kth.sePublications
Change search
Link to record
Permanent link

Direct link
Publications (10 of 235) Show all publications
Font, B., Alcantara-Avila, F., Rabault, J., Vinuesa, R. & Lehmkuhl, O. (2024). Active flow control of a turbulent separation bubble through deep reinforcement learning. In: 5th Madrid Turbulence Workshop 29/05/2023 - 30/06/2023 Madrid, Spain: . Paper presented at 5th Madrid Summer School on Turbulence Workshop, Madrid, Spain, May 29 2023 - Jun 30 2023. IOP Publishing, 2753, Article ID 012022.
Open this publication in new window or tab >>Active flow control of a turbulent separation bubble through deep reinforcement learning
Show others...
2024 (English)In: 5th Madrid Turbulence Workshop 29/05/2023 - 30/06/2023 Madrid, Spain, IOP Publishing , 2024, Vol. 2753, article id 012022Conference paper, Published paper (Refereed)
Abstract [en]

The control efficacy of classical periodic forcing and deep reinforcement learning (DRL) is assessed for a turbulent separation bubble (TSB) at Reτ = 180 on the upstream region before separation occurs. The TSB can resemble a separation phenomenon naturally arising in wings, and a successful reduction of the TSB can have practical implications in the reduction of the aviation carbon footprint. We find that the classical zero-net-mas-flux (ZNMF) periodic control is able to reduce the TSB by 15.7%. On the other hand, the DRL-based control achieves 25.3% reduction and provides a smoother control strategy while also being ZNMF. To the best of our knowledge, the current test case is the highest Reynolds-number flow that has been successfully controlled using DRL to this date. In future work, these results will be scaled to well-resolved large-eddy simulation grids. Furthermore, we provide details of our open-source CFD-DRL framework suited for the next generation of exascale computing machines.

Place, publisher, year, edition, pages
IOP Publishing, 2024
Series
Journal of Physics: Conference Series, ISSN 1742-6596 ; 2753
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-346842 (URN)10.1088/1742-6596/2753/1/012022 (DOI)2-s2.0-85193071647 (Scopus ID)
Conference
5th Madrid Summer School on Turbulence Workshop, Madrid, Spain, May 29 2023 - Jun 30 2023
Note

QC 20240531

Available from: 2024-05-24 Created: 2024-05-24 Last updated: 2024-05-31Bibliographically approved
Koliogeorgi, K., Anagnostopoulos, G., Zampino, G., Sanchis, M., Vinuesa, R. & Xydis, S. (2024). Auto-tuning Multi-GPU High-Fidelity Numerical Simulations for Urban Air Mobility. In: 2024 Design, Automation and Test in Europe Conference and Exhibition, DATE 2024 - Proceedings: . Paper presented at 2024 Design, Automation and Test in Europe Conference and Exhibition, DATE 2024, Valencia, Spain, Mar 25 2024 - Mar 27 2024. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Auto-tuning Multi-GPU High-Fidelity Numerical Simulations for Urban Air Mobility
Show others...
2024 (English)In: 2024 Design, Automation and Test in Europe Conference and Exhibition, DATE 2024 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2024Conference paper, Published paper (Refereed)
Abstract [en]

The aviation field is rapidly evolving towards an era where both typical aviation and Unmanned Aicraft Systems are essential and co-exist in the same airspace. This new territory raises important concerns regarding environmental impact, safety and societal acceptance. The RefMap European Project is an initiative that addresses these issues and aims at optimizing air traffic in terms of the environmental footprint in aviation and drone flights. One of RefMap's objectives is the development of powerful deep-learning models that predict urban flow based on extensive CFD simulations. The excessive time requirements of CFD simulations require the computational power of exascale heterogeneous supercomputer clusters. This work presents RefMap's strategy to mitigate simulation to GPU-enabled high-class solvers and further leverage sophisticated autotuning HPC techniques for creating portable high-performance simulations that can efficiently run on any GPU architecture and parallel system.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Autotuning, Computational Fluid Dynamics, GPU acceleration
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-350535 (URN)2-s2.0-85196526417 (Scopus ID)
Conference
2024 Design, Automation and Test in Europe Conference and Exhibition, DATE 2024, Valencia, Spain, Mar 25 2024 - Mar 27 2024
Note

