> For the complete documentation index, see [llms.txt](https://www.aprioridns.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.aprioridns.org/fundamentals-and-usage/tutorial/data-driven-closure-for-turbulence-chemistry-interaction.md). # Data-Driven Closure for Turbulence-Chemistry interaction ## Prerequisites *** Before reading this tutorial, you should know a bit of Python. If you would like to refresh your memory, take a look at the [Python tutorial](https://docs.python.org/3/tutorial/). We are going to work on the reduced [dataset](https://github.com/LorenzoPiu/aPriori/tree/main/data) that is available on the [project's GitHub page](https://github.com/LorenzoPiu/aPriori/tree/main). Make sure to download the data folder before starting. ## Learner Profile This tutorial is aimed at a user who has a solid understanding of Computational Fluid Dynamics (CFD) for Combustion. A basic understanding of Machine learning is not required. The tutorial could also be beneficial for those who do not have strong competencies in CFD but know Data Science; in fact, apart from some computations to compute interesting quantities in reacting flows, the basic operations that we are going to perform on the data are based on filtering of 3D fields. ## Dataset Description *** The dataset is extracted from a DNS simulation of a [lifted non-premixed hydrogen flame](https://blastnet.github.io/index.html). The variables saved comprise Temperature, Species Mass Fractions, 3 Velocity components, and Pressure.\ The chemical mechanism used comprises 9 species.

## What you will learn You will learn: * how to use aPriori to compute the chemical source terms, * how to filter the results obtained to resemble an LES field, * and build a data-driven closure based on Neural Networks to model the subgrid interactions between Turbulence and Chemistry. ## A-priori methodology