The influence of the learning data on the reduced order model of laminar non-premixed flames

Nicole Lopes Junqueira (1) , Luis Fernando Figueira da Silva (1, 2) , Louise da Costa Ramos (3, 4) , Igor Braga de Paula (1)

1 PUC-Rio - Pontifícia Universidade Católica do Rio de Janeiro [Brasil] = Pontifical Catholic University of Rio de Janeiro [Brazil] = Université catholique pontificale de Rio de Janeiro [Brésil]
2 PPrime [Poitiers] - Institut Pprime [UPR 3346]
3 ANSYS France
4 CAS - Centre Automatique et Systèmes

Computational fluid dynamics (CFD) is often applied to the study of combustion, enabling to optimize the process and control the emission of pollutants. This numerical methodology enables the analysis of different flame properties, such as the components of velocity, temperature, and mass fractions of chemical species. However, reproducing the behavior observed in engineering problems requires a high computational cost associated with memory and simulation time. Reduced order model (ROM) is a machine learning technique that has been applied to several engineering applications, aiming to develop models for complex systems with reduced computational cost. In this way, a high-fidelity model of complex systems is created from available data to learn its behavior and its main characteristics. In this work, different ROMs are created using CFD simulation data. The CFD model solves the mass, species, energy, and momentum conservation equations for a methane/air laminar diffusion flame, stabilized on the Gülder burner. Chemistry is modeled using a 19-species skeletal chemical kinetic mechanism. The static reduced order model uses the singular value decomposition (SVD) algorithm to decompose the CFD data and obtain the system's modes. Then, genetic aggregation response surface interpolation is applied on the higher SVD modes, creating the static ROM. This work analyzes the effect of different data preprocessing approaches on the ROM. The first analysis is the impact of reducing the number of learning data points, showing that this decrease does not directly impact the energy of the SVD modes, but, in the reconstruction field is possible to notice a degradation of the reconstruction. The second analysis is related to the effect of creating a ROM for each uncoupled flame property or treating the properties as a coupled system. The results of the coupled and uncoupled reduced order models are quite similar in terms of properties field reconstruction. However, in the energy analysis the coupled ROM converges rapidly, similarly to the uncoupled temperature ROM, while the uncoupled chemical species ROMs have a slower convergence.

Mots clés: machine learning; computational fluid dynamics; diffusion flames; methane/air combustion

Domaines: Milieux fluides et réactifs

Nicole Lopes Junqueira, Luis Fernando Figueira da Silva, Louise da Costa Ramos, Igor Braga de Paula. The influence of the learning data on the reduced order model of laminar non-premixed flames. 26th International Congress of Mechanical Engineering, Nov 2021, Florianopolis, Brazil. ⟨10.26678/ABCM.COBEM2021.COB2021-0110⟩. ⟨hal-03357849⟩