arXiv subjects

Ivette Rodriguez

Publications and source records attributed to Ivette Rodriguez.

6 recordsLinked to original sources

arXiv2026

High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning

This study investigates active flow control (AFC) of a 30P30N high-lift wing at a Reynolds number Re$_c$ = 450,000 and angle of attack $\alpha$ = 23$^\circ$ using wallresolved large-eddy simulations (LES). Two optimization strategies are explored: open-loop Bayesian optimization (BO) and closed-loop deep reinforcement learning (DRL), both targeting the mitigation of stall and the improvement of aerodynamic efficiency via synthetic jets on the slat, main, and flap elements. The uncontrolled configuration was validated against literature data, confirming the reliability of the LES setup. The BO framework successfully identified steady jet velocities that increased efficiency by +10.9% through a -9.7% drag reduction while maintaining lift. In contrast, the DRL agent, despite leveraging instantaneous flow information from distributed sensors, achieved only minor improvements in lift and drag, with negligible efficiency gain. Training analysis indicated that the penalty-dominated reward constrained exploration. These results highlight the need for carefully designed rewards and computational acceleration strategies in DRL-based flow control at high Reynolds numbers.

physics.flu-dyn↗

arXiv2025

Atmospheric boundary layer over urban roughness: validation of large-eddy simulation

The study presents wall-modeled large-eddy simulations (LES) characterizing the flow features of a neutral atmospheric boundary layer over two urban-like roughness geometries: an array of three-dimensional square prisms and the 'Michel-Stadt' geometry model. The former is an arrangement of idealized building blocks. The latter mimics a typical central European urban geometry. In both cases, the incident wind angle is $0^\circ$. The Reynolds number for each case are $Re_H = 5.0 \times 10^6$ and $8.0 \times 10^6$, respectively ($Re_H = U_{ref} H/\nu$ with $U_{ref}$ and $H$ denoting the reference velocity and building height, respectively, and $\nu$ the kinematic viscosity). The LES employs a high-order, low-dissipation numerical scheme with a spatial resolution of 0.75m within the urban canopy. An online precursor simulation ensures realistic turbulent inflow conditions. The simulations performed successfully captures mean-velocity profiles, wake regions, and rooftop acceleration, with excellent agreement in the streamwise velocity component. While turbulent kinetic energy is well predicted at most locations, minor discrepancies are observed near the ground. The analysis of scatter plots and validation metrics (FAC2 and hit rate) shows that LES predictions outperform the standard criteria commonly used in urban flow simulations, while spectral analysis verifies that LES accurately resolves the turbulent energy cascade over approximately two frequency decades. The Kolmogorov -2/3 slope in the pre-multiplied spectra has been well reproduced below and above the urban canopy. These findings reinforce the importance of spectral analysis in LES validation and highlight the potential of high-order methods for LES of urban flows.

physics.flu-dyn↗

arXiv2024

Differentially heated turbulent channel flow two-point correlations

This study analyzes the behavior of a differentially heated channel flow by means of a direct numerical simulations (DNS) with variable thermophysical properties under low-speed conditions focusing on the impact of the temperature gradient on the turbulence structures near the channel walls. The simulations were conducted at a mean friction Reynolds number of Re{\tau}m = 400 with a temperature ratio between the walls of Thot/Tcold = 2. Results show significant differences between the hot and cold walls that lead to an increased heat transfer at the hot wall and a higher turbulent production in the cold wall.

physics.flu-dyn↗

arXiv2024

Towards Active Flow Control Strategies Through Deep Reinforcement Learning

This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL approach achieved a 9.32% drag reduction and a 78.4% decrease in lift oscillations by learning advanced actuation strategies. The methodology integrates a CFD solver with a DRL model using an in-memory database for efficient communication between

cs.LG↗

arXiv2024

Turbulent Boundary Layer in a 3-Element High-LiftWing: Coherent Structures Identification

A wall-resolved large-eddy simulation (LES) of the fluid flow around a 30P30N airfoil is conducted at a Reynolds number of Rec=750,000 and an angle of attack (AoA) of 9 degrees. The simulation results are validated against experimental data from previous studies and further analyzed, focusing on the suction side of the wing main element. The boundary layer development is investigated, showing characteristics typical of a zero-pressure-gradient turbulent boundary layer (ZPG TBL). In particular, the boundary layer exhibits limited growth, and the outer peak of the streamwise Reynolds stresses is virtually absent, distinguishing it from an adverse-pressure-gradient turbulent boundary layer (APG TBL). A proper orthogonal decomposition (POD) analysis is performed on a portion of the turbulent boundary layer, revealing a significant energy spread across higher-order modes. Despite this, TBL streaks are identified, and the locations of the most energetic structures correspond to the peaks in the Reynolds stresses.

physics.flu-dyn↗

arXiv2024

pyLOM: A HPC open source reduced order model suite for fluid dynamics applications

This paper describes the numerical implementation in a high-performance computing environment of an open-source library for model order reduction in fluid dynamics. This library, called pyLOM, contains the algorithms of proper orthogonal decomposition (POD), dynamic mode decomposition (DMD) and spectral proper orthogonal decomposition (SPOD), as well as, efficient SVD and matrix-matrix multiplication, all of them tailored for supercomputers. The library is profiled in detail under the MareNostrum IV supercomputer. The bottleneck is found to be in the QR factorization, which has been solved by an efficient binary tree communications pattern. Strong and weak scalability benchmarks reveal that the serial part (i.e., the part of the code that cannot be parallelized) of these algorithms is under 10% for the strong scaling and under 0.7% for the weak scaling. Using pyLOM, a POD of a dataset containing 1.14 x 108 gridpoints and 1808 snapshots that takes 6.3Tb of memory can be computed in 81.08 seconds using 10368 CPUs. Additioally, the algorithms are validated using the datasets of a flow around a circular cylinder at ReD = 100 and ReD = 1 x 104, as well as the flow in the Stanford diffuser at Reh = 1 x 104.

physics.flu-dyn↗