A novel data-driven reduced order modelling methodology for simulation of humid blowout in wet combustion applications

Palulli, Rahul; Zhang, Kai; Dybe, Simeon; Paschereit, Christian Oliver; Duwig, Christophe

doi:10.1016/j.energy.2024.131310

Energy

2024

DOI: 10.1016/j.energy.2024.131310

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A novel data-driven reduced order modelling methodology for simulation of humid blowout in wet combustion applications

Rahul Palulli,

Kai Zhang,

Simeon Dybe

et al.

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Cited by 2 publications

References 37 publications

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A Comparative Study of the Hydrogen Auto-Ignition Process in Oxygen–Nitrogen and Oxygen–Water Vapor Oxidizer: Numerical Investigations in Mixture Fraction Space and 3D Forced Homogeneous Isotropic Turbulent Flow Field

Caban,

Tyliszczak

2024

Energies

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In this paper, we analyze the auto-ignition process of hydrogen in a hot oxidizer stream composed of oxygen–nitrogen and oxygen–water vapor with nitrogen/water vapor mass fractions in a range of 0.1–0.9. The temperature of the oxidizer varies from 1100 K to 1500 K and the temperature of hydrogen is assumed to be 300 K. The research is performed in 1D mixture fraction space and in a forced homogeneous isotropic turbulent (HIT) flow field. In the latter case, the Large Eddy Simulation (LES) method combined with the Eulerian Stochastic Field (ESF) combustion model is applied. The results obtained in mixture fraction space aim to determine the most reactive mixture fraction, maximum flame temperature, and dependence on the scalar dissipation rate. Among others, we found that the ignition in H2-O2-H2O mixtures occurs later than in H2-O2-N2 mixtures, especially at low oxidizer temperatures. On the other hand, for a high oxidizer temperature, the ignitability of H2-O2-H2O mixtures is extended, i.e., the ignition occurs for a larger content of H2O and takes place faster. The 3D LES-ESF results show that the ignition time is virtually independent of initial conditions, e.g., randomness of an initial flow field and turbulence intensity. The latter parameter, however, strongly affects the flame evolution. It is shown that the presence of water vapor decreases ignitability and makes flames more prone to extinction.

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A Comparative Study of the Hydrogen Auto-Ignition Process in Oxygen–Nitrogen and Oxygen–Water Vapor Oxidizer: Numerical Investigations in Mixture Fraction Space and 3D Forced Homogeneous Isotropic Turbulent Flow Field

Caban,

Tyliszczak

2024

Energies

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Implementation of Principal Component Analysis (PCA)/Singular Value Decomposition (SVD) and Neural Networks in Constructing a Reduced-Order Model for Virtual Sensing of Mechanical Stress

Melgarejo,

Pérez,

Ruiz

et al. 2024

Sensors

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This study presents the design and validation of a numerical method based on an AI-driven ROM framework for implementing stress virtual sensing. By leveraging Reduced-Order Models (ROMs), the research aims to develop a virtual stress transducer capable of the real-time monitoring of mechanical stresses in mechanical components previously analyzed with high-resolution FEM simulations under a wide range of multiple load scenarios. The ROM is constructed through neural networks trained on Finite Element Method (FEM) outputs from multiple scenarios, resulting in a simplified yet highly accurate model that can be easily implemented digitally. The ANN model achieves a prediction error of MAEtest=(0.04±0.06) MPa for the instantaneous mechanical stress predictions, evaluated over the entire range of stress values (0 to 5.32 MPa) across the component structure. The virtual sensor is capable of producing a quasi-instantaneous, detailed full stress map of the component in just 0.13 s using the ROM, for any combination of 4-load inputs, compared to the 6 min and 31 s required by the FEM. Thus, the approach significantly reduces computational complexity while maintaining a high degree of precision, enabling efficient real-time monitoring. The proposed method’s effectiveness is demonstrated through rigorous ROM validation, underscoring its potential for stress control. This precise AI-driven procedure opens new horizons for predictive maintenance strategies centered on stress cycle monitoring.

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A novel data-driven reduced order modelling methodology for simulation of humid blowout in wet combustion applications

Cited by 2 publications

References 37 publications

A Comparative Study of the Hydrogen Auto-Ignition Process in Oxygen–Nitrogen and Oxygen–Water Vapor Oxidizer: Numerical Investigations in Mixture Fraction Space and 3D Forced Homogeneous Isotropic Turbulent Flow Field

A Comparative Study of the Hydrogen Auto-Ignition Process in Oxygen–Nitrogen and Oxygen–Water Vapor Oxidizer: Numerical Investigations in Mixture Fraction Space and 3D Forced Homogeneous Isotropic Turbulent Flow Field

Implementation of Principal Component Analysis (PCA)/Singular Value Decomposition (SVD) and Neural Networks in Constructing a Reduced-Order Model for Virtual Sensing of Mechanical Stress

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