Enhancing computational fluid dynamics with machine learning.

R. Vinuesa and S. L. Brunton.

Nat. Comput. Sci., 2, 358–366, 2022.

Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows.

H. Eivazi, S. Le Clainche, S. Hoyas and R. Vinuesa.

Expert Syst. Appl., 202, 117038, 2022.

Assessments of model-form uncertainty using Gaussian stochastic weight averaging for fluid-flow regression.

M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa and K. Fukagata.

Phys. D: Nonlinear Phenom., 440, 133454, 2022.

Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems.

Y. Morita, S. Rezaeiravesh, N. Tabatabaei, R. Vinuesa, K. Fukagata and P. Schlatter.

J. Comput. Phys., 449, 110788, 2022.

On the generation and destruction mechanisms of arch vortices in urban
fluid flows.

E. Lazpita, Á. Martínez-Sánchez, A. Corrochano, S. Hoyas, S. Le Clainche and R. Vinuesa.

Phys. Fluids, 34, 051702, 2022.

Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations.

H. Eivazi, M. Tahani, P. Schlatter and R. Vinuesa.

Phys. Fluids, 2022. Accepted.

Predicting the temporal dynamics of turbulent channels through deep learning.

G. Borrelli, L. Guastoni, H. Eivazi, P. Schlatter and R. Vinuesa.

Int. J. Heat Fluid Flow, 96, 109010, 2022

An adverse-pressure-gradient turbulent boundary layer with nearly-constant β ≃ 1.4 up to Reθ ≃ 8, 700.

R. Pozuelo, Q. Li, P. Schlatter and R. Vinuesa.

J. Fluid Mech., 939, A34, 2022.

Decomposition of the mean friction drag on a NACA4412 airfoil under uniform blowing/suction.

Y. Fan, M. Atzori, R. Vinuesa, D. Gatti, P. Schlatter and W. Li.

J. Fluid Mech., 932, A31, 2022.

Control effects on coherent structures in a non-uniform adverse-pressure-gradient boundary layer.

M. Atzori, R. Vinuesa and P. Schlatter.

Int. J. Heat Fluid Flow, 97, 109036, 2022.

Predicting coherent turbulent structures via deep learning.

D. Schmekel, F. Alcántara-Ávila, S. Hoyas and R. Vinuesa.

Front. Phys., 10, 888832, 2022.

Remote sensing and AI for building climate adaptation applications.

B. Sirmacek and R. Vinuesa.

Results Eng., 15, 100524, 2022.

Get out of the BAG! Silos in AI ethics education: Unsupervised topic modeling
analysis of global AI curricula.

R. Tallal Javed, O. Nasir, M. Borit, L. Vanhée, E. Zea, S. Gupta, R. Vinuesa and J. Qadir.

J. Artif. Intell. Res., 73, 933–965, 2022.

The role of robotics in achieving the United Nations Sustainable Development Goals – The Experts’ Meeting at the 2021 IEEE/RSJ IROS Workshop.

V. Mai, B. Vanderborght, T. Haidegger, A. Khamis, N. Bhargava, D. B. O. Boesl, K. Gabriels, A. Jacobs, A. Moon, R. Murphy, Y. Nakauchi, E. Prestes, B. Rao R., R. Vinuesa and C.-M. Mörch.

IEEE Robot. Autom. Mag., 29, 92–107, 2022.

Flow control in wings and discovery of novel approaches via deep reinforcement learning.

R. Vinuesa, O. Lehmkuhl, A. Lozano-Durán and J. Rabault.

Fluids, 7, 62, 2022.

An uncertainty-quantification framework for assessing accuracy, sensitivity and robustness in computational fluid dynamics.

S. Rezaeiravesh, R. Vinuesa and P. Schlatter.

J. Comput. Sci., 62, 101688, 2022.

Model-form uncertainty quantification in neural-network-based fluid-flow estimation.

M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa and K. Fukagata.

Nagare J. Jpn. Soc. Fluid Mech., 41, 2022.

Machine-learning methods for complex flows.

R. Vinuesa and S. Le Clainche.

Energies, 15, 1513, 2022.

Techniques for turbulence tripping of boundary layers in RANS simulations.

N. Tabatabaei, R. Vinuesa, R. Örlü and P. Schlatter.

Flow Turbul. Combust., 108, 661–682, 2022.

RANS modelling of a NACA4412 wake using wind tunnel measurements.

