arXiv subjects

Seyed Morteza Habibi Khorasani

Publications and source records attributed to Seyed Morteza Habibi Khorasani.

6 recordsLinked to original sources

arXiv2025

Turbulent heat transfer in open-channel flows with a thermally-conductive porous wall

Results of direct numerical simulations (DNS) of porous-wall turbulent flows in open channels with conjugate heat transfer are reported in this work. For the conductive porous walls considered here, the change in heat transfer is not monotonic. The heat flux initially decreases when going from a conductive smooth wall to slightly porous walls. In this initial porous-wall turbulence regime, the near-wall flow remains smooth-wall like and the heat transfer is dominated by molecular diffusion. As such, a reduction of the more favorably conducting solid material diminishes the overall heat transfer performance. Beyond a certain level of permeability however, the near-wall flow transitions to the K-H-like regime marked by the presence of cross-stream rollers, and the heat flux undergoes an increasing trend until it eventually surpasses that of smooth-wall turbulence. Neglecting the thermal behavior of the solid material can therefore result in overestimation of any gains in heat transfer. Additionally, thermal performance is assessed in terms of the Reynolds analogy breakdown, which is the disparity between the fractional increases in the Stanton number, $St$, and the fractional increases in the skin-friction coefficient, $C_f$, relative to smooth-wall flow. Similar to rough walls, the breakdown is unfavorable for porous walls. The unfavorable breakdown in Reynolds analogy is due to growing dissimilarities between the transfer of momentum and heat in the vicinity of the porous wall as it becomes more permeable. Turbulent sweep and ejection type events contribute more significantly to momentum transfer across the permeable surface than they do to heat transfer. However, unlike for rough walls, a saturation limit for heat transfer is not observed for the porous walls considered here. How much of a maximum increase in heat transfer can be achieved is something that remains to be determined.

physics.flu-dyn↗

arXiv2024

Data-driven discovery of drag-inducing elements on a rough surface through convolutional neural networks

Understanding the influence of surface roughness on drag forces remains a significant challenge in fluid dynamics. This paper presents a convolutional neural network (CNN) that predicts drag solely by the topography of rough surfaces and is capable of discovering spatial patterns linked to drag-inducing structures. A CNN model was developed to analyze spatial information from the topography of a rough surface and predict the roughness function, $ΔU^+$, obtained from direct numerical simulation. This model enables the prediction of drag from rough surface data alone, which was not possible with previous methods owing to the large number of surface-derived parameters. Additionally, the retention of spatial information by the model enables the creation of a feature map that accentuates critical areas for drag prediction on rough surfaces. By interpreting the feature maps, we show that the developed CNN model is able to discover spatial patterns associated with drag distributions across rough surfaces, even without a direct training on drag distribution data. The analysis of the feature map indicates that, even without flow field information, the CNN model extracts the importance of the flow-directional slope and height of roughness elements as key factors in inducing pressure drag. This study demonstrates that CNN-based drag prediction is grounded in physical principles of fluid dynamics, underscoring the utility of CNNs in both predicting and understanding drag on rough surfaces.

physics.flu-dyn↗

arXiv2024

Drag prediction of rough-wall turbulent flow using data-driven regression

Efficient tools for predicting the drag of rough walls in turbulent flows would have a tremendous impact. However, methods for drag prediction rely on experiments or numerical simulations which are costly and time-consuming. Data-driven regression methods have the potential to provide a prediction that is accurate and fast. We assess the performance and limitations of linear regression, kernel methods and neural networks for drag prediction using a database of 1000 homogeneous rough surfaces. Model performance is evaluated using the roughness function obtained at friction-scaled Reynolds number 500. With two trainable parameters, the kernel method can fully account for nonlinear relations between $ΔU^+$ and surface statistics (roughness height, effective slope, skewness, etc). In contrast, linear regression cannot account for nonlinear correlations and display large errors and high uncertainty. Multilayer perceptron and convolutional neural networks demonstrate performance on par with the kernel method but have orders of magnitude more trainable parameters. For the current database size, the networks' capacity cannot be fully exploited, resulting in reduced generalizability and reliability. Our study provides insight into the appropriateness of different regression models for drag prediction. We also discuss the remaining steps before data-driven methods emerge as useful tools in applications.

