Building a Digital Twin for a Ground Heat Exchanger

Abstract This research investigates the development of a digital twin (DT) for ground heat exchangers (GHEs) and its potential to enhance the efficiency and sustainability of shallow geothermal energy systems. It introduces an innovative approach for building a GHE‐DT that connects the physical and digital systems to monitor key parameters, predict issues, and optimize energy efficiency. The process involves several phases including implicit knowledge codification, data‐driven analysis, model construction, and system design. The study emphasizes real‐time monitoring of the effective parameters: ground temperature and fluid conditions (flow rate, temperature, and pressure). The GHE‐DT's digital system mainly comprises three sections, namely, data storage, mathematical modeling, and data‐driven modeling. The role of the presented mathematical model is to simulate the GHE's behavior and assess its performance characteristics, such as the heat exchanger's effectiveness and efficiency. Additionally, the data‐driven model used in the proposed DT utilizes formal concept analysis and relation concept analysis to identify connections and associations among parameters for a better understanding of the GHE functioning. The GHE‐DT provides useful services including trend analysis, problem prediction, and correlation analysis. These services provide engineers and operators with the opportunity to increase dependability, save maintenance costs, and optimize GHE performance.