Fluent18

Manufactured by ANSYS

Sourced in United States

ANSYS-FLUENT18.0 is a computational fluid dynamics (CFD) software tool used for simulating fluid flow, heat transfer, and related physical processes. It provides a comprehensive suite of modeling capabilities to handle a wide range of incompressible and compressible, laminar and turbulent fluid flow problems.

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Lab products found in correlation

Icem cfd, by ANSYS (1 mentions)

15 protocols using fluent18

Comparative Analysis of Mesh Techniques

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To systematically study the potential benefits of PRM meshing, we also generated UNST meshes for comparison. All our attempt of direct unstructured meshing of the entire large portion of the arterial tree failed due to surface discontinuities, holes and overlap between neighboring surface patches. To overcome this problem for large-scale modeling, we synthesized unstructured tetrahedral/prism meshes by using parametric surface meshes (PRM) and Delaunay method in ANSYS ICEMCFD (ANSYS Inc., Canonsburg, Pa., USA). We compared the performance of PRM with UNST meshes using parallel 14-core processing on dual 2.4 GHz Xenon CPUs. Unsteady hemodynamic simulations were carried out using ANSYS Fluent 18.1 (ANSYS Inc., Canonsburg, PA) using the finite volume method (FVM). For both PRM and UNST, the numerical simulations were carried out using semi-implicit method for pressure-linked equations (SIMPLE) solver with a second-order upwind scheme with 40 iterations per time-step, and time-step size of 0.001 s.
The vessel walls were assumed rigid with no-slip boundary condition. Blood rheology is modeled as a viscous, incompressible, single-phase Newtonian fluid with density 1055 kg/m³ and dynamic viscosity of 4.265 × 10⁻³Pa.s.

Ghaffari M., Tangen K., Alaraj A., Du X., Charbel F.T, & Linninger A.A. (2017). Large-scale subject-specific cerebral arterial tree modeling using automated parametric mesh generation for blood flow simulation. Computers in biology and medicine, 91, 353-365.

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Simulation of sodium hypochlorite irrigation

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5.25% sodium hypochlorite solution was adopted as the irrigation, with density of 1.04 g/cm³ and viscosity of 1.3 × 10⁻³ Pa*s [20 (link)]. The surfaces of the needle and root canal were regarded as rigid, impermeable walls, and a non-slip boundary condition was adopted in the simulation. The k-ω SST turbulence model that had been validated in our earlier studies was used [11 (link), 21 (link)]. Similar to the commonly used clinical irrigation flow of 0.26 mL/s [5 (link), 22 (link)], an inflow velocity of 8.6 m/s was adopted so that the effect of different working depths and root canal curvatures could readily be evaluated. Numerical simulation was carried out with software Ansys Fluent 18.1 (ANSYS Inc., Canonsburg, PA, USA).

Yu M., Li Y., Zhao M., Huang Z., Zhou N, & Jin H. (2024). Computational fluid dynamics investigation on the irrigation of a real root canal with a side-vented needle. BMC Oral Health, 24, 321.

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CFD Simulation of Dust Deposition on PV Panels

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The CFD package ANSYS FLUENT 18.1 is used to simulate air and dust flow around a PV panel and predict the dust particles behavior. The CFD package was used to solve the Navier–Stokes equations of airflow around the PV panel and the dust particles governing equations for dust motion. The Eulerian–Lagrangian approach was used to simulate the airflow and dust particles’ trajectories around the PV panel. The Eulerian approach was used to compute the continuous fluid phase which was assumed to be steady, incompressible 2D air flow, while the Lagrangian technique was adopted to predict particles transport in air.

Shenouda R., Abd-Elhady M.S., Kandil H.A, & Dagher M.M. (2023). Numerical investigation of the effect of dust shields on accumulation of dust over PV panels. Environmental Science and Pollution Research International, 30(22), 62905-62923.

