Advanced search
Publication Cover
Combustion Theory and Modelling Volume 4, 2000 - Issue 1
Submit an article Journal homepage
292
Views
59
CrossRef citations to date
0
Altmetric
Original Articles

A self-organizing-map approach to chemistry representation in combustion applications

J Blasco Fluid Mechanics Group, CPS/LITEC, María de Luna 3, Zaragoza, 50.015, Spain
,
N Fueyo Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA, 94720-1740, USA
,
C Dopazo Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA, 94720-1740, USA
&
J-Y Chen Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA, 94720-1740, USA
Pages 61-76 | Published online: 08 Nov 2010

Citations (59)

Keep up to date with the latest research on this topic with citation updates for this article.

Subscribe to citation updates

Read on this site (4)

Elizabeth Armstrong, Michael A. Hansen, Robert C. Knaus, Nathaniel A. Trask, John C. Hewson & James C. Sutherland. (2022) Accurate Compression of Tabulated Chemistry Models with Partition of Unity Networks. Combustion Science and Technology 0:0, pages 1-18.
Read now
Maximilian Hansinger, Yipeng Ge & Michael Pfitzner. (2022) Deep Residual Networks for Flamelet/progress Variable Tabulation with Application to a Piloted Flame with Inhomogeneous Inlet. Combustion Science and Technology 194:8, pages 1587-1613.
Read now
Rishikesh Ranade, Genong Li, Shaoping Li & Tarek Echekki. (2021) An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure. Combustion Science and Technology 193:7, pages 1258-1277.
Read now
Qing Tang & Stephen B Pope. (2004) A more accurate projection in the rate-controlled constrained-equilibrium method for dimension reduction of combustion chemistry. Combustion Theory and Modelling 8:2, pages 255-279.
Read now

Articles from other publishers (55)

