Digital Commons Network^™

Message From Workshop Chairs, Sushil K. Prasad, Srishti Srivastava, Satish Puri, David Bunde, Shubbhi Taneja, Buddhi Ashan Mallika Kankanamalage

Computer Science Faculty Research & Creative Works

No abstract provided.

Real-Time Testbed For Studying Cyberattacks And Defense In Der-Integrated Smart Inverter Systems, M. Maliha, A. Oluyomi, M. Booge, S. Bhattacharjee, N. Braasch, P. Gomez, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we propose a Hardware-in-the-Loop (HIL) simulation testbed suitable for the implementation and testing of realistic cyberattacks on grid-tied smart inverter systems integrated with Distributed Energy Resources (DER) that use the Distributed Network Protocol-3 (DNP3) protocol for communications between grid components. Specifically, our testbed combines a Real-Time Digital Simulator (RTDS) NovaCor device, outfitted with GNETx2 network interface cards, a grid-tied DER topology implemented via the RTDS software package RSCAD, and a custom virtual network that emulates a man-in-the-middle (MITM) attacker. The MITM attacker captures DNP3 traffic and falsifies telemetry data in DNP3 packets to trigger unwarranted commands from …

Grace-Fl: Green Resource-Aware Communication-Efficient Federated Learning, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, but its deployment on resource-constrained devices is hindered by high communication overhead, inefficient energy usage, and poor convergence under non-IID data distributions. To address these challenges, we propose GRACE-FL: a Green Resource-Aware Communication-Efficient Federated Learning framework that explicitly incorporates device energy capacity into training. Each client adapts its learning rate, number of local epochs, and gradient quantization bit-width based on its available energy, allowing high-capacity devices to sustain more intensive training while low-capacity devices operate with lighter configurations. A novel energy-weighted aggregation strategy ensures that clients …

Alertble: Alert Workzone Hazards Using Hybrid Filtering And Machine-Learning-Enabled Ble, Samuel Akinyede, Sejun Song

Computer Science Faculty Research & Creative Works

Collision hazard detection in industrial work zones faces challenges from signal instability, mobility-induced fluctuations, and nonline-of-sight (NLOS) conditions. While Bluetooth low energy (BLE) offers cost-effective proximity sensing, its received signal strength indicator (RSSI) variability - fluctuating by ±10 dBm even at fixed distances - limits reliability in safety-critical applications. This article presents AlertBLE, a hybrid BLE-based hazard detection system that combines extended Kalman filter (EKF) and adaptive moving average (AMA) algorithms to achieve up to 94% RSSI variance reduction in static NLOS conditions. The system introduces speed-aware safety thresholds based on reaction time and braking distance models, dynamically expanding hazard …

Understanding Heterogeneous Nucleation In Iron Alloys From First Principles: A Review And Discussion, Semen Naumovich Lekakh

Materials Science and Engineering Faculty Research & Creative Works

Heterogeneous nucleation during solidification plays a key role in structure formation of different types of iron-based alloys, including steels and cast irons. A review summarizes the novel theoretical methods which were used to understand the nature of heterogeneous nucleation in iron-based alloys. An ab initio simulation of atomic adsorption was used to predict the activity of heterogeneous nuclei, and the thermodynamic methods were applied to design conditions for desired precipitate formation in the iron melts. A high-resolution TEM study of individual nucleus and statistics of nuclei family, obtained from an automated SEM/EDX analysis, illustrates a possibility to design processes of …

The Effect Of Carbon Equivalent And Nodularity On Multi-Axial Casting Wall Movement During Spheroidal Graphite Iron Solidification And Cooling, Noah J. Brack, Mingzhi Xu, Jingjing Qing, Simon Lekakh

Materials Science and Engineering Faculty Research & Creative Works

Spheroidal graphite iron, also known as ductile iron, is an iron–carbon casting alloy used in industry for its good castability, balanced mechanical properties, and low cost. Ductile iron consists of round graphite nodules in an iron matrix. During solidification and cooling, ductile iron castings experience dynamic volume changes due to the precipitation of graphite nodules and formation of austenite. These dynamic volume changes can distort external casting surfaces, causing swell and shrinkage porosity. A novel apparatus was custom-built to capture the casting wall movement in real time along three axes. This study found that casting expansion increased with carbon equivalent …

A Novel Technique For Improved Measurement Of Graphite And Inclusions In Ductile Iron Contaminated By Boron, Chase Schroeder, Colleen Lehrer, Simon Lekakh, Laura Bartlett

