arXiv subjects

Luigi Marra

Publications and source records attributed to Luigi Marra.

5 recordsLinked to original sources

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↗

arXiv2025

Latent-Space Non-Linear Model Predictive Control for Partially-Observable Systems

This work presents a scalable control framework based on nonlinear Model Predictive Control for high-dimensional dynamical systems. The proposed approach addresses the key challenges of model scalability and partial observability by integrating data-driven reduced order modelling, control in a latent space, and state estimation within a unified formulation. A predictive model is constructed via Operator Inference on a Proper Orthogonal Decomposition basis, yielding a compact latent representation that captures the dominant system dynamics. State estimation is achieved through an Unscented Kalman Filter, which reconstructs the latent space from sparse and noisy measurements, enabling closed-loop control. The input signals are computed directly in the reduced-order latent space, improving computational efficiency with negligible impact on predictive capability. The methodology is validated on the one- and two-dimensional Kuramoto--Sivashinsky equations, serving as benchmarks for chaotic and spatially-extended systems. Numerical experiments demonstrate that the proposed framework achieves accurate stabilisation. Overall, the framework provides a practical approach for nonlinear control of complex, high-dimensional systems where full-state measurements are often inaccessible or infeasible.

physics.flu-dyn↗

arXiv2025

Self-tuning model predictive control for wake flows

This study presents a noise-robust closed-loop control strategy for wake flows employing model predictive control. The proposed control framework involves the autonomous offline selection of hyperparameters, eliminating the need for user interaction. To this purpose, Bayesian optimisation maximises the control performance, adapting to external disturbances, plant model inaccuracies and actuation constraints. The noise robustness of the control is achieved through sensor data smoothing based on local polynomial regression. The plant model can be identified through either theoretical formulation or using existing data-driven techniques. In this work we leverage the latter approach, which requires minimal user intervention. The self-tuned control strategy is applied to the control of the wake of the fluidic pinball, with the plant model based solely on aerodynamic force measurements. The closed-loop actuation results in two distinct control mechanisms: boat tailing for drag reduction and stagnation point control for lift stabilization. The control strategy proves to be highly effective even in realistic noise scenarios, despite relying on a plant model based on a reduced number of sensors.

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↗

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↗