arXiv subjects

Fabio Bonaccorso

Publications and source records attributed to Fabio Bonaccorso.

At least 19 recordsLinked to original sources

arXiv2026

Drift of interfaces in forced stably-stratified turbulence and the role of vertically-sheared helical structures

Experimental investigations of forced stably stratified turbulence (SST) have shown that the step-like density profile, made of well-mixed density layers and sharp interfaces alternating along the gravity direction, undergo a slow coarsening dynamics with either decay or merging of interfaces. In this Letter, we focus on the coarsening dynamics phenomenon, by means of Direct Numerical Simulations of forced SST at moderate resolutions, and very long temporal integration. We show that the vertical drift and merging of interfaces is associated to the emergence of spatially-uniform, vertically-sheared helical structures that break the mirror-symmetry of the system. When these are absent, interfaces decay is observed instead. %how the kinetic energy excursions observed at $Fr=0.076$, occurring in parallel to vertical drift of interfaces, are due to the emergence of spatially-uniform, vertically-sheared helical structures that break the mirror-symmetry of the system. This is absent at larger $Fr=0.22$, where interface decay is observed instead. A dynamical correspondence between helicity dissipation rate by buoyancy effects and the vertical buoyancy flux allows to establish a (causal) connection between the chiral structures and the vertical movement of interfaces leading to merging.

physics.flu-dyn↗

arXiv2026

Stochastic Multiscale Reconstruction of Lagrangian Turbulence via Guided Diffusion Models

Lagrangian turbulence is characterized by intermittent, fat-tailed fluctuations and nontrivial correlations across temporal scales, making a quantitative description of its full multiscale probability distribution a longstanding challenge. A particularly important question is whether unresolved fine-scale fluctuations can be inferred from coarse-grained trajectory information. Here, we address this problem by sampling the conditional distribution of unresolved fluctuations using a diffusion-model prior conditioned on large-scale dynamics obtained through a wavelet-based coarse-graining of Lagrangian trajectories. Using tracer trajectories from direct numerical simulations of homogeneous and isotropic turbulence at $Re_\lambda \simeq 310$, we show that the reconstructed signals recover scale-dependent intermittent statistics, including high-order structure functions, flatness, and local scaling exponents, together with cross-scale temporal correlations between resolved and unresolved fluctuations. The method also reproduces the broad stochastic variability of intermittent acceleration fluctuations conditioned on the same coarse-grained trajectory, whereas Gaussian-process reconstructions in wavelet representation suppress rare events. Our results show that small-scale Lagrangian intermittency can be modeled as a non-Gaussian conditional stochastic process constrained by coarse-scale dynamics and quantitatively reproduced through data-driven generative sampling.

physics.flu-dyn↗

arXiv2026

Physics-Constrained Diffusion Model for Synthesis of 3D Turbulent Data

Synthesizing fully developed three-dimensional turbulent velocity fields remains a long-standing problem in fluid mechanics and an open challenge for generative modeling. The difficulty arises from the coexistence of extreme dimensionality, multiscale rough fluctuations and strong intermittency, together with exact physical constraints such as incompressibility and zero-mean momentum. We propose a physics-constrained diffusion model (PCDM) in which these \emph{a priori} constraints are incorporated directly into the generative dynamics. Using rotating turbulence as a stringent benchmark, we show that the proposed framework enables stable and statistically faithful synthesis of inertial-range three-dimensional turbulent velocity fields at medium resolution, accurately reproducing anisotropic energy spectra, intermittency statistics, and physical constraints. By contrast, standard denoising diffusion probabilistic models without such constraints exhibit multiscale statistical deviations, violations of physical consistency, and substantially slower training convergence. These findings point to broader implications for generative modeling of high-dimensional complex systems under physical constraints.

physics.flu-dyn↗

arXiv2025

Long-time evolution of density layers and interfaces in forced stably-stratified flows

Stably stratified fluids subject to sustained forcing are known to develop step-like density "staircases", where nearly homogeneous layers alternate with thin interfaces of strong stratification. However, long-time numerical investigations of this phenomenon have been limited by the intrinsically slow evolution of large-scale modes and the sensitivity of stratified turbulence to physical parameters. We present direct numerical simulations of forced Boussinesq flows for three stratification strengths (Fr = 0.42, 0.22, 0.076) and of unprecedented time extensions - up to O(10000) turnover times - with the purpose of reproducing and studying the very slow coarsening of the layered state. A large-scale friction term is introduced to arrest shear-mode growth and mimic finite-domain constraints. Staircase formation is observed for both medium and strong stratified cases, following two different coarsening dynamics: interfaces decaying or merging. While kinetic energy remains quasi-stationary during interface decay, it exhibits sharp bursts during merging events. The emergence and persistence of density steps can be explained by the non-monotonic relation between buoyancy flux and buoyancy gradient. Intermittency in vertical velocity and density fluctuations is confined to the vicinity of layer-interface boundaries, indicating that strong events arise from the interaction between turbulent mixing and layer formation rather than from regions of large density gradients alone.