Materials Science and Engineering Faculty Research & Creative Works

The highly heterogeneous microstructure of ductile iron in casting includes graphite particles, non-metallic inclusions, microporosity, and other features, such as carbides distributed in the ferrite–pearlite matrix. Determination and comprehensive quantification of microstructural features are practically important however challenging. An advanced methodology based on an automated SEM/EDX analysis was developed and compared to the standard optical imaging method. In addition to backscattered electron contrast, sensitive to atomic number, a novel methodology added EDX data to identify and classify the multiple structural features at micron resolution thresholding. To quantify the shape of individual phases, the eight chord raster algorithms was used and …

Hydrogen Permeation In Iron-Chromium-Aluminum (Fecral) Alloys And The Effects Of Microstructure And Surface Oxide, Nathan T. Gehmlich, Thomas F. Fuerst, Hanns Gietl, Chase N. Taylor, Joshua Rittenhouse, Haiming Wen, M. Nedim Cinbiz

Materials Science and Engineering Faculty Research & Creative Works

Iron–chromium–aluminum (FeCrAl) class alloys are candidates for use as cladding for accident-tolerant fuels and moderators. In this context, hydrogen isotope permeation in FeCrAl alloys is an important material property. In the present work, the apparent permeability, effective diffusivity, and apparent solubility of hydrogen in the FeCrAl alloys C26M and Kanthal D (KD) were measured with gas-driven hydrogen permeation. Permeation measurements were conducted at temperatures of 400 to 700 °C and at gas-driven pressures from 1 to 100 kPa. In particular, the effect of grain size on hydrogen transport was studied with KD samples with three different microstructures: nanocrystalline (NC), ultra-fine …

Anisotropic And Temperature-Tunable Second-Harmonic Vortex Generation In Ferroelectric Nbocl2 Holograms, Jayanta Deka, Xiaodong Yang, Jie Gao

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Optical vortex beams with helical phase fronts have immense potential to enhance data capacity due to the unbounded values of orbital angular momentum. Chip-scale platforms for producing vortex beams are of paramount importance for a variety of applications. On the other hand, 2D materials with unique optical properties are essential for developing multifunctional ultrathin photonic devices. Here, anisotropic and temperature-tunable second-harmonic vortex beam generation is demonstrated with ultrathin ferroelectric niobium oxide dichloride (NbOCl₂) fork holograms. The polarization-resolved Raman measurements are performed on the NbOCl₂ crystal to understand the anisotropic behavior of the Raman modes. It is demonstrated …

Design And Testing Of An Intrusive Arm For Supersonic Flow Sampling, Caleb Roberts, Davide Viganò

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Intrusive probes are critical tools in supersonic wind tunnel testing, enabling direct measurement of flow properties such as pressure and heat flux. To support these measurements, a single-axis intrusive arm was designed and implemented in the Missouri S&T Supersonic Wind Tunnel. The system is designed to maintains precise probe positioning, while withstanding the aerodynamic loads, and preserving a dynamic seal between the test section and ambient environment. Its modular design accommodates a range of probe types for various research applications. This paper presents the design methodology, including structural, mechanical, and aerodynamic considerations, and details the integration process. System performance was …

Few-Shot Learning-Enhanced Tiered Path Planning For Mars Rover Navigation, Ziyi Wang, Di Yu, Mina Khalilzadeh Fathi, Chaoying Pei

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Path planning for Mars rovers presents significant challenges due to the diverse terrain, ranging from easily navigable areas to hazardous zones. Traditional methods typically classify terrain simply as passable or impassable, failing to account for the nuances of more moderately challenging areas. In this paper, we introduce a tiered terrain-aware path planning strategy, employing few-shot learning to classify and segment Martian terrain into levels of difficulty. The few-shot learning model, trained on Earth, is sent to the rover, enabling real-time processing of images from satellites or helicopters. The flexibility of few-shot learning, which requires minimal data and training time, enables …

Machine Learning Approach For Defect Prediction In Metal 3d Printing For Aerospace Applications, Yerlik Gabdulla, Md Hazrat Ali, Frank Liou, Essam Shehab

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Additive manufacturing (AM) has revolutionized the aerospace industry by enabling the production of lightweight and high-strength components, such as aerospace engine components and structural elements. The ability to create complex geometries and reduce material waste is particularly beneficial for aerospace applications, where performance and weight reduction are paramount. However, ensuring the quality and reliability of these components remains a challenge, particularly in mass production, which is related to material quality, expensive processes, and longer computational times than conventional manufacturing methods. This paper proposes an approach utilizing a Decision Tree Classification Machine Learning Algorithm to predict the possibility of defect occurrence …

