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Category: Technology
  • Bioaugmentation for Enhanced Mitigation of Explosives in Surface Soil

    Abstract: Residual munition constituents (MCs) generated from live-fire training exercises persist in soil and can migrate to groundwater, surface waters, and off-range locations. Techniques to mitigate this potential migration are needed. Since the MC hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) can be biodegraded, soil inoculation with RDX-degrading bacteria (i.e., bioaugmentation) was investigated as a means to reduce the migration potential of RDX. Metagenomic studies using contaminated soils have suggested that a greater diversity of bacteria are capable of RDX biodegradation. However, these bacteria remain uncultivated and are potentially a source of novel enzymes and pathways for RDX biodegradation. In situ soil cultivation of a novel soil array was used to isolate the uncultivated bacteria that had been inferred to degrade RDX. Approximately 10.5% of the bacteria isolated from the soil arrays degraded RDX by the aerobic denitration pathway. Of these, 26.5% were possibly novel species of RDX-degrading bacteria, based on 16S rRNA sequence similarity. Both cell encapsulation in hydrogels and coating cells onto granules of polymeric carbon sources were investigated as carrier/delivery approaches for soil inoculation. However, neither of these approaches could confirm that the observed RDX degradation was by the inoculated bacteria.
  • Experimental and Numerical Analyses of Soil Electrical Resistivity under Subfreezing Conditions

    Abstract: The engineering behavior of frozen soils is critical to the serviceability of civil infrastructure in cold regions. Among various geophysical techniques, electrical resistivity imaging is a promising technique that is cost effective and provides spatially continuous subsurface information. In this study, under freeze–thaw conditions, we carry out lab–scale 1D electrical resistivity measurements on frost–susceptible soils with varying water content and bulk density properties. We use a portable electrical resistivity meter for temporal electrical resistivity measurements and thermocouples for temperature monitoring. Dynamic temperature-dependent soil properties, most notably unfrozen water content, exert significant influences on the observed electrical resistivity. Below 0 ◦C, soil resistivity increases with the decreasing temperature. We also observe a hysteresis effect on the evolution of electrical resistivity during the freeze–thaw cycle, which effect we characterize with a sigmoidal model. At the same temperature, electrical resistivity during freezing is consistently lower than that during thawing. We have implemented this sigmoidal model into a COMSOL finite element model at both laboratory and field scales which enables the simulation of soil electrical resistivity response under both short–term and long–term sub–freezing conditions. Atmospheric temperature variations induce soil temperature change, and thereby phase transition and electrical resistivity change, with the rate of change being a function of the depth of investigation and soil properties include initial water content and initial temperature. This study advances the fundamental understanding of the electrical behaviors of frozen soils and enhance the application of electrical geophysical investigations in cold regions.
  • Standardized NEON Organismal Data for Biodiversity Research

    Abstract: Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is limited by a lack of standardized, high-quality, empirical data spanning large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the US and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be accessible and easily integrated into investigators’ workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format available through the ecocomDP R package; provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included. Our workflows are open-source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks.
  • Neural Ordinary Differential Equations for Rotorcraft Aerodynamics

    Abstract: High-fidelity computational simulations of aerodynamics and structural dynamics on rotorcraft are essential for helicopter design, testing, and evaluation. These simulations usually entail a high computational cost even with modern high-performance computing resources. Reduced order models can significantly reduce the computational cost of simulating rotor revolutions. However, reduced order models are less accurate than traditional numerical modeling approaches, making them unsuitable for research and design purposes. This study explores the use of a new modified Neural Ordinary Differential Equation (NODE) approach as a machine learning alternative to reduced order models in rotorcraft applications—specifically to predict the pitching moment on a rotor blade section from an initial condition, mach number, chord velocity and normal velocity. The results indicate that NODEs cannot outperform traditional reduced order models, but in some cases they can outperform simple multilayer perceptron networks. Additionally, the mathematical structure provided by NODEs seems to favor time-dependent predictions. We demonstrate how this mathematical structure can be easily modified to tackle more complex problems. The work presented in this report is intended to establish an initial evaluation of the usability of the modified NODE approach for time-dependent modeling of complex dynamics over seen and unseen domains.
  • Artificial Intelligence (AI)–Enabled Wargaming Agent Training

    Abstract: Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancements included updates related to supporting agent training, leveraging high-performance computing resources, and developing infrastructure to support AI versus AI agent training and gameplay. After evaluating agent training across different algorithm options, Deep Q-Network–trained agents performed better compared to those trained with Advantage Actor Critic or Proximal Policy Optimization algorithms. Experimentation in varying scenarios revealed acceptable performance from agents trained in the original baseline scenario. By training a blue agent against a previously trained red agent, researchers successfully demonstrated the AI versus AI training and gameplay capability. Observing results from agent gameplay revealed the emergence of behavior indicative of two principles of war, which were economy of force and mass.
  • Enabling Understanding of Artificial Intelligence (AI) Agent Wargaming Decisions through Visualizations

