{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/enhanced-modeling-of-back-mixing-in-chemical","title":"Enhanced Modeling of Back-Mixing in Chemical Reactor Networks","arxiv_id":"2305.11591","date":"2023-05-19","proceeding":null,"authors":["Lisanne Gossel","Mathis Fricke","Dieter Bothe"],"abstract":"Chemical reactor networks (CRNs) enable simulations of combustion reactors with detailed chemical kinetics and strongly simplified flow structure. In this paper, the implementation of a reactor component with less idealized flow structure, namely axial dispersion, to the CRN software NetSMOKE is presented. It is shown exemplarily that different flow models can lead to strongly different results in species prediction. However, computational efficiency remains an issue for this reactor class, which is still to be solved.","url_abs":"https://arxiv.org/abs/2305.11591v1","url_pdf":"https://arxiv.org/pdf/2305.11591v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"enhanced-modeling-of-back-mixing-in-chemical","repo_url":"https://zenodo.org/record/13684524","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}