Prof. Dr. Philipp Schlatter
Institute of Fluid Mechanics (LSTM)
The understanding of fluid dynamics is at the heart of understanding most medical applications, be it inside our lungs, veins or the heart, or external machines such as ventilators. We employ both numerical simulations and experiments to analyse and optimise medical processes of various scales.
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GreenInverter: Nachhaltige Leistungselektronik mit innovativer Kühlung zur Steigerung der Recyclierbarkeit
(Third Party Funds Group – Sub project)
Overall project: Verbundvorhaben: Nachhaltige Leistungselektronik mit innovativer Kühlung zur Steigerung der Recyclierbarkeit
Project leader: , ,
Term: 1. March 2024 - 28. February 2027
Acronym: GreenInverter
Funding source: Bundesministerium für Wirtschaft und Energie (BMWE)The "Green Inverter" project aims to set new standards in terms of recyclable, repairable and upgradeable
hardware in the field of electrical energy conversion. At the same time, energy efficiency is to be
significantly improved. The advantages will be demonstrated using the example of an inverter. The state of
the art, which is based on the principle of individual component developments, is to be replaced by a new
systemic approach.
By means of a disruptively new approach for a combined cooling and insulation strategy, component
temperatures are reduced and the use of new materials is made possible, whereby energy efficiency can
be increased. A significant increase in recyclability, reduction of the product CO2 footprint and increase in
product lifetime due to a more homogeneous temperature distribution in the converter will also be achieved.
In addition, future, new business models are included in the considerations, which means that construction
and design must be less strongly oriented towards manufacturing costs. -
CEEC: Center of Excellence for Exascale CFD
(Third Party Funds Group – Sub project)
Overall project: Center of Excellence for Exascale CFD
Project leader: , ,
Term: 1. January 2023 - 31. December 2026
Acronym: CEEC
Funding source: Europäische Union (EU)For many centuries, scientific discovery relied on performing experiments and the subsequent deduction of new theoretical models. The advent of powerful computers, coupled with new and ever more efficient numerical algorithms, makes it possible to simulate complex systems with increasing realism, and to automatize even model discovery using artificial intelligence (AI) technologies. Computational Fluid DynFor many centuries, scientific discovery relied on performing experiments and the subsequent deduction of new theoretical models. The advent of powerful computers, coupled with new and ever more efficient numerical algorithms, makes it possible to simulate complex systems with increasing realism, and to automatize even model discovery using AI technologies. Computational Fluid Dynamics (CFD) is one of the most prominent areas that clearly requires, and even motivate exascale computing to be part of the engineering and academic workflows. Given the physical scaling and the availability of highly efficient simulation codes, CFD has the potential of reaching exascale performance, as one of the few application areas. This center will implement exascale ready workflows for addressing relevant challenges for future exascale systems, including those procured by EuroHPC. The significant improvement in energy efficiency will be facilitated through efficient exploitation of accelerated hardware architectures (GPUs) and novel adaptive mixed-precision calculations. Emphasis is furthermore given to new or improved algorithms that are needed to exploit upcoming exascale architectures. The efforts of the center are driven by a collection of five different lighthouse cases of physical and engineering interest, ranging from aeronautical to atmospheric flows, with the goal of reaching TRL 4 and even 5 for selected cases. All development is done in five European HPC codes which span the entire spectrum of CFD applications, including compressible, incompressible and multiphase flows.
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CEEC: CEEC: HPC-Exzellenzzentrum für Strömungsmechanik im Exascale-Bereich
(Third Party Funds Group – Sub project)
Overall project: CEEC: HPC-Exzellenzzentrum für Strömungsmechanik im Exascale-Bereich
Project leader: ,
Term: 1. January 2023 - 31. December 2026
Acronym: CEEC
Funding source: BMFTR / Verbundprojekt
2026
- Bagheri, E., Becker, S., & Schlatter, P. (2026). Collapse of turbulence in optimised curved pipe flow. Journal of Fluid Mechanics, 1033. https://doi.org/10.1017/jfm.2026.11427
- Bagheri, E., Stanly, R., Mukha, T., & Schlatter, P. (2026). Influence of turbulence inflow conditions on aeroacoustics of wall-bounded flows. International Journal of Heat and Fluid Flow, 118. https://doi.org/10.1016/j.ijheatfluidflow.2025.110216
- Du, S., Münsch, M., Jansson, N., & Schlatter, P. (2026). Assessment of the gradient jump penalisation in large-eddy simulations of turbulence. Acta Mechanica. https://doi.org/10.1007/s00707-025-04607-z
- Jansson, N., Karp, M., Páll, S., Markidis, S., & Schlatter, P. (2026). Task-decomposed Overlapped Preconditioner for Sustained Strong Scalability on Accelerated Exascale Systems. In Proceedings of Supercomputing Asia and International Conference on High Performance Computing in Asia Pacific Region, SCA/HPCAsia 2026 (pp. 186-193). Osaka, JPN: Association for Computing Machinery, Inc.
