand …

Water Vapor Oxidation Of Sic Layer Of Tristructural Isotropic Particles, Visharad Jalan, Adam Bratten, Matthew Luebbe, Haiming Wen

Materials Science and Engineering Faculty Research & Creative Works

Tri structural isotropic (TRISO) fuel particles, developed for use in high-temperature gas-cooled nuclear reactors, can be subjected to oxidizing environments in off-normal accident scenarios. In this study, surrogate TRISO fuel particles were oxidized at 1000–1350°C for 4 h in 20 kPa water vapor atmosphere balanced with ultrahigh-purity helium gas. The oxide scale morphology and thickness were studied via scanning electron microscopy, focused ion beam, and transmission electron microscopy. The oxide thickness increased as the oxidation temperature was increased. Although the oxide scale at 1000°C was completely amorphous, pockets of crystalline oxide were observed on particles oxidized at 1200 and 1300°C. …

Quantification Of Swelling In Hematite Pellets Reduced Using Hydrogen–Nitrogen Gas Mixture, Amogh Meshram, Yuri Korobeinikov, Xingwen Wei, Ronald J. O'Malley, Seetharaman Sridhar

Materials Science and Engineering Faculty Research & Creative Works

Iron ore pellets are reduced in a 50%H2–50%N2 1 atm gas mixture at 750, 800, 850, 900, and 950 °C while simultaneously documenting swelling (change in pellet radius) and weight change. Swelling increases with increasing temperature, with catastrophic swelling (>20% of reduction swelling index) observed at 850, 900, and 950 °C. As the pellet is reduced, the pellet radius increases until 40–50% reduction is achieved, followed by a decrease in diameter beyond 40–50% reduction at 750 and 850 °C. At 950 °C, the pellet radius continues to increase with additional pellet reduction without any subsequent decrease in diameter. Scanning …

Bipolar Hipims Kick-Pulse For High Hardness In High-Entropy Boride Thin Films, Nathaniel S. Mcilwaine, Nestor O.Marquez Rios, Eva Zurek, Donald W. Brenner, William G. Fahrenholtz, Stefano Curtarolo, Douglas E. Wolfe, Jon Paul Maria

Materials Science and Engineering Faculty Research & Creative Works

We report a microhardness indentation study for multicomponent refractory metal boride thin films that belong to the family of high-entropy ceramics exhibiting superior hardness and high temperature properties. We focus on the nominally equimolar composition (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2) B2, which we refer to as HEB3, on c-plane sapphire substrates. Thin films are prepared using bipolar high power impulse magnetron sputtering (HiPIMS), where the positive kick pulse is optimized to produce films with high density, high crystallinity, and a microstructure that is isometric in nature as revealed by cross-sectional fracture surface imaging, X-ray diffraction, and X-ray reflectometry. Low-load Knoop indentation is used to …

Impact Of Quenching Intensity Conditions On Using A Finite Element Model To Investigate The Microstructure And Hardenability Of Low-Alloy Steel, J. S. Alabi, E. Heidari, M. F. Buchely, K. Chandrashekhara, S. N. Lekakh, R. J. O'Malley, V. Athavale, A. Kumar

Materials Science and Engineering Faculty Research & Creative Works

Quenching is one of the primary processes to improve mechanical properties in steels, particularly hardness. Quenching is well established for different geometries of individually treated steel components; while in-steam quenching of large diameter continuously cast steel bar has several specific features which are difficult and costly to experimentally optimize. The end-quench Jominy test has been used extensively to study the hardenability of different steel grades. Different numerical, analytical, and empirical models have been developed to simulate the Jominy process and to understand quenching of steels. However, it is not straight forward to translate experimental data from Jominy test on instream …

Ijmc/Fef Student Research Competition: Understanding The Effect Of Boron On Graphite Nodularity And Distribution In Ductile Iron Castings, Chase Schroeder, Colleen Lehrer, Simon Lekakh, Laura Bartlett