Investigation Of Dynamic Adsorption And Desorption Of Polymer Nanogel In Porous Media Through Microfluidics, Junchen Liu, Fuqiao Bai, Abdulaziz A. Almakimi, Mingzhen Wei, Xiaoming He, Ibnelwaleed A. Hussein, Baojun Bai

Mathematics and Statistics Faculty Research & Creative Works

Understanding the transport and retention of elastic nanogel and microgel particles in porous media has been a significant research subject for decades, essential to the application of enhanced oil recovery (EOR). However, a lack of dynamic adsorption and desorption studies, in which the kinetics in porous media are seldom investigated, hinders the design and application of polymer nanogel in underground porous media. In this work, we visualized and quantified the transport and dynamic adsorption of polymer nanogel in 3D glass micromodels that were manufactured by packing glass beads in capillaries. Calibrating the linearity of fluorescence intensity to concentration, we calculated …

Using Preformed Particle Gels To Control Transport In Geothermal Reservoirs: Mathematical Modeling, Philip Winterfeld, Baojun Bai, Yu Shu Wu

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

We are developing swellable Preformed Particle Gels (PPG), which can control preferential fluid and heat flow through fracture networks to increase the performance of EGS reservoirs. Part of this development is a mathematical model and numerical simulator to simulate PPG treatments by considering coupled thermal-hydraulic-mechanical effects, and gel swelling kinetics and plugging efficiency, from which an optimized gel treatment design and operation can be achieved. The starting point for our mathematical model is the TOUGH2-CSM formulation and code. The TOUGH2-CSM fluid and heat flow formulation is based on the TOUGH2 one for multiphase, multicomponent, and multi-porosity systems, with the latter …

Predictive Modeling For Optimal Gel Treatment Design In Brownfields Using Ensemble Machine Learning And Data Upsampling Via Generative Ai, Munqith Aldhaheri, Baojun Bai, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Efficiently designed gel treatments play a vital role in extending the lifespan of brownfields through rejuvenating oil production. Recently, a three-mode mathematical methodology named the VCR approach has been proposed for designing effective treatments. To optimize this approach, it is crucial to determine the appropriate design mode systematically rather than relying solely on the intuitive judgments of field operators. This study introduces an advanced methodology for predicting the optimal design type of gel treatments using 12 reservoir and production variables. The methodology integrates ensemble machine-learning (EML) models with historical data from 65 field projects across 11 countries (1985-2020). The Random …

Visual Understanding Of Rock Wettability Distribution To Contact Angle Regions And Oil Displacement Patterns In The Silty Sand Reservoir Via 2d Pore-Scale Modeling, H. Al-Ajaj, W. Al-Bazzaz, Ralph E. Flori, S. Alsayegh, H. Almubarak, D. S. Ibrahim, H. Al-Saedi

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This study, conducted using a silty sand reservoir rock extracted from a Kuwait-producing oilfield, provides crucial insights into fluid distribution patterns and mineralogy, as well as wettability contact angle preferences. The method used for visual identification not only captures rock physics but also suggests effective oil recovery displacement strategies. Visual identification is presented using 2D image technology and a Scanning Electron Microscope (SEM). Analytical data are presented from electron bombardments and backscattering reflections collected in the BSE detector. These analyses were used to characterize the various surface boundary morphology parameters of the silty sand mineral surfaces, including area, perimeter, mineral …

Integration Of Physics-Informed Neural Networks And Transfer Learning For Rainfall Induced Landslide Forecasting, Shian Cao, Weibing Gong

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Rainfall-induced landslides are a significant geological hazard, causing severe economic losses and casualties. Accurate forecasting of these events is particularly challenging due to the complex interactions of spatial and temporal factors governing slope stability. The Iverson model, which uses the Richards equation to describe water infiltration in unsaturated soils, is a widely adopted framework for analyzing rainfall-induced landslides. However, its reliance on traditional numerical methods limits its scalability and efficiency, particularly for complex boundary conditions and transient behaviors near slope failure. To address these limitations, we propose a physics-informed neural network (PINN) enhanced with transfer learning (TL-PINN) to solve the …

Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework For Enhanced Nuclear Reactor Power Prediction, James Daniell, Kazuma Kobayashi, Ayodeji Alajo, Syed Bahauddin Alam

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can be computationally intensive and may not fully capture the complexities of real-world reactor behavior. This paper introduces a novel hybrid digital twin-focused multi-stage deep learning framework that addresses these limitations, offering a faster and more robust solution for predicting the final steady-state power of reactor transients. By leveraging a combination of feed-forward neural networks with both classification and regression stages, and training on a unique dataset that integrates real-world measurements of reactor power and controls state …

