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Category: Publications: Cold Regions Research and Engineering Laboratory (CRREL)
  • Resilience in Distributed Sensor Networks

    Abstract: With the advent of cheap and available sensors, there is a need for intelligent sensor selection and placement for various purposes. While previous research was focused on the most efficient sensor networks, we present a new mathematical framework for efficient and resilient sensor network installation. Specifically, in this work we formulate and solve a sensor selection and placement problem when network resilience is also a factor in the optimization problem. Our approach is based on the binary linear programming problem. The generic formulation is probabilistic and applicable to any sensor types, line-of-site and non-line-of-site, and any sensor modality. It also incorporates several realistic constraints including finite sensor supply, cost, energy consumption, as well as specified redundancy in coverage areas that require resilience. While the exact solution is computationally prohibitive, we present a fast algorithm that produces a near-optimal solution that can be used in practice. We show how such formulation works on 2D examples, applied to infrared (IR) sensor networks designed to detect and track human presence and movements in a specified coverage area. Analysis of coverage and comparison of sensor placement with and without resilience considerations is also performed.
  • Practical Guidance for Numerical Modeling in FUNWAVE-TVD

    Purpose: This technical note describes the physical and numerical considerations for developing an idealized numerical wave-structure interaction modeling study using the fully nonlinear, phase-resolving Boussinesq-type wave model, FUNWAVE-TVD (Shi et al. 2012). The focus of the study is on the range of validity of input wave characteristics and the appropriate numerical domain properties when inserting partially submerged, impermeable (i.e., fully reflective) coastal structures in the domain. These structures include typical designs for breakwaters, groins, jetties, dikes, and levees. In addition to presenting general numerical modeling best practices for FUNWAVE-TVD, the influence of nonlinear wave-wave interactions on regular wave propagation in the numerical domain is discussed. The scope of coastal structures considered in this document is restricted to a single partially submerged, impermeable breakwater, but the setup and the results can be extended to other similar structures without a loss of generality. The intended audience for these materials is novice to intermediate users of the FUNWAVE-TVD wave model, specifically those seeking to implement coastal structures in a numerical domain or to investigate basic wave-structure interaction responses in a surrogate model prior to considering a full-fledged 3-D Navier-Stokes Computational Fluid Dynamics (CFD) model. From this document, users will gain a fundamental understanding of practical modeling guidelines that will flatten the learning curve of the model and enhance the final product of a wave modeling study. Providing coastal planners and engineers with ease of model access and usability guidance will facilitate rapid screening of design alternatives for efficient and effective decision-making under environmental uncertainty.
  • Cold Regions Vehicle Start: Cold Performance of Ultracapacitor-Based Batteries for Stryker Vehicles

    Abstract: Reliable vehicle start is necessary to support mission success, especially for response time. At Department of Defense installations in cold regions, vehicles using rechargeable battery and starter technologies have significant issues starting in the cold. Ultracapacitor engine start modules (ESMs) are an alternate technology to rechargeable lead-acid or lithium-ion batteries. The project develops a performance baseline for the ESM used in the M1126 Stryker Combat Vehicle under cold conditions. To test the performance of the ESMs in a cold room, a mechanical load system was constructed to replicate the load of starting a Stryker engine and instrumented with sensors to monitor parameters such as voltage, torque, and temperature. The ESMs were tested with the load system at temperatures from 24°C to −40°C. The results of the tests showed that there was some degradation of the ultracapacitor’s performance at the colder temperatures, which was expected, but no permanent damage. This work provides a test protocol and capability to evaluate next-generation vehicle battery systems for cold regions applications. Additionally, the ESM cold performance data establish a baseline to compare next-generation vehicle battery storage systems and to support cold regions missions and identify potential performance requirements for future vehicle battery system acquisition.
  • Landform Identification in the Chihuahuan Desert for Dust Source Characterization Applications: Developing a Landform Reference Data Set

    Abstract: ERDC-Geo is a surface erodibility parameterization developed to improve dust predictions in weather forecasting models. Geomorphic landform maps used in ERDC-Geo link surface dust emission potential to landform type. Using a previously generated southwest United States landform map as training data, a classification model based on machine learning (ML) was established to generate ERDC-Geo input data. To evaluate the ability of the ML model to accurately classify landforms, an independent reference landform data set was created for areas in the Chihuahuan Desert. The reference landform data set was generated using two separate map-ping methodologies: one based on in situ observations, and another based on the interpretation of satellite imagery. Existing geospatial data layers and recommendations from local rangeland experts guided site selections for both in situ and remote landform identification. A total of 18 landform types were mapped across 128 sites in New Mexico, Texas, and Mexico using the in situ (31 sites) and remote (97 sites) techniques. The final data set is critical for evaluating the ML-classification model and, ultimately, for improving dust forecasting models.
  • Automated Ground-Penetrating-Radar Post-Processing Software in R Programming

    Abstract: Ground-penetrating radar (GPR) is a nondestructive geophysical technique used to create images of the subsurface. A major limitation of GPR is that a subject matter expert (SME) needs to post-process and interpret the data, limiting the technique’s use. Post-processing is time-intensive and, for detailed processing, requires proprietary software. The goal of this study is to develop automated GPR post-processing software, compatible with Geophysical Survey Systems, Inc. (GSSI) data, in open-source R programming. This would eliminate the need for an SME to process GPR data, remove proprietary software dependencies, and render GPR more accessible. This study collected GPR profiles by using a GSSI SIR4000 control unit, a 100 MHz antenna, and a Trimble GPS. A standardized method for post-processing data was then established, which includes static data removal, time-zero correction, distance normalization, data filtering, and stacking. These steps were scripted and automated in R programming, excluding data filtering, which was used from an existing package, RGPR. The study compared profiles processed using GSSI soft-ware to profiles processed using the R script developed here to ensure comparable functionality and output. While an SME is currently still necessary for interpretations, this script eliminates the need for one to post-process GSSI GPR data.
  • Investigations into the Ice Crystallization and Freezing Properties of the Antifreeze Protein ApAFP752

