arXiv subjects

Xavier Gloerfelt

Publications and source records attributed to Xavier Gloerfelt.

4 recordsLinked to original sources

arXiv2026

A high-fidelity numerical database for free-stream transition

The accurate prediction of laminar-to-turbulent transition is critical for the design of aerodynamic and turbomachinery systems, yet widely used experimental benchmarks, such as the ERCOFTAC T3 series, lack the full-field, three-dimensional, and time-resolved data required for modern model development. To address these limitations, this study presents a high-fidelity numerical database of bypass transition in boundary layers, generated using wall-resolved implicit Large Eddy Simulations (iLES) to rigorously mimic the ERCOFTAC T3 flat-plate experiments. Computations are performed using a high-order compressible Navier-Stokes solver across multiple configurations, encompassing a range of freestream turbulence intensities and both zero and varying pressure gradients. The numerical results demonstrate satisfactory agreement with legacy experimental data for skin friction, mean velocity, and fluctuation profiles. Finally, the resulting database is utilized to evaluate the predictive capabilities of standard Reynolds-Averaged Navier-Stokes (RANS) transition models (SA-BCM and $k-ω-γ$), revealing systemic flaws in predicting transition onset and length. This highlights the dataset's value as a foundational resource for the calibration, assessment, and development of next-generation, physics-informed machine learning transition closures.

physics.flu-dyn↗

arXiv2025

Cost-effective multi-fidelity strategy for the optimization of high-Reynolds number turbine flows guided by LES

A cost-effective multi-objective shape optimization strategy is proposed for high-Reynolds number flows involving complex phenomena such as boundary layer transition, shock-wave interactions, and turbulent wakes. These processes are poorly captured by Reynolds-Averaged Navier--Stokes (RANS) models, necessitating higher-fidelity approaches like Large Eddy Simulation (LES). However, LES is computationally prohibitive at high Reynolds numbers, making its direct use in optimization impractical. To address this, we introduce a low-dimensional design space representation using Singular Value Decomposition (SVD) and construct a multi-fidelity co-Kriging (MFK) surrogate model combining wall-resolved LES (WRLES) and RANS. Adaptive infill criteria are employed to strategically enrich the surrogate model within a limited computational budget (fewer than 10 LES samples). The methodology is applied to optimize a supersonic turbine vane for Organic Rankine Cycles (ORC), operating at Reynolds numbers of $\sim 10^6$. While RANS-LES correlation weakens near the optimal region, the MFK model outperforms single-fidelity Kriging (SFK) trained on the same LES data, effectively leveraging both abundant low-fidelity and scarce high-fidelity data. RANS accurately predicts global objective function trends but fails to resolve key flow features, whereas the MFK model captures fine-detail geometry trends from LES. Loss analysis reveals that LES is essential for identifying performance-detrimental mechanisms, while RANS-only optimization yields sub-optimal designs.

physics.flu-dyn↗

arXiv2024

Space-dependent Aggregation of Stochastic Data-driven Turbulence Models

A stochastic Machine-Learning approach is developed for data-driven Reynolds-Averaged Navier-Stokes (RANS) predictions of turbulent flows, with quantified model uncertainty. This is done by combining a Bayesian symbolic identification methodology for learning stochastic RANS model corrections for selected classes of flows (expert models), and a Mixture-of-Experts methodology that aggregates their predictions. The expert models are learned using the recently proposed SBL-SpaRTA algorithm, which generates sparse analytical expressions of the corrective terms with model parameters described by probability distributions. They outperform the baseline RANS model for flows similar to those used for training, but their generalization to different flows is not warranted. With the aim of quantifying the predictive uncertainty associated with the data-driven models while improving predictive accuracy and generalization capabilities, a space-dependent model aggregation technique (XMA) is then adopted. A gating function, which assigns each model a performance score (weight) based on a vector of local flow features, is trained alongside the expert models. The weights can be interpreted as the probability that a candidate model will outperform its competitors given the flow behavior at a given location. Predictions of unseen flows are formulated as a locally weighted average of the stochastic solutions of the expert models. A prediction uncertainty estimate is obtained by propagating the models' posterior parameter distributions and by evaluating the inter-model prediction variance. The expectancy of the XMA prediction is significantly more accurate than the baseline deterministic solution and the individual solutions of the experts for well-documented benchmark flows not included in the training set, while providing consistent estimates of the predictive variance.

physics.flu-dyn↗

arXiv2020

Bifurcations in curved duct flow based on a simplified Dean model

We present a minimal model of an incompressible flow in square duct subject to a slight curvature. Using a Poincaré-like section we identify stationary, periodic, aperiodic and chaotic regimes, depending on the unique control parameter of the problem: the Dean number (De). Aside from representing a simple, yet rich, dynamical system the present simplified model is also representative of the full problem, reproducing quite accurately the bifurcation points observed in the literature. We analyse the bifurcation diagram from De = 0 (no curvature) to De = 500, observing a periodic segment followed by two separate chaotic regions. The phase diagram of the flow in the periodic regime shows the presence of two symmetric steady states, the system oscillates around these solutions following a heteroclinic cycle. In the appendix some quantitative results are provided for validation purposes, as well as the python code used for the numerical solution of the Navier-Stokes equations.

physics.flu-dyn↗