Virginia Tech - U.S. Forest Service Housing Commentary

Provides information on housing growth and construction, and wood use in the construction industry

Household Water Quality - Culpeper County

Household water quality in Culpeper County, Virginia

2020 Peanut Variety and Quality Evaluation Results. I. Agronomic and Grade Data

Due to suitability to the environmental conditions and existence of a strong peanut industry tailored to process primarily the large-seeded Virginia-type peanut, growers in Virginia, North Carolina, and South Carolina generally grow Virginia-type cultivars. In the view of a common interest in the Virginia-type peanut, the three states are working together through a multi-state project, the Peanut Variety Quality Evaluation (PVQE), to evaluate advanced breeding lines and commercial cultivars throughout their production regions. The objectives of this project are: 1) to determine yield, grade, quality, and disease response of commercial cultivars and advanced breeding lines at various locations in Virginia and the Carolinas, 2) develop a database for Virginia-type peanut to allow research-based selection of the best genotypes by growers, industry, and the breeding programs, and 3) to identify the most-suited peanut genotypes for various regions that can be developed into varieties. This report contains agronomic and grade data of the PVQE tests in 2020

Journal of Biomechanics

Back-support exoskeletons (BSEs) offer the potential to reduce the risk of work-related musculoskeletal disorders of the lower spine. Although surface electromyography (sEMG) is commonly used to evaluate BSE effects on muscle activity, there are practical limitations to monitoring multiple trunk muscles while wearing a BSE. Musculoskeletal models offer an alternative, providing estimates of muscle activity that may complement or substitute direct measurements. We evaluated estimates of muscle activity using the gait-full-body model from the AnyBody™ Modeling System (AMS) and the OpenSim full-body thoracolumbar model. These evaluations were done for two bilateral lumbar extensors (longissimus and iliocostalis), vs. normalized sEMG (nEMG) data obtained from 18 participants who completed symmetric and asymmetric lifting tasks with and without two different BSEs. Comparisons were done using maximum normalized cross-correlation (MNCC), root-mean-square error (RMSE), and peak activity (95th percentile). Both AMS and OpenSim yielded strong associations with measured nEMG (mean MNCC: 0.90–0.95), though moderate errors were found (mean RMSE: 0.09–0.15). Model estimates captured general reductions in peak muscle activity with BSE use, consistent with nEMG, but with varying magnitudes. Muscle activity estimates had smaller MNCC and larger RMSE in BSE conditions, suggesting limitations in current simplified human–BSE interaction models. While both modeling tools show promise for estimating trunk muscle activity during occupational tasks, further refinement is needed—particularly to improve accuracy during complex movements and BSE-assisted scenarios. These findings support the potential utility of musculoskeletal models in ergonomic assessment and exoskeleton evaluation but underscore the need for cautious interpretation.Accepted versio

Toward Sustainable Digital Infrastructure: Thermal and Economic Potential of Data Center Heat Reuse

Data centers consume large amounts of electricity, with a significant fraction dissipated as low grade heat. Generally, this waste heat is released to the environment, but growing energy demands and decarbonization targets have emphasized the interest in its recovery and reuse. This study investigates the initial technoeconomic viability of heat reuse from a modeled system with varying effeciency and other key influencing parameters. By using the thermal approximations, we quantify the available temperature ranges and heat flows for several reuse pathways, including district heating, absorption cooling, and domestic hot water production. The results will reflect on the key parameters including the thermal efficiency of direct reuse of heat for low temperature applications, while higher temperature uses necessitate auxiliary upgrades that reduce overall system performance. A comparative techno-economic analysis highlights the trade-offs between capital investment, operating cost, and key data center parameters across reuse scenarios. In particular, coupling with district heating networks emerges can be one of the most scalable option, though decentralized applications (e.g., building heating) can offer faster payback under certain operating regimes. The findings underscore that while data center heat reuse is technically feasible, its practical deployment depends strongly on local infrastructure compatibility and economic drivers. This work contributes a structured framework for evaluating reuse pathways, providing both performance metrics and cost considerations to guide decision making for sustainable data center operation.Published versionYes, full paper (Peer reviewed?

