arXiv subjects

Armin Sheidani

Publications and source records attributed to Armin Sheidani.

5 recordsLinked to original sources

arXiv2026

Enhancing the accuracy of under-resolved numerical simulations of atmospheric flows with super resolution

Super-resolution (SR) techniques based on deep learning have recently emerged as a promising approach to enhance the spatial resolution of computational fluid dynamics simulations while containing computational cost. In this paper, we investigate several SR architectures to improve coarse-grid simulations of mesoscale atmospheric flows, with training data generated from simulations of the weakly compressible Euler equations. We compare a baseline convolutional neural network (CNN), an attention-enhanced CNN, a multi-scale CNN designed to capture flow structures across different spatial scales, and a diffusion-based SR model. The methods are evaluated on two standard atmospheric benchmarks: the rising thermal bubble and the density current. Results show that the baseline CNN can accurately reconstruct simpler flow features, while more complex flows require multi-scale architectures. Overall, SR based on the multi-scale CNN provides the best balance of accuracy, robustness, and computational efficiency, outperforming even a state-of-the-art diffusion-based approach. We also analyze the sensitivity of the models to the size of the training dataset, highlighting limitations and trade-offs of the proposed SR strategies.

physics.flu-dyn↗

arXiv2024

Deep learning-based reduced-order methods for fast transient dynamics

In recent years, large-scale numerical simulations played an essential role in estimating the effects of explosion events in urban environments, for the purpose of ensuring the security and safety of cities. Such simulations are computationally expensive and, often, the time taken for one single computation is large and does not permit parametric studies. The aim of this work is therefore to facilitate real-time and multi-query calculations by employing a non-intrusive Reduced Order Method (ROM). We propose a deep learning-based (DL) ROM scheme able to deal with fast transient dynamics. In the case of blast waves, the parametrised PDEs are time-dependent and non-linear. For such problems, the Proper Orthogonal Decomposition (POD), which relies on a linear superposition of modes, cannot approximate the solutions efficiently. The piecewise POD-DL scheme developed here is a local ROM based on time-domain partitioning and a first dimensionality reduction obtained through the POD. Autoencoders are used as a second and non-linear dimensionality reduction. The latent space obtained is then reconstructed from the time and parameter space through deep forward neural networks. The proposed scheme is applied to an example consisting of a blast wave propagating in air and impacting on the outside of a building. The efficiency of the deep learning-based ROM in approximating the time-dependent pressure field is shown.

math.NA↗

arXiv2024

Modal Analysis of the Wake Shed Behind a Horizontal Axis Wind Turbine with Flexible Blades

The proper orthogonal decomposition has been applied on a full-scale horizontal-axis wind turbine to shed light on the wake characteristics behind the wind turbine. In reality, the blade tip experiences high deflections even at the rated conditions which definitely alter the wake flow field, and in the case of a wind farm, may complicate the inlet conditions of the downstream wind turbine. The turbine under consideration is the full-scale model of the National Renewable Energy Laboratory 5MW onshore wind turbine which is accompanied by several simulation complexities including turbulence, mesh motion and fluid-structure interaction. Results indicated an almost similar modal behaviour for the rigid and flexible turbines at the wake region. In addition, more flow structures in terms of local vortices and fluctuating velocity fields take place at the far wake region. The flow structures due to the wake shed from the tower tend to move towards the center and merge with that of the nacelle leading to an integral vortical structure 2.5 diameter away from the rotor. Also, it is concluded that the exclusion of the tower leads to missing a major part of the wake structures, especially at far-wake positions.

math.NA↗

arXiv2024

A Predictive Surrogate Model for Heat Transfer of an Impinging Jet on a Concave Surface

This paper aims to comprehensively investigate the efficacy of various Model Order Reduction (MOR) and deep learning techniques in predicting heat transfer in a pulsed jet impinging on a concave surface. Expanding on the previous experimental and numerical research involving pulsed circular jets, this investigation extends to evaluate Predictive Surrogate Models (PSM) for heat transfer across various jet characteristics. To this end, this work introduces two predictive approaches, one employing a Fast Fourier Transformation augmented Artificial Neural Network (FFT-ANN) for predicting the average Nusselt number under constant-frequency scenarios. Moreover, the investigation introduces the Proper Orthogonal Decomposition and Long Short-Term Memory (POD-LSTM) approach for random-frequency impingement jets. The POD-LSTM method proves to be a robust solution for predicting the local heat transfer rate under random-frequency impingement scenarios, capturing both the trend and value of temporal modes. The comparison of these approaches highlights the versatility and efficacy of advanced machine learning techniques in modelling complex heat transfer phenomena.

math.NA↗

arXiv2022

Assessment of URANS and LES Methods in Predicting Wake Shed Behind a Vertical Axis Wind Turbine

In order to shed light on the Vertical-Axis Wind Turbines (VAWT) wake characteristics, in this paper we present high-fidelity CFD simulations of the flow around an exemplary H-shaped VAWT turbine, and we propose to apply Proper Orthogonal Decomposition (POD) to the computed flow field in the near wake of the rotor. The turbine under consideration was widely studied in previous experimental and computational investigations. In the first part of the study, multiple Reynolds-Averaged Navier-Stokes (RANS) simulations were performed at the Tip Speed Ratio (TSR) of peak power coefficient, to select the most accurate turbulence model with respect to available data. In the following step, further RANS numerical simulations were performed at different TSRs to compare the power coefficient against experimental data. Then, Large Eddy Simulation (LES) was applied for multiple TSR conditions. The spatial and temporal POD modes along with modal energy for the RANS and LES results were extracted, and the performance of the turbulence models was assessed. Also, an interpretation of the POD modes with respect to the flow structures was given to highlight the most significant time and length scales of the predictions considering the different dynamical levels of approximations of the computational models.

math.NA↗