- Karp, M., Stanly, R., Mukha, T., Galimberti, L., Toosi, S., Song, H.,... Schlatter, P. (2026). Effects of lower floating-point precision on scale-resolving numerical simulations of turbulence. Journal of Computational Physics, 549. https://doi.org/10.1016/j.jcp.2025.114600
- Massaro, D., Rezaeiravesh, S., & Schlatter, P. (2026). Causally coherent structures in turbulent dynamical systems. Physical Review E, 113(3). https://doi.org/10.1103/16fy-qvdp
- Mohamadi Bagheri, E., Becker, S., & Schlatter, P. (2026). Collapse of turbulence in curved pipe flow. (Unpublished, In review).
- Münsch, M., Wendler, J., & Schlatter, P. (2026). Evaluation of Dynamic σ Sub-grid Scale Models. In (pp. 247-252). Springer Science and Business Media B.V..
- Noor, F., Habla, F., Hofmeister, T., Münsch, M., & Schlatter, P. (2026). CFD modeling of sloshing-induced pressure drop inside LH2 storage tanks used in maritime applications. International Journal of Hydrogen Energy, 222. https://doi.org/10.1016/j.ijhydene.2026.154278
- Stanly, R., Mukha, T., Karp, M., Markidis, S., & Schlatter, P. (2026). Generating wall-bounded turbulent inflows at high Reynolds numbers. Journal of Fluid Mechanics, 1037. https://doi.org/10.1017/jfm.2026.11689
- Wachter, F., Becker, S., & Schlatter, P. (2026). The Influence of Regions of Convex Transverse Curvature and Concave Grooves on the Turbulent Boundary Layer Along a Cylinder in Axial Flow. In Direct and Large Eddy Simulation XIV - Proceedings of DLES14. (pp. 45-51). Springer Science and Business Media B.V..
- Yang, L., Yao, J., Schlatter, P., & Hussain, F. (2026). Direct numerical simulations of axially rotating turbulent pipe flow up to Reτ = 1000. In Journal of Physics: Conference Series. Bertinoro, ITA: Institute of Physics.
2025
- Guastoni, L., Balasubramanian, A.G., Foroozan, F., Güemes, A., Ianiro, A., Discetti, S.,... Vinuesa, R. (2025). Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers. Theoretical and Computational Fluid Dynamics, 39(1). https://doi.org/10.1007/s00162-024-00732-y
- Huusko, L., Mukha, T., Donati, L.L., Sullivan, P.P., Schlatter, P., & Svensson, G. (2025). Large Eddy Simulation of Canonical Atmospheric Boundary Layer Flows With the Spectral Element Method in Nek5000. Journal of Advances in Modeling Earth Systems, 17(10). https://doi.org/10.1029/2025MS005233
- Ju, Y., Huber, D., Perez, A., Ulbl, P., Markidis, S., Schlatter, P.,... Laure, E. (2025). Dynamic Resource Management for In-Situ Techniques Using MPI-Sessions. In Claudia Blaas-Schenner, Christoph Niethammer, Tobias Haas (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 105-120). Perth, WA, AUS: Springer Science and Business Media Deutschland GmbH.
- Ju, Y., Vidal, N., Perez, A., Gainaru, A., Suter, F., Markidis, S.,... Laure, E. (2025). A Performance Model of In-Situ Techniques. In Proceedings - 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025 (pp. 209-216). Turin, IT: Institute of Electrical and Electronics Engineers Inc..
- Lehner, F., Groves, C., Meiller, M., Sładek, S., & Schlatter, P. (2025). Influence of water injection on the formation of CO emissions in oxyfuel combustion of low calorific gaseous fuels. Journal of the Energy Institute, 123. https://doi.org/10.1016/j.joei.2025.102335
- Lupi, V., Massaro, D., Peplinski, A., & Schlatter, P. (2025). Swirl switching in spatially developing bent pipes. Physical Review Fluids, 10(11). https://doi.org/10.1103/ztyq-qk1r
- Mallor, F., Sanmiguel Vila, C., Hajipour, M., Vinuesa, R., Schlatter, P., & Örlü, R. (2025). Experimental characterization of turbulent boundary layers around a NACA 4412 wing profile. Experimental Thermal and Fluid Science, 160. https://doi.org/10.1016/j.expthermflusci.2024.111327
- Mukha, T., Parsani, M., & Schlatter, P. (2025). Wall-modeled large-eddy simulation based on spectral-element discretization. Physics of Fluids, 37(10). https://doi.org/10.1063/5.0283984
- Nobis, H., Schlatter, P., Wadbro, E., Berggren, M., & Henningson, D.S. (2025). Topology optimization of roughness elements to delay modal transition in boundary layers. Computers & Fluids, 299. https://doi.org/10.1016/j.compfluid.2025.106680
- Schlatter, P., Stanly, R., Mohamadi Bagheri, E., Peplinski, A., Toosi, S., Jansson, N.,... Schlatter, P. (2025). Direct numerical simulation of a starting rotor at Rec=15000. Journal of Visualization, 28(6), 1083-1090. https://doi.org/10.1007/s12650-025-01085-2
- Örlü, R., & Schlatter, P. (2025). Editorial of Special Issue (SI) on “Large-scale control of wall-bounded flow”. International Journal of Heat and Fluid Flow. https://doi.org/10.1016/j.ijheatfluidflow.2025.109893
2024
- Jansson, N., Karp, M., Markidis, S., & Schlatter, P. (2024). Neko: A Modern, Portable, and Scalable Framework for Extreme-Scale Computational Fluid Dynamics. In Proceedings - 2024 IEEE International Conference on Cluster Computing Workshops, CLUSTER Workshops 2024 (pp. 156-157). Kobe, JPN: Institute of Electrical and Electronics Engineers Inc..
