Ricardo Vinuesa

KTH Royal Institute of Technology | KTH · Department of Engineering Mechanics

This article reports on one component of a larger study on measurement of the zeropressure- gradient turbulent flat plate boundary layer, in which a detailed investigation was conducted of the suite of corrections required for mean velocity measurements performed using Pitot tubes. In particular, the corrections for velocity shear across the tube a...

Understanding flow structures in urban areas is widely recognized as a challenging concern due to its effect on urban development, air quality, and pollutant dispersion. In this study, state-of-the-art data-driven methods for modal analysis of simplified urban flows are used to study the dominant flow processes in these environments. Higher order d...

The presence of very-large-scale motions in wall-bounded turbulent flows is commonly associated with their footprint in the form of the superposition of the large scales at the wall and the additional amplitude modulation of small-scale near-wall turbulence. These two phenomena are currently understood to be interlinked, with the superposed large-s...

The objective of this study is to assess the capability of convolution-based neural networks to predict wall quantities in a turbulent open channel flow. The first tests are performed by training a fully-convolutional network (FCN) to predict the 2D velocity-fluctuation fields at the inner-scaled wall-normal location $y^{+}_{\rm target}$, using the...

Deep reinforcement learning (DRL) has been applied to a variety of problems during the past decade, and has provided effective control strategies in high-dimensional and non-linear situations that are challenging to traditional methods. Flourishing applications now spread out into the field of fluid dynamics, and specifically of active flow control...

With the availability of new high-Reynolds-number (Re) databases of turbulent boundary layers (TBLs) it has been possible to identify in detail certain regions of the boundary layer with more complex behavior. In this study we consider a unique database at moderately-high Re, with a nearconstant adverse pressure gradient (APG) [Pozuelo et al., J. F...

This study proposes a newly developed deep-learning-based method to generate turbulent inflow conditions for spatially developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced super-resolution generative adversarial network is utilised to predict velocity fields of a spatially developing TBL at...

Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an abundance of high-fidelity databases can be generated by experimental measurements and numerical simulations, but obtaining such accurate data in full-scale applications is curren...

Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem that requires new perspectives to be tackled. One of the key strategies has been to study interactions among the coherent structures in the flow. Such interactions are explored in this study for the first time using an explainable deep-learni...

The 2030 Agenda of the United Nations (UN) revolves around the Sustainable Development Goals (SDGs). A critical step towards that objective is identifying whether scientific production aligns with the SDGs' achievement. To assess this, funders and research managers need to manually estimate the impact of their funding agenda on the SDGs, focusing o...

The objective of the present study is to provide a numerical database of thermal boundary layers and to contribute to the understanding of the dynamics of passive scalars at different Prandtl numbers. In this regard, a direct numerical simulation (DNS) of an incompressible zero-pressure-gradient turbulent boundary layer is performed with the Reynol...

We introduce a reinforcement learning (RL) environment to design and benchmark control strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The environment provides a framework for computationally-efficient, parallelized, high-fidelity fluid simulations, ready to interface with established RL agent programming interface...

Understanding the progress of the Sustainable Development Goals (SDGs) proposed by the United Nations (UN) is important, but difficult. In particular, policymakers would need to understand the sentiment within the public regarding challenges associated with climate change. With this in mind and the rise of social media, this work focuses on the tas...

Deep reinforcement learning (DRL) has been applied to a variety of problems during the past decade, and has provided effective control strategies in high-dimensional and non-linear situations that are challenging to traditional methods. Flourishing applications now spread out into the field of fluid dynamics, and specifically of active flow control...

With each IPCC report, the science basis around climate change increases extensively in terms of scope, depth, and complexity. The challenge is making this knowledge accessible to society – and reforming the way researchers generate, package, and communicate scientific findings to produce real climate action.

Physics-informed neural networks (PINN) are machine-learning methods that have been proved to be very successful and effective for solving governing equations of fluid flow. In this work we develop a robust and efficient model within this framework and apply it to a series of two-dimensional three-component (2D3C) stereo particle-image velocimetry...

The increase in emissions associated with aviation requires deeper research into novel sensing and flow-control strategies to obtain improved aerodynamic performances. In this context, data-driven methods are suitable for exploring new approaches to control the flow and develop more efficient strategies. Deep artificial neural networks (ANNs) used...

The renewed interest from the scientific community in machine learning (ML) is opening many new areas of research. Here we focus on how novel trends in ML are providing opportunities to improve the field of computational fluid dynamics (CFD). In particular, we discuss synergies between ML and CFD that have already shown benefits, and we also assess...

The digital revolution has brought ethical crossroads of technology and behavior, especially in the realm of sustainable cities. The need for a comprehensive and constructive ethical framework is emerging as digital platforms encounter trouble to articulate the transformations required to accomplish the sustainable development goal (SDG) 11 (on sus...

Turbulence is a complicated phenomenon because of its chaotic behavior with multiple spatio-temporal scales. Turbulence also has irregularity and diffusivity, making predicting and reconstructing turbulence more challenging. This study proposes a deep-learning approach to reconstruct three-dimensional (3D) high-resolution turbulent flows from spati...

