Engineering Commons^™

Experimental Investigation And Performance Optimization Of A Double Slope Solar Still Integrated With Nanoparticles In Phase Change Materials, Pronob Das, Md Shahriar Mohtasim, Utpol K. Paul, Md Sanowar Hossain, Barun K. Das, Anik Saha, Md Golam Kibria

Research outputs 2022 to 2026

By utilizing solar energy for desalination, solar stills offer a sustainable and cost-effective means of providing fresh drinking water in remote and arid regions. Existing studies have primarily focused on improving freshwater productivity while considering economic and environmental feasibility. The present work offers an in-depth assessment of solar still (SS) systems by analyzing energy and exergy performance, exergoeconomic factors (energy-economic factor, exergoeconomic factor, and cost of water), environmental impacts (CO2 emissions, exergo-environmental factor, and carbon credit gained), and sustainability indicators (energy payback time and sustainability index). Five different cases were examined: (I) a conventional solar still (CSS), (II) CSS with …

Mission-Focused Multidisciplinary Design Optimization Of Tilt-Rotor Evtol Propulsion System, Tyler Critchfield, Andrew Ning

Faculty Publications

Tilt-rotor propulsion system design requires a multidisciplinary approach to tackle important challenges and competing tradeoffs between disciplines. In this paper, we model rotor aerodynamics, blade structures, vehicle drag, electric propulsion, and tonal/broadband acoustics for a tilt-rotor, electric vertical takeoff and landing aircraft using low-to-mid fidelity tools. We use gradient-based design optimization with automatic differentiation and parameter sensitivity analyses to explore the design space and complex tradeoffs of tilt-rotor distributed electric propulsion systems, exploring effects of variations in payload/empty weight, battery specific energy, and blade tip speed. This framework models multiple operating points with a mission-focused objective to account for the …

Communication-Aware Energy Optimization For Electric Vehicles With Adaptive Cruise Control, Shahriar Shahram

Graduate Studies Theses and Dissertations 2026

This dissertation develops information-driven methods to reduce traction energy in battery electric vehicles during adaptive and cooperative cruise control. Physics-grounded energetics are embedded in a predictive controller that accounts for intermittent V2V preview, sensing noise, packet loss, and powertrain limits. To ensure deployability, the nonconvex traction–power map is replaced by locally convex surrogates so each step solves a small, strictly convex QP in real time (average ≈ 70 ms/step on a desktop CPU: 8 cores/16 threads, 4.2–5.0 GHz), leaving margin at typical sampling rates (Ts =0.05–0.10 s; N=15–25).

Across standardized drive cycles from NREL DriveCAT—including FTP–75 (light duty), NREL Class …

Machine Learning And Multi-Scale Optimization For Control And Energy Management In Connected And Automated Vehicle Propulsion Systems, Joshua D. Orlando

Dissertations, Master's Theses and Master's Reports

This dissertation presents a multi-scale optimization framework leveraging machine learning (ML) to enhance energy efficiency in connected and automated vehicle (CAV) propulsion systems. As transportation transitions toward hybridization and automation, the integration of vehicle-to-everything (V2X) connectivity and advanced control algorithms offers unprecedented opportunities for energy reduction. This research addresses three critical scales of vehicle energy management: multiple vehicle-level coordination, component-level powertrain dynamics, and real-time vehicle parameter estimation.

First, the research investigates the energy consumption characteristics of heterogeneous propulsion systems—ranging from internal combustion engines to battery electric vehicles across light- and heavy-duty sectors—on arterial roadways. Utilizing Particle Swarm Optimization (PSO) and …

Models And Algorithms Of Control Mechanisms In Information Exchange Processes, Madina M. Fozilova, Dilshoda N. Uchqunova

Chemical Technology, Control and Management

In modern digital systems, efficient and reliable information exchange is essential for the stability of corporate systems. Traditional data management models struggle to detect and eliminate invalid, incomplete data at early stages, resulting in reduced accuracy and system inefficiency. This article proposes an advanced framework for controlling information exchange processes through the development of a Verification and Filtering algorithm. The algorithm operates within a multi-layered conceptual model that includes data input, control, validation, optimization, and decision layers. Acting as the core component, the Verification and Filtering algorithm distinguishes valid from invalid records in real time, ensuring data integrity before storage. …

