arXiv subjects

Pourya Forooghi

Publications and source records attributed to Pourya Forooghi.

7 recordsLinked to original sources

arXiv2026

Micro-convection mass transfer following bubble coalescence on a solid wall

The Volume-of-Fluid implementation in the \href{basilisk.fr}{Basilisk} flow solver is employed to study mass transfer after coalescence-induced jump-off of bubbles on solid substrates at four combinations of bubble radius ($R_m=150$ and 25 $\mu$m, hydrogen-water properties) and Schmidt number ($\text{Sc}=210$ and 1). The results show a relatively strong downward entrainment of low-concentration liquid, induced by a rapid movement of the bubble interface at approximately 0.5 to 1 inertio-capillary time units after the moment of coalescence. At $\text{Sc}=210$, this leads to a highly local increase of the Sherwood number at a core region (roughly an area of radius $R_m/2$) below the south pole of the merged bubble, which persists long after the bubble departure due to the slow diffusion at large $\text{Sc}$. The enhancement factor of the Sherwood number is highly dependent on the state of the mass-transfer boundary layer at the moment of coalescence, increasing with smaller boundary layer thicknesses. Shortly after the jump-off, the velocity of the bubble is considerably damped and rapidly approaches its free-rise terminal velocity. The effect of micro-convection on the wall mass transfer coefficient at this stage is insignificant when isolated, similar to what is reported in the literature for purely buoyancy-driven bubble rise -- but slightly stronger.

physics.flu-dyn↗

arXiv2025

Data-driven correlations for thermohydraulic roughness properties

The influence of rough surfaces on fluid flow is characterized by the downward shift in the logarithmic layer of velocity and temperature profiles, namely the velocity roughness function $\Delta U^+$ and the corresponding temperature roughness function $\Delta \Theta^+$. Their computation relies on computational simulations, and hence a simple prediction without such simulation is envisioned. We present a framework, where a data-driven model is developed using the dataset of Yang et al. 2023 \cite{yang_2023} with $93$ high fidelity direct numerical simulations of a fully-developed turbulent channel flow at $Re_\tau \approx 800$ and $Pr = 0.71$. The model provides robust predictive capabilities (mean squared error $\text{MSE}_{k} = 0.09$ and $\text{MSE}_\theta = 0.096$), but lacks interpretability. Simplistic statistical roughness parameters provide a more understandable route, so the framework is extended with a symbolic regression approach to distill an empirical correlation from the data-driven model. The derived expression leads to a predictive correlation for the equivalent sand-grain roughness $k_\text{s} = k_\text{99} (ES_x ( - ES_x + Sk + 2.37) + 0.772)$ with reasonable predictive powers. The predictive capability of the temperature roughness function is subject to limitations due to the missing Prandtl-number variation in the dataset. Nevertheless, the interpretable correlation and the neural network as well as the original dataset can be used to explore the roughness functions. The functional form of the derived correlations, along with visual analysis of these surfaces, suggests a strong relationship with roughness wavelengths, further linking them to explanations based on sheltered and windward regions.

physics.flu-dyn↗

arXiv2023

Prediction of equivalent sand-grain size and identification of drag-relevant scales of roughness -- a data driven approach

The purpose of the present work is to examine two possibilities; firstly, predicting equivalent sand-grain roughness size $k_s$ based on the roughness height probability density function and power spectrum leveraging machine learning as a regression tool, and secondly, extracting information about relevance of different roughness scales to skin-friction drag by interpreting the output of the trained data-driven model. The model is an ensemble neural network consisting of 50 deep neural networks. The data for the training of the model is obtained from direct numerical simulations (DNSs) of turbulent flow in plane channels over 85 irregular multi-scale roughness samples at friction Reynolds number Re$_τ=800$. The 85 roughness samples are selected from a repository of 4200 samples, covering a wide parameter space, through an active learning (AL) framework. The selection is made in several iterations, based on the informativeness of samples in the repository, quantified by the variance of ENN predictions. This AL framework aims to maximize the generalizability of the predictions with a certain amount of data. This is examined using three different testing data sets with different types of roughness, including 21 surfaces from the literature. The model yields an overall mean error of 5\% to 10\% on different testing data sets. Subsequently, a data interpretation technique, known as layer-wise relevance propagation, is applied to measure the contributions of different roughness wave-lengths to the predicted $k_s$. High-pass filtering is then applied to the roughness PS to exclude the wave-numbers identified as drag-irrelevant. The filtered rough surfaces are investigated using DNS, and it is demonstrated that, despite significant impact of filtering on the roughness topographical appearance and statistics, the skin-friction coefficient of the original roughness is successfully preserved.