Impact Of Surface And Physical Property On Multiphase Flow In Sealed Vessel: Liquid Dropdown Performance, Mehedi Hasan Tusar, Palash K. Bhowmik, Kazuma Kobayashi, Syed Bahauddin Alam, Shoaib Usman

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

This study explores multi-phase (i.e., liquid-gas) and multi-fluid (i.e., air-water, and water-silicone oil) flow-pattern and flow-blockage physics phenomena for wall wettability conditions ranging from superhydrophilic to superhydrophobic cases in sealed vessels utilizing computational fluid dynamics (CFD) simulation tools and volume-of-fluid (VOF) method with sharp interface modeling. Detailed modeling and simulation (M&S) of such physics phenomena—in which liquid (e.g., water) stands over top of gas (e.g., air or steam) in a closed channel and exhibited flow blockage, flow reversal related challenges—are pivotal for design, analysis, and qualification of component-level (e.g., heat pipes, heat exchangers) to system-level (e.g., emergency core cooling systems …

Evaluating Feasibility Of Incorporating Fuel Cell Drive Trains Into Existing Haul Trucks, Ayorinde Akinrinlola, Kwame Awuah-Offei, Abdullah Al Moinee

Mining Engineering Faculty Research & Creative Works

Hydrogen, as a fuel source in material handling, has become a viable option for achieving carbon reduction targets. The objective of this work was to apply vehicle drive train and energy simulation in MATLAB/Simulink to guide a discussion of the challenges and opportunities of incorporating hydrogen fuel cell drive trains into the current form factors of mine haul trucks. We built and validated a model based on the Komatsu 830-E truck and used it to estimate the hydrogen requirements for typical short-haul, medium-haul, and long-haul cycles; in the short-haul case (cycle time = 852 s) the truck consumed 23.6 L …

Arc-Jet And Free-Flight Evaluation Of Laminar And Turbulent Diffusion Models On Ablation, Kyle Worden, Andrew Heider, Serhat Hosder

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper investigates the effect of diffusion models on carbon ablation with a parametric study on a slug-calorimeter geometry in arc-jet flow at two enthalpy conditions using two arc-jet flow modeling approaches, and at corresponding enthalpy-matched free-flight conditions using RANS CFD simulations. Additionally, a sphere-cone geometry is modeled at the arc-jet enthalpy matched free-flight conditions and at a high Reynolds number free-flight condition. The approximate-corrected form of Fick's law and the Stefan-Maxwell diffusion models are investigated for equilibrium and finite-rate carbon ablation in laminar and turbulent flows, which is simulated with the Menter-SST turbulence model. The turbulent cases include the …

Study On Field Emission Characteristics Of Carbon Nanotube Arrays Patterned Via Laser Welding Of Dissimilar Materials, Hung Yin Tsai, Yi Hung Chen, Kuan Ching Wang, Paul W. Leu, Ming C. Leu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper describes a new method of growing carbon nanotube (CNT) arrays using laser welding of a catalyst metal onto the surface of a quartz substrate, followed by CNT growth through the chemical vapor deposition (CVD) process. A major advantage of this method is its ability to pattern the catalyst before growing the CNTs, thus allowing for the formation of CNTs at specific locations. The laser pre-treatment method minimized structural damage to CNTs in comparison to the laser post-processing method, achieving a lower ID/IG value of 0.72 in Raman spectroscopy analysis. Using hexagonally patterned CNT arrays on quartz, we achieve …

Advancing Cislunar Space Domain Awareness Through Robust Optimization Framework For Optical Sensors-Based Autonomous Satellite Systems, Smriti Nandan Paul, Siwei Fan, Igor Panfil

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The number of cislunar resident space objects is expected to proliferate rapidly because of strategic interests targeting long-term presence on the Moon and exploration of other planets (e.g., Artemis Accords) and liberalization of space through the entry of private space players (e.g., Intuitive Machines). It necessitates expanding current near-Earth space domain awareness (SDA) operation systems and knowledge to the relatively unexplored cislunar region. Besides the traditional complexities, xGEO orbits (orbits beyond the geosynchronous Earth orbit (GEO) region) face additional challenges because of highly non-linear and non-Keplerian dynamics, which results in inaccuracies in uncertainty propagation and state estimation. Further challenges include …

Improving The Operator-Swarm Dynamic Under Mental Fatigue Constraints In Search And Rescue Operations, Jordan Morrow

