Engineering Commons^™

Impulse, Fall 2025, Jill Fier, Sierra Brown, Jerome J. Lohr College Of Engineering

Impulse (Jerome J. Lohr College of Engineering Publication)

2 | Faculty News
3 | SDSU Vet Hua to Head Civil Engineering
6 | Ad Lunam — To the Moon
10 | Building A Safety Culture
11 | Department News
12 | Surface Mount Technology Kickoff
14 | Construction Management Student, Volleyball Player Building Success Piece by Piece
16 | Student Awards, Honors
18 | Student Competition Results
20 | NASA Win Opens Doors for New Product Development Order
22 | SDSU Engineering Student Tabbed as Noblereach Fellow
26 | Knabach Award Recipient from Apprentice Lineman to Company President
28 | Metzger Create Faculty Endowment for SDSU Engineering
30 …

Inductorless Cascaded Low-Power Dc-Dc Converter: Optimizing Performance Metrics Through Machine Learning Techniques, Ahmed Khaled, Sameh O. Abdellatif

Electrical Engineering

This study presents a groundbreaking methodology for optimizing the operational efficiency of a three-stage boost DC-DC cascaded converter through the application of a Random Forest(RF) machine learning algorithm. A novel figure of merit is meticulously formulated to quantitatively evaluate the converter’s performance, focusing on critical metrics such as power conversion efficiency, output DC ripple levels, and response time. The Random Forest model is trained on a comprehensive dataset encompassing a wide range of resistive and capacitive design parameters, with the figure of merit serving as the output indicator. Rigorous simulations and analyses demonstrate that the integration of LM741 operational amplifiers …

Insect-Foundation: A Foundation Model And Large Multimodal Dataset For Vision-Language Insect Understanding, Thanh-Dat Truong, Hoang-Quan Nguyen, Xuan-Bac Nguyen, Ashley Dowling, Xin Li, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Multimodal conversational generative AI has shown impressive capabilities in various vision and language understanding through learning massive text-image data. However, current conversational models still lack knowledge about visual insects since they are often trained on the general knowledge of vision-language data. Meanwhile, understanding insects is a fundamental problem in precision agriculture, helping to promote sustainable development in agriculture. Therefore, this paper proposes a novel multimodal conversational model, Insect-LLaVA, to promote visual understanding in insect-domain knowledge. In particular, we first introduce a new large-scale Multimodal Insect Dataset with Visual Insect Instruction Data that enables the capability of learning the multimodal foundation …

Evaluation Of Towed Tem Potential For Rapid Characterization Of Levee Foundations, Kolawole Arowoogun, Katherine Grote, Jeremy Maurer

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Assessing the geologic conditions of levee foundation soils is a challenging task owing to the extensive length of most levees and the heterogeneity of many alluvial deposits. Traditional investigation techniques (such as boring and cone penetrometer testing [CPT]) are expensive, invasive, and provide spatially-limited information. As a result, they are restricted to pre-identified problematic zones in the levee. To overcome these challenges, geophysical instruments capable of better spatial coverage are proposed for rapid geoelectrical characterization of levees. In this study, we presented a field-based application of the towed time-domain electromagnetic method (tTEM) system in characterizing the subsurface geology adjacent to …

Research On The Construction Of Domain Knowledge Graph For Assisted Decision Making And Its Scenario-Oriented Application, Hao Xu, Linlin Ge, Yan Zhang, Sanhong Deng

Journal of Scientific Information Research

[Purpose/significance] This research constructed a domain knowledge graph and its scenario-oriented application framework for decision support at four levels: the data foundation layer, the key technology layer, the domain knowledge graph construction layer, and the scenario-oriented application layer. This framework aims to provide systematic support for knowledge discovery.

