Abstract

The scope of this work is to create a model to support a sensor monitoring system of a tailings dam. Such model can be used for monitoring and prediction, but also for the optimal design of the sensor network and the overall optimization of the monitoring system. To accelerate the model response we propose using Model Order Reduction techniques in the transient thermo- hydro-mechanical system. POD-based model reduction, combined with Discrete Empirical Interpolation (DEIM) is used for data assimilation applications, parameter identification (soil mechanical and hydraulic properties) and optimal sensor placement. The efficiency gains in inverse problem solving and the accuracy of the resulting ROM are examined and discussed.