Hostname: page-component-848d4c4894-4rdrl Total loading time: 0 Render date: 2024-07-02T04:35:32.307Z Has data issue: false hasContentIssue false
  • English
  • Français

Towards certification of computational fluid dynamics as numerical experiments for rotorcraft applications

Part of: Rotorcraft Virtual Engineering Conference 2016

Published online by Cambridge University Press:  20 December 2017

M.J. Smith ,
K.E. Jacobson  and
J.-P. Afman
M.J. Smith*
Affiliation:
Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
K.E. Jacobson
Affiliation:
Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
J.-P. Afman
Affiliation:
Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
*
marilyn.smith@ae.gatech.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Virtual Engineering (VE), also known as Model-Based Systems Engineering (MBSE), is necessary in both current operational engineering qualifications and to help reduce the costs of future vertical lift design and analysis. As computational power continues to provide increasing capability to the rotorcraft engineering community to perform simulations in both real time and off line, it is imperative that the community develop verification and validation protocols and processes to certify these methods so that they can be reliably used to help reduce engineering cost and schedule. Computational Fluid Dynamics (CFD) has become a major Computational Science and Engineering (CSE) tool in the fixed wing and vertical lift communities, but it has not been developed to the point where it is accepted as a replacement for testing in certification of new or existing systems or vehicles. Since the rise of modern CFD in the 1980s, the promise of CFD’s capabilities has been met or exceeded, but its role in certification arguably remains less prominent than projected. The ability to implement transformative technologies further drives the need for CFD in design. To meet CFD’s role in certification, several goals must be met to provide a true “numerical experiment” from which accuracies (error estimates), sensitivities, and consistent application results can be extracted. This paper discusses the progress and direction towards developing CFD strategies for certification.

Keywords

certificationCFDerror quantificationrotorcraftvirtual engineering
Type
Research Article
Information
The Aeronautical Journal , Volume 122 , Issue 1247 , January 2018 , pp. 104 - 130
Copyright
Copyright © Royal Aeronautical Society 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This is a version of a paper first presented at the RAeS Virtual Engineering Conference held at Liverpool University, 8-10 November 2016.

