Machine learning intelligent based hydromagnetic thermal transport under Soret and Dufour effects in convergent/divergent channels : a hybrid evolutionary numerical algorithm
© 2023. The Author(s)..
In this research, we analyze the complex dynamics of hydro-magnetic flow and heat transport under Sorent and Dofour effects within wedge-shaped converging and diverging channels emphasizing its critical role in conventional system design, high-performance thermal equipment. We utilized artificial neural networks (ANNs) to investigation the dynamics of the problem. Our study centers on unraveling the intricacies of energy transport and entropy production arising from the pressure-driven flow of a non-Newtonian fluid within both convergent and divergent channel. The weights of ANN based fitness function ranging from - 10 to 10. To optimize the weights and biases of artificial neural networks (ANNs), employ a hybridization of advanced evolutionary optimization algorithms, specifically the artificial bee colony (ABC) optimization integrated with neural network algorithms (NNA). This approach allows us to identify and fine-tune the optimal weights within the neural network, enabling accurate prediction. We compare our results against the established different analytical and numerical methods to assess the effectiveness of our approach. The methodology undergoes a rigorous evaluation, encompassing multiple independent runs to ensure the robustness and reliability of our findings. Additionally, we conduct a comprehensive analysis that includes metrics such as mean squared error, minimum values, maximum values, average values, and standard deviation over these multiple independent runs. The minimum fitness function value is 1.32 × 10-8 computed across these multiple runs. The absolute error, between the HAM and machine learning approach addressed ranging from 3.55 × 10-7 to 1.90 × 10-8. This multifaceted evaluation ensures a thorough understanding of the performance and variability of our proposed approach, ultimately contributing to our understanding of entropy management in non-uniform channel flows, with valuable implications for diverse engineering applications.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
|---|---|
| Enthalten in: |
Scientific reports - 13(2023), 1 vom: 11. Dez., Seite 21973 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Aslam, Muhammad Naeem [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Revised 11.12.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1038/s41598-023-48784-0 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM36572677X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM36572677X | ||
| 003 | DE-627 | ||
| 005 | 20231229123515.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1038/s41598-023-48784-0 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1227.xml |
| 035 | |a (DE-627)NLM36572677X | ||
| 035 | |a (NLM)38081911 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Aslam, Muhammad Naeem |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Machine learning intelligent based hydromagnetic thermal transport under Soret and Dufour effects in convergent/divergent channels |b a hybrid evolutionary numerical algorithm |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 11.12.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2023. The Author(s). | ||
| 520 | |a In this research, we analyze the complex dynamics of hydro-magnetic flow and heat transport under Sorent and Dofour effects within wedge-shaped converging and diverging channels emphasizing its critical role in conventional system design, high-performance thermal equipment. We utilized artificial neural networks (ANNs) to investigation the dynamics of the problem. Our study centers on unraveling the intricacies of energy transport and entropy production arising from the pressure-driven flow of a non-Newtonian fluid within both convergent and divergent channel. The weights of ANN based fitness function ranging from - 10 to 10. To optimize the weights and biases of artificial neural networks (ANNs), employ a hybridization of advanced evolutionary optimization algorithms, specifically the artificial bee colony (ABC) optimization integrated with neural network algorithms (NNA). This approach allows us to identify and fine-tune the optimal weights within the neural network, enabling accurate prediction. We compare our results against the established different analytical and numerical methods to assess the effectiveness of our approach. The methodology undergoes a rigorous evaluation, encompassing multiple independent runs to ensure the robustness and reliability of our findings. Additionally, we conduct a comprehensive analysis that includes metrics such as mean squared error, minimum values, maximum values, average values, and standard deviation over these multiple independent runs. The minimum fitness function value is 1.32 × 10-8 computed across these multiple runs. The absolute error, between the HAM and machine learning approach addressed ranging from 3.55 × 10-7 to 1.90 × 10-8. This multifaceted evaluation ensures a thorough understanding of the performance and variability of our proposed approach, ultimately contributing to our understanding of entropy management in non-uniform channel flows, with valuable implications for diverse engineering applications | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Shaukat, Nadeem |e verfasserin |4 aut | |
| 700 | 1 | |a Riaz, Arshad |e verfasserin |4 aut | |
| 700 | 1 | |a Khan, Ilyas |e verfasserin |4 aut | |
| 700 | 1 | |a Niazai, Shafiullah |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Scientific reports |d 2011 |g 13(2023), 1 vom: 11. Dez., Seite 21973 |w (DE-627)NLM215703936 |x 2045-2322 |7 nnns |
| 773 | 1 | 8 | |g volume:13 |g year:2023 |g number:1 |g day:11 |g month:12 |g pages:21973 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1038/s41598-023-48784-0 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 13 |j 2023 |e 1 |b 11 |c 12 |h 21973 | ||