A study of changes in temperature profile of porous fin model using cuckoo search algorithm
TL;DR: A metaheuristic is combined with neural computing modelling to study the effects of temperature changes in a porous fin model and the Cuckoo search algorithm is used as an efficient optimization technique to find the best weights to reduce the mean squared error in the required temperature profile.
Abstract: For analysis of physical properties of different materials, rectangular porous fins are used to examine the heat transformation through a system. In this paper, a metaheuristic is combined with neural computing modelling to study the effects of temperature changes in a porous fin model. Cuckoo search algorithm is used as an efficient optimization technique to find the best weights to reduce the mean squared error in the required temperature profile. The governing partial differential equation is converted into a non-linear ordinary differential equation subject to certain boundary conditions. Two individual cases, of silicon nitride ( Si 3 N 4 ) and Aluminium (Al), are considered. In the proposed procedure, the Cuckoo Search(CS) algorithm is combined with the artificial neural network (ANN), namely CS-ANN, to solve the differential equations and obtain solutions with better accuracy.
Citations
More filters
TL;DR: The design of stochastic computational intelligent solver for the solution of mathematical model representing the dynamics of mosquito dispersal in a heterogeneous environment by feedforward artificial neural networks trained with genetic algorithms aided with sequential quadratic programming (SQP).
Abstract: In this paper, the design of stochastic computational intelligent solver is presented for the solution of mathematical model representing the dynamics of mosquito dispersal in a heterogeneous environment by feedforward artificial neural networks (FFANNs) trained with genetic algorithms (GAs) aided with sequential quadratic programming (SQP), i.e., FFANN-GASQP. In the scheme FFANN-GASQP, the formulation of fitness function in mean square error sense with continuous mapping-based differential equation models of FFANNs for the mosquito dispersal system and training of these networks are accomplished by integrated competency of GA and SQP. The exactness, reliability and stability of the designed FFANN-GASQP approach are established through comparative studies with Adams numerical results for both single and multiple runs. Outcomes of statistical assessments are used to validate the accuracy and convergence of the designed FFANN-GASQP scheme.
128 citations
TL;DR: This research investigates the heat and mass transfer in 3-D MHD radiative flow of water based hybrid nanofluid over an extending sheet by employing the strength of numerical computing based Lobatto IIIA method and observed that heat transfer rate increases with the increase in magnetic effect, Biot number and rotation parameter.
Abstract: This research investigates the heat and mass transfer in 3-D MHD radiative flow of water based hybrid nanofluid over an extending sheet by employing the strength of numerical computing based Lobatto IIIA method. Nanoparticles of aluminum oxide (Al2O3) and silver (Ag) are being used with water (H2O) as base fluid. By considering the heat transfer phenomenon due to thermal radiation effects. The physical flow problem is then modeled into set of PDEs, which are then transmuted into equivalent set of nonlinear ODEs by utilizing the appropriate similarity transformations. The system of ODEs is solved by the computational strength of Lobatto IIIA method to get the various graphical and numerical results for analyzing the impact of various physical constraints on velocity and thermal profiles. Additionally, the heat transfers and skin friction analysis for the fluid flow dynamics is also investigated. The relative errors up to the accuracy level of 1e-15, established the worth and reliability of the computational technique. It is observed that heat transfer rate increases with the increase in magnetic effect, Biot number and rotation parameter.
106 citations
TL;DR: The proposed ANN-PSO-IPS is implemented for four variants of TONMS-EFEs, and comparison with exact solutions relieved its robustness, correctness and effectiveness, which is further authenticated through statistical explorations.
Abstract: In this study, a novel neuro-swarming computing solver is developed for numerical treatment of third-order nonlinear multi-singular Emden–Fowler equation (TONMS-EFE) by using function approximation ability of artificial neural networks (ANNs) modeling and global optimization mechanism of particle swarm optimization (PSO) integrated with local search of interior-point scheme (IPS), i.e., ANN-PSO-IPS. The inspiration for the design of ANN-PSO-IPS-based numerical solver comes with an objective of presenting a reliable, accurate and viable structure that combines the strength of ANNs optimized with the integrated soft computing frameworks to deal with such challenging systems based on TONMS-EFE. The proposed ANN-PSO-IPS is implemented for four variants of TONMS-EFEs, and comparison with exact solutions relieved its robustness, correctness and effectiveness, which is further authenticated through statistical explorations.
97 citations
TL;DR: In this article, the authors investigated the bimodal spread of COVID-19 with hybrid model based on nonlinear autoregressive with radial base function (NAR-RBFs) neural network for SITR model.
91 citations
TL;DR: The aim of the present study is to solve the singular second-order functional differential model with the development of neuro-swarm intelligent computing solver ANN–PSO–SQP based on mathematical modeling of artificial neural networks (ANNs) optimized globally search efficacy of particle swarm optimization (PSO) aided with local search efficiency of sequential quadratic programming (QP).
Abstract: The aim of the present study is to solve the singular second-order functional differential model with the development of neuro-swarm intelligent computing solver ANN–PSO–SQP based on mathematical modeling of artificial neural networks (ANNs) optimized globally search efficacy of particle swarm optimization (PSO) aided with local search efficiency of sequential quadratic programming (SQP). In the scheme ANN–PSO–SQP, an error-based objective function is assembled with the help of continuous mapping of ANN for second-order singular functional differential model and optimized with combination strength of PSO with SQP. The inspiration for the design of ANN–PSO–SQP comes with an objective to present a precise, reliable and feasible frameworks to handle with stiff singular functional models involving the delayed, pantograph and prediction terms. The designed scheme is tested for three different variants of the singular second-order functional differential models. The obtained outcomes on both single as well as multiple runs of the proposed ANN–PSO–SQP are compared with the exact solutions to validate the efficacy, correctness and viability.
84 citations
References
More filters
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
18,794 citations
9,818 citations
01 Dec 2009
TL;DR: A new meta-heuristic algorithm, called Cuckoo Search (CS), is formulated, based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour ofSome birds and fruit flies, for solving optimization problems.
Abstract: In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.
5,521 citations
Book•
01 Jan 1984
TL;DR: In this paper, a reference record was created on 2005-11-18, modified on 2016-08-08 and used for CFD-based transfert de chaleur.
Abstract: Keywords: CFD ; numerique ; transfert de chaleur ; ecoulement Reference Record created on 2005-11-18, modified on 2016-08-08
3,629 citations
Book•
01 Oct 1991
TL;DR: In this article, the authors identify the principles of transport in porous media and compare the available predicted results, based on theoretical treatments of various transport mechanisms, with the existing experimental results, and the theoretical treatment is based on the volume-averaging of the momentum and energy equations with the closure conditions necessary for obtaining solutions.
Abstract: Although the empirical treatment of fluid flow and heat transfer in porous media is over a century old, only in the last three decades has the transport in these heterogeneous systems been addressed in detail. So far, single-phase flows in porous media have been treated or at least formulated satisfactorily, while the subject of two-phase flow and the related heat-transfer in porous media is still in its infancy. This book identifies the principles of transport in porous media and compares the available predicted results, based on theoretical treatments of various transport mechanisms, with the existing experimental results. The theoretical treatment is based on the volume-averaging of the momentum and energy equations with the closure conditions necessary for obtaining solutions. While emphasizing a basic understanding of heat transfer in porous media, this book does not ignore the need for predictive tools; whenever a rigorous theoretical treatment of a phenomena is not available, semi-empirical and empirical treatments are given.
2,551 citations