Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3769-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-3769-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
24 Jun 2021
Development and technical paper |
| 24 Jun 2021
| 24 Jun 2021
Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flow
Robin Stoffer
CORRESPONDING AUTHOR
Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
Caspar M. van Leeuwen
SURFsara, Amsterdam, the Netherlands
Damian Podareanu
SURFsara, Amsterdam, the Netherlands
Valeriu Codreanu
SURFsara, Amsterdam, the Netherlands
Menno A. Veerman
Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
Martin Janssens
Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
Oscar K. Hartogensis
Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
Chiel C. van Heerwaarden
Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
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Robbert P. J. Moonen, Getachew A. Adnew, Oscar K. Hartogensis, Jordi Vilà-Guerau de Arellano, David J. Bonell Fontas, and Thomas Röckmann
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Bert G. Heusinkveld, Wouter B. Mol, and Chiel C. van Heerwaarden
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Wouter B. Mol, Wouter H. Knap, and Chiel C. van Heerwaarden
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We describe a dataset of detailed measurements of sunlight reaching the surface, recorded at a rate of one measurement per second for 10 years. The dataset includes detailed information on direct and scattered sunlight; classifications and statistics of variability; and observations of clouds, atmospheric composition, and wind. The dataset can be used to study how the atmosphere influences sunlight variability and to validate models that aim to predict this variability with greater accuracy.
Luuk D. van der Valk, Adriaan J. Teuling, Luc Girod, Norbert Pirk, Robin Stoffer, and Chiel C. van Heerwaarden
The Cryosphere, 16, 4319–4341, https://doi.org/10.5194/tc-16-4319-2022, https://doi.org/10.5194/tc-16-4319-2022, 2022
Short summary
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Most large-scale hydrological and climate models struggle to capture the spatially highly variable wind-driven melt of patchy snow cover. In the field, we find that 60 %–80 % of the total melt is wind driven at the upwind edge of a snow patch, while it does not contribute at the downwind edge. Our idealized simulations show that the variation is due to a patch-size-independent air-temperature reduction over snow patches and also allow us to study the role of wind-driven snowmelt on larger scales.
Felipe Lobos-Roco, Oscar Hartogensis, Francisco Suárez, Ariadna Huerta-Viso, Imme Benedict, Alberto de la Fuente, and Jordi Vilà-Guerau de Arellano
Hydrol. Earth Syst. Sci., 26, 3709–3729, https://doi.org/10.5194/hess-26-3709-2022, https://doi.org/10.5194/hess-26-3709-2022, 2022
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This research brings a multi-scale temporal analysis of evaporation in a saline lake of the Atacama Desert. Our findings reveal that evaporation is controlled differently depending on the timescale. Evaporation is controlled sub-diurnally by wind speed, regulated seasonally by radiation and modulated interannually by ENSO. Our research extends our understanding of evaporation, contributing to improving the climate change assessment and efficiency of water management in arid regions.
Anja Ražnjević, Chiel van Heerwaarden, and Maarten Krol
Atmos. Meas. Tech., 15, 3611–3628, https://doi.org/10.5194/amt-15-3611-2022, https://doi.org/10.5194/amt-15-3611-2022, 2022
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We evaluate two widely used observational techniques (Other Test Method (OTM) 33A and car drive-bys) that estimate point source gas emissions. We performed our analysis on high-resolution plume dispersion simulation. For car drive-bys we found that at least 15 repeated measurements were needed to get within 40 % of the true emissions. OTM 33A produced large errors in estimation (50 %–200 %) due to its sensitivity to dispersion coefficients and underlying simplifying assumptions.
Anja Ražnjević, Chiel van Heerwaarden, Bart van Stratum, Arjan Hensen, Ilona Velzeboer, Pim van den Bulk, and Maarten Krol
Atmos. Chem. Phys., 22, 6489–6505, https://doi.org/10.5194/acp-22-6489-2022, https://doi.org/10.5194/acp-22-6489-2022, 2022
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Mobile measurement techniques (e.g., instruments placed in cars) are often employed to identify and quantify individual sources of greenhouse gases. Due to road restrictions, those observations are often sparse (temporally and spatially). We performed high-resolution simulations of plume dispersion, with realistic weather conditions encountered in the field, to reproduce the measurement process of a methane plume emitted from an oil well and provide additional information about the plume.
Carlos Román-Cascón, Marie Lothon, Fabienne Lohou, Oscar Hartogensis, Jordi Vila-Guerau de Arellano, David Pino, Carlos Yagüe, and Eric R. Pardyjak
Geosci. Model Dev., 14, 3939–3967, https://doi.org/10.5194/gmd-14-3939-2021, https://doi.org/10.5194/gmd-14-3939-2021, 2021
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The type of vegetation (or land cover) and its status influence the heat and water transfers between the surface and the air, affecting the processes that develop in the atmosphere at different (but connected) spatiotemporal scales. In this work, we investigate how these transfers are affected by the way the surface is represented in a widely used weather model. The results encourage including realistic high-resolution and updated land cover databases in models to improve their predictions.