- Karp, M., Suarez, E., Meinke, J.H., Andersson, M.I., Schlatter, P., Markidis, S., & Jansson, N. (2024). Experience and analysis of scalable high-fidelity computational fluid dynamics on modular supercomputing architectures. International Journal of High Performance Computing Applications. https://doi.org/10.1177/10943420241303163
- Liu, J., Edwards, T., Durovic, K., Schlatter, P., & Weinkauf, T. (2024). In-Situ Binary Segmentation of 3D time-dependent Flows into Laminar and Turbulent Regions. In ACM International Conference Proceeding Series (pp. 210-219). Gotland, SWE: Association for Computing Machinery.
- Mallor, F., Vinuesa, R., Örlü, R., & Schlatter, P. (2024). High-fidelity simulations of the flow around a NACA 4412 wing section at high angles of attack. International Journal of Heat and Fluid Flow, 110. https://doi.org/10.1016/j.ijheatfluidflow.2024.109590
- Mallor, F., Örlü, R., & Schlatter, P. (2024). Spatial Averaging Effects in Adverse Pressure Gradient Turbulent Boundary Layers. Flow Turbulence and Combustion. https://doi.org/10.1007/s10494-024-00568-w
- Marchioli, C., García-Villalba, M., Salvetti, M.V., & Schlatter, P. (2024). Advances in Direct and Large-Eddy Simulations. Flow Turbulence and Combustion. https://doi.org/10.1007/s10494-023-00524-0
- Pozuelo, R., Cavalieri, A., Schlatter, P., & Vinuesa, R. (2024). Widest scales in turbulent channels. Physics of Fluids, 36(2). https://doi.org/10.1063/5.0189532
- Stanly, R., Du, S., Xavier, D., Perez, A., Mukha, T., Markidis, S.,... Schlatter, P. (2024). Generating synthetic turbulence with vector autoregression of proper orthogonal decomposition time coefficients. Journal of Fluid Mechanics, 1000. https://doi.org/10.1017/jfm.2024.1034
- Toosi, S., Larsson, J., & Schlatter, P. (2024). ASSESSMENT OF THE ACCURACY AND ROBUSTNESS OF DIFFERENT NUMERICAL METHODS IN WALL-BOUNDED FLOWS. In World Congress in Computational Mechanics and ECCOMAS Congress. Lisbon, PRT: Scipedia S.L..
- Xavier, D., Rezaeiravesh, S., & Schlatter, P. (2024). Autoregressive models for quantification of time-averaging uncertainties in turbulent flows. Physics of Fluids, 36(10). https://doi.org/10.1063/5.0211541
2023
- Amor, C., Schlatter, P., Vinuesa, R., & Le Clainche, S. (2023). Higher-order dynamic mode decomposition on-the-fly: A low-order algorithm for complex fluid flows. Journal of Computational Physics, 475. https://doi.org/10.1016/j.jcp.2022.111849
- Andreolli, A., Gatti, D., Vinuesa, R., Örlü, R., & Schlatter, P. (2023). Separating large-scale superposition and modulation in turbulent channels. Journal of Fluid Mechanics, 958. https://doi.org/10.1017/jfm.2023.103
- Atzori, M., Mallor, F., Pozuelo, R., Fukagata, K., Vinuesa, R., & Schlatter, P. (2023). A new perspective on skin-friction contributions in adverse-pressure-gradient turbulent boundary layers. International Journal of Heat and Fluid Flow, 101. https://doi.org/10.1016/j.ijheatfluidflow.2023.109117
- Balasubramanian, A.G., Guastoni, L., Schlatter, P., Azizpour, H., & Vinuesa, R. (2023). Predicting the wall-shear stress and wall pressure through convolutional neural networks. International Journal of Heat and Fluid Flow, 103. https://doi.org/10.1016/j.ijheatfluidflow.2023.109200
- Balasubramanian, A.G., Guastoni, L., Schlatter, P., & Vinuesa, R. (2023). Direct numerical simulation of a zero-pressure-gradient turbulent boundary layer with passive scalars up to Prandtl number Pr = 6. Journal of Fluid Mechanics, 974. https://doi.org/10.1017/jfm.2023.803
- Jansson, N., Karp, M., Perez, A., Mukha, T., Ju, Y., Liu, J.,... Markidis, S. (2023). Exploring the Ultimate Regime of Turbulent Rayleigh-Bénard Convection Through Unprecedented Spectral-Element Simulations. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023. Denver, CO, US: New York, NY: Association for Computing Machinery, Inc.
