Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs

Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs

Abstract 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 therm...

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Bibliographic Details
Main Authors: Tao Hai, Ali Basem, As’ad Alizadeh, Kamal Sharma, Dheyaa J. jasim, Husam Rajab, Mohsen Ahmed, Murizah Kassim, Narinderjit Singh Sawaran Singh, Hamid Maleki
Format: Article
Language:English
Published: Nature Portfolio 2024-08-01
Series:Scientific Reports
Subjects:
Microencapsulated PCM
Thermal energy storage
Gaussian process regression
Genetic algorithm
Particle swarm optimization
Marine predators algorithm
Online Access:https://doi.org/10.1038/s41598-024-71027-9

Internet

https://doi.org/10.1038/s41598-024-71027-9

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