arXiv subjects

Andrea Ianiro

Publications and source records attributed to Andrea Ianiro.

At least 19 recordsLinked to original sources

arXiv2026

Divide and conquer complex flows. Part I: cluster and manifold-based local analysis

This work is a two-part study on the description and prediction of complex fluid flows through the partitioning of the flow domain. In this first part, we propose a framework for a global description of the dynamics of complex flows via clustered spatial representations of the flow, isolating and identifying local dynamics, retrieving different \acp{ST-CNM}. The key enabler is the partitioning of the domain based on a nonlinear manifold learning approach, in which spatial points are clustered based on the similarity of their dynamics, as observed in their compact embedding in manifold coordinates. The method receives as input time-resolved flow fields. The spatial manifold is computed through isometric mapping applied to the vorticity time histories at each spatial location. An unsupervised clustering method, applied in the manifold space, partitions the full flow domain into subdomains. The dynamics of each subdomain are then described with cluster-based modelling. The method is demonstrated on two flow-field datasets obtained with a direct numerical simulation of a fluidic pinball under periodic forcing and with two-dimensional particle image velocimetry measurements of a transitional jet flow. The spatial manifold-based flow partitioning identifies regions with similar dynamics in an automated way. For both cases, \ac{ST-CNM} identifies local dynamics that are not captured by a global approach. In particular, vortex shedding and vortex pairing dynamics are isolated in the jet flow experiment. The proposed fully automated domain partitioning method will benefit the structural description of controlled flows and unveil the actuation mechanisms at play.

physics.flu-dyn↗

arXiv2026

Feature-based manifold model of actuated wakes

We propose a feature-based reduced-order model to predict the transient dynamics of bluff-body wakes under arbitrary time-varying actuation. Starting point is a control-oriented POD Galerkin modeling which is challenged by incorporating time-varying actuations as a free input. Our model includes three key enablers. First, POD modes are replaced by a more accurate feature-based manifold of same dimension. Second, a state space is distilled from dynamic features which encapsulate time-varying coherent structures. Third, this state space is augmented for the transient actuation response. Thus, a simple analytical manifold dynamics is obtained. The approach is applied to the fluidic pinball at Re=30, a canonical configuration of three identical circular cylinders arranged in an equilateral triangle and immersed in uniform flow under symmetric actuation. The model is validated against several representative actuation scenarios and accurately reproduces the transient dynamics without requiring unsteady training data, providing an interpretable, observable-based and control-oriented framework. The proposed description of actuated bluff-body flows is expected to be generalisable to other configurations.

physics.flu-dyn↗

arXiv2026

Information decomposition for disentangled and interpretable manifold learning of fluid flows via variational autoencoders

We introduce an information-theoretic framework that uses variational autoencoders (VAEs) to extract compact, physically interpretable manifolds from high-dimensional flow-field data. To this end, the Kullback--Leibler (KL) divergence in the variational objective is decomposed into three complementary information-theoretic terms: the index-code mutual information, the total correlation, and the dimension-wise KL divergence. These terms explicitly regulate data compression, latent disentanglement, and geometric regularization. This establishes a principled basis for targeted latent-space design, allowing enhanced interpretability without sacrificing information capacity, a common drawback of heavily regularized VAE variants. The approach is evaluated on two synthetic unsteady flow datasets. First, we consider a flow around a cylinder in a channel with variable cylinder position, diameter, and Reynolds number. Later, we also consider the flow around a NACA 0012 airfoil at varying angles of attack and subjected to strong vortex gusts with variable intensity, position, and length scale. Comparisons with Principal Component Analysis, Isometric Feature Mapping, and $β$-VAE demonstrate clear advantages in disentanglement and physical interpretability. The learned latent coordinates successfully separate distinct physical effects. Moreover, the proposed method demonstrates strong robustness to variations in the loss-weighting parameters, despite involving a larger number of such parameters.

physics.flu-dyn↗

arXiv2026

Drag reduction via separation control using plasma actuators on a truck cabin side