Felipe Lobos-Roco, Oscar Hartogensis, Jordi Vilà-Guerau de Arellano, Alberto de la Fuente, Ricardo Muñoz, José Rutllant, and Francisco Suárez
Atmos. Chem. Phys., 21, 9125–9150, https://doi.org/10.5194/acp-21-9125-2021, https://doi.org/10.5194/acp-21-9125-2021, 2021
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We investigate the influence of regional atmospheric circulation on the evaporation of a saline lake in the Altiplano region of the Atacama Desert through a field experiment and regional modeling. Our results show that evaporation is controlled by two regimes: (1) in the morning by local conditions with low evaporation rates and low wind speed and (2) in the afternoon with high evaporation rates and high wind speed. Afternoon winds are connected to the regional Pacific Ocean–Andes flow.
Cited articles
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: A system for
large-scale machine learning, in: 12th (USENIX) Symposium on Operating
Systems Design and Implementation (OSDI 16), 265–283, 2016. a
Bardina, J., Ferziger, J., and Reynolds, W.: Improved subgrid-scale models for
large-eddy simulation, in: 13th fluid and plasmadynamics conference, 1357,
https://doi.org/10.2514/6.1980-1357, 1980. a
Bolton, T. and Zanna, L.: Applications of deep learning to ocean data inference
and subgrid parameterization, J. Adv. Model. Earth Sy.,
11, 376–399, https://doi.org/10.1029/2018MS001472, 2019. a
Bou-Zeid, E., Meneveau, C., and Parlange, M.: A scale-dependent Lagrangian
dynamic model for large eddy simulation of complex turbulent flows, Phys. Fluids, 17, 025105, https://doi.org/10.1063/1.1839152, 2005. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a
Brenowitz, N. D. and Bretherton, C. S.: Spatially extended tests of a neural
network parametrization trained by coarse-graining, J. Adv.
Model. Earth Sy., 11, 2728–2744, https://doi.org/10.1029/2019MS001711, 2019. a
Brunton, S. L., Noack, B. R., and Koumoutsakos, P.: Machine learning for fluid
mechanics, Annu. Rev. Fluid Mech., 52, 477–508,
https://doi.org/10.1146/annurev-fluid-010719-060214, 2020. a
Chow, F. K. and Moin, P.: A further study of numerical errors in large-eddy
simulations, J. Comput. Phys., 184, 366–380,
https://doi.org/10.1016/S0021-9991(02)00020-7, 2003. a, b
Clark, R. A., Ferziger, J. H., and Reynolds, W. C.: Evaluation of subgrid-scale
models using an accurately simulated turbulent flow, J. Fluid
Mech., 91, 1–16, https://doi.org/10.1017/S002211207900001X, 1979. a, b
Denaro, F. M.: What does Finite Volume-based implicit filtering really resolve
in Large-Eddy Simulations?, J. Comput. Phys., 230,
3849–3883, https://doi.org/10.1016/j.jcp.2011.02.011, 2011. a, b
Duraisamy, K., Iaccarino, G., and Xiao, H.: Turbulence modeling in the age of
data, Annu. Rev. Fluid Mech., 51, 357–377,
https://doi.org/10.1146/annurev-fluid-010518-040547, 2019. a
Fisher, A., Rudin, C., and Dominici, F.: All Models are Wrong, but Many are
Useful: Learning a Variable's Importance by Studying an Entire Class of
Prediction Models Simultaneously, J. Mach. Learn. Res., 20,
1–81, 2019. a
Gamahara, M. and Hattori, Y.: Searching for turbulence models by artificial
neural network, Phys. Rev. Fluids, 2, 054604,
https://doi.org/10.1103/PhysRevFluids.2.054604, 2017. a, b, c, d
Ghosal, S.: An analysis of numerical errors in large-eddy simulations of
turbulence, J. Comput. Phys., 125, 187–206,
https://doi.org/10.1006/jcph.1996.0088, 1996. a
Giacomini, B. and Giometto, M. G.: On the suitability of second-order accurate finite-volume solvers for the simulation of atmospheric boundary layer flow, Geosci. Model Dev., 14, 1409–1426, https://doi.org/10.5194/gmd-14-1409-2021, 2021. a
