Skip to main content

Advertisement

SpringerLink
Log in

Hierarchical optimization of the composite blade of a stratospheric airship propeller based on genetic algorithm

  • RESEARCH PAPER
  • Published:
Structural and Multidisciplinary Optimization Aims and scope Submit manuscript

Abstract

High-altitude propellers equipped with solar energy systems are widely adopted in stratospheric airships because of their light weight, excellent mechanical performance, and high efficiency. To optimize the composite laminated structure of the blade, a hierarchical optimization method based on genetic algorithm is carried out. Global and local layers are combined according to the structural and loading properties of the blade, and each partitioned region in the local layer is optimized independently. Combined with the finite element method, a subprogram based on the classical lamination theory is developed to simulate the stiffness matrix of the blade and obtain the deflection, weight, etc. as objects. The restricted condition, whether the structure has failed, is determined by the Tsai-Wu criterion. In addition, multiple tasks are delivered and read simultaneously by a specific program for the sake of improving computation efficiency. After verification with a case study, the stacking sequence and thickness of the blade of a stratospheric airship propeller is optimized and an ideal result is obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Abbreviations

T :

Thickness of each composite layer

N r :

Number of local region layers

N w :

Number of global region layers

p c :

Crossover rate between individuals

p m :

Mutation rate of individuals

N elite :

Number of elites

F i :

Fitness function of the ith individual

D ik :

Nondimensional value of the kth weighting parameter for the ith individual

ω k :

Weight coefficient of the kth weighting parameter

N F :

Number of weighting parameters

\( {F}_i^{\ast } \) :

Modified fitness function of the ith individual

b :

Ideal minimum value of F i

a :

Difference between average and minimum value of F i

α , β :

Modified indexes of the fitness function

E i :

Evaluation function

P i :

Penalty function

Δb k :

Violation for the kth chromosome

ε :

A small positive number

g k :

Objective value of F i

b k :

Actual minimum value of F i

N Gen :

Decimal expression of the gene sequence

f 1 , f 2 :

Two uniformly distributed loads

ρ :

Density of each layup

η c :

Crossover possibility

η m :

Mutation possibility

p m :

Heritability

M :

Population size

G :

Maximum generation number

ξ :

Growth rate of optimal fitness

References

  • Akbulut M, Sonmez FO (2011) Design optimization of laminated composites using a new variant of simulated annealing. Comput Struct 89:1712–1724

    Article  Google Scholar 

  • Almeida F, Awruch A (2009) Design optimization of composite laminated structures using genetic algorithms and finite element analysis. Compos Struct 88:443–454

    Article  Google Scholar 

  • An H, Chen S, Huang H (2014) Laminate stacking sequence optimization with strength constraints using two-level approximations and adaptive genetic algorithm. Struct Multidiscip Optim 51:903–918. doi:10.1007/s00158-014-1181-0

    Article  Google Scholar 

  • Cho M, Rhee SY (2003) Layup optimization considering free-edge strength and bounded uncertainty of material properties. AIAA J 41:2274–2282

    Article  Google Scholar 

  • Cho HK, Rowlands RE (2015) Enhancing buckling performance of perforated composite laminates by manipulating fiber direction using a genetic algorithm. J Mech Sci Technol 29:3727–3737. doi:10.1007/s12206-015-0818-2

    Article  Google Scholar 

  • Colozza A (1998) High altitude propeller design and analysis overview. Federal Data Systems Cleveland Ohio 44135

  • Corz A, Gomez-Ruiz JA, Pelaez JI, Tenorio E, Veintimilla J (2012) Design and optimization of symmetric laminated composites using a variable neighbourhood search-based model. Eng Optim 44:505–520. doi:10.1080/0305215x.2011.588225

    Article  MathSciNet  Google Scholar 

  • Ferreira RTL, Rodrigues HC, Guedes JM, Hernandes JA (2014) Hierarchical optimization of laminated fiber reinforced composites. Compos Struct 107:246–259. doi:10.1016/j.compstruct.2013.07.051

    Article  Google Scholar 

  • Gen M, Cheng R (1996) Optimal design of system reliability using interval programming and genetic algorithms. Comput Ind Eng 31:237–240. doi:10.1016/0360-8352(96)00120-9

    Article  Google Scholar 

  • Gürdal Z, Haftka RT, Hajela P (1999) Design and optimization of laminated composite materials. John Wiley & Sons

  • Haftka RT, Gürdal Z (2012) Elements of structural optimization vol 11. Springer Science & Business Media

  • Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press

  • Huang ZM (2007) Inelastic and failure analysis of laminate structures by ABAQUS incorporated with a general constitutive relationship. J Reinf Plast Compos 26:1135–1181. doi:10.1177/0731684407079753

    Article  Google Scholar 

  • Hwang SF, Hsu YC (2014) Optimization of composite laminates by a genetic algorithm. Innovation, communication and engineering. Crc Press-Taylor & Francis Group, Boca Raton

  • Hwang SF, Hsu YC, Chen YD (2014) A genetic algorithm for the optimization of fiber angles in composite laminates. J Mech Sci Technol 28:3163–3169. doi:10.1007/s12206-014-0725-y

