Decoupling and Enhanced-Synergy Based Optimization for Multi-Fan Power Allocation in Highway Tunnel Ventilation

Energy-efficient operation of highway tunnel ventilation systems remains challenging, and optimal power allocation among multiple fans is essential for reducing overall energy consumption. This study begins with a quantitative analysis of multi-fan synergistic effects, decoupling the interactions into sequential transverse and longitudinal superpositions. An equivalent predictive model is then established for rapid and accurate calculation of the overall ventilation supply, where a neural-network surrogate model is integrated to predict the superposition effects. Building on this model, an improved particle swarm optimization (PSO) algorithm is applied to determine the optimal power allocation, demonstrating robust applicability across tunnels of different lengths and fan configurations. Validation against CFD simulations shows that the predictive model yields an error of about 3%. By enhancing both transverse and longitudinal synergies, the optimized power allocation scheme can reduce ventilation energy consumption by 36%. Thus, the proposed framework provides a practical and scalable solution for multi-fan power allocation in highway tunnel ventilation systems.