Self-Organizing Map Network for the Decision Making in Combined Mode Conduction-Radiation Heat Transfer in Porous Medium

Anand, Kshitij; Bhardwaj, Abhishek; Chaudhuri, Sumanta; Mishra, Vijay Kumar

doi:10.1007/s13369-021-06489-4

Cited by 8 publications

(2 citation statements)

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“…As compared to any numerical/experimental approach, ANN offers the following advantages: (1) only correct pair of data (input-output) is required; that is, exact relation between the output and input is not needed, (2) a trained ANN model can give the result in much less time, that is, time taken to solve any similar problem is drastically less as compared to other means, (3) both the experimental and/or numerical data can be used to develop an ANN model, and (4) inverse problem can be solved easily. [7][8][9][10][11] Various advantages of the ANN approach make it a useful tool to solve scientific problems in different fields, like, manufacturing, 12,13 oil exploration, 14 biofuels, 15,16 solar, 17 lubrication, 18 automobile, 19 power plants, 20 and so on. Various thermal and fluid problems solved by employing ANN and various optimization tools are listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…), when interconnected. As compared to any numerical/experimental approach, ANN offers the following advantages: (1) only correct pair of data (input–output) is required; that is, exact relation between the output and input is not needed, (2) a trained ANN model can give the result in much less time, that is, time taken to solve any similar problem is drastically less as compared to other means, (3) both the experimental and/or numerical data can be used to develop an ANN model, and (4) inverse problem can be solved easily 7–11 …”

Section: Introductionmentioning

confidence: 99%

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Retrieval of parameters in micromixer with obstacle by cascade‐forward‐type artificial neural network: Comparison of four training algorithms under noisy data

et al. 2023

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Two parameters are retrieved in a passive Y‐type micromixer with circular obstacle by cascade‐forward‐type artificial neural network (CFANN). The governing equations are solved by the finite volume method, under specific boundary conditions. The numerical model is then used to compute velocity profile and mixing efficiency, for different values of the Reynolds number. Thus, the velocity profiles along with Reynolds number (Re) and mixing efficiency (η) constitute the input–output pair of data. These data are used to train CFANN, and the network is monitored through different means, like, histograms, performance curves, and so forth. For inverse analysis, the trained CFANN model is fed with a new velocity profile as input, and corresponding values of Reynolds number and mixing efficiency are obtained as output. In an attempt to construct the optimum CFANN model, various combinations were explored, like, (1) different numbers of neurons in the hidden layer, (2) different noise levels in input data, and (3) different algorithms in the training stage. Finally, the CFANN with 10 hidden layer neurons with Levenberg–Marquardt (LM) algorithm was found to give retrieved values with up to 0.96% absolute error for all levels of noise in the input data. Also, the CFANN model with the LM algorithm has a very high value of regression coefficient of greater than 0.998, under all the noise values. Scaled conjugate gradient algorithm gives good results for the no‐noise case, but fails poorly with the rise of noise. Other algorithms, like, Bayesian regularization and resilient backpropagation, perform poorly even in the no‐noise case. The present approach is highly simple, accurate, and time efficient for applying inverse analysis in micromixers.

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