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
From PFAS source attribution to collaborative management in a One Water system
Krauss, Lauren; Rashid, Md Redowan; Noble-Blair, Mishelle; Furst, Kirin; Spiesman, Anne; Kaushal, Sujay; Dadiala, Rhea; Bhide, Shantanu V.; Rippy, Megan A.; Curtis, Shannon; Grant, Stanley B. (2026)
A major barrier to preventing per- and polyfluoroalkyl substances (PFAS) from entering drinking water supplies is identifying and quantifying their upstream sources, particularly in One Water systems that integrate diverse water inputs. Here we combine high-frequency measurements with mass-balance analysis to quantify PFAS and major-ion loading to the Occoquan Reservoir, a drinkingwater supply serving one million people in Northern Virginia, USA. Mass-balance analysis at the confluence of watershed inflows and treated wastewater inputs reveals seasonally varying contributions from domestic wastewater, watershed runoff, and a single significant industrial user (SIU) of the sanitary sewer system. A one-month, system-scale diversion of SIU effluent confirms this source attribution for several short-chain PFAS and major ions, with concentration deficits closely matching withheld mass. These results demonstrate that traditional mass-balance approaches can inform collaborative management of PFAS contamination in One Water systems.
Análise descritiva por ordenação na caracterização sensorial de iogurte diet sabor morango enriquecido com concentrado protéico do soro
Loures, Milene Moreira Ribeiro; Minim, Valéria Paula Rodrigues; Ceresino, Elaine Berger; Carneiro, Renata C. V.; Minim, Luis Antonio (Universidade Estadual de Londrina, 2010)
Este estudo avaliou as características sensoriais de iogurte diet sabor morango enriquecidos com concentrado protéico de soro (CPS). Três formulações contendo 0,5%, 1% e 1,5% de CPS (F2, F3 e F4 em ordem crescente de concentração) e uma formulação controle sem adição de CPS (F1) foram desenvolvidas e avaliadas por meio da Análise Descritiva por Ordenação. Vinte provadores selecionados e treinados avaliaram as amostras caracterizadas pelos atributos: cor rosa, viscosidade, aroma característico de iogurte de morango, sabor característico de iogurte de morango, gosto doce, gosto ácido e consistência. As formulações diferiram significativamente (p < 0.05) nos atributos gosto doce e consistência. As amostras F3 e F4 apresentaram maior consistência confirmando a eficiência do CPS no aumento da consistência.
RAPID Enabled Physics-Based Neural Networks for Predicting 3-D Fission Distributions in JSI TRIGA Reactor
Franck, Timothy; Haghighat, Alireza; Snoj, Luka (2026-04-23)
The current methods for high-fidelity simulations of nuclear reactor systems are complex and computationally expensive. To reduce computation time, artificial intelligence (AI) and machine learning (ML) are being considered. Despite showing promise for solving various neutronics problems, the limited availability of high-fidelity data constrains ML applications to simpler problems or systems. This paper utilizes the RAPID code system for its effectiveness at rapidly producing large quantities of high-fidelity data. This has enabled the development of physics-based neural networks (NN) to predict 3-D fission distributions as a function of CR positions for the JSI TRIGA Mark-II research reactor. We developed a NN architecture that contains two hidden layers, 4400 neurons per hidden layer, with Leaky ReLU activation functions. This model was capable of predicting more than 99% of the fission values in the fuel elements within ±0.5% rel. diff. The model also predicted about 98% of the fission values in the fuel followers within ±10% rel. diff. It was determined that errors in the fuel follower predictions did not significantly impact calculated power peaking factors, which fell within the range of -0.39% to 0.91% rel. diff. Hyperparameter tuning and its effect on model performance is also discussed, with some comparisons to simpler ML models developed in a previous study.
Next-Generation Research Reactors: Novel Virginia Research and Education Reactor (VA-RER)
Haghighat, Alireza; Franck, Timothy; Seidulla, Beksultan; Mascolino, Valerio (2026-04-21)
The Virginia Research and Education Reactor (VA-RER) is a next-generation research reactor concept engineered to meet the urgent national need for modern, flexible, and AI-enabled modeling, design, operation and monitoring for diagnostics/prognostics. Built on the patented Test and Education Microreactor (TEM) architecture, the design integrates a central irradiation cavity, a neutron-spectrum-tailoring buffer zone, and a circular TRIGA-fuel lattice to support advanced applications in materials testing, isotope production, reactor physics research, and workforce development. The VA-RER is envisioned as a dual-reactor system, a zero-power unit dedicated to training and hands-on education, paired with a 5 to 10 MWth reactor enabling high-flux irradiation experiments and validation of physics-based AI-driven digital-twin and autonomous monitoring technologies.
This paper presents preliminary neutronics analyses using the OpenMC Monte Carlo code system with ENDF/B-VIII.0 nuclear data to evaluate the reactivity behavior of reactor configurations with irradiation cavity radii ranging from 6.25 cm to 100 cm. Five select core configurations are analyzed in this paper. The resulting eigenvalues range from 1.01581 to 1.06814, with reactivity gains diminishing for larger annular radii due to increased neutron leakage. Notably, an optimal moderator-to-fuel ratio emerges near a 25 cm radius, where the eigenvalue increases even with fewer total fuel rods. These findings provide early design guidance for maximizing irradiation volume while maintaining favorable neutronic performance, supporting ongoing development of a transformative research reactor for next-generation nuclear science and engineering.
Feasibility Test for Determination of Dosimeter Response for the Watts Bar Unit 1 Reactor Extended Belt-line Region Using the DRF Method
Friedman, Cole N.; Haghighat, Alireza (2026-04-21)
New methods for solving 3-D reactor pressure vessel (RPV) dosimetry problems are emerging as the operating lifetimes of nuclear power reactors continue to increase. Accurately quantifying neutron fluence and its associated damage in RPV regions beyond the traditional beltline has become increasingly important. This work applies the detector response function (DRF) methodology to a 3-D RPV dosimetry problem using the TVA Watts Bar Unit 1 Benchmark as the reference model. A test problem consisting of four axial fuel segments is used to evaluate the effectiveness of the DRF approach for pressure vessel fluence calculations. DRF predictions are compared against reference MCNP Monte Carlo results. The good agreement between the two methods demonstrates the soundness of the DRF approach; however, additional analysis and optimization are required before extending the method to a full-core DRF solution.