Guan, Y., Chattopadhyay, A., Subel, A., and Hassanzadeh, P.: Stable a
posteriori LES of 2D turbulence using convolutional neural networks:
Backscattering analysis and generalization to higher Re via transfer
learning, arXiv preprint arXiv:2102.11400,
available at: https://arxiv.org/pdf/2102.11400.pdf (last access: 1 March 2021), 2021. a, b, c, d, e, f
He, K., Zhang, X., Ren, S., and Sun, J.: Delving Deep into Rectifiers:
Surpassing Human-Level Performance on ImageNet Classification, in: The IEEE
International Conference on Computer Vision (ICCV), 1026–1034,
https://doi.org/10.1109/ICCV.2015.123, 2015. a
Hornik, K., Stinchcombe, M., and White, H.: Multilayer feedforward networks are
universal approximators., Neural Networks, 2, 359–366,
https://doi.org/10.1016/0893-6080(89)90020-8, 1989. a
Jimenez, J. and Moser, R. D.: Large-eddy simulations: where are we and what can
we expect?, AIAA journal, 38, 605–612, https://doi.org/10.2514/2.1031, 2000. a
Kaandorp, M. L. A. and Dwight, R. P.: Data-driven modelling of the Reynolds
stress tensor using random forests with invariance, Comput. Fluids, 202,
104497, https://doi.org/10.1016/j.compfluid.2020.104497, 2020. a
Kravchenko, A. G. and Moin, P.: On the effect of numerical errors in large eddy
simulations of turbulent flows, J. Comput. Phys., 131,
310–322, https://doi.org/10.1006/jcph.1996.5597, 1997. a
Kutz, J. N.: Deep learning in fluid dynamics, J. Fluid Mech., 814,
1–4, https://doi.org/10.1017/jfm.2016.803, 2017. a
Langford, J. A. and Moser, R. D.: Optimal LES formulations for isotropic
turbulence, J. Fluid Mech., 398, 321–346,
https://doi.org/10.1017/S0022112099006369, 1999. a, b
Langford, J. A. and Moser, R. D.: Breakdown of continuity in large-eddy
simulation, Phys. Fluids, 13, 1524–1527, https://doi.org/10.1063/1.1358876, 2001. a
Lilly, D. K.: The representation of small-scale turbulence in numerical
simulation experiments, in: Proceedings of the IBM Scientific Computing
Symposium on Environmental Sciences, 195–210, https://doi.org/10.5065/D62R3PMM,
1967. a, b, c
Ling, J., Jones, R., and Templeton, J.: Machine learning strategies for systems
with invariance properties, J. Comput. Phys., 318, 22–35,
https://doi.org/10.1016/j.jcp.2016.05.003, 2016a. a
Ling, J., Kurzawski, A., and Templeton, J.: Reynolds averaged turbulence
modelling using deep neural networks with embedded invariance, J.
Fluid Mech., 807, 155–166, https://doi.org/10.1017/jfm.2016.615,
2016b. a, b
Liu, S., Meneveau, C., and Katz, J.: On the properties of similarity
subgrid-scale models as deduced from measurements in a turbulent jet, J. Fluid Mech., 275, 83–119, https://doi.org/10.1017/S0022112094002296, 1994. a, b, c
Maulik, R., San, O., Rasheed, A., and Vedula, P.: Subgrid modelling for
two-dimensional turbulence using neural networks, J. Fluid Mech.,
858, 122–144, https://doi.org/10.1017/jfm.2018.770, 2019. a, b, c, d
McMillan, O. J. and Ferziger, J. H.: Direct testing of subgrid-scale models,
AIAA Journal, 17, 1340–1346, https://doi.org/10.2514/3.61313, 1979. a, b
Milano, M. and Koumoutsakos, P.: Neural network modeling for near wall
turbulent flow, J. Comput. Phys., 182, 1–26,
https://doi.org/10.1006/jcph.2002.7146, 2002. a, b
Molnar, C.: Interpretable Machine Learning, lulu.com,
available at: https://christophm.github.io/interpretable-ml-book/ (last access: 14 April 2021), 2019. a
Moser, R. D., Kim, J., and Mansour, N. N.: Direct numerical simulation of
turbulent channel flow up to Re τ= 590, Phys. Fluids, 11, 943–945,
https://doi.org/10.1063/1.869966, 1999. a
Nadiga, B. T. and Livescu, D.: Instability of the perfect subgrid model in
implicit-filtering large eddy simulation of geostrophic turbulence, Phys.
