Effective cooling is essential for maintaining the performance, reliability, and energy efficiency of server systems. However, optimizing airflow parameters to achieve efficient thermal management is often constrained by the high computational cost of iterative simulations. This work presents an intelligent decision-support framework that integrates Computational Fluid Dynamics (CFD) with numerical optimization to dynamically refine airflow properties, ensuring efficient cooling while minimizing energy consumption.

The framework employs the L-BFGS-B algorithm to iteratively adjust inlet velocity within predefined constraints, efficiently converging to an optimal cooling configuration without the need for exhaustive CFD simulations. By leveraging optimization-driven adjustments, the system reduces computational overhead while maintaining accuracy, making it a practical approach for real-world applications. The framework operates autonomously, reducing the need for manual tuning and accelerating decision-making in server cooling design.

Beyond improving cooling performance, this approach demonstrates the broader potential of intelligent optimization in engineering workflows. The integration of CFD with data-driven decision-making enhances adaptability, streamlines design cycles, and supports real-time adjustments to operational conditions. The results highlight how automated optimization can significantly improve thermal management strategies in data centers and other thermally constrained environments, offering a scalable, computationally efficient solution for intelligent airflow control.