physics.flu-dyn↗

arXiv2025

TURB-Scalar. A large database of passive scalar fields advected by 2D Navier-Stokes in the turbulent inverse cascade regime

We introduce TURB-Scalar, an open-access database comprising approximately $400$ uncorrelated snapshots of two-dimensional turbulent velocity and passive scalar fields, obtained from the turbulent inverse cascade regime. These data are generated through Direct Numerical Simulations (DNS) of the advection-diffusion equation for a passive scalar, $θ$, with resolution $N=4096$. The database serves as a versatile benchmark for the development and testing of both physics-based and data-driven modeling approaches. The scalar field exhibits intermittent statistics with universal anomalous scaling, making TURB-Scalar a valuable resource for studying turbulent transport phenomena. The database is available at http://smart-turb.roma2.infn.it.

physics.flu-dyn↗

arXiv2025

TURB-Smoke. A database of Lagrangian pollutants emitted from point-sources and dispersed in turbulent flows

Identifying the location and characteristics of pollution sources in turbulent flows is challenging, especially for environmental monitoring and emergency response, due to sparse, stochastic, and infrequent cue detection. Even in idealized settings, accurately modeling these phenomena remains highly complex, with realistic representations typically achievable only through experimental or simulation-based data. We introduce TURB-Smoke, a cutting-edge numerical dataset designed for investigating odor and contaminant dispersion in turbulent environments with and without mean wind. Generated via direct numerical simulations of the fully resolved three-dimensional Navier-Stokes equations, TURB-Smoke tracks hundreds of millions of Lagrangian particles released from five distinct point sources in fully developed turbulence, thus providing a reliable ground-truth framework for developing and evaluating source-tracking strategies using stationary sensors or mobile agents in realistic flows. Each particle's trajectory is continuously tracked on many characteristic turbulence timescales, recording both the position and the local flow velocity. Additionally, we provide coarse-grained concentration fields in 3D and in quasi-2D slabs containing the source, ideal for quickly testing and optimizing search algorithms under varying flow conditions.

physics.flu-dyn↗

arXiv2025

Deterministic diffusion models for Lagrangian turbulence: robustness and encoding of extreme events

Modeling Lagrangian turbulence remains a fundamental challenge due to its multiscale, intermittent, and non-Gaussian nature. Recent advances in data-driven diffusion models have enabled the generation of realistic Lagrangian velocity trajectories that accurately reproduce statistical properties across scales and capture rare extreme events. This study investigates three key aspects of diffusion-based modeling for Lagrangian turbulence. First, we assess architectural robustness by comparing a U-Net backbone with a transformer-based alternative, finding strong consistency in generated trajectories, with only minor discrepancies at small scales. Second, leveraging a deterministic variant of diffusion model formulation, namely the deterministic denoising diffusion implicit model (DDIM), we identify structured features in the initial latent noise that align consistently with extreme acceleration events. Third, we explore accelerated generation by reducing the number of diffusion steps, and find that DDIM enables substantial speedups with minimal loss of statistical fidelity. These findings highlight the robustness of diffusion models and their potential for interpretable, scalable modeling of complex turbulent systems.

physics.flu-dyn↗

arXiv2025

Hidden symmetry in passive scalar advected by 2D Navier-Stokes turbulence

Here we show that passive scalars possess a hidden scaling symmetry when considering suitably rescaled fields. Such a symmetry implies (i) universal probability distribution for scalar multipliers and (ii) Perron-Frobenius scenario for the anomalous scaling of structure functions. We verify these predictions with high resolution simulations of a passive scalar advected by a 2D turbulent flow in inverse cascade.

physics.flu-dyn↗

arXiv2025

TURB-MHD: an open-access database of forced homogeneous magnetohydrodynamic turbulence

We present TURB-MHD, a database formed by six datasets of three-dimensional incompressible homogeneous magnetohydrodynamic turbulence maintained by a large-scale random forcing with minimal injection of cross helicity. Five of them describe a stationary state including one characterised by a weak background magnetic field. The remaining dataset is non-stationary and is featured by a strong background magnetic field. The aim is to provide datasets that clearly exhibit the phenomenon of the total energy cascade from the large to the small scales generated by the large-scale energy injection and one showing a partial inverse kinetic energy cascade from the small to the large scales. This database offers the possibility to realize a wide variety of analyses of fully developed magnetohydrodynamic turbulence from the sub-grid scale filtering up to the validation of an a posteriori LES. TURB-MHD is available for download using the SMART-Turb portal http://smart-turb.roma2.infn.it.