Anxiong Liu, Tianjie Ding, Runzhi Liu, Stelios Rigopoulos & Kun Luo. (2024) Machine learning tabulation of thermochemistry for turbulent dimethyl ether (DME) flames. Fuel 360, pages 130338.
Crossref
Jiyun He, Yue Li, Luigi Acampora, Francesco Saverio Marra & Lin Ji. (2024) Community analysis of bifurcation maps of diluted hydrogen combustion in well stirred reactors. International Journal of Hydrogen Energy 49, pages 126-136.
Crossref
Thomas Readshaw, Lucas L.C. Franke, W.P. Jones & Stelios Rigopoulos. (2023) Simulation of turbulent premixed flames with machine learning - tabulated thermochemistry. Combustion and Flame 258, pages 113058.
Crossref
Ahmed Almeldein & Noah Van Dam. (2023) Accelerating Chemical Kinetics Calculations With Physics Informed Neural Networks. Journal of Engineering for Gas Turbines and Power 145:9.
Crossref
Cédric Mehl & Damien Aubagnac-Karkar. (2023) On-the-fly accuracy evaluation of artificial neural networks and hybrid method to improve the robustness of neural network accelerated chemistry solving. Physics of Fluids 35:6.
Crossref
Rohit Mishra, Sarvesh Mayilvahanan & Dorrin Jarrahbashi. (2023) Hybrid Unsupervised Cluster-wise Regression Approach for Representing the Flamelet Tables. Energy & Fuels 37:4, pages 3056-3070.
Crossref
Shubhangi Bansude, Farhad Imani & Reza Sheikhi. (2023) Performance Assessment of Chemical Kinetics Neural Ordinary Differential Equations in Pairwise Mixing Stirred Reactor. ASME Open Journal of Engineering 2.
Crossref
S. Iavarone, H. Yang, Z. Li, Z. X. Chen & N. Swaminathan. 2023. Machine Learning and Its Application to Reacting Flows. Machine Learning and Its Application to Reacting Flows 209 243 .
T. Echekki, A. Farooq, M. Ihme & S. M. Sarathy. 2023. Machine Learning and Its Application to Reacting Flows. Machine Learning and Its Application to Reacting Flows 117 147 .
Tianjie Ding, Stelios Rigopoulos & W.P. Jones. (2022) Machine learning tabulation of thermochemistry of fuel blends. Applications in Energy and Combustion Science 12, pages 100086.
Crossref
Tianhan Zhang, Yuxiao Yi, Yifan Xu, Zhi X. Chen, Yaoyu Zhang, Weinan E & Zhi-Qin John Xu. (2022) A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics. Combustion and Flame 245, pages 112319.
Crossref
Gregor D. Wehinger, Matteo Ambrosetti, Raffaele Cheula, Zhao-Bin Ding, Martin Isoz, Bjarne Kreitz, Kevin Kuhlmann, Martin Kutscherauer, Kaustav Niyogi, Jeroen Poissonnier, Romain Réocreux, Dominik Rudolf, Janika Wagner, Ronny Zimmermann, Mauro Bracconi, Hannsjörg Freund, Ulrike Krewer & Matteo Maestri. (2022) Quo vadis multiscale modeling in reaction engineering? – A perspective. Chemical Engineering Research and Design 184, pages 39-58.
Crossref
Junsu Shin, Maximilian Hansinger, Michael Pfitzner & Markus Klein. (2022) A Priori Analysis on Deep Learning of Filtered Reaction Rate. Flow, Turbulence and Combustion 109:2, pages 383-409.
Crossref
Matthias Ihme, Wai Tong Chung & Aashwin Ananda Mishra. (2022) Combustion machine learning: Principles, progress and prospects. Progress in Energy and Combustion Science 91, pages 101010.
Crossref
Jian An, Fei Qin, Jian Zhang & Zhuyin Ren. (2022) Explore artificial neural networks for solving complex hydrocarbon chemistry in turbulent reactive flows. Fundamental Research 2:4, pages 595-603.
Crossref
Zeinab Shadram, Tuan M. Nguyen, Athanasios Sideris & William A. Sirignano. (2022) Physics-aware neural network flame closure for combustion instability modeling in a single-injector engine. Combustion and Flame 240, pages 111973.
Crossref
Xu Han, Ming Jia, Yachao Chang & Yaopeng Li. (2022) An improved approach towards more robust deep learning models for chemical kinetics. Combustion and Flame 238, pages 111934.
Crossref
Rishikesh Ranade, Tarek Echekki & Assaad R. Masri. (2021) Experiment-Based Modeling of Turbulent Flames with Inhomogeneous Inlets. Flow, Turbulence and Combustion 108:4, pages 1043-1067.
Crossref
Lei Zhou, Yuntong Song, Weiqi Ji & Haiqiao Wei. (2022) Machine learning for combustion. Energy and AI 7, pages 100128.