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Numerical Simulation of Nasal Airflow

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After the reconstruction of geometries, the flow field and particle transport equations were solved numerically using the CFD-DPM method. The mass and momentum conservation equations were solved for resting and low activity breathing conditions (laminar airflow) and then particle transport equations were solved using Lagrangian approach. ICEM-CFD software package and ANSYS Fluent 18.0 were used for meshing and performing numerical simulation, respectively. Grid independence test was done to ensure that the results are independent of the grid size. For this purpose, an unstructured quadratic grid with 2.7 million cells was used for the external hemisphere which covers the exterior nose domain and a hybrid grid was used for the inside of the nasal cavity (consists of 4.6 million cells of quadratic elements and 7–9 layers of prism cells near the wall). The final size of the grid for the computational domain is 7.3 million. In addition, the independence of the simulation results for particle deposition fractions from the number of injected particles was investigated. Also, the maximum number of injected particles that have been used for small particles is 1.4 million. Details of grid generation, particle number study, and CFD-DPM method are given, respectively, in Appendices A, B and C.

Hazeri M., Faramarzi M., Sadrizadeh S., Ahmadi G, & Abouali O. (2020). Regional deposition of the allergens and micro-aerosols in the healthy human nasal airways. Journal of Aerosol Science, 152, 105700.

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Quantitative Evaporation Rate Simulation

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The maximum evaporation rate was characterized quantitatively using the engineering simulation software ANSYS. The 3-D simulation was conducted with the same dimensions of the microfluidic chip (see Figure 1). The fluid field of the microchannel was extracted as the simulation domain, which was divided into 446,349 elements and 407,375 nodes. The meshing result was imported into ANSYS Fluent 18.0. The modules including Viscous–Laminar model, Evaporation–Condensation model and Schiller–Naumann model were adopted to obtain a steady solution of flow rate. In the Evaporation–Condensation model, evaporation effect of micropores drives the fluid flow to the outlet cavity until saturated vapor pressure is reached, corresponding to the steady status. In this case, the evaporation rate was equivalent to the maximum flow rate for sweat collection. The maximum flow rate was obtained at a temperature of 20

^{\circ}

C. The fluid used in the simulation was water with the density of 1000 kg/m

^{3}

Chen X.M., Li Y.J., Han D., Zhu H.C., Xue C.D., Chui H.C., Cao T, & Qin K.R. (2019). A Capillary-Evaporation Micropump for Real-Time Sweat Rate Monitoring with an Electrochemical Sensor. Micromachines, 10(7), 457.

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Theoretical Analysis of Tubular Micromotor Motion

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Here we show a simplified version of the micromotor motion theory based on driving force and drag force. A detailed theoretical study of drag force can be found in Wang et al. [23 (link)]. According to Newton’s second law, the movement of a tubular micromotor immersed in a fluid can be expressed as

m \dot{v} = Σ F = F_{jet} - F_{d}

where F_d is the drag force experienced by the micromotor. The relationship between F_d and Reynolds number, micromotor geometry, and the drag coefficient is obtained by the computational fluid dynamics software FLUENT 18.0 (ANSYS).
The mass of the micromotor m is

\begin{array}{l} m = ρ V_{j} = & ρ [\frac{π}{3} L ({R_{\max}}^{2} + R_{\max} R_{\min} + {R_{\min}}^{2}) \\ - \frac{π}{3} L ({R_{\max}}^{' 2} + {R_{\max}}^{'} {R_{\min}}^{'} + {R_{\min}}^{' 2})] \end{array}

The drag force is [23 (link)]

F_{d} = \frac{π}{4} μ v R_{\max} [(a e^{b λ} + \frac{c}{ξ} + \frac{d}{ξ^{2}}) \tan δ + \frac{f}{ξ}]

Solving the differential equation of Equation (51), the expression of the motion velocity of the tubular micromotor could be obtained on the basis of driving force and drag force.

Lin Y., Geng X., Chi Q., Wang C, & Wang Z. (2019). Driving Forces of the Bubble-Driven Tubular Micromotor Based on the Full Life-Cycle of the Bubble. Micromachines, 10(6), 415.

