Engineering Commons^™

Model Predictive Control Of A Grid-Scale Thermal Energy Storage System In Relap5-3d, Jaron Wallace, John Hedengren, Kody Powell, Matthew Memmott

Faculty Publications

This research delves into the integration and control of a Thermal Energy Storage (TES) system with a Small Modular Reactor (SMR), specifically the NuScale VOYGR SMR module in RELAP5-3D. The research methodology centered on modeling the NuScale VOYGR SMR, a light water pressurized water reactor (LWR) with a power output capacity of 77 MWe per module. The reactor and plant details were sourced from NuScale's final safety analysis report and supplemented by information from the NuScale website. The SMR plays a crucial role in energy generation, and to manage and dispatch the produced energy effectively, a robust storage system …

Introduction To Robotics Systems Assignment 2, Tarek Elderini

AI Assignment Library

An assignment was reformatted with respect to TILT using both Claude 3 and ChatGPT. One of the problems is that whatever pics in the file will not be added to the reformatted file. Also, I asked ChatGPT and Claude 3 to make a comparison between the two created assignments showing the pros and cons for each at the end. It was funny that both agreed that ChatGPT was more professional from the aspect of an engineering assignment. This document contains the original file, the 2 formatted assignments, and the comparison at the end.

Comparative Analysis Of Muskingum Routing: Traditional Vs. Ai-Assisted Methods, Vida Atashi

AI Assignment Library

In this assignment, students will apply traditional and AI-assisted Muskingum routing methods to real-world discharge data from the USGS over a 10-day period. The goal is to compare these approaches, enhancing skills in hydrological modeling and data analysis crucial for water resources engineering.

A Simulated Annealing Approach To The Scheduling Of Battery-Electric Bus Charging, Alexander Brown, Greg Droge

Electrical and Computer Engineering Faculty Publications

With an increasing adoption of battery-electric bus (BEB) fleets, developing a reliable charging schedule is vital to a successful migration from their fossil fuel counterparts. In this paper, a simulated annealing (SA) implementation is developed for a charge scheduling framework for a fixed-schedule fleet of BEBs that utilizes a proportional battery dynamics model, accounts for multiple charger types, allows partial charging, and further considers the total energy consumed by the schedule as well as peak power use. Two generation mechanisms are implemented for the SA algorithm, denoted as the "quick" and "heuristic" implementations, respectively. The model validity is demonstrated by …

Understanding And Enhancing Linux Kernel-Based Packet Switching On Wifi Access Points, Shiqi Zhang

Computer Science and Engineering Master's Theses

As the number of WiFi devices and their traffic demands continue to rise, the need for a scalable and highperformance wireless infrastructure becomes increasingly essential. Central to this infrastructure are WiFi Access Points (APs), which facilitate packet switching between Ethernet and WiFi interfaces. Despite APs’ reliance on the Linux kernel’s data plane for packet switching, the detailed operations and complexities of switching packets between Ethernet and WiFi interfaces have not been investigated in existing works. This paper makes the following contributions towards filling this research gap. Through macro and micro-analysis of empirical experiments, our study reveals insights in two distinct …

09.09.2024 Orsp Connect, Liz Williamson

ORED Newsletter

New training course available: Sponsored Travel Guidance

OpenScholar Overview

Learn about Nature Masterclasses On-Demand Training

NSF Sessions on Phase 1 ISBIR/STTR Proposals

All You Need Is Unary: End-To-End Bit-Stream Processing In Hyperdimensional Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi

Faculty Scholarship

Hyperdimensional Computing (HDC) is a brain-inspired computing paradigm introduced to achieve energy efficiency with a lightweight and single-pass training model. Hypervectors (HVs) at the heart of the HDC systems play a fundamental role in elevating the accuracy and obtaining the desired performance. Image-based HV encoding requires two types of HVs: Position and Level HVs. State-of-the-art approaches utilize pseudo-random methods for generating these HVs, which might degrade system performance and cause higher power consumption due to poor randomness in HV generation. These conventional methods require iteratively calculating orthogonal Positional HVs for acceptable accuracy. This work proposes a fast, ultra-lightweight, and high-quality …

The Role Of Micromobility In The Changing Urban Mobility Landscape, Mostafa Jafarzadehfadaki

