Abstract
Although the grid convergence index is a widely used for the estimation of discretization error in computational fluid dynamics, it still has some problems. These problems are mainly rooted in the usage of the order of a convergence variable within the model which is a fundamental variable that the model is built upon. To improve the model, a new perspective must be taken. By analyzing the behavior of the gradient within simulation data, a gradient-based model was created. The performance of this model is tested on its accuracy, precision, and how it will affect a computational time of a simulation. The testing is conducted on a dataset of 36 simulated variables, simulated using the method of manufactured solutions, with an average of 26.5 meshes/case. The result shows the new gradient based method is more accurate and more precise then the grid convergence index(GCI). This allows for the usage of a coarser mesh for its analysis, thus it has the potential to reduce the overall computational by at least by 25% and also makes the discretization error analysis more available for general usage.
Highlights
As the usage of computational fluid dynamics (CFD) is getting more common in the engineering field, it is important to ensure that the result of these CFD simulations is accurate
The method is based on the behavior of the gradient in a CFD simulation result
This method is tested on an method of manufactured solution (MMS) simulation dataset, in which the results show that even by changing the center of estimation from f 1 to f 0,guess, the gradient-based method can still estimate the value of f 0 even to a degree that is more accurate than the other method tested in this study
Summary
As the usage of computational fluid dynamics (CFD) is getting more common in the engineering field, it is important to ensure that the result of these CFD simulations is accurate. As CFD generally works by modeling a governing equation and solving it, both of these processes must be done accurately to ensure an overall accurate simulation. It is important that both processes are accurate, the accuracy of the solver/calculation should be prioritized since the process of checking that a model is accurate depends on the accuracy of the calculation itself [1,2]. Since verification could be classified as a process to assess the errors in the calculation of a model [7], it is possible to separate the two types of verification by the error that it determines. Code verification is to check if there is any programming error, and solution verification determines the effect of the numerical error within the solution
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