In this paper we present a new method (DCE – Drift and Conditional Estimation), coupling Infinite Line Source (ILS) theory with geostatistics, to interpret thermal response test (TRT) data and the relative implementing user-friendly software (GA-TRT). Many methods (analytical and numerical) currently exist to analyze TRT data. The innovation derives from the fact that we use a probabilistic approach, able to overcome, without excessively complicated calculations, many interpretation problems (choice of the guess value of ground volumetric heat capacity, identification of the fluctuations of recorded data, inability to provide a measure of the precision of the estimates obtained) that cannot be solved otherwise. The new procedure is based on a geostatistical drift analysis of temperature records which leads to a precise equivalent ground thermal conductivity (λg) estimation, confirmed by the calculation of its estimation variance. Afterwards, based on λg, a monovariate regression on the original data allows for the identification of the theoretical relationship between ground volumetric heat capacity (cg) and borehole thermal resistance (Rb). By assuming the monovariate Probability Distribution Function (PDF) for each variable, the joint conditional PDF to the cg−Rb relationship is found; finally, the conditional expectation allows for the identification of the correct and optimal couple of the cg−Rb estimated values.

A software tool for geostatistical analysis of thermal response test data: GA-TRT

FOCACCIA, SARA;TINTI, FRANCESCO;BRUNO, ROBERTO
2013

Abstract

In this paper we present a new method (DCE – Drift and Conditional Estimation), coupling Infinite Line Source (ILS) theory with geostatistics, to interpret thermal response test (TRT) data and the relative implementing user-friendly software (GA-TRT). Many methods (analytical and numerical) currently exist to analyze TRT data. The innovation derives from the fact that we use a probabilistic approach, able to overcome, without excessively complicated calculations, many interpretation problems (choice of the guess value of ground volumetric heat capacity, identification of the fluctuations of recorded data, inability to provide a measure of the precision of the estimates obtained) that cannot be solved otherwise. The new procedure is based on a geostatistical drift analysis of temperature records which leads to a precise equivalent ground thermal conductivity (λg) estimation, confirmed by the calculation of its estimation variance. Afterwards, based on λg, a monovariate regression on the original data allows for the identification of the theoretical relationship between ground volumetric heat capacity (cg) and borehole thermal resistance (Rb). By assuming the monovariate Probability Distribution Function (PDF) for each variable, the joint conditional PDF to the cg−Rb relationship is found; finally, the conditional expectation allows for the identification of the correct and optimal couple of the cg−Rb estimated values.
COMPUTERS & GEOSCIENCES
Focaccia Sara; Francesco Tinti; Roberto Bruno
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/230080
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