Part of ISBN 9798350348590

QC 20240716

Available from: 2024-07-16 Created: 2024-07-16 Last updated: 2024-07-16Bibliographically approved
Martin, J. A., Rosti, M. E., Le Clainche, S., Navarro, R. & Vinuesa, R. (2024). Direct numerical simulations of a novel device to fight airborne virus transmission. Physics of fluids, 36(2), Article ID 023352.
Open this publication in new window or tab >>Direct numerical simulations of a novel device to fight airborne virus transmission
Show others...
2024 (English)In: Physics of fluids, ISSN 1070-6631, E-ISSN 1089-7666, Vol. 36, no 2, article id 023352Article in journal (Refereed) Published
Abstract [en]

The SARS-CoV-2 (COVID-19) pandemic has highlighted the crucial role of preventive measures in avoiding the spread of disease and understanding the transmission of airborne viruses in indoor spaces. This study focuses on a novel personal protective equipment consisting of a fan-peaked cap that creates a jet flow of air in front of the individual's face to reduce the concentration of airborne viruses and decrease the risk of infection transmission. Direct numerical simulation is used to analyze the effectiveness of the device under certain conditions, such as the velocity of the airflow, flow orientation, ambient conditions, and geometrical factors.

Place, publisher, year, edition, pages
AIP Publishing, 2024
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-344202 (URN)10.1063/5.0187736 (DOI)001177282500007 ()2-s2.0-85185902871 (Scopus ID)
Note

QC 20240308

Available from: 2024-03-06 Created: 2024-03-06 Last updated: 2024-04-02Bibliographically approved
Wälchli, D., Guastoni, L., Vinuesa, R. & Koumoutsakos, P. (2024). Drag reduction in a minimal channel flow with scientific multi-agent reinforcement learning. In: : . Paper presented at 5th Madrid Summer School on Turbulence Workshop, Madrid, Spain, May 29 2023 - Jun 30 2023. IOP Publishing, Article ID 012024.
Open this publication in new window or tab >>Drag reduction in a minimal channel flow with scientific multi-agent reinforcement learning
2024 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We study drag reduction in a minimal turbulent channel flow using scientific multi-agent reinforcement learning (SMARL). The flow is controlled by blowing and suction at the wall of an open channel, with observable states derived from flow velocities sensed at adjustable heights. We explore the actions, state, and reward space of SMARL using the off-policy algorithm V-RACER. We compare single- and multi-agent setups, and compare the identified control policies against the well-known mechanism of opposition-control. Our findings demonstrate that off-policy SMARL reduces drag in various experimental setups, surpassing classical opposition-control by up to 20 percentage points.

Place, publisher, year, edition, pages
IOP Publishing, 2024
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-346841 (URN)10.1088/1742-6596/2753/1/012024 (DOI)2-s2.0-85193046458 (Scopus ID)
Conference
5th Madrid Summer School on Turbulence Workshop, Madrid, Spain, May 29 2023 - Jun 30 2023
Note

QC 20240603

Available from: 2024-05-24 Created: 2024-05-24 Last updated: 2024-06-03Bibliographically approved
Baxerres, V., Vinuesa, R. & Nagib, H. (2024). Evidence of quasiequilibrium in pressure-gradient turbulent boundary layers. Journal of Fluid Mechanics, 987, Article ID R8.
Open this publication in new window or tab >>Evidence of quasiequilibrium in pressure-gradient turbulent boundary layers
2024 (English)In: Journal of Fluid Mechanics, ISSN 0022-1120, E-ISSN 1469-7645, Vol. 987, article id R8Article in journal (Refereed) Published
Abstract [en]

Two sets of measurements utilizing hot-wire anemometry and oil-film interferometry for flat-plate turbulent boundary layers, exposed to various controlled adverse and favourable pressure gradients, are used to evaluate history effects of the imposed and varying free-stream gradients. The results are from the NDF wind tunnel at Illinois Tech (IIT) and the MTL wind tunnel at KTH, over the range 800 < Re-tau <22000 (where Re-tau is the friction Reynolds number). The streamwise pressure-gradient parameter beta equivalent to (-& ell;/tau(w))& sdot;(partial derivative P-e/partial derivative x) varied between -2 < beta < 7, where & ell; is an outer length scale for boundary layers equivalent to the half-height of channel flow and the radius of pipe flow, and is estimated for each boundary-layer profile; note that tau(w) is the wall-shear stress and P-e is the free-stream static pressure. Extracting from each profile the three parameters of the overlap region, following the recent work of Monkewitz & Nagib (J. Fluid Mech., vol. 967, 2023, p. A15) that led to an overlap region of combined logarithmic and linear parts, we find minimum history effects in the overlap region. Thus, the overlap region in this range of pressure-gradient boundary layers appears to be in 'quasiequilibrium'.