[Method/process] Based on the construction of a domain knowledge graph and its scenario-oriented application framework for decision support, this research focuses on the improvement of models and performance evaluation for fine-grained entity and relationship extraction at the discourse level within texts. The optimal model is selected to construct a domain knowledge graph. …

High Torque Density Dual-Stator Vernier Motors With Flux Concentrating Rotors, Esmaeil Mohammadi, Ali Mohammadi, Mohammad Amin Jalali Kondelaji, Pedram Asef, Ion G. Boldea, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Axial Flux Permanent Magnet (AFPM) machines are increasingly being studied for low-speed, direct-drive applications due to their compact structure and high torque output. This study proposes two novel dual-stator AFPM vernier machine topologies: a spoke-type rotor configuration and a back-to-back Halbach array rotor. Both designs employ dual outer stators with 12 double-layer concentrated windings, and high-polarity rotor configurations to enhance flux concentration. A three-dimensional finite element model, which was previously validated by a laboratory prototype motor was utilized to evaluate the electromagnetic characteristics of the proposed topologies. These characteristics include torque density, airgap flux distribution, and harmonic content. Comparative results …

Multi-Phase Wireless Power Transfer With High Power Density Inductive Coils For Electric Drone Charging, Lucas A. Gastineau, Donovin D. Lewis, Omer Onar, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Wireless charging of unmanned ground vehicles and aircraft has been proposed to increase charging reliability and security, allow for autonomous functionality, and either reduce battery size or increase continuous flight time. This paper proposes a three-phase Litz wire primary and a two-phase PCB secondary for high secondary-side power density considering misalignment tolerances, surface and volumetric power density, and coil sizing. Electromagnetic 3D finite element analysis (FEA) simulations are conducted to study variation in mutual inductance and coupling coefficient with different secondary coil sizes and number of turns, horizontal and vertical misalignment between the primary and secondary, and a combination of …

Nonlinear Design Scaling Of Electric Machines Based On Hybrid De And Meta-Modeling Application To Synchronous Motors With Combined Pm Stator And Reluctance Rotor Excitation, Oluwaseun A. Badewa, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents an innovative method for nonlinear scaling of electric machines by integrating machine learning (ML)-based meta-modeling with a differential evolution (DE) algorithm. The technique is applied to high-performance combined-excitation synchronous electric motors which exhibit highly nonlinear characteristics, making performance scaling challenging. The proposed approach employs an ML meta-model trained on data obtained from finite element analysis (FEA), utilizing an experimentally validated model for nonlinear scaling and performance prediction at different power ratings. The accuracy of the meta-model in capturing the nonlinear relationships between design parameters and motor performance is first assessed using metrics such as R-squared (R2) and …

Multi Electric Machines With Series And Parallel Electromechanical Combinations For Aircraft, David R. Stewart, Donovin D. Lewis, Matin Vatani, Diego A. Lopez-Guerrero, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

High-performance electric propulsion systems require fault tolerant, power dense, electric machines capable of maintaining high efficiency across a dynamic range of operation. To address these inherently conflicting requirements, multi-motor architectures employing electromechanically coupled modular configurations have been proposed to enhance system efficiency, fault tolerance, and redundancy. This paper investigates four mechanically coupled configurations for a coreless axial flux permanent magnet (CAFPM) motor unit integrating series, parallel, and hybrid architectures with differential and gearbox coupling. Performance and optimal sizing for motors in each configuration are determined through 3D finite element analysis (FEA). To assess fault tolerance and system redundancy, Markov chain …

Design Optimization And Scaling Of Coreless Afpm Machines Using Hybrid Fea-Based Differential Evolution And Machine Learning, Matin Vatani, David R. Stewart, Donovin D. Lewis, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents a machine learning (ML) based design framework for the fast and accurate optimization of coreless axial flux permanent magnet (AFPM) machines. Although the absence of magnetic cores eliminates material nonlinearity, the design process remains highly nonlinear due to the complex influence of geometric parameters. To overcome the computational challenges of finite element analysis (FEA)-based optimization, a series of multi-objective differential evolution (MODE) optimizations were conducted across various machine sizes at constant power output. The resulting design data was used to train an artificial neural network (ANN), enabling rapid prediction of machine performance without the need for repeated …

