| description abstract | The design of various biomedical, electronics cooling, and microfluidic devices relies on geometry-specific models and empirical correlations for flow and heat transfer through microscale pin fin geometries. Machine learning (ML) techniques are being used across many branches of science to develop more generalized surrogate models that can predict such transport processes. To collapse the simulation of flow and thermal properties across many different pin fin surfaces into a single predictive tool, the present study develops machine-learning-based surrogate models for the friction factor and Nusselt number (for constant wall temperature conditions) for fully developed low Reynolds number flow across pin fin geometries of differing cross section shape (circular, square, triangular) in aligned or staggered arrangements, oriented at any angle to the incoming flow, and for a range of transverse and longitudinal pitches, with water as the working fluid. The model training data are generated using an automated workflow that allows thousands of numerical simulations to be carried out on across different geometric and flow configurations. A total of ∼14,800 distinct simulation cases, for both friction factor and Nusselt number, are generated while varying the Reynolds number and aforementioned geometric parameters to train and test the machine learning models. The machine learning model architecture takes inputs of both image and vector data, and then outputs a scalar friction factor or Nusselt number. The trained models yield a goodness of fit (R2) value of 0.98 on unseen data. | |
