XFlow Meshless Particle-based CFD Code - MSC...
Transcript of XFlow Meshless Particle-based CFD Code - MSC...
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XFlow ™Meshless Particle -based CFD Code
Presented By: Fausto Gill Di Vincenzo24-05-2012
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• XFlow Overview - Application fields & Theory
Topics
• GUI et Validation cases for external aerodynamics
• CFD Process approach - Mesh-based vs XFlow (Meshless)
• UAV case study
• CFD Process approach - Mesh-based vs XFlow (Meshless)
• Wing case study - Static Aeroelasticity application
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XFlow ™ Key Technology• XFlow ™ developed by XFlow Technlogy™ - Division of Next Limit Technologies™
• Next Limit Technology™ mission is to provide simulation technologies for a
broad range of applications in Visualization, Science and Engineering
• XFlow ™ uses a proprietary, particle-based fully lagrangian, meshless approach which
can easily predict Flow, Thermal and Acoustic behavior on complex geometric domain
involving Moving Boundaries, Free Surface and Fluid-Structure Interaction (FSI)
• The Particle-base Kinetic algorithm resolves the Boltzmann (mesoscopic) and the
compressible Navier-Stokes (macroscopic) equations - Specifically designed to
perform fast with accessible hardware
• Complexity of surfaces is not a limiting factor
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XFlow ™ ApplicationsXFlow is a Particle-based CFD software able to solve complex transient fluid dynamics
problems such as Aerodynamics, Aero-acoustic, Moving parts, Free surface flow including
Fluid-Structures interaction
Analysis capabilities
• External and internal aerodynamics
• Free surface flows
• Thermal analysis: convection, radiation, conjugated heat transfer
• Flow through porous media
• Advanced modeling: moving parts, forced and constrained motion, contact
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Xflow application fieldsHelicopter - No moving Mesh
Automotive application
Landing gear - No moving Mesh
Free surface
Acoustic
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Aeronautic application - Surface Refinement
Xflow application fields
Boundary Layer modeling - High lift device Wing turbine - Wake Refinement
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Large Eddy Simulation - Turbulence ModelingLarge Eddy Simulation (LES) originally implemented in the 1970s by
atmospheric scientists to study the weather
• Larger Scales of turbulence carry the majority of the energy hence more important
• Smaller Scales of turbulence found to be more universal and extensively studied hence
more easily modeled
LES directly solves large spatial scales while modeling the smaller scales
• Sub-grid scale modeling done by a low-pass filter applied to Navier-Stokes equation
• XFlow LES scheme - Wall-Adapting Local Eddy-viscosity (WALE)
• Direct Numerical Simulation (DNS) attempts to solve all time
and scales - Computational unrealistic
• Reynolds Average Navier-Stokes (RANS) splits variables into Time-
Average “mean” part and Turbulent part - Not suitable for wake
flows or flows with large separation when turbulence part could
have the same order of the mean
• Large Eddy Simulation is a method between DNS and RANS
which filters of the N-S equation to separate scales to be
modeled from those will be solved directly
Good properties both near and far from the wall and with laminar and turbulent flow
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Lattice Boltzmann Method Lattice Boltzmann Method (LBM) is an extension of the Lattice Gas Automata
• LGA schemes use discrete numbers to represent the state of the molecules
• Chapman-Enskog expansion shows that it is possible to design LGA schemes that recover the
hydrodynamic macroscopic behavior at low Mach numbers
Compressible Navier-Stokes equation is recovered
• LBM method makes use of statistical distribution functions with real variables preserving by
construction the conservation of mass, linear momentum and energy
• From Chapman-Enskog & Boltzmann’s equation
• Boltzmann’s Transport equation identifies the distribution function fi
2D LBM schemes D2Q7 - D2Q9
Compressible Navier-Stokes equation is recovered
with the Collision operator
• LBM schemes classified by spatial dimension d and number of distribution function b
DdQb
3D LBM schemes D3Q19 - D3Q27
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• XFlow Overview - Application fields & Theory
Topics
• GUI et Validation Cases for External Aerodynamics
• CFD Process approach - Mesh-based vs XFlow (Meshless)
• UAV case study
• CFD Process approach - Mesh-based vs XFlow (Meshless)
• Wing case study - Static Aeroelastic application
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XFlow ™ InterfaceXFlow™ provides a unique GUI with pre-processor, solver and post-processor fully
integrated in the same environment
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1st Validation Test CaseTwo-Dimensional Simulation of Flow Past NACA-0012 Airfoil
• Simulation Conditions
NACA-0012 Airfoil
• XFlow - CFL3D Comparison
CFL3D Domain & MeshXFlow Wind Tunnel
• XFlow - CFL3D Comparison
Reference: David P. Lockard, Li-Shi Luo, Bart A. Singer, Evaluation of the Lattice-Boltzmann
Equation Solver PowerFLOW for Aerodynamic Applications, October 2000.
