5 Aug 2022

South Pole Station Snowdrift Model

Abstract: The elevated building at Scott-Amundsen South Pole Station was designed to mitigate the effects of windblown snow on it and the surrounding infrastructure. Because the elevation of the snow surface increases annually, the station is periodically lifted on its support columns to maintain its design height above the snow surface. To assist with planning these lifts, this effort developed a computational model to simulate snowdrift formation around the elevated building. The model uses computational fluid dynamics methods and synthetic wind record generation derived from statistical analysis of meteorological data. Simulations assessed the impact of several options for the lifting operation on drifts surrounding the elevated building. Simulation results indicate that raising the eastern-most building section (Pod A), or the entire station all at once, can reduce drift accumulation rates over the nearby arches structures. Long-term analyses, spanning 5–6 years, determine whether an equilibrium drift condition may be reached after a long period of undisturbed drift development. These simulations showed that after about 6 years, the rate of growth of the upwind drift slows, appearing to approach an equilibrium condition. However, the adjacent drifts were still increasing in depth at a roughly linear rate, indicating that equilibrium for those drifts was still several seasons away.

1 Dec 2020

Improving Design Methodologies and Assessment Tools for Building on Permafrost in a Warming Climate

Abstract: The U.S. Department of Defense (DoD) operates numerous Arctic and Subarctic installations, including Alaska. Changes to permafrost can threaten critical built infrastructure. It is critical to accurately characterize and compare site conditions in permafrost regions to enable the efficient, cost-effective design and construction of an infrastructure well suited to the permafrost environment and that meets DoD requirements. This report describes three research efforts to establish (1) field investigation approaches for ground ice detection and delineation, (2) methods and modeling for early warning detection of thawing permafrost under infrastructure, and (3) an outline of a decision support system that determines the most applicable foundation design for warming and degrading permafrost. Outcomes of these interrelated efforts address needs to improve construction of DoD mission critical infrastructure on Arctic and Subarctic permafrost terrains. Field investigation processes used systematic methodologies including borehole data and geophysical measurements to effectively characterize subsurface permafrost information. The Permafrost Foundation Decision Support System (PFFDSS) tool implements and logically links field survey information and foundation type assessments. The current version of PFFDSS is designed to be accessible to design-engineers of a broad range of experience, that will reduce the effort and cost, and improve the effectiveness of site assessment.

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.