On the relevance of Reynolds stresses in resolvent analyses of turbulent wall-bounded flows

The ability of linear stochastic response analysis to estimate coherent motions is investigated in turbulent channel flow at friction Reynolds number Re$_\tau$ = 1007. The analysis is performed for spatial scales characteristic of buffer-layer and large-scale motions by separating the contributions of different temporal frequencies. Good agreement between the measured spatio-temporal power spectral densities and those estimated by means of the resolvent is found when the effect of turbulent Reynolds stresses, modelled with an eddy-viscosity associated to the turbulent mean flow, is included in the resolvent operator. The agreement is further improved when the flat forcing power spectrum (white noise) is replaced with a power spectrum matching the measures. Such a good agreement is not observed when the eddy-viscosity terms are not included in the resolvent operator. In this case, the estimation based on the resolvent is unable to select the right peak frequency and wall-normal location of buffer-layer motions. Similar results are found when comparing truncated expansions of measured streamwise velocity power spectral densities based on a spectral proper orthogonal decomposition to those obtained with optimal resolvent modes

Nonlinear jet-flap interactions: a dynamical-systems analysis

International audienceWe analyze the temporal dynamics associated with the jet-flap interactions by carrying-out a dynamical-systems analysis. The experimental cases are characterized by three different setups of the jet-flap system, running in the range M a = 0.6 − 1.0. The analysis is based on data presented by Jordan et al., 1 where the self-sustained oscillations were analyzed by means of linear models. Nonlinear competition among the modes was observed: here we analyze this interplay by investigating the system using statistical tools, phase portraits, Poincaré sections, and return maps. We estimate the minimal number of degrees of freedom necessary for the description of a nonlinear model. The correlation dimension is assessed for four representative cases. Finally, we analyze the toroidal geometry in the phase-space and identify the main ingredients necessary for nonlinear reduced-order models of this system

Enhancing Data-Assimilation in CFD using Graph Neural Networks

We present a novel machine learning approach for data assimilation applied in fluid mechanics, based on adjoint-optimization augmented by Graph Neural Networks (GNNs) models. We consider as baseline the Reynolds-Averaged Navier-Stokes (RANS) equations, where the unknown is the meanflow and a closure model based on the Reynolds-stress tensor is required for correctly computing the solution. An end-to-end process is cast; first, we train a GNN model for the closure term. Second, the GNN model is introduced in the training process of data assimilation, where the RANS equations act as a physics constraint for a consistent prediction. We obtain our results using direct numerical simulations based on a Finite Element Method (FEM) solver; a two-fold interface between the GNN model and the solver allows the GNN's predictions to be incorporated into post-processing steps of the FEM analysis. The proposed scheme provides an excellent reconstruction of the meanflow without any features selection; preliminary results show promising generalization properties over unseen flow configurations.Comment: Presented at: Machine Learning and the Physical Sciences Workshop, NeurIPS 202

Leveraging the structure of dynamical systems for data-driven modeling

The reliable prediction of the temporal behavior of complex systems is required in numerous scientific fields. This strong interest is however hindered by modeling issues: often, the governing equations describing the physics of the system under consideration are not accessible or, when known, their solution might require a computational time incompatible with the prediction time constraints. Nowadays, approximating complex systems at hand in a generic functional format and informing it ex--nihilo from available observations has become a common practice, as illustrated by the enormous amount of scientific work appeared in the last years. Numerous successful examples based on deep neural networks are already available, although generalizability of the models and margins of guarantee are often overlooked. Here, we consider Long-Short Term Memory neural networks and thoroughly investigate the impact of the training set and its structure on the quality of the long-term prediction. Leveraging insights from ergodic theory, we perform a thorough computational analysis to assess the amount of data sufficient for a priori guaranteeing a faithful model of the physical system. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models, opening up avenues for research within the context of active learning. Further, the non-trivial effects of the memory initializations when relying on memory-capable models will be illustrated. Our findings provide evidence-based good-practice on the amount and the choice of data required for an effective data-driven modeling of any complex dynamical system

Closed-loop optimal control for shear flows using reinforcement learning

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Active Control and Modal Structures in Transitional Shear Flows

Flow control of transitional shear flows is investigated by means of numerical simulations. The attenuation of three-dimensional wavepackets of Tollmien-Schlichting (TS) and streaks in the boundary layer is obtained using active control in combination with localised sensors and actuators distributed near the rigid wall. Due to the dimensions of the discretized Navier-Stokes operator, reduced-order models are identified, preserving the dynamics between the inputs and the outputs of the system. Balanced realizations of the system are computed using balanced truncation and system identification. We demonstrate that the energy growth of the perturbations is substantially and efficiently mitigated, using relatively few sensors and actuators. The robustness of the controller is analysed by varying the number of actuators and sensors, the Reynolds number, the pressure gradient and by investigating the nonlinear, transitional case. We show that delay of the transition from laminar to turbulent flow can be achieved despite the fully linear approach. This configuration can be reproduced in experiments, due to the localisation of sensing and actuation devices. The closed-loop system has been investigated for the corresponding twodimensional case by using full-dimensional optimal controllers computed by solving an iterative optimisation based on the Lagrangian approach. This strategy allows to compare the results achieved using open-loop model reduction with model-free controllers. Finally, a parametric analysis of the actuators/ sensors placement is carried-out to deepen the understanding of the inherent dynamics of the closed-loop. The distinction among two different classes of controllers – feedforward and feedback controllers - is highlighted. A second shear flow, a confined turbulent jet, is investigated using particle image velocimetry (PIV) measurements. Proper orthogonal decomposition (POD) modes and Koopman modes via dynamic mode decomposition (DMD) are computed and analysed for understanding the main features of the flow. The frequencies related to the dominating mechanisms are identified; the most energetic structures show temporal periodicity.QC 20130207</p

Reinforcement Learning for Control of Spatially Developing Distributed Systems

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Analysis of time-resolved PIV measurements of a confined co-flowing jet using POD and Koopman modes

Modal analysis by proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) of experimental data from a fully turbulent flow is presented. The flow case is a turbulent confined jet with co-flow, with Reynolds number based on the jet thickness of Re=10700. Experiments are performed with time-resolved Particle Image Velocimetry (PIV). The jet is created in a square channel with the confinement ratio is 1:5. Statistics of the flow are presented in terms of mean and fluctuating fields. Analysis of spatial spectra and temporal spectra reveal the presence of dominant wavelengths and frequenciesembedded in broad-band turbulent spectrum. Frequencies in the shearlayer migrate from St ≈ 1 near the jet inlet to St &lt; 0.1 at 18 jet thickness downstream. This flow case provides an interesting and challenging benchmark for testing POD and DMD and discussing their efficacy in describing a fully turbulent case. At first, issues related to convergence and physical interpretation of the modes are discussed, then the results are analyzed and compared. POD analysis reveals the most energetic spatial structures that are related to the flapping of the jet; a low frequency peak (St = 0.02) is found when the associated temporalmode is analyzed. Higher order modes revealed the presence of fasteroscillating shear flow modes combined to a recirculation zone near the inner jet. The flapping of the inner jet is sustained by this region. A good agreement is found between DMD and POD; however, DMD is able to rank the modes by frequencies, isolating structures associated to harmonics of the flow.QC 20110214</p