Crossref
Majid Haghshenas, Peetak Mitra, Niccolò Dal Santo & David P. Schmidt. (2021) Acceleration of Chemical Kinetics Computation with the Learned Intelligent Tabulation (LIT) Method. Energies 14:23, pages 7851.
Crossref
Tianjie Ding, Thomas Readshaw, Stelios Rigopoulos & W.P. Jones. (2021) Machine learning tabulation of thermochemistry in turbulent combustion: An approach based on hybrid flamelet/random data and multiple multilayer perceptrons. Combustion and Flame 231, pages 111493.
Crossref
Florian SetzweinMartin GraderTimo SeitzPeter EssPeter Gerlinger. (2021) Application of Artificial Neural Networks for the Simulation of a Perfectly Premixed Chemical Reactor. Application of Artificial Neural Networks for the Simulation of a Perfectly Premixed Chemical Reactor.
Thomas Readshaw, Tianjie Ding, Stelios Rigopoulos & W. P. Jones. (2021) Modeling of turbulent flames with the large eddy simulation–probability density function (LES–PDF) approach, stochastic fields, and artificial neural networks. Physics of Fluids 33:3.
Crossref
Tianhan Zhang, Yaoyu Zhang, Weinan E & Yiguang Ju. (2021) DLODE: a deep learning-based ODE solver for chemistry kinetics. DLODE: a deep learning-based ODE solver for chemistry kinetics.
Opeoluwa Owoyele, Prithwish Kundu & Pinaki Pal. (2021) Efficient bifurcation and tabulation of multi-dimensional combustion manifolds using deep mixture of experts: An a priori study. Proceedings of the Combustion Institute 38:4, pages 5889-5896.
Crossref
Giuseppe D’Alessio, Antonio Attili, Alberto Cuoci, Heinz Pitsch & Alessandro Parente. 2021. 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) 460 469 .
Jian An, Guoqiang He, Kaihong Luo, Fei Qin & Bing Liu. (2020) Artificial neural network based chemical mechanisms for computationally efficient modeling of hydrogen/carbon monoxide/kerosene combustion. International Journal of Hydrogen Energy 45:53, pages 29594-29605.
Crossref
Jian An, Hanyi Wang, Bing Liu, Kai Hong Luo, Fei Qin & Guo Qiang He. (2020) A deep learning framework for hydrogen-fueled turbulent combustion simulation. International Journal of Hydrogen Energy 45:35, pages 17992-18000.
Crossref
Rishikesh Ranade & Tarek Echekki. (2019) A framework for data-based turbulent combustion closure: A posteriori validation. Combustion and Flame 210, pages 279-291.
Crossref
Rishikesh Ranade, Sultan Alqahtani, Aamir Farooq & Tarek Echekki. (2019) An extended hybrid chemistry framework for complex hydrocarbon fuels. Fuel 251, pages 276-284.
Crossref
Vladimir P. Berishvili, Valentin O. Perkin, Andrew E. Voronkov, Eugene V. Radchenko, Riyaz Syed, Chittireddy Venkata Ramana Reddy, Viness Pillay, Pradeep Kumar, Yahya E. Choonara, Ahmed Kamal & Vladimir A. Palyulin. (2019) Time-Domain Analysis of Molecular Dynamics Trajectories Using Deep Neural Networks: Application to Activity Ranking of Tankyrase Inhibitors. Journal of Chemical Information and Modeling 59:8, pages 3519-3532.
Crossref
Rishikesh Ranade, Sultan Alqahtani, Aamir Farooq & Tarek Echekki. (2019) An ANN based hybrid chemistry framework for complex fuels. Fuel 241, pages 625-636.
Crossref
Biswanath Panda, Johannes Gehrke & Mirek Riedewald. 2018. Encyclopedia of Database Systems. Encyclopedia of Database Systems 979 985 .
Alireza Shakeri, Karim Mazaheri & Mohammad Owliya. (2017) Using Sensitivity Analysis and Gradual Evaluation of Ignition Delay Error To Produce Accurate Low-Cost Skeletal Mechanisms for Oxidation of Hydrocarbon Fuels under High-Temperature Conditions. Energy & Fuels 31:10, pages 11234-11252.
Crossref
Payam Sinaei & Sadegh Tabejamaat. (2016) Large eddy simulation of methane diffusion jet flame with representation of chemical kinetics using artificial neural network. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231:2, pages 147-163.
Crossref
Krzysztof Wawryn, Robert Suszynski & Jacek Marciniak. (2016) A competitive WTA ANN for classificationa calorific value of a coal fuel in combustion chambers using FPAA. A competitive WTA ANN for classificationa calorific value of a coal fuel in combustion chambers using FPAA.