Graph Based Planning With Guarantees, Trazon Tyrik Jimerson

Mechanical Engineering ETDs

This thesis presents two graph-based methods with formal guarantees for motion planning and routing. First, the invariant-set motion planner (ISMP), which uses constraint admissible positive invariant (CAPI) sets of closed-loop dynamics, is adapted for spacecraft attitude planning to avoid moving keep-out zones. Contributions include time bounds for maneuvers via exponential stability, a single-stage reachability graph from one-step backward reachable CAPI sets, and its multi-stage expansion to certify node safety over time. Simulations verify safe attitude control with moving obstacles. Second, we formulate a convex optimization problem over a network for evacuation planning with operational constraints such as helicopter capacity and …

Nonlinear Excitation Control Of Multimachine Systems Via The Invariant-Set Design, Ehab Bayoumi

Mechanical Engineering

Power grids are inherently vulnerable to many uncertainties. All power networks are prone to instability because of the uncertainties inherent in the operation of power systems. Rotor-angle instability is a challenging issue, and if not properly managed, could give rise to cascading failures and even blackouts. This paper addresses the generator excitation system’s state feedback sliding mode control (SMC). The global system is divided into multiple subsystems to achieve decentralized control. A disturbance is defined as the influence of the system as a whole on a specific subsystem. The state-feedback controller is to be designed taking into account the disturbance …

Optimizing Xo Throughput At Imerys, Derek Beasley, Brennan Chandler, Baris Erarslan, Cynthia Grogin

Senior Design Project For Engineers

This project is conducted in collaboration with Imerys to optimize the performance of the VSI (Vertical Shaft Impactor) system at their Plant 4 facility, focusing on improving the production of XO, a fine material used in products such as textured coatings, acid neutralization, and water filtration systems. The VSI operates continuously, producing several materials at the Marble Hill, Georgia location, including #1 Chip, #2 Chip, OZ, XO, Z, and 30–50. The main focus of this project is to increase the throughput of XO, as it requires both throughput improvement and product quality consistency to meet growing market demand. Imerys’ quality …

Design And Optimization Of High-Frequency Medium-Voltage Dual Active Bridge Dc/Dc Converter Using Sic Device, Hui Cao

Graduate Theses and Dissertations

High frequency Dual Active Bridge (DAB) converters have become a key enabling technology in medium- and high-power applications such as renewable energy integration, electric vehicle fast charging, and emerging DC power distribution systems. However, when operating at high power and high frequency, DAB converters face critical challenges including limited soft-switching range, uneven semiconductor loss distribution, deadtime-induced resonance, switching-frequency constraints of wide-bandgap (WBG) devices, and transformer DC bias during dynamic transitions. This dissertation addresses these challenges through advancements in modulation strategies and converter architecture. First, an enhanced triple-phase-shift (E-TPS) modulation scheme is proposed to achieve balanced switching and conduction losses across …

Optimization Of Size And Siting Of Distributed Generation In Unbalanced Distribution Systems: A Literature Review, Pema Dorji, Stefan Lachowicz, Octavian Bass

Research outputs 2022 to 2026

Renewable energy sources (RES) are essential for meeting the rising global electricity demand while reducing greenhouse gas emissions from conventional generation. As traditional systems approach capacity saturation, the integration of RES into power grids becomes increasingly vital. However, the intermittent and variable nature of RES introduces significant technical, economic, and operational challenges. This review focuses on the optimal planning and integration of distributed generation in unbalanced distribution systems, which more accurately reflect real-world power network conditions. Emphasis is placed on siting and sizing strategies aimed at enhancing voltage stability, minimizing power losses, and reducing system costs and emissions. The review …

Optimization Of Bioethanol From Plantain Peel Using Saccharomyces Cerevisiae, Akinjide A. Akinola, Olawole O. Olanipekun, Paul A. David