physics.flu-dyn↗

arXiv2023

Dewetting of a corner film wrapping a wall-mounted cylinder

In this study, we investigate the stability of a liquid film that partially wets a corner between a cylinder and a substrate, using a combination of theoretical and numerical approaches. The film stability, which depends on the film size and the wall wettability, is firstly predicted by a standard linear stability analysis (LSA) within the long-wave theoretical framework. We find that the film size plays the most important role in controlling the film stability. Specifically, the thinner the film is, the more sensitive it becomes to the large-wavenumber perturbation. The wall wettability mainly impacts the growth rates of perturbations and slightly influences the marginal stability and post-instability patterns of wrapping films. We compare the LSA predictions with numerical results obtained from a disjoining pressure model (DPM) and Volume-of-Fluid (VOF) simulations, which provide more insights into the film breakup process. At the early stage there is a strong agreement between the LSA predictions and the DPM results. Notably, as the perturbation grows, thin film regions connecting two neighboring satellite droplets form which may eventually lead to a stable or temporary secondary droplet, an aspect which the LSA is incapable of capturing. In addition, the VOF simulations suggest that beyond a critical film size the crest coalescence mechanism becomes involved during the breakup stage. Therefore, the LSA predictions are able to provide only an upper limit on the final number of satellite droplets.

physics.flu-dyn↗

arXiv2023

Turbulent flows over porous lattices: alteration of near-wall turbulence and pore-flow amplitude modulation

Turbulent flows over porous lattices consisting of rectangular cuboid pores are investigated using scale-resolving direct numerical simulations. Beyond a certain threshold which is primarily determined by the wall-normal Darcy permeability, ${K_y}^+$, near-wall turbulence transitions from its canonical regime, marked by the presence of streak-like structures, to another marked by the presence of spanwise coherent structures reminiscent of the Kelvin-Helmholtz (K-H) type of instability. This permeability threshold agrees well with that previously established in studies where permeable-wall boundary conditions had been used as surrogates for a porous substrate. None of the substrates investigated demonstrate any drag reduction relative to smooth-wall turbulent flow. At the permeable surface, a significant component of the flow is that which adheres to the pore geometry and undergoes amplitude modulation (AM). This pore-coherent flow remains notable within the substrates, highlighting the importance of the porous substrate's microstructure when the overlying flow is turbulent, an aspect which cannot be accounted for when using continuum-based approaches to model porous media flows or effective representations such as wall boundary conditions. The severity of the AM is enhanced in the K-H-like regime, which has implications when designing porous substrates for transport processes. This suggests that the surface of the substrate can have a geometry which is different than the rest of it and tailored to influence the overlying flow in a particular way.

physics.flu-dyn↗

arXiv2021

Near-wall turbulence alteration with the Transpiration-Resistance Model

A set of boundary conditions called the Transpiration-Resistance Model (TRM) are investigated in altering near-wall turbulence. The TRM has been previously proposed by \citet{Lacis2020} as a means of representing the net effect of surface micro-textures on their overlying bulk flows. It encompasses conventional Navier-slip boundary conditions relating the streamwise and spanwise velocities to their respective shears through the slip lengths $\ell_x$ and $\ell_z$. In addition, it features a transpiration condition accounting for the changes induced in the wall-normal velocity by expressing it in terms of variations of the wall-parallel velocity shears through the transpiration lengths $m_x$ and $m_z$. Greater levels of drag increase occur when more transpiration takes place at the boundary plane, with turbulent transpiration being predominately coupled to the spanwise shear component for canonical near-wall turbulence. The TRM can reproduce the effect of a homogeneous and structured roughness up to ${k^+}\,{\approx18}$. In this transitionally rough flow regime, the transpiration lengths of the TRM must be empirically determined. The \emph{transpiration factor} is defined as the product between the slip and transpiration lengths, i.e. $(m\ell)_{x,z}$. This factor contains the compound effect of the wall-parallel velocity occurring at the boundary plane and increased permeability, both of which lead to the transport of momentum in the wall-normal direction. A linear relation between the transpiration factor and the roughness function is observed for regularly textured surfaces in the transitionally rough regime of turbulence. The relations obtained between the transpiration factor and the roughness function show that such effective flow quantities can be suitable measures for characterizing rough surfaces in this flow regime.

physics.flu-dyn↗