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Machine learning cl...
Machine learning classification of in-tube condensation flow patterns using visualization
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- Seal, M. K. (författare)
- Clean Energy Research Group, Department of Mechanical and Aeronautical Engineering, University of Pretoria,Hatfield (ZAF)
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- Noori Rahim Abadi, Seyyed Mohammad Ali, 1985- (författare)
- Högskolan Väst,Avdelningen för svetsteknologi (SV),PTW
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- Mehrabi, M. (författare)
- Clean Energy Research Group, Department of Mechanical and Aeronautical Engineering, University of Pretoria, Hatfield (ZAF)
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- Meyer, J. P. (författare)
- Clean Energy Research Group, Department of Mechanical and Aeronautical Engineering, University of Pretoria, Hatfield (ZAF)
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(creator_code:org_t)
- Elsevier, 2021
- 2021
- Engelska.
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Ingår i: International Journal of Multiphase Flow. - : Elsevier. - 0301-9322 .- 1879-3533. ; 143
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Identifying two-phase flow patterns is fundamental to successfully design and subsequently optimize highprecision heat transfer equipment, given that the heat transfer efficiency and pressure gradients occurring in such thermo-hydraulic systems are dependent on the flow structure of the working fluid. This paper shows that with visualization data and artificial neural networks, the flow pattern images of condensation of R-134a refrigerant in inclined smooth tubes can be classified with more than 98% accuracy. The study considers 10 classes of flow pattern images acquired from previous experimental works for a wide range of flow conditions and the full range of tube inclination angles. Although not the focus of this paper, the use of a Principal Component Analysis allowed feature dimensionality reduction, dataset visualization, and decreased associated computational cost when used together with multilayer perceptron neural networks. In addition, the superior two-dimensional spatial learning capability of convolutional neural networks allowed improved image classification and generalization performance. In both cases, the classification was performed sufficiently fast to enable real-time implementation in two-phase flow systems.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Energiteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Energy Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Condensation flow pattern; Convolutional neural network; Machine learning
- Production Technology
- Produktionsteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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