Advertisement

Aerotecnica Missili & Spazio

, Volume 98, Issue 1, pp 85–104 | Cite as

NERONE: An Open-Source Based Tool for Aerodynamic Transonic Optimization of Nonplanar Wings

  • Luca Pustina
  • Rauno CavallaroEmail author
  • Giovanni Bernardini
Original article

Abstract

Transonic shape optimization of nonplanar wings is a relevant topic in current aeronautical world. Although widely studied, an appropriate level of robustness and automation is still lacking for the inclusion of aerodynamic shape optimization in a Multi-Disciplinary Optimization (MDO) process. In this paper, a new framework, opeNsource-mEsh-geneRation-fOr-aerodyNamic-Evaluations (NERONE), is presented that allows for an automatic transonic shape optimization process within an MDO loop design using open-source libraries, namely OpenCasCade (OCC) for the geometric description, GMSH for grid generation and SU2 multi-physics for aerodynamic analysis and optimization. Wing geometry is defined in the CPACS standard, converted to a mathematical continuous description (NURBS) and passed to the mesher. Grid generation process is driven by user-defined mesh dimensions defined on wing regions and on support domain boundary surfaces. The aerodynamic analysis and optimization are then launched on the obtained grid. SU2 software has been augmented to overcome robustness issues regarding point-inversion algorithm and to control geometric quality during optimization of nonplanar wings. Capability of NERONE is first demonstrated for 2D Euler and RANS simulations (on the RAE2822 airfoil) and on a 3D Euler case (ONERA M6). With very few user-specified parameters, high-quality grids are obtained providing results that correlate well with the literature data. Finally, NERONE is applied to the local shape optimization of a wing–winglet configuration in transonic flight conditions.

Keywords

Aerodynamic shape optimization Adjoint method Nonplanar wings OpenCascade SU2 GMSH 