N. Tabatabaei, M. Hajipour, F. Mallor, R. Örlü, R. Vinuesa and P. Schlatter

Fluids, 7, 153, 2022.

Environmental thresholds for mass-extinction events.

G. R. McPherson, B. Sirmacek and R. Vinuesa.

Results Eng., 13, 100342, 2022.

Application and advances in radiographic and novel technologies used for non-intrusive object inspection.

D. Mamchur, J. Peksa, S. Le Clainche and R. Vinuesa.

Sensors, 22, 2121, 2022.

Understanding the bibliometric patterns of publications in IEEE Access.

R. Raman, P. Singh, V. K. Singh, R. Vinuesa and P. Nedungadi.

IEEE Access, 10, 35561–35577, 2022.

Analysis of the state of the art on non-intrusive object-screening techniques.

D. Mamchur, J. Peksa, S. Le Clainche and R. Vinuesa.

Prz. Elektrotech., 98, 168–173, 2022.

Innovative software systems for managing the impact of the COVID-19 pandemic.

S. Singh Gill, R. Vinuesa, V. Balasubramanian and S. K. Ghosh.

Softw.: Pract. Exper., 52, 821–823, 2022.

2021

Interpretable deep-learning models to help achieve the Sustainable Development Goals.

R. Vinuesa and B. Sirmacek.

Nat. Mach. Intell., 3, 926, 2021.

Convolutional-network models to predict wall-bounded turbulence from wall
quantities.

L. Guastoni, A. Güemes, A. Ianiro, S. Discetti, P. Schlatter, H. Azizpour and R. Vinuesa.

J. Fluid Mech., 928, A27, 2021.

From coarse wall measurements to turbulent velocity fields through deep learning.

A. Güemes, S. Discetti, A. Ianiro, B. Sirmacek, H. Azizpour and R. Vinuesa.

Phys. Fluids, 33, 075121, 2021.

Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence.

H. Eivazi, L. Guastoni, P. Schlatter, H. Azizpour and R. Vinuesa.

Int. J. Heat Fluid Flow, 90, 108816, 2021.

An interpretable framework of data-driven turbulence modeling using deep neural networks.

C. Jiang, R. Vinuesa, R. Chen, J. Mi, S. Laima and H. Li.

Phys. Fluids, 33, 055133, 2021.

Data deprivations, data gaps and digital divides: lessons from the COVID-19 pandemic.

W. Naudé and R. Vinuesa.

Big Data Soc., 8, 1–12, 2021.

Spanwise coherent hydrodynamic waves around at plates and airfoils.

L. I. Abreu, A. Tanarro, A. V. G. Cavalieri, P. Schlatter, R. Vinuesa, A. Hanifi and D. S. Henningson.

J. Fluid Mech., 927, A1, 2021.

High-fidelity simulations in complex geometries: towards better flow understanding and development of turbulence models.

R. Vinuesa.

Results Eng., 11, 100254, 2021.

Intense Reynolds-stress events in turbulent ducts.

M. Atzori, R. Vinuesa, A. Lozano-Durán and P. Schlatter.

Int. J. Heat Fluid Flow, 89, 108802, 2021.

Uniform blowing and suction applied to non-uniform adverse-pressure-gradient wing boundary layers.

M. Atzori, R. Vinuesa, A. Stroh, D. Gatti, B. Frohnapfel and P. Schlatter.

Phys. Rev. Fluids, 6, 113904, 2021.

Regulating artificial-intelligence applications to achieve the sustainable development goals.

H.-H. Goh and R. Vinuesa.

Discov. Sustain., 2, 52, 2021.

On numerical uncertainties in scale-resolving simulations of canonical wall turbulence.

S. Rezaeiravesh, R. Vinuesa and P. Schlatter.

Comput. Fluids, 227, 105024, 2021.

Investigation of blowing and suction for turbulent flow control on airfoils.

G. Fahland, A. Stroh, B. Frohnapfel, M. Atzori, R. Vinuesa, P. Schlatter and D. Gatti.

AIAA J., 59, 4422–4436, 2021.

Spectral-element simulation of the turbulent flow in an urban environment.

M. Stuck, A. Vidal, P. Torres, H. M. Nagib, C. Wark and R. Vinuesa.

Appl. Sci., 11, 6472, 2021.