References

REFERENCES

1. Office of the Deputy Army Chief of Staff, G-8 Future Force Division. Army equipment modernization strategy, http://www.g8.army.mil/pdf/AEMS_31MAR15.pdf, Last accessed 09/23/2016, 2015.Google Scholar
2. Merritt, L. Overview, aviation development directorate industry day, 2 March 2016, Huntsville, Alabama, US. See also 2017 Industry Day Briefings, https://www.amrdec.army.mil/amrdec/pdf/IndustryDay2017briefings.pdf, Last accessed 07/11/2017.Google Scholar
3. Gorton, S.A. NASA vertical lift strategic direction, http://rotorcraft.arc.nasa.gov/02%20NASA%202016%20University%20Day%20for%20VLRCOE%20final.pdf, January 22, 2016.Google Scholar
4. Shechter, E. Bearing heavy loads, Aerospace America, June 2014, pp 18–20.Google Scholar
5. Head, E. FAA Policy Change Could Restrict Development, Use Of Inlet Barrier Filters, Last accessed 04/07/2016, March 20 2016. Vertical Magazine, http://www.verticalmag.com/news/article/FAA-policy-change-could-restrictdevelopment-use-of-inlet-barrier-filters, Last accessed 04/07/2016.Google Scholar
6. Hodara, J., Lind, A., Jones, A. and Smith, M.J. Collaborative investigation of the aerodynamic behavior of airfoils in reverse flow, American Helicopter Society 71st Annual Forum, May 2015, Virginia Beach, Virginia, US.Google Scholar
7. Reich, D., Shenoy, R., Schmitz, S. and Smith, M.J. A review of 60 years of rotor hub drag and wake physics: 1954–2014, J of the American Helicopter Soc, January 2016, 61, pp 022007, 1-17. DOI:10.4050/JAHS.61.022007.Google Scholar
8. Datta, A., Yeo, H. and Norman, T. Experimental investigation and fundamental understanding of a full-scale slowed rotor at high advance ratios, J of the American Helicopter Soc, 2013, 58, (2), pp 1–17.Google Scholar
9. Potsdam, M., Datta, A. and Jayaraman, B. Computational investigation and fundamental understanding of a slowed UH-60A rotor at high advance ratios, J of the American Helicopter Soc, 2016, 61, (2), pp 1–17.Google Scholar
10. Schmitz, S., Reich, D., Smith, M.J. and Centolanza, L.R. First rotor hub flow prediction workshop experimental data campaigns and computational analyses, AHS International 73rd Annual Forum, 9–11 May 2017, Fort Worth, Texas, US.Google Scholar
11. Shenoy, R. and Smith, M.J. Computational deconstruction of hub drag, part II: Computational investigation, Tech Rep, ONR Final Report, September 2013, Atlanta, Georgia, US.Google Scholar
12. Chapman, D.R. Computational aerodynamics development and outlook, AIAA J, 1979, 17, (12), pp 1293–1313.Google Scholar
13. Bousman, W. Rotorcraft airloads measurements – extraordinary costs, extraordinary benefits, Tech Rep, August 2014, Moffett Field, California, US. doi: NASA/TP2014-218374 Google Scholar
14. van der Wall, B.G., Lim, J.W., Smith, M.J., Jung, S., Bailly, J., Baeder, J. and Boyd, J., D.D. The HART II international workshop: An assessment of the state of the art in comprehensive code prediction, CEAS Aeronautical J, September 2013, 4, (3), pp 223–252. doi: 10.1007/s13272-013-0077-9 Google Scholar
15. Oberkampf, W. and Trucano, T.G. Verification and validation in computational fluid dynamics, Progress in Aerospace Sciences, 2002, 38, pp 209–272.Google Scholar
16. Roy, C.J. and Oberkampf, W.L. A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing, Comput. Methods Appl. Mech. Engrg., 2011, 200, pp 2231–2144.Google Scholar
17. Shenoy, R. Overset Adaptive Strategies for Complex Rotating Systems, PhD thesis, Georgia Institute of Technology, Atlanta, Georgia, https://smartech.gatech.edu/handle/1853/51796, 2014.Google Scholar
18. Prosser, D. Advanced Computational Techniques for Unsteady Aerodynamic-dynamic Interactions of Bluff Bodies, PhD thesis, Georgia Institute of Technology, Atlanta, Georgia, https://smartech.gatech.edu/handle/1853/53899, 2015.Google Scholar
