Estimation of the performance of different pumps using non-Newtonian fluids in various operating conditions with artificial neural network

被引:1
作者
Yemenici, Onur [1 ]
Donmez, Muhammed [2 ]
机构
[1] Bursa Uludag Univ, Dept Mech Engn, TR-16059 Bursa, Turkiye
[2] Bursa Uludag Univ, Dept Automot Engn, TR-16059 Bursa, Turkiye
关键词
Centrifugal pump; Artificial neural network; Pseudoplastic; Carboxy methyl cellulose; PREDICTION; VISCOSITY; EROSION; ANN;
D O I
10.1007/s13369-024-08729-9
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The performance of three centrifugal pumps designed to operate at a rotational speed of 151.84 rad/s and flow rates of 1, 25, and 45 kg/s is being investigated for both water and non-Newtonian fluids at various rotational speeds and flow rates. The analyses are being conducted experimentally and numerically within the flow rate range of 0.25-55 kg/s and rotational speed values between 52.36 and 151.84 rad/s. Additionally, artificial neural networks (ANN) trained using experimental pump performance data are being tested with experimental and numerical values obtained at a new rotational speed of 130.9 rad/s. The non-Newtonian fluids being tested include CMC 0.2% and CMC 0.4%, comprising carboxy methyl cellulose (CMC) solution and water. The results indicate that the pump's performance when handling non-Newtonian fluids is significantly influenced by the pump's geometry, rotational speed, and flow rate. In design parameters, the head obtained with 0.2% CMC for pump 1 is 3.3% greater than that in water. For pump 2, the highest head is in water according to design parameters. Pump 3 exhibits the highest head at a CMC of 0.4 in design parameters, and this value is 0.81% higher than the value with water. Experimental and numerical results demonstrate good agreement, especially in design parameters. The head obtained from numerical analyses with the RNG k-epsilon turbulence model for pumps 1, 2, and 3 at design parameters is 3, 10, and 9.83 m, respectively. The corresponding experimental heads are 3, 10, and 9.84 m, respectively. However, discrepancies between these results increase with higher flow rates and the use of non-Newtonian fluids. The compatibility of ANN results with experimental results is better than with numerical results, particularly at higher flow rates than the design condition. Pump performance values estimated by ANNs are 2% lower than the experimental results. This study provides comprehensive experimental data on the use of non-Newtonian fluids in different centrifugal pumps, and it also offers important guidance for future research by comparing ANN and computational fluid dynamics.
引用
收藏
页码:14607 / 14623
页数:17
相关论文
共 49 条
[31]  
Mahmood MA., 2013, IOSR J MECH CIVIL EN, V9, P43, DOI 10.9790/1684-0924352
[32]   Neural network prediction of biodiesel kinematic viscosity at 313 K [J].
Meng, Xiangzan ;
Jia, Ming ;
Wang, Tianyou .
FUEL, 2014, 121 :133-140
[33]   Modeling and numerical simulation of non-Newtonian arterial blood flow for mild to severe stenosis [J].
Nadeem, Sohail ;
Ali, Shahbaz ;
Akkurt, Nevzat ;
Ben Hamida, Mohamed Bechir ;
Almutairi, Shahah ;
Ghazwani, Hassan Ali ;
Eldin, Sayed M. ;
Khan, Zareen A. ;
Al-Shafay, A. S. .
ALEXANDRIA ENGINEERING JOURNAL, 2023, 72 :195-211
[34]   FUZZY LOGIC-BASED MODELING OF A CENTRIFUGAL BLOOD PUMP PERFORMANCE VIA EXPERIMENTAL DATA OF NEWTONIAN AND NON-NEWTONIAN FLUIDS [J].
Onder, Ahmet ;
Guzel, Muhammed Huseyin ;
Incebay, Omer ;
Sen, Muhammed Arif ;
Yapici, Rafet ;
Kalyoncu, Mete .
JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY, 2023, 23 (03)
[35]  
Ozturk A, 2009, J SCI IND RES INDIA, V68, P203
[36]   Comparative analysis of an electrical submersible pump's performance handling viscous Newtonian and non-Newtonian fluids through experimental and CFD approaches [J].
Pablo Valdes, Juan ;
Becerra, Deisy ;
Rozo, Daniel ;
Cediel, Alexandra ;
Torres, Felipe ;
Asuaje, Miguel ;
Ratkovich, Nicolas .
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2020, 187
[37]   Coupling of an IOT wear sensor and numerical modelling in predicting wear evolution of a slurry pump [J].
Qin, Jiangyi ;
Chen, Wei .
POWDER TECHNOLOGY, 2022, 404
[38]   Artificial Neural Networking (ANN) Model for Drag Coefficient Optimization for Various Obstacles [J].
Rehman, Khalil Ur ;
Colak, Andac Batur ;
Shatanawi, Wasfi .
MATHEMATICS, 2022, 10 (14)
[39]   Significance of the physical quantities for the non-Newtonian fluid flow in an irregular channel with heat and mass transfer effects: Lie group analysis [J].
Saleem, Musharafa ;
Tufail, Muhammad Nazim ;
Chaudhry, Qasim Ali .
ALEXANDRIA ENGINEERING JOURNAL, 2022, 61 (03) :1968-1980
[40]   Enhancement of R600a vapour compression refrigeration system with MWCNT/TiO2 hybrid nano lubricants for net zero emissions building [J].
Senthilkumar, A. ;
Prabhu, L. ;
Sathish, T. ;
Saravanan, R. ;
Jeyaseelan, G. Antony Casmir ;
Agbulutc, Umit ;
Mahmoud, Z. ;
Shaik, Saboor ;
Saleel, C. Ahamed .
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, 2023, 56