Mansoura Engineering Journal

This research aimed to evaluate the potential of using plantain peels as a raw material for producing bioethanol with the help of Saccharomyces cerevisiae. The study utilized a four-factor Box-Behnken design (BBD) and response surface methodology (RSM) to optimize the fermentation conditions. The factors considered for optimization were substrate concentration (1-4 g), pH (5-7), temperature (30-45°C), and fermentation time (24-96 hours). Through this optimization process, the study found that the optimal conditions for bioethanol production were 4 g substrate concentration, pH 6, 45°C temperature, and 60 hours of fermentation time. Utilizing these optimal conditions resulted in a bioethanol yield of …

A Novel Hybrid Intrusion Detection Model: A New Metaheuristic Approach For Feature Selection Based On Ai Techniques For Cyber Threat Detection, Maryam Mahdi Alhusseini, Alireza Rouhi

Iraqi Journal for Computer Science and Mathematics

The rapid increase in internet usage, digital transformation, and the rise of interconnected devices have greatly expanded the attack surface, introducing new and evolving cybersecurity challenges. Conventional security solutions frequently have difficulty adjusting to complex threats and the vast dimensionality of network traffic data, particularly in the case of imbalanced datasets. To tackle these challenges, this research introduces a Hybrid Intrusion Detection System (HyIDS-EVO) that combines the Energy Valley Optimizer (EVO) for feature selection and dimensionality reduction with machine learning classifiers, which include Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN). The system’s effectiveness …

Integrated Optimization And Data-Driven Modeling For Seawater Intrusion Mitigation And Prediction, Assaad Hassan Kassem

Thesis/ Dissertation Defenses

Seawater intrusion (SWI) threatens the reliability of coastal groundwater especially in hyper-arid settings, where climatic stress and pumping accelerate salinization. This dissertation advances two complementary approaches to managing SWI: Part A optimizes mitigation measures, hydraulic (pumping/injection) and physical barriers (cutoff walls, subsurface dams) on benchmark models; Part B predicts SWI in the hyper-arid Fujairah (UAE) coastal aquifer using total dissolved solids (TDS) as a proxy, through machine learning-based models, spatially and dynamically. A bibliometric synthesis first maps the evolution of SWI models and mitigation strategies, identifying gaps that motivate the subsequent methodological developments. Part A employs the classical Henry problem …

Intelligent Decision-Making Systems In Smart Greenhouses, Muso Berdiyor Ugli Allanov

Chemical Technology, Control and Management

Smart greenhouses offer a solution to sustainable food production under climate uncertainty, yet their management often depends on fixed rules or human intuition. This study proposes an intelligent decision-making framework that integrates optimization, simulation, and a neural set into a self-learning system. By generating “conditionally real data” through simulation and evolutionary algorithms, the system can predict microclimatic changes and optimize control of water, energy, and nutrients. Continuous digital feedback enables adaptive, data-efficient operation even with limited real data. Experimental results demonstrate reduced resource use and improved yield stability, advancing the development of autonomous and resilient greenhouse ecosystems.

Microstrip Antenna Design Based On Ai And Machine Learning, Waleed Mohamed Sha Moulavi

Theses

Microstrip patch antennas (MPAs) rely on precise impedance matching for efficient power transfer between the antenna and feed line. This is often achieved using a number of different techniques, one of which is the quarter-wavelength transformer (QWT). While commercial electromagnetic (EM) solvers offer robust optimization capabilities, they often operate as "black boxes" without providing physical insights into parameter interdependencies. Furthermore, this thesis focuses on the specific scenario where the antenna input impedance is purely real. To address the lack of explicit design relationships for these specific conditions, this thesis develops and comparatively evaluates artificial intelligence (AI) models for QWT width …

Environment Mapping And Gps-Based Trailer Parking Using Low-Cost Peripheral Sensors And Post-Processing Algorithms, Connor Best

Journal of Undergraduate Research at Minnesota State University, Mankato

This paper explores the merit of software data optimization through two practical examples: environment mapping & GPS navigation.

