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 50 freely available and openly licensed textbooks are among our most downloaded items.
Communities in VTechWorks
Select a community to browse its collections.
Recent Submissions
Using the SECI Model to Map Knowledge Systems of Urban Farmers in Virginia
McCausland, William Alexander (Virginia Tech, 2026-08-28)
Urban farmers face significant issues with representation and the ability to access information from institutions designed to provide support for agricultural producers. This study examines the knowledge systems of urban farmers in Virginia through the Socialization, Externalization, Combination, and Internalization (SECI) model of knowledge creation to better understand how agricultural knowledge is acquired, exchanged, and applied in Virginia. A qualitative research design was employed using semi-structured interviews with self-identified urban farmers across Virginia who produced for either financial production or public good. Participants were recruited through statewide urban agriculture networks, conferences, and snowball sampling. Interview transcripts were analyzed using in vivo and descriptive coding, followed by thematic analysis aligned with the SECI framework. Four themes emerged from the analysis: (1) agricultural identity through experience; (2) navigating diversified knowledge systems; (3) knowledge transmission and experiential learning through reciprocal relationships; and (4) envisioning decentralized, community driven futures. The findings demonstrate that urban agricultural knowledge systems are dynamic, relationship-based networks that depend on the interaction of formal organizations, peer communities, and experiential learning rather than a single centralized source of expertise. This research is significant because it serves to strengthen collaboration among knowledge institutions, while also supporting existing community networks for urban farmers. This study also addresses research gaps that relate to educational access, knowledge dissemination, awareness of urban agriculture nationally and in Virginia, and the connection between knowledge systems and urban agriculture.
Wave Holtz: Theory and High Performance Applications
Rotem, Amit (Virginia Tech, 2026-08-28)
The solution to the Helmholtz equation describes the spatial waveform of a time-harmonic wave and arises in acoustics, electromagnetism, and seismology. Its efficient numerical treatment makes engineering problems tractable but presents two central difficulties. First, the waves must be sufficiently resolved. Engineering problems often span hundreds to thousands of wavelengths, over which phase error accumulates, demanding many points per wavelength and yielding systems with millions to billions of degrees of freedom. Second, standard discretizations produce a complex-valued, highly indefinite linear system on which general-purpose iterative methods perform poorly or fail outright, making specialized methods for the equation a challenging, active research topic. This dissertation advances one such method: WaveHoltz, which relates the Helmholtz equation to its time-dependent counterpart, the wave equation. WaveHoltz evolves the wave equation and filters its solution over a short time interval to estimate the Helmholtz solution; this estimate is fed back as an initial condition, iteratively improving the estimate until convergence; this iteration can be accelerated by standard Krylov methods. On the theoretical side, convergence of the discrete iteration is established, the temporal error of the wave solve is eliminated, and WaveHoltz-HMM, a homogenization method with error estimates, is introduced. On the computational side, a high-performance GPU implementation combining multigrid-accelerated time stepping with massively parallel algorithms and a GPU-tailored domain decomposition approach made efficient by using WaveHoltz as a subdomain solver are presented. Altogether, WaveHoltz proves robust and efficient for solving the Helmholtz equation.
Assessment of Peanut (Arachis hypogaea L.) Responses to Heat and Drought Stress Through Physiological, Machine Learning, and Transcriptomic Approaches
Vennam, Ranadheer Reddy (Virginia Tech, 2026-08-28)
Heat-exacerbated drought stress is an increasing constraint on peanut (Arachis hypogaea L.) production in the rainfed Virginia–Carolina region, yet the physiological, phenotypic, and molecular consequences of this combined stress remain incompletely characterized. This dissertation integrated growth chamber, multi-year field, high-throughput phenotyping, and transcriptomic approaches across five interconnected studies to address this gap. In growth chamber and field trials, combined heat and drought stress (HS+DS) significantly reduced stomatal conductance and transpiration relative to control or rainfed treatment. Under sustained mid- to late-season HS+DS imposed with rainout shelters across two field seasons, canopy temperature increased by up to 6 °C, accompanied by greater leaf wilting severity and pod yield reductions of 52–73%. Yield, grade, and economic analyses across 12 genotypes showed that HS+DS reduced the percentages of extra-large kernels and total sound mature kernels and eliminated profitability, with losses exceeding $2,000 ha⁻¹ in 2025. However, breeding line 14x009-1-5-1-1 maintained comparatively stable pod yield across both years. Leaf wilting and stomatal conductance were the strongest predictors of yield loss (R² = 0.86–0.89). To scale wilting assessment beyond manual scoring, a machine learning framework combining UAV multispectral imagery and accumulated growing degree days was developed utilizing data across three years and five sites; Extreme Gradient Boosting achieved the highest predictive accuracy (R² = 0.82–0.87), enabling pixel-level mapping of wilting severity. Finally, RNA sequencing of four virginia-type genotypes under field HS+DS identified 1,733 differentially expressed genes, revealing that genotypes with similar physiological responses employed largely distinct transcriptional strategies. Collectively, these findings demonstrate that HS+DS imposes convergent physiological and yield penalties on peanut via distinct molecular pathways, while also establishing scalable phenotyping tools that can support management and breeding strategies for improved resilience to heat and drought in rainfed peanut production.
Experimental and Computational Characterization of a Hydrofoil System for a Retrofit Inflatable Boat Platform
Joshi, Keyur Sachin (Virginia Tech, 2026-08-28)
The Center for Marine Autonomy and Robotics at Virginia Tech has been contracted by the Office of Naval Research to design, test, and validate a hydrofoil retrofit kit for a rigid- hull inflatable boat, using fully submerged, flapped hydrofoils to improve small-craft speed and ride quality. The fore foil of this system was first characterized analytically, then measured directly in a tow tank across a wide matrix of speed, incidence, and flap deflection.
A three-dimensional CFD model of the isolated foil was developed and validated against these measurements. The validated model was then extended to the complete, integrated starboard-side foil system, and used to evaluate a strut fairing and a wingtip endplate as drag-reduction and lift-recovery devices. The fairing was found to increase lift and reduce drag at the same time, while the endplate recovered further lift at a moderate drag cost.
Together, these results indicate good potential for hydrofoil retrofits of this kind. However, full validation of the craft's dynamic takeoff behavior would require further development of a coupled fluid-body simulation and real world testing of the complete system.
Experimental Evaluation of Radar-Based Velocity Estimation for Navigation in GNSS-Denied Environments
Mongold, Garrett Andrew (Virginia Tech, 2026-08-27)
Resilient navigation in GNSS-denied environments is critical for safe autonomous ground vehicle operation. LiDAR, radar, cameras, and magnetometers fused with an IMU can provide a navigation solution that limits the inertial drift to maintain safety. Among these sensing modalities, radar is a particularly promising technology as it performs well in poor weather conditions and directly provides relative velocity measurements. This thesis develops a multi-sensor data collection platform to enable radar-inertial algorithm development. Datasets are collected in various environments and include raw LiDAR, radar, and IMU data along with corresponding ground truth. Using these datasets, this work evaluates radar-based vehicle velocity estimation for use as a measurement update within an Error-State Kalman Filter (ESKF). An offline lever arm and mounting angle calibration, posed as a multi-variable optimization problem, is introduced to better characterize radar-to-IMU lever arm and mounting angle parameters, improving measurement consistency. Furthermore, a radar-based vehicle yaw-rate estimation method is proposed and validated against ground truth. The results demonstrate the potential of radar measurements to aid in navigation through GNSS-denied conditions.