![]() Using Ns2QueryServer, the developer can provide the users of hisher application the ability to use SQL to select data records and process them through the trained neural network. This component also includes plotting and processing methods to allow analyses of table data.ĭatabase Query Component The TNs2QueryServer component combines the functionality of the Delphi VCL TTQuery with that of the basic TNs2Server component. Using Ns2TableServer, the developer can provide the users of hisher application the ability to process data contained in a database table record through the trained neural network. This extends the functionality of TNs2Server by adding database connectivity. The properties and methods are explained in detail in the accompanying help file.ĭatabase Table Component The TNs2TableServer component combines the functionality of the Delphi VCL TTable with that of the basic TNs2Server component. There are no new Events associated with the Ns2Server component. The results showed that it is possible to design and train certain architectures of NNs to accurately predict θ and ψ distribution within the enclosure, and hence impart confidence in the legitimacy of CFD solutions for new cases.The Basic Wrapper Component TNs2Server is the basic Delphi wrapper component for the NeuroShell 2 DLL Server. ![]() The trained NNs were used to verify CFD solutions in cases of higher Ra ranges for which CFD simulations produce different solutions for different discretization schemes. The robustness of the trained NNs was tested by applying them to a number of “production” data sets that the networks have never “seen” before. The results of the CFD were used to train and test the neural networks. The CFD analysis was used to calculate the normalized temperature θ and stream function ψ throughout a partitioned enclosure. Three types of NNs were evaluated and parametric studies were performed to optimize network designs for best predictions of the flow variables.Ī validated CFD code was used to generate a database that covered the range of Rayleigh number Ra from 10 4 to 4.7 × 10 6. ![]() Attention is focused on using NNs trained on a database generated by numerically-stable CFD analysis to predict flow variables for the aforementioned ill-posed cases, thereby giving confidence in steady-state CFD results for these cases. ![]() The objective of this paper is to investigate the feasibility of using neural networks (NNs) as a means lending support to the authenticity of steady-state CFD solutions for such ill-posed problems through inter-model comparisons. In some cases, it was reported in the literature that different CFD solutions (due to different numerical stability characteristics) were obtained for different mesh quality, time step, and discretization order. CFD analysis of heat and mass flow due to natural convection in partitioned enclosures has recently been the focus of many CFD researchers. ![]()
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January 2023
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