Ansys Fluent 16.0
Ansys Fluent 16.0 is a computational fluid dynamics (CFD) software package that provides advanced modeling capabilities for fluid flow, turbulence, heat transfer, and other related phenomena. It offers a comprehensive suite of tools for simulating complex fluid dynamics problems across a wide range of industries.
Lab products found in correlation
18 protocols using Ansys Fluent 16.0
Finite Volume RANS Modeling in Fluent
Computational Fluid Dynamics Simulations
For pressure–velocity coupling, the SIMPLE algorithm was used [16 (link)]. The discretization schemes were defined as second for the pressure interpolation and the convection and viscous terms. The gradients were computed by the least-squares cell-based method. Pressure and momentum were defined as second-order and second-order upwind. The turbulent kinetic energy and dissipation rate were defined as first order upwind. The convergence occurred automatically by the Ansys Fluent 16.0 before 1404 interactions.
Fluent CFD Numerical Code Simulation
For pressure-speed coupling, the SIMPLE algorithm was used. The pressure, convection terms and viscosity were defined as second. The least squares cell-based technique allowed us to compute the gradients. Pressure and moment were defined as second and first order upwind. The turbulence kinetic energy and dissipation rate were set as first order upwind. For all the simulations, an automatic convergence occurred before 1404 interactions (Ansys Fluent 16.0, Ansys Inc., Pennsylvania, PA, USA).
Drag Force Computation from Simulations
FD is the drag force, Cd represents the drag coefficient, v the velocity, A the surface area and ρ is the air density (1.292 kg/m3).
Aerodynamic Analysis of Bicycle-Cyclist System
Mean velocity in tours is near 11.1 m/s (~40 km/h) [28 (link),29 (link)]. Knowing that, velocities up to 13 m/s with increments of 1 m/s. The velocities were set at the inlet portion of the enclosure surface (-z direction) in the opposite direction of the bicycle-cyclists models’ orientation. The turbulence intensity in numerical simulations were assumed as 1 × 10−6%. It was established that the bicycle–cyclist system had a zero roughness non-slip wall, and scalable wall functions were assigned.
Numerical Simulation of Hollow OSR Flow
Computational Fluid Dynamics Analysis of Cyclist Drag
On the Ansys Workbench software (Ansys Fluent 16.0, Ansys Inc., Pennsylvania, PA, USA), three-dimensional frontiers were generated as a domain around the model (domain: 7 m in length, 2.5 m in height and 2.5 m in width; model: placed at 2.5 m distance of the inlet end). The mesh was created with more than 42 million elements [16 (link)]. The elements were the volumes in which equations of motion were applied around the geometry [11 (link),13 (link)]. The cell size was ~25 µm [11 (link)]. The mesh processing time was about 12 h.
The numerical simulations to assess drag were run between 1 m/s and 22 m/s, with increments of 1 m/s. Typically, during downhill or sprinting events, cyclists may reach the top speeds selected in this study [17 (link),18 ]. Thus, each speed was set in the inlet portion of the domain (-z direction). The turbulence intensity was set as 1 × 10%−6%, and the system was set with the scalable walls function. Each computation took about 48 h to reach the simulation’s convergence.
Extracting Drag Coefficient and Surface Area
where, Fd is the drag force, Cd represents the drag coefficient, v the velocity, A the surface area and ρ is the air density (1.292 kg/m3). The Cd is given by re-arranging Equation 5:
Finite Volume Mesh and CFD Analysis of Central Venous System
Sketch of mesh generation and the zoom-in view of the boundary layer mesh.
Aerodynamic Analysis of Cyclist Performance
Typically, a professional road cyclist reaches mean speeds of about 11 m/s (≈ 40 km/h) during a stage [5 (link)]. Thus, the numerical simulations were conducted between 1 and 22 m/s with increments of 1 m/s (22 speeds). The speeds were set at the inlet portion of the enclosure (-z direction). The turbulence intensity was assumed as 1 × 10−6% for different positions. The non-slip wall and scalable wall functions were assigned.
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