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Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J.

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Presentation on theme: "Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J."— Presentation transcript:

1 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Schematic of a pin-fin array at the trailing edge of a gas turbine airfoil Figure Legend:

2 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Measured versus predicted array-averaged heat transfer using correlations of (a) Metzger et al. [10] and (b) VanFossen [11] Figure Legend:

3 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Model of a perceptron, the building block of the artificial neural network Figure Legend:

4 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Example network for predicting Nu D with 3-3-3-1 architecture, where the transfer functions are shown above each layer Figure Legend:

5 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Effect of activation function on learning rate for a 3-3-3-1 network having (a) logistic-logistic-logistic, (b) logistic-logistic-linear, and (c) hyperbolic tangent-hyperbolic tangent-linear activation functions. Arrow indicates minimum RMSE of combined training/validation data. Figure Legend:

6 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Effect of network size and training algorithm on predicting array-average heat transfer using (a) BPM, (b) BFGS, and (c) TNC Figure Legend:

7 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Variation of RMSE and maximum percent error for repeated training attempts using (a) BPM, (b) BFGS, and (c) TNC Figure Legend:

8 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Effect of network size on the cumulative distribution of prediction error for array-averaged heat transfer Figure Legend:

9 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Sensitivity of trained networks to changes in (a) Re D, (b) X/D, (c) S/D, (d) H/D using 50% of data in Table 1 for network training Figure Legend:

10 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Sensitivity of trained networks to changes in (a) Re D, (b) X/D, (c) S/D, (d) H/D using 75% of data in Table 1 for network training Figure Legend:

11 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Sensitivity of trained networks to changes in (a) Re D, (b) X/D, (c) S/D, (d) H/D using 100% of data in Table 1 for network training Figure Legend:

12 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Predicted and measured array-averaged Nu D for a 4-4-1 network trained using 50% of the available data in Table 1 Figure Legend:

13 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Effect of network size on the cumulative distribution of prediction error for row-averaged heat transfer Figure Legend:

14 Date of download: 6/3/2016 Copyright © ASME. All rights reserved. From: Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks J. Turbomach. 2013;136(5):051010-051010-9. doi:10.1115/1.4025217 Predicted and measured row-averaged Nu D for a 5-4-1 network trained using 50% of the available data in Table 1 Figure Legend:


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