Coreless Axial Flux Permanent Magnet Machines With Concentrated Coils And Various Pole/Coil Combinations, Matin Vatani, Spencer M. Goode-Kulchar, John F. Eastham, Xiaoze Pei, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper comprehensively analyzes coreless stator axial flux permanent magnet (AFPM) machines by investigating rotor magnetic fields, stator winding factors, and 2D/3D finite element analysis (FEA) simulations. The torque production theory in coreless AFPM machines is studied with detailed derivations for flux density and current density. The impact of rotor permanent magnet (PM) width is examined for both surface-mounted and Halbach array configurations, followed by a discussion of its influence on the air-gap harmonic spectrum. The effect of stator coil side width is analyzed through a detailed winding factor study across various pole-to-coil ratios and a discussion on the trade-off …

Voltage And Reactive Power Combined Control Of Utility Devices And Smart Inverters On A Distribution Grid With Solar Pv, Steven B. Poore, Rosemary E. Alden, Evan S. Jones, Thomas Morstyn, Aron Patrick, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

With adoption of distributed energy resources (DERs) expected in future grids, voltage regulation methods need to be reevaluated and improved to ensure their effectiveness under the high volatility of renewable generation. A multi-timescale cluster-based method is proposed to optimize and disperse operation of voltage controlling utility devices including capacitor banks (CBs) and load tap changers (LTCs) while allowing faster response time with customer-owned smart inverters (SIs) in-between switching operations. The proposed method is tested on a digital twin (DT) of a very large utility distribution grid with 2,018 nodes and 8.65MW peak load to evaluate its effectiveness in future grid …

Tensile And Fatigue Properties Of Haynes ® 233 Manufactured By Wire-Arc Additive Manufacturing, Samuel Onimpa Alfred, Frank W. Liou, Mehdi Amiri

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Haynes® 233 is a newly developed nickel-based superalloy currently in the early stages of commercial adoption. With the growing interest in fabricating large and complex components using wire-arc additive manufacturing (WAAM), this alloy presents a promising option for industrial applications. This study investigates the microstructure, tensile, and fatigue properties of heat-treated (HT) WAAM Haynes ® 233 and compares them to its wrought counterpart. Yield strength (YS), ultimate tensile strength (UTS), and fatigue strength of WAAM Haynes ® 233 are 709.4 MPa, 890.1 MPa, and 253.8 MPa, respectively. These values indicate a 63.8 % increase in YS, a 1.11 % decrease …

Detection Of Two Anomalies Behind The Eastern Face Of The Menkaure Pyramid Using A Combination Of Non-Destructive Testing Techniques, Khalid Helal, Polina Pugacheva, Hussien Allam, Mohamed Fath-Elbab, Mohamed Sholqamy, Olga Popovych, Simon Schmid, Benedikt Maier, Alejandro Ramirez, Johannes Rupfle, Thomas Schumacher, Multiple Additional Authors

Civil and Environmental Engineering Faculty Publications and Presentations

The Menkaure Pyramid is the smallest of the three main pyramids on the Giza Plateau. Recently, the possibility of a second entrance to the Pyramid has been hypothesized by Van den Hoven [1], based on similarities between the polished granite blocks covering the Eastern face and the blocks around the main entrance on the Northern face. To test this hypothesis, measurement campaigns using three non-destructive techniques, Electrical Resistivity Tomography (ERT), Ground Penetrating Radar (GPR), and Ultrasonic Testing (UST), were carried out on the Eastern face of Menkaure Pyramid. ERT data was obtained from measurements of four long parallel profiles using …

Recovery Of Daily Water Levels In The Sacramento-San Joaquin Delta, 1915–2023, Serena B. Lee, Steven Dykstra, Reyna Gomez‐Sanchez, Cole Wilkenson, Ricardo Estrada, Nick Mcguire, David A. Jay, Stefan A. Talke

Civil and Environmental Engineering Faculty Publications and Presentations

This manuscript documents the data rescue, digitization, and quality assurance of archival daily maximum and minimum water levels at twenty-five sites within the Sacramento-San Joaquin Delta. The records encompass 1846 total unique years, where 915 years are newly digitized from the 1915–1985 era. The period of record for each gauge location varies from 40 to 109years (median=80 years). Quality assurance procedures and datum corrections were applied to both archival and digital records to generate a time series referenced to a common geocentric datum. Both riverine and coastal influences on mean sea level and great diurnal range are evident in the …