Assessing whether artificial intelligence is an enabler or an inhibitor of sustainability at indicator level.

S. Gupta, S. D. Langhans, S. Domisch, F. Fuso Nerini, A. Felländer, M. Battaglini, M. Tegmark and R. Vinuesa.

Transp. Eng., 4, 100064, 2021.

Two-dimensional compact-finite-difference schemes for solving the bi-Laplacian operator with homogeneous wall-normal derivatives.

J. Amo-Navarro, R. Vinuesa, J. A. Conejero and S. Hoyas.

Mathematics, 9, 2508, 2021.

Improved learning of Mechanics through augmented reality.

C. Hedenqvist, M. Romero and R. Vinuesa.

Technol. Knowl. Learn., 2021. Accepted.

On the experimental, numerical and data-driven methods to study urban flows.

P. Torres, S. Le Clainche and R. Vinuesa.

Energies, 14, 1310, 2021.

COVIDTAS COVID-19 tracing app scale – An evaluation framework.

R. Raman, K. Achuthan, R. Vinuesa and P. Nedungadi.

Sustainability, 13, 2912, 2021.

COVID-19 digital contact tracing applications and techniques: a review post
initial deployments.

M. Shahroz, F. Ahmad, M. S. Younis, N. Ahmad, M. N. K. Boulos, R. Vinuesa and J. Qadir.

Transp. Eng., 5, 100072, 2021.

In-situ visualization of large-scale turbulence simulations in Nek5000 with ParaView Catalyst.

M. Atzori, W. Köpp, S. W. D. Chien, D. Massaro, F. Mallor, A. Peplinski, M. Rezaei, N. Jansson, S. Markidis, R. Vinuesa, E. Laure, P. Schlatter and T. Weinkauf.

J. Supercomput., 2021. Accepted.

Flow structures on a planar Food and Drug Administration (FDA) nozzle at low and intermediate Reynolds number.

A. Corrochano, D. Xavier, P. Schlatter, R. Vinuesa and S. Le Clainche.

Fluids, 6, 4, 2021.

Aerodynamic free-flight conditions in wind-tunnel modelling through reduced-order wall inserts.

N. Tabatabaei, R. Örlü, R. Vinuesa and P. Schlatter.

Fluids, 6, 265, 2021.

UQit: A Python package for uncertainty quantification (UQ) in computational fluid dynamics (CFD).

S. Rezaeiravesh, R. Vinuesa and P. Schlatter.

J. Open Source Softw., 6, 2871, 2021.

Bibliometric analysis of SARS, MERS, and COVID-19 studies from India and connection to Sustainable Development Goals.

R. Raman, R. Vinuesa and P. Nedungadi.

Sustainability, 13, 7555, 2021.

Acquisition and user behavior in online science laboratories before and during Covid-19 pandemic.

R. Raman, R. Vinuesa and P. Nedungadi.

Multimodal Technol. Interact., 5, 46, 2021.

International scientific collaboration is needed to bridge science to society: USERN2020 consensus statement.

Momtazmanesh et al.

SN Compr. Clin. Med., 3, 1699–1703, 2021.

2020

The role of artificial intelligence in achieving the Sustainable Development Goals.

R. Vinuesa, H. Azizpour, I. Leite, M. Balaam, V. Dignum, S. Domisch, A. Felländer, S. D. Langhans, M. Tegmark and F. Fuso Nerini.

Nat. Commun., 11, 233, 2020.

Instantaneous wall-shear-stress measurements: advances and application to near-wall extreme events.

R. Örlü and R. Vinuesa

Meas. Sci. Technol., 31, 112001, 2020.

Decomposition of the mean friction drag in adverse-pressure-gradient turbulent boundary layers.

Y. Fan, W. Li, M. Atzori, R. Pozuelo, P. Schlatter and R. Vinuesa.

Phys. Rev. Fluids, 5, 114608, 2020.

Effect of adverse pressure gradients on turbulent wing boundary layers.

A. Tanarro, R. Vinuesa and P. Schlatter.

J. Fluid Mech., 883, A8, 2020.

SPOD and resolvent analysis of near-wall coherent structures in turbulent pipe flows.

L. I. Abreu, A. V. G. Cavalieri, P. Schlatter, R. Vinuesa and D. S. Henningson.

J. Fluid Mech., 900, A11, 2020.

Separating adverse-pressure-gradient and Reynolds-number effects in turbulent boundary layers.