The 2030 Agenda of the United Nations (UN) revolves around the Sustainable Development Goals (SDGs). A critical step towards that objective is identifying whether scientific production aligns with the SDGs' achievement. To assess this, funders and research managers need to manually estimate the impact of their funding agenda on the SDGs, focusing o...

Artificial Intelligence (AI) should aim at benefiting society, the economy, and the environment, i.e., AI should aim to be socially good. The UN-defined Sustainable Development Goals (SDGs) are the best depiction to measure social good. For AI to be socially good, it must support all 17 UN SDGs. Our work provides a unique insight into AI on all fro...

This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (experimental and numerical), aerodynamics, acoustics, combustion and structural health monitoring. We review the state of the art, gathering the advantages and challenges of...

The goal of the present work is to perform a systematic study of the formation of wing-tip vortices and their interaction with and impact on the surrounding flow in more details. Of particular interest is the interaction of these vortices with wall turbulence and the turbulent wake. This is done by considering two wing geometries, i.e., infinite-sp...

We examine the effects of three basic but effective control strategies, namely uniform blowing, uniform suction, and body-force damping, on the intense Reynolds-stress events in the turbulent boundary layer (TBL) developing on the suction side of a NACA4412 airfoil. This flow is subjected to a non-uniform adverse pressure gradient (APG), which subs...

The aim of this work is to analyse the formation mechanisms of large-scale coherent structures in the flow around a wall-mounted square cylinder, due to their impact on pollutant transport within cities. To this end, we assess causal relations between the modes of a reduced-order model obtained by applying proper-orthogonal decomposition to high-fi...

Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an abundance of high-fidelity databases can be generated by experimental measurements and numerical simulations, but obtaining such accurate data in full-scale applications is curren...

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 representat...

The success of recurrent neural networks (RNNs) has been demonstrated in many applications related to turbulence, including flow control, optimization, turbulent features reproduction as well as turbulence prediction and modeling. With this study we aim to assess the capability of these networks to reproduce the temporal evolution of a minimal turb...

With the availability of new high-Reynolds-number ($Re$) databases of turbulent boundary layers (TBLs) it has been possible to identify in detail certain regions of the boundary layer with more complex behavior. In this study we consider a unique database at moderately-high $Re$, with a near-constant adverse pressure gradient (APG) (Pozuelo {\it et...

High-fidelity large-eddy simulations of the flow around two rectangular obstacles are carried out at a Reynolds number of 10,000 based on the free-stream velocity and the obstacle height. The incoming flow is a developed turbulent boundary layer. Mean-velocity components, turbulence fluctuations, and the terms of the turbulent-kinetic-energy budget...

Urban areas are not only one of the biggest contributors to climate change, but also they are one of the most vulnerable areas with high populations who would together experience the negative impacts. In this paper, we address some of the opportunities brought by satellite remote sensing imaging and artificial intelligence (AI) in order to measure...

We use Gaussian stochastic weight averaging (SWAG) to assess the epistemic uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG approximates a posterior Gaussian distribution of each weight, given training data, and a constant learning rate. Having access to this distribution, it is able to create mu...

View Video Presentation: https://doi.org/10.2514/6.2022-3334.vid This work applies resolvent analysis to incompressible flow through a rectangular duct, in order to identify dominant linear energy-amplification mechanisms present in such flows. In particular, we formulate the resolvent operator from linearizing the Navier--Stokes equations about a...

Physics-informed neural networks (PINNs) are successful machine-learning methods for the solution and identification of partial differential equations (PDEs). We employ PINNs for solving the Reynolds-averaged Navier-Stokes (RANS) equations for incompressible turbulent flows without any specific model or assumption for turbulence, and by taking only...

Since the derivation of the Navier Stokes equations, it has become possible to numerically solve real world viscous flow problems (computational fluid dynamics (CFD)). However, despite the rapid advancements in the performance of central processing units (CPUs), the computational cost of simulating transient flows with extremely small time/grid sca...

This study proposes a newly-developed deep-learning-based method to generate turbulent inflow conditions for spatially-developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced super-resolution generative adversarial network is utilized to predict velocity fields of a spatially-developing TBL at...

This study proposes a newly-developed deep-learning-based method to generate turbulent inflow conditions for spatially-developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced super-resolution generative adversarial network is utilized to predict velocity fields of a spatially-developing TBL at...

Machine learning is rapidly becoming a core technology for scientific computing, with numerous opportunities to advance the field of computational fluid dynamics. Here we highlight some of the areas of highest potential impact, including to accelerate direct numerical simulations, to improve turbulence closure modeling and to develop enhanced reduc...

This work applies resolvent analysis to incompressible flow through a rectangular duct, in order to identify dominant linear energy-amplification mechanisms present in such flows. In particular, we formulate the resolvent operator from linearizing the Navier--Stokes equations about a two-dimensional base/mean flow. The laminar base flow only has a...