Progress in Organic Coatings

This study proposes a sustainable alternative to conventional plastic coatings in packaging by developing a biodegradable coating system based on polylactic acid (PLA) and polyhydroxyalkanoate (PHA). A novel spray coating technique followed by hot pressing was used to apply PLA/PHA blends onto kraft pulp paper. This approach aimed to enhance mechanical strength, barrier properties, and water resistance while maintaining compostability. The coating behavior was strongly influenced by the PLA to PHA ratio. PLA formed a dense surface layer that effectively sealed pores, while PHA penetrated more deeply into the fibrous matrix, filling internal voids. These complementary roles contributed differently to the mechanical and barrier properties. In particular, the 50:50 PLA/PHA blend showed the most balanced results, achieving the lowest oxygen transmission rate and improved tensile strength. The thermogravimetric analysis further confirmed enhanced thermal stability in all coated samples compared to uncoated paper, with the degradation temperature profile shifting depending on the polymer composition. However, coatings with excessive PHA content showed surface irregularities and reduced barrier performance due to poor film formation. Overall, this work demonstrates that compositional tuning of PLA and PHA enables multifunctional coatings with improved mechanical, thermal, and barrier properties. The proposed spray-based method offers a scalable, eco-friendly solution for high-performance biodegradable packaging.Accepted versio

Lecture Notes on Configuration Aerodynamics

Practical applications of aerodynamic theory are critically important for aerodynamic design of aircraft configurations, but are often omitted from aerospace engineering curricula. Lecture Notes on Configuration Aerodynamics is an incredible resource for educating the next generation of aerospace engineering students who aspire to engage in the aerodynamic design of aircraft configurations. It shows how aerodynamic theory is applied in practice, giving students insight into what a career in aerodynamics entails. This textbook offers a design-oriented perspective of the development and analysis of aircraft aerodynamics. Based on his academic experience as a professor and his industrial experience at Grumman, Mason presents decades of relevant knowledge and wisdom of a large number of exceptional researchers and practicing engineers. Extensive references throughout the book encourage further study of configuration aerodynamics. This open textbook encompasses the aerodynamic design of flight vehicles with emphasis on flow fields and configuration concepts. Mason covers methodologies for aerodynamic analysis and design for flows ranging from low speed to high speed and includes case studies of classic configurations. Are you reviewing or adopting this book for a course? Please help us understand your use by filling out this form. How to access this book The main landing page for this book is https://doi.org/10.21061/configurationaerodynamics. The open textbook is freely available online in multiple formats, including: PDF, EPUB, and Pressbooks. A paperback print version (in color) is available for order here. ISBNs ISBN (PDF): 978-1-962841-52-8 ISBN (Pressbooks): 978-1-962841-53-5 ISBN (EPUB): 978-1-962841-51-1 ISBN (print): 978-1-962841-50-4 Table of contents 1. Introduction to Configuration Aerodynamics 2. Foundations of Fluid Mechanics: Governing Equations 3. Fundamentals of Aerodynamic Drag 4. Configuration Aerodynamic Design: Use of Computational Aerodynamics 5. Subsonic Aerodynamics: Airfoils and Wings 6. Transonic Aerodynamics: Airfoils and Wings 7. High-Lift Aerodynamics 8. High-Angle-of-Attack (High-α) Aerodynamics 9. Supersonic Aerodynamics 10. Hypersonic Aerodynamics 11. End Note Appendix A: Geometry for Aerodynamicists Appendix B: Fifteen Minutes of Stealth in Aircraft Design Appendix C: Government Regulations Affecting Configuration Aerodynamics Appendix D: Examples of Aerodynamic Design Appendix E: Software for Aerodynamic Analysis and Aircraft Design Appendix F: Configuration Aerodynamics Reading List Appendix G: The Configuration Aerodynamicist’s Bookshelf About the author and editor William H. Mason, author William H. Mason (1947–2019) developed a deep passion for airplanes quite early in his life. Growing up in Southwest Virginia, he spent countless hours building and flying model airplanes as a teenager. When he was an undergraduate student at Virginia Tech, he seized upon opportunities to gain practical experience, working summers at McDonnell Douglas in St. Louis, Missouri, where he was involved with various F-4 aircraft projects, including the swing-wing F-4, and at the Edwards Air Force Base, California, working on US Army Huey Cobra helicopters. In 1974, he began his fifteen-year professional aerospace engineering career with Grumman, where he made valuable contributions to many high-profile projects, such as: (i) the X-29, an experimental aircraft with a forward-swept wing and canard; (ii) the NASA/Grumman Research Fighter Configuration with supercruise and maneuvering capabilities; and (iii) the SC3 Wing Concept, which set a record for low drag at high-lift supersonic performance. From 1989 until his passing in 2019, he was a dedicated educator at Virginia Tech. Right after returning to VT in 1989, he devoted himself to sharing his knowledge and insights with students and colleagues. His legacy lives on with a large number of students who either took the courses he offered in aircraft design, applied computational aerodynamics, and configuration aerodynamics or performed research in aerospace systems design and multidisciplinary optimization. He co-authored Applied Computational Aerodynamics: A Modern Engineering Approach—one of the first textbooks on this topic for undergraduates—published by Cambridge University Press in 2015. He also authored or co-authored more than 100 technical papers and reports. He was a lifelong Hokie, having earned his BS degree in 1971, a MS in 1972, and a PhD in 1975, all in aerospace engineering from Virginia Tech. Mason was an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA). Pradeep Raj, editor Pradeep Raj is a Collegiate Professor Emeritus at Virginia Tech and spent twelve years serving as a faculty advisor of student capstone aircraft design teams and conducting collaborative research in simulation driven design to enable development of quality affordable aerial vehicles. He joined VT in 2012 after thirty-two years (1979–2011) with Lockheed Martin, a premier aerospace and defense corporation. For the first twenty years there, he held key technical leadership positions and made noteworthy contributions to advancing the effectiveness of computational simulation capabilities for meeting aircraft design needs. For the next twelve years, he held executive leadership and management positions before retiring from the Advanced Development Programs organization commonly known as the Skunk Works®, which is world renowned for creating breakthrough technologies and landmark aircraft. He is a Fellow of the American Institute of Aeronautics and Astronautics and of the Royal Aeronautical Society (RAeS). He earned a PhD in aerospace engineering from Georgia Institute of Technology in 1976 after earning a master’s degree in aeronautical engineering and a bachelor’s in electrical technology, both from the Indian Institute of Science in Bangalore, India. Project support This project was made possible in part through financial support from the University Libraries’ Open Education Initiative and the Kevin T. Crofton Department of Aerospace and Ocean Engineering. Suggested citation William H. Mason and Pradeep Raj, Lecture Notes on Configuration Aerodynamics (2026). CC BY-NC-SA 4.0. https://doi.org/10.21061/configurationaerodynamics. View errata | Report an error Accessibility Virginia Tech is committed to making its publications accessible in accordance with the Americans with Disabilities Act of 1990. The text, images, and links in the PDF versions of this text are tagged structurally and include alternative text, which allows for machine readability. Virginia Tech Publishing is continuously working to improve accessibility and welcomes any feedback from readers