- Ju, Y., Li, M., Perez, A., Bellentani, L., Jansson, N., Markidis, S.,... Laure, E. (2023). In-Situ Techniques on GPU-Accelerated Data-Intensive Applications. In Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023. Limassol, CYP: Institute of Electrical and Electronics Engineers Inc..
- Karp, M., Liu, F., Stanly, R., Rezaeiravesh, S., Jansson, N., Schlatter, P., & Markidis, S. (2023). Uncertainty Quantification of Reduced-Precision Time Series in Turbulent Channel Flow. In ACM International Conference Proceeding Series (pp. 387-390). Denver, CO, USA: Association for Computing Machinery.
- Karp, M., Massaro, D., Jansson, N., Hart, A., Wahlgren, J., Schlatter, P., & Markidis, S. (2023). Large-Scale direct numerical simulations of turbulence using GPUs and modern Fortran. International Journal of High Performance Computing Applications. https://doi.org/10.1177/10943420231158616
- Mallor, F., Semprini-Cesari, G., Mukha, T., Rezaeiravesh, S., & Schlatter, P. (2023). Bayesian Optimization of Wall-Normal Blowing and Suction-Based Flow Control of a NACA 4412 Wing Profile. Flow Turbulence and Combustion. https://doi.org/10.1007/s10494-023-00475-6
- Massaro, D., Lupi, V., Peplinski, A., & Schlatter, P. (2023). Global stability of 180-bend pipe flow with mesh adaptivity. Physical Review Fluids, 8(11). https://doi.org/10.1103/PhysRevFluids.8.113903
- Massaro, D., Peplinski, A., & Schlatter, P. (2023). Coherent structures in the turbulent stepped cylinder flow at ReD=5000. International Journal of Heat and Fluid Flow, 102. https://doi.org/10.1016/j.ijheatfluidflow.2023.109144
- Massaro, D., Rezaeiravesh, S., & Schlatter, P. (2023). On the potential of transfer entropy in turbulent dynamical systems. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-49747-1
- Nobis, H., Schlatter, P., Wadbro, E., Berggren, M., & Henningson, D.S. (2023). Modal laminar–turbulent transition delay by means of topology optimization of superhydrophobic surfaces. Computer Methods in Applied Mechanics and Engineering, 403. https://doi.org/10.1016/j.cma.2022.115721
- Offermans, N., Massaro, D., Peplinski, A., & Schlatter, P. (2023). Error-driven adaptive mesh refinement for unsteady turbulent flows in spectral-element simulations. Computers & Fluids, 251. https://doi.org/10.1016/j.compfluid.2022.105736
- Pozuelo, R., Li, Q., Schlatter, P., & Vinuesa, R. (2023). Spectra of near-equilibrium adverse-pressure-gradient turbulent boundary layers. Physical Review Fluids, 8(2). https://doi.org/10.1103/PhysRevFluids.8.L022602
- Yao, J., Rezaeiravesh, S., Schlatter, P., & Hussain, F. (2023). Direct numerical simulations of turbulent pipe flow up to. Journal of Fluid Mechanics, 956. https://doi.org/10.1017/jfm.2022.1013
2022
- Amor, C., Perez, J.M., Schlatter, P., Vinuesa, R., & Le Clainche, S. (2022). Modeling the Turbulent Wake Behind a Wall-Mounted Square Cylinder. Logic Journal of the Igpl, 30(2), 263-276. https://doi.org/10.1093/jigpal/jzaa060
- Atzori, M., Kopp, W., Chien, S.W.D., Massaro, D., Mallor, F., Peplinski, A.,... Weinkauf, T. (2022). In situ visualization of large-scale turbulence simulations in Nek5000 with ParaView Catalyst. Journal of Supercomputing, 78(3), 3605-3620. https://doi.org/10.1007/s11227-021-03990-3
- Atzori, M., Stroh, A., Gatti, D., Fukagata, K., Vinuesa, R., & Schlatter, P. (2022). A NEW POINT OF VIEW ON SKIN-FRICTION CONTRIBUTIONS IN ADVERSE-PRESSURE-GRADIENT TURBULENT BOUNDARY LAYERS. In 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022. Osaka, Virtual, JPN: International Symposium on Turbulence and Shear Flow Phenomena, TSFP.
