Engineering Commons^™

Akronauts Turbopump Research Project, Gerald Drabeck Iii, Michael Mccalla, Joshua Skelton

Williams Honors College, Honors Research Projects

The idea of this project is to design a turbo pump template capable of fitting into future Akronauts rocket designs. This allows the rockets to travel higher due to the reduced net weight. Many methods were used to verify the rigidity and feasibility of the design, such as Finite Element Analysis and fluid dynamics simulations.

Urotropine As A Corrosion Inhibitor In Steel Rebar: A Molecular Dynamics Study, Tyler Zadrozny

Williams Honors College, Honors Research Projects

This project investigates the behavior of corrosion in rebar, a critical component in infrastructure such as roads and skyscrapers. Corrosion of rebar can lead to structural instability, visible as cracks or pitting on the surface. The primary focus is on the potential of urotropine as a corrosion inhibitor. Urotropine, known for its ability to inhibit corrosion in mild steel in acidic environments, is hypothesized to be effective in preventing corrosion on rebar. To explore this, molecular dynamics simulations using LAMMPS were conducted, modeling urotropine molecules in an acidic solution (PH = 1) interacting with iron atoms, mimicking rebar. Simulations were …

Examining Correlations Between Inhibition Efficiency And Compound Properties For Corrosion Inhibitors On Carbon Steel Rebar In Concrete, Nicole Langenfeld

Williams Honors College, Honors Research Projects

This project will look to determine if correlations exist between the inhibition efficiency and particular chemical properties of different corrosion inhibitors for carbon steel rebar in concrete affected with chlorides. Examining the inhibition efficiency of different corrosion inhibitors and attempting to find correlations or a general rule of thumb for what denotes a better corrosion inhibitor would be valuable. This similar concept has been seen with Lipinski's rule of 5 where he created a general guide to predict if compounds were orally active in humans. Since little information is readily available and no known correlations exist for corrosion inhibitors on …

Quantifying The Effectiveness Of Rhamnolipid As A Biocide: Assessing Comparability Of A Modified Solution, Rosemary Sterling

Williams Honors College, Honors Research Projects

The CO2 induced corrosion of carbon steel in oil and gas pipelines, exacerbated by sulfate-reducing bacteria (SRB), poses a serious threat to infrastructure. This study evaluates the effectiveness of rhamnolipid (RhL), a biosurfactant, as a corrosion inhibitor and potential SRB biocide in simulated pipeline environments. Using a Postgate-C Modified Produced Water Simulant, which demonstrated electrochemical behavior comparable to industry-standard solutions while sustaining SRB, corrosion trials were conducted with and without RhL. Mass loss and electrochemical data revealed that RhL significantly reduced corrosion rates—by 85% on average and up to 99% instantaneously in a 4-day trial. Visual and analytical results from …

Automatic Hole Saw Testing For Mk Morse, Jason Watson, Justin Zapotoczny

Williams Honors College, Honors Research Projects

M.K. Morse has a hole saw tester they use for their hole saw product line. The tester currently requires constant operator attention for a process that could be automated. The goal of this project is to create a new hole saw tester that can run without the need for an operator's complete attention and give reliable data for the hole saws.

Kb Bioenergy Mobile Dewatering System Research, Wyatt Balser, Nathan Szymczak

Williams Honors College, Honors Research Projects

This report outlines the design and development of a mobile skid-mounted dewatering system intended for high-solids sludge processing at wastewater treatment facilities. The goal was to create a self-contained, transportable unit capable of reliably separating water from sludge using a screw press, while integrating upstream processes such as polymer conditioning, sludge maceration, and pumping. The final design prioritizes serviceability, structural integrity, and operational simplicity, featuring a consolidated equipment layout, engineered piping diagrams, and vendor-specified components.

