arXiv subjects

Ali Shamooni

Publications and source records attributed to Ali Shamooni.

5 recordsLinked to original sources

arXiv2026

Large-eddy simulation of moderately dense evaporating sprays with particle-informed super-resolution

In large-eddy simulation (LES) of dense sprays or sprays with pronounced clustering, evaporation rates can be inaccurate when the mesh is too coarse to provide realistic boundary conditions for the widely employed single droplet evaporation model. This is especially relevant to liquid spray combustion in practical applications. Deep learning-based super-resolution (SR) has recently emerged as a promising method for LES subgrid-scale modeling, capable of enhancing flow field resolution. This technique appears well-suited to reconstruct the local gas fields within the inter-droplet space that can be used to correct the evaporation rates. However, it has not yet been applied for this purpose. This paper presents an innovative SR approach $-$ particle-informed super-resolution (PISR) $-$ that approximates high-resolution flow fields for improved evaporation computation. It is validated with a priori, a posteriori and generalization tests on moderately dense sprays. The results show that PISR-LES can closely replicate the evaporation rates computed in a carrier-phase direct numerical simulation (CP-DNS), significantly reducing the discrepancy in the fuel mass fraction field between LES and CP-DNS. Furthermore, the PISR model exhibits robust generalization to cases unseen in training when varying air temperature, droplet diameter, turbulent Reynolds number and spray pattern.

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arXiv2025

Super-resolution of turbulent velocity fields in two-way coupled particle-laden flows

This paper introduces a deep learning-based super-resolution (SR) framework specifically developed for accurately reconstructing high-resolution velocity fields in two-way coupled particle-laden turbulent flows. Leveraging conditional generative adversarial networks (cGANs), the generator network architecture incorporates explicit conditioning on physical parameters, such as effective particle mass density and subgrid kinetic energy, while the discriminator network is conditioned on low-resolution data as well as high-frequency content of the input data. High-fidelity direct numerical simulation (DNS) datasets, covering a range of particle Stokes numbers, particle mass loadings, and carrier gas turbulence regimes, including forced- and decaying-turbulence, serve as training and testing datasets. Extensive validation studies, including detailed analyses of energy spectra, probability density functions (PDFs), vorticity distributions, and wavelet-based decomposition demonstrate the model's accuracy and generalization capabilities across different particle parameters. The results show that the network utilizes particle data, mainly in the reconstruction of high-frequency details modulated by particles. Additionally, systematic assessment of the model's performance in capturing previously unseen flow regimes further validates its predictive capabilities.

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arXiv2025

Super-resolution of turbulent velocity and scalar fields using different scalar distributions

In recent years, sub-grid models for turbulent mixing have been developed by data-driven methods for large eddy simulation (LES). Super-resolution is a data-driven deconvolution technique in which deep convolutional neural networks are trained using direct numerical simulation (DNS) data to learn mappings between the input data from a low resolution domain to the super-resolved high resolution output domain. While the technique has been of a great success in a-priori tests, the assessment of its generalization capabilities is required for further a-posteriori applications. In this study we assess the generalization capability of a super-resolution generative adversarial network (GAN) in reconstructing scalars with different distributions. Forced turbulence mixing DNS data with a fixed Reynolds number but different bulk scalar distributions, are generated and used as training/testing datasets. The results show that the velocity vector field can be reconstructed well, but the model fails to super-resolve the scalars from out-of-sample distributions. Including two extreme mixture fraction distributions, namely double Pareto and semi-Gaussian, in the training dataset significantly improves the performance of the model, not only for those distributions, but also for previously unseen bimodal distributions.

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arXiv2024

Carrier-phase DNS of ignition and combustion of iron particles in a turbulent mixing layer

Three-dimensional CP-DNS of reacting iron particle dust clouds in a turbulent mixing layer are conducted. The simulation approach considers the Eulerian transport equations for the reacting gas phase and resolves all scales of turbulence, whereas the particle boundary layers are modelled employing the Lagrangian point-particle framework for the dispersed phase. The CP-DNS employs an existing sub-model for iron particle combustion that considers the oxidation of iron to FeO and that accounts for both diffusion- and kinetically-limited combustion. At first, the particle sub-model is validated against experimental results for single iron particle combustion considering various particle diameters and ambient oxygen concentrations. Subsequently, the CP-DNS approach is employed to predict iron particle cloud ignition and combustion in a turbulent mixing layer. The upper stream of the mixing layer is initialised with cold particles in air, while the lower stream consists of hot air flowing in the opposite direction. Simulation results show that turbulent mixing induces heating, ignition and combustion of the iron particles. Significant increases in gas temperature and oxygen consumption occur mainly in regions where clusters of iron particles are formed. Over the course of the oxidation, the particles are subjected to different rate-limiting processes. While initially particle oxidation is kinetically-limited it becomes diffusion-limited for higher particle temperatures and peak particle temperatures are observed near the fully-oxidised particle state. Comparing the present non-volatile iron dust flames to general trends in volatile-containing solid fuel flames, non-vanishing particles at late simulation times and a stronger limiting effect of the local oxygen concentration on particle conversion is found for the present iron dust flames in shear-driven turbulence.

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arXiv2019

Velocity Spectra and Model Spectrum in Non-Premixed Jet Flames

In this contribution, velocity and its dissipation spectra in turbulent non-premixed jet flames are investigated by using two Direct Numerical Simulation (DNS) databases of temporally evolving jets with different bulk jet Reynolds numbers. In the DNS differential diffusion and flame dynamics effects have been accounted for and the flames experience high levels of extinction followed by re-ignition. It turns out that the spectra extracted from different statistically homogeneous planes across the jets and selected times corresponding to various occurring flame regimes (extinction and re-ignition phases) collapse reasonably when normalized by Favre averaged turbulent quantities. Especially (1) in the inertial range, the $κ^{-5/3}$ power law is observed with the constant of proportionality of $C_{K}=2.3$. (2) In the dissipation range $\exp(βκ)$ scaling exists with $β=7.2$ (instead of $β=5.2$). (3) The location of the peak of the normalized 1D dissipation spectra is in a lower normalized waver number ($\approx 0.08$) compared to the peak of non-reactive model spectrum ($\approx 0.11$). (4) Finally, an adapted 3D dissipation model spectrum proposed which peaks at a normalized waver number $\approx 0.19$ (instead of $\approx 0.26$).

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