THREE-DIMENSIONAL SUPER-RESOLUTION RECONSTRUCTION OF TURBULENT SPRAY FLOW FIELDS
Source abstract
Abstract
Super-resolution (SR) with deep neural networks has emerged as a promising technique for enhancing the resolution of turbulent flow fields in experiments and numerical simulations. Substantial research has been conducted on single-phase turbulent flows, but little effort has been made for turbulent spray combustion. This paper presents the first application of three-dimensional SR to two-way coupled particle-laden flows with an SR factor of eight. Appropriate data treatment is investigated. An SR model is developed that is capable of learning and reconstructing the momentum interaction between turbulence and spray droplets. The results demonstrate that particle information is an essential input for SR models to correctly learn and reconstruct the subgrid modulation of turbulence by particles. A data padding mode consistent with the boundary conditions of the flow fields and sample-wise data normalization are recommended based on the test outcomes. Moreover, the applicability of a recently proposed spectral loss to three-dimensional turbulence SR is verified.
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Research context
OpenAlex topics: Digital Holography and Microscopy · Orbital Angular Momentum in Optics · Biomimetic flight and propulsion mechanisms
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References (36)
Show source-provided references
1. Abdelsamie, A.H. and Lee, C., Decaying versus Stationary Turbulence in Particle-Laden Isotropic Turbulence: Turbulence Modulation Mechanism, Phys. Fluids, vol. 24, no. 1, Article 015106, 2012.10.1063/1.3678332 ↗
2. Anagun, Y., Isik, S., and Seke, E., SRLibrary: Comparing Different Loss Functions for Super-Resolution over Various Convolutional Architectures, J. Vis. Commun. Image Represent., vol. 61, pp. 178-187, 2019.10.1016/j.jvcir.2019.03.027 ↗
3. Bode, M., Gauding, M., Goeb, D., Falkenstein, T., and Pitsch, H., Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-Like Flame Kernel Direct Numerical Simulation Data, Proc. Combust. Inst., vol. 39, no. 4, pp. 5289-5298, 2023.10.1016/j.proci.2022.07.254 ↗
4. Bode, M., Gauding, M., Lian, Z., Denker, D., Davidovic, M., Kleinheinz, K., Jitsev, J., and Pitsch, H., Using Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks for Subfilter Modeling in Turbulent Reactive Flows, Proc. Combust. Inst., vol. 38, no. 2, pp. 2617-2625, 2021.10.1016/j.proci.2020.06.022 ↗
5. Cai, S., Zhou, S., Xu, C., and Gao, Q., Dense Motion Estimation of Particle Images via a Convolutional Neural Network, Exp. Fluids, vol. 60, no. 4, Article 73, 2019.10.1007/s00348-019-2717-2 ↗
6. Chakraborty, P., Balachandar, S., and Adrian, R.J., On the Relationships between Local Vortex Identification Schemes, J. Fluid Mech., vol. 535, pp. 189-214, 2005.10.1017/S0022112005004726 ↗
7. Cheng, R., Shamooni, A., Zirwes, T., and Kronenburg, A., Improved Super-Resolution Reconstruction of Turbulent Flows with Spectral Loss Function, Phys. Fluids, vol. 37, no. 3, Article 035208, 2025.10.1063/5.0258090 ↗
8. Chung, W.T., Akoush, B., Sharma, P., Tamkin, A., Jung, K.S., Chen, J.H., Guo, J., Brouzet, D., Talei, M., Savard, B., Poludnenko, A.Y., and Ihme, M., Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data, Proc. of the 37th Conf. on Neural Information Processing Systems Track on Datasets and Benchmarks, 2023.10.52202/075280-3386 ↗
9. Elghobashi, S., On Predicting Particle-Laden Turbulent Flows, Appl. Sci. Res., vol. 52, no. 4, pp. 309-329, 1994.10.1007/BF00936835 ↗
10. Faeth, G.M., Evaporation and Combustion of Sprays, Prog. Energy Combust. Sci., vol. 9, no. 1, pp. 1-76, 1983.10.1016/0360-1285(83)90005-9 ↗
11. Ferrante, A. and Elghobashi, S., On the Physical Mechanisms of Two-Way Coupling in Particle-Laden Isotropic Turbulence, Phys. Fluids, vol. 15, no. 2, pp. 315-329, 2003.10.1063/1.1532731 ↗
12. Fukami, K., Fukagata, K., and Taira, K., Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows, Theor. Comput. Fluid Dyn., vol. 37, no. 4, pp. 421-444, 2023.10.1007/s00162-023-00663-0 ↗
13. Hunt, J., Wray, A., and Moin, P., Eddies, Streams, and Convergence Zones in Turbulent Flows, Proc. of Summer Program, Center for Turbulence Research, Stanford, CA, pp. 193-208, 1988.