Place, publisher, year, edition, pages
Cambridge University Press (CUP), 2024
Keywords
turbulent boundary layers, boundary layer structure, turbulence theory
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-347169 (URN)10.1017/jfm.2024.440 (DOI)001227934300001 ()2-s2.0-85194000148 (Scopus ID)
Note

QC 20240603

Available from: 2024-06-03 Created: 2024-06-03 Last updated: 2024-06-03Bibliographically approved
Nerini, F. F., Mazzucato, M., Rockström, J., van Asselt, H., Hall, J. W., Matos, S., . . . Sachs, J. (2024). Extending the Sustainable Development Goals to 2050-a road map. Nature, 630(8017), pp. 555-558
Open this publication in new window or tab >>Extending the Sustainable Development Goals to 2050-a road map
Show others...
2024 (English)In: Nature, ISSN 0028-0836, E-ISSN 1476-4687, Vol. 630, no 8017, p. 555-558Article in journal, News item (Other (popular science, discussion, etc.)) Published
Abstract [en]

The world should redouble its efforts on the SDGs, not abandon them. Here's how to progress the United Nations' agenda towards 2050.

Place, publisher, year, edition, pages
Springer Nature, 2024
Keywords
Sustainability, Climate change, Economics, Policy
National Category
Climate Research Economics
Identifiers
urn:nbn:se:kth:diva-350109 (URN)10.1038/d41586-024-01754-6 (DOI)001253524500002 ()38886551 (PubMedID)2-s2.0-85196187379 (Scopus ID)
Note

QC 20240708

Available from: 2024-07-08 Created: 2024-07-08 Last updated: 2024-07-08Bibliographically approved
Cremades, A., Hoyas, S., Deshpande, R., Quintero, P., Lellep, M., Lee, W. J., . . . Vinuesa, R. (2024). Identifying regions of importance in wall-bounded turbulence through explainable deep learning. Nature Communications, 15(1), Article ID 3864.
Open this publication in new window or tab >>Identifying regions of importance in wall-bounded turbulence through explainable deep learning
Show others...
2024 (English)In: Nature Communications, E-ISSN 2041-1723, Vol. 15, no 1, article id 3864Article in journal (Refereed) Published
Abstract [en]

Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem in classical physics that requires new perspectives to be tackled. One of the key strategies has been to study interactions among the energy-containing coherent structures in the flow. Such interactions are explored in this study using an explainable deep-learning method. The instantaneous velocity field obtained from a turbulent channel flow simulation is used to predict the velocity field in time through a U-net architecture. Based on the predicted flow, we assess the importance of each structure for this prediction using the game-theoretic algorithm of SHapley Additive exPlanations (SHAP). This work provides results in agreement with previous observations in the literature and extends them by revealing that the most important structures in the flow are not necessarily the ones with the highest contribution to the Reynolds shear stress. We also apply the method to an experimental database, where we can identify structures based on their importance score. This framework has the potential to shed light on numerous fundamental phenomena of wall-bounded turbulence, including novel strategies for flow control.

Place, publisher, year, edition, pages
Nature Research, 2024
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-346812 (URN)10.1038/s41467-024-47954-6 (DOI)001221986200028 ()38740802 (PubMedID)2-s2.0-85192880434 (Scopus ID)
Note

QC 20240527

Available from: 2024-05-24 Created: 2024-05-24 Last updated: 2024-07-03Bibliographically approved
Mallor, F., Liu, J., Peplinski, A., Vinuesa, R., Örlü, R., Weinkauf, T. & Schlatter, P. (2024). In-Situ Analysis of Backflow Events and Their Relation to Separation in Wings Through Well-Resolved LES. In: ERCOFTAC Series: (pp. 17-22). Springer Science and Business Media B.V., 31
Open this publication in new window or tab >>In-Situ Analysis of Backflow Events and Their Relation to Separation in Wings Through Well-Resolved LES
Show others...
2024 (English)In: ERCOFTAC Series, Springer Science and Business Media B.V. , 2024, Vol. 31, p. 17-22Chapter in book (Other academic)
Abstract [en]

Wall-bounded turbulent flows as those occurring in transportation (e.g. aviation) or industrial applications (e.g turbomachinery), are usually subjected to pressure gradients (PGs). The presence of such PGs affects greatly the development and physics of the turbulent boundary layer (TBL), making it an open research area. An important phenomena associated with the presence of strong adverse PGs (APGs) as appearing in wings, is the separation of the boundary layer, which can lead to stall.

