Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison Article Swipe
Farag M. A. Altalbawy
,
Ahmad Alkhayyat
,
Ramdevsinh Jhala
,
Anupam Yadav
,
Thiagarajan Ramachandran
,
Aman Shankhyan
,
A. Karthikeyan
,
Dhirendra Nath Thatoi
,
Vladimir Vladimirovich Sinitsin
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-03187-1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-03187-1
This paper introduces an approach to temperature prediction by employing three distinct machine learning models: K-Nearest Neighbors (KNN), Gaussian Process Regression (GPR), and Multi-layer Perceptron (MLP) which are integrated into Computational Fluid Dynamics (CFD). The dataset consists of inputs, represented by the features x and y, and the corresponding output, which is the temperature. The case study is fluid flow of nanofluid through a pipe and the nanofluid contains CuO particles. To enhance model efficacy, the Political Optimizer (PO) algorithm was utilized for fine-tuning purposes. The findings substantiate the capability of the optimized frameworks in delivering precise temperature estimations. GPR yielded a notable R2 value of 0.998, reflecting a strong concordance between the estimated and actual measurements. In a comparable manner, the KNN approach exhibited outstanding predictive performance, also attaining an R2 value of 0.998. MLP, although slightly lower in performance compared to the other models, still proves to be a reliable predictor of temperature values, with an R-squared score of 0.984. Overall, the combination of the PO algorithm and the machine learning models showcases promising results in temperature prediction. The study's findings offer valuable insights for decision-making processes in a variety of domains that rely on accurate temperature forecasting.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-03187-1
- https://www.nature.com/articles/s41598-025-03187-1.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410889988
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410889988Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-03187-1Digital Object Identifier
- Title
-
Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparisonWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-30Full publication date if available
- Authors
-
Farag M. A. Altalbawy, Ahmad Alkhayyat, Ramdevsinh Jhala, Anupam Yadav, Thiagarajan Ramachandran, Aman Shankhyan, A. Karthikeyan, Dhirendra Nath Thatoi, Vladimir Vladimirovich SinitsinList of authors in order
- Landing page
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https://doi.org/10.1038/s41598-025-03187-1Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-025-03187-1.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.nature.com/articles/s41598-025-03187-1.pdfDirect OA link when available
- Concepts
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Nanofluid, Computational fluid dynamics, Computer science, Fluid dynamics, Flow (mathematics), Distribution (mathematics), Mechanics, Materials science, Nanotechnology, Mathematics, Nanoparticle, Physics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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