    Article  Google Scholar 

  • Inamoto Y, Saito K, Shibasaki K, Sasa S, Kohno T, Harada K (2003) Flight control testing for the development of stratospheric platform airships. In: AIAA’s 3rd Aviation Technology, Integration, and Operations (ATIO) Conferences, Denver, USA

  • Jahani E, Shayanfar MA, Barkhordari MA (2013) Structural reliability based on genetic algorithm-Monte Carlo (GAMC). Adv Struct Eng 16:419–426

    Article  Google Scholar 

  • Jamison L, Sommer GS, Porche III IR (2005) High-altitude airships for the future force army. DTIC Document

  • Jing Z, Sun Q, Silberschmidt VV (2016) Sequential permutation table method for optimization of stacking sequence in composite laminates. Compos Struct 141:240–252. doi:10.1016/j.compstruct.2016.01.052

    Article  Google Scholar 

  • Kogiso N, Watson LT, Gürdal Z, Haftka RT (1994) Genetic algorithms with local improvement for composite laminate design. Struct Optim 7:207–218. doi:10.1007/bf01743714

    Article  Google Scholar 

  • Le Riche R, Haftka RT (1993) Optimization of laminate stacking sequence for buckling load maximization by genetic algorithm. AIAA J 31:951–956

    Article  MATH  Google Scholar 

  • Lee Y-G, Kim D-M, Yeom C-H (2006) Development of Korean high altitude platform systems. Int J Wireless Inf Networks 13:31–42

    Article  Google Scholar 

  • Le-Manh T, Lee J (2014) Stacking sequence optimization for maximum strengths of laminated composite plates using genetic algorithm and isogeometric analysis. Compos Struct 116:357–363. doi:10.1016/j.compstruct.2014.05.011

    Article  Google Scholar 

  • Liu DZ, Toropov VV, Barton DC, Querin OM (2015) Weight and mechanical performance optimization of blended composite wing panels using lamination parameters. Struct Multidiscip Optim 52:549–562. doi:10.1007/s00158-015-1244-x

    Article  MathSciNet  Google Scholar 

  • Park J, Hwang J, Lee C, Hwang W (2001) Stacking sequence design of composite laminates for maximum strength using genetic algorithms. Compos Struct 52:217–231

    Article  Google Scholar 

  • Rechenberg I (1965) Cybernetic solution path of an experimental problem

  • Roney JA (2007) Statistical wind analysis for near-space applications. J Atmos Sol Terr Phys 69:1485–1501

    Article  Google Scholar 

  • Shakeri M, Yas M, Gol MG (2005) Optimal stacking sequence of laminated cylindrical shells using genetic algorithm. Mech Adv Mater Struct 12:305–312

    Article  Google Scholar 

  • Smith M, Rainwater L (2003) Applications of scientific ballooning technology to high altitude airships. In: AIAA's 3 rd Annual Aviation Technology, Integration, and Operations(ATIO) Technology Conference

  • Vo-Duy T, Ho-Huu V, Do-Thi TD, Dang-Trung H, Nguyen-Thoi T (2017) A global numerical approach for lightweight design optimization of laminated composite plates subjected to frequency constraints. Compos Struct 159:646–655. doi:10.1016/j.compstruct.2016.09.059

    Article  Google Scholar 

  • Wang L, Kolios A, Nishino T, Delafin PL, Bird T (2016) Structural optimisation of vertical-axis wind turbine composite blades based on finite element analysis and genetic algorithm. Compos Struct 153:123–138. doi:10.1016/j.compstruct.2016.06.003

    Article  Google Scholar 

  • Xu C, Lin S, Yang YZ (2015) Optimal design of viscoelastic damping structures using layerwise finite element analysis and multi-objective genetic algorithm. Comput Struct 157:1–8. doi:10.1016/j.compstruc.2015.05.005

    Article  Google Scholar 

  • Zehnder N, Ermanni P (2006) A methodology for the global optimization of laminated composite structures. Compos Struct 72:311–320. doi:10.1016/j.compstruct.2005.01.021

    Article  Google Scholar 

  • Zheng Z, Huo W (2013) Planar path following control for stratospheric airship. IET Control Theory & Applications 7:185–201. doi:10.1049/iet-cta.2011.0462

    Article  MathSciNet  Google Scholar 

  • Zheng Z, Zhu M, Shi D, Wu Z (2014) Hovering control for a stratospheric airship in unknown wind. doi:10.2514/6.2014-0973

Download references

Acknowledgements

This research was supported by the China Postdoctoral Science Foundation under Grant No. 2016 M600891. The authors thank all the people involved in the past and present progress of the experiment. The authors are also grateful to the reviewer and the executive editor for their valuable suggestions regarding this paper.

Author information

Authors and Affiliations

  1. School of Aeronautic Science and Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, China

    Junhui Meng, Houdi Xiao & Mingyun Lv

  2. Commercial Aircraft Corporation of China, Shanghai, China

    Jie Hu

Authors
  1. Junhui Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Houdi Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mingyun Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mingyun Lv.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meng, J., Hu, J., Xiao, H. et al. Hierarchical optimization of the composite blade of a stratospheric airship propeller based on genetic algorithm. Struct Multidisc Optim 56, 1341–1352 (2017). https://doi.org/10.1007/s00158-017-1725-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00158-017-1725-1

Keywords

Search

Navigation