Laboratory Investigation Of High-Temperature Preformed Particle Gels For Fluid Control In Granite Cores For Geothermal Applications, K. Caleb Darko, Yanbo Liu, Thomas P. Schuman, Mingzhen Wei, Baojun Bai

Chemistry Faculty Research & Creative Works

To understand the applicability of high-temperature preformed particle gel (HT-PPG) for control of short-circuiting in enhanced geothermal systems (EGSs), core flooding experiments were conducted on fractured granite cores under varying fracture widths, gel particle sizes and swelling ratios. Key parameters such as injection pressure, water breakthrough pressure, and residual resistance factor were measured to evaluate HT-PPG performance. The gel exhibited strong injectability, entering granite fractures at pressure gradients as low as 0.656 MPa/m; HT-PPG yields a superior sealing performance by significantly reducing the permeability; and dehydration occurs during HT-PPG propagation, with a dehydration ratio ranging from 4.71% to 11.36%. This …

Stochastic Modeling Of Electromagnetic Wave Propagation Through Extreme Dust Conditions In Underground Mines Using Vector Parabolic Approach, Emmanuel Atta Antwi, Samuel Frimpong, Muhammad Azeem Raza, Sanjay Madria

Mining Engineering Faculty Research & Creative Works

Post-disaster underground (UG) mine environments are characterized by complex and rapidly changing conditions, adding extra attenuation to propagating electromagnetic (EM) waves. One such complex condition is the extreme generation of dust and sudden rise in humidity contributing to extra attenuation effects to propagating waves, especially under varying airborne humidity and dust levels. The existing wave propagation prediction models, especially those that factor in the effect of dust particles, are deterministic in nature, limiting their ability to account for uncertainties, especially during emergency conditions. In this work, the vector parabolic equation (VPE) model is modified to include dust attenuation effects. Using …

Machine Learning Classification Of Eeg Responses To Pain-Related Vs Non-Pain-Related Stimulus In Preterm Infants, Lojain Hamwi, Hang Du, Sara Jasim, Xiaogang Wang, Vibhuti Shah, Carol Cheng, Lorenzo Fabrizi, Maria Fitzgerald, Judith Meek, Nicole Racine, Ian Stedman, Rebecca Pillai Riddell

Michigan Tech Publications

INTRODUCTION: Unmanaged pain in preterm infants can lead to long-term developmental consequences. Current pain assessment methods lack specificity, resulting in possible pain mismanagement in Neonatal Intensive Care Units (NICUs). This study explores the application of machine learning (ML) to differentiate between pain-related and non-pain-related cortical activity in preterm infants. OBJECTIVE: To evaluate the performance of ML models in distinguishing cortical EEG activity during a painful procedure in preterm infants across different postmenstrual ages (PMAs). METHODS: This observational study was conducted from June 2015 to May 2024 at Mount Sinai Hospital in Toronto, Canada, and University College London Hospital, United Kingdom. …

Torque-Speed Characteristic Estimation Based On Gaussian Processes And Adaptive Sampling Strategy For Permanent Magnet Synchronous Machines, Marcelo D. Silva, Pedram Asef, Oluwaseun A. Badewa, Rosemary E. Alden, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Internal Permanent Magnet Synchronous Machines (IPMs) are widely used and typically optimized to meet specific performance requirements. Parameters such as base speed, maximum torque, and maximum speed commonly define the torque- speed characteristic of a given design. This study introduces a novel machine learning approach for statistically estimating the torque-speed characteristics of IPMs using Gaussian Process Regression (GPR), which models predictions as random variables. By leveraging uncertainty quantification, the study explores sampling strategies that enable the construction of a high-precision meta-model with minimal error and uncertainty. The proposed adaptive sampling strategy, combined with GPR, accurately estimates torque-speed characteristics and associated …

