# Modeling and Key Parameter Interaction Analysis for Ship Central Cooling Systems
## 1 Introduction
[13] both indicate that, especially under low load conditions such as slow steaming, dynamically adjusting the pump load can effectively reduce unnecessary energy losses, further highlighting the critical role played by optimized control strategies in achieving multi-condition energy efficiency improvement. Currently, researchers commonly analyze and optimize the energy efficiency and operational efficiency of systems by applying variable speed drive (VSD/VFD) technology or by adjusting operating parameters based on specific working conditions. These studies undoubtedly lay an important theoretical foundation and provide valuable engineering guidance for the design selection and overall performance optimization of ship cooling systems. However, a ship cooling system is essentially a complex coupled system involving multiple physical fields and transient processes, whose energy consumption is influenced by the combined effects of various operating parameters and environmental factors [14; 15]. Solely relying on VSD/VFD for the speed regulation of a single piece of equipment, or adjusting individual parameters based on simplified assumptions, often struggles to fully address the intricate dynamic coupling effects and interactions among multiple factorsin actual operation. This, to some extent, limits the potential for the system to achieve globally optimal energy efficiency. Simultaneously, these methods often find it difficult to accurately capture the nuances of all key factors and their mutual influences, leading to potentially limited optimization effects or effects that are only evident under specific operating conditions. Furthermore, gaining a deep understanding of these complex interactions through traditional multi-physical field coupled simulation modeling is not only inherently challenging but also computationally intensive and time-consuming. Conversely, conducting comprehensive experimental studies is often constrained by high costs and demanding implementation conditions. Therefore, there is an urgent need for a new modeling approach that can efficiently reveal how key factors and their interactions affect system performance. Response surface methodology (RSM) is precisely such a powerful statistical modeling technique. Leveraging optimized experimental design, it can efficiently construct mathematical models of the relationship between input parameters and system responses using relatively few data points. Such models not only clearly reveal the influence patterns of individual parameters and their interactions on the system response, making them particularly suitable for analyzing highly coupled multi-parameter problems like ship cooling systems, but their modeling process is also relatively fast, and the resulting predictive models demonstrate excellent computational efficiency. The core objective of RSM is to obtain an empirical model with good predictive accuracy at a reasonable experimental or simulation cost, thereby providing an efficient tool for in-depth analysis, accurate prediction, and effective optimization of the system [16, 17, 18]. To accurately predict the energy consumption of ship central cooling systems and gain a deep understanding of the influence of key operating parameters on system performance, this paper focused on the central cooling system of a certain type of bulk carrier. The research employed a combined approach of numerical simulation and experimental design. First, based on the multi-physical domain modeling technique, a dynamic simulation model of the system was constructed and validated using actual ship data. Secondly, using this model and the experimental design method, the influence patterns of several key operating parameters on system energy consumption were systematically investigated.