Notes

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

References

  1. 1.
    Open cascade community edition. https://github.com/tpaviot/oce
  2. 2.
    Open cascade technology. https://www.opencascade.com/
  3. 3.
  4. 4.
  5. 5.
    An aerospace coder drags a stodgy industry toward open source (2018). https://www.wired.com/2017/04/aerospace-coder-drags-stodgy-industry-toward-open-source/
  6. 6.
    A NASA open government initiative website (2018). https://code.nasa.gov/
  7. 7.
    Andreson, W.K., Bonhaus, D.L.: Aerodynamic design on unstructured grids for turbulent flows. NASA Technical Memorandum 112867, NASA (1997)Google Scholar
  8. 8.
    Baalbergen, E., Lammen., W., Noskov, N., Ciampa, P., Moerland, E.: Integrated collaboration capabilities for competitive aircraft design. In: EASN-CEAS 2018, Glasgow (2018). https://www.agile-project.eu/cloud/index.php/s/SoIufBsbYMbGvg8
  9. 9.
    Cavallaro, R., Demasi, L.: Challenges, ideas, and innovations of joined-wing configurations: a concept from the past, an opportunity for the future. Progr. Aerosp. Sci. 87, 1–93 (2016).  https://doi.org/10.1016/j.paerosci.2016.07.002. http://www.sciencedirect.com/science/article/pii/S0376042116300471
  10. 10.
    Cavallaro, R., Frediani, A.: A code for shape generation and aerodynamic design of aircraft. In: G. Buttazzo, A. Frediani (eds.) Variational Analysis and Aerospace Engineering: Mathematical Challenges for Aerospace Design, Springer Optimization and Its Applications, vol. 66, pp. 117–139. Springer US (2012).  https://doi.org/10.1007/978-1-4614-2435-2_6
  11. 11.
    Chen, S., Lyu, Z., Gaetan, Kenway, K.W., Martins, J.R.R.A.: Aerodynamic shape optimization of common research model wingbodytail configuration. J. Aircr. 53(1), 276–293 (2016).  https://doi.org/10.2514/1.C033328 Google Scholar
  12. 12.
    Deb, K.: Simple genetic algorithm code in C (2001). https://www.iitk.ac.in/kangal/codes.shtml
  13. 13.
    Demasi, L., Dipace, A., Monegato, G., Cavallaro, R.: Invariant formulation for the minimum induced drag conditions of nonplanar wing systems. AIAA J. 52(10), 2223–2240 (2014).  https://doi.org/10.2514/1.J052837 Google Scholar
  14. 14.
    Demasi, L., Monegato, G., Cavallaro, R.: Minimum induced drag theorems for multiwing systems. AIAA J. 55(10), 3266–3287 (2017).  https://doi.org/10.2514/1.J055652 Google Scholar
  15. 15.
    Demasi, L., Monegato, G., Cavallaro, R., Rybarczyk, R.: Minimum induced drag conditions for truss-braced wings. AIAA J. (2018).  https://doi.org/10.2514/1.J057225
  16. 16.
    Demasi, L., Monegato, G., Dipace, A., Cavallaro, R.: Minimum induced drag theorems for joined wings, closed systems, and generic biwings: theory. J. Optim. Theory Appl. 169(1), 200–235 (2016).  https://doi.org/10.1007/s10957-015-0849-y MathSciNetzbMATHGoogle Scholar
  17. 17.
    Dillmann, A., Heller, G., Krämer, E., Wagner, C., Breitsamter, C.: New Results in Numerical and Experimental Fluid Mechanics X. In: Contributions to the 19th STAB/DGLR Symposium Munich, Germany, 2014, vol. 132. Springer (2016)Google Scholar
  18. 18.
    Economon, T.D., Palacios, F., Copeland, S.R., Lukaczyk, T.W., Alonso, J.J.: Su2: an open-source suite for multiphysics simulation and design. AIAA J. 54(3), 828–846 (2016).  https://doi.org/10.2514/1.J053813 Google Scholar
  19. 19.
    Frediani, A., Cipolla, V., Salem, K.A., Binante, V., Scardaoni, M.P.: On the preliminary design of Prandtl plane civil transport aircraft. In: 7th European Conference for Aeronautics and Space Sciences (EUCASS) (2017).  https://doi.org/10.13009/EUCASS2017-546
  20. 20.
    Gallard, F.: Optimisation de forme dun avion pour sa performance sur une mission. Ph.D. thesis, École Doctorale Aéronautique-Astronautique (Toulouse), 142618039 (2014)Google Scholar
  21. 21.
    Hicks, Rm, Henne, P.A.: Wing design by numerical optimization. J. Aircr. 15(7), 407–412 (1978).  https://doi.org/10.2514/3.58379 Google Scholar
  22. 22.