Throughout the project, multiple design alternatives were evaluated using a weighted decision matrix, leading to the selection of a skid platform over a trailer configuration …

Aeg Improved Football Helmet Liner, Matthew Dyson, John Clore, Brady Slates, Nathan Walker

Williams Honors College, Honors Research Projects

The National Football League (NFL) is a leading professional American football organization experiencing rapid growth in popularity. However, the high-contact nature of the sport frequently results in player injuries, with head trauma and concussions being major concerns. This project aims to mitigate these risks by developing an advanced helmet liner designed to reduce the impact of direct collisions and torsional shear forces. In order to do this, we will utilize an impact testing machine with a modified helmet mount. By enhancing the liner's effectiveness, the proposed design seeks to improve player safety without compromising performance. The study focuses on innovative …

Zips Racing Electric - Vehicle Control Unit, Ryan Stoller, Brian Glen, John Wozencraft, Tetra Engdahl

Williams Honors College, Honors Research Projects

Zips Racing Electric is a design team at the university that builds and races electric cars. The racing team wants to design a four-wheel drive system to increase performance, but the current computer on the vehicle cannot control the more complex power train. The goal of the project will be to build a hardware device that can read sensor inputs and control the torque output to each motor individually. The control device being designed will need to interface with the existing systems on the car and control regenerative breaking, torque vectoring, and other auxilliary systems on the car.

Using The Historical Equity Action Lens (Heal) To Identify And Remedy Transportation Inequities From The Akron Innerbelt Project, Olivia Lane

Williams Honors College, Honors Research Projects

This project will utilize the Historical Equity and Action Lens (HEAL) to address transportation inequities in marginalized communities by incorporating historical and cultural insights into data collection and analysis. Specifically, the project will focus on the Akron Innerbelt, an infamous highway project that significantly impacted historically Black neighborhoods. By examining the historical, economic, and social context of the Innerbelt, the project will identify the project's long-term impacts Akron communities. The goal is to use this knowledge to inform improve transportation equity in the affected areas by developing a plan for the Innerbelt since its vacancy in 2016. The project will …

Material Suppliers’ Perspective On Collaboration In Industrial Construction Projects, Seogjae Choi, William J. O'Brien

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Material suppliers' involvement in construction project planning has been recommended as an effective measure to improve project performance by using their expertise. However, the recommendation is not universal, and some researchers disputed the value of early involvement of suppliers depending on material type. Moreover, prior studies considered main contractors' and owners' perspectives mostly in examining how to increase the involvement of suppliers. To add material suppliers' perspective, this study conducted interviews with experienced professionals from 16 material suppliers including standardized, make-to-order, and custom products. The results are specified by separating the type of materials and sub-phases of planning. The results …

Polarimetry Based Radar Estimation Of Extreme Rainfall: Case Studies, Bong Chul Seo, Witold F. Krajewski, James A. Smith

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The study evaluated radar-derived polarimetric rainfall estimates for extreme rain events that occurred in the Kansas City Metropolitan area in the United States. To derive quantitative precipitation estimates (QPE), we implemented two polarimetric algorithms based on specific attenuation (A) and specific differential phase (KDP), along with the reflectivity (Z) based one using data from two radars in the study area. The analysis to assess radar-rainfall estimates (R) utilizes ground observations from a dense network of about 170 rain gauges. Based on our analysis results, the two polarimetric estimates from R(A) and R(KDP) outperform the conventional estimation R(Z). R(A) appeared to …

Every Publication Needs An Author Verified Tl;Dr Statement, Sarah Oerther, Daniel B. Oerther

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

No abstract provided.

Nonlinear Control Of Buck-Type Converters For Micro-Wind Generators, Noah Wilding, Shuzan Kumar Sarkar, Shruti Pandey, Michael L. Mcintyre

Electrical and Computer Engineering Faculty Research & Creative Works

Small-scale wind turbines offer a promising solution for distributed renewable energy generation. However, this approach often leads to wasted energy when battery capacity is reached, as excess energy is typically dissipated into resistors. The reliance on batteries further increases the cost and complexity of such systems. This paper presents a nonlinear control algorithm for regulating buck-type converters, providing a more efficient energy management solution. By employing a grid-connected inverter, excess energy is utilized rather than dissipated, potentially eliminating the need for batteries and reducing micro-wind turbine installation costs. The proposed control strategy manages the DC-link voltage for the inverter by …

