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https://hdl.handle.net/2115/87539

Improvement of accuracy with uncertainty quantification in the simulation of a ground heat exchanger by combining model prediction and observation

英語

Ensemble Kalman Filter

2023-01

Elsevier

Geothermics

107

102611

With the utilization of shallow geothermal heat as a renewable energy source in recent times, several studies have focused on ground heat exchanger simulation. Ground heat exchanger simulation is an important factor that contributes to the design and control of shallow geothermal systems. Thus far, various models and parameter estimation methods have been proposed to represent actual phenomena; however, errors inevitably occur be-tween the model predictions and actual values. Hence, a method that can explicitly account for this uncertainty is desirable. Thus, in this study, we show that data assimilation-a method that combines simulation and obser-vation for more accurate state estimation and uncertainty quantification-can be applied to ground heat exchanger simulation. To this end, we perform an in situ transient heating experiment using a single-borehole heat exchanger, and we assimilate the actual observations using an ensemble Kalman filter for a reproductive simulation. The results of the data assimilation experiment indicate that the model parameter, i.e., the soil effective thermal conductivity, is modified from 1.19 W m-1 K-1 estimated from the geologic column to 1.70 +/- 0.05 W m-1 K-1, and it reproduces the standard estimate of 1.69 W m-1 K-1 from the thermal response test. Further, for the ground heat exchanger inlet/outlet temperature, simulation without data assimilation yielded a maximum error of approximately 2.0 K, whereas simulation with data assimilation produced a highly accurate state estimate with a standard deviation of 0.08 K. The proposed method allows a posteriori estimation of soil properties from the operational data of ground heat exchanger systems installed without thermal response tests as well as the correction of deviations between the model and observation values through statistical support and uncertainty quantification.

学術雑誌論文

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© 2022 The Authors. Published by Elsevier Ltd.

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