    Jameson, A., Martinelli, L., Pierce, N.: Optimum aerodynamic design using the Navier–Stokes equations. Theor. Comput. Fluid Dyn. 10(1), 213–237 (1998).  https://doi.org/10.1007/s001620050060 zbMATHGoogle Scholar
  23. 23.
    Jameson, A., Ou, K.: Optimization Methods in Computational Fluid Dynamics. American Cancer Society (2010).  https://doi.org/10.1002/9780470686652.eae059
  24. 24.
    Jameson, A., Shankaran, S., Martinelli, L.: Continuous adjoint method for unstructured grids. AIAA J. 46(5), 1226–1239 (2008).  https://doi.org/10.2514/1.25362 Google Scholar
  25. 25.
    Jones, E., Oliphant, T., Peterson, P., et al.: SciPy: Open source scientific tools for Python (2001) [Online]. http://www.scipy.org/
  26. 26.
    Kraft, D.: A software package for sequential quadratic programming. Tech. Rep. DFVLR-FB 88-28, DLR German Aerospace Center Institute for Flight Mechanics, Koln (1988)Google Scholar
  27. 27.
    Liem, R.P., Martins, J.R., Kenway, G.K.: Expected drag minimization for aerodynamic design optimization based on aircraft operational data. Aerosp. Sci. Technol. 63, 344–362 (2017).  https://doi.org/10.1016/j.ast.2017.01.006. http://www.sciencedirect.com/science/article/pii/S1270963816302978
  28. 28.
    Morino, L., Bernardini, G., Mastroddi, F.: Multi-disciplinary optimization for the conceptual design of innovative aircraft configurations. Comput. Model. Eng. Sci. 13(1), 1 (2006)Google Scholar
  29. 29.
    Nagel, B., Bhnke, D., Gollnick, V., Schmollgruber, P., Rizzi, A., Rocca, G.L., Alonso, J.: Communication in aircraft design: can we establish a common language? In: 28th International Congress of the Aeronautical Sciences, Brisbane (2012)Google Scholar
  30. 30.
    Papadimitriou, D.I., Papadimitriou, C.: Aerodynamic shape optimization for minimum robust drag and lift reliability constraint. Aerosp. Sci. Technol. 55, 24–33 (2016).  https://doi.org/10.1016/j.ast.2016.05.005. http://www.sciencedirect.com/science/article/pii/S1270963816301754
  31. 31.
    Peter, J.E., Dwight, R.P.: Numerical sensitivity analysis for aerodynamic optimization: a survey of approaches. Comput. Fluids 39(3), 373–391 (2010).  https://doi.org/10.1016/j.compfluid.2009.09.013. http://www.sciencedirect.com/science/article/pii/S0045793009001388
  32. 32.
    Piegl, L., Tiller, W.: The NURBS Book, 2 edn. Springer, Berlin (1997).  https://doi.org/10.1007/978-3-642-59223-2
  33. 33.
    Prandtl, L.: Induced drag of multiplanes. Tech. Rep. TN 182, NACA (1924). Reproduction of “Der induzierte Widerstand von Mehrdeckern”. Technische Ber. 3, pp. 309–315 (1918)Google Scholar
  34. 34.
    Pustina, L.: Towards transonic aerodynamic shape optimization of unconventional aircraft with opensource software. Master’s thesis, Universitá degli Studi di Roma Tre. Rauno Cavallaro and Giovanni Bernardini, Tutors (2018)Google Scholar
  35. 35.
    Schmitt, V., Charpin, F.: Pressure distributions on the onera-m6-wing at transonic mach numbers. In: Experimental Data Base for Computer Program Assessment AGARD AR 138, AGARD (1979)Google Scholar
  36. 36.
    Sobieski, J.: The case for aerodynamic sensitivity analysis. Tech. Rep. 19870009428, NASA (1987). https://ntrs.nasa.gov/search.jsp?R=19870009428
  37. 37.
    van Rossum, G.: Python tutorial. Tech. Rep. CS-R9526, Centrum voor Wiskunde en Informatica (CWI), Amsterdam (1995)Google Scholar
  38. 38.
    Whitcomb, R.: A design approach and selected wind tunnel results at high subsonic speeds for wing-tip mounted winglets. Tech. Rep. NASA-TN-D-8260, NASA (1976)Google Scholar
  39. 39.
    Yu, Y., Lyu, Z., Xu, Z., Martins, J.R.: On the influence of optimization algorithm and initial design on wing aerodynamic shape optimization. Aerosp. Sci. Technol. 75, 183–199 (2018).  https://doi.org/10.1016/j.ast.2018.01.016. http://www.sciencedirect.com/science/article/pii/S1270963817314475

Copyright information

© AIDAA Associazione Italiana di Aeronautica e Astronautica 2019

Authors and Affiliations

  1. 1.Bionegineering and Aerospace Engineering DepartmentUniversity Carlos III of MadridMadridSpain
  2. 2.Department of EngineeringRoma Tre UniversityRomeItaly

Personalised recommendations