All ETDs from UAB

The growing prevalence of micromobility has significantly impacted urban mo-‎bility, offering a sustainable and convenient alternative for short trips in urban settings. ‎As cities grapple with increasing congestion, pollution, and the demand for efficient ‎mobility solutions, micromobility options like e-scooters and e-bikes have emerged as ‎popular modes of transportation, particularly in densely populated areas. While earlier ‎research has contributed to understanding micromobility and its impact on urban set-‎tings, significant knowledge gaps still exist. Therefore, a comprehensive study is neces-‎sary to explore the multifaceted dimensions of e-scooter adoption, usage patterns of mi-‎cromobility, and their implications for urban transportation systems and traffic …

Reduced Order Modeling (Rom) Using Machine Learning Techniques For Analyzing Fluid Flow Around A Ground Vehicle, Emmanuel Ong'aro Ramogi

All ETDs from UAB

Predicting wind and temperature fields around military vehicles during extended missions is crucial to avoid detectability by infrared (IR) devices. Also, the abrupt shifting of wind direction can have a significant impact on vehicle stability. This is a challenging task due to the vehicles' geometric complexity and the unpredictable nature of wind direction, which can shift abruptly. Computational Fluid Dynamics (CFD) is routinely used for calculating the flow fields around ground vehicles. However, this requires extensive computational time and memory, making it unsuitable for real-time analysis. To address these challenges, this research focuses on machine learning (ML) techniques for accurate …

Redwater Test Data, Paul Van Susante, Kris Zacny

Michigan Tech Research Data

The RedWater mission is to extract water from subterranean glaciers found on Mars. Honeybee robotics has contracted the PSTDL to conduct small scale tests in order to collect data on the power consumed by a high-density cartridge heater to melt through cryogenic clear ice at Martian atmospheric pressure.

High-Frequency-Based Transmission Line Percentage Differential Protection With Traveling Wave Alignment, Igor F. Prado, Flavio Costa, Kleber M. Silva, Rodrigo P. Medeiros, Bruce A. Mork

Michigan Tech Publications

This paper presents a wavelet-based differential protection algorithm for transmission lines. It uses restraint and operating components obtained with high-frequency components of a few kHz from instantaneous energy values of the real-time boundary stationary wavelet transform instead of low-frequency components from phasor estimation. It does not require capacitive current suppression as well. Therefore, the proposed method overcomes the limitations of conventional percentage differential protection. Furthermore, the proposed technique uses traveling wave theory to perform the current sample alignment at a few kHz, thereby, not requiring the global positioning system (GPS). The performance of the proposed method is evaluated through extensive …

Integrated Environmental Vulnerability Assessment And Adaptation Strategies For Coastal Areas Under Sustainable Development, Lien-Kwei Chien, Yu-Chi Li, Chia-Feng Hsu

Journal of Marine Science and Technology–Taiwan

This research focuses on the holistic management and environmental vulnerability of coastal areas in Taiwan within the framework of sustainable development. With economic and social growth gravitating towards coastal regions, the strain on the natural environment is increasing. Therefore, discovering a balance between economic progress and environmental conservation is paramount. To decipher the vulnerability of Taiwan's coastal zones, this study first defines ‘Integrated Environmental Vulnerability of Coastal Areas.’Key vulnerability factors were identified across environmental, social, and economic dimensions. Seven core determinants were determined using the Fuzzy Delphi method: biodiversity, coastal erosion, water pollution, population density, population aging, land utilization, and …

Evalution Of The Ability To Infer Tilt Angle And Size Distributions Of Fish Using A Broadband Scientific Echosounder Based On Simulation, Jing Liu

Journal of Marine Science and Technology–Taiwan

The biological information, such as species, size, and tilt angle, is crucial for converting the echo data into biomass information in acoustic surveys. Typically, the information can be obtained through trawl net sampling or underwater camera observations. However, both methods have some limitations. To overcome these limitations, scientists have utilized inversion methods with multi-frequency and broadband echosounders to derive biological information about fish, plankton, and krill. However, evaluating the reliability and accuracy of these inversion methods has been challenging due to the difficulty in obtaining accurate biological information. In this study, a numerical simulation method was used to generate fish …