We investigate the drag reduction on a heavy-duty vehicle using dielectric-barrier discharge plasma actuators located on the A-pillars. An experimental campaign is carried out on a generalized truck model, the Ground Transportation System (GTS), which is known for its lateral separation bubbles on both sides of the truck's cabin. Measurements are performed for several yaw angles up to $7.5\degree$. Actuation is applied individually on the leeward and windward sides as well as simultaneously. Load cell measurements show that the plasma actuators effectively reduce the axial force on the GTS, with symmetric actuation achieving the highest reduction. Leeward actuation demonstrates greater control authority than the windward one; at large yaw angles the latter has a negligible effect on the axial force. Regarding side force, the leeward actuation produces a drop in its magnitude while windward actuation produces an increase. Interestingly, actuating symmetrically also augments the side force. Particle image velocimetry reveals that the plasma actuator causes a reduction in the length and width of the separation bubble on the cabin side, reducing the apparent frontal area of the truck and thus its drag. Under crosswind conditions, the stronger authority of the leeward actuator is explained by the larger separation bubble. The side force variation is driven by the net lateral suction force, which correlates with the size of the lateral recirculation regions controlled by the actuators.

physics.flu-dyn↗

arXiv2026

On the turbulent wake of the actuated fluidic pinball: dynamics, bifurcations and control authority

We present the first comprehensive experimental and numerical study featuring the turbulent wake of the fluidic pinball for a large actuation range. The fluidic pinball is a cluster of three equal circular cylinders centered on the vertices of an equilateral triangle, pointing upstream in uniform flow. This configuration has become a canonical benchmark for control-oriented reduced-order modeling, for nonlinear control design and for a large kaleidoscope of drag reduction mechanisms. While the literature covers well the laminar two-dimensional Reynolds number regime, we focus on unexplored terra incognita: experiments of the symmetrically actuated turbulent regime at a Reynolds number of Re=9100. In other words, the upstream cylinder is kept stationary, while the two downstream cylinders rotate with equal and opposite angular velocities. A large range of base-bleeding and boat-tailing actuation parameters is investigated with time-resolved particle image velocimetry and aerodynamic force measurement with a companion Reynolds-averaged Navier-Stokes simulation. Our results indicate that the turbulent wake of the fluidic pinball can be approximated by a three-dimensional actuation manifold comprising two inverse pitchfork bifurcations. In the boat-tailing limit, a reduced control authority with a new low-frequency shedding state is observed.

physics.flu-dyn↗

arXiv2026

Meshless Super-Resolution of Scattered Data via constrained RBFs and KNN-Driven Densification

We propose a novel meshless method to achieve super resolution from scattered data obtained from sparse, randomly positioned sensors such as the particle tracers of particle tracking velocimetry. The method combines K Nearest Neighbor Particle Tracking Velocimetry (KNN PTV, Tirelli et al. 2023) with meshless Proper Orthogonal Decomposition (meshless POD, Tirelli et al. 2025) and constrained Radial Basis Function regression (c RBFs, Sperotto et al. 2022). The main idea is to enhance the spatial resolution of flow fields by blending data from locally similar flow regions available in the time series. This similarity is assessed in terms of statistical coherency with leading features identified by meshless POD applied directly to scattered data, without interpolation onto a grid and relying instead on RBFs to compute the relevant inner products. The denser scattered distributions are then used within a constrained RBF framework to derive an analytical representation of the flow fields that incorporates physical constraints. The approach is fully meshless and does not require a grid at any stage, offering flexibility in complex geometries. An ablation study highlights the role of penalties and physical constraints in regularizing the regression and ensuring physically consistent reconstructions. The method is validated using three dimensional measurements of a jet flow in air. The assessment focuses on statistics, spectra, and modal analysis. Performance is evaluated against standard Particle Image Velocimetry, KNN PTV, and c RBFs. The results show improved accuracy, with an average error of about ten percent compared to twelve to thirteen percent for the other methods, nearly halved errors in reduced order reconstructions, and a higher frequency cutoff based on the noise floor.

physics.flu-dyn↗

arXiv2025

Sensor optimization for urban wind estimation with cluster-based probabilistic framework

We propose a physics-informed machine-learned framework for sensor-based flow estimation for drone trajectories in complex urban terrain. The input is a rich set of flow simulations at many wind conditions. The outputs are velocity and uncertainty estimates for a target domain and subsequent sensor optimization for minimal uncertainty. The framework has three innovations compared to traditional flow estimators. First, the algorithm scales proportionally to the domain complexity, making it suitable for flows that are too complex for any monolithic reduced-order representation. Second, the framework extrapolates beyond the training data, e.g., smaller and larger wind velocities. Last, and perhaps most importantly, the sensor location is a free input, significantly extending the vast majority of the literature. The key enablers are (1) a Reynolds number-based scaling of the flow variables, (2) a physics-based domain decomposition, (3) a cluster-based flow representation for each subdomain, (4) an information entropy correlating the subdomains, and (5) a multi-variate probability function relating sensor input and targeted velocity estimates. This framework is demonstrated using drone flight paths through a three-building cluster as a simple example. We anticipate adaptations and applications for estimating complete cities and incorporating weather input.