14. Kim, H., Kim, J., Won, S., and Lee, C., Unsupervised Deep Learning for Super-Resolution Reconstruction of Turbulence, J. Fluid Mech., vol. 910, Article A29, 2021.10.1017/jfm.2020.1028 ↗
15. Lai, W.S., Huang, J.B., Ahuja, N., and Yang, M.H., Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution, IEEE Conf. on Computer Vision and Pattern Recognition, IEEE, New York, pp. 5835-5843, 2017.10.1109/CVPR.2017.618 ↗
16. Lin, J., Divergence Measures Based on the Shannon Entropy, IEEE Trans. Inf. Theory, vol. 37, no. 1, pp. 145-151, 1991.10.1109/18.61115 ↗
17. Lucci, F., Ferrante, A., and Elghobashi, S., Is Stokes Number an Appropriate Indicator for Turbulence Modulation by Particles of Taylor-Length-Scale Size?, Phys. Fluids, vol. 23, no. 2, Article 025101, 2011.10.1063/1.3553279 ↗
18. Maejima, S. and Kawai, S., Unsupervised and Supervised Machine Learning Pipeline for Super-Resolution-Based Subgrid Scale Modelling in Coarse-Grid Large-Eddy Simulations, J. Fluid Mech., vol. 1013, Article A28, 2025.10.1017/jfm.2025.10211 ↗
19. Morimoto, M., Fukami, K., and Fukagata, K., Experimental Velocity Data Estimation for Imperfect Particle Images Using Machine Learning, Phys. Fluids, vol. 33, no. 8, Article 087121, 2021.10.1063/5.0060760 ↗
20. Nista, L., Pitsch, H., Schumann, C.D.K., Bode, M., Grenga, T., MacArt, J.F., and Attili, A., Influence of Adversarial Training on Super-Resolution Turbulence Reconstruction, Phys. Rev. Fluids, vol. 9, no. 6, Article 064601, 2024.10.1103/PhysRevFluids.9.064601 ↗
21. Nista, L., Schumann, C.D.K., Grenga, T., Attili, A., and Pitsch, H., Investigation of the Generalization Capability of a Generative Adversarial Network for Large Eddy Simulation of Turbulent Premixed Reacting Flows, Proc. Combust. Inst., vol. 39, no. 4, pp. 5279-5288, 2023.10.1016/j.proci.2022.07.244 ↗
22. Pope, S.B., Turbulent Flows, Cambridge, UK: Cambridge University Press, 2000.10.1017/CBO9780511840531 ↗
23. Reveillon, J. and Vervisch, L., Spray Vaporization in Nonpremixed Turbulent Combustion Modeling: A Single Droplet Model, Combust. Flame, vol. 121, no. 1, pp. 75-90, 2000.10.1016/S0010-2180(99)00157-1 ↗
24. Shamooni, A., Cheng, R., Zirwes, T., Stein, O.T., and Kronenburg, A., Super-Resolution Reconstruction of Scalar Fields from the Pyrolysis of Pulverised Biomass Using Deep Learning, Proc. Combust. Inst., vol. 41, Article 105982, 2025a.10.1016/j.proci.2025.105982 ↗
25. Shamooni, A., Cheng, R., Zirwes, T., Tofighian, H., Stein, O.T., and Kronenburg, A., Super-Resolution of Turbulent Velocity Fields in Two-Way Coupled Particle-Laden Flows, Phys. Fluids, vol. 37, no. 9, Article 093383, 2025b.10.1063/5.0288515 ↗