Prompt Engineering Principles For Generative Ai Use In Extension, Paul A. Hill, Lendel K. Narine, Aubree L. Miller

Journal of Extension

The prevalence of Generative AI (GenAI) and Large Language Models (LLMs) is increasing rapidly. For Extension professionals, the utilization of prompt engineering is key to leveraging GenAI and LLMs effectively. Prompt engineering involves crafting prompts that elicit desired LLM responses. This article discusses prompt engineering principles, providing examples and guidance. The application of prompt engineering in Extension is explored, showcasing its potential to enhance programs, deliver personalized advice, engage audiences, and disseminate research-based information. By learning prompt engineering skills, Extension professionals can harness the power of GenAI and LLMs, enhancing their ability to address complex challenges in the 21st century.

Common Ground Newsletter Fall 2024, Missouri University Of Science And Technology

Common Ground

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Mechanics Cognitive Diagnostic: Mathematics Skills Tested In Introductory Physics Courses, Vy Le, Ben Van Dusen, Jayson M. Nissen, Xiuxiu Tang, Yuxiao Zhang, Hua Hua Chang, Jason Morphew

School of Engineering Education Faculty Publications

Physics instructors and education researchers use research-based assessments (RBAs) to evaluate students' preparation for physics courses. This preparation can cover a wide range of constructs including mathematics and physics content. Using separate mathematics and physics RBAs consumes course time. We are developing a new RBA for introductory mechanics as an online test using both computerized adaptive testing and cognitive diagnostic models. This design allows the adaptive RBA to assess mathematics and physics content knowledge within a single assessment. In this article, we used an evidence-centered design framework to inform the extent to which our models of skills students develop in …

Characterising Stem Ways Of Thinking In Engineering Design (Ed)-Based Tasks, Ravishankar Chatta Subramaniam, Caden L’Fontaine, Amir Bralin, Jason Morphew, Carina M. Rebello, Sanjay Rebello

School of Engineering Education Faculty Publications

Investigating students' thinking in routine classroom tasks, especially in science and engineering, is crucial. Given the rising interest in STEM Ways of Thinking (SWoT), in this exploratory study, we focus on two multi-week Engineering Design tasks within an undergraduate physics laboratory. Given that the term 'ways of thinking' has varied interpretations, we aim to further the discourse by identifying four SWoTs: Design Thinking, Physics Concepts, Mathematical Constructs, and Metacognitive Reflection. Analyzing discussions from 14 student-groups reveals notable differences in how students solve an instructor-assigned challenge given earlier in the semester and a student-generated challenge later in the semester. Students considered …

Multimodal Mixing Convolutional Neural Network And Transformer For Alzheimer’S Disease Recognition, Junde Chen, Yun Wang, Adnan Zeb, M. D. Suzauddola, Yuxin Wen

Engineering Faculty Articles and Research

Early recognition of Alzheimer’s disease (AD) and its precursor state, mild cognitive impairment (MCI), is pivotal in interrupting the progression of the disease and providing suitable treatment. Recent development in deep learning techniques has drawn great research attention for improving the efficacy of AD recognition. However, numerous current methods solely utilize data from a single auxiliary domain, limiting their ability to harness valuable intrinsic insights from multiple domains. To cope with the challenge, this paper is devoted to establishing an innovative multimodal medical data fusion model, termed as MMDF, to perform Alzheimer’s disease recognition. Multimodal data including clinical records and …

Cyber Threat Intelligence Sharing In Nigeria, Muhammad Abubakar Nainna, Julian Bass, Lee Speakman

Communications of the IIMA

Cybersecurity challenges are common in Nigeria. Sharing cyber threat intelligence is essential in addressing the extensive challenges posed by cyber threats. It also helps in meeting regulatory compliance. There are a range of impediments that prevent cyber threat intelligence sharing. We hypothesise that we want to maximise this cyber threat intelligence sharing to resist malicious attackers. Therefore, this research investigates factors influencing threat intelligence sharing in Nigeria's cyber security practitioners. To achieve this aim, we conducted research interviews with 14 cyber security practitioners using a semi-structured, open-ended interview guide, which was recorded and transcribed. We analysed the data using an …

