Tikhonov regularization stabilizes multi-parameter estimation of geothermal heat exchangers
2023
Du, Yufang | Li, Min | Li, Yong | Lai, Alvin CK.
Parameter estimation from thermal response tests (TRTs) becomes unreliable when testing time reduces or the number of estimated parameters increases because of low identifiability and ill-posed mathematical feature. To overcome this challenge, this paper reports an inversion algorithm integrating a short-time temperature response model and the zero-order Tikhonov regularization strategy. We applied the algorithm to a reference sandbox dataset and examined four scenarios: simultaneous estimation of four, five, six, or seven parameters of U-shaped geothermal heat exchangers. The preliminary results indicate that the Tikhonov regularization can improve the accuracy and precision of the nonlinear multi-parameter estimation of ground heat exchangers for both long (>48 h) and short (<48 h) tests. The improved performance is contributed to the short-time model, which enables the short-time high-sensitivity data to be useable, and the regularization, which stabilizes the iterative optimization-solving procedure.
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