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A deep-learning-based closure model to address energy loss in low-dimensional surrogate models based on proper-orthogonal-decomposition (POD) modes is introduced. Using a transformer-encoder block with easy-attention mechanism, the model predicts the spatial probability density function of fluctuations not captured by the truncated POD modes. The methodology is demonstrated on the wake of the Windsor body at yaw angles of [2.5,5,7.5,10,12.5], with 7.5 as a test case. Key coherent modes are identified by clustering them based on dominant frequency dynamics using Hotelling T2 on the spectral properties of temporal coefficients. These coherent modes account for nearly 60% of the total energy while comprising less than 10% of all modes. A common POD basis is created by concatenating coherent modes from training angles and orthonormalizing the set, reducing the basis vectors from 142 to 90 without losing information. Transformers with different size on the attention layer, (64, 128 and 256), are trained to model the missing fluctuations. Larger attention sizes always improve predictions for the training set, but the transformer with an attention layer of size 256 overshoots the fluctuations predictions in the test set because they have lower intensity than in the training cases. Adding the predicted fluctuations closes the energy gap between the reconstruction and the original flow field, improving predictions for energy, root-mean-square velocity fluctuations, and instantaneous flow fields. The deepest architecture reduces mean energy error from 37% to 12% and decreases the Kullback--Leibler divergence of velocity distributions from KL=0.2 to below KL=0.026.

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., Comment: Proceedings paper, ECCOMAS 2024

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., Comment: DLES13 2022 conference proceeding paper

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, Comment: ECOMMAS 2024 conference proceeding paper

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.