Robert Suszynski, Jacek Marciniak & Krzysztof Wawryn. (2016) An artificial neural network for classification a quality of a coal fuel in combustion chambers using FPAA. An artificial neural network for classification a quality of a coal fuel in combustion chambers using FPAA.
Biswanath Panda, Johannes Gehrke & Mirek Riedewald. 2016. Encyclopedia of Database Systems. Encyclopedia of Database Systems 1 7 .
Tamás Turányi & Alison S. TomlinTamás Turányi & Alison S. Tomlin. 2014. Analysis of Kinetic Reaction Mechanisms. Analysis of Kinetic Reaction Mechanisms 183 312 .
A.K. Chatzopoulos & Stelios Rigopoulos. (2013) A chemistry tabulation approach via Rate-Controlled Constrained Equilibrium (RCCE) and Artificial Neural Networks (ANNs), with application to turbulent non-premixed CH4/H2/N2 flames. Proceedings of the Combustion Institute 34:1, pages 1465-1473.
Crossref
Ramón Piloto-Rodríguez, Yisel Sánchez-Borroto, Magin Lapuerta, Leonardo Goyos-Pérez & Sebastian Verhelst. (2013) Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression. Energy Conversion and Management 65, pages 255-261.
Crossref
Tamás Turányi & Alison S. Tomlin. 2013. Cleaner Combustion. Cleaner Combustion 485 512 .
Tore Risch, Samuel Madden, Hari Balakrishan, Lewis Girod, Ryan Newton, Milena Ivanova, Erik Zeitler, Johannes Gehrke, Biswanath Panda & Mirek Riedewald. 2009. Scientific Data Management. Scientific Data Management.
Tianfeng Lu & Chung K. Law. (2009) Toward accommodating realistic fuel chemistry in large-scale computations. Progress in Energy and Combustion Science 35:2, pages 192-215.
Crossref
Biswanath Panda, Johannes Gehrke & Mirek Riedewald. 2009. Encyclopedia of Database Systems. Encyclopedia of Database Systems 733 738 .
Dan‐Ping Sun, Qing‐Yan Fang, Hua‐Jian Wang & Huai‐Chun Zhou. (2008) A compact optimization strategy for combustion in a 125 MW tangentially anthracite‐fired boiler by an artificial neural network. Asia-Pacific Journal of Chemical Engineering 3:4, pages 432-439.
Crossref
Matthias IhmeAlison L. MarsdenHeinz Pitsch. (2008) Generation of Optimal Artificial Neural Networks Using a Pattern Search Algorithm: Application to Approximation of Chemical Systems. Neural Computation 20:2, pages 573-601.
Crossref
Chung Law & Tianfeng Lu. (2008) Towards Accomodating Realistic Fuel Chemistry in Large-Scale Computations. Towards Accomodating Realistic Fuel Chemistry in Large-Scale Computations.
Danping Sun, Qingyan Fang, Huajian Wang & Huaichun Zhou. 2007. Challenges of Power Engineering and Environment. Challenges of Power Engineering and Environment 644 651 .
Mohammad Janbozorgi, Yue Gao, Hameed Metghalchi & James C. Keck. (2006) Rate-Controlled Constrained-Equilibrium Calculations of Ethanol-Oxygen Mixture. Rate-Controlled Constrained-Equilibrium Calculations of Ethanol-Oxygen Mixture.
Mauro Valorani, Francesco Creta, Dimitris A. Goussis, Jeremiah C. Lee & Habib N. Najm. (2006) An automatic procedure for the simplification of chemical kinetic mechanisms based on CSP. Combustion and Flame 146:1-2, pages 29-51.
Crossref
C. P. Taylor & M. S. Bourqui. (2006) A new fast stratospheric ozone chemistry scheme in an intermediate general‐circulation model. I: Description and evaluation. Quarterly Journal of the Royal Meteorological Society 131:610, pages 2225-2242.
Crossref
Soteris A. Kalogirou. (2003) Artificial intelligence for the modeling and control of combustion processes: a review. Progress in Energy and Combustion Science 29:6, pages 515-566.
Crossref
R. Homma & J.-Y. Chen. (2001) Reduced Mechanisms for Prediction of NO2 Formation and Ignition Delay in Methane-Air Combustion. Journal of Engineering for Gas Turbines and Power 123:2, pages 303-307.
Crossref
Jiyun He, Yue Li, Luigi Acampora, Francesco Saverio Marra & Lin Ji. (2022) Community Analysis of Bifurcation Maps of Diluted Hydrogen Combustion in WSRs. SSRN Electronic Journal.
Crossref

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.