Research graph
References from On the role of error metrics in the assessment of chemical kinetic mechanisms. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
2014 · External reference
On the applicability of directed relation graphs to the reduction of reaction mechanisms
10.1016/j.combustflame.2006.04.017 · 2006 · External reference
An efficient error-propagation-based reduction method for large chemical kinetic mechanisms
10.1016/j.combustflame.2007.10.020 · 2008 · External reference
Complex CSP for chemistry reduction and analysis
10.1016/s0010-2180(01)00252-8 · 2001 · External reference
State-of-the-art in premixed combustion modeling using flamelet generated manifolds
10.1016/j.pecs.2016.07.001 · 2016 · External reference
Modeling of turbulent flames with the large eddy simulation probability density function (LES-PDF) approach, stochastic fields, and artificial neural networks
10.1063/5.0041122 · 2021 · External reference
A physics-based approach to modeling real-fuel combustion chemistry–IV. HyChem modeling of combustion kinetics of a bio-derived jet fuel and its blends with a conventional Jet A
10.1016/j.combustflame.2018.07.012 · 2018 · External reference
Development of a virtual optimized chemistry method. Application to hydrocarbon/air combustion
10.1016/j.combustflame.2019.09.013 · 2020 · External reference
Determination of rate parameters based on both direct and indirect measurements
10.1002/kin.20717 · 2012 · External reference
ORCh: A package to reduce and optimize chemical kinetics. Application to tetrafluoromethane oxidation
10.1016/j.softx.2024.101819 · 2024 · External reference
Skeletal mechanism generation for surrogate fuels using directed relation graph with error propagation and sensitivity analysis
10.1016/j.combustflame.2009.12.022 · 2010 · External reference
Reduction of very large reaction mechanisms using methods based on simulation error minimization
10.1016/j.combustflame.2008.11.001 · 2009 · External reference
Automated simulation error based reduction (ASER) of large chemical reaction mechanisms
10.1016/j.compchemeng.2019.106560 · 2019 · External reference
Genetic algorithms for optimisation of chemical kinetics reaction mechanisms
10.1016/j.pecs.2004.02.002 · 2004 · External reference
OptiSMOKE++: A toolbox for optimization of chemical kinetic mechanisms
10.1016/j.cpc.2021.107940 · 2021 · External reference
pyMechOpt: A Python toolbox for optimizing of reaction mechanisms
10.1016/j.softx.2024.102001 · 2025 · External reference
A skeletal mechanism for n-dodecane/ammonia combustion and an open-source reaction scheme optimization tool
10.1016/j.fuel.2024.132168 · 2024 · External reference
Generating simplified ammonia reaction model using genetic algorithm and its integration into numerical combustion simulation of 1 MW test facility
2023 · External reference
Assessing the performance of various stochastic optimization methods on chemical kinetic modeling of combustion
10.1021/acs.iecr.0c04009 · 2020 · External reference
Unresolved reference
2026 · External reference
Development of reduced and optimized mechanism for ammonia/hydrogen mixture based on genetic algorithm
10.1016/j.energy.2023.126927 · 2023 · External reference
A reduced mechanism for predicting the ignition timing of a fuel blend of natural-gas and n-heptane in HCCI engine
10.1016/j.enconman.2013.12.005 · 2014 · External reference
Unresolved reference
External reference
Machine learned compact kinetic models for methane combustion
10.1016/j.combustflame.2023.112755 · 2023 · External reference
Clustering-enhanced deep learning method for computation of full detailed thermochemical states via solver-based adaptive sampling
10.1021/acs.energyfuels.3c01955 · 2023 · External reference
Machine learning methods for modeling the kinetics of combustion in problems of space safety
10.1016/j.actaastro.2024.09.039 · 2024 · External reference
Unresolved reference
2021 · External reference
A new detailed kinetic model for surrogate fuels: C3MechV3.3
2022 · External reference
An experimental and kinetic modeling study of ammonia/n-heptane blends
10.1016/j.combustflame.2022.112428 · 2022 · External reference
Assessing the predictions of a NOx kinetic mechanism on recent hydrogen and syngas experimental data
