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Aerodynamic improvements primarily result in decreased fuel usage and carbon dioxide emissions into the atmosphere. Numerous governments support ongoing aerodynamics development initiatives as a means of addressing the energy problem and reducing air pollution, Ahmed body investigation helps research to investigate versatile approaches and flexibility of design. This study is carried over a generic design of Ahmed body model. We attempted a passive arrangement system to reduce drag coefficient with a correlation of cases such as in primary objective varying parameter of slant angle from 20^∘ to 30^∘ proposed to monitor the behavior of drag coefficient. Once we finalized the optimum slant angle, which gives a lower drag coefficient, the next proposed configuration is to vary passive arrangement between lower and upper blend length to see the deflection of the boundary layer in correlation with the drag coefficient. The final topology is selected, which gives the lowest drag coefficient. The post-process correlation study was proposed by using an artificial neural network (ANN) scheme. The ANN model is developed between an achieved set of data from CFD investigation, ANN model indicates a strong correlation between the varying percentage of blend angle and increment percentage of the drag coefficient.

In computational fluid dynamics (CFD), there is a transformation of methods over the years for building commercially coded software. Each method has predicted its own set of importance, but the exportation and prediction of data are some of the crucial elements for post-processing and validating results. In the present investigation, a detailed comparative analysis is performed over finite difference method (FDM) and finite volume method (FVM) method for the 1D steady-state heat conduction problem over a 1-m-long plate. The comparison was made between solution creation and validation between FDM and FVM for the analytical and computational scheme. The convergence-dependent study is performed as multi-objective optimization to predict how artificial neural network (ANN) can be used to verify and validate the solution of CFD.