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Simulation-based Stenosis Pressure Drop Assessment

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Flow in phantoms was also simulated to compute “true”
PD. The Reynolds number (Re) was in the range of 340 to 1360, and the
transitional Re has been reported as 400 for stenosis with 50% diameter
constriction ratio (56 ). Thus, the
Reynolds-averaged Navier-Stokes (RANS) equations with
k-ω turbulence model
(k and ω are turbulence kinetic
energy and specific dissipation rate, respectively) were solved numerically
using Fluent 18.2 (ANSYS Inc, PA, USA). Computational mesh consisted of 3 to
4 million anisotropic hexahedral cells refined near the wall, to ensure a
dimensionless wall distance less than one,
y⁺<1, for accurate modeling of near-wall
flow (57 ). Fully-developed parabolic
velocity profiles matched with the FRs for in-vitroexperiments (0.1–0.4 L/min) were used as inlet boundary condition.
Flow outlet was placed 25 unconstricted diameters downstream from the
stenosis with a constant pressure as the boundary condition, and no-slip
condition was applied at walls. A dynamic viscosity of 0.001 Pa.s and a
density of 1000 kg/m³ were used for water. PD between two points
proximal and at the center of stenosis was calculated from CFD and
considered as a reference for examining PD from in-vitroscans.

Vali A., Aristova M., Vakil P., Abdalla R., Prabhakaran S., Markl M., Ansari S.A, & Schnell S. (2019). Semi-Automated Analysis of 4D Flow MRI to Assess the Hemodynamic Impact of Intracranial Atherosclerotic Disease. Magnetic resonance in medicine, 82(2), 749-762.

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Simulating Atrial Hemodynamics with ANSYS Fluent

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The CFD analysis was performed using ANSYS Fluent 18.2, in which blood was modeled as an incompressible Newtonian fluid (density of ρ= 1,060 kg/m³; dynamic viscosity of μ = 0.0035 Pa·s). Blood was simulated using the incompressible Navier-Stokes and continuity equations. Model dynamics was introduced with a sinusoidal function representing a heart cycle of a healthy patient (Fernandez-Perez et al., 2012 (link); García-Isla et al., 2018 (link)). Boundary conditions at systole (first 0.40 s) were modeled with the PVs as velocity-inlets and the MV as a wall, to represent that the valve is closed during this cardiac phase (Fernandez-Perez et al., 2012 (link)). The opening of the MV in diastole (duration of 0.65 s) was modeled applying an outlet pressure of 8 mmHg (Nagueh et al., 2008 (link)) to the MV. All LA walls were modeled as rigid walls with no slip, representing the worst scenario in (persistent) AF, where the atrium barely contracts anymore. The LAAO device was also modeled as a wall to represent that no blood could go through it.

Aguado A.M., Olivares A.L., Yagüe C., Silva E., Nuñez-García M., Fernandez-Quilez Á., Mill J., Genua I., Arzamendi D., De Potter T., Freixa X, & Camara O. (2019). In silico Optimization of Left Atrial Appendage Occluder Implantation Using Interactive and Modeling Tools. Frontiers in Physiology, 10, 237.

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CFD Analysis of Rhino-Based Geometry Models

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The geometry models are first drawn using Rhino 6.0 software. These models will then be imported as Parasolid files to run the CFD simulations using the ANSYS Fluent 18.2 software.

Zheng K., Ortner P., Lim Y.W, & Zhi T.J. (2021). Ventilation in worker dormitories and its impact on the spread of respiratory droplets. Sustainable Cities and Society, 75, 103327.

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Airflow Simulation in ANSYS-FLUENT

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The airflow was simulated using ANSYS-FLUENT18.0 assuming steady flow. The ambient environment was set to atmospheric pressure and inhalation was initiated by a negative pressure difference between external environment and the airway exit. This allowed the ambient flow field to be influenced only by the inhaled air. The continuity and momentum equation of the fluid flow are:

\frac{\partial}{\partial x_{i}} (ρ u_{i}) = 0

ρ u_{j} \frac{\partial u_{i}}{\partial x_{j}} = - \frac{\partial p}{\partial x_{i}} + \frac{\partial}{\partial x_{j}} [μ \frac{\partial u_{i}}{\partial x_{j}}]

where ρ, u and p are density, velocity and pressure of the air, respectively. A second order upwind scheme was used to approximate the momentum equation, while the pressure-velocity coupling was handled through the SIMPLE method. Further details of the fluid flow modeling are given in [43 (link)].

Tian L., Shang Y., Chen R., Bai R., Chen C., Inthavong K, & Tu J. (2019). Correlation of regional deposition dosage for inhaled nanoparticles in human and rat olfactory. Particle and Fibre Toxicology, 16, 6.

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