    Abstract: Antifreeze proteins (AFPs) allow biological organisms, including insects, fish, and plants, to survive in freezing temperatures. While in solution, AFPs impart cryoprotection by creating a thermal hysteresis (TH), imparting ice recrystallization inhibition (IRI), and providing dynamic ice shaping (DIS). To leverage these ice-modulating effects of AFPs in other scenarios, a range of icing assays were performed with AFPs to investigate how AFPs interact with ice formation when tethered to a surface. In this work, we studied ApAFP752, an AFP from the beetle Anatolica polita, and first investigated whether removing the fusion protein attached during protein expression would result in a difference in freezing behavior. We performed optical microscopy to examine ice-crystal shape, micro-structure, and the recrystallization behavior of frozen droplets of AFP solutions. We developed a surface chemistry approach to tether these proteins to glass surfaces and conducted droplet-freezing experiments to probe the interactions of these proteins with ice formed on those surfaces. In solution, ApAFP752 did not show any DIS or TH, but it did show IRI capabilities. In surface studies, the freezing of AFP droplets on clean glass surfaces showed no dependence on concentration, and the results from freezing water droplets on AFP-decorated surfaces were inconclusive.
  • Buried-Object-Detection Improvements Incorporating Environmental Phenomenology into Signature Physics

    Abstract: The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environmental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, this study developed an approach using a Canny edge methodology to identify regions of interest potentially harboring a target object. Finally, an ML method was developed to improve automatic target detection and recognition performance by accounting for environmental phenomenological conditions, improving performance by 50% over standard automatic target detection and recognition software.
  • Ground-penetrating Radar Studies of Permafrost, Periglacial, and Near-surface

    Abstract: Installations built on ice, permafrost, or seasonal frozen ground require careful design to avoid melting issues. Therefore, efforts to rebuild McMurdo Station, Antarctica, to improve operational efficiency and consolidate energy resources require knowledge of near-surface geology. Both 200 and 400 MHz ground-penetrating radar (GPR) data were collected in McMurdo during January, October, and November of 2015 to detect the active layer, permafrost, excess ice, fill thickness, solid bedrock depth, and buried utilities or construction and waste debris. Our goal was to ultimately improve surficial geology knowledge from a geotechnical perspective. Radar penetration ranged between approximately 3 and 10 m depth for the 400 and 200 MHz antennas, respectively. Both antennas successfully detect buried utilities and near-surface stratified material to ~0.5–3.0 m whereas 200 MHz profiles were more useful for mapping deeper stratified and un-stratified fill over bedrock. Artificially generated excess ice which appears to have been created from runoff, water pooling and refreezing, aspect shading from buildings, and snowpack buried under fill, are prevalent. Results show that McMurdo Station has a complex myriad of ice-rich fill, scoria, fractured volcanic bedrock, permafrost, excess ice, and buried anthropogenically generated debris, each of which must be considered during future construction.
  • Extracting Sintered Snow Properties from MicroCT Imagery to Initialize a Discrete Element Method Model

    Abstract: Modeling snow’s mechanical behavior is important for many cold regions engineering problems. Because snow’s microstructure plays a significant role in its mechanical response, it is imperative to initialize models with accurate bond characteristics and realistic snow-grain geometries to precisely capture the microstructure interactions. Previous studies have processed microcomputed tomography scans of snow samples with a watershed method to extract grain geometries. This approach relies on identification of seed points to segment each grain. Our new methodology, called the “moving window method,” does not require prior knowledge of the snow-grain-size distribution to identify seed points. We use the interconnectivity of the segmented grains to identify bond characteristics. We compare the resultant grain-size and bond-size distributions to the known grain sizes of the laboratory-made snow samples. The grain-size distributions from the moving window method closely match the known grain sizes, while both results from the traditional method produce grains that are too large. We propose that the bond net-work identified using the traditional method underestimates the number of bonds and overestimates bond radii. Our method allows us to segment realistic snow grains and their associated bonds, without prior knowledge of the samples, from which we can initialize numerical models of the snow.
  • Short-range Near-surface Seismic Ensemble Predictions and Uncertainty Quantification for Layered Medium

    Abstract: To make a prediction for seismic signal propagation, one needs to specify physical properties and subsurface ground structure of the site. This information is frequently unknown or estimated with significant uncertainty. This paper describes a methodology for probabilistic seismic ensemble prediction for vertically stratified soils and short ranges with no in situ site characterization. Instead of specifying viscoelastic site properties, the methodology operates with probability distribution functions of these properties taking into account analytical and empirical relationships among viscoelastic variables. This yields ensemble realizations of signal arrivals at specified locations where statistical properties of the signals can be estimated. Such ensemble predictions can be useful for preliminary site characterization, for military applications, and risk analysis for remote or inaccessible locations for which no data can be acquired. Comparison with experiments revealed that measured signals are not always within the predicted ranges of variability. Variance-based global sensitivity analysis has shown that the most significant parameters for signal amplitude predictions in the developed stochastic model are the uncertainty in the shear quality factor and the Poisson ratio above the water table depth.