    Abstract: The process to develop options for military planning course of action (COA) development and analysis relies on human subject matter expertise. Analyzing COAs requires examining several factors and understanding complex interactions and dependencies associated with actions, reactions, proposed counteractions, and multiple reasonable outcomes. In Fiscal Year 2021, the Institute for Systems Engineering Research team completed efforts resulting in a wargaming maritime framework capable of training an artificial intelligence (AI) agent with deep reinforcement learning (DRL) techniques within a maritime scenario where the AI agent credibly competes against blue agents in gameplay. However, a limitation of using DRL for agent training relates to the transparency of how the AI agent makes decisions. If leaders were to rely on AI agents for COA development or analysis, they would want to understand those decisions. In or-der to support increased understanding, researchers engaged with stakeholders to determine visualization requirements and developed initial prototypes for stakeholder feedback in order to support increased understanding of AI-generated decisions and recommendations. This report describes the prototype visualizations developed to support the use case of a mission planner and an AI agent trainer. The prototypes include training results charts, heat map visualizations of agent paths, weight matrix visualizations, and ablation testing graphs.
  • Financing Natural Infrastructure: The Elizabeth River Project, Chesapeake Bay, VA

    Purpose: Knowledge gaps surrounding natural infrastructure (NI) life cycles and performance thwart widespread implementation of NI in civil works projects. In particular, information about funding or financing the scoping, design, construction, monitoring, and adaptive management of NI projects constitutes a key need as there is no standard process for securing funds. This technical note is part of a series documenting successful examples of funding NI projects and sharing lessons learned about a variety of funding and financing methods to increase the implementation of NI projects. The research effort is a collaboration between the Engineering With Nature® (EWN®) and Systems Approach to Geomorphic Engineering (SAGE) programs of the US Army Corps of Engineers (USACE). This technical note explores how the Elizabeth River Project (ERP), a nonprofit organization based in Norfolk, Virginia, developed a homeowner cost-sharing program to fund NI projects—living shorelines, rain gardens, and riparian buffers—within an urban watershed.
  • The Use of Rhodamine Water Tracer (RWT) Dye to Improve Submersed Herbicide Applications

    Abstract: The inert fluorescent dye rhodamine water tracer (RWT) has been widely used in freshwater aquatic systems for many years to quantify bulk water exchange patterns and as a tracer for submersed herbicide movement. The dye is well-suited for tracer work due to its high solubility and detectability in water (<0.01 μg/L). Federal guidelines limit the aqueous concentration 0f RWT to <10 μg/L at drinking water intakes. The dye has proven to be harmless to aquatic organisms and humans in low concentrations and is relatively inexpensive. Since 1991, RWT has been used by Engineer Re-search and Development Center (ERDC) researchers to simulate aqueous herbicide applications in large, hydrodynamic systems in over 12 states. Such simulations have improved the effectiveness of herbicide treatments by linking in situ water exchange processes with appropriate herbicide selection and application rates. Understanding these parameters can be critical for mitigating herbicide exposure in environmentally sensitive settings and around potable water and irrigation intakes. A data-based estimate of water exchange patterns usually results in successful submersed herbicide applications—both with target-plant efficacy and limited injury to nontarget vegetation. Using RWT dye to simulate submersed herbicide applications is an important predictive and real-time tool in both experimental and operational settings.
  • Development and Testing of the Sediment Distribution Pipe (SDP): A Pragmatic Tool for Wetland Nourishment

    Abstract: Standard dredging operations during thin layer placement (TLP) projects are labor intensive as crews are necessary to periodically move the outfall location, which can have lasting adverse effects on the marsh surface. In an effort to increase efficiency during TLP, a novel Sediment Distribution Pipe (SDP) system was investigated. This system offers multiple discharge points along the pipeline to increase the sediment distribution while reducing pipeline movements. An SDP Modeling Application (SDPMA) was developed to assist in the design of SDP field applications by quickly assessing the pressure and velocity inside the discharge pipe and approximating the slurry throw distances. An SDP field proof of concept was performed during a two-phase TLP on Sturgeon Island, New Jersey, in 2020. The SDPMA was shown to be an accurate method of predicting performance of the SDP. The SDP was successful at distributing dredge material across the placement site; however, further research is warranted to better quantify performance metrics.
  • Uncrewed Survey-Vessel Conversion

    Purpose: The purpose of this study was to investigate the uses of an uncrewed survey vessel to maintain mission readiness of all federal navigation channels and ports. Developing an uncrewed survey vessel capable of collecting data in a riverine environment may increase the efficiency and resiliency of the US Army Corps of Engineers (USACE) missions and USACE districts to conduct surveys during post natural disasters and pandemics. This document describes the installation, enhancement, and modification of the commercial-off-the-shelf (COTS) system, the Sea Machines SM300, on a US Army Engineer Research and Development Center (ERDC) survey vessel to create a semiautonomous survey capability.