## N.A.K. Doan

58 records found

1

## Authored

## SGS Reaction rate modelling for MILD combustion based on machine-learning combustion mode classification

### Development and a priori study

A neural network (NN) aided model is proposed for the filtered reaction rate in moderate or intense low-oxygen dilution (MILD) combustion. The framework of the present model is based on the partially stirred reactor (PaSR) approach, and the fraction of the reactive structure a ...

Multi-scale analysis of turbulence-flame interaction is performed using experimental data sets from three methane- and propane-fired premixed, turbulent V-flames, at an approach flow turbulent Reynolds number of 450 and a ratio of r.m.s. fluctuating velocity from the mean to l ...

In this chapter, the research dedicated to moderate or intense low-oxygen dilution (MILD) combustion (also called flameless combustion) that relied on direct numerical simulations (DNS) is summarized. In particular, the various DNS carried out are detailed and three different ...

## Short- And long-term predictions of chaotic flows and extreme events

### A physics-constrained reservoir computing approach

We propose a physics-constrained machine learning method - based on reservoir computing - to time-accurately predict extreme events and long-term velocity statistics in a model of chaotic flow. The method leverages the strengths of two different approaches: empirical modelling ...

We explore the possibility of combining a knowledge-based reduced order model (ROM) with a reservoir computing approach to learn and predict the dynamics of chaotic systems. The ROM is based on proper orthogonal decomposition (POD) with Galerkin projection to capture the essen ...

We present an Auto-Encoded Reservoir-Computing (AE-RC) approach to learn the dynamics of a 2D turbulent flow. The AE-RC consists of an Autoencoder, which discovers an efficient manifold representation of the flow state, and an Echo State Network, which learns the time evolutio ...

## Learning hidden states in a chaotic system

### A physics-informed echo state network approach

We extend the Physics-Informed Echo State Network (PI-ESN) framework to reconstruct the evolution of an unmeasured state (hidden state) in a chaotic system. The PI-ESN is trained by using (i) data, which contains no information on the unmeasured state, and (ii) the physical eq ...

## Filtered Reaction Rate Modelling in Moderate and High Karlovitz Number Flames

### An a Priori Analysis

Direct numerical simulations (DNS) of statistically planar flames at moderate and high Karlovitz number (Ka) have been used to perform an a priori evaluation of a presumed-PDF model approach for filtered reaction rate in the framework of large eddy simulation (LES) for differe ...

Subgrid correlation of mixture fraction, Z, and progress variable, c, is investigated using direct numerical dimulation (DNS) data of a hydrogen lifted jet flame. Joint subgrid behaviour of these two scalars are obtained using a Gaussian-type filter for a broad range of filter ...

## Contributed

## Physics-informed neural networks for highly compressible flows

### Assessing and enhancing shock-capturing capabilities

## Unsteady SpaRTA

### Data-driven turbulence modelling for unsteady applications

This study aims to investigate the potential of the application of PINNs in fluid mechanics pro ...