TY - JOUR
T1 - Tikhonov regularization stabilizes multi-parameter estimation of geothermal heat exchangers
AU - Du, Yufang
AU - Li, Min
AU - Li, Yong
AU - Lai, Alvin CK.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
KW - Ground heat exchangers
KW - Parameter estimation
KW - Short-time G function
KW - Thermal response tests
KW - Tikhonov regularization
UR - http://www.scopus.com/inward/record.url?scp=85138781959&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85138781959&origin=recordpage
U2 - 10.1016/j.energy.2022.125479
DO - 10.1016/j.energy.2022.125479
M3 - 21_Publication in refereed journal
VL - 262
JO - Energy
JF - Energy
SN - 0360-5442
IS - Part B
M1 - 125479
ER -