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Ansys Fluent 16.0

Manufactured by ANSYS
Sourced in United States

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.

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18 protocols using Ansys Fluent 16.0

The finite volume approach method allowed solving Reynolds-averaged Navier–Stokes (RANS) equations in Fluent CFD code (Ansys Fluent 16.0, Ansys Inc., Pennsylvania, USA). The Realizable k-e turbulence model was selected, and the SIMPLE algorithm used. The governing equations of the discretization schemes were defined as second and the gradients were computed by the least-squares cell-based method. Pressure and momentum were set as second order and second order upwind. The turbulent kinetic energy and dissipation rate were defined as first order upwind (Defraeye et al., 2010b (link)). The convergence occurred automatically by the Ansys Fluent 16.0 (Ansys Fluent 16.0, Ansys Inc., Pennsylvania, USA).
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The Fluent CFD code (Ansys Fluent 16.0, Ansys Inc., Pennsylvania, PA, USA) solve the Reynolds-averaged Navier–Stokes (RANS) equations by the finite volume approach to. The Realizable k-e turbulence model was selected.
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.
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The Fluent CFD numerical code (Ansys Fluent 16.0, Ansys Inc., Pennsylvania, PA, USA) uses the finite volumes approach method to solve the Reynolds-averaged Navier–Stokes (RANS) equations. For that, a turbulence model is required, and the Realizable k-e was selected. This model was used with low-Reynolds number modelling (LRNM) to deal with the viscosity-affected region. This model presented higher convergence stability in comparison to standard k-e. Moreover, the Realizable k-e turbulence model presented a higher computation economy and velocity histograms very similar to the standard k-e, RST and RNG k-e models [15 (link),18 ].
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).
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The coefficients of drag and effective surface were obtained from the numerical simulations (Ansys Fluent 16.0, Ansys Inc., Pennsylvania, PA, USA). The drag force was computed by Equation (1)
FD=0.5ρACdv2
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).
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The three-dimensional boundaries with 7 m of length, 2.5 m of width and 2.5 m of height were created in Ansys Workbench software (Ansys Fluent 16.0, Ansys Inc., Pennsylvania, PA, USA) around the bicycle-cyclist system for each geometry. The Ansys meshing module allowed to generate a grid with more than 42 million of elements to represent the fluid flow in the opposite direction to the bicycle-cyclists systems at 2.5 m distance of the fluid flow inlet portion [17 (link)].
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.
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All simulations in this paper were performed in ANSYS FLUENT 16.0 (ANSYS Inc., Canonsburg, PA, USA). In this paper, the volume of fluid (VOF) model was used to obtain the gas-liquid interface. The VOF model was employed to track the moving gas-liquid interface21 (link). The k-ω-SST turbulence model was used to enclose the governing equations of the fluid motion22 (link). All the boundary conditions were set to the wall. The PISO algorithm was used to solve the velocity and pressure. The time step size was 0.0001 seconds. The maximum courant number was 0.25. The grid for this hollow OSR was generated using Gambit 2.4.6 (ANSYS Inc., Canonsburg, PA, USA).
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An ASUS (ASUS, N751, Taipé, Taiwan) machine running on an Intel processor was used (Core i7 4720HQ 2,6GHz). The Central Process Unit (CPU) had 4 cores and 8 threads, a maximal turbo boost of 3.6 GHz and a speed of 5 GT/s. The computer had 12 Gb of Random Access Memory (RAM) memory and an Solid-State Drive (SSD) hard disk of 256 Gb.
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.
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After the numerical simulations, it is possible to extract the coefficient of drag and surface area from Ansys Fluent Software (Ansys Fluent 16.0, Ansys Inc., Pennsylvania, USA). Then, the effective surface area (ACd) was computed. For the drag force, equation 4 was used:
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:
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9

Finite Volume Mesh and CFD Analysis of Central Venous System

A finite volume mesh with 590086 or 315019 unstructured tetrahedral elements and 170521 or 103490 nodes were generated for the central venous system without or with CVC respectively, using an automatic mesh generator ICEM (ANSYS Inc., Pittsburgh, PA). The boundary layer was resolved by placing the first four grid nodes at approximately 15, 17, 19 and 21μm away from the wall, and we supplemented the zoom-in view of the boundary layer mesh as shown in Fig. 8.

Sketch of mesh generation and the zoom-in view of the boundary layer mesh.

The flow visualization and analysis were completed by the commercial CFD software Ansys FLUENT 16.0 which was based on the finite volume method. The default segregate implicit 3D solver was applied. Discretization of the equations involved a second order upwind differencing scheme, SIMPLE, that was adopted for the pressure velocity correction and the residual error convergence threshold was set as 1e-6.
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The Ansys Workbench geometry module software (Ansys Fluent 16.0, Ansys Inc., Canonsburg, PA, USA) enabled us to create a three-dimensional domain (length = 7 m; width = 2.5 m; height = 2.5 m) around the cyclist. The domain was meshed with more than 42 million elements to represent the fluid. The elements were prismatic and tetrahedral with cell size near 25.72 µm. The cyclist geometry was at 2.5 m from the inlet portion for each simulation [28 (link)].
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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