Characterizing Surface Waviness Of Aluminum Alloy: An Approach To Minimize Post-Processing In Wire Arc Additive Manufacturing (Waam) Production, Shammas Mahmood Shafi, Anis Fatima, Nicholas V. Hendrickson

Michigan Tech Publications

Wire Arc Additive Manufacturing (WAAM) offers high deposition rates and cost-effective production of large metal components but suffers from poor surface quality, particularly surface waviness, which increases post-processing requirements and limits industrial adoption. Since waviness directly impacts structural integrity, resource efficiency, and industrial applicability, understanding how process parameters govern this feature is critical for reducing post-processing requirement. This study systematically investigated the influence of voltage, travel speed, and wire feed speed on surface waviness in aluminum alloy walls fabricated by WAAM. A two-level factorial design with 16 experiments was conducted, and surface waviness was quantified using height gauge measurements relative …

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 …

Lifting-Line Predictions For Optimal Dihedral Distributions In Ground Effect, Amanda K. Olsen, Zachary S. Montgomery, Douglas F. Hunsaker

Mechanical and Aerospace Engineering Student Publications and Presentations

When a flying wing comes within close proximity to the ground, a phenomenon called ground effect occurs where the lift is increased and the induced drag is decreased. This research seeks to determine the optimal dihedral distribution predicted by lifting-line theory that minimizes induced drag in ground effect. Despite some limitations, using lifting-line theory for this study allows for quick results across a large range of design variables, which would be infeasible for high-fidelity methods. The SLSQP optimization method is used along with a numerical lifting-line code to find the dihedral distribution that minimizes induced drag. Results are presented showing …

Pressure And Force Dynamics In Artificial Muscle Actuators: A State-Space And Optimization-Based Approach, Mohammad Elzein

Dissertations and Theses

This two-part investigation explores the dynamic behavior of braided pneumatic actuators (BPAs) under bio-inspired pulse modulation, with the aim of improving their biomimetic force output and control. The first study examines the effect of pulse length and inter-pulse timing on BPA performance, revealing that force output is highly sensitive to the temporal structure of input pulses mirroring biological muscle behavior. Using dual-pulse actuation schemes, the results demonstrate that force responses exceed the additive contributions of individual pulses, with peak amplification occurring consistently at a 27 ms inter-pulse gap. Shorter pulse lengths (10–20 ms) yielded the highest normalized force increases, up …

Tools To Design Algorithms For Implementing Control Over Quantum Computers, Shilpa Narashimhan, Jihan Abou Halloun, Kip Nieman, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Quantum computers (QCs) may find future applications within control systems that operate manufacturing processes. For application within control engineering, quantum algorithm development must be led by control engineers. However, control engineers may face challenges in designing quantum algorithms for control engineering problems. In this work, we provide several path-finding studies that leverage engineering tools such as optimization, encryption, and computational "short-cuts" toward making algorithm design for QC easier for control engineers.

Optimized Hybrid Watermarking: Dual-Scheme Strategies For Enhanced Robustness, Ooi Jessie, Liew Siau Chuin, Syifak Izhar Bt Hisham, Khor Hui Liang, Khoo Bee Ee, Jasni Mohamad Zain

Iraqi Journal for Computer Science and Mathematics

Digital watermarking is crucial in content identification and copyright protection, particularly multimedia and medical imaging. This paper introduces two novel hybrid watermarking methods, Entropy-Guided Singular Embedding (EGSE) and Entropy-Guided Hybrid Embedding (EGHE), that improve upon existing techniques by integrating entropy-based adaptive block selection with Particle Swarm Optimization (PSO) for dynamic embedding strength determination. Unlike traditional methods, which rely on fixed embedding regions or manual parameter tuning, the proposed approaches automatically identify high-entropy regions to embed watermark signals, ensuring stronger resistance to distortion while maintaining image quality. EGSE employs Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD), whereas EGHE enhances …

Genetic Algorithm Optimization To Maximize Sensitivity Of Triboelectric Mems Accelerometers, Yu Tian, Benyamin Davaji, Shahrzad Towfighian

Mechanical Engineering Faculty Scholarship

A genetic algorithm is used to optimize the sensitivity of a triboelectric MEMS accelerometer fabricated using CMOS-compatible processes. The optimization focuses on the structural parameters of an awl-shaped serpentine microspring (ASSM) connected to the proof mass. Constraints are defined by microfabrication standards and the available chip area. Three different constraint problems are considered, and the algo- rithm yields designs with maximized sensitivity in each case. Simulations under various sinusoidal vibration scenarios show that the optimized designs significantly outperform the previous version, achieving a 2.72× improvement at 1000 Hz and up to 20× at 500 Hz.

