Neuro-computing intelligent networks to analyze Casson nanofluid flow over a curved stretching surface

Shoaib, Muhammad; Abbasi, Aqsa Zafar; Raja, Muhammad Asif Zahoor; Nisar, Kottakkaran Sooppy; Rafiq, Ayesha

doi:10.1080/17455030.2023.2168787

Cited by 5 publications

(3 citation statements)

References 49 publications

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“…Khalil et al [27] analysed the thermally magnetised Casson fluid flow under the influence of mixed convection, heat generation and viscous dissipation using an ANN model and the Levenberg-Marquardt method. Shoaib et al [28] examined the Casson nanofluid flow by analysing the complex model of non-linear governing equations using the Levenberg Marquardt Scheme (LMS).…”

Section: Introductionmentioning

confidence: 99%

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ANN model for magnetised Casson fluid flow under the influence of thermal radiation and temperature stratification: Comparative analysis

Tanveer,

Ashraf

2024

Z Angew Math Mech

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The article compares the performance of multi‐layer back‐propagated neural networks using the Levenberg–Marquardt (LMS) method and Bayesian Regularization Scheme (BRS) for analysing heat transfer in magnetized Casson fluid over flat and cylindrical surfaces. Factors like mixed convection, Joule heating, thermal radiation and temperature stratification are considered. Both LMS and BRS prove effective in handling complex interactions in numerical studies. Non‐linear ODE systems are solved using a shooting technique to obtain Nusselt number datasets, which are then divided for training, testing and validation purposes. The accuracy of the artificial neural network (ANN) results is confirmed by the agreement between predicted and target Nusselt numbers. Analysis of mean square error (MSE), regression fitness and error histogram further validates ANN adeptness. Furthermore, heat transfer rate is increasing for the Casson parameter, Grashof number and radiation parameter, while, it declines for stratification parameter, Prandtl and Eckert number. The study explores the impact of various parameters on velocity and temperature profiles, revealing that ANN provides accurate Nusselt number results for both flat and cylindrical surfaces. The research contributes a novel perspective to understanding ANN behaviour in heat transfer analysis, particularly for magnetized Casson fluid flow over two stretching surfaces.

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Section: Introductionmentioning

confidence: 99%

“…Shoaib et al. [28] examined the Casson nanofluid flow by analysing the complex model of non‐linear governing equations using the Levenberg Marquardt Scheme (LMS).…”

Section: Introductionmentioning

confidence: 99%

ANN model for magnetised Casson fluid flow under the influence of thermal radiation and temperature stratification: Comparative analysis

Tanveer,

Ashraf

2024

Z Angew Math Mech

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“…Soft computing methods are important in a variety of applications [27–29]. Few research paradigms related to this neuro computing technique are given as cross nanofluid system [30], Darcy–Forchheimer flow of nanofluidic model [31], Carreau‐nanoliquid model [32], Lane–Emden system [33, 34], double diffusion convection floe models [35, 36], pine wilt disease model [37], and COVID‐19 models [38–40]. These computing paradigms are the motivational factor for researchers to exploit a reliable and accurate framework using the artificial intelligence infrastructure to solve the NRS‐CNFS based on parametric investigations to observe the numerous physical measures of velocity, temperature, and concentration states.…”

Section: Introductionmentioning

confidence: 99%

A stagnation point flow of cross nanofluid flow: Levenberg Marquardt backpropagation computational approach

Shoaib,

Nisar,

Raja

et al. 2023

Z Angew Math Mech

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In the present article, the intelligence based numerical investigation by Levenberg–Marquardt algorithm with Back‐propagated Artificial Neural Networks (LMA–BANN) is exploited to analyze the nonlinear radiative stagnation point flow of cross nanofluid system (NRS‐CNFS) past a stretching surface. The designed NRS‐CNFS, initially represented by system of partial differential equations (PDEs), is converted into system of non‐linear ODEs through the applicability of mathematical conversion analysis. The desired reference solution in the form of a dataset for LMA–BANN is achieved from the Adam method for NRS‐CNFS's different scenarios by varying the magnetic parameter (Ha), Prandlt number (Pr), thermal radiation parameter(R), Eckert number (Ec), thermophoresis diffusion coefficients (Nt), and non‐dimensional activation energy (E). The obtained results are construed for the NRS‐CNFS through the performances of the testing, validation, and training. In addition, the comparison is provided using the LMA ‐BANN analysis, which is validated in terms of regression, fitness estimations, and histograms along with MSE performances. Hence, the article provides an impeccable innovative approach to problem‐solving by implementing a soft computation paradigm. It promotes the use of an effective and dependable alternative framework that is based on soft computing environments and emphasizes descriptive analysis to successfully handle the problems brought on by varied physical features.

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Heat Transfer in Chemically Reactive Dual Diffusive Casson Nanofluid Flow: An Intelligent Computing Paradigm

Butt,

Ahmad,

Raja

et al. 2024

Int. J. Appl. Comput. Math

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Neuro-computing intelligent networks to analyze Casson nanofluid flow over a curved stretching surface

Cited by 5 publications

References 49 publications

ANN model for magnetised Casson fluid flow under the influence of thermal radiation and temperature stratification: Comparative analysis

ANN model for magnetised Casson fluid flow under the influence of thermal radiation and temperature stratification: Comparative analysis

A stagnation point flow of cross nanofluid flow: Levenberg Marquardt backpropagation computational approach

Heat Transfer in Chemically Reactive Dual Diffusive Casson Nanofluid Flow: An Intelligent Computing Paradigm

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