Rev. E, 75, 046 303, https://doi.org/10.1103/PhysRevE.75.046303, 2007. a
Rasp, S.: Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0), Geosci. Model Dev., 13, 2185–2196, https://doi.org/10.5194/gmd-13-2185-2020, 2020. a, b
Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning representations
by back-propagating errors, Nature, 323, 533–536, https://doi.org/10.1038/323533a0,
1986. a
Sarghini, F., De Felice, G., and Santini, S.: Neural networks based subgrid
scale modeling in large eddy simulations, Comput. Fluids, 32, 97–108,
https://doi.org/10.1016/S0045-7930(01)00098-6, 2003. a, b, c
Schmitt, F. G.: About Boussinesq's turbulent viscosity hypothesis: historical
remarks and a direct evaluation of its validity, Comptes Rendus
Mécanique, 335, 617–627, https://doi.org/10.1016/j.crme.2007.08.004, 2007. a
Singh, A. P., Duraisamy, K., and Zhang, Z. J.: Augmentation of turbulence
models using field inversion and machine learning, in: 55th AIAA Aerospace
Sciences Meeting, 0993, https://doi.org/10.2514/6.2017-0993, 2017. a
Smagorinsky, J.: General circulation experiments with the primitive equations:
I. The basic experiment, Mon. Weather Rev., 91, 99–164,
https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2, 1963. a, b
Stoffer, R.: robinstoffer/microhh2: Rep corresponding to GMD publication
Stoffer et al. (2021) (Version ANN_SGS_v1.2-alpha) [code], Zenodo,
https://doi.org/10.5281/zenodo.4767902, 2021. a
Van Driest, E. R.: On turbulent flow near a wall, J. Aeronaut.
Sci., 23, 1007–1011, https://doi.org/10.2514/8.3713, 1956. a, b
van Heerwaarden, C. C., van Stratum, B. J. H., and Heus, T.: microhh/microhh:
1.0.0 (Version 1.0.0) [code], Zenodo, https://doi.org/10.5281/zenodo.822842, 2017a. a
van Heerwaarden, C. C., van Stratum, B. J. H., Heus, T., Gibbs, J. A., Fedorovich, E., and Mellado, J. P.: MicroHH 1.0: a computational fluid dynamics code for direct numerical simulation and large-eddy simulation of atmospheric boundary layer flows, Geosci. Model Dev., 10, 3145–3165, https://doi.org/10.5194/gmd-10-3145-2017, 2017b. a, b, c, d, e, f
Völker, S., Moser, R. D., and Venugopal, P.: Optimal large eddy simulation
of turbulent channel flow based on direct numerical simulation statistical
data, Phys. Fluids, 14, 3675–3691, https://doi.org/10.1063/1.1503803, 2002. a
Vollant, A., Balarac, G., and Corre, C.: Subgrid-scale scalar flux modelling
based on optimal estimation theory and machine-learning procedures, J. Turbulence, 18, 854–878, https://doi.org/10.1080/14685248.2017.1334907, 2017. a, b
Wang, J., Wu, J., and Xiao, H.: Physics-informed machine learning approach for
reconstructing Reynolds stress modeling discrepancies based on DNS data,
Phys. Rev. Fluids, 2, 034603, https://doi.org/10.1103/PhysRevFluids.2.034603,
2017. a
Wang, Z., Luo, K., Li, D., Tan, J., and Fan, J.: Investigations of data-driven
closure for subgrid-scale stress in large-eddy simulation, Phys. Fluids,
30, 125101, https://doi.org/10.1063/1.5054835, 2018. a, b, c
Wu, J., Xiao, H., and Paterson, E.: Physics-informed machine learning approach
for augmenting turbulence models: A comprehensive framework, Phys. Rev.
Fluids, 3, 074602, https://doi.org/10.1103/PhysRevFluids.3.074602, 2018.
a
Xie, C., Wang, J., Li, K., and Ma, C.: Artificial neural network approach to
large-eddy simulation of compressible isotropic turbulence, Phys. Rev.
E, 99, 053113, https://doi.org/10.1103/PhysRevE.99.053113, 2019. a, b, c
Yuval, J. and O'Gorman, P. A.: Stable machine-learning parameterization of
subgrid processes for climate modeling at a range of resolutions, Nat.
Commun., 11, 1–10, https://doi.org/10.1038/s41467-020-17142-3, 2020. a
Zhou, Z., He, G., Wang, S., and Jin, G.: Subgrid-scale model for large-eddy
simulation of isotropic turbulent flows using an artificial neural network,
Comput. Fluids, 195, 104319, https://doi.org/10.1016/j.compfluid.2019.104319,
2019. a, b, c, d
Short summary
Turbulent flows are often simulated with the large-eddy simulation (LES) technique, which requires subgrid models to account for the smallest scales. Current subgrid models often require strong simplifying assumptions. We therefore developed a subgrid model based on artificial neural networks, which requires fewer assumptions. Our data-driven SGS model showed high potential in accurately representing the smallest scales but still introduced instability when incorporated into an actual LES.
Turbulent flows are often simulated with the large-eddy simulation (LES) technique, which...