Method Of Termination With Absorbers For Far-End Crosstalk Measurements, Daniel L. Commerou, Reza Asadi, Sathvika Bandi, Seyed Mostafa Mousavi, Xiaoning Ye, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing demand for higher data rates in modern electronic systems has heightened the challenges of maintaining signal integrity, particularly in addressing farend crosstalk (FEXT). This paper presents a novel approach using absorber-based terminations to perform signal integrity measurements in high-speed PCB designs. The performance of magnetically and electrically loaded absorber materials is evaluated against traditional 50Ω terminations with performance parameters such as S-parameters, Time-Domain reflectometry (TDR), and induced far-end crosstalk voltage. Simulations and experimental measurements demonstrate that electrically loaded absorbers can achieve performance characteristics comparable to high-quality terminations, particularly for reflections and impedance matching. The results indicate that absorbers …

Multi-Objective Inverse Optimization Of High-Speed Interconnects Using Cascaded Deep Neural Network, Yicheng Zhang, Ling Zhang, Hyunwook Park, Bo Pu, Xiao Ding Cai, Chulsoon Hwang, Bidyut Sen, Jun Fan, Er Ping Li, James L. Drewniak

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel multi-objective inverse optimization method for high-speed interconnects based on a cascaded deep neural network (DNN) structure, which can efficiently optimize characteristic impedance, insertion loss, and far-end crosstalk (FEXT) simultaneously. Parameter optimization for high-speed interconnects is essential to the signal integrity and electrical performance of complex designs such as multilayer printed circuit boards (PCBs) and chiplets. Conventional optimization approaches often rely on numerous optimization iterations, which is highly time-consuming, especially in high dimensional parameter spaces. This paper proposes a novel DNN based method by cascading an inverse-prediction network and a forward-prediction network to achieve multi-objective optimization …

Data Representation And Preprocessing Effects On S-Parameter Modeling Of High-Speed Channels Using Machine Learning, Hyunwook Park, Davit Kharshiladze, Yifan Ding, Ling Zhang, Natalia Bondarenko, Hanqin Ye, Kaushal Sanjay Mhalgi, Brice Achkir, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the effects of data representations and preprocessing on machine learning based S-parameter modeling of high-speed channels are investigated. Using a transformer network as a base model, two S-parameter representations in real/imaginary and magnitude/phase are compared and studied. Considering S-parameter data distributions, various preprocessing techniques including MinMax normalization, standardization, robust scaling, power transformation, and quantile transformation are compared and analyzed to improve accuracy. Moreover, the accuracy results are compared depending on the electrical length of target channels.

Enhanced Continual Reinforcement Learning-Based Output Feedback Control Of Heterogeneous Quadrotors Formation, Ehsan Soleimani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a unified framework for the safe and optimal control of heterogeneous quadrotor unmanned aerial vehicles (QUAVs) in formation, enabling multitask missions without requiring precise system dynamics. To address partial state observability, a multilayer neural network (MNN) observer is designed to estimate unmeasured states. Reinforcement learning (RL) is employed for optimal control utilizing an MNN ensuring adaptability. Barrier Lyapunov Functions (BLFs) are integrated into the RL framework to enforce safety by maintaining QUAVs within predefined constraints. An enhanced continual learning (ECL) method is proposed to improve the adaptability of MNNs. This method enables effective multitask learning while mitigating …

Structural Analysis Of Multi-Domain Dynamic Systems Modeled Via Bond Graphs, Arnold A. Fernandes, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

Structural Observability (SO) and Structural Monitorability (SM) are structural properties utilized to determine the state and fault-free operation of components, respectively, in a bond graph (BG) model. BGs enable qualitative system analysis, evaluating whether existing sets of sensors and actuators ensure Structural Observability (SO) and Structural Controllability (SC) without knowledge of parametric values. Furthermore, the analysis determines whether there are sufficient sensors available to identify component faults accurately. This work provides a framework for automated sensor placement in a multi-domain physical system while analyzing the SO and SM properties. The MATLAB Structural Analysis Toolbox (MATSAT) conducts sensor placement in a …

The Effect Of Need For Cognition & Need For Affect On Human Reliance And Artificial Intelligence Interactions, Aliyah Mcgowan