cs.LG↗

arXiv2025

Model-based time super-sampling of turbulent flow field sequences

We propose a novel method for model-based time super-sampling of turbulent flow fields. The key enabler is the identification of an empirical Galerkin model from the projection of the Navier-Stokes equations on a data-tailored basis. The basis is obtained from a Proper Orthogonal Decomposition (POD) of the measured fields. Time super-sampling is thus achieved by a time-marching integration of the identified dynamical system, taking the original snapshots as initial conditions. Temporal continuity of the reconstructed velocity fields is achieved through a forward-backwards integration between consecutive measured Particle Image Velocimetry measurements of a turbulent jet flow. The results are compared with the interpolation of the POD temporal coefficients and the low-order reconstruction of data measured at a higher sampling rate. In both cases, the results obtained show the ability of the method to reconstruct the dynamics of the flow with small errors during several flow characteristic times.

physics.flu-dyn↗

arXiv2025

Full-domain POD modes from PIV asynchronous patches

A method is proposed to obtain full-domain spatial modes based on Proper Orthogonal Decomposition (POD) of Particle Image Velocimetry (PIV) measurements performed at different (overlapping) spatial locations. This situation occurs when large domains are covered by multiple non-simultaneous measurements and yet the large-scale flow field organization is to be captured. The proposed methodology leverages the definition of POD spatial modes as eigenvectors of the spatial correlation matrix, where local measurements, even when not obtained simultaneously, provide each a portion of the latter, which is then analyzed to synthesize the full-domain spatial modes. The measurement domain coverage is found to require regions overlapping by 50-75% to yield a smooth distribution of the modes. The procedure identifies structures twice as large as each measurement patch. The technique, referred to as Patch POD, is applied to planar PIV data of a submerged jet flow where the effect of patching is simulated by splitting the original PIV data. Patch POD is then extended to 3D robotic measurement around a wall-mounted cube. The results show that the patching technique enables global modal analysis over a domain covered with a multitude of non-simultaneous measurements.

physics.flu-dyn↗

arXiv2025

Instantaneous convective heat transfer at the wall: a depiction of turbulent boundary layer structures

We demonstrate the ability to experimentally measure fluctuations of the convective heat transfer coefficient at the wall in a turbulent boundary layer. For this, we measure two-dimensional fields of wall-temperature fluctuations beneath a zero-pressure-gradient turbulent boundary layer, at two moderate friction Reynolds numbers ($Re_τ\approx 990$ and $Re_τ\approx 1800$). Spatiotemporal data of wall-temperature are acquired by means of a heated-thin-foil sensor as sensing hardware, and an infrared camera as temperature detector. At low $Re_τ$ conditions, the fields of the Nusselt number fluctuations are populated by elongated structures comprising streamwise and spanwise length scales comparable to those of near-wall streaks. At higher $Re_τ$ conditions, the effective width and length of the coherent $Nu$ fluctuations increases. These findings are based on two-point correlations, as well as streamwise-spanwise energy spectra of $Nu$ fluctuations. The convective velocities of the $Nu$ fluctuations are also computed with the available time resolution from the measurements. This allows for resolving the multi-scale nature of convective footprints of wall-bounded turbulence: our experimental data reflect that larger streaks in the footprint convect at velocities in the order of the free-stream velocity, while the more energetic smaller-scale features move at velocities in the order of $10u_τ$. Measurements of the kind presented here offer a promising method for sensing, as they can be used as input to flow control systems.