C. Sanmiguel Vila, R. Vinuesa, S. Discetti, A. Ianiro, P. Schlatterand R. Örlü.

Phys. Rev. Fluids, 5, 064609, 2020.

Back-flow events under the effect of secondary flow of Prandtl’s first kind.

R. C. Chin, R. Vinuesa, R. Örlü, J. I. Cardesa, A. Noorani, M. S. Chong and P. Schlatter.

Phys. Rev. Fluids, 5, 074606, 2020.

A socio-technical framework for digital contact tracing.

R. Vinuesa, A. Theodorou, M. Battaglini and V. Dignum.

Results Eng., 8, 100163, 2020.

A description of turbulence intensity profiles for boundary layers with adverse pressure gradient.

A. Dróżdż, W. Elsner, P. Niegodajew, R. Vinuesa, R. Örlü and P. Schlatter.

Eur. J. Mech. B/Fluids, 84, 470–477, 2020.

Experimental realisation of near-equilibrium adverse-pressure-gradient turbulent boundary layers.

C. Sanmiguel Vila, R. Vinuesa, S. Discetti, A. Ianiro, P. Schlatter and R. Örlü.

Exp. Thermal Fluid Sci., 112, 109975, 2020.

Modeling the turbulent wake behind a wall-mounted square cylinder.

C. Amor, J. M. Pérez, P. Schlatter, R. Vinuesa and S. Le Clainche.

Log. J. IGPL, jzaa060, 2020.

Simulation strategies for the Food and Drug Administration nozzle using Nek5000.

N. Sánchez Abad, R. Vinuesa, P. Schlatter, M. Andersson and M. Karlsson.

AIP Adv., 10, 025033, 2020.

Aerodynamic effects of uniform blowing and suction on a NACA4412 airfoil.

M. Atzori, R. Vinuesa, G. Fahland, A. Stroh, D. Gatti, B. Frohnapfel and P. Schlatter.

Flow Turbul. Combust., 105, 735–759, 2020.

Enabling adaptive mesh refinement for spectral-element simulations of turbulence around wing sections.

A. Tanarro, F. Mallor, N. Offermans, A. Peplinski, R. Vinuesa and P. Schlatter.

Flow Turbul. Combust., 105, 415–436, 2020.

Resolvent modelling of near-wall coherent structures in turbulent channel flow.

L. I. Abreu, A. V. G. Cavalieri, P. Schlatter, R. Vinuesa and D. S. Henningson.

Int. J. Heat Fluid Flow, 85, 108662, 2020.

Organic growth theory for corporate sustainability.

A. Karnama and R. Vinuesa.

Sustainability, 12, 8523, 2020.

2019

Predictions of turbulent shear flows using deep neural networks.

P. A. Srinivasan, L. Guastoni, H. Azizpour, P. Schlatter and R. Vinuesa.

Phys. Rev. Fluids, 4, 054603, 2019.

Transfer functions for flow predictions in wall-bounded turbulence.

K. Sasaki, R. Vinuesa, A. V. G. Cavalieri, P. Schlatter and D. S. Henningson.

AIAA J., 2021. Accepted.

Quantification of amplitude modulation in wall-bounded turbulence.

E. Dogan, R. Örlü, D. Gatti, R. Vinuesa and P. Schlatter.

Fluid Dyn. Res., 51, 011408, 2019.

Vorticity fluxes: A tool for three-dimensional and secondary flows in turbulent shear flows.

H. M. Nagib, A. Vidal and R. Vinuesa.

J. Fluids Struct., 89, 39–48, 2019.

Enhanced large-scale atmospheric flow interaction with ice sheets at high model resolution.

F. Schenk and R. Vinuesa.

Results Eng., 3, 100030, 2019.

Flow organization in the wake of a rib in a turbulent boundary layer with pressure gradient.

A. Guëmes, C. Sanmiguel Vila, R. Örlü, R. Vinuesa, P. Schlatter, A. Ianiro and S. Discetti.

Exp. Thermal Fluid Sci., 108, 115–124, 2019.

The influence of thermal boundary conditions on turbulent forced convection pipe flow at two Prandtl numbers.

S. Straub, P. Forooghi, L. Marocco, T. Wetzel, R. Vinuesa, P. Schlatter and B. Frohnapfel.

Int. J. Heat Mass Transf., 144, 118601, 2019.