Virtual Wind Tunnel: 60x40 m
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Far field - 2.56m Region of Refinement - 0.005m
Resolution of the Solution• Resolved scale set up to ensure the symmetry of the Flow
Region of Refinement
Target resolved Scale - Shape Naca 0012
Resolved Scale
Resolved Scale
Region of Refinement
Shape
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Data plot lines: x/L = 0.0, 0.25, 0.5, 0.75 and 1.0
Post Processing of Results
• X Component of Velocity
• Y Component of Velocity
• Pressure Coefficient
Plot lines on shape
Results monitored:
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X - component of the Velocity along the Data Plot Li nesx = 0 x = 0.25 x = 0.5
Courtesy of David P. Lockard and the NASA Langley Research Center
x = 0.75 x = 1
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Y - component of the Velocity along the Data Plot Li nesx = 0 x = 0.25 x = 0.5
Courtesy of David P. Lockard and the NASA Langley Research Center
x = 0.75 x = 1
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Pressure Coefficient along the Data Plot Linesx = 0 x = 0.25 x = 0.5
Courtesy of David P. Lockard and the NASA Langley Research Center
x = 0.75 x = 1
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XFlow CFL3D ObjectiveRelative Error to
Objective
Cd 0.1705 0.1741 0.1741 -2.0678 %
Comparison of the Drag and Lift Coefficient
Cd 0.1705 0.1741 0.1741 -2.0678 %
Cl 1.0 x 10-13 -0.538 x 10-5 0 0 %
Courtesy of David P. Lockard and the NASA Langley Research Center
Results are in accordance with objective
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2nd Validation Test Case
Two-Dimensional Simulation of Flow Past S825 Airfoil
S825 Airfoil
Angles of
AttackMach Number
Reynolds
Number
Reference
Velocity
From -4 to 10º 0.1 2E+06 43.7493 m/s
Reference: D. Somers, Design and Experimental Results for the S825 Airfoil; Period of Performance: 1998-
1999, tech. report, National Renewable Energy Laboratory, January 2005.
Flight condition
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Far field - 1.28m Wall & Wake Refinement - 0.0025m
Resolved Scale
Resolution of the Solution
Resolved Scale
Target resolved Scale - AirfoilS825
Wake Resolution
Dynamically Scale Adaption
• Region of Dynamical Refinement scale: Wall and Wake
Virtual Wind Tunnel: 60x40 m
Resolved Scale
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Projection of the Pressure Coefficient
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Pressure Coefficient for different AOA
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Pressure Coefficient for different AOA
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Pressure Coefficient for different AOA
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3th Validation Test Case
3D Simulation of Flow Past the ASMO model
ASMO model
• Flow velocity: V = 50 m/s
• Density: ρ = 1 Kg/m-3
• Dynamic viscosity: µ = 1.5e-5 Pa⋅s
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Turbulent wake structure
Isosurface of vorticity
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• Comparison with experimental data - Volvo and Daimler Benz
Pressure distribution along the symmetry plane
Pressure filed in the symmetry plane
Front pressure distribution along the symmetry plane
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• Comparison with experimental data - Volvo and Daimler Benz
Pressure distribution along the symmetry plane
Roof Pressure filed in the symmetry plane
Underbody pressure distribution along the symmetry plane
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• Comparison with experimental data - Volvo and Daimler Benz
Drag estimation
Overall drag time history
Comparison with experiments - Drug
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4th High lift prediction
3D Simulation of Flow Past a wing with model with flap and slat
Wing geometry
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4th High lift prediction
• Wind Tunnel definition - 40 x 15 x 30 m
• Ground wall effect enabled
Virtual wind tunnel
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4th High lift predictionResolved scaled definition with adaptive refinement - Wall and Wake
• Resolved scale (far field): 1.28 m
Resolved scale resolution
• Target scale for wake : 0.01 m s
• Target scale for wall : 0.005 m
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Results overviewVelocity - Static pressure - Vorticity visualization
• AOA: 13 degrees
• Simulation time: 0.26 s
Contours of wing section
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Vorticity
Volumetric Rendering of Vorticity - Ortho view Volumetric Rendering of Vorticity - Top view
• AOA: 13 degrees
• Simulation time: 0.26 s
