Araştırma Makalesi
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ONERA M6 kanadının belirsizlik altında şekil optimizasyonu ile tasarımı

Yıl 2024, Cilt: 39 Sayı: 2, 771 - 784, 30.11.2023
https://doi.org/10.17341/gazimmfd.1190263

Öz

Aerodinamik şekil optimizasyon süreçleri, tekli veya çoklu tasarım hedeflerini sağlayan kompleks problemler için sıklıkla kullanılmaktadır. Geleneksel ve belirsizliğin dahil edilmediği problemlere nazaran belirsizliklerin dahil edildiği ve yüksek değişken sayısına sahip sağlam optimizasyon yöntemlerinin hesaplama yükü oldukça yüksektir. Bu problemin önüne geçmek için, bu çalışmada, temel bileşenler analizi, tümevarımsal tasarım araştırma yöntemi ile entegre edilerek ONERA M6 kanadının sağlam şekil optimizasyonu gerçekleştirilmiştir. Temel bileşenler analizi yöntemi, kanat geometrisinin tasarım değişkeni sayısını azaltmak için tercih edilmiştir. Hesaplamalı akışkanlar dinamiği analizi kullanımı sonucunda ortaya çıkan yüksek çözüm süreleri ise, temel bileşenler analizi yönteminin bir vekil model tekniği olan radyal bazlı fonksiyon ile birlikte kullanılmasıyla oluşturulan bir veri tahmin modeli ile azaltılmıştır. Transonik akış rejimi için Mach sayısındaki belirsizlikler, önerilen tümevarımsal tasarım araştırma yöntemi tabanlı yönteme dahil edilerek sağlam optimizasyon gerçekleştirilmiştir. Sağlam tasarımların performans tahminlerinin hesaplamalı akışkanlar dinamiği analiz sonuçlarına oldukça yakın elde edilmesi, önerilen yöntemin etkinliğini göstermiştir.