Materials Science and Engineering Faculty Research & Creative Works

Ductile iron is renowned for its superior mechanical properties, higher strength, and ductility, combined with excellent castability and the economy of using a scrap melting process. The matrix structure as well as morphology and distribution of graphite nodules significantly influences mechanical properties of as-cast ductile iron. Boron contamination of the scrap supply has presented some difficulties in controlling the microstructure and mechanical properties of certain gray and ductile iron grades. In the current study, the effect of boron levels up to 95 ppm was determined on graphite nodularity and distribution in as-cast ductile iron castings as a function of section …

Kinetic Modeling Of Electrostatic Sieving For Lunar Regolith Beneficiation: Case Studies, Easton Ingram, Emmanuela Amen Eze, Jeffrey Smith, Fateme Rezaei, David Bayless, William Schonberg, Daoru Han

Materials Science and Engineering Faculty Research & Creative Works

A new kinetic particle modeling framework was developed to investigate electrostatic transport of lunar regolith particles with applications to the concept of electrostatic sieving. the new approach is based on kinetic particle dynamics and includes major modules of sampling the particle size distribution, solving electric fields, and tracking motion of charged dust grains. Case studies for a concept of electrostatic sieving were chosen to validate the new model. the simulation achieved similar yields reported in previous works, which served as validation of the model. the new model is computationally efficient and could serve as a design, analysis, and optimization tool.

Impact Of Quenching Intensity Conditions On Using A Finite Element Model To Investigate The Microstructure And Hardenability Of Low-Alloy Steel, J. S. Alabi, E. Heidari, M. F. Buchely, K. Chandrashekhara, S. N. Lekakh, R. J. O'Malley, V. Athavale, A. Kumar

Materials Science and Engineering Faculty Research & Creative Works

Quenching is one of the primary processes to improve mechanical properties in steels, particularly hardness. Quenching is well established for different geometries of individually treated steel components; while in-steam quenching of large diameter continuously cast steel bar has several specific features which are difficult and costly to experimentally optimize. the end-quench Jominy test has been used extensively to study the hardenability of different steel grades. Different numerical, analytical, and empirical models have been developed to simulate the Jominy process and to understand quenching of steels. However, it is not straight forward to translate experimental data from Jominy test on instream …

A Lab-Scale Mold Simulator Employing An Optical-Fiber-Instrumented Mold To Characterize Initial Steel Shell Growth Phenomena, Muhammad A. Nazim, Rony K. Saha, Mario F. Buchely, Ronald J. O'Malley, Jie Huang, Arezoo Emdadi

PSMRC Faculty Research

A mold simulator was developed to replicate the mold oscillation and casting speed conditions of a continuous caster on the lab scale. A mold was designed incorporating fiber optics to capture internal temperature gradients and transient heat transfer phenomena during the initial solidification of steel. Casting parameters (casting speed, oscillation stroke and oscillation frequency) were investigated using the mold simulator. Solidified steel shells and mold thermal data were collected and characterized after initial solidification to investigate the impacts of mold oscillation on shell growth and mold heat transfer.

Femtosecond Laser–Inscribed Fiber Bragg Grating Sensors: Enabling Distributed High- Temperature Measurements And Strain Monitoring In Steelmaking And Foundry Applications, Ogbole Collins Inalegwu, Yeshwanth Reddy Mekala, Rony Kumer Saha, Farhan Mumtaz, Dinesh Reddy Alla, Deva Prasaad Neelakandan, Jeffrey D. Smith, Ronald J. O'Malley, Rex Gerald, Jie Huang

PSMRC Faculty Research

This study demonstrates the use of fiber Bragg grating (FBG) sensors for distributed temperature and strain monitoring in steelmaking and foundry applications. Integrated into inexpensive optical fibers, FBGs offer accurate and real-time remote sensing, detecting shifts in wavelength due to temperature (up to 1,800 °C), strain, or structural wear and tear. Furthermore, the intrinsic features of FBG sensors: compact size, immunity to electromagnetic interference and corrosion, robustness to vibration, ease of integration into existing composite structures, and non-intrusive measurement capacity in harsh environments make them ideal for steelmaking. FBGs optimize production, ensure quality, and enhance safety within the steel industry.

