1 Dec 2020

Snow-Covered Obstacles’ Effect on Vehicle Mobility

ABSTRACT:  The Mobility in Complex Environments project used unmanned aerial systems (UAS) to identify obstacles and to provide path planning in forward operational locations. The UAS were equipped with remote-sensing devices, such as photogrammetry and lidar, to identify obstacles. The path-planning algorithms incorporated the detected obstacles to then identify the fastest and safest vehicle routes. Future algorithms should incorporate vehicle characteristics as each type of vehicle will perform differently over a given obstacle, resulting in distinctive optimal paths. This study explored the effect of snow-covered obstacles on dynamic vehicle response. Vehicle tests used an instrumented HMMWV (high mobility multipurpose wheeled vehicle) driven over obstacles with and without snow cover. Tests showed a 45% reduction in normal force variation and a 43% reduction in body acceleration associated with a 14.5 cm snow cover. To predict vehicle body acceleration and normal force response, we developed two quarter-car models: rigid terrain and deformable snow terrain quarter-car models. The simple quarter models provided reasonable agreement with the vehicle test data. We also used the models to analyze the effects of vehicle parameters, such as ground pressure, to understand the effect of snow cover on vehicle response.

1 Jul 2020

PUBLICATION NOTICE: Spatial Analysis of Precipitation and Snow Water Equivalent Extremes for the Columbia River Basin

Abstract: Recent advances in the spatial statistics of extremes and model calibration were applied to develop and deliver areal-exceedance estimates for precipitation (PREC), by season and duration, and snow water equivalent (SWE), by cool season month and for the water year, for 758 delineated sub-basins of the Columbia River Basin (CRB), which correspond to a new CRB hydrology model watershed delineation. Understanding that future US Army Corps of Engineers, Northwestern Division, mission requirements may change, project execution also included the development and delivery of an application guidance document to credibly compute areal-exceedance estimates, including uncertainty, for PREC or SWE for any arbitrary area within the CRB. R, a free software environment for statistical computing and graphics (https://www.r-project.org/), and QGIS, a free and open source geographic information system (https://qgis.org/en/site/index.html), were the primary tools used for product development and delivery. The following R software packages were primarily used during project execution: evd, Glmnet, maps, raster, rgdal, SDMTools, sp, and SpatialExtremes.

25 Jun 2020

PUBLICATION NOTICE: A Generalized Approach for Modeling Creep of Snow Foundations

ABSTRACT:  When an external load is applied, snow will continue to deform in time, or creep, until the load is removed. When using snow as a foundation material, one must consider the time-dependent nature of snow mechanics to understand its long-term structural performance. In this work, we develop a general approach for predicting the creep behavior of snow. This new approach spans the primary (nonlinear) to secondary (linear) creep regimes. Our method is based on a uniaxial rheological Burgers model and is extended to three dimensions. We parameterize the model with density- and temperature-dependent constants that we calculate from experimental snow creep data. A finite element implementation of the multiaxial snow creep model is derived, and its inclusion in an ABAQUS user material model is discussed. We verified the user material model against our analytical snow creep model and validated our model against additional experimental data sets. The results show that the model captures the creep behavior of snow over various time scales, temperatures, densities, and external loads. By furthering our ability to more accurately predict snow foundation movement, we can help prevent unexpected failures and extend the useful lifespan of structures that are constructed on snow.

30 Apr 2020

PUBLICATION NOTICE: Analysis of Snow Water Equivalent Annual Maxima in the Upper Connecticut River Basin Using a Max-Stable Spatial Process Model

Abstract: Recent advances from the science of spatial extremes and model regularization were applied to develop areal-based extremes of snow water equivalent (SWE) data for the upper Connecticut River Basin. Development of areal-based SWE exceedance probability estimates are of relevance for cool season probabilistic flood hazard analyses (PFHA). The approach profiled in this case study is applicable for other hydrometeor-ological variables of relevance to PFHA. The methodology conforms with Extreme Value Theory (EVT) for the analysis of spatial extremes; hence, there is a firm theoretical basis for extrapolation. Trend surface development is guided by EVT theory and recent advances for regularizing general linear models. R, a free software environment for statistical computing and graphics, and QGIS, a free and open-source geographic information system, were the primary tools used for product development and delivery. The following R software packages were primarily used during project execution: evd, Glmnet, maps, raster, rgdal, SDMTools, sp, and SpatialExtremes. R software packages exist in the public domain and support PFHA analyses of varying complexities. Their application herein is not an endorsement or recommendation. It is recommended that one would need to evaluate any particular R software package regarding its suitability for use for any specific application.