In this study, a new well-resolved large-eddy simulation of an incompressible near-equilibrium adverse-pressure-gradient (APG) turbulent boundary layer (TBL) over a flat plate is presented. In this simulation, we have established a near-equilibrium APG over a wide Reynolds-number range. In this so-called region of interest, the Rotta–Clauser pressu...

This study uses higher-order dynamic mode decomposition to analyze a high-fidelity database of the turbulent flow in an urban environment consisting of two buildings separated by a certain distance. We recognize the characteristics of the well-known arch vortex forming on the leeward side of the first building and document this vortex's generation...

Wake analysis plays a significant role in wind-farm planning through the evaluation of losses and energy yield. Wind-tunnel tests for wake studies have high costs and are time-consuming. Therefore, computational fluid dynamics (CFD) emerges as an efficient alternative. An especially attractive approach is based on the solution of the Reynolds-avera...

Modal-decomposition techniques are computational frameworks based on data aimed at identifying a low-dimensional space for capturing dominant flow features: the so-called modes. We propose a deep probabilistic-neural-network architecture for learning a minimal and near-orthogonal set of non-linear modes from high-fidelity turbulent-flow data useful...

Turbulent flow is widespread in many applications, such as airplane wings or turbine blades. Such flow is highly chaotic and impossible to predict far into the future. Some regions exhibit a coherent physical behavior in turbulent flow, satisfying specific properties; these regions are denoted as coherent structures. This work considers structures...

Combining different existing uncertainty quantification (UQ) techniques, a framework is obtained to assess a set of metrics in computational physics problems, in general, and computational fluid dynamics (CFD), in particular. The metrics include accuracy, sensitivity and robustness of the simulator’s outputs with respect to uncertain inputs and par...

High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy. Deep-learning approaches have been shown suitable for such super-resolution tasks. However, a high...

The domain of Artificial Intelligence (AI) ethics is not new, with discussions going back at least 40 years. Teaching the principles and requirements of ethical AI to students is considered an essential part of this domain, with an increasing number of technical AI courses taught at several higher-education institutions around the globe including c...

Increase in trading and travelling flows has resulted in the need for non-intrusive object inspection and identification methods. Traditional techniques proved to be effective for decades; however, with the latest advances in technology, the intruder can implement more sophisticated methods to bypass inspection points control techniques. The presen...

The success of recurrent neural networks (RNNs) has been demonstrated in many applications related to turbulence, including flow control, optimization, turbulent features reproduction as well as turbulence prediction and modeling. With this study we aim to assess the capability of these networks to reproduce the temporal evolution of a minimal turb...

The exact placement of the laminar-turbulent transition has a significant effect on relevant characteristics of the boundary layer and aerodynamics , such as drag, heat transfer and flow separation on e.g. wings and turbine blades. Tripping, which fixes the transition position, has been a valuable aid to wind-tunnel testing during the past 70 years...

The development and deployment of robotic technologies can have an important role in efforts to achieve the United Nations’ (UN) Sustainable Development Goals (SDGs)—with both enabling and inhibiting impacts. During a workshop at the 2021 IEEE/Robotics Society of Japan International Conference on Intelligent Robots and Systems (IROS 2021), experts...

We are delighted to introduce this Special Issue focused on novel machine-learning (ML) methods aimed at predicting, modeling, and controlling a variety of complex fluid flow scenarios [...]

In this chapter we extend earlier work (Vinuesa et al., Nature Communications 11, 2020) on the potential of artificial intelligence (AI) to achieve the 17 Sustainable Development Goals (SDGs) proposed by the United Nations (UN) for the 2030 Agenda. The present contribution focuses on three SDGs related to healthy and sustainable societies, i.e. SDG...

Studying and interpreting the different flow patterns present in urban areas is becoming essential since they help develop new approaches to fight climate change through an improved understanding of the dynamics of the pollutants in urban environments. This study uses higher order dynamic mode decomposition (HODMD) to analyze a high-fidelity databa...

Understanding flow structures in urban areas is being widely recognized as a challenging concern due to its effect on urban development, air quality, and pollutant dispersion. In this study, state-of-the-art data-driven methods for modal analysis of urban flows are used to understand better the dominant flow processes that occur in this phenomenon....

In-situ visualization on high-performance computing (HPC) systems allows us to analyze simulation results that would otherwise be impossible , given the size of the simulation data sets and offline post-processing execution time. We develop an in-situ adaptor for Paraview Catalyst and Nek5000, a massively parallel Fortran and C code for computation...

In this review, we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards a...

While the global-average temperatures are rapidly rising, more researchers have been shifting their focus towards the past mass-extinction events in order to show the relations between temperature increase and temperature thresholds which might trigger extinction of species. These temperature and mass-extinction relation graphs are found practical...

Received xx; revised xx; accepted xx) 9 The application of drag-control strategies on canonical wall-bounded turbulence, such as 10 periodic channel and zero-or adverse-pressure-gradient boundary layers, raises the equation 11 on how to distinguish consistently the origin of control effects under different reference 12 conditions. We employ the RD...

In this review we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards ac...