Fiber-Optic Raman Probe For On-Line Eaf Slag Analysis, Bohong Zhang, Hanok Tekle, Jeffrey D. Smith, Todd Sander, Ronald J. O'Malley, Jie Huang

PSMRC Faculty Research

In Electric Arc Furnace (EAF) steelmaking, the push for improved efficiency requires accurate analysis of the chemical composition of its slag system to control slag foaming, provide refractory protection, and maintain high furnace iron yield. Therefore, the ability to obtain real-time slag chemistry data would provide a useful tool to improve the control and efficiency of the process. The work reported here aims to assess the structure and chemistry of EAF slags at high temperatures using a portable fiber-optic Raman probe. The ability to relate Raman spectra peaks to chemistry and structure is demonstrated in experimental result with EAF slags …

The Melting Behavior Of Hydrogen Direct Reduced Iron In Molten Steel And Slag: An Integrated Computational And Experimental Study, Fabian Andres Calderon Hurtado, Joseph Govro, Arezoo Emdadi, Ronald J. O'Malley

PSMRC Faculty Research

Direct reduced iron (DRI) and hot briquetted iron (HBI) are essential feedstocks for tramp element control in the electric arc furnace (EAF). Due to greenhouse gas (GHG) concerns related to CO₂ emissions, hydrogen as a substitute for natural gas and a reductant in DRI production is being widely explored to reduce GHG emissions in ironmaking. This study examines the melting behavior of hydrogen DRI (H-DRI) pellets in the EAF containing low-carbon (0.1 wt.%) molten steel and molten slag. A computational heat transfer model was developed to predict the melting behavior of H-DRI pellets. To validate the model, a set …

Advancing Environmental Engineering: The Role Of Artificial Intelligence In Sustainable Solutions - A Short Review, Amirreza Talaie, Hesam Kamyab, Ashkan Razmfarsa

Management Faculty Publications

Artificial intelligence (AI) has emerged as a transformative force in environmental engineering, offering innovative solutions to complex environmental challenges. From air pollution monitoring and water resource management to waste management, climate change mitigation, and ecological preservation, AI is revolutionizing the way we address environmental issues. Machine learning, neural networks, and other AI technologies are enabling more accurate predictions, optimizing resource use, and improving conservation efforts. However, despite its many advantages, AI also faces challenges such as data availability, energy consumption, ethical concerns, and the need for transparency. This review explores the diverse applications of AI in environmental engineering, highlighting the …

A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu

Computer Science Faculty Publications

The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …

Autonomous Strike Uavs For Counterterrorism Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu

Computer Science Faculty Publications

UAVs are becoming a crucial tool in modern warfare, primarily due to their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. The use of autonomous UAVs to conduct strike missions against highly valuable targets is the focus of this research. Due to developments in ledger technology, smart contracts, and machine learning, such activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of challenges and potential solutions for successful implementation of an autonomous UAV mission.

Speculative Anisotropic Mesh Adaptation On Shared Memory For Cfd Applications, Christos Tsolakis, Nikos Chrisochoides

Computer Science Faculty Publications

Efficient and robust anisotropic mesh adaptation is crucial for Computational Fluid Dynamics (CFD) simulations. The CFD Vision 2030 Study highlights the pressing need for this technology, particularly for simulations targeting supercomputers. This work applies a fine-grained speculative approach to anisotropic mesh operations. Our implementation exhibits more than 90% parallel efficiency on a multi-core node. Additionally, we evaluate our method within an adaptive pipeline for a spectrum of publicly available test-cases that includes both analytically derived and error-based fields. For all test-cases, our results are in accordance with published results in the literature. Support for CAD-based data is introduced, and its …

Mosaic: A Prune-And-Assemble Approach For Efficient Model Pruning In Privacy-Preserving Deep Learning, Yifei Cai, Qiao Zhang, Rui Ning, Chunsheng Xin, Hongyi Wu