19. Prosser, D. and Smith, M.J. Aerodynamics of finite cylinders in quasi-steady flow, Paper AIAA-2015-1931, AIAA 53rd Aerospace Sciences Meeting, January 2015, Kissimmee, Florida, US. doi: 10.2514/6.2015-1931 Google Scholar
20. Oberkampf, W., DeLand, S.M., Rutherford, B.M., Diegert, K. and Alvin, K.F. Error and uncertainty in modeling and simulation, Reliability Engineering & System Safety, 2002, 35, pp 333–357.Google Scholar
21. Prosser, D. and Smith, M.J. Physics-based aerodynamic simulation models suitable for dynamic behavior of complex bluff body configurations, American Helicopter Society 71st Annual Forum, May 2015, Virginia Beach, Virginia, US.Google Scholar
22. Lorieau, A. and Smith, M.J. Towards certification of the modeling of complex systems: Slung loads, AHS Development, Affordability and Qualification of Complex Systems Specialists Meeting, 9–11 February 2016, Huntsville, Alabama, US.Google Scholar
23. NATO RTO Task Group, Unsteady aerodynamic response of rigid wings in gust encounters (AVT-282), 2017, https://www.cso.nato.int/ACTIVITY_META.asp?ACT=8389.Google Scholar
24. Shenoy, R., Smith, M.J. and Park, M.A. Unstructured overset mesh adaptation with turbulence modeling for unsteady aerodynamic interactions, AIAA J of Aircr, January 2014, 51, (1), pp 161–174. doi: 10.2514/1.C032195 Google Scholar
25. Park, M.A. Adjoint-based, three-dimensional error prediction and grid adaptation, AIAA J, 2004, 42, (9), pp 1854–1864.Google Scholar
26. Müller, J.-D. and Cusdin, P. On the performance of discrete adjoint CFD codes using automatic differentiation, Int J for Numerical Methods in Fluids, 2005, 47, (8-9), pp 939–945.Google Scholar
27. Lyu, Z., Kenway, G.K., Paige, C. and Martins, J. Automatic differentiation adjoint of the Reynolds-averaged Navier-Stokes equations with a turbulence model, Paper AIAA-2013-2581, 21st AIAA Computational Fluid Dynamics Conference, 2013, San Diego, California, US.Google Scholar
28. Venditti, D.A. and Darmofal, D.L. Anisotropic grid adaptation for functional outputs: Application to two-dimensional viscous flows, J Computational Physics, 2003, 187, (1), pp 22–46.Google Scholar
29. Venditti, D. and Darmofal, D. A grid adaptive methodology for functional outputs of compressible flow simulations, Paper AIAA-2001-2659, 15th AIAA Computational Fluid Dynamics Conference, 2001, Anaheim, California, US.Google Scholar
30. Fidkowski, K.J. and Luo, Y. Output-based space-time mesh adaptation for the compressible Navier-Stokes equations, J of Computational Physics, 2011, 230, (14), pp 5753–5773.Google Scholar
31. Fidkowski, K.J. and Darmofal, D.L. Review of output-based error estimation and mesh adaptation in computational fluid dynamics, AIAA J, 2011, 49, (4), pp 673–694.Google Scholar
32. Wang, Q., Hu, R. and Blonigan, P. Least squares shadowing sensitivity analysis of chaotic limit cycle oscillations, J of Computational Physics, 2014, 267, pp 210–224.Google Scholar
33. Hand, M.M., Simms, D.A., Fingersh, L.J., Jager, D.W., Cotrell, J.R., Schreck, S. and Larwood, S.M. Unsteady aerodynamics experiment phase VI: Wind tunnel test configurations and available data campaigns, Tech Rep, December 2001, Golden, Colorado, US. doi: NREL/TP-500-29955 Google Scholar
34. Simms, D.A., Schreck, S., Hand, M. and Fingersh, L.J. NREL unsteady aerodynamics experiment in the NASA-Ames wind tunnel: A comparison of predictions to measurements, Tech Rep, June 2001, Golden, Colorado, US. doi: NREL/TP-500-29494 Google Scholar
35. NASA FUN3D Development Team, FUN3D-Analysis and Design, http://fun3d.larc.nasa.gov, 2016.Google Scholar
36. Spalart, P.R. and Allmaras, S.R. A one-equation turbulence model for aerodynamic flows, Recherche Aerospatiale, 1991, 1, pp 5–21.Google Scholar
37. Loseille, A. Metric-orthogonal anisotropic mesh generation, Procedia Engineering, 2014, 82, pp 403–415.Google Scholar
38. Loseille, A. and Menier, V. Serial and parallel mesh modification through a unique cavity-based primitive, Proceedings of the 22nd International Meshing Roundtable, 2013, Orlando, Florida, US.Google Scholar