Degradation Minimization Of Utility-Scale Li-Ion Bess Through Operational Optimization Employing An Equivalent Circuit Model, Kwabena A. Kyeremeh, Grant M. Fischer, Donovin D. Lewis, Aron Patrick, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

The increasing deployment of utility-scale battery energy storage systems (BESS) necessitates effective strategies for supporting grid services while minimizing degradation that may compromise system longevity. High charge/discharge rates (C-rate) and imbalanced operation of multi-unit BESS configurations may accelerate degradation. This paper proposes a degradation-aware operational optimization based on Model Predictive Control (MPC) for coordinating multiple BESS units under physical and operational constraints. The multi-objective optimization model imposes penalties on C-rate magnitude, operational state-of-charge (SoC) disparity, and battery internal resistance modeled using an equivalent circuit model. A case study conducted for a fleet of BESS units over a one-week load profile …

Impact Of Ionizable Lipid Variation On The Immunogenicity Of Lipid Nanoparticles, Chloe M. Krueger, Abbey L. Stokes, Shilpi Agrawl, Christopher E. Nelson

Annual Student Research Poster Session

Lipid nanoparticles (LNPs) are a leading nonviral delivery system for nucleic acid therapeutics due to their scalability and efficiency. However, certain formulations may trigger undesired immune responses. This study aimed to assess the inflammatory potential of LNPs formulated with different ionizable lipids using a murine macrophage reporter cell line (RAW-IRCs). RAW-IRCs express GFP upon successful mRNA delivery and mRFP1 upon activation of inflammatory pathways. A library of LNPs was synthesized via vortex mixing and characterized for hydrodynamic diameter, polydispersity index, and encapsulation efficiency. Treated RAW-IRCs were analyzed by flow cytometry to evaluate GFP and mRFP1 expression. Our results highlight seven …

Evaluating Machine-Learning Models For Drone-Based Image Analysis Of Coastal Ecosystem Dynamics, Katharine Mcdaniel, Pragati Toppo

College of Engineering Summer Undergraduate Research Program

Previous work from our group utilized drone-based images and a novel machine learning classification model to quickly and accurately quantify spatial variability in eelgrass in Morro Bay. In this SURP project we aim to develop and evaluate quantifiable estimates of the model’s prediction uncertainty. Our model produces a binary labeling indicating the presence or absence of eelgrass at each pixel. However, internally the model produces a probabilistic output indicating the likelihood of detection at each pixel that we propose to use to produce uncertainty estimates and a confidence interval for the cumulative extent of eelgrass detected. This would allow for …

Design Of Ballistic Armor Using Multi-Composite Sandwich Structures With Nanomaterial Additives, Carmen Alaichamy

College of Engineering Summer Undergraduate Research Program

This study explores the bulletproof capabilities of Kevlar composites fabricated using advanced additive manufacturing techniques. Continuous fiber reinforced 3D printing (CFRP) is employed to produce Kevlar-reinforced polymer matrix composites (PMCs) with optimized structural configurations for ballistic protection. The primary objective is to evaluate the impact resistance of these composites against 9mm projectiles through systematic experimental testing. A series of ballistics tests are conducted using a high-velocity gas-gun system to fire 9mm projectiles at varying speeds angles. The tests will be conducted at the San Luis Obispo Range, with projectiles fired at various angles. The effects of fiber volume fraction, reinforcement …

Investigation Of Its-Cvo Technologies, Mallory Brown, Andrew Martin, Valerie Keathley-Helil, Jennifer Walton

Kentucky Transportation Center Research Report

Staff shortages and reduced budgets have made transportation agencies increasingly reliant on intelligent transportation systems (ITS) for commercial motor (CMV) vehicle enforcement activities. Technologies such as license plate readers, US Department of Transportation (USDOT) number readers, weigh-in-motion scales, static scales, and preclearance systems like PrePass and Drivewyze help automate enforcement and free up personnel to focus on inspecting those vehicles most likely to pose compliance and/or safety risks. To document adoption rates of different technologies, researchers surveyed transportation agencies, vendors, and law enforcement agencies. Surveys responses provided useful insights into how jurisdictions are approaching the use of advanced technologies to …