Computer Science Faculty Publications

To enable common users to capitalize on the power of deep learning, Machine Learning as a Service (MLaaS) has been proposed in the literature, which opens powerful deep learning models of service providers to the public. To protect the data privacy of end users, as well as the model privacy of the server, several state-of-the-art privacy-preserving MLaaS frameworks have also been proposed. Nevertheless, despite the exquisite design of these frameworks to enhance computation efficiency, the computational cost remains expensive for practical applications. To improve the computation efficiency of deep learning (DL) models, model pruning has been adopted as a strategic …

Flexible Fitting Of Alphafold2-Predicted Models To Cryo-Em Density Maps Using Elastic Network Models: A Methodological Affirmation, Maytha Alshammari, Jing He, Willy Wriggers

Computer Science Faculty Publications

Motivation: This study investigates the flexible refinement of AlphaFold2 models against corresponding cryo-electron microscopy (cryo-EM) maps using normal modes derived from elastic network models (ENMs) as basis functions for displacement. AlphaFold2 generally predicts highly accurate structures, but 18 of the 137 models of isolated chains exhibit a TM-score below 0.80. We achieved a significant improvement in four of these deviating structures and used them to systematically optimize the parameters of the ENM motion model.

Results: We successfully refined four AlphaFold2 models with notable discrepancies: lipid-preserved respiratory supercomplex (TM-score increased from 0.52 to 0.69), flagellar L-ring protein (TM-score increased from 0.53 …

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

Triphlapan: Predicting Hla Molecules Binding Peptides Based On Triple Coding Matrix And Transfer Learning, Meng Wang, Chuqi Lei, Jianxin Wang, Yaohang Li, Min Li

Computer Science Faculty Publications

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding …

Autonomous Strike Uavs In Support Of Homeland Security Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming crucial tools in modern homeland security applications, primarily because of their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. This study focuses on the use of autonomous UAVs to conduct, as part of homeland security applications, strike missions against high-value terrorist targets. Owing to developments in ledger technology, smart contracts, and machine learning, activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of the challenges and preliminary solutions for the successful implementation of an autonomous UAV mission. Specifically, we …

Enhancing Research Productivity: Seamless Integration Of Personal Devices And Hpc Resources With The Cybershuttle Notebook Gateway, Yasith Jayawardana, Dimuthu Wannipurage, Eroma Abeysinghe, Suresh Marru

Computer Science Faculty Publications

Scientists often utilize personal laptops and workstations for initial research stages and turn to high-performance computing (HPC) supercomputers for compute-intensive tasks. However, seamless transitions between these environments are vital for enhancing productivity and accelerating research progress. Our paper presents the Cybershuttle Notebook Gateway, an open-source framework crafted to streamline this transition, optimize resource utilization, and reduce time-to-science for researchers. Leveraging JupyterLab, the framework extends kernel mechanics for seamless provisioning and connection to remote HPC cluster kernels. We delve into its architecture, which separates user authentication, kernel provisioning, and remote file system access. Additionally, we highlight practical capabilities like analyzing network …

Enhanced Skin Cancer Diagnosis Through Grid Search Algorithm-Optimized Deep Learning Models For Skin Lesion Analysis, Rudresh Pillai, Neha Sharma, Sheifali Gupta, Deepali Gupta, Sapna Juneja, Saurav Malik, Hong Qin, Mohammed S. Alqahtani, Amel Ksibi

Computer Science Faculty Publications

Skin cancer is a widespread and perilous disease that necessitates prompt and precise detection for successful treatment. This research introduces a thorough method for identifying skin lesions by utilizing sophisticated deep learning (DL) techniques. The study utilizes three convolutional neural networks (CNNs)-CNN1, CNN2, and CNN3-each assigned to a distinct categorization job. Task 1 involves binary classification to determine whether skin lesions are present or absent. Task 2 involves distinguishing between benign and malignant lesions. Task 3 involves multiclass classification of skin lesion images to identify the precise type of skin lesion from a set of seven categories. The most optimal …

H-Nobs: Achieving Certified Fairness And Robustness In Distributed Learning On Heterogeneous Datasets, Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian

Electrical and Computer Engineering Faculty Publications

Fairness and robustness are two important goals in the design of modern distributed learning systems. Despite a few prior works attempting to achieve both fairness and robustness, some key aspects of this direction remain underexplored. In this paper, we try to answer three largely unnoticed and unaddressed questions that are of paramount significance to this topic: (i) What makes jointly satisfying fairness and robustness difficult? (ii) Is it possible to establish theoretical guarantee for the dual property of fairness and robustness? (iii) How much does fairness have to sacrifice at the expense of robustness being incorporated into the system? To …

Photoexcited Charge Carrier Dynamics And Electronic Properties Of Two-Dimensional Mxene, Nb2ctx, Andrew M. Fitzgerald, Emily Sutherland, Tarek A. Elmelegy, Mary Qin Hassig, Julia Martin, Erika Colin-Ulloa, Ken Ngo, Ronald L. Grimm, Joshua R. Uzarski, Michel W. Barsoum, N. Aaron Deskins, Lyubov V. Titova, Kateryna Kushnir Friedman

Mechanical Engineering

Two-dimensional, 2D, niobium carbide MXene, Nb2CTx, has attracted attention due to its extraordinarily high photothermal conversion efficiency that has applications ranging from medicine, for tumor ablation, to solar energy conversion. Here, we characterize its electronic properties and investigate the ultrafast dynamics of its photoexcitations with a goal of shedding light onto the origins of its unique properties. Through density functional theory, DFT, calculations, we find that Nb2CTx is metallic, with a small but finite density of states at the Fermi level for all experimentally relevant terminations of Nb2CTx that can be achieved using HF or molten salt etching of the …

Corrosion Characteristics Of Ti And Al2o3/Ti Thin Films Sputtered On 316lss, Iman El-Mahallawi, Hanan ِ Abd El-Fattah Dr., Lamiaa Mohamed Dr., Aliaa Abdelfattah

Mechanical Engineering

In this work, two thin films (Ti and Al2O3/Ti) were deposited on 316LSS substrates by the physical vapor deposition (PVD) sputtering technique. Heat treatment was applied for thin films at 400 °C for 2 h. Thin films were divided into two groups, The First group cooled in the air (normalized), and the second cooled in the furnace (annealed). The electrochemical characteristics were investigated by potentiodynamic polarization (POT) and impedance spectroscopy (EIS) tests. The corrosion resistance (CRST) for all thin films before and after heat treatment was determined in 3.5% NaCl at room temperature (RT). Scanning electron microscope (SEM), and surface …

A Practical Framework For Component-Level Structural Health Monitoring Of The Gerald Desmond Bridge, Mehran Rahmani, Vesna Terzic, Andrea Calabrese, Brittany Cambell

Mineta Transportation Institute

Bridges serve as critical transportation infrastructure, but traditional maintenance inspection to ensure their safety is time-consuming, costly, and labor-intensive, especially for larger and more complex structures. This study presents a practical framework for the instrumentation, data acquisition, and remote condition assessment of the Gerald Desmond Bridge, California’s largest cable-stayed bridge. The framework aims to establish a foundation for real-time or near real-time remote health monitoring of the bridge’s critical elements. The study highlights the advantages of remote monitoring in terms of efficiency, cost-effectiveness, and early detection of damage.

Underlying Substrate Effect On Electrochemical Activity For Hydrogen Evolution Reaction With Low-Platinum-Loaded Catalysts, Baleeswaraiah Muchharla, Peter V. Sushko, Kishor K. Sadasivuni, Wei Cao, Akash Tomar, Hani Elsayed-Ali, Adetayo Adedeji, Abdennaceur Karoui, Joshua M. Spurgeon, Bijandra Kumar

Electrical & Computer Engineering Faculty Publications

Platinum is known as the best catalyst for the hydrogen evolution reaction (HER) but the scarcity and high cost of Pt limit its widespread applicability. Herein, the role of the underlying substrate on the HER activity of dispersed Pt atoms is uncovered. A direct current magnetron sputtering technique is utilized to deposit transition metal (TM) thin films of W, Ti, and Ta as underlying substrates for extremely low loading of Pt (