Kaynakça

  • Yu Y., Lyu Z., Xu Z., Martins J.R.R.A., On the influence of optimization algorithm and initial design on wing aerodynamic shape optimization, Aerosp. Sci and Technol., 75, 183-199, 2018.
  • Bird G.D., Gorrell S.E., Salmon J.L., Dimensionality-reduction-based surrogate models for real-time design space exploration of a jet engine compressor blade, Aerosp. Sci. and Technol., 118, 2021.
  • Amrit, A. and Leifsson L., Koziel S., Multi-fidelity aerodynamic design trade-off exploration using point-by-point Pareto set identification, Aerosp. Sci. and Technol., 79, 399-412, 2018.
  • Li J., Zhang M., Data-based approach for wing shape design optimization, Aerosp. Sci. and Technol., 112, 2021.
  • Sripawadkul V., Padulo M., Guenov M., A Comparison of airfoil shape parametrization techniques for early design optimization, AIAA/ISSMO Multidiscp. Anal. Optim. Conf., 13-15 Eylül, 2010.
  • Du X., He P., Martins J.R.R.A., Rapid airfoil design optimization via neural networks-based parametrization and surrogate modeling, Aerosp. Sci. and Technol., 113, 2021.
  • Ye Y., Wang X., Zhang X., Cascade ensemble-RBF-based optimization algorithm for aero-engine transient control schedule design optimization, Aerosp. Sci. and Technol., 115, 2021
  • Zhu Y., Ju Y., Zhang C., Proper orthogonal decomposition assisted inverse design optimisation method for the compressor cascade airfoil, Aerosp. Sci. and Technol., 105, 2020.
  • Lim H.D., Wei X.F., Zang B., Vevek U.S., Mariani R., New T.H., Cui Y.D., Short time proper orthogonal decomposition of time-resolved schlieren images for transient jet screech characterization, Aerosp. Sci. and Technol., 107, 2020.
  • Zhou L.L., Jiu L.J., Jun Z., Kuan L., Ni Y.M., Aerodynamic shape optimization by continually moving ROM, Aerosp. Sci. and Technol., 99, 2020.
  • Raul V., Leifsson L., Surrogate-based aerodynamic shape optimization for delaying airfoil dynamic stall using Kriging regression and infill criteria, Aerosp. Sci. and Technol., 111, 2021.
  • Iuliano E., Global Optimization of benchmark aerodynamic cases using physics-based surrogate models, Aerosp. Sci. and Technol., 67, 273-286, 2017.
  • Benaissa B., Köppen M., Wahab M.A., Khatir S., Application of proper orthogonal decomposition and radial basis functions for crack size estimation using particle swarm optimization, J. Phys. Conf. Ser., 842, 2017.
  • Fusi F., Quaranta G., Assessment of robust optimization for design of rotorcraft airfoils in forward flight, Aerosp. Sci. and Technol., 107, 2020.
  • Zhang J., Tang H., Chen M., Robust design of an adaptive cycle engine performance under component performance uncertainty, Aerosp. Sci. and Technol., 113, 2021.
  • Choi H.J., Allen J.K., Rosen D., McDowell D.L., Mistree F., An Inductive design exploration method for robust multiscale materials design, J. of Mech. Des., 130, 2005.
  • Jang S., Choi H.J., Choi S.K., Oh J.S., Inductive Design Exploration Method with Active Learning for Complex Design Problems, Appl. Sci., 8, 2018.
  • Kumar Y., Srivastava S.K., Bajpai S.K., Kumar N., Development of CAD algorithms for Bezier Curves/Surfaces Independent of Operating System, WSEAS Trans. on Computers, 11, 2012.
  • Sirovich L., Chaotic Dynamics of coherent structures, Physica, 37, 126-145, 1997.
  • Deane A.E., Kevrekidis I.G., Karniadakis G.E., Orszag S.A., Low-dimensional models for complex geometry flows: Application to grooved channels and circular cylinders, Phys. Of Fluids A: Fluid Dyn., 3, 1991.
  • Wu X., Zhang W., Peng X., Wang Z., Benchmark aerodynamic shape optimization with the POD-based CST airfoil parametric method, Aerosp. Sci. and Technol., 84, 632-640, 2018.
  • Kutkan H., Aerothermodynamic shape optimization using DSMC and POD-RBF methods, Yüksek Lisans Tezi, Orta Doğu Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, 2018.
  • Rogers C.A., Kassab A.J., Divo A., Ostrowski Z., Bialecki A., An inverse POD-RBF network approach to parameter estimation in mechanics, Inverse Probl. Sci. Eng., 20, 749-767, 2012.
  • Romani L., Rossini M., Schenone D., Edge detection methods based on RBF interpolation, J. of Comput. and Appl. Math., 349, 532-547, 2019.
  • Wendland H. (1995) Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree. Adv. in Comput. Math., 4, 389-396, 1995.
  • Caboni M., Minisci E., Riccardi A., Aerodynamic design optimization of wind turbine airfoils under aleatory and epistemic uncertainty, J. of Phys., 1037, 2018.
  • Emory M., Pecnik R., Iaccarino G., Modeling structural uncertainties in reynolds-averaged computations of shock/boundary layer interactions, 49th AIAA Aerosp. Sci. Meet. Incl. the New Horiz. Forum and Aerosp. Expos., 4-7 Ocak, 2011.
  • Mishra A.A., Mukhopadhaya J., Iaccarino G., Alonso J., An uncertainty estimation module for turbulence model predictions in SU2, AIAA J., 57, 2018.
  • Kern P.C., Priddy M.W., Ellis B.D., McDowell D.L., pyDEM: A generalized implementation of the inductive design exploration method, Mater. and Des., 134, 293-300, 2017.
  • Afzal A., Kim K.Y. and Seo J.W., Effects of Latin hypercube sampling on surrogate modeling and optimization, Intern. J. of Fluid Mach. and Syst., 10, 240-253, 2017.
  • Raymer design a conceptual approach, American Institute of Aeronautics and Astronautics, Reston, VA, 2018.
  • Kaygan E., Ulusoy C., Effectiveness of twist morphing wing on aerodynamic performance and control of an aircraft, J. of Avi., 2, 77-86, 2018.
  • Lyu Z., Martins J.R.R.A., Aerodynamic design optimization studies of a blended-wing-body aircraft, J. of Aircr., 51, 2014.
  • Liang Y., Cheng X., Li Z., Xiang J., Robust multi-objective wing design optimization via CFD approximation model, Eng. Appl. of Comp. Fluid Mech., 5, 286-300, 2011.

Design of the ONERA M6 wing by shape optimization under uncertainty

Yıl 2024, Cilt: 39 Sayı: 2, 771 - 784, 30.11.2023
https://doi.org/10.17341/gazimmfd.1190263

Öz

Aerodynamic shape optimization processes are often used for complex problems that meet single or multi-objective design requirements. The robust aerodynamic shape optimization techniques with a high number of design variables that consider uncertainties have a huge computational burden compared to the traditional aerodynamic shape optimization techniques without considering uncertainties. To overcome this issue, in this study, the proper orthogonal decomposition is integrated with the inductive design exploration method to use for the robust shape optimization of the ONERA M6 wing. The proper orthogonal decomposition method is utilized for reducing the number of design variables of the wing geometry. The cost due to the computational fluid dynamics analysis is mitigated by incorporating the proper orthogonal decomposition with a surrogate modeling technique called the radial basis function. The robust optimization is conducted by the proposed approach based on the inductive design exploration method by accounting for uncertainties of the Mach number in the transonic flow regime. The agreement between the performance predictions of the robust designs and the computational fluid dynamics analysis results showed the effectiveness of the proposed approach.