Remote Sensing of Environment

Accurate and high spatiotemporal resolution soil moisture (SM) monitoring in cropland is important for water resource management, drought forecasting, and nutrient transport estimation at the field scale for sustainable crop production. Although recent research has applied machine learning (ML) to downscale coarse-resolution satellite SM products, most of this past work has focused only on surface SM estimation, and the performance of rootzone SM products has not been intensively evaluated in cropland. This study introduces a novel framework that integrates multi-source satellite-based ML models with the Layered Green and Ampt Infiltration with Redistribution (LGAR) model to produce high-resolution (100 m, hourly) SM products for both the surface layer (0–5 cm) and rootzone (0–100 cm) across cropland in the contiguous United States (CONUS). First, six ML models were trained using multiple high-resolution remote sensing datasets (Sentinel-1, Sentinel-2, and Landsat) to predict surface and rootzone SM. These ML predictions were then assimilated into the LGAR model using the ensemble Kalman filter (EnKF). The framework was developed and validated using an eight-fold cross-validation scheme with in-situ data from 431 cropland sites across CONUS, sourced from three networks (SCAN, USCRN, and PSA). The 100-m hourly SM data from this framework surpasses existing products (9-km SMAP L4, SMAP-based 1-km thermal hydraulic disaggregation of SM product) in spatial and temporal resolution and captures rootzone SM that is not available in the SMAP-HydroBlocks SM product. It achieves good performance, with median bias-corrected root mean squared error (ubRMSE) of 0.053 m3/m3 and median Kling-Gupta efficiency (KGE) of 0.379 in the surface layer, and median ubRMSE of 0.027 m3/m3 and median KGE of 0.302 in the rootzone. While the framework demonstrates strong performance, its accuracy varies across climatic regimes, with surface SM performing better in non-humid areas (median KGE = 0.375 versus median KGE = 0.416) and rootzone SM in humid regions (median KGE = 0.313 versus median KGE = 0.127). This high-resolution cropland SM product can potentially benefit multiple agricultural applications, such as irrigation management and nutrient leaching estimation, and provide valuable insights to support farmers and land managers in decision-making processes.Published versio

