article · Case Studies in Thermal Engineering
This study addressed the complexity of modelling fluid flow and heat transfer in heat exchangers by developing an advanced machine learning model. The model, which uses a Random Vector Functional Link (RVFL) optimised by a Fire Hawk Optimizer, predicts the thermo-hydraulic behaviour of helical plate heat exchangers (HPHE). Specifically, it forecasts the outlet temperatures of hot and cold fluids, considering variations in cross-sectional areas and pitches of the flow channels. When compared against models optimised by grey wolf, jellyfish, sine-cosine, and hybrid salp swarm-whale optimisers, the Fire Hawk Optimizer demonstrated superior accuracy, achieving an R2 value of 0.999.
Accurate prediction of heat exchanger performance is vital for efficient design and operation across various industries. This research offers a sophisticated machine learning approach that can simplify the complex task of modelling these systems, potentially leading to more optimised and energy-efficient heat exchanger designs.
This research provides a predictive modelling tool that can help manufacturers of heat exchangers estimate thermal performance and characteristics. The model, which is based on advanced machine learning, could be integrated into design and simulation software to optimise heat exchanger configurations, such as channel cross-sectional areas and pitches. This appears to be an applied research outcome, offering a tool for industrial use in the development and testing phases of heat exchanger design.
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The study of the fluid flow in heat exchangers and its related mass and heat transfer processes is a very complicated research subject. Modeling of coupled fluid flow and heat transfer processes using numerical and analytical approaches are cumbersome problems. Hence, the current study investigates the modeling thermo-hydraulic behavior of a helical plate heat exchanger (HPHE) using advanced machine learning models. The outlet temperatures of hot and cold fluids are predicted considering different cross-sectional areas and pitches of the flow channels. This paper fulfills the research gap about fluid flow and heat transfer mechanisms by considering advanced machine learning approaches. The proposed model helps different manufacturers of heat exchangers to estimate the thermal performance and characteristics. The developed model aims to provide an advanced random vector functional link (RVFL) optimized fire hawk optimizer to predict outlet temperatures of working fluids of HPHE. The proposed model was compared with four optimized models using grey wolf, jellyfish, sine-cosine, and hybrid salp swarm-whale optimizers. The results of all models were compared and fire hawk optimizers showed superior accuracy compared with other models. Fire hawk optimizer had the highest R2 (0.999) followed by jellyfish (0.998) for both investigated responses. Hybrid salp swarm-whale and sine-cosine optimizers had the lowest R2 (0.987) in the case of hot fluid.
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DOI: 10.1016/j.csite.2023.103294
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