A study of changes in temperature profile of porous fin model using cuckoo search algorithm

Waseem, Waseem; Sulaiman, Muhammad; Islam, Saeed; Kumam, Poom; Nawaz, Rashid; Raja, Muhammad Asif Zahoor; Farooq, Muhammad; Shoaib, Muhammad

doi:10.1016/j.aej.2019.12.001

Cited by 79 publications

(30 citation statements)

References 53 publications

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“…In the future, one may explore, investigate, or exploit the stochastic numerical computing approaches based on the artificial intelligence paradigm [37][38][39][40][41][42][43][44] for alternate, accurate, robust, and stable solutions, not only for the given biological fluidic model involving nano-materials, but also for other stiff nonlinear systems, which are still a challenge for traditional/classical numerical methods.…”

Section: Discussionmentioning

confidence: 99%

Heat transfer analysis of biological nanofluid flow through ductus efferentes

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A theoretical investigation on heat transfer of nanofluid transport in a ciliated channel is presented. A mathematical model for cilia oriented nanofluid transport through the human male reproductive tract is exhibited. Considering the structure of tubules, the Reynolds number is considered very small. The concept of a long wavelength approximation is incorporated to model the nanofluid flow problem. The exact analytical expressions for the x-component of velocity, temperature, nanoparticle concentration, stream function, volume flow rate, and pressure gradient are calculated. The solution for the transverse component of velocity, pressure rise per wavelength, and friction force on the wall of the channel is explored numerically, while variations in crucial flow parameters are examined graphically.

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Section: Discussionmentioning

confidence: 99%

Heat transfer analysis of biological nanofluid flow through ductus efferentes

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“…(6) via Meyer wavelet neural networks (MWNN) optimized with global search efficacy of genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., MWNN-GASQP. The solvers based on meta-heuristic intelligent computing have been extensively applied for the analysis of linear/nonlinear, singular/non-singular systems using neural networks optimized with evolutionary/swarming-based computing schemes (Lodhi 2019;Raja et al 2017a;Bukhari 2020;Waseem 2020;Ahmad 2020Ahmad ,2019. Some recent applications of the evolutionary/swarming-based numerical computing are Painlevé equation-based models in random matrix theory (Raja et al 2018a), nonlinear prey-predator models (Umar 2019), Bagley-Torvik systems in fluid mechanics.…”

Section: Problem Statement and Related Workmentioning

confidence: 99%

FMNEICS: fractional Meyer neuro-evolution-based intelligent computing solver for doubly singular multi-fractional order Lane–Emden system

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In the present study, a novel fractional Meyer neuro-evolution-based intelligent computing solver (FMNEICS) is presented for numerical treatment of doubly singular multi-fractional Lane-Emden system (DSMF-LES) using combined heuristics of Meyer wavelet neural networks (MWNN) optimized with global search efficacy of genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., MWNN-GASQP. The design of novel FMNE-ICS for DSMF-LES is presented after derivation from standard Lane-Emden equation, and the singular points and shape factors along with fractional-order terms are analyzed. The MWNN modeling strength is used to represent the system model DSMF-LES in the meansquared error-based merit function and optimization of the networks is carried out with integrated optimization ability of GASQP. The verification, validation, and perfection of the FMNEICS for three different cases of DSMF-LES are established through comparative studies from reference solutions on convergence, robustness, accuracy, and stability measures. Moreover, the observations through the statistical analysis further authenticate the worth of proposed fractional MWNN-GASQP-based stochastic solver. Keywords Multi-fractional Lane-Emden model • Multi-singular systems • Artificial neural networks • Meyer wavelet neural networks • Sequential quadratic programming • Genetic algorithms Mathematics Subject Classification 34-XX • 34A08 • 34A34 • 82B31

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“…f (∞) = S ic , T = 0, X > 0, (38) transforming the boundary condition Eq (38) into variational condition, we have…”

Section: B Homogenous Porous Mediummentioning

confidence: 99%

“…The optimal design of heat fins is proposed in [36]. A study of temperature distribution in heat fins is carried out by using a hybrid of the Cuckoo Search (CS) algorithm and Artificial Neural Network architecture [37], [38]. Neuro-fuzzy modeling is used to predict the summer precipitation in targeted metrological sites [39].…”

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Analysis of Multi-Phase Flow Through Porous Media for Imbibition Phenomena by Using the LeNN-WOA-NM Algorithm

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The flow of fluids in multi-phase porous media results due to many interesting natural phenomena. The counter-current water imbibition phenomena, that occur during oil extraction through a cylindrical well is an interesting problem in petroleum engineering. During the secondary oil recovery process, water is injected into a porous media having heterogenous and homogenous characteristics. Due to the difference in viscosities of fluids in oil wells, the counter-current imbibition phenomenon occurs. At that moment, the imbibition equation V i = −V n is satisfied by the viscosities of oil and water. In this paper, we have analyzed the governing mathematical model of the imbibition phenomenon occurring during the secondary oil recovery process. A new soft computing algorithm is designed and adapted to analyze the mathematical model of dual-phase flow in detail. Weighted Legendre polynomials based artificial neural networks are hybridized with an efficient global optimizer the Whale Optimization Algorithm (WOA) and a local optimizer the Nelder-Mead algorithm. It is established, that our algorithm LeNN-WOA-NM is efficient and reliable in calculating high-quality solutions in less time. We have compared our experimental outcome with state-of-the-art results. The quality of our solutions is judged based on values of absolute errors, MAD, TIC, and ENSE. It is obvious that LeNN-WOA-NM algorithm can solve real application problems efficiently and accurately. INDEX TERMS Counter-current imbibition phenomena, homogeneous porous media, heterogeneous porous media, capillary pressure, soft computing algorithm, weighted Legendre neural networks, Nelder-Mead algorithm, whale optimization algorithm. I. INTRODUCTION T HIS paper models the phenomena of counter-current imbibition in the multi-phase flow of two immiscible fluids through heterogeneous and homogenous porous mediums that occurs during the secondary oil recovery process or water flooding [1]. In applied sciences, various changes occur in porous mediums having multi-phase flows of different fluids. In the past few decades, researchers are focused on studying the flows in underground petroleum reservoirs [2]. The primary recovery process includes the production of oil by simple natural decompression with no effect of external forces at wells [3]. In secondary oil recovery or water flooding process, fluids like water, polymer, steam are injected into a reservoir through injection wells located in rocks that have fluid communication with production wells. This process is called the immiscible displacement process [3].

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A study of changes in temperature profile of porous fin model using cuckoo search algorithm

Cited by 79 publications

References 53 publications

Heat transfer analysis of biological nanofluid flow through ductus efferentes

Heat transfer analysis of biological nanofluid flow through ductus efferentes

FMNEICS: fractional Meyer neuro-evolution-based intelligent computing solver for doubly singular multi-fractional order Lane–Emden system

Analysis of Multi-Phase Flow Through Porous Media for Imbibition Phenomena by Using the LeNN-WOA-NM Algorithm

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