Prediction of undisturbed ground temperature using analytical and numerical modeling. Part I: Model development and experimental validation

The interaction of buildings and ground source heat pump systems with the surrounding ground is quite important for design and energy calculation procedures. This article describes a one-dimensional finite volume numerical model that can be used to estimate the undisturbed ground temperatures under various ground covers—short grass, tall grass, bare soil, concrete, and asphalt; and a two-harmonic analytical model, which requires minimum computational time and is intended for engineering application. The analytical model relies on five model parameters: annual average ground temperature, two temperature amplitudes, and two phase lags to estimate the ground temperatures. The parameters are estimated using the numerical model results. This article presents experimental validations of both models: Nineteen geographically and climatically diverse measurement sites, covered by short grass or tall grass, are chosen for validating the models for a 1-year period using weather data at the sites. Validation results show that both models satisfactorily predict the undisturbed ground temperatures for these sites; the mean root mean square errors of the numerical model at all sites are 1.3°C–1.6°C (2.3°F–2.9°F) at 5, 20, 50, and 100 cm depths; the mean root mean square errors of the analytical model at all sites are 1.4°C–2.4°C (2.5°F–4.3°F) at the four depths. In companion articles by Xing and Spitler (2017 Xing, L., and J.D. Spitler. 2017. Prediction of undisturbed ground temperature using analytical and numerical modeling. Part II: Methodology for developing a world-wide dataset. Science and Technology for Built Environment 23:809–825.[Taylor & Francis Online] , [Google Scholar]) and Xing et al. (2017 Xing, L., J.D. Spitler, and A. Bandyopadhyay. 2017. Prediction of undisturbed ground temperature using analytical and numerical modeling. Part III: Experimental validation of a world-wide dataset. Science and Technology for Built Environment 23:826–842.[Taylor & Francis Online] , [Google Scholar]), the authors develop automatic procedures using the two models to generate a worldwide dataset of both typical year ground temperatures and design year ground temperatures.