Novel discretization method to calculate g-functions of vertical geothermal boreholes with improved accuracy and efficiency

TL;DR

This paper presents a new discretization method for calculating g-functions of geothermal boreholes, significantly improving computational efficiency and accuracy over existing models by using Gauss-Legendre quadrature and regularization techniques.

Contribution

A novel discretization approach employing Gauss-Legendre quadrature and regularization to enhance g-function calculations for geothermal boreholes.

Findings

Achieves 20 to 200 times faster computation than SFLS model.

Maintains or improves accuracy with optimized discretization.

Ensures stable solutions through regularization of ill-conditioned systems.

Abstract

The calculation of g-functions is essential for the design and simulation of geothermal boreholes. However, existing methods, such as the stacked finite line source (SFLS) model, face challenges regarding computational efficiency and accuracy, particularly with fine-grained discretization. This paper introduces a novel discretization method to address these limitations. We reformulate the g-function calculation under the uniform borehole wall temperature boundary condition as the solution to spatio-temporal integral equations. The SFLS model is identified as a special case using stepwise approximation of the heat extraction rate. Our proposed method employs the Gauss-Legendre quadrature to approximate the spatial integrals with a weighted sum of function values at strategically chosen points. This transforms the time-consuming segment-to-segment integral calculations in SFLS model into simpler and analytical point-to-point response factors. Furthermore, we identify that the governing integral equations are of the Fredholm first kind, leading to ill-conditioned linear systems that can cause g-function to diverge at high discretization orders. To address this, a regularization technique is implemented to ensure stable and convergent solutions. Numerical tests demonstrate that the proposed method is significantly more efficient, achieving comparable or improved accuracy at speeds 20 to 200 times faster than the SFLS model with optimized nonuniform discretization schemes.