Organic data centers: a sustainable solution for computing facilities.

A. Karnama, E. B. Haghighi and R. Vinuesa.

Results Eng., 4, 100063, 2019.

2018

Secondary flow in spanwise-periodic in-phase sinusoidal channels.

A.Vidal, H. M. Nagib, P. Schlatter and R.Vinuesa

J. Fluid Mech., 851, 288–316, 2018.

Turbulent boundary layers around wing sections up to Rec = 1,000,000.

R. Vinuesa, P. S. Negi, M. Atzori, A. Hanifi, D. S. Henningson and P. Schlatter.

Int. J. Heat Fluid Flow, 72, 86–99, 2018.

Turbulent structure of a simplified urban fluid flow studied through stereoscopic particle image velocimetry.

B. Monnier, S. A. Goudarzi, R. Vinuesa and C. Wark.

Boundary- Layer Meteorol., 166, 239–268, 2018.

Secondary flow in turbulent ducts with increasing aspect ratio.

R. Vinuesa, P. Schlatter and H. M. Nagib.

Phys. Rev. Fluids, 3, 054606, 2018.

Assessment of uncertainties in hot-wire anemometry and oil-film interferometry measurements for wall-bounded turbulent flows.

S. Rezaeiravesh, R. Vinuesa, M. Liefvendahl and P. Schlatter.

Eur. J. Mech. B/Fluids, 72, 57–73, 2018.

Lossy data compression effects on wall-bounded turbulence: bounds on data reduction.

E. Otero, R. Vinuesa, O. Marin, E. Laure and P. Schlatter.

Flow Turbul. Combust., 101, 365–387, 2018.

Unsteady aerodynamic effects in small-amplitude pitch oscillations of an airfoil.

P. S. Negi, R. Vinuesa, A. Hanifi, P. Schlatter and D. S. Henningson.

Int. J. Heat Fluid Flow, 71, 378–391, 2018.

Turbulent rectangular ducts with minimum secondary flow.

A. Vidal, R. Vinuesa, P. Schlatter and H. M. Nagib.

Int. J. Heat Fluid Flow, 72, 317–328, 2018.

Vorticity fluxes and secondary flow: Relevance for turbulence modelling.

A. Vidal, H. M. Nagib and R. Vinuesa.

Phys. Rev. Fluids, 3, 072602(R), 2018.

2017

Pressure-gradient turbulent boundary layers developing around a wing section.

R. Vinuesa, S. M. Hosseini, A. Hanifi, D. S. Henningson and P. Schlatter.

Flow Turbul. Combust., 99, 613–641, 2017.

Revisiting history effects in adverse-pressure-gradient turbulent boundary layers.

R. Vinuesa, R. Örlü, C. Sanmiguel Vila, A. Ianiro, S. Discetti and P. Schlatter.

Flow Turbul. Combust., 99, 565–587, 2017.

Turbulent duct flow controlled with spanwise wall oscillations.

S. Straub, R. Vinuesa, P. Schlatter, B. Frohnapfel and D. Gatti.

Flow Turbul. Combust., 99, 787–806, 2017.

Adverse- pressure-gradient effects on turbulent boundary layers: statistics and flow-field organiza- tion.

C. Sanmiguel Vila, R. Örlü, R. Vinuesa, P. Schlatter, A. Ianiro and S. Discetti.

Flow Turbul. Combust., 99, 589–612, 2017.

Influence of corner geometry on the secondary flow in turbulent square ducts.

A. Vidal, R. Vinuesa, P. Schlatter and H. M. Nagib.

Int. J. Heat Fluid Flow, 67, 69–78, 2017.

History effects and near equilibrium in adverse-pressure-gradient turbulent boundary layers.

A. Bobke, R. Vinuesa, R. Örlü and P. Schlatter.

J. Fluid Mech., 820, 667–692, 2017.

On the identification of well-behaved turbulent boundary layers.

C. Sanmiguel Vila, R. Vinuesa, S. Discetti, A. Ianiro, P. Schlatter and R. Örlü.

J. Fluid Mech., 822, 109–138, 2017.

Characterisation of back flow events over a wing section.

R.Vinuesa, R. Örlü and P. Schlatter.

J. Turbul., 18, 170–185, 2017.

Impact simulation and optimisation of elastic fuel tanks reinforced with exoskeleton for aerospace applications.

