Shape optimization under uncertainty for rotor blades of horizontal axis wind turbines

Keshavarzzadeh, Vahid; Ghanem, Roger G.; Tortorelli, Daniel A.

doi:10.1016/j.cma.2019.05.015

Title: Shape optimization under uncertainty for rotor blades of horizontal axis wind turbines

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Abstract

We present a computational framework for the shape optimization of a Horizontal-Axis Wind Turbine (HAWT) rotor blade under uncertainty. Our framework integrates aerodynamic simulations based on the blade element method which utilizes reduced order models of the blade structure and wind load with design sensitivity analysis and nonlinear programming. The wind velocity is modeled as a stochastic process to account for variations in time and space. An additional stochastic process accounts for uncertainties in the structural material properties. The uncertainty propagation is based on a non-intrusive polynomial chaos expansion that allows accurate estimation of stochastic performance metrics such as generated power and structural compliance. Sensitivities of cost and constraint functions with respect to shape parameters namely twist angles are computed with an efficient scheme to enable gradient based optimization. To demonstrate the effect of uncertainty, designs obtained from optimization under uncertainty are compared to those obtained from deterministic optimization.

Authors:

^[1];

^[2]; Tortorelli, Daniel A. ^[3]

Univ. of Illinois at Urbana-Champaign, IL (United States). Dept. of Materials Science and Engineering
Univ. of Southern California, Los Angeles, CA (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Publication Date:: Sun Sep 01 00:00:00 EDT 2019

Research Org.:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1598120

Alternate Identifier(s):: OSTI ID: 1702469

Report Number(s):: LLNL-JRNL-757333
Journal ID: ISSN 0045-7825; 945206

Grant/Contract Number:: AC52-07NA27344

Resource Type:: Accepted Manuscript

Journal Name:: Computer Methods in Applied Mechanics and Engineering

Additional Journal Information:: Journal Volume: 354; Journal Issue: C; Journal ID: ISSN 0045-7825

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 42 ENGINEERING; Shape optimization under uncertainty; Blade element method; Polynomial chaos expansion; Reduced order models; Horizontal-axis wind turbine

Citation Formats


                    Keshavarzzadeh, Vahid, Ghanem, Roger G., and Tortorelli, Daniel A. Shape optimization under uncertainty for rotor blades of horizontal axis wind turbines.  United States: N. p., 2019. 
Web.  doi:10.1016/j.cma.2019.05.015.

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                    Keshavarzzadeh, Vahid, Ghanem, Roger G., & Tortorelli, Daniel A. Shape optimization under uncertainty for rotor blades of horizontal axis wind turbines.  United States.  https://doi.org/10.1016/j.cma.2019.05.015

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                    Keshavarzzadeh, Vahid, Ghanem, Roger G., and Tortorelli, Daniel A. Sun .  
"Shape optimization under uncertainty for rotor blades of horizontal axis wind turbines".  United States.  https://doi.org/10.1016/j.cma.2019.05.015.  https://www.osti.gov/servlets/purl/1598120.

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@article{osti_1598120,

  title        = {Shape optimization under uncertainty for rotor blades of horizontal axis wind turbines},

  author       = {Keshavarzzadeh, Vahid and Ghanem, Roger G. and Tortorelli, Daniel A.},

  abstractNote = {We present a computational framework for the shape optimization of a Horizontal-Axis Wind Turbine (HAWT) rotor blade under uncertainty. Our framework integrates aerodynamic simulations based on the blade element method which utilizes reduced order models of the blade structure and wind load with design sensitivity analysis and nonlinear programming. The wind velocity is modeled as a stochastic process to account for variations in time and space. An additional stochastic process accounts for uncertainties in the structural material properties. The uncertainty propagation is based on a non-intrusive polynomial chaos expansion that allows accurate estimation of stochastic performance metrics such as generated power and structural compliance. Sensitivities of cost and constraint functions with respect to shape parameters namely twist angles are computed with an efficient scheme to enable gradient based optimization. To demonstrate the effect of uncertainty, designs obtained from optimization under uncertainty are compared to those obtained from deterministic optimization.},

  doi          = {10.1016/j.cma.2019.05.015},

  journal      = {Computer Methods in Applied Mechanics and Engineering},

  number       = C,

  volume       = 354,

  place        = {United States},

  year         = {Sun Sep 01 00:00:00 EDT 2019},

  month        = {Sun Sep 01 00:00:00 EDT 2019}

}

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https://doi.org/10.1016/j.cma.2019.05.015

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