Improving efficiency and optimizing heat transfer in a novel tesla valve through multi-layer perceptron models
Over the past few years, the distinctive design and versatile applications of Tesla valves have captured considerable interest across diverse industries. In contrast to conventional check valves, Tesla valves employ interconnected channels, establishing a highly efficient and reliable fluid flow con...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
|
| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X23006974 |
| _version_ | 1797729917154099200 |
|---|---|
| author | Peng Cheng Xu Jianjun Jitendra Kumar Hamad Almujibah H. Elhosiny Ali Tamim Alkhalifah Salem Alkhalaf Fahad Alturise Raymond Ghandour |
| author_facet | Peng Cheng Xu Jianjun Jitendra Kumar Hamad Almujibah H. Elhosiny Ali Tamim Alkhalifah Salem Alkhalaf Fahad Alturise Raymond Ghandour |
| author_sort | Peng Cheng |
| collection | DOAJ |
| description | Over the past few years, the distinctive design and versatile applications of Tesla valves have captured considerable interest across diverse industries. In contrast to conventional check valves, Tesla valves employ interconnected channels, establishing a highly efficient and reliable fluid flow control mechanism. This research delves into an investigation of the optimum geometric parameters that significantly influence the performance of a novel Tesla valve. The study focuses on three key geometric characteristics: the valve angle (α), the distance between consecutive stages (D), and the distance between the divider wall in the second stage of each step group and the wall of the straight channel (H). The authors carried out a numerical study using computational fluid dynamics to acquire the results. Four multi-layer perceptron models, each with a structure of 3-2-2-1, were applied to predict the selected responses of Nusselt numbers in the forward (Nuf) and reverse (Nur) directions, as well as pressure drops in the forward (ΔPf) and reverse (ΔPr) directions. The findings revealed that among all the variables examined, the parameter H exerted the most substantial influence on all measured responses. It was concluded that by incorporating specific values of α = 34.065°, D = 9 mm, and H = 5.624 mm during the manufacturing process of the valve and altering the flow direction from forward to reverse, a remarkable improvement of approximately 271.7% in pressure diodicity was achieved. |
| first_indexed | 2024-03-12T11:36:31Z |
| format | Article |
| id | doaj.art-82b4ebadacd34a5fbc81b2c30d00658a |
| institution | Directory Open Access Journal |
| issn | 2214-157X |
| language | English |
| last_indexed | 2024-03-12T11:36:31Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj.art-82b4ebadacd34a5fbc81b2c30d00658a2023-09-01T05:02:07ZengElsevierCase Studies in Thermal Engineering2214-157X2023-09-0149103391Improving efficiency and optimizing heat transfer in a novel tesla valve through multi-layer perceptron modelsPeng Cheng0Xu Jianjun1Jitendra Kumar2Hamad Almujibah3H. Elhosiny Ali4Tamim Alkhalifah5Salem Alkhalaf6Fahad Alturise7Raymond Ghandour8School of Electrical and Information Engineering, Northeast Petroleum University, ChinaSchool of Electrical and Information Engineering, Northeast Petroleum University, ChinaDepartment of Electronics and Communication Engineering, GLA University, Mathura, 281406, Uttar Pradesh, India; Corresponding author.Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif City, 21974, Saudi ArabiaDepartment of Physics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha, Saudi Arabia; Corresponding author.Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi ArabiaDepartment of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi ArabiaDepartment of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi ArabiaCollege of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait; Corresponding author.Over the past few years, the distinctive design and versatile applications of Tesla valves have captured considerable interest across diverse industries. In contrast to conventional check valves, Tesla valves employ interconnected channels, establishing a highly efficient and reliable fluid flow control mechanism. This research delves into an investigation of the optimum geometric parameters that significantly influence the performance of a novel Tesla valve. The study focuses on three key geometric characteristics: the valve angle (α), the distance between consecutive stages (D), and the distance between the divider wall in the second stage of each step group and the wall of the straight channel (H). The authors carried out a numerical study using computational fluid dynamics to acquire the results. Four multi-layer perceptron models, each with a structure of 3-2-2-1, were applied to predict the selected responses of Nusselt numbers in the forward (Nuf) and reverse (Nur) directions, as well as pressure drops in the forward (ΔPf) and reverse (ΔPr) directions. The findings revealed that among all the variables examined, the parameter H exerted the most substantial influence on all measured responses. It was concluded that by incorporating specific values of α = 34.065°, D = 9 mm, and H = 5.624 mm during the manufacturing process of the valve and altering the flow direction from forward to reverse, a remarkable improvement of approximately 271.7% in pressure diodicity was achieved.http://www.sciencedirect.com/science/article/pii/S2214157X23006974Tesla valveHeat transferEfficiency improvementArtificial neural networkThermal performance optimization |
| spellingShingle | Peng Cheng Xu Jianjun Jitendra Kumar Hamad Almujibah H. Elhosiny Ali Tamim Alkhalifah Salem Alkhalaf Fahad Alturise Raymond Ghandour Improving efficiency and optimizing heat transfer in a novel tesla valve through multi-layer perceptron models Case Studies in Thermal Engineering Tesla valve Heat transfer Efficiency improvement Artificial neural network Thermal performance optimization |
| title | Improving efficiency and optimizing heat transfer in a novel tesla valve through multi-layer perceptron models |
| title_full | Improving efficiency and optimizing heat transfer in a novel tesla valve through multi-layer perceptron models |
| title_fullStr | Improving efficiency and optimizing heat transfer in a novel tesla valve through multi-layer perceptron models |
| title_full_unstemmed | Improving efficiency and optimizing heat transfer in a novel tesla valve through multi-layer perceptron models |
| title_short | Improving efficiency and optimizing heat transfer in a novel tesla valve through multi-layer perceptron models |
| title_sort | improving efficiency and optimizing heat transfer in a novel tesla valve through multi layer perceptron models |
| topic | Tesla valve Heat transfer Efficiency improvement Artificial neural network Thermal performance optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X23006974 |
| work_keys_str_mv | AT pengcheng improvingefficiencyandoptimizingheattransferinanovelteslavalvethroughmultilayerperceptronmodels AT xujianjun improvingefficiencyandoptimizingheattransferinanovelteslavalvethroughmultilayerperceptronmodels AT jitendrakumar improvingefficiencyandoptimizingheattransferinanovelteslavalvethroughmultilayerperceptronmodels AT hamadalmujibah improvingefficiencyandoptimizingheattransferinanovelteslavalvethroughmultilayerperceptronmodels AT helhosinyali improvingefficiencyandoptimizingheattransferinanovelteslavalvethroughmultilayerperceptronmodels AT tamimalkhalifah improvingefficiencyandoptimizingheattransferinanovelteslavalvethroughmultilayerperceptronmodels AT salemalkhalaf improvingefficiencyandoptimizingheattransferinanovelteslavalvethroughmultilayerperceptronmodels AT fahadalturise improvingefficiencyandoptimizingheattransferinanovelteslavalvethroughmultilayerperceptronmodels AT raymondghandour improvingefficiencyandoptimizingheattransferinanovelteslavalvethroughmultilayerperceptronmodels |