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<p>This study presents a comparative analysis of energy destruction in integral rolled spiral finned tube (IRSFT) bundles in heat exchangers, employing advanced machine learning techniques to enhance efficiency. Traditional normal finned tube (NFT) designs often encounter significant energy losses due to suboptimal geometric parameters. To address this, machine learning algorithms were applied&#xa0;to optimize the design and performance of integral rolled SFT bundles. The research involved extensive numerical simulations using computational fluid dynamics (CFD) to model heat transfer and energy destruction. Key geometric parameters, including fin tip and root thickness, were varied and analysed. Machine learning models, particularly deep neural networks (DNNs), were then trained on this simulation data to predict the optimal configurations that minimize energy destruction. The results demonstrate that machine learning techniques can significantly improve the thermal performance of integral rolled SFT bundles. The optimized designs, identified through machine learning, exhibited lower entropy generation and higher heat transfer efficiency compared to traditional designs. Specifically, the optimized SFT bundles achieved a reduction in energy destruction by up to 20%, highlighting the potential of these advanced techniques in enhancing heat exchanger performance. Furthermore, the study developed performance evaluation criteria (PEC) to assess and predict the impact of various fin configurations on system efficiency. These criteria provided a comprehensive framework for understanding and mitigating energy losses. The integration of machine learning techniques in the design of integral rolled SFT bundles results in substantial improvements in efficiency of 7%. This approach not only reduces energy destruction but also offers a robust methodology for optimizing heat exchanger performance in various industrial applications.</p>

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Analysis of energy destruction in integral rolled spiral finned tube bundles in heat exchangers using machine learning techniques

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Praveen Barmavatu

摘要

This study presents a comparative analysis of energy destruction in integral rolled spiral finned tube (IRSFT) bundles in heat exchangers, employing advanced machine learning techniques to enhance efficiency. Traditional normal finned tube (NFT) designs often encounter significant energy losses due to suboptimal geometric parameters. To address this, machine learning algorithms were applied to optimize the design and performance of integral rolled SFT bundles. The research involved extensive numerical simulations using computational fluid dynamics (CFD) to model heat transfer and energy destruction. Key geometric parameters, including fin tip and root thickness, were varied and analysed. Machine learning models, particularly deep neural networks (DNNs), were then trained on this simulation data to predict the optimal configurations that minimize energy destruction. The results demonstrate that machine learning techniques can significantly improve the thermal performance of integral rolled SFT bundles. The optimized designs, identified through machine learning, exhibited lower entropy generation and higher heat transfer efficiency compared to traditional designs. Specifically, the optimized SFT bundles achieved a reduction in energy destruction by up to 20%, highlighting the potential of these advanced techniques in enhancing heat exchanger performance. Furthermore, the study developed performance evaluation criteria (PEC) to assess and predict the impact of various fin configurations on system efficiency. These criteria provided a comprehensive framework for understanding and mitigating energy losses. The integration of machine learning techniques in the design of integral rolled SFT bundles results in substantial improvements in efficiency of 7%. This approach not only reduces energy destruction but also offers a robust methodology for optimizing heat exchanger performance in various industrial applications.