Masters Theses

"Human-robot applications that allow for work to be done remotely are largely dependent on the lassitude of the operators. The exhaustion of these operators is a result of work completion and duration. Previous research attempts to evaluate the impact on reaction by quantiying human weariness. This paper examines how human weariness affects the human-robot dynamic in UAV-assisted search and rescue missions. An explanation of the connection between mental exhaustion and operator responsiveness over prolonged durations is provided by the search and rescue operations using UAV swarms (SAROUS) model. Through the use of artificial intelligence, SAROUS is modernized. This allows the …

Computation Of Natural Relative Trajectories In The Elliptic Restricted Three-Body Problem, Dane Huck

Masters Theses

"Humankind’s presence in deep space is certain to increase throughout future decades. Future missions will continue to exploit the complex dynamics making use of liberation point orbits. Along with this trend is the rise of small satellites ("small sats") and fractionated spacecraft in general. Compared to traditional monolithic spacecraft, small sats can often have higher maneuverability, be a fraction of the cost, and be more expendable in nature. The growing interest in these two areas drives the need for improvements in the area of relative motion in deep space. One obvious use of small sats in deep space is for …

Advanced 3d Lidar-Based Systems For Urban Traffic And Pedestrian Monitoring: Integrating Elevated Lidar, Data Collection, And Deep Learning For Precise Detection And Activity Classification, Nawfal Guefrachi

Masters Theses

"Accurate and real-time monitoring of urban traffic and pedestrian activities is crucial for intelligent transportation systems (ITS) and smart cities. Traditional camera-based methods struggle with issues like lighting and privacy. This research leverages advanced three-dimension light detection and ranging (3D LiDAR) technology and computational frameworks to address these challenges, providing transformative solutions for urban traffic management and pedestrian safety. By strategically deploying elevated LiDAR sensors, detailed 3D point cloud data is captured, enabling precise monitoring of urban environments. Enhancements to LiDAR-based frameworks, such as fine-tuning the Point Voxel Region-Based Convolutional Neural Network (PV-RCNN), improve the detection of vehicles and pedestrians …

Bond Behavior Of Magnesium Potassium Phosphate Cement (Mkpc) Coating For Steel Reinforcement, Eric Williams

Masters Theses

"This research investigates the bond behavior of magnesium potassium phosphate cement (MKPC) coated mild steel deformed bars in conventional concrete and concrete repair materials. For typical steel reinforcement in concrete, a thin “passive layer” develops around the reinforcement, which protects the reinforcement from corrosion. The concrete carbonates over time, reducing the protection of the passive layer and allowing chlorides, which corrode the reinforcement, to penetrate at an increasing rate. In some cases, to combat this corrosion, a protective layer is added to the reinforcement prior to installation. Common protective layers include epoxy coatings or zinc galvanization. MKPC is a novel …

Effects Of Climate Change On Fungal Diversity And Function In Streams, Syeda Tasfia Imam

Masters Theses

"This study investigated the impact of climate change, particularly drought, on leaf decomposition and associated fungal and macroinvertebrate communities in freshwater ecosystems. With climate change, streams may experience altered hydrology, leading to shifts in community composition and biogeochemical processes. Fungi decompose organic matter, facilitate nutrient cycling, and support food webs. However, drought can disrupt these processes by reducing water availability, causing declines in fungal diversity and activity. I examined leaf breakdown and associated biota in 13 stream sites across varying hydrologic conditions, from perennial to ephemeral flow patterns. Flow duration varied during the 8-week study. Leaf breakdown was significantly related …

Hyper B-Ary Representations Of A Number, Mingway Wang

Masters Theses

In this work, we summarize combinatorial, analytical, and computational properties of Stern's diatomic sequence, and do the same for a generalization of a combinatorial property of this sequence to higher bases. We then construct special ``bit conversion'' functions $g_{b}$ that allows us to find a semi-explicit formula for the generalized sequences. Lastly, we analyze some of the properties of these $g_{b}$ functions.

Characterization Of The Microstructural Changes Of Zrb2 Under Heavy Ion Irradiation For Molten Salt Reactor Applications, Narrie Loftus

Masters Theses

A primary challenge in finding suitable materials for Molten Salt Reactors (MSRs) lies in identifying those that concurrently exhibit high corrosion resistance, stability at elevated temperatures, resistance to radiation, and chemical compatibility with molten salts. 11B-enriched zirconium diboride (ZrB₂) is a potential candidate for applications in MSRs due to its unique combination of properties, including high melting point, high thermal conductivity, and resistance to corrosion in harsh environments. This ultra-high temperature ceramic (UHTC) has exhibited stability under extreme conditions, making it an attractive material for components within MSRs.

The microstructural response of ZrB₂ under heavy ion irradiation …