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Category: Publications: Engineer Research & Development Center (ERDC)
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  • Radio Frequency Heating of Washable Conductive Textiles for Bacteria and Virus Inactivation

    Abstract: The ongoing COVID-19 pandemic has increased the use of single-use medical fabrics such as surgical masks, respirators, and other personal protective equipment (PPE), which have faced worldwide supply chain shortages. Reusable PPE is desirable in light of such shortages; however, the use of reusable PPE is largely restricted by the difficulty of rapid sterilization. In this work, we demonstrate successful bacterial and viral inactivation through remote and rapid radio frequency (RF) heating of conductive textiles. The RF heating behavior of conductive polymer-coated fabrics was measured for several different fabrics and coating compositions. Next, to determine the robustness and repeatability of this heating response, we investigated the textile’s RF heating response after multiple detergent washes. Finally, we show a rapid reduction of bacteria and virus by RF heating our conductive fabric. 99.9% of methicillin¬resistant Staphylococcus aureus (MRSA) was removed from our conductive fabrics after only 10 min of RF heating; human cytomegalovirus (HCMV) was completely sterilized after 5 min of RF heating. These results demonstrate that RF heating conductive polymer-coated fabrics offer new opportunities for applications of conductive textiles in the medical and/or electronic fields.
  • Influence of Chemical Coatings on Solar Panel Performance Snow Accumulation

    Abstract: Solar panel performance can be impacted when panel surfaces are coated with substances like dust, dirt, snow, or ice that scatter and/or absorb light and may reduce efficiency. As a consequence, time and resources are required to clean solar panels during and after extreme weather events or whenever surface coating occurs. Treating solar panels with chemical coatings that shed materials may decrease the operating costs associated with solar panel maintenance and cleaning. This study investigates three commercial coatings for use as self-cleaning glass technologies. Optical and thermal properties (reflectivity, absorption, and transmission) are investigated for each coating as well as their surface wettability and particle size. Incoming solar radiation was continuously monitored and snow events were logged to estimate power production capabilities and surface accumulation for each panel. In terms of power output, the commercial coatings made little impact on overall power production compared to the control (uncoated) panels. This was attributable to the overall high transmission, low absorption, and low reflection of each of the commercial coatings, making their presence on the surface of solar panels have minimal impact besides to potentially shed snow While the coatings made no observable difference to increase power production compared to the control panels, the shedding results from video monitoring suggest both the hydrophilic or hydrophobic test coatings decreased snow accumulation to a greater extent than the control panels (uncoated). Controlling the wettability properties of the solar panel surfaces has the potential to limit snow accumulation when compared to uncoated panel surfaces.
  • Dockerization of the Coastal Model Test Bed Toolkit

    Purpose: The purpose of this technical note is to document and describe changes made to the Coastal Model Test Bed (CMTB) suite of software in conjunction with the version 2 (V2) update.
  • ERDC-PT: A Multidimensional Particle Tracking Model

    Abstract: This report describes the technical engine details of the particle- and species-tracking software ERDC-PT. The development of ERDC-PT leveraged a legacy ERDC tracking model, “PT123,” developed by a civil works basic research project titled “Efficient Resolution of Complex Transport Phenomena Using Eulerian-Lagrangian Techniques” and in part by the System-Wide Water Resources Program. Given hydrodynamic velocities, ERDC-PT can track thousands of massless particles on 2D and 3D unstructured or converted structured meshes through distributed processing. At the time of this report, ERDC-PT supports triangular elements in 2D and tetrahedral elements in 3D. First-, second-, and fourth-order Runge-Kutta time integration methods are included in ERDC-PT to solve the ordinary differential equations describing the motion of particles. An element-by-element tracking algorithm is used for efficient particle tracking over the mesh. ERDC-PT tracks particles along the closed and free surface boundaries by velocity projection and stops tracking when a particle encounters the open boundary. In addition to passive particles, ERDC-PT can transport behavioral species, such as oyster larvae. This report is the first report of the series describing the technical details of the tracking engine. It details the governing equation and numerical approaching associated with ERDC-PT Version 1.0 contents.
  • Using an Object-Based Machine Learning Ensemble Approach to Upscale Evapotranspiration Measured from Eddy Covariance Towers in a Subtropical Wetland

    Abstract: Accurate prediction of evapotranspiration (ET) in wetlands is critical for understanding the coupling effects of water, carbon, and energy cycles in terrestrial ecosystems. Multiple years of eddy covariance (EC) tower ET measurements at five representative wetland ecosystems in the subtropical Big Cypress National Preserve (BCNP), Florida (USA) provide a unique opportunity to assess the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) ET operational product MOD16A2 and upscale tower measured ET to generate local/regional wetland ET maps. We developed an object-based machine learning ensemble approach to evaluate and map wetland ET by linking tower measured ET with key predictors from MODIS products and meteorological variables. The results showed MOD16A2 had poor performance in characterizing ET patterns and was unsatisfactory for estimating ET over four wetland communities where Nash-Sutcliffe model Efficiency (NSE) was less than 0.5. In contrast, the site-specific machine learning ensemble model had a high predictive power with a NSE larger than 0.75 across all EC sites. We mapped the ET rate for two distinctive seasons and quantified the prediction diversity to identify regions easier or more challenging to estimate from model-based analyses. An integration of MODIS products and other datasets through the machine learning upscaling paradigm is a promising tool for local wetland ET mapping to guide regional water resource management.
  • Residual Strength of a High-Strength Concrete Subjected to Triaxial Prestress

