arXiv subjects

Shaghayegh Zamani Ashtiani

Publications and source records attributed to Shaghayegh Zamani Ashtiani.

2 recordsLinked to original sources

arXiv2026

Data-driven time-dependent bases for turbulent airfoil wake-extreme gust interactions

We analyze interactions between turbulent airfoil wakes and extreme gusts using a data-driven framework with time-dependent bases. The current approach represents each snapshot with time-varying bases consisting of two-dimensional in-plane modes and one-dimensional spanwise modes, together with a reduced covariance matrix. By deriving closed-form evolution equations, we advance these components using a small rolling window, eliminating the need for full-history storage. Applied to extreme vortex gust-airfoil interactions at Re=5000, we reveal that the first in-plane mode dominates before impingement, while the second mode gains energy post-impingement, amplified by gust intensity. During impingement, leading modes exhibit multiscale features such as shear-layer roll-up and fine-scale turbulence. The temporal evolution of the mode-energy spectrum and its rank gaps further quantifies these interactions. A large leading-mode gap indicates energy concentrated in a few coherent structures, leading to faster recovery, whereas a smaller gap indicates energy distributed across many modes, consistent with richer multiscale activity and delayed re-stabilization. These trends also follow the transient lift dynamics, with higher amplitude and more oscillations indicated by a rise in the leading singular values. This work may provide a foundation for time-varying data-driven modal analysis of extreme gust encounters.

physics.comp-ph↗

arXiv2022

Scalable In Situ Compression of Transient Simulation Data Using Time-Dependent Bases

Large-scale simulations of time-dependent problems generate a massive amount of data and with the explosive increase in computational resources the size of the data generated by these simulations has increased significantly. This has imposed severe limitations on the amount of data that can be stored and has elevated the issue of input/output (I/O) into one of the major bottlenecks of high-performance computing. In this work, we present an in situ compression technique to reduce the size of the data storage by orders of magnitude. This methodology is based on time-dependent subspaces and it extracts low-rank structures from multidimensional streaming data by decomposing the data into a set of time-dependent bases and a core tensor. We derive closed-form evolution equations for the core tensor as well as the time-dependent bases. The presented methodology does not require the data history and the computational cost of its extractions scales linearly with the size of data -- making it suitable for large-scale streaming datasets. To control the compression error, we present an adaptive strategy to add/remove modes to maintain the reconstruction error below a given threshold. We present four demonstration cases: (i) analytical example, (ii) incompressible unsteady reactive flow, (iii) stochastic turbulent reactive flow, and (iv) three-dimensional turbulent channel flow.

cs.CE↗