Machine learning clustering algorithms for the automatic generation of chemical reactor networks from CFD simulations

Predicting the thermal and environmental performances of combustion systems can be difficult and computa-tionally expensive. Chemical Reactor Networks (CRN) represent an appealing solution for performing faster numerical simulations since they can carry out calculations with detailed kinetics in a significantly reduced amount of time. However, the design of such models is challenging, as it usually requires a considerable amount of expertise from the user. In this work, we present a novel, automatic methodology for the design of CRN models, which consists of the post-processing of CFD data via a combination of unsupervised clustering and graph scanning algorithms. The methodology was tested on a semi-industrial furnace which can operate in Moderate or Intense Low oxygen Dilution (MILD) combustion. The furnace operates at a nominal power input of 15 kW and is fed with several CH4-H2 mixtures, with two different air injector diameters. First, RANS simulations of the different cases were performed and validated against experimental data. Subsequently, different CRNs were extracted singularly for each case and simulated using detailed kinetics mechanisms. The different CRNs showed good performances in predicting NO emissions for the entire range of cases, as the results were in reasonable agreement with experimental data. Last, the capability of a single CRN to extrapolate towards different operating conditions was also assessed by using one single CRN for the simulation of different fuel mixtures. The approach showed a good level of generalization since the NO predictions were close to the experimental values.

Automated summary

In this study, a novel methodology for the design of CRN models was proposed, which involves the post-processing of CFD data using unsupervised clustering and graph scanning algorithms. The methodology was tested on a semi-industrial furnace and showed promising predictive performances.