physics.flu-dyn↗

arXiv2024

Lightweight Lattice Boltzmann

A GPU-accelerated version of the lattice Boltzmann method for efficient simulation of soft materials is introduced. Unlike standard approaches, this method reconstructs the distribution functions from available hydrodynamic variables (density, momentum, and pressure tensor) without storing the full set of discrete populations. This scheme shows satisfactory numerical stability, significantly lower memory requirements, and data access cost. A series of benchmark tests of relevance to soft matter, such as collisions of fluid droplets, is discussed to validate the method. The results can be of particular interest for high-performance simulations of soft matter systems on future exascale computers.

physics.flu-dyn↗

arXiv2024

Stochastic Reconstruction of Gappy Lagrangian Turbulent Signals by Conditional Diffusion Models

We present a stochastic method for reconstructing missing spatial and velocity data along the trajectories of small objects passively advected by turbulent flows with a wide range of temporal or spatial scales, such as small balloons in the atmosphere or drifters in the ocean. Our approach makes use of conditional generative diffusion models, a recently proposed data-driven machine learning technique. We solve the problem for two paradigmatic open problems, the case of 3D tracers in homogeneous and isotropic turbulence, and 2D trajectories from the NOAA-funded Global Drifter Program. We show that for both cases, our method is able to reconstruct velocity signals retaining non-trivial scale-by-scale properties that are highly non-Gaussian and intermittent. A key feature of our method is its flexibility in dealing with the location and shape of data gaps, as well as its ability to naturally exploit correlations between different components, leading to superior accuracy, with respect to Gaussian process regressions, for both pointwise reconstruction and statistical expressivity. Our method shows promising applications also to a wide range of other Lagrangian problems, including multi-particle dispersion in turbulence, dynamics of charged particles in astrophysics and plasma physics, and pedestrian dynamics.

physics.flu-dyn↗

arXiv2024

TURB-Lagr. A database of 3d Lagrangian trajectories in homogeneous and isotropic turbulence

We present TURB-Lagr, a new open database of 3d turbulent Lagrangian trajectories, obtained by Direct Numerical Simulations (DNS) of the original Navier-Stokes equations in the presence of a homogeneous and isotropic forcing. The aim is to provide the community interested in data-assimilation and/or Lagrangian-properties of turbulence a new testing-ground made of roughly 300K different Lagrangian trajectories. TURB-Lagr data are influenced by the strong non-Gaussian fluctuations characteristic of turbulence and by the rough and non differentiable fields. In addition, coming from fully resolved numerical simulations of the original partial differential equations, they offer the possibility to apply a wide range of approaches, from equation-free to physics-based models. TURB-Lagr data are reachable at http://smart-turb.roma2.infn.it

physics.flu-dyn↗

arXiv2024

Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence

Heavy and light particles are commonly found in many natural phenomena and industrial processes, such as suspensions of bubbles, dust, and droplets in incompressible turbulent flows. Based on a recent machine learning approach using a diffusion model that successfully generated single tracer trajectories in three-dimensional turbulence and passed most statistical benchmarks across time scales, we extend this model to include heavy and light particles. Given the particle type - tracer, light, or heavy - the model can generate synthetic, realistic trajectories with correct fat-tail distributions for acceleration, anomalous power laws, and scale dependent local slope properties. This work paves the way for future exploration of the use of diffusion models to produce high-quality synthetic datasets for different flow configurations, potentially allowing interpolation between different setups and adaptation to new conditions.

physics.flu-dyn↗

arXiv2024

Synthetic Lagrangian Turbulence by Generative Diffusion Models

Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, bio-fluids, atmosphere, oceans, and astrophysics. Despite exceptional theoretical, numerical, and experimental efforts conducted over the past thirty years, no existing models are capable of faithfully reproducing statistical and topological properties exhibited by particle trajectories in turbulence. We propose a machine learning approach, based on a state-of-the-art diffusion model, to generate single-particle trajectories in three-dimensional turbulence at high Reynolds numbers, thereby bypassing the need for direct numerical simulations or experiments to obtain reliable Lagrangian data. Our model demonstrates the ability to reproduce most statistical benchmarks across time scales, including the fat-tail distribution for velocity increments, the anomalous power law, and the increased intermittency around the dissipative scale. Slight deviations are observed below the dissipative scale, particularly in the acceleration and flatness statistics. Surprisingly, the model exhibits strong generalizability for extreme events, producing events of higher intensity and rarity that still match the realistic statistics. This paves the way for producing synthetic high-quality datasets for pre-training various downstream applications of Lagrangian turbulence.