- Atzori, M., Vinuesa, R., & Schlatter, P. (2022). Control effects on coherent structures in a non-uniform adverse-pressure-gradient boundary layer. International Journal of Heat and Fluid Flow, 97. https://doi.org/10.1016/j.ijheatfluidflow.2022.109036
- Borrelli, G., Guastoni, L., Eivazi, H., Schlatter, P., & Vinuesa, R. (2022). Predicting the temporal dynamics of turbulent channels through deep learning. International Journal of Heat and Fluid Flow, 96. https://doi.org/10.1016/j.ijheatfluidflow.2022.109010
- Chan, C.I., Orlu, R., Schlatter, P., & Chin, R.C. (2022). Large-scale and small-scale contribution to the skin friction reduction in a modified turbulent boundary layer by a large-eddy break-up device. Physical Review Fluids, 7(3). https://doi.org/10.1103/PhysRevFluids.7.034601
- Eivazi, H., Tahani, M., Schlatter, P., & Vinuesa, R. (2022). Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations. Physics of Fluids, 34(7). https://doi.org/10.1063/5.0095270
- Fan, Y., Atzori, M., Vinuesa, R., Gatti, D., Schlatter, P., & Li, W. (2022). Decomposition of the mean friction drag on an NACA4412 airfoil under uniform blowing/suction. Journal of Fluid Mechanics, 932. https://doi.org/10.1017/jfm.2021.1015
- 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.
- Ju, Y., Perez, A., Markidis, S., Schlatter, P., & Laure, E. (2022). Understanding the Impact of Synchronous, Asynchronous, and Hybrid In-Situ Techniques in Computational Fluid Dynamics Applications. In Proceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022 (pp. 295-305). Salt Lake City, UT, USA: Institute of Electrical and Electronics Engineers Inc..
- Karp, M., Jansson, N., Podobas, A., Schlatter, P., & Markidis, S. (2022). Reducing Communication in the Conjugate Gradient Method: A Case Study on High-Order Finite Elements. In Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2022. Basel, CHE: Association for Computing Machinery, Inc.
- Karp, M., Podobas, A., Kenter, T., Jansson, N., Plessl, C., Schlatter, P., & Markidis, S. (2022). A High-Fidelity Flow Solver for Unstructured Meshes on Field-Programmable Gate Arrays: Design, Evaluation, and Future Challenges. In ACM International Conference Proceeding Series (pp. 125-136). Virtual, Online, JPN: Association for Computing Machinery.
- Massaro, D., Peplinski, A., & Schlatter, P. (2022). DIRECT NUMERICAL SIMULATION OF TURBULENT FLOW AROUND 3D STEPPED CYLINDER WITH ADAPTIVE MESH REFINEMENT. In 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022. Osaka, Virtual, JPN: International Symposium on Turbulence and Shear Flow Phenomena, TSFP.
- Morita, Y., Rezaeiravesh, S., Tabatabaei, N., Vinuesa, R., Fukagata, K., & Schlatter, P. (2022). Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems. Journal of Computational Physics, 449. https://doi.org/10.1016/j.jcp.2021.110788
- Mukha, T., & Schlatter, P. (2022). WALL-MODELLED LES USING HIGH- AND LOW-ORDER CFD CODES: APPLICATION TO A FLAT-PLATE BOUNDARY LAYER. In 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022. Osaka, Virtual, JPN: International Symposium on Turbulence and Shear Flow Phenomena, TSFP.
- Nobis, H., Schlatter, P., Wadbro, E., Berggren, M., & Henningson, D.S. (2022). Topology optimization of unsteady flows using the spectral element method. Computers & Fluids, 239. https://doi.org/10.1016/j.compfluid.2022.105387
- Perez, A., Orlu, R., Talamelli, A., & Schlatter, P. (2022). Appraisal of cavity hot-wire probes for wall-shear-stress measurements. Experiments in Fluids, 63(9). https://doi.org/10.1007/s00348-022-03498-3
- Pozuelo, R., Li, Q., Schlatter, P., & Vinuesa, R. (2022). An adverse-pressure-gradient turbulent boundary layer with nearly constant up to. Journal of Fluid Mechanics, 939. https://doi.org/10.1017/jfm.2022.221
- Rezaeiravesh, S., Vinuesa, R., & Schlatter, P. (2022). An uncertainty-quantification framework for assessing accuracy, sensitivity, and robustness in computational fluid dynamics. Journal of Computational Science, 62. https://doi.org/10.1016/j.jocs.2022.101688
- Rezaeiravesh, S., Xavier, D., Vinuesa, R., Yao, J., Hussain, F., & Schlatter, P. (2022). ESTIMATING UNCERTAINTY OF LOW- AND HIGH-ORDER TURBULENCE STATISTICS IN WALL TURBULENCE. In 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022. Osaka, Virtual, JPN: International Symposium on Turbulence and Shear Flow Phenomena, TSFP.
