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Archive: November, 2022
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  • Cold Regions Vehicle Start: Next-Generation Lithium-Ion Battery Technologies for Stryker Vehicles

    Abstract: Operating vehicles in extremely cold environments is a significant problem for not only the public but also the military. The Department of Defense has encountered issues when trying to reliably cold start large, heavy-duty military vehicles, specifically the M1126 Stryker Combat Vehicle, in cold regions. As noted in previous work, the issue stems from the current battery technology’s limited temperature range. This current project utilized the protocol established in the previous phase to evaluate next-generation lithium-ion battery technologies for use in cold regions. Selected battery technologies met necessary military specifications for use in large military combat vehicles and were evaluated using a mechanical load system developed in previous work to simulate the starting of a Stryker engine. This work also evaluated the performance of the existing battery technology of a Stryker under Alaskan winter temperatures, which will verify the accuracy of the simulated cold room testing on the mechanical load system. The results of the tests showed that while the system was able to reliably operate down to −20°C, the battery management system encountered challenges at the lower end of the temperature range. This technology has a potential to reliably support cold regions operations but needs further evaluation.
  • Dissolution of NTO, DNAN, and Insensitive Munitions Formulations and Their Fates in Soils: SERDP ER-2220

    Abstract: The US military is interested in replacing TNT (2,4,6-trinitrotoluene) and RDX (1,3,5-hexahydro-1,3,5-trinitro-1,3,5-triazine) with DNAN (2,4-dinitroanisole) and NTO (3-nitro-1,2,4-triazol-5-one), which have similar explosive characteristics but are less likely to detonate unintentionally. Although these replacements are good explosives, basic information about their fate and transport was needed to evaluate their environmental impact and life-cycle management. This project measured their dissolution, photodegradation, and how aqueous solutions interact with soils, data critical to determining exposure potential and, consequently, risk.
  • Environmentally Informed Buried Object Recognition

    The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a dataset that consists of thermal images containing buried objects paired with the corresponding environmental and meteorological conditions, we employ a machine learning approach to determine which environmental conditions are the most impactful on the visibility of the buried objects. We find the key environmental conditions include incoming short-wave solar radiation, soil volumetric water content, and average air temperature. For each image, ROIs are computed using a computer vision approach and these ROIs are coupled with the most important environmental conditions to form the input for the classification algorithm. The environmentally informed classification algorithm produces a decision on whether the ROI contains a buried object by simultaneously learning on the ROIs with a classification neural network and on the environmental data using a tabular neural network. On a given set of ROIs, we have shown that the environmentally informed classification approach improves the detection of buried objects within the ROIs.
  • Willis Coupling in One-dimensional Layered Bulk Media

    Abstract: Willis coupling, which couples the constitutive equations of an acoustical material, has been applied to acoustic metasurfaces with promising results. However, less is understood about Willis coupling in bulk media. In this paper a multiple-scales homogenization method is used to analyze the source and interpretation of Willis coupling in one-dimensional bulk media without any hidden degrees of freedom, or one-dimensional layered media. As expected from previous work, Willis coupling is shown to arise from geometric asymmetries, but is further shown to depend greatly on the measurement position. In addition, a discussion of the predicted material properties, including Willis coupling, of macroscopically inhomogeneous media is presented.
  • Snow-Covered Region Improvements to a Support Vector Machine-Based Semi-Automated Land Cover Mapping Decision Support Tool

    Abstract: This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overall accuracy across the continental United States for Version 2 is 75% on snow-covered pixels and 69% on snow-free pixels, versus 16% and 66% for Version 1. However, combining the “crop” and “low vegetation” classes improves these values to 86% for snow and 83% for snow-free, compared to 19% and 83% for Version 1. This merging is justified by their spectral similarity, the difference between crop and low vegetation falling closer to land use than land cover. The Version 2 tool is built into a Python-based ArcGIS toolbox, allowing users to leverage the pre-trained model—along with image splitting and parallel processing techniques—for their land cover type map generation needs.