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Basis Neural Networks (basis + neural_network)

Distribution by Scientific Domains
Engineering75%

Kinds of Basis Neural Networks

  • radial basis neural network
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    Selected Abstracts

    Application of the Radial Basis Neural Network to Optimization of a Micromixer

    CHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 7 2007
    A. Ansari
    Abstract The radial basis neural network (RBNN) method has been applied to shape optimization of a staggered herringbone groove micromixer using three-dimensional Navier-Stokes analysis. The calculation of the variance of the mass fraction at various nodes on a plane in the channel is used to quantify the mixing. Optimization techniques based on the RBNN method are used to optimize the shape of the grooves on a single wall of the channel. Three design variables, i.e., the ratio of the groove depth to channel height, the ratio of the groove width to groove pitch, and the angle of the groove, are selected for optimization. The mixing index at the end of the patterned groove is employed as the objective function. The dependence of the objective function on the design variables is analyzed. The RBNN method is successfully applied to improve the degree of mixing with modification of the groove shape. [source]

    Surrogate model-based strategy for cryogenic cavitation model validation and sensitivity evaluation

    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 9 2008
    Tushar Goel
    Abstract The study of cavitation dynamics in cryogenic environment has critical implications for the performance and safety of liquid rocket engines, but there is no established method to estimate cavitation-induced loads. To help develop such a computational capability, we employ a multiple-surrogate model-based approach to aid in the model validation and calibration process of a transport-based, homogeneous cryogenic cavitation model. We assess the role of empirical parameters in the cavitation model and uncertainties in material properties via global sensitivity analysis coupled with multiple surrogates including polynomial response surface, radial basis neural network, kriging, and a predicted residual sum of squares-based weighted average surrogate model. The global sensitivity analysis results indicate that the performance of cavitation model is more sensitive to the changes in model parameters than to uncertainties in material properties. Although the impact of uncertainty in temperature-dependent vapor pressure on the predictions seems significant, uncertainty in latent heat influences only temperature field. The influence of wall heat transfer on pressure load is insignificant. We find that slower onset of vapor condensation leads to deviation of the predictions from the experiments. The recalibrated model parameters rectify the importance of evaporation source terms, resulting in significant improvements in pressure predictions. The model parameters need to be adjusted for different fluids, but for a given fluid, they help capture the essential fluid physics with different geometry and operating conditions. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks

    HYDROLOGICAL PROCESSES, Issue 2 2009
    Özgür Ki
    Abstract This paper reports on investigations of the abilities of three different artificial neural network (ANN) techniques, multi-layer perceptrons (MLP), radial basis neural networks (RBNN) and generalized regression neural networks (GRNN) to estimate daily pan evaporation. Different MLP models comprising various combinations of daily climatic variables, that is, air temperature, solar radiation, wind speed, pressure and humidity were developed to evaluate the effect of each of these variables on pan evaporation. The MLP estimates are compared with those of the RBNN and GRNN techniques. The Stephens-Stewart (SS) method is also considered for the comparison. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE) and determination coefficient (R2) statistics. Based on the comparisons, it was found that the MLP and RBNN computing techniques could be employed successfully to model the evaporation process using the available climatic data. The GRNN was found to perform better than the SS method. Copyright © 2008 John Wiley & Sons, Ltd. [source]