Determination of Thermal Conductivity Properties of Coastal Soils for GSHPs and Energy Geostructure Applications in Mexico

Abstract

The thermal conductivity of soils is a fundamental parameter for the design of ground-source heat pump systems (GSHPs) and energy geostructures. This paper presents a comprehensive evaluation of the physical, mineralogical, and thermal characteristics of typical coastal soils from Tabasco, Mexico. Twenty-five soil samples from four different strata were studied using the thermal needle probe method, X-ray diffractometry, scanning electron microscopy, and standard geotechnical soil classification tests. The results showed a significant correlation between the dry density and porosity with the thermal conductivity of the studied samples, which ranged between 1.17 and 2.32 W m−1 K−1. The performed statistical analyses indicated that coarse-grained soils had larger thermal conductivities and higher variability than fine-grained soils. Additionally, the performance of six models to estimate the thermal conductivity of soils was validated against the experimental data. All models provided accurate estimations for fine-grained soils, but only the effective medium theory (EMT) showed an adequate fit for coarse-grained soils. The results represent one of the first datasets for the thermal properties of Mexican soils. They will contribute to the implementation of GSHPs and energy geostructures in the country and locations with similar subsoil conditions, especially where time and resources are not available for their experimental determination.

Interpretation of Thermal Response Test (TRT) in energy piles using Bayesian Inference

“…In the present study, a stochastic method based on Bayes' Theorem [1] has been adapted to interpret the results of a shortduration TRT carried out in an energy pile in Mexico [5]. The aim is to infer Posterior Probability Density Functions (PPDFs) for the parameters of interest (ground thermal conductivity  and concrete thermal resistance Rc), taking into account our previous knowledge (prior distributions) and the given measurements of the TRT (likelihood).…”