Cyberattack Detection And Handling For Neural Network-Approximated Economic Model Predictive Control, Jihan Abou Halloun, Helen E. Durand

Chemical Engineering and Materials Science Faculty Research Publications

Cyberattacks on control systems can create unprofitable and unsafe operating conditions. To enhance safety and attack resiliency of control systems, cyberattack detection strategies can be developed. Prior work in our group has sought to develop cyberattack detection strategies that are integrated with an advanced control formulation known as Lyapunov-based economic model predictive control (LEMPC), in the sense that the controller properties can be used to analyze closed-loop stability in the presence or absence of undetected attacks. In this work, we consider neural network-approximated control laws, concepts for mitigating cyberattacks on such control laws, and how these ideas elucidate concepts in …

Lyapunov-Based Cyberattack Detection For Distinguishing Between Sensor And Actuator Attacks, Dominic Messina, Helen E. Durand

Chemical Engineering and Materials Science Faculty Research Publications

Control-theoretic cyberattack detection strategies are control strategies where control theory can be used in the design of the detection policies and analysis of stability properties with and without cyberattacks. This work provides a step toward understanding how to diagnose cyberattacks using control-theoretic cyberattack detection mechanisms. Specifically, we analyze the conditions under which a control-theoretic cyberattack detection strategy developed in our prior work to handle detection of simultaneous actuator and sensor attacks can be extended to distinguish between whether attacks are occurring on sensors or actuators. We present and evaluate heuristic concepts for attempting to diagnose sensor attacks; these again demonstrate …

Study Of The Application Of Blender For Simulation Of A Closed-Loop Image-Based Greenhouse Supplemental Lighting Control, Kip Nieman, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Image-based control and sensing has been applied in a wide variety of next generation manufacturing fields. Utilizing methods of simulating closed-loop image-based control may be advantageous for improving control performance and design without the need for an experimental setup. One software capable of these simulations is the open-source 3D modeling software Blender, which has many capabilities aided by a Python API. This work explores the use of Blender as an image-based control test bed, where both the process and the controller are simulated, in the context of a greenhouse supplemental lighting control system.

Safety With Non-Deterministic Control Action Selection Using Quantum Devices, Kip Nieman, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Recent increasing interest in quantum computers has spurred research into practical engineering applications for quantum algorithms. One potential application is process control. The unique quantum phenomena involved with quantum computing brings up interesting considerations for control. This work focuses on non-determinism, first through a motivating simulation utilizing a continuous stirred-tank reactor. Following this, two methods of potentially ensuring system stability in the presence of non-determinism are discussed. The first involves including an additional gate to the modified Grover’s algorithm presented in our previous work, which is designed to prevent a qubit state corresponding to an undesired control input from being …

Profit Considerations For Nonlinear Control-Integrated Cyberattack Detection On Process Actuators, Keshav Kasturi Rangan, Helen E. Durand

Chemical Engineering and Materials Science Faculty Research Publications

Prior research from our group developed a control-integrated active actuator cyberattack detection strategy. This strategy continuously probed for cyberattacks by updating target steady-states at every sampling time and then moving the process state toward these over the subsequent sampling period. Attacks were fagged if a Lyapunov function around the target steady-state did not decrease over a sampling period. This strategy had the benefit of ensuring safety of the process until an attack was detected. However, the continuous probing for attacks could decrease profit from the process compared to not probing for the attacks, which could limit the attractiveness of the …

Low-Power Sensor Design And Fusion To Edge Devices For High-Speed Object Detection And Enhanced Soldier Situational Awareness, Scott Patrick Wood

Theses and Dissertations

Threat detection and physiological monitoring of soldiers from fused sensor data collected in real time is currently limited to running deep neural networks with substantial computing needs. The lack of data acquisition from sensor readings and efficient detection of novel enemy signatures motivates the need for a low-power, low-cost, wireless multisensor fusion computing system. We propose the current trends in Internet of Things to deploy a chargeable, wireless multi-channel acquisition system that can be interfaced to a high speed, Single Board Computer (SBC) such as the NVIDIA Jetson Orin capable of running object detection models, such as YOLOv7-tiny to enable …

