Machine learning for predictive coal combustion CFD simulations—From detailed kinetics to HDMR Reduced-Order models

Feature extraction and artificial neural networks for the on-the-fly classification of high-dimensional thermochemical spaces in adaptive-chemistry simulations

“…In fact, as also mathematically proved in Aggarwal et al (2001) and Verleysen et al (2003), as the number of dimensions increases, the concepts of distance and nearest neighbors both lose meaningfulness. Thus, Artificial Neural Networks (ANNs) represent a valid alternative to improve the classification efficiency when dealing with high-dimensional spaces, and they have also already been successfully exploited for combustion and chemical kinetics applications (Christo et al, 1995(Christo et al, , 1996Blasco et al, 1998;Hao et al, 2001;Galindo et al, 2005;Chen et al, 2020;Dalakoti et al, 2020;Debiagi et al, 2020;Angelilli et al, 2021). ANNs have also been introduced in the context of Large Eddy Simulations of reactive flows in Ihme et al (2009), and the comparison with conventional tabulation techniques for chemistry representation led to excellent results in terms of accuracy.…”

Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

“…Qiao and Zeng 143 also applied the ANN framework to predict the gas products of heavy oil gasification under oxy-fuel conditions but the authors have not clarified how they trained and validated their ANN models. Debiagi et al 144 developed a reduced-order model based on ML, which can accurately predict different phases of coal particle combustion at a reduced computation cost. They used a High Dimensional Model Representation (HDMR) method to develop the supervised ML models (See Figure 5).…”

“…They used a High Dimensional Model Representation (HDMR) method to develop the supervised ML models (See Figure 5). Unlike the case with the previous work, the training and test datasets were generated from an accurate, detailed solid fuel kinetic model that considered a wide range of operation conditions obtained from a novel gas-assisted coal combustor 144 . This journal is © The Royal Society of Chemistry 20xx…”

A Review on Detailed Kinetic Modeling and Computational Fluid Dynamics of Thermochemical Processes of Solid Fuels

“…Many researchers are still working on speeding up CFD techniques for detailed mechanisms. Despite the development of new combustion models or mechanism reduction, some possible approaches may be the utilization of the field programmable gate array (FPGA), the graphics processing unit (GPU)-accelerated chemistry solver, − and the application of machine learning. − It should be noted that the GPU-based solver and machine learning are both software-based approaches, while the FPGA approach adjusts the hardware to solve a specific CFD problem instead of software optimization. For instance, in Ebrahimi and Zandsalimy’s work, the authors found that the FPGA improved the solution speed up to 20 times faster in the case of the Laplace equation (compared to the conventional CPU).…”