26. Sontheimer, M., Kronenburg, A., and Stein, O.T., Grid Dependence of Evaporation Rates in Euler-Lagrange Simulations of Dilute Sprays, Combust. Flame, vol. 232, Article 111515, 2021.10.1016/j.combustflame.2021.111515 ↗
27. Sontheimer, M., Kronenburg, A., and Stein, O.T., A Comparative Study of Two-Phase Coupling Models for a Sparse-Lagrangian Particle Method, Proc. Combust. Inst., vol. 39, no. 2, pp. 2643-2652, 2023.10.1016/j.proci.2022.07.188 ↗
28. Tofighian, H., Denev, J.A., and Kornev, N., A Conditional Deep Learning Model for Super-Resolution Reconstruction of Small-Scale Turbulent Structures in Particle-Laden Flows, Phys. Fluids, vol. 36, no. 11, Article 115173, 2024.10.1063/5.0235192 ↗
29. Vo, S., Kronenburg, A., Stein, O.T., and Hawkes, E.R., Direct Numerical Simulation of Non-Premixed Syngas Combustion Using OpenFOAM, High Performance Computing in Science and Engineering '16, W.E. Nagel, D.B. Kroner, and M.M. Resch, Eds., New York: Springer, pp. 245-257, 2017.10.1007/978-3-319-47066-5_17 ↗
30. Wang, B., Kronenburg, A., and Stein, O.T., Modelling Sub-Grid Passive Scalar Statistics in Moderately Dense Evaporating Sprays, Flow Turbul. Combust., vol. 103, no. 2, pp. 519-535, 2019a.10.1007/s10494-019-00024-0 ↗
31. Wang, B., Kronenburg, A., and Stein, O.T., A New Perspective on Modelling Passive Scalar Conditional Mixing Statistics in Turbulent Spray Flames, Combust. Flame, vol. 208, pp. 376-387, 2019b.10.1016/j.combustflame.2019.07.016 ↗
32. Wang, X., Xie, L., Yu, K., Chan, K.C., Loy, C.C., and Dong, C., BasicSR: Open Source Image and Video Restoration Toolbox, Accessed 2024-01-03, from: https://github.com/XPixelGroup/BasicSR, 2022.
33. Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., and Loy, C.C., ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks, Computer Vision-European Conference on Computer Vision 2018 Workshops, Munich, Germany, pp. 63-79, 2019c.10.1007/978-3-030-11021-5_5 ↗
34. Yao, S., Wang, B., Kronenburg, A., and Stein, O.T., Conditional Scalar Dissipation Rate Modeling for Turbulent Spray Flames Using Artificial Neural Networks, Proc. Combust. Inst., vol. 38, no. 2, pp. 3371-3378, 2021.10.1016/j.proci.2020.06.135 ↗
35. Yousif, M.Z., Yu, L., and Lim, H.C., Super-Resolution Reconstruction of Turbulent Flow Fields at Various Reynolds Numbers Based on Generative Adversarial Networks, Phys. Fluids, vol. 34, no. 1, Article 015130, 2022.10.1063/5.0074724 ↗
36. Zirwes, T., Sontheimer, M., Zhang, F., Abdelsamie, A., Hernandez Perez, F.E., Stein, O.T., Im, H.G., Kronenburg, A., and Bockhorn, H., Assessment of Numerical Accuracy and Parallel Performance of Open-FOAM and Its Reacting Flow Extension EBIdnsFoam, Flow Turbul. Combust., vol. 111, pp. 567-602, 2023.10.1007/s10494-023-00449-8 ↗