Bioconvection mechanism using third-grade nanofluid flow with Cattaneo–Christov heat flux model and Arrhenius kinetics
Abstract: This paper aims to study the effects of activation energy and thermal radiation in the bioconvection flow of nanofluid (third-grade nanofluid) containing swimming microorganisms in the presence of a heat source-sink past a stretching sheet. Brownian movement and thermophoresis diffusion are used in mathematical modeling. The given flow phenomenon is modeled in the form of governing partial differential equations. Furthermore, appropriate dimensionless transformation is used to transfer the governing system of … Show more
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Cited by 17 publications
References 40 publications
Computational intelligence refers to an algorithm inspired by the body structure of natural animals and plants and unique landscapes. It can perform simultaneous multithreaded work. In the processing process of the food industry, multiple factors must be considered at the same time to optimize various parameters simultaneously. Therefore, this research combines computational intelligence algorithms with the processing flow of the food industry, conducts research on the optimization of food processing systems and processes, and fully understands the needs of the market, the competitiveness of the company’s products, and the people facing marketing. The MA-CI model was used to optimize the entire process of food processing and production, and multiple variables were integrated. The following conclusions were drawn: (1) When the node size = 100 , calculate the optimal value of the intelligent model MA − CI = 0.3196 , the worst value = 0.3120 , and the average plus − minus variance = 0.3155 ± 4.6278 × 10 − 6 , in each index. Both are better than the Greedy algorithm model and the EA model. When the node size = 200 , the optimal value of the calculation intelligence model MA − CI = 0.3050 , the worst value = 0.2900 , and the average plus and minus variance = 0.2970 ± 8.9251 × 10 − 6 ; when the node size = 300 , the calculation intelligence model (MA-CI) optimal value = 0.2608 , the worst value = 0.2499 , and the mean plus − minus variance = 0.2551 ± 1.0028 × 10 − 5 ; when the node size = 500 , calculate the optimal value of intelligent model MA − CI = 0.2857 , the worst value = 0.2812 , and the mean plus and minus variance = 0.2834 ± 2.8230 × 10 − 6 . (2) In the electronic circuit network, calculate the intelligent model (MA-CI): Rc = 0.3174 , NMI = 0.7388 , Δ Ec = 0 , and k = 14.43 ; in the USAir network, the computational intelligence model (MA-CI) is as follows: Rc = 0.3041 , NMI = 0.8632 , Δ Ec = 0 , and k = 7.43 ; the computational intelligence model (the uniqueness and optimality of MA-CI) has resulted in the optimal solution for the optimization of the global machining process. (3) Using six online databases to predict and verify the MA-CI model for process optimization in the food industry, it was found that the MA-CI model for process optimization in the food industry was better than the standard value. (4) Perform performance testing on the optimized MA-CI model. It is found that under the MA-CI model, MUTAG = 98.2 ± 4.3 , ENZYMES = 96.2 ± 4.3 , PTC = 85.2 ± 4.3 , PROTEINS = 82.0 ± 3.2 , NCI 1 = 80.2 ± 2.0 , and D & D = 91.9 ± 0.5 , which are better than the other models. The optimized MA-CI model can control the profit problem in food processing and production, and it is found that the MA-CI model can make the enterprise obtain the maximum profit in the shortest period.
Computational intelligence refers to an algorithm inspired by the body structure of natural animals and plants and unique landscapes. It can perform simultaneous multithreaded work. In the processing process of the food industry, multiple factors must be considered at the same time to optimize various parameters simultaneously. Therefore, this research combines computational intelligence algorithms with the processing flow of the food industry, conducts research on the optimization of food processing systems and processes, and fully understands the needs of the market, the competitiveness of the company’s products, and the people facing marketing. The MA-CI model was used to optimize the entire process of food processing and production, and multiple variables were integrated. The following conclusions were drawn: (1) When the node size = 100 , calculate the optimal value of the intelligent model MA − CI = 0.3196 , the worst value = 0.3120 , and the average plus − minus variance = 0.3155 ± 4.6278 × 10 − 6 , in each index. Both are better than the Greedy algorithm model and the EA model. When the node size = 200 , the optimal value of the calculation intelligence model MA − CI = 0.3050 , the worst value = 0.2900 , and the average plus and minus variance = 0.2970 ± 8.9251 × 10 − 6 ; when the node size = 300 , the calculation intelligence model (MA-CI) optimal value = 0.2608 , the worst value = 0.2499 , and the mean plus − minus variance = 0.2551 ± 1.0028 × 10 − 5 ; when the node size = 500 , calculate the optimal value of intelligent model MA − CI = 0.2857 , the worst value = 0.2812 , and the mean plus and minus variance = 0.2834 ± 2.8230 × 10 − 6 . (2) In the electronic circuit network, calculate the intelligent model (MA-CI): Rc = 0.3174 , NMI = 0.7388 , Δ Ec = 0 , and k = 14.43 ; in the USAir network, the computational intelligence model (MA-CI) is as follows: Rc = 0.3041 , NMI = 0.8632 , Δ Ec = 0 , and k = 7.43 ; the computational intelligence model (the uniqueness and optimality of MA-CI) has resulted in the optimal solution for the optimization of the global machining process. (3) Using six online databases to predict and verify the MA-CI model for process optimization in the food industry, it was found that the MA-CI model for process optimization in the food industry was better than the standard value. (4) Perform performance testing on the optimized MA-CI model. It is found that under the MA-CI model, MUTAG = 98.2 ± 4.3 , ENZYMES = 96.2 ± 4.3 , PTC = 85.2 ± 4.3 , PROTEINS = 82.0 ± 3.2 , NCI 1 = 80.2 ± 2.0 , and D & D = 91.9 ± 0.5 , which are better than the other models. The optimized MA-CI model can control the profit problem in food processing and production, and it is found that the MA-CI model can make the enterprise obtain the maximum profit in the shortest period.
Analytical approach on magnetohydrodynamic Casson fluid flow past a stretching sheet via Adomian decomposition method
Flow phenomena of three-dimensional conductingCasson fluid through a stretching sheet are proposed in the present investigation with the impact of the magnetic parameter in a permeable medium. The adaptation of particular transformations is useful to modify
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