Development and Validation of a General Framework for Automated Chemical Reactor Network Generation Based on CFD Simulation Results

Donghwi Kim; Namsu Kim; Youngtae Guahk; Seongkyun Im

doi:10.15231/jksc.2026.31.3.013

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2026 Vol.31, Issue 3 Preview Page Next Page

Research Article

Development and Validation of a General Framework for Automated Chemical Reactor Network Generation Based on CFD Simulation Results CFD 시뮬레이션 결과 기반 자동 화학 반응기 네트워크 생성을 위한 범용 프레임워크의 개발 및 검증

30 September 2026. pp. 13-23

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Abstract

This study presents a framework for the automated generation of chemical reactor networks (CRNs) based on computational fluid dynamics (CFD) simulation results, eliminating the need for complex parameter configurations. In the proposed framework, CFD solutions serve as the primary input for zone partitioning and CRN construction, with inter-zonal mass flow rates determined by computing the mass flux across each computational zone boundary. The framework was validated against the well-established Sandia Flame D benchmark, and the CRN predictions were compared with available experimental data. Furthermore, the statistical convergence of the k-means clustering algorithm was verified to account for its inherent randomness. K=30 was selected as the optimal value, balancing computational cost and accuracy. In the optimal case, the CRN enabled the prediction of emission characteristics using detailed reaction mechanisms at a significantly lower computational cost than CFD. In addition, the framework demonstrated satisfactory qualitative agreement in predicting the distribution of major thermochemical state quantities. Therefore, this framework is expected to serve as a valuable tool for predicting the distribution of thermochemical state variables and for analyzing sensitivity to various chemical reaction mechanisms.

Keywords

CFD

CRN

Automated generation

Emission prediction

References

I. Larki, A. Zahedi, M. Asadi, M.M. Forootan, M. Farajollahi, R. Ahmadi, A. Ahmadi, Mitigation approaches and techniques for combustion power plants flue gas emissions: A comprehensive review, Sci. Total Environ. 903 (2023) 166108.

10.1016/j.scitotenv.2023.166108

WHO, WHO global air quality guidelines, Available at: <https://iris.who.int/server/api/core/bitstreams/ 551b515e-2a32-4e1a-a58c-cdaecd395b19/content>, 2021.

환경부, 제3차 대기환경개선 종합계획(23~32), Available at: <https://me.go.kr/m/mob/policy_data/read.do;jsessionid=0WNkKj7kgEjScaNm53Mu5Vrg.mehome1?menuId=36&condition.code=A3&seq=8025>, 2023.

Eastern Kern Air Pollution Control District, Rule 425: Stationary Gas Turbines (Oxides of Nitrogen), Available at: <https://ww2.arb.ca.gov/sites/default/files/classic/technology-clearinghouse/rules/RuleID1431.pdf>, 2018.

H. Khodayari, F. Ommi, Z. Saboohi, A review on the applications of the chemical reactor network approach on the prediction of pollutant emissions, Aircr. Eng. Aerosp. Technol. 92 (2020) 551-570.

10.1108/AEAT-08-2019-0178

M. Falcitelli, L. Tognotti, S. Pasini, An algorithm for extracting chemical reactor network models from CFD simulation of industrial combustion systems, Combust. Sci. Technol. 174 (2002) 27-42.

10.1080/713712951

I.V. Novosselov, P.C. Malte, Development and Application of an Eight-Step Global Mechanism for CFD and CRN Simulations of Lean-Premixed Combustors, J. Eng. Gas Turbines Power 130 (2008) 021502.

10.1115/1.2795787

C. Romano, M. Cerutti, G. Babazzi, L. Miris, R. Lamioni, C. Galletti, L. Mazzotta, D. Borello, Ammonia blends for gas-turbines: Preliminary test and CFD-CRN modelling, Proc. Combust. Inst., 40 (2024) 105494.

10.1016/j.proci.2024.105494

V. Fichet, M. Kanniche, P. Plion, O. Gicquel, A reactor network model for predicting NOx emissions in gas turbines, Fuel 89 (2010) 2202-2210.

10.1016/j.fuel.2010.02.010

A. Stagni, A. Cuoci, A. Frassoldati, T. Faravelli, E. Ranzi, A fully coupled, parallel approach for the post-processing of CFD data through reactor network analysis, Comput. Chem. Eng. 60 (2014) 197-212.

10.1016/j.compchemeng.2013.09.002

OpenCFD Ltd, OpenFOAM, Available at: <https://www.openfoam.com>.

Cantera Developers, Cantera, Available at: <https:// www.cantera.org>.