Place, publisher, year, edition, pages
Springer Science and Business Media B.V., 2024
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-340780 (URN)10.1007/978-3-031-47028-8_3 (DOI)2-s2.0-85178156992 (Scopus ID)
Note

QC 20231214

Available from: 2023-12-14 Created: 2023-12-14 Last updated: 2023-12-14Bibliographically approved
Lopez-Doriga, B., Atzori, M., Vinuesa, R., Bae, H. J., Srivastava, A. & Dawson, S. T. (2024). Linear and nonlinear Granger causality analysis of turbulent duct flows. In: Journal of Physics: Conference Series, Volume 2753, 5th Madrid Turbulence Workshop 29/05/2023 - 30/06/2023: . Paper presented at 5th Madrid Summer School on Turbulence Workshop, Madrid, Spain, May 29 2023 - Jun 30 2023. Institute of Physics, 2753, Article ID 012017.
Open this publication in new window or tab >>Linear and nonlinear Granger causality analysis of turbulent duct flows
Show others...
2024 (English)In: Journal of Physics: Conference Series, Volume 2753, 5th Madrid Turbulence Workshop 29/05/2023 - 30/06/2023, Institute of Physics , 2024, Vol. 2753, article id 012017Conference paper, Published paper (Refereed)
Abstract [en]

This research focuses on the identification and causality analysis of coherent structures that arise in turbulent flows in square and rectangular ducts. Coherent structures are first identified from direct numerical simulation data via proper orthogonal decomposition (POD), both by using all velocity components, and after separating the streamwise and secondary components of the flow. The causal relations between the mode coefficients are analysed using pairwise-conditional Granger causality analysis. We also formulate a nonlinear Granger causality analysis that can account for nonlinear interactions between modes. Focusing on streamwise-constant structures within a duct of short streamwise extent, we show that the causal relationships are highly sensitive to whether the mode coefficients or their squared values are considered, whether nonlinear effects are explicitly accounted for, and whether streamwise and secondary flow structures are separated prior to causality analyses. We leverage these sensitivities to determine that linear mechanisms underpin causal relationships between modes that share the same symmetry or anti-symmetry properties about the corner bisector, while nonlinear effects govern the causal interactions between symmetric and antisymmetric modes. In all cases, we find that the secondary flow fluctuations (manifesting as streamwise vorticial structures) are the primary cause of both the presence and movement of near-wall streaks towards and away from the duct corners.

Place, publisher, year, edition, pages
Institute of Physics, 2024
Series
Journal of Physics: Conference Series, ISSN 1742-6588 ; 2753
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-346840 (URN)10.1088/1742-6596/2753/1/012017 (DOI)2-s2.0-85193067653 (Scopus ID)
Conference
5th Madrid Summer School on Turbulence Workshop, Madrid, Spain, May 29 2023 - Jun 30 2023
Note

QC 20240527

Available from: 2024-05-24 Created: 2024-05-24 Last updated: 2024-05-27Bibliographically approved
Vinuesa, R. (2024). Perspectives on predicting and controlling turbulent flows through deep learning. Physics of fluids, 36(3), Article ID 031401.
Open this publication in new window or tab >>Perspectives on predicting and controlling turbulent flows through deep learning
2024 (English)In: Physics of fluids, ISSN 1070-6631, E-ISSN 1089-7666, Vol. 36, no 3, article id 031401Article in journal (Refereed) Published
Abstract [en]

The current revolution in the field of machine learning is leading to many interesting developments in a wide range of areas, including fluid mechanics. Fluid mechanics, and more concretely turbulence, is an ubiquitous problem in science and engineering. Being able to understand and predict the evolution of turbulent flows can have a critical impact on our possibilities to tackle a wide range of sustainability problems (including the current climate emergency) and industrial applications. Here, we review recent and emerging possibilities in the context of predictions, simulations, and control of fluid flows, focusing on wall-bounded turbulence. When it comes to flow control, we refer to the active manipulation of the fluid flow to improve the efficiency of processes such as reduced drag in vehicles, increased mixing in industrial processes, enhanced heat transfer in heat exchangers, and pollution reduction in urban environments. A number of important areas are benefiting from ML, and it is important to identify the synergies with the existing pillars of scientific discovery, i.e., theory, experiments, and simulations. Finally, I would like to encourage a balanced approach as a community in order to harness all the positive potential of these novel methods.

Place, publisher, year, edition, pages
AIP Publishing, 2024
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-344549 (URN)10.1063/5.0190452 (DOI)001180556400023 ()2-s2.0-85187222247 (Scopus ID)
Note

QC 20240326

Available from: 2024-03-20 Created: 2024-03-20 Last updated: 2024-04-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6570-5499

Search in DiVA

Show all publications