| Summary: | Accurate prediction of the dimensionless heat transfer (j factor) and friction factor (f factor) is crucial for optimizing plate-fin heat exchanger (PFHE) performance. However, current models, including existing ANN models, have difficulty predicting these factors for complex fin geometries such as offset strip fins and wavy fins. To address this challenge, this research proposes a novel, unified artificial neural network (ANN) model, specifically a multilayer perceptron (MLP), capable of accurately predicting both factors across various fin types, including offset strip fins and wavy fins. The ANN was trained and validated using a comprehensive dataset that encompasses a wide range of fin configurations and Reynolds numbers, from 300 to 10,000, covering both laminar and turbulent flow conditions. Advanced learning techniques, like Bayesian regularization (BR), were employed to enhance the model's performance. The proposed model surpasses existing models, achieving a predictive accuracy with a deviation of just 1–2% from actual values, compared to the typical ±20 % seen in previous models. This significant improvement implies the new ANN model could enhance PFHE performance, energy efficiency, and cost-effectiveness across diverse applications. Introducing a unified ANN model, the research underscores machine learning's potential in thermal engineering and paves the way for future advancements.
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