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  Real -Time Optimization of Artificial Frost-wall Model Predictions in Deep -Shafts

  During planning and execution of an artificial ground freezing project, the frost development over time is commonly prognosed using numerical models. The prediction quality of these models relies, however, directly on the given input parameters representing the thermal properties of the lithological layers. These input parameters are, nevertheless, naturally associated with unavoidable uncertainties. To enhance the reliability of the numerical models, a computational steering procedure is developed. This procedure is based on real-time monitoring data and deterministic back calculation of 2D numerical models representing defined homogeneous layers. Subsequently, a coherent 3D model encompassing all homogeneous layers is used for predicting the subsurface temperature field utilizing the calibrated parameters from the 2D models. In the developed workflow, the model parameters are continuously updated through the entire life-time of the project and more precise numerical predictions are achieved. As a result, risks related to frost wall instability and groundwater inflow are mitigated leading to optimized project planning and safer execution.

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