Kaynakça

  • Yu Y., Lyu Z., Xu Z., Martins J.R.R.A., On the influence of optimization algorithm and initial design on wing aerodynamic shape optimization, Aerosp. Sci and Technol., 75, 183-199, 2018.
  • Bird G.D., Gorrell S.E., Salmon J.L., Dimensionality-reduction-based surrogate models for real-time design space exploration of a jet engine compressor blade, Aerosp. Sci. and Technol., 118, 2021.
  • Amrit, A. and Leifsson L., Koziel S., Multi-fidelity aerodynamic design trade-off exploration using point-by-point Pareto set identification, Aerosp. Sci. and Technol., 79, 399-412, 2018.
  • Li J., Zhang M., Data-based approach for wing shape design optimization, Aerosp. Sci. and Technol., 112, 2021.
  • Sripawadkul V., Padulo M., Guenov M., A Comparison of airfoil shape parametrization techniques for early design optimization, AIAA/ISSMO Multidiscp. Anal. Optim. Conf., 13-15 Eylül, 2010.
  • Du X., He P., Martins J.R.R.A., Rapid airfoil design optimization via neural networks-based parametrization and surrogate modeling, Aerosp. Sci. and Technol., 113, 2021.
  • Ye Y., Wang X., Zhang X., Cascade ensemble-RBF-based optimization algorithm for aero-engine transient control schedule design optimization, Aerosp. Sci. and Technol., 115, 2021
  • Zhu Y., Ju Y., Zhang C., Proper orthogonal decomposition assisted inverse design optimisation method for the compressor cascade airfoil, Aerosp. Sci. and Technol., 105, 2020.
  • Lim H.D., Wei X.F., Zang B., Vevek U.S., Mariani R., New T.H., Cui Y.D., Short time proper orthogonal decomposition of time-resolved schlieren images for transient jet screech characterization, Aerosp. Sci. and Technol., 107, 2020.
  • Zhou L.L., Jiu L.J., Jun Z., Kuan L., Ni Y.M., Aerodynamic shape optimization by continually moving ROM, Aerosp. Sci. and Technol., 99, 2020.
  • Raul V., Leifsson L., Surrogate-based aerodynamic shape optimization for delaying airfoil dynamic stall using Kriging regression and infill criteria, Aerosp. Sci. and Technol., 111, 2021.
  • Iuliano E., Global Optimization of benchmark aerodynamic cases using physics-based surrogate models, Aerosp. Sci. and Technol., 67, 273-286, 2017.
  • Benaissa B., Köppen M., Wahab M.A., Khatir S., Application of proper orthogonal decomposition and radial basis functions for crack size estimation using particle swarm optimization, J. Phys. Conf. Ser., 842, 2017.
  • Fusi F., Quaranta G., Assessment of robust optimization for design of rotorcraft airfoils in forward flight, Aerosp. Sci. and Technol., 107, 2020.
  • Zhang J., Tang H., Chen M., Robust design of an adaptive cycle engine performance under component performance uncertainty, Aerosp. Sci. and Technol., 113, 2021.
  • Choi H.J., Allen J.K., Rosen D., McDowell D.L., Mistree F., An Inductive design exploration method for robust multiscale materials design, J. of Mech. Des., 130, 2005.
  • Jang S., Choi H.J., Choi S.K., Oh J.S., Inductive Design Exploration Method with Active Learning for Complex Design Problems, Appl. Sci., 8, 2018.
  • Kumar Y., Srivastava S.K., Bajpai S.K., Kumar N., Development of CAD algorithms for Bezier Curves/Surfaces Independent of Operating System, WSEAS Trans. on Computers, 11, 2012.
  • Sirovich L., Chaotic Dynamics of coherent structures, Physica, 37, 126-145, 1997.
  • Deane A.E., Kevrekidis I.G., Karniadakis G.E., Orszag S.A., Low-dimensional models for complex geometry flows: Application to grooved channels and circular cylinders, Phys. Of Fluids A: Fluid Dyn., 3, 1991.
  • Wu X., Zhang W., Peng X., Wang Z., Benchmark aerodynamic shape optimization with the POD-based CST airfoil parametric method, Aerosp. Sci. and Technol., 84, 632-640, 2018.
  • Kutkan H., Aerothermodynamic shape optimization using DSMC and POD-RBF methods, Yüksek Lisans Tezi, Orta Doğu Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, 2018.
  • Rogers C.A., Kassab A.J., Divo A., Ostrowski Z., Bialecki A., An inverse POD-RBF network approach to parameter estimation in mechanics, Inverse Probl. Sci. Eng., 20, 749-767, 2012.
  • Romani L., Rossini M., Schenone D., Edge detection methods based on RBF interpolation, J. of Comput. and Appl. Math., 349, 532-547, 2019.
  • Wendland H. (1995) Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree. Adv. in Comput. Math., 4, 389-396, 1995.
  • Caboni M., Minisci E., Riccardi A., Aerodynamic design optimization of wind turbine airfoils under aleatory and epistemic uncertainty, J. of Phys., 1037, 2018.
  • Emory M., Pecnik R., Iaccarino G., Modeling structural uncertainties in reynolds-averaged computations of shock/boundary layer interactions, 49th AIAA Aerosp. Sci. Meet. Incl. the New Horiz. Forum and Aerosp. Expos., 4-7 Ocak, 2011.
  • Mishra A.A., Mukhopadhaya J., Iaccarino G., Alonso J., An uncertainty estimation module for turbulence model predictions in SU2, AIAA J., 57, 2018.
  • Kern P.C., Priddy M.W., Ellis B.D., McDowell D.L., pyDEM: A generalized implementation of the inductive design exploration method, Mater. and Des., 134, 293-300, 2017.
  • Afzal A., Kim K.Y. and Seo J.W., Effects of Latin hypercube sampling on surrogate modeling and optimization, Intern. J. of Fluid Mach. and Syst., 10, 240-253, 2017.
  • Raymer design a conceptual approach, American Institute of Aeronautics and Astronautics, Reston, VA, 2018.
  • Kaygan E., Ulusoy C., Effectiveness of twist morphing wing on aerodynamic performance and control of an aircraft, J. of Avi., 2, 77-86, 2018.
  • Lyu Z., Martins J.R.R.A., Aerodynamic design optimization studies of a blended-wing-body aircraft, J. of Aircr., 51, 2014.
  • Liang Y., Cheng X., Li Z., Xiang J., Robust multi-objective wing design optimization via CFD approximation model, Eng. Appl. of Comp. Fluid Mech., 5, 286-300, 2011.
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Görkem Demir 0000-0002-7261-1762