Doctoral Dissertations and Master's Theses

Abstract

With the increased use of Artificial Intelligence (AI) automations in fields like medical diagnoses and mental health queries, there are concerns regarding an individual’s trust and reliance on the technology. Reliance on AI output may lead an individual to accept inaccurate or incorrect information without further analysis. Trust may influence reliance and trust formation may be a product of affective processing. This study investigated the relationship between Need for Affect (NFA), Need for Cognition (NFC), and trust and reliance on AI interactions. Participants were assessed on the NFA scale for willingness to approach or avoid emotional stimuli, the NFC …

Mxene-Based Materials For Enhanced Water Quality: Advances In Remediation Strategies, Ali Mohammad Amani, Milad Abbasi, Atena Najdian, Farzaneh Mohamadpour, Seyed Reza Kasaee, Hesam Kamyab, Shreeshivadasan Chelliapan, Mostafa Shafiee, Lobat Tayebi, Ahmad Vaez, Atefeh Najafian, Ehsan Vafa, Sareh Mosleh-Shirazi

Electrical & Computer Engineering Faculty Publications

Two-dimensional MXenes are promising candidates for water treatment because of their large surface area (e.g., exceeding 1000 m²/g for certain structures), high electrical conductivity (e.g., >1000 S/m), hydrophilicity, and chemical stability. Their strong sorption selectivity and effective reduction capacity, exemplified by heavy metal adsorption efficiencies exceeding 95 % in several studies, coupled with facile surface modification, make them suitable for removing diverse contaminants. Applications include the removal of heavy metals (e.g., achieving >90 % removal of Pb(II)), dye removal (e.g., demonstrating >80 % removal of methylene blue), and radioactive waste elimination. Furthermore, 3D MXene architecture exhibit enhanced performance in antibacterial …

A Customized Large Single-Piece Bifrontal Implant For Post-Craniectomy Defect Reconstruction: A Case Study, Omid Ghaderzadeh, Ehsan Amirbeyk, Seyed Roholah Ghodsi, Zahra Namazi, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

Background: Large bifrontal defects pose unique reconstruction challenges due to their complex curvature and mechanical requirements. This case demonstrated how computer-aided design/manufacturing (CAD/CAM) enabled precise single-piece polymethyl methacrylate (PMMA) implant fabrication, thereby overcoming traditional limitations. Case presentation: A 25-year-old male who had undergone bifrontal decompressive craniectomy suffered a severe traumatic brain injury. The autologous bone flap had been temporarily stored in a subcutaneous fat area of the abdomen for 3 months to preserve its viability. A secondary cranioplasty was then performed using titanium miniplates and self-tapping screws for final fixation. After 2 years, the patient developed empyema and a brain …

Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions …

An Extendable Soft-Switched Step-Up Interleaved Converter Integrated With Voltage Multiplier Cells, Amir Hasan Babanezhad, Koosha Choobdari Omran, Reza Beiranvand, Pourya Shamsi

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a multiphase high step-up interleaved converter is introduced, which ensures low-input current ripple, low-component currents, and voltage stresses. The proposed circuit guarantees ZVS operation for power switches and ZCS operation for power diodes, which significantly reduce converter switching losses and EMI emission and improve its efficiency. Therefore, its passive components volume can be reduced by using high switching frequencies. In addition, the interleaved technique reduces input current ripple and input filter volume and provides high-power density. High-voltage gain and low-voltage stresses on the components are also achieved due to the integration of the converter structure with the …

Deep Learning For Uav Classification: Impact Of Noise And Multipath Fading In Rf Signals, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Kumar Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing presence of unmanned aerial vehicles (UAVs) raises serious security concerns, particularly regarding unauthorized drone operations. Recent U.S. security statistics report a sharp rise in unauthorized UAV activities, with the Federal Aviation Administration (FAA) receiving over 100 monthly reports of illegal drone operations near airports. In 2024 alone, Dedrone records 1.19 million unauthorized drone flights across major U.S. cities, highlighting the need for robust UAV detection and classification systems. In this work, a lightweight Convolutional Neural Network (CNN) model is proposed for RF-based UAV classification under noisy and multipath fading conditions. The proposed CNN consists of multiple convolutional blocks, …

