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Category: Publications: Cold Regions Research and Engineering Laboratory (CRREL)
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  • The Influence of Mesoscale Atmospheric Convection on Local Infrasound Propagation

    Abstract: Infrasound—that is, acoustic waves with frequencies below the threshold of human hearing—has historically been used to detect and locate distant explosive events over global ranges (≥1,000 km). Simulations over these ranges have traditionally relied on large-scale, synoptic meteorological information. However, infrasound propagation over shorter, local ranges (0–100 km) may be affected by smaller, mesoscale meteorological features. To identify the effects of these mesoscale meteorological features on local infrasound propagation, simulations were conducted using the Weather Research and Forecasting (WRF) meteorological model to approximate the meteorological conditions associated with a series of historical, small-scale explosive test events that occurred at the Big Black Test Site in Bovina, Mississippi. These meteorological conditions were then incorporated into a full-wave acoustic model to generate meteorology-informed predictions of infrasound propagation. A series of WRF simulations was conducted with varying degrees of horizontal resolution—1, 3, and 15 km—to investigate the spatial sensitivity of these infrasound predictions. The results illustrate that convective precipitation events demonstrate potentially observable effects on local infrasound propagation due to strong, heterogeneous gradients in temperature and wind associated with the convective events themselves. Therefore, to accurately predict infrasound propagation on local scales, it may be necessary to use convection-permitting meteorological models with a horizontal resolution ≤4 km at locations and times that support mesoscale convective activity.
  • An MCA Linear Additive Method for Research Project Analysis

    Abstract: This report describes a business intelligence (BI) model developed by the Cold Regions Research and Engineering Laboratory (CRREL) to evaluate multiple projects simultaneously and help researchers learn descriptive phrases found in alignment sources representative of their projects. The BI model combines the linear additive model with the analytical hierarchy process to take advantage of the qualitative and quantitative nature of both methods. The model has five variations, all built along the same objectives but with different criteria due to the specialized emphasis areas of each variation. The BI model operates around three central concepts for evaluating the projects: Alignment Variables, Timing, and Customer Relationship. A use-case scenario with ten projects shows the effectiveness of the model and compares it with another model from the United States Military Academy. This new BI model will assist researchers in developing and proposing research ideas that are more relevant and fundable.
  • CRREL Environmental Wind Tunnel Upgrades and the Snowstorm Library

    Abstract: Environmental wind tunnels are ideal for basic research and applied physical modeling of atmospheric conditions and turbulent wind flow. The Cold Regions Research and Engineering Laboratory's own Environmental Wind Tunnel (EWT)—an open-circuit suction wind tunnel—has been historically used for snowdrift modeling. Recently the EWT has gone through several upgrades, namely the three-axis chassis motors, variable frequency drive, and probe and data acquisition systems. The upgraded wind tunnel was used to simulate various snowstorm conditions to produce a library of images for training machine learning models. Various objects and backgrounds were tested in snowy test conditions and no-snow control conditions, producing a total of 1.4 million training images. This training library can lead to improved machine learning models for image-cleanup and noise-reduction purposes for Army operations in snowy environments.
  • Preliminary Permafrost Predictions within the Chena River Watershed, Alaska, Using Landscape Characteristics

    Purpose: This Technical Note presents a method to create permafrost predictions in the Chena River watershed near Fairbanks, Alaska, using landscape characteristics. We produced probabilities for near-surface permafrost in the Chena River watershed using a published algorithm applied in a nearby region. The methodology presented serves as a proof of concept for developing permafrost maps using similar data in other cold regions.
  • 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.
  • 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.
  • 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.
  • Challenges and Limitations of Using Autonomous Instrumentation for Measuring In Situ Soil Respiration in a Subarctic Boreal Forest in Alaska, USA

    Abstract: Subarctic and Arctic environments are sensitive to warming temperatures due to climate change. As soils warm, soil microorganisms break down carbon and release greenhouse gases such as methane (CH4) and carbon dioxide (CO2). Recent studies examining CO2 efflux note heterogeneity of microbial activity across the landscape. To better understand carbon dynamics, our team developed a predictive model, Dynamic Representation of Terrestrial Soil Predictions of Organisms’ Response to the Environment (DRTSPORE), to estimate CO2 efflux based on soil temperature and moisture estimates. The goal of this work was to acquire respiration rates from a boreal forest located near the town of Fairbanks, Alaska, and to provide in situ measurements for the future validation effort of the DRTSPORE model estimates of CO2 efflux in cold climates. Results show that soil temperature and seasonal soil thaw depth had the greatest impact on soil respiration. However, the instrumentation deployed significantly altered the soil temperature, moisture, and seasonal thaw depth at the survey site and very likely the soil respiration rates. These findings are important to better understand the challenges and limitations associated with the in situ data collection used for carbon efflux modeling and for estimating soil microbial activity in cold environments.
  • Vehicle Modeling in Unreal Engine 4

    Abstract: Vehicle modeling software has presented considerable challenges in properly representing vehicle mobility in extreme conditions. We have recently been developing new vehicle models and scenes in Unreal Engine. Unreal Engine is best known as a video game creation platform focused on graphics and has relatively few options for real world accurate physics modeling. UE4 allows for lots of customization internally or via supplemental C++ code, so this can be mitigated by the addition of various functions to account for different situations a vehicle might be in. We have successfully implemented the following: accurately functioning wheeled vehicles, tracked vehicles, and created simulated and real world environments, downloaded through Geowatch heightmaps. Each environment can have various terrain conditions including soil, rock, snow, and sand applied across its surface. Modeling snow in these environments is of particular interest and recent motion resistance and sinkage models have been integrated into the software to affect graphics and vehicle performance. This new model for vehicle mobility offers an opportunity to improve the physics and graphics of differing terrains especially for winter conditions. The new model also allows for features to be updated and added with ease in the future.
  • Investigation of Steam Adsorption Chillers to Modernize Existing Central Steam Plant Systems

    Abstract: This report investigates the integration of steam adsorption chillers as a modernization strategy for conventional central steam plant systems. Our objective is to assess the feasibility, advantages, and challenges of incorporating steam adsorption chillers into existing steam plant setups to enhance energy efficiency and cooling capabilities. Central steam plant systems have historically been used for steam-based heating but often lack cooling capabilities, necessitating additional cooling infrastructure. Steam adsorption chillers offer a potential solution by using waste steam for cooling, optimizing energy utilization and reducing reliance on traditional cooling methods. Through a comprehensive analysis, this report evaluates the technical compatibility and potential cost implications of implementing steam adsorption chillers. It explores factors such as system integration, operational dynamics, and maintenance requirements to provide a holistic view of the feasibility and benefits of this modernization approach. The findings aim to offer valuable insights to decision-makers and Army facility managers seeking innovative ways to upgrade central steam plant systems. By considering the technical and economic aspects of adopting steam adsorption chillers, this report contributes to the knowledge base for sustainable and efficient energy utilization in central plant operations.