The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/5423254e-bc6e-4006-ad76-a2c4ee13f6bc

Ghasemi Khourinia, Alireza; Derksen, Marco; Kok, Jim (2025): Greencode Project Dataset for Chemistry Reduction with CSP. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/5423254e-bc6e-4006-ad76-a2c4ee13f6bc.v1

Categories

• Mechanical Engineering
• Applied Mathematics
• Chemical Engineering
• Numerical and Computational Mathematics
• Engineering
• Mathematical Sciences

Keywords

Computational Fluid Dynamics Computational Singular Perturbance Eddy Dissipation Concept Flamelet Generated Manifold Intrinsic Low Dimensional Manifolds Machine Learning MILD Combustion Reduced Chemistry

Time coverage

2022-2024

Licence

CC BY-ND 4.0

Interoperability

• RO-Crate Metadata

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by Alireza Ghasemi , Marco Derksen, Jim Kok

This dataset contains results of the numerical analysis of the combustion of lean methane-air with flue gas recirculation (FGR) intended for ultra low NOx applications, Hydrogen rich simulations and MILD conditions using a CSP-based reduced mechanism.

History

• 2025-09-09 first online, published, posted

Publisher

4TU.ResearchData

Format

h5, dat, keras

References

• https://projecten.topsectorenergie.nl/projecten/ontwikkeling-van-een-innovatief-en-industrieel-toepasbaar-model-voor-turbulente-verbranding-van-h2-36878

Funding

• Greencoode (grant code TKITOETKI 2021-H2-09) [more info...] Top Sector Energy, TKI Nieuw Gas

Organizations

University of Twente, Faculty of Engineering Technology, Thermal Engineering
Mateq Process B.V.