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In much of the public literature on pin-fin heat transfer, Nusselt number is presented as a function of Reynolds number using a power-law correlation. Power-law correlations typically have an accuracy of 20% while the experimental uncertainty of such measurements is typically between 5% and 10%. Additionally, the use of power-law correlations may require many sets of empirical constants to fully characterize heat transfer for different geometrical arrangements. In the present work, artificial neural networks were used to predict heat transfer as a function of streamwise spacing, spanwise spacing, pin-fin height, Reynolds number, and row position. When predicting experimental heat transfer data, the neural network was able to predict 73% of array-averaged heat transfer data to within 10% accuracy while published power-law correlations predicted 48% of the data to within 10% accuracy. Similarly, the neural network predicted 81% of row-averaged data to within 10% accuracy while 52% of the data was predicted to within 10% accuracy using power-law correlations. The present work shows that first-order heat transfer predictions may be simplified by using a single neural network model rather than combining or interpolating between power-law correlations. Furthermore, the neural network may be expanded to include additional pin-fin features of interest such as fillets, duct rotation, pin shape, pin inclination angle, and more making neural networks expandable and adaptable models for predicting pin-fin heat transfer.Copyright © 2013 by ASME

Pin-fin arrays are commonly used as compact heat exchangers for cooling the trailing edge of gas turbine airfoils. While much research has been devoted to the heat transfer characteristics of various pin-fin configurations, little work has been done to investigate the flowfield in pin-fin arrays. Such information may allow for further optimization of pin-fin configurations. A new pin-fin facility at large scale has been constructed to allow optical access for the use of non-intrusive measurement techniques such as laser Doppler velocimetry and time-resolved, digital particle image velocimetry. Using these techniques, the flow through a single row of pin-fins having a height-to-diameter ratio of 2 and span-to-diameter ratio of 2.5 was investigated. Results show that the length of the wake region decreases with increasing Reynolds number. At higher Reynolds numbers, Karman vortices developed closer to the pin-fins than for single, infinitely long cylinders. Transverse fluctuations correlated well with endwall heat transfer indicating that the Karman vortices play a key role in energy transport.Copyright © 2011 by ASME