    Abstract: This study investigates simplified mechanical loading paths that represent more complex loading paths observed during penetration using a triaxial chamber and a high-strength concrete. The objective was to determine the effects that stress-strain (load) paths have on the material’s unconfined compressive (UC) residual strength. The loading paths included hydrostatic compression (HC), uniaxial strain in compression (UX), and uniaxial strain load biaxial strain unload (UXBX). The experiments indicated that the load paths associated with nonvisible microstructural damage were HC and UX—which produced minimal impact on the residual UC strength (less than 30%)—while the load path associated with visible macro-structural damage was UXBX, which significantly reduced the UC strength (greater than 90%). The simplified loading paths were also investigated using a material model driver code that was fitted to a widely used Department of Defense material model. Virtual experiment data revealed that the investigated material model overestimated material damage and produced poor results when compared to experimental data.
  • Instrumented Manikin Data Experiments 1 & 2

    Abstract: In this report, pressure-time histories from a shock front propagating past an instrumented manikin head are presented for two separate experiments. Data represents physical measurements to support an ongoing collaboration between with the US Army Medical Research and Development Center (MRDC) and the US Army Engineer Research and Development Center (ERDC).
  • Graphene in Cementitious Materials

    Abstract: This project aims to determine the influence of laboratory-generated graphene (LGG) and commercial-grade graphene (CGG) on the chemical structure and compressive strength of graphene-cement mixtures. Determining the graphene-cement structure/processing/property relationships provides the most useful information for attaining the highest compressive strength. Graphene dose and particle size, speed of mixing, and dispersant agent were found to have important roles in graphene dispersion by affecting the adhesion forces between calcium silicate hydrate (CSH) gels and graphene surfaces that result in the enhanced strength of cement-graphene mixtures. X-ray diffraction (XRD), Raman, and scanning electron microscope (SEM) analyses were used to determine chemical microstructure, and compression testing for mechanical properties characterization, respectively. Based on observed results both LGG and CGG graphene cement mixtures showed an increase in the compressive strength over 7-, 14-, and 28-day age curing periods. Preliminary dispersion studies were performed to determine the most effective surfactant for graphene dispersion. Future studies will continue to research graphene—cement mortar and graphene—concrete composites using the most feasible graphene materials. These studies will prove invaluable for military programs, warfighter support, climate change, and civil works.
  • Ranking Ports by Vessel Demand for Depth

    Abstract: The US Army Corps of Engineers (USACE) traditionally uses two metrics to evaluate the maintenance of coastal navigation projects: tonnage at the associated port (representing relative importance) and the controlling depth in the channel (representing operating condition). These are incorporated into a risk-based decision framework directing funds where channel conditions have deteriorated and the disrupted tonnage potential is the highest. However, these metrics fail to capture shipper demand for the maintained depth service provided by the USACE through dredging. Using automatic identification system (AIS) data, the USACE is pioneering new metrics describing vessel demand for the channel depth, represented by vessel encroachment volume (VEV). VEV describes the volume of the hull intruding into a specified clearance margin above the bed and captures how much vessels use the deepest portions of USACE-dredged channels. This study compares the VEV among 13 ports over 4 years by combining AIS, tidal elevations, channel surveys, and sailing draft. The ports are ranked based on the services demanded by their user base to inform the decision framework driving dredge funding allocations. Integrating demand for-depth metrics into the Harbor Maintenance Fee assessment and/or Trust Fund disbursements could alleviate the constitutionality concerns and several criticisms levied against Harbor Maintenance funding.
  • Challenges and Limitations of Using Autonomous Instrumentation for Measuring In Situ Soil Respiration in a Subarctic Boreal Forest in Alaska, USA

    Abstract: Subarctic and Arctic environments are sensitive to warming temperatures due to climate change. As soils warm, soil microorganisms break down carbon and release greenhouse gases such as methane (CH4) and carbon dioxide (CO2). Recent studies examining CO2 efflux note heterogeneity of microbial activity across the landscape. To better understand carbon dynamics, our team developed a predictive model, Dynamic Representation of Terrestrial Soil Predictions of Organisms’ Response to the Environment (DRTSPORE), to estimate CO2 efflux based on soil temperature and moisture estimates. The goal of this work was to acquire respiration rates from a boreal forest located near the town of Fairbanks, Alaska, and to provide in situ measurements for the future validation effort of the DRTSPORE model estimates of CO2 efflux in cold climates. Results show that soil temperature and seasonal soil thaw depth had the greatest impact on soil respiration. However, the instrumentation deployed significantly altered the soil temperature, moisture, and seasonal thaw depth at the survey site and very likely the soil respiration rates. These findings are important to better understand the challenges and limitations associated with the in situ data collection used for carbon efflux modeling and for estimating soil microbial activity in cold environments.