Structural Optimization and Carbon Emission Sensitivity Analysis of Multi-Temperature Cold Chain Warehouses Based on Thermal Resistance Network and Heat Transfer Entropy Generation

Abstract

This paper addresses the core issues of significant heat loss in the building envelope and the increased irreversibility of heat transfer processes, which aggravate energy consumption and carbon emissions in multi-temperature cold chain warehouses. Existing research lacks a key quantitative coupling mechanism between thermal resistance networks and heat transfer entropy generation. To fill this gap, this paper proposes a low-carbon structural design method that integrates thermal resistance network modeling, heat transfer entropy generation analysis, and multi-objective optimization. First, the thermal system of a multi-temperature warehouse is deconstructed, and an unsteady thermal resistance network model, which includes elements such as the building envelope and partition walls, is established to quantify the heat flux and temperature distribution along different paths. Based on the second law of thermodynamics, a coupling equation between the thermal resistance network output parameters and the heat transfer entropy generation rate is derived, establishing a direct link between “structural parameters, " “heat transfer processes, " and “irreversible energy consumption.” Next, a multi-objective optimization model is developed with the goals of minimizing the heat transfer entropy generation rate, reducing total carbon emissions, and optimizing construction costs. Key design variables such as insulation thickness and partition thermal conductivity are selected, and the model incorporates thermal and structural constraints. The NSGA-III algorithm, combined with Kriging surrogate modeling, is used to solve the optimization problem. Dual verification is carried out through Fluent-COMSOL joint simulation and field experiments. Finally, global sensitivity analysis based on Sobol’s method is performed to identify the key parameters influencing carbon emissions. The optimization results are validated using international case studies. The “thermal resistance-entropy generation” coupling modeling paradigm established in this study overcomes the limitations of traditional optimization methods that rely solely on macroscopic energy consumption, providing a new approach to low-carbon design for cold chain warehouses that is both physically grounded and practically applicable. This approach aligns with the United Nations Sustainable Development Goals and international low-carbon building standards.