TNF Workshop, Piloted Jet: CH4-AIR, Available at: <https://tnfworkshop.org/data-archives/pilotedjet/ch4-air>.

OpenCFD Ltd., k-ε, Available at: <https://doc.openfoam.com/2306/tools/processing/models/turbulence/ras/linear-evm/rtm/kEpsilon>.

OpenCFD Ltd., EDC, Available at: <https://github.com/OpenFOAM/OpenFOAM-5.x/blob/master/src/combustionModels/EDC/EDC.H>.

OpenCFD Ltd., SandiaD_LTS/chemkin, Available at: <https://gitlab.com/openfoam/core/openfoam/-/ tree/master/tutorials/combustion/reactingFoam/RAS/SandiaD_LTS/chemkin?ref_type=heads>.

OpenCFD Ltd., scalarTransportFoam, Available at: <https://www.openfoam.com/documentation/guides/latest/doc/guide-applications-solvers-basic-scalarTransportFoam.html>.

J.A. van Oijen, A. Donini, R.J.M. Bastiaans, J.H.M. ten Thije Boonkkamp, L.P.H. de Goey, State-of- the-art in premixed combustion modeling using flamelet generated manifolds, Prog. Energy Combust. Sci. 57 (2016) 30-74.

10.1016/j.pecs.2016.07.001

OpenCFD Ltd., BilgerMixtureFraction, Available at: <https://www.openfoam.com/documentation/guides/v2206/api/classFoam_1_1functionObjects_1_1BilgerMixtureFraction.html>.

J.B. MacQueen, Some Methods for Classification and Analysis of Multivariate Observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, University of California Press, Berkeley, CA, 1967, pp. 281-297.

D. Arthur, S. Vassilvitskii, K-means++: The advantages of careful seeding, in: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, Society for Industrial and Applied Mathematics, 2007, pp. 1027-1035.

OpenCFD Ltd., Mesh description, Available at: <https://www.openfoam.com/documentation/user-guide/4-mesh-generation-and-conversion/4.1-mesh-description>.

M. Savarese, A. Cuoci, W.D. Paepe, A. Parente, Machine learning clustering algorithms for the automatic generation of chemical reactor networks from CFD simulations, Fuel 343 (2023) 127945.

10.1016/j.fuel.2023.127945

A. Innocenti, A. Andreini, D. Bertini, B. Facchini, M. Motta, Turbulent flow-field effects in a hybrid CFD-CRN model for the prediction of NOx and CO emissions in aero-engine combustors, Fuel 215 (2018) 853-864.

10.1016/j.fuel.2017.11.097

M.M. Borujeni, H.R. Norouzi, How to simulate combustion of a flame using OpenFOAM, Available at: <https://www.cemf.ir/PDFs/OpenFOAM/Sandia-D-Flame-reactingFoam.pdf>.

G.P. Smith, D.M. Golden, M. Frenklach, N.W. Moriarty, B. Eiteneer, M. Goldenberg, C.T. Bow man, R.K. Hanson, S. Song, W.C. Gardiner Jr., V.V. Lissianski, Z. Qin, GRI-Mech 3.0, Available at: <http://combustion.berkeley.edu/gri-mech/version30/text30.html>.

University of California at San Diego, Chemical- Kinetic Mechanisms for Combustion Applications, San Diego Mechanism web page, Available at: <https://web.eng.ucsd.edu/mae/groups/combustion/mechanism.html>.

T. Li, Ø. Skreiberg, T. Løvås, P. Glarborg, Skeletal mechanisms for prediction of NOx emission in solid fuel combustion, Fuel 254 (2019) 115569.

10.1016/j.fuel.2019.05.152

M.L. Lavadera, C. Brackmann, A.A. Konnov, Experimental and modeling study of laminar burning velocities and nitric oxide formation in premixed ethylene/air flames, Proc. Combust. Inst. 38 (2021) 395-404.

10.1016/j.proci.2020.06.062

P. Glarborg, J.A. Miller, B. Ruscic, S.J. Klippen stein, Modeling nitrogen chemistry in combustion, Prog. Energy Combust. Sci. 67 (2018) 31-68.

10.1016/j.pecs.2018.01.002

Information

Publisher :The Korean Society of Combustion
Publisher(Ko) :한국연소학회
Journal Title :Journal of the Korean Society of Combustion
Journal Title(Ko) :한국연소학회지
Volume : 31
No :3
Pages :13-23
Received Date : 2026-06-09
Revised Date : 2026-08-10
Accepted Date : 2026-08-10
DOI :https://doi.org/10.15231/jksc.2026.31.3.013

Journal of the Korean Society of Combustion ISSN:1226-0959(Print) 2466-2089(Online) 한국연소학회지

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