39. Lohner, R. and Onate, E. An advancing front point generation technique, Communications in Numerical Methods in Engineering, 1998, 14, (12), pp 1097–1108.Google Scholar
40. Vassberg, J.C., et al. Summary of the fourth AIAA computational fluid dynamics drag prediction workshop, J of Aircr, 2014, 51, (4), pp. 1070–1089.Google Scholar
41. Pierce, N.A. and Giles, M.B. Adjoint and defect error bounding and correction for functional estimates, J of Computational Physics, 2004, 200, (2), pp 769–794.Google Scholar
42. Phillips, T. and Roy, C.J. Defect correction and error transport discretization error estimation for applications in CFD, 32nd AIAA Applied Aerodynamics Conference, 2014, Atlanta, Georgia, US.Google Scholar
43. Derlaga, J.M. and Park, M.A. Application of exact error transport equations and adjoint error estimation to AIAA workshops, 55th AIAA Aerospace Sciences Meeting, 2017, Grapevine, Texas, US.Google Scholar
44. Tischler, M.B. Aircraft and Rotorcraft System Identification, American Institute of Aeronautics and Astronautics, Blacksburg, Virginia, 2nd ed, 2012.Google Scholar
45. Crozier, P. Recent improvements in rotor testing capabilities in the ONERA S1MA wind tunnel, Proceedings of the 20th European Rotorcraft Forum, October 1994, Amsterdam, NI.Google Scholar
46. Ortun, B., Potsdam, M., Yeo, H. and van Truong, K. Rotor loads prediction on the ONERA 7A rotor using loose fluid/structure coupling, J of the American Helicopter Soc, 2017, 62, (3), 032005, pp. 1-13. DOI: 10.4050/JAHS.62.032005.Google Scholar
47. Wissink, A., Sitaraman, J., Jayaraman, B., Roget, B., Lakshminarayan, V., Potsdam, M., Jain, R., Leffell, J., Forsythe, J. and Bauer, A. Recent advancements in the Helios rotorcraft simulation code, AIAA-2016-0563, 54th AIAA Science and Technology Forum and Exposition, 4–8 January 2016, San Diego, California, US.Google Scholar
48. Saberi, H., Hasbun, M., Hong, J., Yeo, H. and Ormiston, R.A. Overview of RCAS capabilities, validations, and rotorcraft applications, Proceedings of the American Helicopter Society 71st Annual Forum, 5–7 May 2015, Virginia Beach, Virginia, US.Google Scholar
49. Cambier, L., Heib, S. and Plot, S. The ONERA elsA CFD software: Input from research and feedback from industry, Mech & Industry, 2013, 14, pp 159–174.Google Scholar
50. Benoit, B., Dequin, A., Kampa, K., Grnhagen, W., Basset, P. and Gimonet, B. HOST, a general helicopter simulation tool for Germany and France, Proceedings of the American Helicopter Society 56th Annual Forum, May 2–4 2000, Virginia Beach, Virginia, US.Google Scholar
51. Potsdam, M., Yeo, H. and Johnson, W. Rotor airloads prediction using loose aerodynamic/structural coupling, J of Aircr, 2006, 43, (3), pp 732–742.Google Scholar
52. Morton, S., Kholodar, D., Billingsley, T., Forsythe, J., Wurtzler, K., Squires, K., Cummings, R. and Spalart, P. Multidisciplinary applications of detached-eddy simulation to separated flows at high Reynolds numbers, Proceedings of the Department of Defense High Performance Computing Modernization Program Users Group Conference, June 2004, Williamsburg, Virginia, US.Google Scholar
53. Smith, M.J., Jacobson, K. and Afman, J.P. Certification of computational fluid dynamics as numerical experiments, Proceedings of the Royal Aeronautical Society Rotorcraft Virtual Engineering Conference, November 2016, Liverpool, UK.Google Scholar
54. Anonymous, Aeronautical design standard performance specification, handling qualities requirements for military rotorcraft, ADS-33E-PRF, https://www.amrdec.army.mil/amrdec/rdmr-se/tdmd/Documents/ads33front.pdf, 2000.Google Scholar
55. AIAA Computational Fluid Dynamics Committee, AIAA guide for the verification and validation of computational fluid dynamics simulations, https://arc.aiaa.org/doi/10.2514/4.472855.001, 2002.Google Scholar