Journal of Extension

Accepted versio

Ion-Currents in Oxyfuel Cutting Flames Exposed to External Bias Voltages

Computational Fluid Dynamics (CFD) and predictive models are presented in this dissertation that illustrates the detailed electrical characteristics, and the current-voltage (i-v) relationship throughout the preheating process of premixed methane-oxygen (CH4-O2) oxyfuel cutting flame subject to electric bias voltages. As such, the equations describing combustion, electrochemical transport for charged species, and potential are solved through a commercially available finite-volume CFD code. The reactions of the methane-oxygen (CH4 – O2) flame were combined with the GRI 3.0 mechanism and a 25-species reduced mechanism, respectively, and additional ionization reactions that generate three chemi-ions, H3O+, HCO+, and e– , to describe the chemistry of ions in flames. The electrical characteristics such as ion migrations and ion distributions are investigated for a range of electric potential, V ∈ [−10V, +10V ]. Since the physical flame is comprised of twelve Bunsen-like conical flame, inclusion of the third dimension imparts the resolution of fluid mechanics and the interaction among the individual cones. As for developing the predictive models, four different supervised machine learning (ML) algorithms, decision tree (DT), random forest (RF), K-nearest neighbors (KNN), and artificial neural network (ANN), were employed to predict the i-v relationship. An experimental dataset of ≈ 10050 was utilized where a 60:20:20 split was adopted, allocating 60% for training, 20% for validation, and 20% for testing. It was concluded that charged 'sheaths' are formed at both torch and workpiece surfaces, subsequently forming three distinct regimes in the i-v relationship. The i-v characteristics obtained have been compared to the previous experimental study for premixed flame. In this way, the overall model generates a better understanding of the physical behavior of the oxyfuel cutting flames, along with a more validated i-v characteristics. Such understanding might provide critical information towards achieving an autonomous oxyfuel cutting process.Doctor of PhilosophyOxyfuel flame cutting is a century-old technique having widespread applications in heavy industries, including, but not limited to, building construction, defense, shipyards, etc. However, the mechanized oxyfuel cutting process has never benefited from the degree of autonomy due to contemporary sensing technologies' limitations at high-temperature working conditions. As a result, an experienced labor force is required to operate the system, thereby lowering the efficacy associated with this cutting process. A potential solution to this problem is motivated by preliminary measurements demonstrating that electrical events called 'ion currents' associated with the flame itself can reliably indicate vital process states. Provided that an autonomous process is achieved, this work could realize reliable cost-effective control of the oxyfuel cutting process, a capability of great interest to many core US industries involved in construction, and major equipment manufacture for defense and energy applications. Critical parameters (standoff, F/O ratio, flow rate, etc.) must be detected during operation to ensure an autonomous oxyfuel cutting process. The motivation stems from the fact that by measuring such co-dependence between critical parameters and electrical characteristics through a data acquisition unit (DAQ) and power supply, the shortcomings of sensing suites in a harsh operating environment can be compromised. Experimental data in the literature indicated the current-voltage (i-v) relationship with different critical parameters of oxyfuel flame to be the salient electrical characteristic in the preheating process when cutting steel. A comprehensive two-dimensional computational simulation using StarCCM+ only with the reduced combustion chemical mechanism with ion-exchange reactions has already been completed to elucidate the experimental results and to investigate the electrical characteristics such as ion migrations and ion distributions. Nonetheless, the findings exhibit some magnitude of differences compared to the experimental results. Thereby to further improve the results and better understand the underlying physics, further computational models using ANSYS FLUENT are proposed herein, having the reduced surface chemical mechanism considered. In addition, predictive models were developed based on machine learning (ML) algorithms. Four supervised ML algorithms - decision tree (DT), random forest (RF), Knearest neighbors (KNN), and artificial neural network (ANN) - were adopted to predict the current-voltage (i-v) relationship at different process states. ML offers a more data-driven, adaptable, and scalable approach to prediction compared to traditional methods. Its ability to handle large, noisy, and complex data makes it especially powerful for tasks that are challenging for conventional analytical techniques. The results of this study illustrate the detailed electrical characteristics of premixed methane-oxygen (CH4 – O2) oxyfuel cutting flame subject to an electric field, for both the computational fluid dynamics (CFD) and ML models. Since the physical flame is comprised of twelve Bunsen-like conical flame, inclusion of the third dimension will impart the resolution of fluid mechanics and the interaction among the individual cones. Moreover, the chemical activity at the work surface will also be considered, however, with a substantial simplification of the three-dimensional model as a cost. The overall model will generate a better understanding of the physical behavior of the oxyfuel cutting flames, along with a more validated currentvoltage (i-v) relationship. Consequently, this relationship could then be embedded into a control algorithm to detect the critical process parameters that may facilitate a step towards achieving an autonomous oxyfuel cutting process