NON-INTRUSIVE SENSING IN TURBULENT BOUNDARY LAYERS VIA DEEP FULLY-CONVOLUTIONAL NEURAL NETWORKS
Guastoni L, Balasubramanian AG, Güemes A, Ianiro A, Discetti S, Schlatter P, Azizpour H, Vinuesa R (2022)
Publication Type: Conference contribution
Publication year: 2022
Publisher: International Symposium on Turbulence and Shear Flow Phenomena, TSFP
Conference Proceedings Title: 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022
Event location: Osaka, Virtual, JPN
Abstract
Flow-control techniques are extensively studied in fluid mechanics, as a means to reduce energy losses related to friction, both in fully-developed and spatially-developing flows. These techniques typically rely on closed-loop control systems that require an accurate representation of the state of the flow to compute the actuation. Such representation is generally difficult to obtain without perturbing the flow. For this reason, in this work we propose a fully-convolutional neural-network (FCN) model trained on direct-numerical-simulation (DNS) data to predict the instantaneous state of the flow at different wall-normal locations using quantities measured at the wall. Our model can take as input the heat-flux field at the wall from a passive scalar with Prandtl number Pr = ν/α = 6 (where ν is the kinematic viscosity and α is the thermal diffusivity of the scalar quantity). The heat flux can be accurately measured also in experimental settings, paving the way for the implementation of a non-intrusive sensing of the flow in practical applications.
Authors with CRIS profile
Involved external institutions
Royal Institute of Technology / Kungliga Tekniska Högskolan (KTH)
Sweden (SE)
Universidad Carlos III de Madrid (UC3M)
Spain (ES)
Sweden (SE)
Universidad Carlos III de Madrid (UC3M)
Spain (ES)
How to cite
APA:
Guastoni, L., Balasubramanian, A.G., Güemes, A., Ianiro, A., Discetti, S., Schlatter, P.,... Vinuesa, R. (2022). NON-INTRUSIVE SENSING IN TURBULENT BOUNDARY LAYERS VIA DEEP FULLY-CONVOLUTIONAL NEURAL NETWORKS. In 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022. Osaka, Virtual, JPN: International Symposium on Turbulence and Shear Flow Phenomena, TSFP.
MLA:
Guastoni, Luca, et al. "NON-INTRUSIVE SENSING IN TURBULENT BOUNDARY LAYERS VIA DEEP FULLY-CONVOLUTIONAL NEURAL NETWORKS." Proceedings of the 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022, Osaka, Virtual, JPN International Symposium on Turbulence and Shear Flow Phenomena, TSFP, 2022.
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