Machine learning techniques, such as reduced order models (ROM), have demonstrated low cost when creating models of complex systems while aiming at the same accuracy as high-fidelity models, such as computational fluid dynamics (CFD). However, reduced models must preserve some properties tied to the studied system. For a combustion problem, those are in particular monotonicity, positivity, and boundedness. Here, ROM are created using data from CFD simulations of non-premixed laminar flames with detailed chemistry and transport. The data obtained for variable fuel velocity is reduced using singular value decomposition (SVD), and then a genetic aggregation response surface algorithm is applied to predict the properties fields for an arbitrary fuel inlet velocity. This work analyzes the effect of different data preprocessing approaches on the ROM, i.e., (1) the properties treated as an uncoupled or as a coupled system; (2) normalization of different properties; (3) the logarithm of the chemical species. For all constructed ROM, the energy content of the reduction process and the reconstructed fields of the flame properties evidence the slow convergence of SVD modes for the uncoupled ROM, while a faster one is seen when the logarithm preprocessing is applied. Also, the learning is shown to be achieved with a smaller number of modes for two of the coupled ROM and for the ROM using the logarithm. The reconstruction of the mass fraction fields is characterized by regions of negative values, underscoring that the baseline ROM methodology does not preserve the properties of monotonicity, positivity, and boundedness. The proposed logarithm preprocessing enables to overcome the positivity problem and to accurately reproduce the original data.

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.

Computational model of combustion chambers is a major topic of research on the past decades.The modelling of such physical systems enables the analysis of several flame properties, such as velocity, species mass fraction and temperature.However, there is a high computational burden associated with the parametric exploration and the complexity of such systems.To overcome this problem, reduced order models (ROM) have been used to predict the behavior of those systems, decreasing the cost associated to the computational modeling.Accordingly, this work presents modeling of non premixed laminar flames stabilized in the Gülder burner, using a skeletal kinetic mechanism.The computational fluid dynamics flame model has been developed using Fluent 2019 R3 for the isothermal and reactive cases, for different prescribed fuel inlet velocities.The analysis of the flow and flame structures are respectively by means of the axial and radial velocity components and the mass fraction of CH 4 fields in the isothermal case; and the temperature, mass fraction of OH and CO for the reactive case.Then, the ROM is construct with StaticROM from Twin Builder based on the CFD results.The results of the ROM have a maximum absolute error of 0.0025 m/s for the velocity ROM, 0.0737 for the mass fraction of CH 4 , 98.91 K for the temperature, 1.47 10 -3 for the mass fraction of OH and 0.018 for the mass fraction of CO.These errors are acceptable for the isothermal case and for the temperature, however, for the mass fractions of OH and CO it shows a significant difference since the concentration of these species is small.