VTechWorks
VTechWorks provides global access to Virginia Tech scholarship, including journal articles, books, theses, dissertations, conference papers, slide presentations, technical reports, working papers, administrative documents, videos, images, and more by faculty, students, and staff. Faculty can deposit items to VTechWorks from Elements, including journal articles covered by the University open access policy. Email vtechworks@vt.edu for help.
Open Access Policy
Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
Theses and Dissertations
Virginia Tech was first in the world to require ETDs in 1997, and continues to add scans of older theses and dissertations.
Open Textbooks
More than 40 freely available and openly licensed textbooks are among our most downloaded items.
Communities in VTechWorks
Select a community to browse its collections.
Recent Submissions
Mechanical design for advancing small-scale solid state additive manufacturing to enable deposition of commercial metal alloys
Remmert, Max Peter (Virginia Tech, 2025-06-05)
Labor Market Adjustments Under Economic Shocks: Evidence from the U.S.
Mun, Byungki (Virginia Tech, 2025-06-05)
This dissertation employs a quasi-experimental design to examine how major economic shocks—the COVID-19 pandemic, immigration restrictions, and the U.S.-China tariff war (2018–2020)—reshaped labor demand and employer skill requirements in the United States. Using econometric methods on panel data from online job postings, the Current Population Survey, and trade exposure measures, the analysis provides empirical evidence on how firms adjust hiring criteria in response to disruptions in labor supply, market conditions, and global trade. The first chapter shows that, in contrast to the upskilling trend following the Great Recession, the COVID-19 pandemic led to widespread downskilling in education requirements, driven by labor market tightness and accelerated technological adoption, especially in tradable industries and routine-manual occupations. The second chapter applies a shift-share difference-in-differences (DiD), finding that visa bans reduced immigrant employment but increased native employment, particularly among less-educated workers. The immigration shock also induced firms to adopt more automation and broadband technologies, raising demand for technical and digital skills. The third chapter uses a staggered DiD with Shift-Share IV and CSDiD models to analyze the tariff war, revealing export tariffs reduced high-skill job postings and wages while increasing low-skill roles, and import tariffs boosted engineering skills but lowered wages. These studies underscore how firms dynamically adjust skill demand under uncertainty, highlighting the role of labor market tightness, technological advancements, and trade policy in shaping hiring strategies.
Measurement of Liquid Film in Annular Flow Using X-Ray Densitometry
Harvey, Devlin McCaul (Virginia Tech, 2025-06-05)
Two-phase annular flow film thickness is a crucial parameter in nuclear thermal hydraulics, yet conventional measurement techniques such as impedance probes may compromise accuracy by directly contacting and potentially disturbing the flow. Non-intrusive measurement methods are needed to provide more reliable film thickness data for improved reactor safety calculations in Boiling Water Reactors (BWR), Pressurized Water Reactors (PWR), and accident scenarios such as Loss of Coolant Accidents (LOCA), where accurate film thickness data is essential for reliable predictions in reactor analysis codes. This thesis evaluates x-ray densitometry as a non-intrusive alternative that measures film thickness without flow disturbance. Film thickness measurements were obtained for two-phase annular flow in a 9.5 mm pipe across various superficial gas and liquid velocities and compared against impedance probe data and established correlations. Through a calibration procedure, a correction factor is determined to enhance the x-ray densitometry system's measurement accuracy for film thickness. Using a specially designed test facility available at RPI, comprehensive film thickness data across annular flow conditions is generated, while simultaneously capturing dynamic wave characteristics including wave height, velocity and frequency. The experimental results were benchmarked against RPI's parallel-wire impedance probe data and established film thickness correlations. Based on these comparisons, one film thickness correlation was refined to achieve better agreement with both the x-ray densitometry results and two supplementary experimental datasets.
Human-Guided Learning for Personalizing Robot Behaviors
Ramirez Sanchez, Robert Javier (Virginia Tech, 2025-06-05)
The presence of robots performing tasks in real-world environments is rapidly growing. These robots will interact with various humans with different personal preferences, highlighting the need for robots that adapt their behavior accordingly. In this thesis, we develop tools and interfaces to convey task-critical information and personalize robot behavior. First, we explore settings where humans provide demonstrations for multiple tasks. For this setting, we introduce PECAN (Personalizing Robot Behavior through a Learned Canonical Space), a learning and interface-based approach that enables users to directly select their desired style. PECAN learn a continuous canonical space from demonstrations, where each point in the space corresponds to a style consistent across each task. Our simulation experiments and user studies indicate that humans prefer using PECAN to personalize robot behavior compared to existing methods. We then examine scenarios where robots complete a task in dynamic environments. A fundamental limitation when learning from demonstrations is causal confusion due to observations containing both task-relevant and extraneous information. Because the robot does not know what aspects of its observations are important a priori, it may fail to learn the intended task. We propose RECON (Reducing Causal Confusion with Human-Placed Markers), a framework that leverages beacons (UWB trackers) attached to task-relevant objects by the human before providing demonstrations. RECON learns a compact observation embedding correlated to the beacon information, and autonomously filters out extraneous information. Our experiments indicate that RECON significantly reduces the number of demonstrations required for teaching a task to the robot.
Compact, Self-Cleaning Wet Electrostatic Precipitators
Meeting, Livia Marie (Virginia Tech, 2025-06-05)