Journal
ENTROPY
Volume 23, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/e23111448
Keywords
fluid dynamics; numerical methods; computational science; computational fluid dynamics; differential equations; Falkner-Skan system; artificial neural networks; Sine-Cosine Algorithm; sequential quadratic programming; hybrid computing; mass transfer
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Funding
- Direccion General de Investigaciones of Universidad Santiago de Cali [01-2021]
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In this work, a new hybrid neurocomputing algorithm was applied to analyze an important model in fluid dynamics, resulting in the introduction of the ANN-SCA-SQP algorithm. Through experimental comparisons with reference solutions, the effectiveness and robustness of the algorithm were validated.
In this work, an important model in fluid dynamics is analyzed by a new hybrid neurocomputing algorithm. We have considered the Falkner-Skan (FS) with the stream-wise pressure gradient transfer of mass over a dynamic wall. To analyze the boundary flow of the FS model, we have utilized the global search characteristic of a recently developed heuristic, the Sine Cosine Algorithm (SCA), and the local search characteristic of Sequential Quadratic Programming (SQP). Artificial neural network (ANN) architecture is utilized to construct a series solution of the mathematical model. We have called our technique the ANN-SCA-SQP algorithm. The dynamic of the FS system is observed by varying stream-wise pressure gradient mass transfer and dynamic wall. To validate the effectiveness of ANN-SCA-SQP algorithm, our solutions are compared with state-of-the-art reference solutions. We have repeated a hundred experiments to establish the robustness of our approach. Our experimental outcome validates the superiority of the ANN-SCA-SQP algorithm.
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