Hai Tao, Basem Ali, Alizadeh As'ad, Sharma Kamal, Jasim Dheyaa J, Rajab Husam, Ahmed Mohsen, Kassim Murizah, Singh Narinderjit Singh Sawaran, Maleki Hamid
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, China.
School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China.
Sci Rep. 2024 Aug 31;14(1):20271. doi: 10.1038/s41598-024-71027-9.
Suspensions containing microencapsulated phase change materials (MPCMs) play a crucial role in thermal energy storage (TES) systems and have applications in building materials, textiles, and cooling systems. This study focuses on accurately predicting the dynamic viscosity, a critical thermophysical property, of suspensions containing MPCMs and MXene particles using Gaussian process regression (GPR). Twelve hyperparameters (HPs) of GPR are analyzed separately and classified into three groups based on their importance. Three metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and marine predators algorithm (MPA), are employed to optimize HPs. Optimizing the four most significant hyperparameters (covariance function, basis function, standardization, and sigma) within the first group using any of the three metaheuristic algorithms resulted in excellent outcomes. All algorithms achieved a reasonable R-value (0.9983), demonstrating their effectiveness in this context. The second group explored the impact of including additional, moderate-significant HPs, such as the fit method, predict method and optimizer. While the resulting models showed some improvement over the first group, the PSO-based model within this group exhibited the most noteworthy enhancement, achieving a higher R-value (0.99834). Finally, the third group was analyzed to examine the potential interactions between all twelve HPs. This comprehensive approach, employing the GA, yielded an optimized GPR model with the highest level of target compliance, reflected by an impressive R-value of 0.999224. The developed models are a cost-effective and efficient solution to reduce laboratory costs for various systems, from TES to thermal management.
含有微胶囊相变材料（MPCMs）的悬浮液在热能存储（TES）系统中起着至关重要的作用，并在建筑材料、纺织品和冷却系统中有应用。本研究聚焦于使用高斯过程回归（GPR）准确预测含有MPCMs和MXene颗粒的悬浮液的动态粘度，这是一种关键的热物理性质。分别分析了GPR的十二个超参数（HPs），并根据其重要性将它们分为三组。采用三种元启发式算法，即遗传算法（GA）、粒子群优化算法（PSO）和海洋捕食者算法（MPA）来优化超参数。使用这三种元启发式算法中的任何一种对第一组中四个最重要的超参数（协方差函数、基函数、标准化和西格玛）进行优化都取得了优异的结果。所有算法都获得了合理的R值（0.9983），证明了它们在这种情况下的有效性。第二组探讨了纳入其他中度重要的超参数（如拟合方法、预测方法和优化器）的影响。虽然所得模型比第一组有一些改进，但该组中基于PSO的模型表现出最显著的增强，获得了更高的R值（0.99834）。最后，分析第三组以研究所有十二个超参数之间的潜在相互作用。采用GA的这种综合方法产生了一个优化的GPR模型，其目标符合度最高，令人印象深刻的R值为0.999224。所开发的模型是一种经济高效的解决方案，可降低从TES到热管理等各种系统的实验室成本。
