DPW 6 Summary of Participant Data Case 1: Code Verification
Transcript of DPW 6 Summary of Participant Data Case 1: Code Verification
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
DPW 6 Summary of Participant DataCase 1: Code Verification
Chris Roy (Virginia Tech)
and the DPW Organizing Committee
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Outline:
• Motivation for Case #1
• Problem description
• Participant data
• Forces and moments
• Pressure and shear stress distributions
• Effects to examine
• Turbulence model
• Grid type
• Order of accuracy of turbulence equations
• Conclusions
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Motivation for Case #1:• Past DPW Workshops had many instances of (ostensibly) the
same turbulence model converging to different results with
mesh refinement
• There should only be a single asymptotic answer for each
model
• These problems could be attributed to a number of sources:
− Coding mistakes
− Inconsistent algorithms or boundary conditions
− Variations in the version of the turbulence model
− Effects of grid type (structured, unstructured, etc.)
− Ad hoc limiters on the turbulence equations (e.g., production)
− Insufficient iterative convergence
• Case #1 was designed to help identify contributions which
suffered from these issues
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Problem description: boundary conditions• NACA 0012 airfoil
• Mach 0.15
• Rec = 6M
• Fixed Riemann BCs
at ~500 chords
• Problem definition
and grids supplied
by the NASA
Langley
TurbModels web
site:
http://turbmodels.larc.
nasa.gov/naca0012nu
merics_val.html
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Problem description: C-grids
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Problem description: primary grids used
Family I Family II (recommended)
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Participant data summary for Case #1:• 30 Data Total Data Submittals
• 15 Teams/Organizations
• Turbulence Models:
− 22 SA (all types)
− 4 SST
− 1 k-kl, 1 k-e Lam, 1 EARSM, 1 LBM-VLES
• Grid Types:
- 4 Structured Grid Family I (2 teams)
- 21 Structured Grid Family II (11 teams) recommended grids
- 1 O-Grid
- 2 Cartesian (2 teams)
- 1 Unstructured
- 1 Adapted Unstructured
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Results:
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Results:
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Results:
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Results:
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Results:
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Effects of turbulence model
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Effects of turbulence model
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Effects of turbulence model
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Effects of grid type
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Effects of grid type
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Effects of grid type
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Effects of grid type: SA only
Correct Value
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Effects of grid type: SA only
Correct Value
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Effects of grid type: SA only
Correct Value
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Effects of grid type: SA only
Correct Value
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Effects of grid type: SA only
Correct Value
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Conclusions for Case #1• Grid effects and other numerical issues (iterative convergence?)
are still polluting the results
• Grid type is important
- Cartesian methods appear to either converge slowly or not converge
• Grid adaptation helps: the single adapted grid case honed in on
the (correct) converged values at much lower cell counts
• Adequate code verification is still not being done
- Ideal approach is to demonstrate order of accuracy using
Manufactured Solutions
- This numerical benchmark allows an easier path to code verification
• Errors that occur for this simple 2D case are expected to be
(much) larger for the more complex 3D cases
• Code verification should be a prerequisite for application of a
code to analysis, model validation, etc.
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Thanks go to:
• Chris Rumsey of NASA Langley for helping
with formatting for data files
• Ed Tinoco for doing the initial screening of
the Case 1 data
• All of the DPW-6 participants
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Extra Slides
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Effects of order of accuracy of turbulence models
• TBD
− TBD
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Pressure distributions:• TBD
− TBD
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Skin friction distributions:• TBD
− TBD
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6th CFD Drag Prediction WorkshopWashington D.C. – June 2016
Order of accuracy plots for SA models