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Pressure distribution
Static pressure - “Pressure” side Volumetric Rendering of Vorticity – “Suction side”
• AOA: 13 degrees
• Simulation time: 0.26 s
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Friction coefficient distribution
• AOA: 13 degrees
• Simulation time: 0.26 s
Friction coefficient - “Pressure” side Volumetric Rendering of Vorticity - “Suction side”
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Drag coefficient distribution
Drag coefficient vs Alpha
• Comparison with experiments - NASA Langley Research Center
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Lift coefficient distribution
Lift coefficient vs Alpha
• Comparison with experiments - NASA Langley Research Center
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Polar curve evaluation
Polar curve
• Comparison with experiments - NASA Langley Research Center
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• XFlow Overview - Application fields & Theory
Topics
• GUI et Validation Cases for External Aerodynamics
• CFD Process approach - Mesh-based vs XFlow (Meshless)
• UAV case study
• CFD Process approach - Mesh-based vs XFlow (Meshless)
• Wing case study - Static Aeroelastic application
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• The reliability of the solution highly depends on the quality of the mesh - Mesh
Sensitivity
Traditional Mesh -based CFD codes
Mesh
• Domain to be solved is divided into finite control volumes - Mesh
• Several solver parameters for turbulence model affect the solution stability -
Convergence criteria
• Parallel process computing - CPU resources
Several engineering time needs to be spent
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Traditional Mesh -based process analysis CAD Model
Nuovo PignoneGE Energy Oil & Gas
GRID Generation
MESH
BOUNDARY LAYER• Multi blocks
• Element topology
• Boundary layer
• Mesh Quality Check
Boundary conditions
GE Energy Oil & Gas
WALL
PERIODIC BOUNDARY
OUTFLOW
INFLOW
• Solver
• Turbulence Model
• Check Residual
• Convergence
• Parallelization
Results - Exp ComparisonMesh sensitivity
More than 1 year of work
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Motivation for modeling with XFlowParticle-based Kinetic Approach - Lagrangian Formulation with LES Turbulence Model
Mesh-based - 3D Wing model XFlow - 3D Wing model
Meshless
• CAD import
• Mesh generation (Multiblocks, Boundary Layer, Topology)
• Convergence criteria (Residual check for Energy, Momentum, Mass)
• Turbulence parameters (RANS k-ε)
• Mesh Sensitivity � Remeshing
• CAD import
• Resolved scale (Automatic shapes and wake refinement)
No more Mesh needed
Easy to useFast Pre-design purpose
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XFlow - Overview process analysisCAD Model XFlow Model
Resolved scaleResults - Exp Comparison
Import
geometry
Surface refinement
Post
process
Saving engineering time
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• XFlow Overview - Application fields & Theory
Topics
• GUI et Validation Cases for External Aerodynamics
• CFD Process approach - Mesh-based vs XFlow (Meshless)
• UAV case study
• CFD Process approach - Mesh-based vs XFlow (Meshless)
• Wing case study - Static Aeroelastic application
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Geometry tessellation - “Mesh”CAD Model - Wing Geometry tessellation
• Geometry is automatically tessellated by Xflow into an
irregular “triangular Mesh” (Vertex, Polygons)
Wing Tip
irregular “triangular Mesh” (Vertex, Polygons)
• All surface entities (Cp, Velocity, Pressure etc) to be
exported are extrapolated from particles to vertex
Pressure data difficult to be processed (Nastran purpouse)
Wing Root
STL geometry creation “ad hoc”.. FE model + python algorithm..
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“Regular” STL Geometry creation via FEMCAD Model FE model
Python
XFlow model
CQUAD4 & CTRIA3 Vertex & Polygons
STL
geometry
creation
• CQUAD4 & CTRIA3 converted into a regular “triangular mesh” (Vertex & Polygons) by python code - STL geometry format
• SimXpert or Patran to create the FE model from CAD
STL Geometry into XFlow FE model
• Vertex posiOons ≡ Nodes posiOons (ID vertex ≠ID node)
Aerodynamic Pressure data can now be properly managed (all vertex positions are known)
CQUAD4 & CTRIA3Vertex & Polygons
≡
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FE Model XFlow - STL Geometry
CQUAD4 & CTRIA3 Vertex & Polygons
From CAD to FE Model (CQUAD4 & CTRIA3) via SimXpert or Patran
Wing case study
CAD Model
.IGES
Three-Dimensional Simulation of Flow around an UAV Wing
• STL Geometry creation
From FEM to STL Geometry (Vertex & Polygons) and Aero Mesh (AEGRID..)