An International Consensus Panel On The Potential Value Of Digital Surgery, Jamie Erskine, Payam Abrishami, Jean Christophe Bernhard, Richard Charter, Richard Culbertson, Jo Carol Hiatt, Ataru Igarashi, Gretchen Purcell Jackson, Matthew Lien, Guy Maddern, Joseph Soon Yau Ng, Anita Patel, Koon Ho Rha, Prasanna Sooriakumaran, Scott Tackett, Giuseppe Turchetti, Anastasia Chalkidou

School of Public Health Faculty Publications

OBJECTIVES: The use of digital technology in surgery is increasing rapidly, with a wide array of new applications from presurgical planning to postsurgical performance assessment. Understanding the clinical and economic value of these technologies is vital for making appropriate health policy and purchasing decisions. We explore the potential value of digital technologies in surgery and produce expert consensus on how to assess this value. DESIGN: A modified Delphi and consensus conference approach was adopted. Delphi rounds were used to generate priority topics and consensus statements for discussion. SETTING AND PARTICIPANTS: An international panel of 14 experts was assembled, representing relevant …

A Panel Data Regression Model For Defense Merger And Acquisition Activity, Corey D. Mack, Clay Koschnick, Michael Brown, Jonathan D. Ritschel, Brandon M. Lucas

Faculty Publications

Excerpt: This paper examines the relationship between a prime contractor's financial health and its mergers and acquisitions (M&A) spending in the defense industry. It aims to provide models that give the United States Department of Defense (DoD) indications of future M&A activity, informing decision-makers and contributing to ensuring competitive markets that benefit the consumer.

The results show a significant relationship between efficiency and M&A spending, indicating that companies with lower efficiency tend to spend more on M&As. However, there was no significant relationship between M&A spending and a company's profitability or solvency. These results were consistent with previous research and …

Performance Studies Of An Axial Flow Waterjet Pump Using An Unsteady Reynolds-Averaged Navier-Stokes Model, Stephen E. Monroe, Junfeng Wang, Chunlei Liang

Northeast Journal of Complex Systems (NEJCS)

In this study, an Unsteady Reynolds-Averaged Navier-Stokes (URANS) model is demonstrated its suitability for studying the flow and performance of open marine propellers and waterjet pumps. First, the accuracy of the URANS model is validated by studying turbulent flow past counter-rotating propellers (CRPs). Specifically, experimental data from Miller (1976) is employed for comparison against the URANS results. Subsequently, URANS is used to study the flow and performance of an Office of Naval Research (ONR) axial flow waterjet pump (AxWJ-2). Due to the large number of degrees of freedom for both simulations, parallel computations over 80 cores are performed. For the …

A Marine Knowledge System For Ocean Affairs: Integrating Data, Evaluating Usage, And Enabling Sustainable Marine Management, Yu-Jen Pan

Journal of Marine Science and Technology–Taiwan

This study presents the evolution and assessment of the Marine Knowledge Education System (MKES), designed to improve user acceptance among students in professional marine science courses in Taiwan. The MKES leverages real-world maritime cases from the General Coast Guard Administration and is built upon existing technologies like cloud services, social networks, and data analysis tools. The technology acceptance model (TAM) provides the theoretical underpinning for the assessment of user confidence. Data was collected from 190 participants through purposive sampling. Path analysis confirmed all hypothesized relationships within the TAM with statistical significance (p < 0.001). Additionally, paired-sample t-tests revealed a significant increase in student acceptance of the MKES after integrating it into the marine science curriculum. These findings underscore the capacity of the MKES as a digital learning tool to enrich course pedagogy and improve student learning outcomes, thereby offering valuable support in advancing the education of professional marine managers.

Smart Toys For Sensing And Soothing Distress In Pediatric Patients, Jonathan Daniel Bonilla

Dartmouth College Master’s Theses

This thesis develops and evaluates child-centered technologies to support remote monitoring of pediatric patients, with an emphasis on childhood cancer and pediatric chronic pain. For these and many other health conditions, current therapies tend to focus on treating physical symptoms yet neglect the psycho-emotional aspects of managing the illness. However, such mental states play a major role in recovery. Research has shown that cancer patients with better mental health have better prognoses, higher possibilities of remission, and faster healing processes, while chronic pain patients with better mental health report lower levels of pain. To enable more personalized, continuous, and scalable …