10.1016/j.combustflame.2017.03.019 · 2017 · External reference
Reduced chemistry for numerical combustion of NH3/H2 fuel blend
10.1016/j.combustflame.2025.114287 · 2025 · External reference
Determination of rate parameters based on both direct and indirect measurements
10.1002/kin.20717 · ExternalCitation · doi-reference
Machine learning methods for modeling the kinetics of combustion in problems of space safety
10.1016/j.actaastro.2024.09.039 · ExternalCitation · doi-reference
On the applicability of directed relation graphs to the reduction of reaction mechanisms
10.1016/j.combustflame.2006.04.017 · ExternalCitation · doi-reference
An efficient error-propagation-based reduction method for large chemical kinetic mechanisms
10.1016/j.combustflame.2007.10.020 · ExternalCitation · doi-reference
Reduction of very large reaction mechanisms using methods based on simulation error minimization
10.1016/j.combustflame.2008.11.001 · ExternalCitation · doi-reference
Skeletal mechanism generation for surrogate fuels using directed relation graph with error propagation and sensitivity analysis
10.1016/j.combustflame.2009.12.022 · ExternalCitation · doi-reference
Assessing the predictions of a NOx kinetic mechanism on recent hydrogen and syngas experimental data
10.1016/j.combustflame.2017.03.019 · ExternalCitation · doi-reference
A physics-based approach to modeling real-fuel combustion chemistry–IV. HyChem modeling of combustion kinetics of a bio-derived jet fuel and its blends with a conventional Jet A
10.1016/j.combustflame.2018.07.012 · ExternalCitation · doi-reference
Development of a virtual optimized chemistry method. Application to hydrocarbon/air combustion
10.1016/j.combustflame.2019.09.013 · ExternalCitation · doi-reference
An experimental and kinetic modeling study of ammonia/n-heptane blends
10.1016/j.combustflame.2022.112428 · ExternalCitation · doi-reference
Machine learned compact kinetic models for methane combustion
10.1016/j.combustflame.2023.112755 · ExternalCitation · doi-reference
Reduced chemistry for numerical combustion of NH3/H2 fuel blend
10.1016/j.combustflame.2025.114287 · ExternalCitation · doi-reference
Automated simulation error based reduction (ASER) of large chemical reaction mechanisms
10.1016/j.compchemeng.2019.106560 · ExternalCitation · doi-reference
OptiSMOKE++: A toolbox for optimization of chemical kinetic mechanisms
10.1016/j.cpc.2021.107940 · ExternalCitation · doi-reference
A reduced mechanism for predicting the ignition timing of a fuel blend of natural-gas and n-heptane in HCCI engine
10.1016/j.enconman.2013.12.005 · ExternalCitation · doi-reference
Development of reduced and optimized mechanism for ammonia/hydrogen mixture based on genetic algorithm
10.1016/j.energy.2023.126927 · ExternalCitation · doi-reference
A skeletal mechanism for n-dodecane/ammonia combustion and an open-source reaction scheme optimization tool
10.1016/j.fuel.2024.132168 · ExternalCitation · doi-reference
Genetic algorithms for optimisation of chemical kinetics reaction mechanisms
10.1016/j.pecs.2004.02.002 · ExternalCitation · doi-reference
State-of-the-art in premixed combustion modeling using flamelet generated manifolds
10.1016/j.pecs.2016.07.001 · ExternalCitation · doi-reference
ORCh: A package to reduce and optimize chemical kinetics. Application to tetrafluoromethane oxidation
10.1016/j.softx.2024.101819 · ExternalCitation · doi-reference
pyMechOpt: A Python toolbox for optimizing of reaction mechanisms
10.1016/j.softx.2024.102001 · ExternalCitation · doi-reference
Complex CSP for chemistry reduction and analysis
10.1016/s0010-2180(01)00252-8 · ExternalCitation · doi-reference
Clustering-enhanced deep learning method for computation of full detailed thermochemical states via solver-based adaptive sampling
10.1021/acs.energyfuels.3c01955 · ExternalCitation · doi-reference
Assessing the performance of various stochastic optimization methods on chemical kinetic modeling of combustion
10.1021/acs.iecr.0c04009 · ExternalCitation · doi-reference
Modeling of turbulent flames with the large eddy simulation probability density function (LES-PDF) approach, stochastic fields, and artificial neural networks
10.1063/5.0041122 · ExternalCitation · doi-reference
