arXiv subjects

Guillermo Barragán

Publications and source records attributed to Guillermo Barragán.

3 recordsLinked to original sources

arXiv2026

Divergence-aware adaptive prediction framework for accelerating CFD simulations of unsteady flows

Reliable long-horizon prediction remains a challenge for data-driven CFD surrogates, because offline-trained models accumulate autoregressive errors and lose accuracy when operating conditions change. This work develops a divergence-aware adaptive CFD-surrogate framework that couples a CFD solver with a proper orthogonal decomposition-deep learning (POD-DL) surrogate in a closed-loop workflow. CFD snapshots are compressed by POD, and a neural-network predictor advances the reduced state in time. The surrogate performs autoregressive forecasting, while its reliability is monitored online. When a prescribed update interval is reached or prediction degradation is detected, the CFD solver is automatically recalled to generate new snapshots and update the surrogate. The framework is assessed for three-dimensional flow past a circular cylinder at Re = 160-400. Baseline non-adaptive predictions exhibit progressive error growth over long forecast horizons, confirming the need for online correction. With prescribed update intervals, the adaptive framework preserves the dominant wake dynamics and reduces error growth after retraining compared with the non-adaptive model. For a representative 200-snapshot interval, the framework achieves a speed-up ratio of approximately 92 relative to CFD. An event-triggered mode is introduced using ensemble uncertainty and dynamically estimated thresholds. This mode terminates unreliable forecasts without requiring ground-truth CFD data during prediction, and the detected triggers are consistent with the onset of deterioration in the lift-coefficient evolution. Under varying inlet conditions, the framework detects regime changes, recalls CFD, and recovers reliable predictions. These results demonstrate that divergence-aware CFD-surrogate coupling provides a robust and efficient route for adaptive long-horizon flow prediction under evolving operating conditions.

physics.flu-dyn↗

arXiv2026

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems

We introduce MoTIF, a mode-structured tensor framework for multi-parametric approximation, super-resolution, and temporal forecasting of high-dimensional unsteady systems. The methodology leverages High-Order Singular Value Decomposition (HOSVD) to obtain a structured multilinear representation of multi-dimensional datasets, separating physical parameters, spatial coordinates, and temporal evolution into distinct modal components. This decomposition enables the application of dedicated approximation operators to each mode. Gaussian Process Regression is employed to interpolate and extrapolate parametric and spatial modal matrices, enabling database completion and resolution enhancement, while recurrent neural networks are applied to the temporal mode to forecast system evolution. This decoupled operator-learning strategy preserves the intrinsic tensor structure while providing a flexible non-intrusive reduced-order modelling framework. The proposed methodology is validated on a database of unsteady laminar flow simulations with varying Reynolds numbers and angles of attack. Accurate reconstruction of unseen flow configurations and temporal prediction are achieved, with relative root mean square errors consistently below 2\% compared to high-fidelity simulations. The framework provides a scalable and mathematically structured alternative to conventional surrogate modelling approaches for high-dimensional parametric dynamical systems.

physics.flu-dyn↗

arXiv2025

HOSVD-SR: A Physics-Based Deep Learning Framework for Super-Resolution in Fluid Dynamics

In this work we present a novel methodology that combines Higher Order Singular Value Decomposition (HOSVD) with Deep Learning (DL) techniques for super-resolution in computational fluid dynamics (CFD) and sparse experimental datasets. This approach, referred to as HOSVD-SR 1, integrates modal decomposition techniques with Machine Learning (ML), creating a hybrid model grounded in the underlying physics of the studied phenomena and capable of enhancing data dimensionality. The proposed methodology leverages HOSVD, a robust variant of SVD, ideal for high dimensional data, which extracts the singular values and modes (spatial and temporal) associated with each dimension of the database in tensor form, reducing noise and addressing challenges related to turbulent flows. HOSVD is employed to capture the key physical patterns from a under-resolved fluid mechanics database. Each spatial mode matrix serves as input for a decoder/autoencoder type neural network trained to increase the dimensionality of the tensor and accurately reconstruct experimental and CFD-like databases. The HOSVD-SR methodology has been tested on both two- and three-dimensional numerical databases of flow past a circular cylinder, as well as an experimental database of circular cylinder wake flows under a turbulent flow regime. HOSVD-SR has successfully addressed both laminar and turbulent cases, outperforming a previously proposed SVD-based methodology in terms of accuracy. HOSVD-SR is physics-based, making it a robust and highly generalizable method that can also be implemented for other data generation approaches in fluid mechanics problems.

physics.flu-dyn↗