The lid-driven cavity is an important fluid mechanical system that serves as a benchmark for testing numerical methods and studying fundamental aspects of incompressible flow in confined volume. This paper concerned with the calculation LDC on 2-D 3-D model. commercial software ANSYS Fluent was used simulation. In these models simulated cases, lid moves x-direction. solutions driven are calcula...

in this study, the effects of strength and direction of the magnetic field on lid-driven cavity flow in the whole range of magnetic field strength variations, which already used in practice up to present (0 to 10 tesla), are studied. the governing equations, including conservative equations of momentum, continuity and magnetic field equations were solved numerically. momentum and continuity equ...

In this manuscript, the LBM is applied for simulating of Mixed Convection in a Lid-Driven cavity with an open side. The cavity horizontal walls are insulated while the west Lid-driven wall is maintained at a uniform temperature higher than the ambient. Prandtl number (Pr) is fixed to 0.71 (air) while Reynolds number (Re) , Richardson number (Ri) and aspect ratio (A) of the cavity are changed in...

It is known that an increased flow rate can be achieved in channel flows when smooth walls are replaced by superhydrophobic surfaces. This reduces friction and increases the flux for a given driving force. Applications include thermal management microelectronics, where competition between convective conductive resistance must accounted order to evaluate any advantages of these Of particular int...

Conclusion: We have shown that amplifier saturation in a driven unlocked oscillator causes the inherent one-sided beat frequency spectrum to be converted into a two-sided beat frequency spectrum of asymmetric amplitude, but equal beat frequency separation. This can be of importance in injectionlocking oscillator design as it can affect the approach to the threshold-lock condition with large inj...

One-shot neural architecture search (NAS) substantially improves the efficiency by training one supernet to estimate performance of every possible child (i.e., subnet). However, inconsistency characteristics among subnets incurs serious interference in optimization, resulting poor ranking correlation subnets. Subsequent explorations decompose weights via a particular criterion, e.g., gradient m...