C. Prus, R. Vinuesa, P. Schlatter, E. Tembrás, E. Mestres and J. P. Berro Ramírez.

S. M. Hosseini, R. Vinuesa, P. Schlatter, A. Hanifi and D. S. Henningson.

Int. J. Heat Fluid Flow, 61, 117–128, 2016.

Characterization of the secondary flow in hexagonal ducts.

O. Marin, R. Vinuesa, A. V. Obabko and P. Schlatter

Phys. Fluids, 28, 125101, 2016.

Convergence of numerical simula- tions of turbulent wall-bounded flows and mean cross-flow structure of rectangular ducts.

R. Vinuesa, C. Prus, P. Schlatter and H. M. Nagib.

Meccanica, 51, 3025–3042, 2016.

Aspect ratio effect on particle transport in turbulent duct flows.

A. Noorani, R. Vinuesa, L. Brandt and P. Schlatter.

Phys. Fluids, 28, 105103, 2016.

On determining characteristic length scales in pressure-gradient turbulent boundary layers.

R.Vinuesa, A. Bobke, R. Örlü and P. Schlatter.

Phys. Fluids, 27, 105107, 2016.

Alternative interpretation of the Superpipe data and motivation for CICLoPE: the effect of a decreasing viscous length scale.

R. Vinuesa, R. D. Duncan and H. M. Nagib.

Eur. J. Mech. B/Fluids, 58, 109–116, 2016.

Simulations and experiments of heat loss from a parabolic trough absorber tube over a range of pressures and gas compositions in the vacuum chamber.

R. Vinuesa, L. F. de Arévalo, M. Luna and H. Cachafeiro.

J. Renew. Sustain. Energy, 8, 023701, 2016.

Enhancing the accuracy of measurement techniques in high Reynolds number turbulent boundary layers for more representative comparison to their canonical representations.

R. Vinuesa and H. M. Nagib.

Eur. J. Mech. B/Fluids, 55, 300–312, 2016.

2015

Enhanced secondary motion of the turbulent flow through a porous square duct.

A. Samanta, R. Vinuesa, I. Lashgari, P. Schlatter and L. Brandt.

J. Fluid Mech., 784, 681–693, 2015.

Documentation of the role of large-scale structures in the bursting process in turbulent boundary layers.

R. Vinuesa, M. H. Hites, C. E. Wark and H. M. Nagib.

Phys. Fluids, 27, 105107, 2015.

Direct numerical simulation of the flow around a wall-mounted square cylinder under various inflow conditions.

R. Vinuesa, P. Schlatter, J. Malm, C. Mavriplis and D. S. Henningson.

J. Turbul., 16, 555–587, 2015.

On minimum aspect ratio for duct-flow facilities and the role of side walls in generating secondary flows.

R. Vinuesa, P. Schlatter and H. M. Nagib.

J. Turbul., 16, 588–606, 2015.

2014

Aspect ratio effects in turbulent duct flows studied through direct numerical simulation.

R. Vinuesa, A Noorani, A. Lozano-Durán, G. K. El Khoury, P. Schlatter, P. F. Fischer and H. M. Nagib.

This paper provided one of the cover images for the International Symposium on Turbulence & Shear Flow Phenomena (TSFP-10).

J. Turbul., 15, 677–706, 2014.

New insight into flow development and two dimensionality of turbulent channel flows.

R. Vinuesa, E. Bartrons, D. Chiu, K. M. Dressler, J.-D. Ruëdi, Y. Suzuki and H. M. Nagib.

Exp. Fluids, 55, 1759, 2014.

Role of data uncertainties in identifying the logarithmic region of turbulent boundary layers.

R. Vinuesa, P. Schlatter and H. M. Nagib.

Exp. Fluids, 55, 1751, 2014.

Experiments and computations of localized pressure gradients with different history effects.

R. Vinuesa, P. H. Rozier, P. Schlatter and H. M. Nagib.

AIAA J., 52, 368–384, 2014.

2013

Obtaining accurate mean velocity measurements in high Reynolds number turbulent boundary layers using Pitot tubes.

S. C. C. Bailey, M. Hultmark, J. P. Monty, P. H. Alfredsson, M. S. Chong, R. D. Duncan, J. H. M. Fransson, N. Hutchins, I. Marusic, B. J. McKeon, H. M. Nagib, R. Örlü, A. Segalini, A. J. Smits and R. Vinuesa.