From CAD to FE Model (CQUAD4 & CTRIA3) via SimXpert or Patran
• Simulation Conditions
• Free-stream velocity Vref = 25m/s
• Density ρ = 1.225 Kg m-3
• Dynamic viscosity µ =0.1 Pa s
• Chord lenght L = 0.402 m
• AOA [0 ÷ 8]
XFlow GUI
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Wing case studyThree-Dimensional Simulation of Flow around an UAV Wing
• Resolved Scale
• Far field - 1.26 m
• Target resolved scale Wing - 0.005 m
Far field - 2.56m Region of Refinement - 0.005m
ShapeShape
Run simulation..
Function viewer- FzStability parameter
Get results
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FE Model XFlow - STL Geometry XFlow - Pressure Coefficient field
CQUAD4 & CTRIA3 Vertex & Polygons
From FEM to STL Geometry (Vertex & Polygons) and Aero Mesh (AEGRID..)
XFlow
From CAD to FE Model (CQUAD4 & CTRIA3) via SimXpert or Patran
CFD simulation and Cp field extraction from Xflow on Vertex
From XFlow to Nastran - Static Aeroelasticity
XFlow – Force in Z direction
FZ = 16.80 N
AOA = 0°
From XFlow Cp to DMIJ (Nastran “aero nodal” Cp) - Python code
Aerodynamic pressure is quite well mapped on the Aerodynamic Mesh..
CFD simulation and Cp field extraction from Xflow on Vertex
Z - Component
Nastran - Aero monitor pointNastran - Cp on AEGRID (DMIJ)
To be improved by increasing Resolved Scale and Geometry quality
Nastran - Rigid Aerodynamic Mesh
FZ = 17.28 N
Aerodynamic Monitor point to check the mapped Aerodynamic load
AEGRID/AEQUAD4
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Longitudinal Trim Sol144
• UAV Flight condition
FE model with mapped FORCEs Aero Mesh with mapped FORCEs
“Aerodynamic Mesh”
SPLINE 6/7AEPRESS
XFlow
NASTRAN
• Air flowing over the Left Wing of the UAV
• Freestreem velocity is 25 m/s
• AOA [ 0°÷ 8°]
From XFlow to Nastran - Static Aeroelasticity
Static Pressure Field on the Wing Load Mapping
DMIJ
CFD Results
Aerodynamic Mesh
Fringe of Nodal forces Structural Model
Structural deformation
Trim condition obtained by using CFD pressure distribution
α ≈ 4.31° δE ≈ 0.8°
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• XFlow Overview - Application fields & Theory
Topics
• GUI et Validation cases for external aerodynamics
• CFD Process approach - Mesh-based vs XFlow (Meshless)
• UAV case study
• CFD Process approach - Mesh-based vs XFlow (Meshless)
• Wing case study
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UAV - Geometry creation via FEM
FE model - Half geometry
Wing
Tail
Elevator
FinRudder
FE model is divided into 6 different parts to be properly used into Xflow
• Fuselage.bdf
• Wing.bdf
• Tail.bdf
• Elevator.bdf
• Fin.bdf
• Rudder.bdf
Fuselage
Python code
• Fuselage.stl
• Wing.stl
• Tail.stl
• Elevator.stl
• Fin.stl
• Rudder.stl
FE model (CQUAD4 & CTRIA3) STL Geometry (Vertex & Polygons) Geometry into XFlow
Six different components
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UAV - Import geometry
Surfaces need to be reorientedAll surfaces well oriented toward outside
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UAV - Environment set up
Ext Aero
4 m20 m
20 mWind
4 m
ON
Domain
Flight Vel
Sym
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UAV - Post processing
Static pressure distribution
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UAV - Post processing
Velocity distribution
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Overall Load distribution - Z Component
3.56 N
22.5 N
-0.74 N
-1.2 N
UAV - Post processing
Wing
Elev
Tail Fuselage
22.5 N
All components could be taken into account for Static Aeroelastic Analysis..
Wing
Future Static Aeroelasticity application
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Thanks!Thanks!