physics.flu-dyn↗

arXiv2025

Actuation manifold from snapshot data

We propose a data-driven methodology to learn a low-dimensional manifold of controlled flows. The starting point is resolving snapshot flow data for a representative ensemble of actuations. Key enablers for the actuation manifold are isometric mapping as encoder and a combination of a neural network and a k-nearest-neighbour interpolation as decoder. This methodology is tested for the fluidic pinball, a cluster of three parallel cylinders perpendicular to the oncoming uniform flow. The centres of these cylinders are the vertices of an equilateral triangle pointing upstream. The flow is manipulated by constant rotation of the cylinders, i.e. described by three actuation parameters. The Reynolds number based on a cylinder diameter is chosen to be 30. The unforced flow yields statistically symmetric periodic shedding represented by a one-dimensional limit cycle. The proposed methodology yields a five-dimensional manifold describing a wide range of dynamics with small representation error. Interestingly, the manifold coordinates automatically unveil physically meaningful parameters. Two of them describe the downstream periodic vortex shedding. The other three describe the near-field actuation, i.e. the strength of boat-tailing, the Magnus effect and forward stagnation point. The manifold is shown to be a key enabler for control-oriented flow estimation.

physics.flu-dyn↗

arXiv2025

A meshless method to compute the proper orthogonal decomposition and its variants from scattered data

Complex phenomena can be better understood when broken down into a limited number of simpler "components". Linear statistical methods such as the principal component analysis and its variants are widely used across various fields of applied science to identify and rank these components based on the variance they represent in the data. These methods can be seen as factorisations of the matrix collecting all the data, assuming it consists of time series sampled from fixed points in space. However, when data sampling locations vary over time, as with mobile monitoring stations in meteorology and oceanography or with particle tracking velocimetry in experimental fluid dynamics, advanced interpolation techniques are required to project the data onto a fixed grid before the factorisation. This interpolation is often expensive and inaccurate. This work proposes a method to decompose scattered data without interpolating. The approach employs physics-constrained radial basis function regression to compute inner products in space and time. The method provides an analytical and mesh-independent decomposition in space and time, demonstrating higher accuracy. Our approach allows distilling the most relevant "components" even for measurements whose natural output is a distribution of data scattered in space and time, maintaining high accuracy and mesh independence.

physics.data-an↗

arXiv2024

Machine-learned flow estimation with sparse data -- exemplified for the rooftop of a UAV vertiport

We propose a physics-informed data-driven framework for urban wind estimation. This framework validates and incorporates the Reynolds number independence for flows under various working conditions, thus allowing the extrapolation for wind conditions far beyond the training data. Another key enabler is a machine-learned non-dimensionalized manifold from snapshot data. The velocity field is modeled using a double encoder-decoder approach. The first encoder normalizes data using the oncoming wind speed, while the second encoder projects this normalized data onto the isometric feature mapping manifold. The decoders reverse this process, with $k$-nearest neighbor performing the first decoding and the second undoing the normalization. The manifold is coarse-grained by clustering to reduce the computational load for de- and encoding. The sensor-based flow estimation is based on the estimate of the oncoming wind speed and a mapping from sensor signal to the manifold latent variables. The proposed machine-learned flow estimation framework is exemplified for the flow above an Unmanned Aerial Vehicle vertiport. The wind estimation is shown to generalize well for rare wind conditions, not included in the original database.

physics.flu-dyn↗

arXiv2024

Measuring time-resolved heat transfer fluctuations on a heated-thin foil in a turbulent channel airflow

We present an experimental setup to perform time-resolved convective heat transfer measurements in a turbulent channel flow with air as the working fluid. We employ a heated thin foil coupled with high-speed infrared thermography. The measurement technique is challenged by the thermal inertia of the foil, the high frequency of turbulent fluctuations, and the measurement noise of the infrared camera. We discuss in detail the advantages and drawbacks of all the design choices that were made, thereby providing a successful implementation strategy to obtain high-quality data. This experimental approach could be useful for experimental studies employing wall-based measurements of turbulence, such as flow control applications in wall-bounded turbulence.

physics.flu-dyn↗

arXiv2024

Some effects of limited wall-sensor availability on flow estimation with 3D-GANs

In this work we assess the impact of the limited availability of wall-embedded sensors on the full 3D estimation of the flow field in a turbulent channel with Reτ = 200. The estimation technique is based on a 3D generative adversarial network (3D-GAN). We recently demonstrated that 3D-GANs are capable of estimating fields with good accuracy by employing fully-resolved wall quantities (pressure and streamwise/spanwise wall shear stress on a grid with DNS resolution). However, the practical implementation in an experimental setting is challenging due to the large number of sensors required. In this work, we aim to estimate the flow fields with substantially fewer sensors. The impact of the reduction of the number of sensors on the quality of the flow reconstruction is assessed in terms of accuracy degradation and spectral length-scales involved. It is found that the accuracy degradation is mainly due to the spatial undersampling of scales, rather than the reduction of the number of sensors per se. We explore the performance of the estimator in case only one wall quantity is available. When a large number of sensors is available, pressure measurements provide more accurate flow field estimations. Conversely, the elongated patterns of the streamwise wall shear stress make this quantity the most suitable when only few sensors are available. As a further step towards a real application, the effect of sensor noise is also quantified. It is shown that configurations with fewer sensors are less sensitive to measurement noise.

