PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
A comprehensive Study for Predicting the Geometrical Characteristics of an Inclined Negatively Buoyant Jet Using Group Method of Data Handling (GMDH) Neural Network
Version 1
: Received: 8 December 2023 / Approved: 12 December 2023 / Online: 12 December 2023 (10:58:09 CET)
How to cite:
Alfaifi, H.; Bonakdari, H. A comprehensive Study for Predicting the Geometrical Characteristics of an Inclined Negatively Buoyant Jet Using Group Method of Data Handling (GMDH) Neural Network. Preprints2023, 2023120853. https://doi.org/10.20944/preprints202312.0853.v1
Alfaifi, H.; Bonakdari, H. A comprehensive Study for Predicting the Geometrical Characteristics of an Inclined Negatively Buoyant Jet Using Group Method of Data Handling (GMDH) Neural Network. Preprints 2023, 2023120853. https://doi.org/10.20944/preprints202312.0853.v1
Alfaifi, H.; Bonakdari, H. A comprehensive Study for Predicting the Geometrical Characteristics of an Inclined Negatively Buoyant Jet Using Group Method of Data Handling (GMDH) Neural Network. Preprints2023, 2023120853. https://doi.org/10.20944/preprints202312.0853.v1
APA Style
Alfaifi, H., & Bonakdari, H. (2023). A comprehensive Study for Predicting the Geometrical Characteristics of an Inclined Negatively Buoyant Jet Using Group Method of Data Handling (GMDH) Neural Network. Preprints. https://doi.org/10.20944/preprints202312.0853.v1
Chicago/Turabian Style
Alfaifi, H. and Hossein Bonakdari. 2023 "A comprehensive Study for Predicting the Geometrical Characteristics of an Inclined Negatively Buoyant Jet Using Group Method of Data Handling (GMDH) Neural Network" Preprints. https://doi.org/10.20944/preprints202312.0853.v1
Abstract
A new approach for predicting the geometrical characteristics of the mixing behavior of an inclined dense jet for angles ranging from 15° to 85° is proposed in this study. This approach is called group method of data handling (GMDH) which is based on the artificial neural network (ANN) technique. The proposed model was trained and tested using existing experimental data reported in literature. The model was then evaluated using statistical indices as well as compared with analytical models from previous studies. The results of the coefficient of determination (R2) indicate a high accuracy of the proposed model with values of 0.9719 and 0.9513 for training and testing for the dimensionless of the distance from the nozzle to the return point xr/D, and 0.9454 and 0.9565 for training and testing for the dimensionless of the terminal rise height yt/D . Moreover, four previous analytical models were used to evaluate the GMDH model. The results showed the superiority of the proposed model in predicting the geometrical characteristics of the inclined dense jet for all tested angles. Finally, the standard error of estimate (SEE) was applied to demonstrate which model performed the best in terms of getting closer to the actual data. The results illustrate that all fitting lines of the GMDH model performed very well for all geometrical parameter predictions and was the best model with approximately 10% error, which was the lowest value of error among the models. Therefore, this study confirms that the GMDH model can be used to predict the geometrical properties of the inclined negatively buoyant jet with high performance and accuracy.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