Air Traffic–Collegiate Training Initiative Program Feasibility In Kentucky, Gayle Marks, Bryan Gibson

Kentucky Transportation Center Research Report

Each day, Federal Aviation Administration (FAA) air traffic control specialists (ATCSs) handle over 44,000 flights and oversee 29 million square miles of the United States National Airspace System. However, staffing shortages have grown more acute over the past 10 years, which has led to delayed and cancelled flights, a higher number of close misses over a short period of time, and frustrating air travel experiences. While the FAA has redoubled its efforts to recruit more ATCSs, it has limited training capacity. To expand training options available to prospective ATCSs, the FAA is working with postsecondary educational institutions to expand its …

Learn To Fly: Enabling Deep Learning Based Perception And Control In Aerial Robotics, Krishna Muvva

School of Computing: Dissertations, Theses, and Student Research

Uncrewed Aerial Vehicles (UAVs) are increasingly deployed in dynamic, GPS degraded, and cluttered environments, yet their autonomy remains fundamentally constrained by limitations in onboard perception and real-time control. This dissertation addresses these challenges by proposing a unified framework that co-designs deep learning-based perception and model-based control, organized around three core thrusts: Learn to Track, Learn to Localize, and Learn to Evade.

Learn to Track develops dynamic and adaptive perception control mechanisms that optimize CNN inference for target tracking. A control-aware CNN framework dynamically adjusts inference frequency based on UAV motion, reducing latency while maintaining visual lock. An adaptive CNN with …

Technology Demonstration, University Of North Dakota. Energy And Environmental Research Center

EERC Brochures and Fact Sheets

Fact sheet about the Energy & Environmental Research Center’s technology demonstration abilities. Includes information on combustion systems, gasification and gas cleanup systems, and chemical and liquid processing.

The Effect Of Fascicular Elastin On The Mechanical And Functional Properties Of Healthy, Damaged, And Healing Tendon, Shawn Pavey

McKelvey School of Engineering Graduate Student Theses & Dissertations

Mechanical properties of tendon are highly influenced by structural protein composition and microscopic sub-structures. Within the largest subunit of tendon, the fascicle, the role of the elastin protein remains understudied despite impressive extensibility and fatigue resistance of its resulting elastic fibers. While previous work catalogued contributions of fascicular elastin across tendon type and species, the anticipated effects of elastin in fatigue and healing have not yet been explored. While previous knockout mouse models showed that disruption of elastic fibers led to altered mechanical properties (e.g., increased linear modulus), these models depended on heterozygous elastin deficiency or indirect knockout of proteins …

Mems 4110: Arduino-Controlled Precise Water Distribution System, Jack M. Williams, Joe Sieracki, Nina Woodward

Mechanical Engineering Design Project Class

Christina Youngepeter is a Graduate Student of Archaeology at Washington University in St. Louis. She is studying an ancient plant that fell out of use after 1400 CE, whose ideal watering conditions are lost to time. To better characterize the plant, Christina is running an experiment to determine the ideal watering conditions for plant development. This involves watering sets of the plant with different volumes of water twice a day. The different volumes of water received by the groups of plants are 933 mL, 833 mL, 733 mL, 617 mL, 517 mL, and 417 mL. The acceptable percent error in …

Mems 4110: Trash-E (Terrain-Adaptive Remote Autonomous Sanitation Hauling Engineer), Jackson R. Weisbard, Raza Rabbani, Dhruv Goel, Kj Kernan

Mechanical Engineering Design Project Class

The TRASH-E (Terrain-Adaptive Remote Autonomous Sanitation Hauling Engineer) project was created to compete in the 2025 ASME Student Design Challenge. For 2025, the ASME SDC asked for the development of a remotecontrolled robot that could collect and deposit trash from receptacles placed around a miniature city. The robot had to adhere to specific size and weight constraints, as well as obey traffic laws in the same manner as a real driver operating a motor vehicle.