DIGITAL TWIN-DRIVEN THERMODYNAMIC AND FLUID DYNAMIC SIMULATION FOR EXERGY EFFICIENCY IN INDUSTRIAL POWER SYSTEMS

Abstract

This study addresses the persistent problem that many industrial power systems still exhibit low exergy efficiency even after decades of energy efficiency programs, largely because second law metrics are not embedded in day-to-day digital decision tools. The purpose is to examine how digital twin driven thermodynamic and fluid dynamic simulation, deployed through cloud and enterprise platforms, relates to exergy efficiency at plant level. Using a quantitative, cross sectional, case-based design, data were collected via structured questionnaires from 214 engineers, operators and managers in steam, combined cycle and cogeneration power plants that have implemented digital twin solutions. Key variables include digital twin adoption, simulation sophistication, data quality, organizational support and perceived exergy efficiency, measured on five-point Likert scales. Descriptive statistics and reliability analysis (Cronbach’s alpha 0.84 to 0.89) were followed by correlation and multiple regression, including interaction terms. The core model explained 51.9 percent of the variance in exergy efficiency (R² = 0.519), with simulation sophistication (β = 0.34, p < .001) and digital twin adoption (β = 0.27, p < .001) as the strongest predictors; data quality (β = 0.18, p = .002) and organizational support (β = 0.16, p = .006) were also significant. When moderation effects were added, explained variance rose to 57.3 percent (R² = 0.573), and interactions between digital twin adoption and data quality (β = 0.15, p = .004) and between adoption and organizational support (β = 0.12, p = .015) were significant, showing that high quality data and strong management backing amplify exergy gains. The findings imply that exergy-oriented improvements are maximized when plants jointly invest in high fidelity digital twin models, robust data governance and sustained organizational support for simulation-based decision making in industrial power systems.