- Tabatabaei, N., Hajipour, M., Mallor, F., Orlu, R., Vinuesa, R., & Schlatter, P. (2022). RANS Modelling of a NACA4412 Wake Using Wind Tunnel Measurements. Fluids, 7(5). https://doi.org/10.3390/fluids7050153
- Tabatabaei, N., Vinuesa, R., Orlu, R., & Schlatter, P. (2022). Correction to: Techniques for Turbulence Tripping of Boundary Layers in RANS Simulations (Flow, Turbulence and Combustion, (2022), 108, 3, (661-682), 10.1007/s10494-021-00296-5). Flow Turbulence and Combustion, 108(4), 1193-. https://doi.org/10.1007/s10494-021-00300-y
- Tabatabaei, N., Vinuesa, R., Orlu, R., & Schlatter, P. (2022). Techniques for Turbulence Tripping of Boundary Layers in RANS Simulations. Flow Turbulence and Combustion, 108(3), 661-682. https://doi.org/10.1007/s10494-021-00296-5
- Vincent, J., Gong, J., Karp, M., Peplinski, A., Jansson, N., Podobas, A.,... Schlatter, P. (2022). Strong Scaling of OpenACC enabled Nek5000 on several GPU based HPC systems. In ACM International Conference Proceeding Series (pp. 94-102). Virtual, Online, JPN: Association for Computing Machinery.
- Xavier, D., Rezaeiravesh, S., Vinuesa, R., & Schlatter, P. (2022). AUTOMATIC ESTIMATION OF INITIAL TRANSIENT IN A TURBULENT FLOW TIME SERIES. In World Congress in Computational Mechanics and ECCOMAS Congress. Oslo, NOR: Scipedia S.L..
2021
- Abreu, L., Tanarro, A., Cavalieri, A.V.G., Schlatter, P., Vinuesa, R., Hanifi, A., & Henningson, D.S. (2021). Spanwise-coherent hydrodynamic waves around flat plates and airfoils. Journal of Fluid Mechanics, 927. https://doi.org/10.1017/jfm.2021.718
- Atzori, M., Vinuesa, R., Lozano-Duran, A., & Schlatter, P. (2021). Intense reynolds-stress events in turbulent ducts. International Journal of Heat and Fluid Flow, 89. https://doi.org/10.1016/j.ijheatfluidflow.2021.108802
- Atzori, M., Vinuesa, R., Stroh, A., Gatti, D., Frohnapfel, B., & Schlatter, P. (2021). Uniform blowing and suction applied to nonuniform adverse-pressure-gradient wing boundary layers. Physical Review Fluids, 6(11). https://doi.org/10.1103/PhysRevFluids.6.113904
- Chan, C.I., Schlatter, P., & Chin, R.C. (2021). Interscale transport mechanisms in turbulent boundary layers. Journal of Fluid Mechanics, 921. https://doi.org/10.1017/jfm.2021.504
- Chan, I.C., Orlu, R., Schlatter, P., & Chin, R.C. (2021). The skin-friction coefficient of a turbulent boundary layer modified by a large-eddy break-up device. Physics of Fluids, 33(3). https://doi.org/10.1063/5.0043984
- Corrochano, A., Xavier, D., Schlatter, P., Vinuesa, R., & Le Clainche, S. (2021). Flow structures on a planar food and drug administration (FDA) nozzle at low and intermediate reynolds number. Fluids, 6(1). https://doi.org/10.3390/fluids6010004
- Eivazi, H., Guastoni, L., Schlatter, P., Azizpour, H., & Vinuesa, R. (2021). Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence. International Journal of Heat and Fluid Flow, 90. https://doi.org/10.1016/j.ijheatfluidflow.2021.108816
- Fahland, G., Stroh, A., Frohnapfel, B., Atzori, M., Vinuesa, R., Schlatter, P., & Gatti, D. (2021). Investigation of blowing and suction for turbulent flow control on airfoils. AIAA Journal, 59(11), 4422-4436. https://doi.org/10.2514/1.J060211
- Guastoni, L., Guemes, A., Ianiro, A., Discetti, S., Schlatter, P., Azizpour, H., & Vinuesa, R. (2021). Convolutional-network models to predict wall-bounded turbulence from wall quantities. Journal of Fluid Mechanics, 928. https://doi.org/10.1017/jfm.2021.812
- Karp, M., Podobas, A., Jansson, N., Kenter, T., Plessl, C., Schlatter, P., & Markidis, S. (2021). High-performance spectral element methods on field-programmable gate arrays : iimplementation, evaluation, and future projection. In Proceedings - 2021 IEEE 35th International Parallel and Distributed Processing Symposium, IPDPS 2021 (pp. 1077-1086). Virtual, Online: Institute of Electrical and Electronics Engineers Inc..
- Köpp, W., Friederici, A., Atzori, M., Vinuesa, R., Schlatter, P., & Weinkauf, T. (2021). Notes on Percolation Analysis of Sampled Scalar Fields. In Ingrid Hotz, Talha Bin Masood, Filip Sadlo, Julien Tierny (Eds.), Mathematics and Visualization (pp. 39-54). Nyköping, SWE: Springer Science and Business Media Deutschland GmbH.