Recep Muhammet Görgülüarslan 0000-0002-0550-8335

Selin Aradağ Çelebioğlu 0000-0002-2034-0008

Erken Görünüm Tarihi 18 Ekim 2023
Yayımlanma Tarihi 30 Kasım 2023
Gönderilme Tarihi 17 Ekim 2022
Kabul Tarihi 18 Nisan 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 39 Sayı: 2

Kaynak Göster

APA Demir, G., Görgülüarslan, R. M., & Aradağ Çelebioğlu, S. (2023). ONERA M6 kanadının belirsizlik altında şekil optimizasyonu ile tasarımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39(2), 771-784. https://doi.org/10.17341/gazimmfd.1190263
AMA Demir G, Görgülüarslan RM, Aradağ Çelebioğlu S. ONERA M6 kanadının belirsizlik altında şekil optimizasyonu ile tasarımı. GUMMFD. Kasım 2023;39(2):771-784. doi:10.17341/gazimmfd.1190263
Chicago Demir, Görkem, Recep Muhammet Görgülüarslan, ve Selin Aradağ Çelebioğlu. “ONERA M6 kanadının Belirsizlik altında şekil Optimizasyonu Ile tasarımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39, sy. 2 (Kasım 2023): 771-84. https://doi.org/10.17341/gazimmfd.1190263.
EndNote Demir G, Görgülüarslan RM, Aradağ Çelebioğlu S (01 Kasım 2023) ONERA M6 kanadının belirsizlik altında şekil optimizasyonu ile tasarımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39 2 771–784.
IEEE G. Demir, R. M. Görgülüarslan, ve S. Aradağ Çelebioğlu, “ONERA M6 kanadının belirsizlik altında şekil optimizasyonu ile tasarımı”, GUMMFD, c. 39, sy. 2, ss. 771–784, 2023, doi: 10.17341/gazimmfd.1190263.
ISNAD Demir, Görkem vd. “ONERA M6 kanadının Belirsizlik altında şekil Optimizasyonu Ile tasarımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39/2 (Kasım 2023), 771-784. https://doi.org/10.17341/gazimmfd.1190263.
JAMA Demir G, Görgülüarslan RM, Aradağ Çelebioğlu S. ONERA M6 kanadının belirsizlik altında şekil optimizasyonu ile tasarımı. GUMMFD. 2023;39:771–784.
MLA Demir, Görkem vd. “ONERA M6 kanadının Belirsizlik altında şekil Optimizasyonu Ile tasarımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 39, sy. 2, 2023, ss. 771-84, doi:10.17341/gazimmfd.1190263.
Vancouver Demir G, Görgülüarslan RM, Aradağ Çelebioğlu S. ONERA M6 kanadının belirsizlik altında şekil optimizasyonu ile tasarımı. GUMMFD. 2023;39(2):771-84.