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Archive: 2022
  • Network Development and Autonomous Vehicles: A Smart Transportation Testbed at Fort Carson

    Abstract: In this work, a smart transportation testbed was utilized at Fort Carson to demonstrate three use cases for the primary purpose to plan, develop, demonstrate, and employ autonomous vehicle technologies at military installations and within the surrounding communities to evaluate commercially available Connected and Automated Vehicles and the potential to reduce base operating costs, improve safety and quality of life for military service members and their families, and deliver services more efficiently and effectively. To meet this purpose, an automated vehicle shuttle, an unmanned aerial system, and a wireless network were used and tested during the project. Results for the automated shuttle indicated that de-spite the quantity of data generated by operations, the contractors may not be ready to share information in a readily usable format. Additionally, successful use by the public is predicated on both knowing their mobility patterns and staff members promoting trust in the technology to prospective riders. Results for the unmanned aerial system showed successful identification of foreign object debris and runway cracks at the airfield. The wireless network is now operational and is used for additional work which utilizes the installed traffic cameras.
  • A 𝘬-Means Analysis of the Voltage Response of a Soil-Based Microbial Fuel Cell to an Injected Military-Relevant Compound (Urea)

    Abstract: Biotechnology offers new ways to use biological processes as environmental sensors. For example, in soil microbial fuel cells (MFCs), soil electro-genic microorganisms are recruited to electrodes embedded in soil and produce electricity (measured by voltage) through the breakdown of substrate. Because the voltage produced by the electrogenic microbes is a function of their environment, we hypothesize that the voltage may change in a characteristic manner given environmental disturbances, such as the contamination by exogenous material, in a way that can be modelled and serve as a diagnostic. In this study, we aimed to statistically analyze voltage from soil MFCs injected with urea as a proxy for gross contamination. Specifically, we used 𝘬-means clustering to discern between voltage output before and after the injection of urea. Our results showed that the 𝘬-means algorithm recognized 4–6 distinctive voltage regions, defining unique periods of the MFC voltage that clearly identify pre- and postinjection and other phases of the MFC lifecycle. This demonstrates that 𝘬-means can identify voltage patterns temporally, which could be further improve the sensing capabilities of MFCs by identifying specific regions of dissimilarity in voltage, indicating changes in the environment.
  • 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.
  • The Impact of Practitioners’ Personality Traits on Their Level of Systems-Thinking Skills Preferences

    Abstract: In this study, we used a structural equation modeling method to investigate the relationship between systems engineers and engineering managers’ Systems-Thinking (ST) skills preferences and their Personality Traits (PTs) in the domain of complex system problems. As organizations operate in more and more turbulent and complex environments, it has become increasingly important to assess the ST skills preferences and PTs of engineers. The current literature lacks studies related to the impact of systems engineers and engineering managers’ PTs on their ST skills preferences, and this study aims to address this gap. A total of 99 engineering managers and 104 systems engineers provided the data to test four hypotheses posed in this study. The results show that the PTs of systems engineers and engineering managers have a positive impact on their level of ST skills preferences and that the education level, the current occupation type, and the managerial experience of the systems engineers and engineering managers moderate the main relationship in the study.
  • Numerical Modeling of Mesoscale Infrasound Propagation in the Arctic

    Abstract: The impacts of characteristic weather events and seasonal patterns on infrasound propagation in the Arctic region are simulated numerically. The methodology utilizes wide-angle parabolic equation methods for a windy atmosphere with inputs provided by radiosonde observations and a high-resolution reanalysis of Arctic weather. The calculations involve horizontal distances up to 200 km for which interactions with the troposphere and lower stratosphere dominate. Among the events examined are two sudden stratospheric warmings, which are found to weaken upward refraction by temperature gradients while creating strongly asymmetric refraction from disturbances to the circumpolar winds. Also examined are polar low events, which are found to enhance negative temperature gradients in the troposphere and thus lead to strong upward refraction. Smaller-scale and topographically driven phenomena, such as low-level jets, katabatic winds, and surface-based temperature inversions, are found to create frequent surface-based ducting out to 100 km. The simulations suggest that horizontal variations in the atmospheric profiles, in response to changing topography and surface property transitions, such as ice boundaries, play an important role in the propagation.
  • Data Acquisition Software for Impedance Tube Measurements

    Abstract: Transmission impedance tube measurements are necessary to measure the asymmetric acoustic property known as Willis coupling. However, software is required to measure and store data from an impedance tube for acoustic material characterization. This report details the overall structure of custom-developed software built from low-level functions. Software libraries from the data acquisition system as well as the HDF5 file system are the basis for the code. A command line user interface guides a user through the necessary steps in data collection.