- Rezaeiravesh, S., Morita, Y., Tabatabaei, N., Vinuesa, R., Fukagata, K., & Schlatter, P. (2021). Bayesian Optimisation with Gaussian Process Regression Applied to Fluid Problems. In Ramis Örlü, Alessandro Talamelli, Joachim Peinke, Martin Oberlack (Eds.), Springer Proceedings in Physics (pp. 137-143). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
- Rezaeiravesh, S., Vinuesa, R., & Schlatter, P. (2021). On numerical uncertainties in scale-resolving simulations of canonical wall turbulence. Computers & Fluids, 227. https://doi.org/10.1016/j.compfluid.2021.105024
- Rezaeiravesh, S., Vinuesa, R., & Schlatter, P. (2021). Towards multifidelity models with calibration for turbulent flows. In World Congress in Computational Mechanics and ECCOMAS Congress (pp. 1-12). Virtual, Online: Scipedia S.L..
- Tabatabaei, N., Orlu, R., Vinuesa, R., & Schlatter, P. (2021). Aerodynamic free-flight conditions in wind tunnel modelling through reduced-order wall inserts. Fluids, 6(8). https://doi.org/10.3390/fluids6080265
2020
- Abreu, L., Cavalieri, A.V.G., Schlatter, P., Vinuesa, R., & Henningson, D.S. (2020). Resolvent modelling of near-wall coherent structures in turbulent channel flow. International Journal of Heat and Fluid Flow, 85. https://doi.org/10.1016/j.ijheatfluidflow.2020.108662
- Abreu, L., Cavalieri, A.V.G., Schlatter, P., Vinuesa, R., & Henningson, D.S. (2020). Spectral proper orthogonal decomposition and resolvent analysis of near-wall coherent structures in turbulent pipe flows. Journal of Fluid Mechanics. https://doi.org/10.1017/jfm.2020.445
- Amor, C., Perez, J.M., Schlatter, P., Vinuesa, R., & Le Clainche, S. (2020). Soft Computing Techniques to Analyze the Turbulent Wake of a Wall-Mounted Square Cylinder. In Francisco Martínez Álvarez, Alicia Troncoso Lora, José António Sáez Muñoz, Emilio Corchado, Héctor Quintián (Eds.), Advances in Intelligent Systems and Computing (pp. 577-586). Seville, ESP: Springer Verlag.
- Appelquist, E., Schlatter, P., Alfredsson, P.H., & Lingwood, R.J. (2020). Transition to turbulence in the rotating-disk boundary layer. In ETC 2013 - 14th European Turbulence Conference. Lyon, FRA: Zakon Group LLC.
- Atzori, M., Vinuesa, R., Fahland, G., Stroh, A., Gatti, D., Frohnapfel, B., & Schlatter, P. (2020). Aerodynamic Effects of Uniform Blowing and Suction on a NACA4412 Airfoil. Flow Turbulence and Combustion, 105(3), 735-759. https://doi.org/10.1007/s10494-020-00135-z
- Atzori, M., Vinuesa, R., Gatti, D., Stroh, A., Frohnapfel, B., & Schlatter, P. (2020). Effects of Different Friction Control Techniques on Turbulence Developing Around Wings. In (pp. 305-311). Springer.
- Atzori, M., Vinuesa, R., Lozano-Durán, A., & Schlatter, P. (2020). Coherent structures in turbulent boundary layers over an airfoil. In Javier Jimenez (Eds.), Journal of Physics: Conference Series. Madrid, ESP: Institute of Physics Publishing.
- Beneitez, M., Duguet, Y., Schlatter, P., & Henningson, D.S. (2020). Edge manifold as a Lagrangian coherent structure in a high-dimensional state space. Physical Review Research, 2(3). https://doi.org/10.1103/PhysRevResearch.2.033258
- Brethouwer, G., Wei, L., Schlatter, P., & Johansson, A.V. (2020). Turbulence and cyclic bursts in rotating channel flow. In ETC 2013 - 14th European Turbulence Conference. Lyon, FRA: Zakon Group LLC.
- Canton, J., Rinaldi, E., Orlu, R., & Schlatter, P. (2020). Critical Point for Bifurcation Cascades and Featureless Turbulence. Physical Review Letters, 124(1). https://doi.org/10.1103/PhysRevLett.124.014501
- Chin, R.C., Vinuesa, R., Orlu, R., Cardesa, J., Noorani, A., Chong, M.S., & Schlatter, P. (2020). Backflow events under the effect of secondary flow of Prandtl's first kind. Physical Review Fluids, 5(7). https://doi.org/10.1103/PhysRevFluids.5.074606
- Cimarelli, A., de Angelis, E., Schlatter, P., Brethouwer, G., Talamelli, A., & Casciola, C.M. (2020). Scalings of the outer energy source of wall-turbulence. In ETC 2013 - 14th European Turbulence Conference. Lyon, FRA: Zakon Group LLC.
- Drozdz, A., Elsner, W., Niegodajew, P., Vinuesa, R., Orlu, R., & Schlatter, P. (2020). A description of turbulence intensity profiles for boundary layers with adverse pressure gradient. European Journal of Mechanics B-Fluids, 84, 470-477. https://doi.org/10.1016/j.euromechflu.2020.07.003
- Eitel-Amor, G., Örlü, R., & Schlatter, P. (2020). The significance of hairpin vortices in turbulent boundary layers. In ETC 2013 - 14th European Turbulence Conference. Lyon, FRA: Zakon Group LLC.
