Abstract
With increasing computational power, direct numerical and large eddy simulation (DNS and LES) of reacting flows with complex chemistry are becoming common, e.g. Yoo et al (Proc Combust Inst, 34(2):2985–2993, 2013, [1]), Duwig and Iudiciani (Fuel 123:256–273, 2014, [2]), Fooladgar et al (Comput Fluids 146:42–50, 2017, [3]). The resulting data which may occupy hundreds of gigabytes of storage, consists of millions to billions of points each of which is described by tens to hundreds of chemical species. To explore and analyze this large, high-dimensional data, conventional visualization techniques such as scatter plots, histograms and pairs plots are limited. Human visual perception is well tuned to identify patterns and trends in graphs with one or a few data variables at a time, calling for new automated identification tools.
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Yoo, C.S., Luo, Z., Lu, T., Kim, H., Chen, J.H.: A DNS study of ignition characteristics of a lean iso-octane/air mixture under HCCI and SACI conditions. Proc. Combust. Inst. 34(2), 2985–2993 (2013)
Duwig, C., Iudiciani, P.: Large Eddy Simulation of turbulent combustion in a stagnation point reverse flow combustor using detailed chemistry. Fuel 123, 256–273 (2014)
Fooladgar, E., Chan, C.K., Nogenmyr, K.J.: An accelerated computation of combustion with finite-rate chemistry using LES and an open source library for In-Situ-Adaptive Tabulation. Comput. Fluids 146, 42–50 (2017)
Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)
Hamel, P., Eck, D.: Learning features from music audio with deep belief networks. In: ISMIR. Utrecht, The Netherlands, pp. 339–344 (2010)
Jamieson, A.R., Giger, M.L., Drukker, K., Li, H., Yuan, Y., Bhooshan, N.: Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and t-SNE. Med. Phys. 37(1), 339–351 (2010)
Wallach, I., Lilien, R.: The protein-small-molecule database, a non-redundant structural resource for the analysis of protein-ligand binding. Bioinformatics 25(5), 615–620 (2009)
Birjandtalab, J., Pouyan, M.B., Nourani, M.: Nonlinear dimension reduction for EEG-based epileptic seizure detection. In: 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, pp. 595–598 (2016)
Van Der Maaten, L.: Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15(1), 3221–3245 (2014)
Duwig, C., Stankovic, D., Fuchs, L., Li, G., Gutmark, E.: Experimental and numerical study of flameless combustion in a model gas turbine combustor. Combust. Sci. Technol. 180(2), 279–295 (2007)
Chemical-Kinetic Mechanisms for Combustion Applications, Mechanical and Aerospace Engineering, University of California at San Diego. http://combustion.ucsd.edu
Acknowledgements
The financial support of the Swedish Energy Agency (Energimyndigheten) is greatly acknowledged. The simulations were run on LUNARC and HPC2N super-computing facilities within SNIC ressource allocation.
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Fooladgar, E., Duwig, C. (2019). Identification of Combustion Trajectories Using t-Distributed Stochastic Neighbor Embedding (t-SNE). In: Salvetti, M., Armenio, V., Fröhlich, J., Geurts, B., Kuerten, H. (eds) Direct and Large-Eddy Simulation XI. ERCOFTAC Series, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-030-04915-7_33
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DOI: https://doi.org/10.1007/978-3-030-04915-7_33
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