# Dynamic Data-Driven Smagorinky Closure for LES

## 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.&#x20;

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.

<figure><img src="https://1950852502-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FeduUusc0XYcvg7KKfhnz%2Fuploads%2F2D6bHuzjRAifrvtGvKoe%2FDataset%20Description.png?alt=media&#x26;token=889e4723-85ee-4e54-bfe0-6aaaeab4a1f9" alt=""><figcaption></figcaption></figure>

## What you will learn

You will learn:&#x20;

* how to use aPriori to compute the Strain Rate,
* 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 stresses.

## A-priori methodology

<figure><img src="https://1950852502-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FeduUusc0XYcvg7KKfhnz%2Fuploads%2F2B1wcHz223IlSttOld0t%2FA%20priori%20validation%20methodology.png?alt=media&#x26;token=289f4ea8-58cb-4ab3-ac49-ef1257c1ef7f" alt=""><figcaption></figcaption></figure>



{% hint style="info" %}
**Work In Progress:**

This section will be written soon. Thank you for your patience 🙏
{% endhint %}