Fleet Algorithm Design For Pooled Rideshare: Integrating Human Factors, Simulation, And Optimization, Joseph Paul

All Dissertations

This dissertation explores the study the integration of human factors modeling and rideshare fleet control algorithms. Pooled rideshare is a unique transportation mode offering that allows riders increased flexibility and accessibility over public transportation, and decreased cost relative to personal vehicles or traditional rideshare. Additionally, relative to personal vehicles, pooled rideshare offers reduced costs and options for those with difficulty obtaining transportation. Prior research in the space typically focused on modeling human behavior, or optimizing system performance, but a lack of integration of the concepts leads to unrealistic or underutilized outcomes. To tackle this problem, novel rideshare assignment, and repositioning …

A Provable Semi-Infinite Programming Approach For Solving Dynamic Nash Games, Tyler C. Gardner

All Graduate Theses and Dissertations, Fall 2023 to Present

Many engineering problems must account for the non-cooperative decisions and actions of multiple players. These problems can be modeled within a game-theoretic framework. The approach herein is to model problems as Nash games, convert them to semi-infinite programs, and leverage provable semi-infinite algorithms to solve the original problem. A particular algorithm that leverages off-the-shelf solvers is used to solve four low-dimensional benchmark problems successfully. Two types of linear quadratic dynamic games are then investigated: ones where each player’s problem is convex and ones where at least one player’s problem is nonconvex. Within each type, variations based on information structure, communication …

Evaluating The Impact Of Maximum Aero-Thermal Heating On A High-Speed Fixed-Wing Atmospheric Aircraft Optimum Trajectory, Benjamin M. Ford

All Graduate Theses and Dissertations, Fall 2023 to Present

This study explores how extreme heating affects the flight paths of hypersonic aircraft—vehicles that travel at speeds many times faster than sound. Analyzing how to fly a high-speed aircraft without overheating it can prevent structural failure while operating at maximum possible performance. The results shown in this section indicate that it is possible to operate at very high speeds in an aircraft while keeping peak vehicle temperature below our limits while saving fuel.

Impact Of Print Speed And Nozzle Temperature On Tensile Strength Of 3d Printed Abs For Permanent Magnet Turbine Systems, Wirawan Wirawan, Hilmi Iman Firmansyah, Satworo Adiwidodo, Mohammad Sukri Mustapa

Journal of Mechanical Engineering Science and Technology (JMEST)

Operational parameters must be integrated into turbine systems' main components, which are determined by turbine systems' functional requirements. The need for producing component designs more effectively raises the possibility of using additive manufacturing. The study focuses on the optimization of the mechanical properties of the principal components of magnetic turbines manufactured with 3D printers using Acrylonitrile Butadiene Styrene (ABS), by changing the temperature and speed of the nozzle. The approach consisted of modeling a standard test piece in CAD software and producing ABS-based test pieces using a 3D printer with print speeds of 50, 70, 90, and 110 mm/s and …

Logistical Automation In Hospitals In The Dach Region And Approaches To Increasing Process Efficiency Through Automation, Emily C. Spicker, Sabrina Forster, Sonja Stabenow, Sidra Rashid, Johannes Fottner, Dirk Wilhelm

International Material Handling Research Colloquium

No abstract provided.

Being Efficient Or Responsive In The Order Picking Process, Jelmer Pier Van Der Gaast

International Material Handling Research Colloquium

No abstract provided.

Multi-Parameter Optimization Of Brine Desalination Using Machine Learning, Elaf Mman Seif

Theses and Dissertations

Air Gap Membrane Distillation (AGMD) is a promising desalination technology with significant potential for addressing global water scarcity. However, the interplay of operational parameters significantly impacts its performance, making optimization a challenging task. This research focuses on brine desalination as a means to mitigate the negative environmental impacts of brine disposal which will eventually help in provide a sustainable solution for handling brine while producing freshwater. The study seeks to develop a predictive model and optimize the AGMD process for efficient brine desalination. To achieve this, Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) were utilized to develop predictive …