- El Khoury, G.K., Schlatter, P., Brethouwer, G., & Johansson, A.V. (2020). Turbulent pipe flow: New DNS data and large-scale structures. In ETC 2013 - 14th European Turbulence Conference. Lyon, FRA: Zakon Group LLC.
- Fan, Y., Li, W., Atzori, M., Pozuelo, R., Schlatter, P., & Vinuesa, R. (2020). Decomposition of the mean friction drag in adverse-pressure-gradient turbulent boundary layers. Physical Review Fluids, 5(11). https://doi.org/10.1103/PhysRevFluids.5.114608
- Guastoni, L., Encinar, M.P., Schlatter, P., Azizpour, H., & Vinuesa, R. (2020). Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks. In Javier Jimenez (Eds.), Journal of Physics: Conference Series. Madrid, ESP: Institute of Physics Publishing.
- Kleusberg, E., Schlatter, P., & Henningson, D.S. (2020). Parametric dependencies of the yawed wind-turbine wake development. Wind Energy, 23(6), 1367-1380. https://doi.org/10.1002/we.2395
- Lupi, V., Canton, J., & Schlatter, P. (2020). Global stability analysis of a 90°-bend pipe flow. International Journal of Heat and Fluid Flow, 86. https://doi.org/10.1016/j.ijheatfluidflow.2020.108742
- Offermans, N., Peplinski, A., Marin, O., Merzari, E., & Schlatter, P. (2020). Performance of preconditioners for large-scale simulations using nek5000. In Spencer J. Sherwin, Joaquim Peiró, Peter E. Vincent, David Moxey, Christoph Schwab (Eds.), Lecture Notes in Computational Science and Engineering (pp. 263-272). London, GBR: Springer.
- Offermans, N., Peplinski, A., Marin, O., & Schlatter, P. (2020). Adaptive mesh refinement for steady flows in Nek5000. Computers & Fluids, 197. https://doi.org/10.1016/j.compfluid.2019.104352
- Offermans, N., Peplinski, A., & Schlatter, P. (2020). Mesh Optimization Using Dual-Weighted Error Estimators: Application to the Periodic Hill. In (pp. 397-403). Springer.
- Orlu, R., & Schlatter, P. (2020). Comment on "evolution of wall shear stress with Reynolds number in fully developed turbulent channel flow experiments". Physical Review Fluids, 5(12). https://doi.org/10.1103/PhysRevFluids.5.127601
- Peplinski, A., Offermans, N., Fischer, P.F., & Schlatter, P. (2020). Non-conforming elements in nek5000: Pressure preconditioning and parallel performance. In Spencer J. Sherwin, Joaquim Peiró, Peter E. Vincent, David Moxey, Christoph Schwab (Eds.), Lecture Notes in Computational Science and Engineering (pp. 599-609). London, GBR: Springer.
- Samie, M., Baars, W.J., Rouhi, A., Schlatter, P., Orlu, R., Marusic, ., & Hutchins, N. (2020). Near wall coherence in wall-bounded flows and implications for flow control. International Journal of Heat and Fluid Flow, 86. https://doi.org/10.1016/j.ijheatfluidflow.2020.108683
- Sanchez Abad, N., Vinuesa, R., Schlatter, P., Andersson, M., & Karlsson, M. (2020). Simulation strategies for the Food and Drug Administration nozzle using Nek5000. AIP Advances, 10(2). https://doi.org/10.1063/1.5142703
- Sanmiguel Vila, C., Vinuesa, R., Discetti, S., Ianiro, A., Schlatter, P., & Orlu, R. (2020). Separating adverse-pressure-gradient and Reynolds-number effects in turbulent boundary layers. Physical Review Fluids, 5(6). https://doi.org/10.1103/PhysRevFluids.5.064609
- Tanarro, ., Vinuesa, R., & Schlatter, P. (2020). Power-Spectral Density in Turbulent Boundary Layers on Wings. In (pp. 11-16). Springer.
- Tanarro, A., Mallor, F., Offermans, N., Peplinski, A., Vinuesa, R., & Schlatter, P. (2020). Enabling Adaptive Mesh Refinement for Spectral-Element Simulations of Turbulence Around Wing Sections. Flow Turbulence and Combustion, 105(2), 415-436. https://doi.org/10.1007/s10494-020-00152-y
- Vila, C.S., Vinuesa, R., Discetti, S., Ianiro, A., Schlatter, P., & Orlu, R. (2020). Experimental realisation of near-equilibrium adverse-pressure-gradient turbulent boundary layers. Experimental Thermal and Fluid Science, 112. https://doi.org/10.1016/j.expthermflusci.2019.109975
- Von Deyn, L.H., Forooghi, P., Frohnapfel, B., Schlatter, P., Hanifi, A., & Henningson, D.S. (2020). Direct numerical simulations of bypass transition over distributed roughness. AIAA Journal, 58(2), 702-711. https://doi.org/10.2514/1.J057765
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