The performance of a gradient-based method to estimate the discretization error in computational fluid dynamics

Adhika Satyadharma, Harinaldi

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number10
Pages (from-to)1-16
Number of pages16
JournalComputation
Volume9
Issue number2
DOIs
Publication statusPublished - Feb 2021

Keywords

  • Computational fluid dynamics
  • Discretization error
  • Grid convergence index
  • Uncertainty quantification
  • Verification and validation

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