Standing column wells achieve enhanced heat transfer through convergent groundwater flow, which is primarily stimulated by bleed operation. In the literature, thermal response tests applied to standing column wells have generally been interpreted using conventional conductive analytical models or detailed numerical models. Interpreting thermal response tests under such conditions is challenging because purely conductive models cannot capture advective effects, while numerical modeling requires substantial effort in model development, parameterization, and calibration. The analytical β -ILS model, recently introduced in the literature, addresses these challenges by accounting for advective heat transport in standing column wells operating with bleed. However, the β -ILS model has not yet been applied to interpret thermal response tests. This study presents a first interpretation methodology for the β -ILS model, which targets five key thermal and hydraulic properties and demonstrates that using impulse response functions improves estimation precision. Key thermal and hydraulic properties are estimated from thermal response test data using Bayesian inference to quantify parameter uncertainty. Two calibration strategies are compared: a conventional temperatures-based inversion and an alternative approach based on deconvolved impulse response functions derived from the same dataset. Results show that impulse-responses-based calibration improves the precision of inferred parameters. Notably, the uncertainty in effective heat exchange length decreases from 59% to 13%, while the mean absolute error on simulated temperatures improves from 0.38 ± 0.15 ° C (temperatures-based) to 0.32 ± 0.04 ° C (impulse-responses-based). These findings demonstrate that conducting impulse-response based interpretation with the β -ILS model enables more precise and computationally efficient property identification for standing column wells, supporting integration into design workflows for ground source heat pump systems. • First TRT interpretation framework with the β -ILS model for SCWs with bleed. • Bayesian inference of properties comparing inversions with IRFs or temperatures. • Calibration on deconvolution-based IRFs reduce uncertainty in inferred parameters. • Provides an alternative to numerical models for SCW property estimation. • Improved parameter estimates for GSHP design and simulation.
Jacques et al. (Tue,) studied this question.