Near Field Sonic Boom Predictions Using STAR-CCM

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Near Field SonicBoom PredictionsUsing STAR-CCM 3rd AIAA Sonic Boom Prediction WorkshopChris Nelson, Mario Castillo, and Oisin TongSiemens Digital Industries SoftwareUnrestricted Siemens 2019Realize innovation.

Agenda:Summary of casesFlow solver/computing platformComputational meshesBiconvex case resultsC608 case resultsSummary/conclusions

Summary of Cases Biconvex 9x7 Three workshop-provided grids biconvex-visc-mixed-157 biconvex-visc-mixed-128 biconvex-visc-mixed-100 C608 Three workshop-provided grids c608-visc-mixed-128.cgns c608-visc-mixed-100.cgns c608-visc-mixed-080.cgnsUnrestricted Siemens 2019Page 3YYYY-MM-DDSiemens Digital Industries Software

Flow Solver and Computing PlatformFlow Solver STAR-CCM v2020.1 (pre-release) Flux scheme 2nd order in space (cell-centered) Venkat limiter Pre-conditioned Roe (Biconvex case) AUSM up (C608) Mentor SST turbulence model Ideal gas (Sutherland’s Law) “Expert Driver CFL” used for most cases New “AutoCFL” tested as well (C608)Computing Platform Various Siemens Linux clusters MPI for parallelizationUnrestricted Siemens 2019Page 4YYYY-MM-DDSiemens Digital Industries Software

Computational Meshes All cases run (so far) used workshop-provided meshes Native STAR-CCM poly meshes under development Application of new AMR algorithm also beinginvestigated Results to be included in paper for AVIATION 2020Unrestricted Siemens 2019Page 5YYYY-MM-DDSiemens Digital Industries Software

Agenda:Summary of casesFlow solver/computing platformComputational meshesBiconvex case resultsC608 case resultsSummary/conclusions

Convergence Criteria Residuals not a good indicator of convergence forthis case Limit cycle reached early on Possibly due to non-native mesh topology (tets) Looked at convergence of drag and mass flowthrough the nozzle instead Convergence obtained for all meshes Drag appears to be converging with increasedresolution Mass flow very similar for all cases Note- initial runs with ‘128’ and ‘100’ resolutionmeshes used incorrect units. Meshes wererescaled and solutions continuedUnrestricted Siemens 2019Page 7YYYY-MM-DDSiemens Digital Industries Software

Pressure Contours‘157’ mesh Major flowfield features very similar for all meshresolutions‘100’ mesh‘128’ meshUnrestricted Siemens 2019Page 8YYYY-MM-DDSiemens Digital Industries Software

Pressure Contours- Nozzle Exit Region‘157’ mesh Differences in pressure contours most apparent invicinity of nozzle exit Increasing resolution improves smoothness ofresults‘100’ mesh‘128’ meshUnrestricted Siemens 2019Page 9YYYY-MM-DDSiemens Digital Industries Software

Near Field Pressure Signatures0 deg All three meshes produce similar results Finer resolution sharpens peaks Waves emanating from jet plume not seen atmeasurement locations (likely due to meshresolution)30 deg15 degUnrestricted Siemens 2019Page 10YYYY-MM-DDSiemens Digital Industries Software

Agenda:Summary of casesFlow solver/computing platformComputational meshesBiconvex case resultsC608 case resultsSummary/conclusions

Convergence CriteriaDrag Coefficient As with Biconvex case, C608 residuals reach limitcycle early Convergence assessed using other means: Drag coefficient Engine intake/exit mass flow imbalance ECS intake/exit mass flow imbalanceMass Imbalance- ECS SystemMass Imbalance- EngineUnrestricted Siemens 2019Page 12YYYY-MM-DDSiemens Digital Industries Software

Pressure Contours‘128’ mesh Major flowfield features very similar for all meshresolutions‘80’ mesh‘100’ meshUnrestricted Siemens 2019Page 13YYYY-MM-DDSiemens Digital Industries Software

Numerical Shadowgraph‘128’ mesh Effects of mesh resolution more apparentPossible impact of cell topology changes‘80’ mesh‘100’ meshUnrestricted Siemens 2019Page 14YYYY-MM-DDSiemens Digital Industries Software

AutoCFL vs. Expert DriverExpert Driver Both are algorithms for controlling solutionadvancement (e.g. CFL number, desired numberof AMG cycles, solution under-relaxation factor) Final solutions very similar AutoCFL intended to replace Expert Driver More stable Less (or no) tweaking required to reachconverged solution With minimal adjustment, can usually obtaingood solutions faster than with Expert Driver Current runs used deliberately conservativesettings for AutoCFL Increased target AMG cycles No attempt made (yet) to optimize settingsAutoCFLUnrestricted Siemens 2019Page 15YYYY-MM-DDSiemens Digital Industries Software

Near Field Pressure Signatures0 deg Solutions similar for all meshes As expected, higher resolution yields sharpersignal Waves much stronger above vehicle (as designed) Unlike Biconvex case, waves emanating fromplume reach data collection region, but are lowamplitude by comparison60 deg30 degUnrestricted Siemens 2019Page 16YYYY-MM-DDSiemens Digital Industries Software

Near Field Pressure Signatures90 deg120 deg150 deg180 degUnrestricted Siemens 2019Page 17YYYY-MM-DDSiemens Digital Industries Software

SummaryCaveat: Drawing conclusions before seeing comparison data for a blind test isalways risky Current solutions suggest that even the coarsest workshop meshes givereasonable results As expected, increasing mesh resolution sharpens waves and provides moredetail in plume region Specific to STAR-CCM : New AutoCFL algorithm (with conservative settings)takes longer to converge, but seems to provide improved stability of resultscompared to previous Expert Driver algorithmUnrestricted Siemens 2019Page 18YYYY-MM-DDSiemens Digital Industries Software

Thank you.

Chris NelsonAerospace Applications SpecialistChris.Nelson@siemens.com

Mentor SST turbulence model Ideal gas (Sutherland’s Law) “Expert Driver CFL” used for most cases New “AutoCFL” tested as well (C608) Computing Platf

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