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Machine Learning Tool for Rapid Wind Turbine Bearing Capacity Prediction (2021)

Wind farm construction requires large cranes to lift massive wind turbine structures over 300 feet tall and exceeding 160 tons. Installing these structures requires many crane “walks”, moving the heavy cranes around 50 miles along soil surfaces of varying strengths. Moving the cranes quickly is critical to installation economics, but this must be done safely by ensuring soil strength stability to avoid sinking or toppling the crane. Conventional best practices require cone penetrometer tests (CPTs) and performing numerical modeling to establish a safe path for moving the cranes requires on the order of four to six weeks. Itasca developed a rapid bearing capacity prediction tool using Python scripts, FLAC3D, and machine learning to provide near real-time feedback on the soil bearing capacity at a location, allowing enhanced crane walk planning.

Simulating Spalling With a Flat-Jointed Material (2020)

Long-term storage of spent fuel is critical to the nuclear energy industry. The Swedish Nuclear Fuel and Waste Management Company (SKB) is developing an approach for the storage of spent nuclear fuel in an underground repository in competent crystalline rock. In order to better understand the spalling damage process, an in-situ test involving the drilling of two boreholes was performed in Äspö diorite at SKB’s underground hard rock laboratory in Äspö. Tests and monitoring were performed on the pillar that separated the boreholes. In order to further investigate the damage process, Itasca performed numerical modeling using PFC3D and FLAC3D.

Modeling of Spalling in PFC3D — A Quantitative Assessment (2020)

SKB is interested in developing a 3D discrete model to predict spalling on the excavation boundaries of underground repositories for the long-term storage of spent nuclear fuel. This project provided a quantitative assessment of modeling spalling using PFC3D to study both lab- and tunnel-scale behavior.

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