physics.flu-dyn↗

arXiv2024

Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements

Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising methodologies, due to their more accurate estimations and better perceptual quality. This work tackles this flow sensing problem in the vicinity of the wall, addressing for the first time the reconstruction of the entire three-dimensional (3-D) field with a single network, i.e. a 3-D GAN. With this methodology, a single training and prediction process overcomes the limitation presented by the former approaches based on the independent estimation of wall-parallel planes. The network is capable of estimating the 3-D flow field with a level of error at each wall-normal distance comparable to that reported from wall-parallel plane estimations and at a lower training cost in terms of computational resources. The direct full 3-D reconstruction also unveils a direct interpretation in terms of coherent structures. It is shown that the accuracy of the network depends directly on the wall footprint of each individual turbulent structure. It is observed that wall-attached structures are predicted more accurately than wall-detached ones, especially at larger distances from the wall. Among wall-attached structures, smaller sweeps are reconstructed better than small ejections, while large ejections are reconstructed better than large sweeps as a consequence of their more intense footprint.

physics.flu-dyn↗

arXiv2023

Genetically-inspired convective heat transfer enhancement in a turbulent boundary layer

The convective heat transfer in a turbulent boundary layer (TBL) on a flat plate is enhanced using an artificial intelligence approach based on linear genetic algorithms control (LGAC). The actuator is a set of six slot jets in crossflow aligned with the freestream. An open-loop optimal periodic forcing is defined by the carrier frequency, the duty cycle and the phase difference between actuators as control parameters. The control laws are optimised with respect to the unperturbed TBL and to the actuation with a steady jet. The cost function includes the wall convective heat transfer rate and the cost of the actuation. The performance of the controller is assessed by infrared thermography and characterised also with particle image velocimetry measurements. The optimal controller yields a slightly asymmetric flow field. The LGAC algorithm converges to the same frequency and duty cycle for all the actuators. It is noted that such frequency is strikingly equal to the inverse of the characteristic travel time of large-scale turbulent structures advected within the near-wall region. The phase difference between multiple jet actuation has shown to be very relevant and the main driver of flow asymmetry. The results pinpoint the potential of machine learning control in unravelling unexplored controllers within the actuation space. Our study furthermore demonstrates the viability of employing sophisticated measurement techniques together with advanced algorithms in an experimental investigation.

physics.flu-dyn↗

arXiv2023

A simple trick to improve the accuracy of PIV/PTV data

Particle Image Velocimetry (PIV) estimates velocities through correlations of particle images within interrogation windows, leading to a spatial modulation of the velocity field. Although in principle Particle Tracking Velocimetry (PTV) estimates locally a non-modulated particle displacement, to exploit the scattered data from PTV it is necessary to interpolate these data on a structured grid, which implies a spatial modulation effect that biases the resulting velocity field. This systematic error due to finite spatial resolution inevitably depends on the interrogation window size and on the interparticle spacing. It must be observed that all these operations (cross-correlation, direct interpolation or averaging in windows) induce modulation on both the mean and the fluctuating part. We introduce a simple trick to reduce this systematic error source of PIV/PTV measurements exploiting ensemble statistics. Ensemble Particle Tracking Velocimetry (EPTV) can be leveraged to obtain the high-resolution mean flow by merging the different instantaneous realisations. The mean flow can be estimated with EPTV, and the fluctuating part can be measured from PIV/PTV. The high-resolution mean can then be superposed to the instantaneous fluctuating part to obtain velocity fields with lower systematic error. The methodology is validated against datasets with a progressively increasing level of complexity: two virtual experiments based on direct numerical simulations (DNS) of the wake of a fluidic pinball and a channel flow and the experimental data of a turbulent boundary layer. For all the cases both PTV and PIV are analysed.

physics.flu-dyn↗