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arXiv2022

Frozen propagation of Reynolds force vector from high-fidelity data into Reynolds-averaged simulations of secondary flows

Successful propagation of information from high-fidelity sources (i.e., direct numerical simulations and large-eddy simulations) into Reynolds-averaged Navier-Stokes (RANS) equations plays an important role in the emerging field of data-driven RANS modeling. Small errors carried in high-fidelity data can propagate amplified errors into the mean flow field, and higher Reynolds numbers worsen the error propagation. In this study, we compare a series of propagation methods for two cases of Prandtl's secondary flows of the second kind: square-duct flow at a low Reynolds number and roughness-induced secondary flow at a very high Reynolds number. We show that frozen treatments result in less error propagation than the implicit treatment of Reynolds stress tensor (RST), and for cases with very high Reynolds numbers, explicit and implicit treatments are not recommended. Inspired by the obtained results, we introduce the frozen treatment to the propagation of Reynolds force vector (RFV), which leads to less error propagation. Specifically, for both cases at low and high Reynolds numbers, propagation of RFV results in one order of magnitude lower error compared to RST propagation. In the frozen treatment method, three different eddy-viscosity models are used to evaluate the effect of turbulent diffusion on error propagation. We show that, regardless of the baseline model, the frozen treatment of RFV results in less error propagation. We combined one extra correction term for turbulent kinetic energy with the frozen treatment of RFV, which makes our propagation technique capable of reproducing both velocity and turbulent kinetic energy fields similar to high-fidelity data.

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arXiv2022

DNS-based characterization of pseudo-random roughness in minimal channels

Direct numerical simulation is used to study turbulent flow over irregular rough surfaces in the periodic minimal channel configuration. The generation of irregular rough surface is based on a random algorithm, in which the power spectrum of the roughness height function along with its probability density function can be directly prescribed. The hydrodynamic properties of the roughness are investigated and compared to those obtained from full-size DNS for 12 roughness topographies with systematically varied PDF and PS at four roughness height. The comparison confirms the viability of the minimal channel approach for characterization of rough surfaces providing excellent agreement in roughness function and zero-plane displacement across various types of roughness and different regimes. Results also indicates that different realizations of roughness, with a fixed PS and PDF, translate to similar values of roughness function with a small scatter. In addition to the global flow properties, the distribution of time-averaged surface force exerted by the roughness onto the fluid is examined and compared to the roughness height distribution for different cases. It is shown that the surface force distribution has an anisotropic structure with spanwise-elongated coherent regions. The anisotropy translates into a very small streamwise integral length scale, which weakly depends on the considered roughness topography, while the larger spanwise integral length scale shows a stronger dependence on roughness characteristics. It is also shown that the sheltering model describes well the spatial distribution of the surface force. Finally, existing roughness correlations are assessed using the present dataset. It was shown that the most correlations can reproduce the values of equivalent sand-grain roughness from DNS within+-30% error while none of the correlations shows a superior predictive accuracy.

physics.flu-dyn↗

arXiv2021

Predicting drag on rough surfaces by transfer learning of empirical correlations

Recent developments in neural networks have shown the potential of estimating drag on irregular rough surfaces. Nevertheless, the difficulty of obtaining a large high-fidelity dataset to train neural networks is deterring their use in practical applications. In this study, we propose a transfer learning framework to model the drag on irregular rough surfaces even with a limited amount of direct numerical simulations. We show that transfer learning of empirical correlations, reported in the literature, can significantly improve the performance of neural networks for drag prediction. This is because empirical correlations include `approximate knowledge' of the drag dependency in high-fidelity physics. The `approximate knowledge' allows neural networks to learn the surface statistics known to affect drag more efficiently. The developed framework can be applied to applications where acquiring a large dataset is difficult, but empirical correlations have been reported.

physics.flu-dyn↗

arXiv2018

A systematic study of turbulent heat transfer over rough walls

Direct Numerical Simulations are used to solve turbulent flow and heat transfer over a variety of rough walls in a channel. The wall geometries are exactly resolved in the simulations. The aim is to understand the effect of roughness morphology and its scaling on the augmentation of heat transfer relative to that of skin friction. A number of realistic rough surface maps obtained from the scanning of gas turbine blades and internal combustion engines as well as several artificially generated rough surfaces are examined. In the first part of the paper, effects of statistical surface properties, namely surface slope and roughness density, at constant roughness height are systematically investigated, and it is shown that Reynolds analogy factor (two times Stanton number divided by skin friction coefficient) varies meaningfully but moderately with the surface parameters except for the case with extremely low slope or density where the Reynolds analogy factor grows significantly and tends to that of a smooth wall. In the second part of the paper, the roughness height is varied (independently in both inner and outer units) while the geometrical similarity is maintained. Considering all the simulated cases, it is concluded that Reynolds analogy factor correlates fairly well with the equivalent sand roughness scaled in inner units and asymptotically tends to a plateau.

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