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 …

Optimizing Uav Swarm Deployment For Efficient Communication Signal Strength Alignment In Disaster Scenarios, Mina Khalilzadeh Fathi, Chaoying Pei

Mechanical and Aerospace Engineering Faculty Research & Creative Works

In disaster scenarios, establishing reliable communication infrastructure is critical, and unmanned aerial vehicle (UAV) swarms offer a promising solution as temporary base stations. This study models communication demand in disaster-affected areas by applying Gaussian kernels to building data, forming a spatial demand distribution. Signal strength is estimated using the normalized inverse Free Space Path Loss (FSPL) to account for realistic attenuation. To guide UAV placement, we extract high-demand regions from the demand distribution using a gradient-based thresholding method. Based on this information, we develop a greedy algorithm to iteratively position UAVs for optimal coverage in areas with the greatest communication …

Fast Demodulation Of Ofdr-Based Distributed Sensing Based On Enhanced Buneman Frequency Estimation, Zhaopeng Zhang, Bo Liu, Xiao Liu, Caiyun Li, Osamah Alsalman, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

Aiming at realizing high-efficiency distributed strain sensing through optical frequency domain reflectometry (OFDR), this paper introduces a fast demodulation algorithm to determine strain-induced spectral shifts from coarse Rayleigh backscattering (RBS) cross-correlation spectra. The proposed approach employs an enhanced Buneman frequency estimation (BFE) algorithm, enabling direct spectral shift analysis across coarse signals. By applying this algorithm, the need for dense interpolation in the conventional cross-correlation demodulation process - typically required for a finer spectral sampling interval but at the cost of demodulation efficiency - can be eliminated. Both theoretical analysis and experimental investigation reveal the equivalence of the BFE and conventional …

Enhancing Risk And Crisis Communication With Computational Methods: A Systematic Literature Review, Madison H. Munro, Ross J. Gore, Christopher J. Lynch, Yvette D. Hastings, Ann Marie Reinhold

VMASC Publications

Recent developments in risk and crisis communication (RCC) research combine social science theory and data science tools to construct effective risk messages efficiently. However, current systematic literature reviews (SLRs) on RCC primarily focus on computationally assessing message efficacy as opposed to message efficiency. We conduct an SLR to highlight any current computational methods that improve message construction efficacy and efficiency. We found that most RCC research focuses on using theoretical frameworks and computational methods to analyze or classify message elements that improve efficacy. For improving message efficiency, computational and manual methods are only used in message classification. Specifying the computational …

From Cyclones To Cybersecurity: A Call For Convergence In Risk And Crisis Communications Research, Ann Marie Reinhold, Ross J. Gore, Barry Ezell, Clemente I. Izurieta, Elizabeth A. Shanahan

VMASC Publications

Effective risk and crisis communication can improve health and safety and reduce harmful effects of hazards and disasters. A robust body of literature investigates mechanisms for improving risk and crisis communication. While effective risk and crisis communication strategies are equally desired across different hazard types (e.g., natural hazards, cyber security), the extent to which risk and crisis communication experts utilize the “lessons learned” from scientific domains outside their own is suspect. Therefore, we hypothesized that risk and crisis communication research is siloed according to academic disciplines at the detriment to the advancement of the field of risk communications research writ …

Age Of Information-Based Optimal Scheduling With Energy Cost Trade-Off For Smart Warehouse: A Deep Reinforcement Learning-Based Approach, Sandip Roy, Abhishek Bisht, Ashok Kumar Das, Sachin Shetty

VMASC Publications

Recent advances in the integration of high-speed mobile networks and real-time IoT devices have facilitated in building of smart warehouses, where a set of beacons and Internet of Things (IoT) devices (or source nodes) can monitor the status of various physical processes in a time-critical way. In real-time status monitoring systems, like smart warehouses, quantifying the freshness of the Internet of Things (IoT) data based on the age of information (AoI) metrics becomes quite crucial. As source nodes are battery-constrained, a balanced trade-off between AoI minimization and preservation of source node battery energy is essential. In this paper, in a …