ENGINEERING PROJECT SUMMARIES

Data center facilities are essential parts of today’s economy and culture since they house the information technology equipment containing online applications (e.g., banking, cloud storage, audio/video streaming) and high-performance computing (i.e., enabling many computers work together to solve large problems). Data centers consume large amounts of energy – roughly 3% of all U.S. electricity use – so improvements in energy efficiency will have significant economic and environmental benefits. One emerging area of research in data centers is their integration into the hydrogen economy by providing on-site energy storage, which is an essential step towards drawing energy from sustainable sources. It is therefore important to garner an understanding of the state-of-the-art for using hydrogen in data centers since it enables the exploration of future research directions. The research for this project will therefore consist of examining the various current and proposed approaches for integrating hydrogen into data centers, providing insights into further areas for research to improve data center energy efficiency and environmental sustainability. This project will result in a publication and poster presentations to members of the data center industry through our industry-university collaborative research center called the Center for Energy-Smart Electronic Systems (ES2), along with Villanova-sponsored poster symposia.

The researcher will perform a thorough literature review through both industrial and academic sources, compiling information into a publication that will be disseminated to members of the ES2 industrial advisory board. In the publication, the researcher will indicate the state-of-practice for the data center industry and also discuss research and development efforts undertaken in academia. The researcher will then work with the faculty mentor to propose areas of future research for discussion with the industrial advisory board members. These findings and recommendations will be captured in a poster presentation at one of the ES2 industrial advisory board meetings, along with Villanova Symposia such as the Sigma Xi Forum and the CRF Undergraduate Researcher Poster Symposium. The researcher may also present updates on their work at monthly web-based meetings to members of the industrial advisory board.

Pennsylvania Bluestone saw shops in Northeast PA cut a tremendous amount of stone throughout the year. They use water in the process which goes through a recycling system. The process creates a grey super slime slurry material. Tons of this waste material are produced annually with no environmentally friendly recycling options available. The goal of this project is to incorporate this waste product into concrete mixes to provide a sustainable solution.

The student will work closely with a team that includes a graduate student, two faculty members, and another freshman. The student will be responsible for making concrete mixes with grey super slime slurry material and conducting standardized tests for slump, strength and elastic modulus. All results will be compared to traditional concrete mix results.

The objective of the proposed research is to evaluate and improve the durability of Portland cement concrete produced with lightweight expanded glass aggregates.  The expanded glass aggregates being evaluated are produced by a local company (Aero Aggregates) from recycled glass.  The use of this material in concrete has the potential to both improve the sustainability of the concrete as well as increase the structural efficiency by reducing the weight of structures in areas where strength is less critical such as floor systems and building cladding.

The student will work closely with a team that includes a graduate student, two faculty members, and another freshman. The student will be responsible for making concrete mixes with light weight recycled glass aggregates and conducting standardized tests for slump, strength and elastic modulus. All results will be compared to traditional concrete mix results.

Cellular steel beams have circular patterns in their webs while castellated steel beams have hexagonal openings. The are currently marketed as C-Beams in the US. The principle advantage of C-Beams is that you increase the depth of a beam to increase its strength, without increasing its weight. C-Beams allow for increasing the length of beam spans to create wide-span and wide-open bay designs. C-Beam characteristics make them ideal for almost any structure that seeks to minimize foundation and column costs while maximizing any structures open spaces. The openings in the web make them an ideal structural element for routing of mechanical systems within buildings. These openings also increase the complexity of structural behavior. Significant research has occurred in the past 40 years to fully understand the structural behavior.  This project will include a detailed literature of this research as well as experimental testing of a conventional steel beam and a cellular counterpart to compare the performance. Experimental work will occur in the Structural Engineering Teaching and Research Laboratory.

The student is responsible for conducting a detailed literature of research on castellated and cellular beams. The focus on the review will be in the last 10 years. The student will organize a shared site of all relevant research and summarize the findings for a technical paper. The student will work in the Structural Engineering Teaching and Research Laboratory, where they will load a conventional steel beam and a cellular counterpart to compare the performance. Load deflection and load strain plots will be generated to show the differences in structural behavior. Results will be summarized and presented in a technical report and a poster session.

This project is a comprehensive exploration of social robotics. It combines creativity, technology, and human-centered design to advance the development of intelligent robotic companions. This project aims to analyze and evaluate existing social robot designs using a meta-analysis approach. By dissecting design elements like appearance, behavior, and interaction modalities, we will learn about patterns and trends that can guide the development of future social robots. The evaluation work will aim to measure the impact of design choices on safety, user engagement, emotional connection, and overall satisfaction. The desired outcome is a set of practical guidelines for building socially intelligent robots that can seamlessly integrate into campus lives.

Student responsibilities include:

1. Conducting literature reviews and gathering and analyzing data. Using meta-analyses methods to summarize the state-of-the-art

2. Organizing research materials, maintaining records, and coordinating communication with mentors and stakeholders

3. Conducting interviews, focus groups, and observations to document the needs for a social robot for campus use

Metastatic lesions increase the risk of pathological fractures in femur. Pathological fractures of femur increase morbidity and adversely affect quality of life. The current clinical approaches do not reliably assess if the metastatic lesions compromise the mechanical integrity of the femur and determine whether the patient requires an invasive surgical intervention. As a result, the goal of this study is to develop a finite element modeling approach that can evaluate the structural integrity of femurs with metastatic lesions. This will be accomplished by mechanically evaluating femur models with varying lesion size and location. The outcomes of this research aim to establish a mechanics-based systematic approach to determine patients under high fracture risk.

