Bayesian inference of structural error in inverse models of thermal response tests

Wonjun Choi, Kathrin Menberg, Hideki Kikumoto, Yeonsook Heo, Ruchi Choudhary, Ryozo Ooka

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

For the design of ground-source heat pumps (GSHPs), two design parameters, namely the ground thermal conductivity and borehole thermal resistance are estimated by interpreting thermal response test (TRT) data using a physical model. In most cases, the parameters are fitted to the measured data assuming that the chosen model can fully reproduce the actual physical response. However, two significant sources of error make the estimation uncertain: random error from experiments and structural bias error that describes the discrepancy between the model and actual physical phenomena. Generally, these two error sources are not evaluated separately. As a result, the suitability of selected models to correctly infer parameters from TRTs are not well understood. In this study, the Bayesian calibration framework proposed by Kennedy and O'Hagan is employed to estimate the GSHP design parameters and quantify the random and structural errors in the inference. The calibration framework enables us to examine structural errors in the commonly used infinite line source model arising due to the conditions in which the TRT takes place. Two in situ TRT datasets were used: TRT1, influenced by contextual disturbances from the outdoor environment, and TRT2, influenced by a strong groundwater flow caused by heavy rainfall. We show that the Bayesian calibration framework is able to quantify the structural errors in the TRT interpretation and therefore can yield more accurate estimates of design parameters with full quantification of uncertainties.

Original languageEnglish
Pages (from-to)1473-1485
Number of pages13
JournalApplied Energy
Volume228
DOIs
Publication statusPublished - 2018 Oct 15

Fingerprint

Geothermal heat pumps
calibration
Calibration
physical phenomena
thermal conductivity
Random errors
Groundwater flow
groundwater flow
Boreholes
Heat resistance
borehole
Rain
Hot Temperature
test
parameter
Thermal conductivity
disturbance
rainfall
experiment
Experiments

Keywords

  • Bayesian calibration
  • Ground-source heat pump (GSHP)
  • Groundwater flow
  • Parameter estimation
  • Structural biased error
  • Thermal response test (TRT)
  • Uncertainty assessment

ASJC Scopus subject areas

  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

Cite this

Bayesian inference of structural error in inverse models of thermal response tests. / Choi, Wonjun; Menberg, Kathrin; Kikumoto, Hideki; Heo, Yeonsook; Choudhary, Ruchi; Ooka, Ryozo.

In: Applied Energy, Vol. 228, 15.10.2018, p. 1473-1485.

Research output: Contribution to journalArticle

Choi, Wonjun ; Menberg, Kathrin ; Kikumoto, Hideki ; Heo, Yeonsook ; Choudhary, Ruchi ; Ooka, Ryozo. / Bayesian inference of structural error in inverse models of thermal response tests. In: Applied Energy. 2018 ; Vol. 228. pp. 1473-1485.
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