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  • Scaled-Up Synthesis of Water-Retaining Alginate-Based Hydrogel

    Purpose: Synthesis of a scaled-up version of a lithium-ion-based alginate/poly(acrylamide-co-stearyl methacrylate) [Li-alginate/P(AAm-co-SMA)] hydrogel with several optimizations for thermal signature investigations on various environmental substrates.
  • Review of Regressive Channel Erosion and Grade Control Options on the Rio Coca, Ecuador

    Purpose: The US Army Corps of Engineers (USACE) is assisting the Ecuadorian state-run Corporación Eléctrica del Ecuador (CELEC) in addressing a water resource issue involving regressive channel erosion on the Rio Coca. Reconnaissance of the site was completed the week of 21 February 2022; parts of the river system were viewed to determine if improvements could be made to the current grade control structure (GCS) mitigation plan for reducing channel erosion and stabilizing the river system downstream of the Coca Coda Sinclair (CCS) Dam. The Rio Coca is a tributary to the Amazon River system in South America. It originates on the east side of the Andes Mountains and generally flows from southwest to northeast through the project area and then turns and flows east into the Amazon basin (Figure 1).* The Rio Coca valley is a current example of how damaging regressive erosion can be to a fluvial system (Figure 2).
  • Assessing Differences in the Wetland Functional Capacity of Wet Pine Flatwood Compensatory Mitigation Sites Managed with Prescribed Fire and Mechanical Mowing

    Abstract: This report assesses the functional capacity of wet pine flatwood wetland habitats in the Gulf Coastal region of the United States, with a specific focus on compensatory mitigation sites maintained using mowing or prescribed fire, or both, as understory management strategies. The use of mowing in lieu of prescribed fire treatments has been proposed for a variety of reasons, including when mitigation sites are located near residential areas or where fires pose a risk to private property and public safety. This study evaluates the effects of mechanized mowing on ecosystem functions by using the hydrogeomorphic (HGM) wetland functional-assessment method to compare mitigation sites managed by mowing to sites managed by prescribed-fire regimes. Assessing mowing as a vegetation-control strategy in lieu of prescribed-fire regimes provides valuable information that can improve the design and management of wet pine flatwoods mitigation sites throughout portions of the southeastern United States, where this wetland class occurs.
  • RISC TAMER Framework: Resilient Installation Support Against Compound Threats Analysis and Mitigation for Equipment and Resources Framework

    Every day, decision-makers must allocate resources based on the best available information at the time. Military installations face a variety of threats which challenge sustained functionality of their supporting and supported deployable systems. Considering the compounding and interdependent impacts of the threats, both specified (what is known) and unspecified (what is not known) and the investments needed to address these threats adds value to the decision-making process. Current risk management practices are generally evaluated via scenario analyses that do not consider compound threats, resulting in limited risk management solutions. Current practices also challenge the ability of decision-makers to increase resilience against such threats. The Resilient Installation Support against Compound Threats Analysis and Mitigation for Equipment and Resources (RISC TAMER) Framework establishes a decision support structure to identify and categorize system components, compound threats and risks, and system relationships to provide decision-makers with more complete and comprehensive information from which to base resilience-related decisions, for prevention and response. This paper focuses on the development process for RISC TAMER framework to optimize resilience enhancements for a wide variety of deployable systems in order to implement resilience strategies to protect assets, to increase adaptability, and to support power projection and global operations.
  • 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.