In this work an artificial neural network model was developed with the aim of predicting fouling resistance for heat exchanger, the network was designed and trained by means of 375 experimental data points that were selected from the literature. This data points contains 6 inputs, including time, volumetric concentration, heat flux, mass flow rate, inlet temperature, thermal conductivity and fouling resistance as an output. The experimental data are used for training, testing and validation the ANN using multiple layer perceptron (MLP). The comparison of statistical criteria of different networks shows that the optimal structure for predicting the fouling resistance of the nanofluid is the MLP network with 20 hidden neurons, which has been trained with Levenberg–Marquardt (LM) algorithm. The accuracy of the model was assessed based on three known statistical metrics including mean square error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). The obtained model was found with the performance of {MSE = 6.5377 × 10−4, MAPE = 2.40% and R2 = 0.99756} for the training stage, {MSE = 3.9629 × 10−4, MAPE = 1.8922% and R2 = 0.99835} for the test stage and {MSE = 5.8303 × 10−4, MAPE = 2.57% and R2 = 0.99812} for the validation stage. In order to control the fouling procedure, and after conducting a sensitivity analysis, it found that all input variables have strong effect on the estimation of the fouling resistance.
The flow and heat transfer characteristics in a nuclear power plant in the
event of a serious accident are simulated by boiling water in an inclined
rectangular channel. In this study an artificial neural network model was
developed with the aim of predicting heat transfer coefficient (HTC) for
flow boiling of water in inclined channel, the network was designed and
trained by means of 520 experimental data points that were selected from
within the literature. orientation ,mass flux, quality and heat flow which
were employed to serve as variables of input of multiple layer perceptron
(MLP) neural network, whereas the analogous HTC was selected to be its
output. Via the method of trial-and-error, MLP network with 30 neurons in
the hidden layer was attained as optimal ANN structure. The fact that is was
enabled to predict accurately the HTC. For the training set, the mean
relative absolute error (MRAE) is about 0.68 % and the correlation
coefficient (R) is about 0.9997. As for the testing and validation set they
are respectively about 0.60 % and 0.9998 and about 0.79 % and 0.9996. The
comparison of the developed ANN model with experimental data and empirical
correlations in vertical channel under the low flow rate and low quality
shows a good agreement.
This work consists of a Computational Fluid Dynamics (CFD) modeling of a reference experiment on boron dilution in the Rossendorf coolant mixing Model (ROCOM) as part of a coordinated research project of the International Atomic Energy Agency, namely, "Application of numerical codes of fluid dynamics to the design of nuclear power plants". This coordinated project aims to address the application of CFD codes to the process of optimizing the design of nuclear power plants related to pressurized water reactors and to evaluate the performance and predictive capabilities of these codes and to contribute to their validation. In this context, a three-dimensional numerical simulation study was carried out using CFD code ANSYS CFX v14.5, to study the boron mixing phenomenon at the core inlet and the downcomer of the ROCOM test facility. The phenomenon of experimental mixing occurs by the injection of a tracer (sodium chloride) into one of the loops of the ROCOM installation mainly containing demineralized water in its primary circuit. The concentration field of the tracer is measured and simulated at the entrance of the heart and in the lowering. The SST-kω turbulence model used in this study could reasonably predict the distribution of the injected tracer in measurement locations within the test facility. The results of this numerical simulation were compared to the Benchmark data provided by the ROCOM experimental facility of the Helmholtz-Zentrum Dresden-Rossendorf Institute.
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