The student will be responsible for:
1. Processing bone images to generate finite element models
2. Running finite element simulations to determine the mechanical response of bone
3. Post-processing simulation outcomes and evaluate results
4. Performing literature to determine the existing studies related to the research project
5. Reading and evaluating research papers related to the project.

Have you ever wondered about the future of vehicles? Are you passionate about autonomous driving? What about humans? Can humans and machines work together for a safer future? If you are interested in any of these topics, this project is for you! We are a group of researchers across civil engineering, electrical engineering, psychology, and brain sciences who are interested in building systems that can understand the interaction between autonomous vehicles and humans. Autonomous vehicles at their current stage often ask the driver to control the vehicle and continue the drive, but the driver....well, the driver could be distracted or as we refer to it "the driver can be out of the loop". We are interested in building models that can predict driver's performance in responding to the vehicle in real-time based on how distracted they are at any given time, and how they have been over the past two weeks prior to our experiment (such as their sleep quality, stress level, and similar factors). We have access to a real driving simulator with close to natural conditions! Within this driving simulator, we are designing an experiment to collect such data and analyze the result to be able to understand how driver's distraction affect the quality of interaction with the car; how can we use driver's physiological sensors to predict the quality of the interaction, and how much having historical data of the driver can help us building more accurate models! If you are interested but don't have any experience, we will train you! Don't Worry! As long as you are passionate and interested in the topics above, you are in the right spot!

Through this research experience, an undergraduate student will observe the start to finish of designing and conducting an experiment that involves human subjects interacting with an autonomous vehicle. The undergraduate researcher will be working closely with a graduate student and will be mentored directly by Dr. Tavakoli. The undergraduate student will be responsible for learning the basics of using a driving simulator, learning the steps towards designing a successful experiment through helping the graduate student and reading supplementary materials, helping the graduate student develop aspects of the experiment such as data collection through smartwatches, and lastly becoming involved in the pilot version of the study which is a smaller version of the final study to detect any problems with the design.

We will be exploring the impact of a faculty journal club on their attitudes regarding engineering education. Currently, there is a knowledge gap among engineering faculty regarding education methodologies and the broader field of engineering education. By providing a scaffolded opportunity to engineering faculty members to read and discuss seminal and up-and-coming research, it is proposed that they will be more likely to implement engineering education methods in their courses and potentially engage in related research.

The student research assistant will assist in setting-up for these journal club sessions and facilitate discussion. They will also participate in the collection and analysis of qualitative research related to faculty attitudes.

We are building a human-robot system to investigate adversarial training for athletes competing in head-to-head races by leveraging state-of-the-art wearable body sensors, real-time AI, and robotics. During training and time-trial exercises, track athletes wear sensors such as heart rate monitors to observe performance; our mobile robot will tap into this treasure of real-time data via standard wireless protocols to give personalized training that adapts to runners’ needs in real-time. By using head-to-head psyche from a physical competition in the form of a robot, and adapting speed profiles in real-time based on vital signs and coaching designs, we can improve competitiveness and monitor athlete health.

The robot will perform sprints of 400m or less, middle distances less than two miles, and races of longer distances. Data from robot tests in each of these distances will be mixed with similar logs captured in simulation to form a robust dataset of real and simulated data with state-of-the-art data augmentation approaches for robust machine learning. We will leverage Villanova Augie Compute cluster for training our neural networks and optimizing autonomy routines, sensor layouts, and machine learning approaches. Critical data logs that make this project unique include biometric information.

The student research assistant will develop algorithms to analyze data from the biometric sensor(s), conducting experiments with real-time signal processing, and explore integration of robot behavior driven by the aforementioned biometric signal processing. The goal is to have a real (wheeled) robot that modulates its speed based on a runner’s heart rate, using the algorithms implemented by the student researcher.

There is increasing investment in nature-based solutions such as green stormwater infrastructure (GSI) in response to stressors such as urbanization and climate change. GSI manages stormwater runoff while also providing other benefits such as heat mitigation, aesthetic value, among others. With climate change leading to more frequent and more intense rainfall in many areas, it is essential to understand GSI design and performance under various rainfall scenarios, including high intensity events. GSI are often design with a synthetic rainfall distribution for a selected design storm, which may not be representative because rain events have substantial variability. Thus, exploring historic rain events by air mass signature, or weather front, is of interest. For this project, rain events from a 15-year period of record (2006-2022) will be compiled from robust weather station networks for several major cities in the contiguous United States across various climate zones. The identified rain events will be matched with air mass signatures from NOAA’s Air Surface Map archive. Unique identifiers for each air mass are present on these maps within the following categories: Warm Front, Cold Front, Stationary Front, and Occluded Front. Time series analysis of the archived maps for the duration of identified events will reveal associated air mass signatures that trigger significant precipitation. Data analysis and visualization as well as statistical methods will be employed on results obtained for enhanced historical rainfall investigations.

The student will collect and organize precipitation and air surface data from publicly available sources. Precipitation thresholds and time between events are implemented as criterion for rain event selection, with event duration, event volume, and peak rain intensity being attributed to each event. Screenshots of NOAA surface maps will be saved for each rain event identified. The student will meet weekly with the project team.