physics.flu-dyn↗

arXiv2024

TURB-Hel: an open-access database of helically forced homogeneous and isotropic turbulence

We present TURB-Hel, a database formed by two datasets of incompressible homogeneous and isotropic turbulence, maintained in a statistically stationary state by fully helical forcing. The aim is to provide a dataset that clearly exhibits the phenomenon of the helicity cascade from the large to the small scales generated by a large-scale forcing that breaks the mirror symmetry. This database offers the possibility to realize a wide variety of analyses of fully developed turbulence from the sub-grid scale filtering up to the validation of an a posteriori LES. TURB-Hel is available for download using the SMART-Turb portal http://smart-turb.roma2.infn.it.

physics.flu-dyn↗

arXiv2023

Multi-scale Reconstruction of Turbulent Rotating Flows with Generative Diffusion Models

We address the problem of data augmentation in a rotating turbulence set-up, a paradigmatic challenge in geophysical applications. The goal is to reconstruct information in two-dimensional (2D) cuts of the three-dimensional flow fields, imagining to have spatial gaps present within each 2D observed slice. We evaluate the effectiveness of different data-driven tools, based on diffusion models (DMs), a state-of-the-art generative machine learning protocol, and generative adversarial networks (GANs), previously considered as the best-performing method both in terms of point-wise reconstruction and the statistical properties of the inferred velocity fields. We focus on two different DMs recently proposed in the specialized literature: (i) RePaint, based on a heuristic strategy to guide an unconditional DM for flow generation by using partial measurements data and (ii) Palette, a conditional DM trained for the reconstruction task with paired measured and missing data. Systematic comparison shows that (i) DMs outperform the GAN in terms of the mean squared error and/or the statistical accuracy; (ii) Palette DM emerges as the most promising tool in terms of both point-wise and statistical metrics. An important property of DMs is their capacity for probabilistic reconstructions, providing a range of predictions based on the same measurements, enabling for uncertainty quantification and risk assessment.

physics.flu-dyn↗

arXiv2023

Multi-scale data reconstruction of turbulent rotating flows with Gappy POD, Extended POD and Generative Adversarial Networks

Data reconstruction of rotating turbulent snapshots is investigated utilizing data-driven tools. This problem is crucial for numerous geophysical applications and fundamental aspects, given the concurrent effects of direct and inverse energy cascades, which lead to non-Gaussian statistics at both large and small scales. Data assimilation also serves as a tool to rank physical features within turbulence, by evaluating the performance of reconstruction in terms of the quality and quantity of the information used. Additionally, benchmarking various reconstruction techniques is essential to assess the trade-off between quantitative supremacy, implementation complexity, and explicability. In this study, we use linear and non-linear tools based on the Proper Orthogonal Decomposition (POD) and Generative Adversarial Network (GAN) for reconstructing rotating turbulence snapshots with spatial damages (inpainting). We focus on accurately reproducing both statistical properties and instantaneous velocity fields. Different gap sizes and gap geometries are investigated in order to assess the importance of coherency and multi-scale properties of the missing information. Surprisingly enough, concerning point-wise reconstruction, the non-linear GAN does not outperform one of the linear POD techniques. On the other hand, supremacy of the GAN approach is shown when the statistical multi-scale properties are compared. Similarly, extreme events in the gap region are better predicted when using GAN. The balance between point-wise error and statistical properties is controlled by the adversarial ratio, which determines the relative importance of the generator and the discriminator in the GAN training. Robustness against the measurement noise is also discussed.

physics.flu-dyn↗

arXiv2023

Generative Adversarial Networks to infer velocity components in rotating turbulent flows

Inference problems for two-dimensional snapshots of rotating turbulent flows are studied. We perform a systematic quantitative benchmark of point-wise and statistical reconstruction capabilities of the linear Extended Proper Orthogonal Decomposition (EPOD) method, a non-linear Convolutional Neural Network (CNN) and a Generative Adversarial Network (GAN). We attack the important task of inferring one velocity component out of the measurement of a second one, and two cases are studied: (I) both components lay in the plane orthogonal to the rotation axis and (II) one of the two is parallel to the rotation axis. We show that EPOD method works well only for the former case where both components are strongly correlated, while CNN and GAN always outperform EPOD both concerning point-wise and statistical reconstructions. For case (II), when the input and output data are weakly correlated, all methods fail to reconstruct faithfully the point-wise information. In this case, only GAN is able to reconstruct the field in a statistical sense. The analysis is performed using both standard validation tools based on $L_2$ spatial distance between the prediction and the ground truth and more sophisticated multi-scale analysis using wavelet decomposition. Statistical validation is based on standard Jensen-Shannon divergence between the probability density functions, spectral properties and multi-scale flatness.

physics.flu-dyn↗