3D MULTIDISCIPLINARY AUTOMATED DESIGN OPTIMIZATION TOOLBOX FOR WIND TURBINE BLADES
| dc.contributor.author | Sagimbayev, Sagi | |
| dc.contributor.author | Kylyshbek, Yestay | |
| dc.contributor.author | Batay, Sagidolla | |
| dc.contributor.author | Zhao, Yong | |
| dc.contributor.author | Fok, Sai | |
| dc.contributor.author | Soo Lee, Teh | |
| dc.date.accessioned | 2021-08-23T04:51:49Z | |
| dc.date.available | 2021-08-23T04:51:49Z | |
| dc.date.issued | 2021-03-26 | |
| dc.description.abstract | This paper presents two novel automated optimization approaches. The first one proposes a framework to optimize wind turbine blades by integrating multidisciplinary 3D parametric modeling, a physics-based optimization scheme, the Inverse Blade Element Momentum (IBEM) method, and 3D Reynolds-averaged Navier-Stokes (RANS) simulation; the second method introduces a framework combining 3D parametric modeling and an integrated goal-driven optimization together with a 4D Unsteady Reynolds-averaged Navier-Stokes (URANS) solver. In the first approach, the optimization toolbox operates concurrently with the other software packages through scripts. The automated optimization process modifies the parametric model of the blade by decreasing the twist angle and increasing the local angle of attack (AoA) across the blade at locations with lower than maximum 3D lift/drag ratio until a maximum mean lift/drag ratio for the whole blade is found. This process exploits the 3D stall delay, which is often ignored in the regular 2D BEM approach. The second approach focuses on the shape optimization of individual cross-sections where the shape near the trailing edge is adjusted to achieve high power output, using a goal-driven optimization toolbox verified by 4D URANS Computational Fluid Dynamics (CFD) simulation for the whole rotor. The results obtained from the case study indicate that (1) the 4D URANS whole rotor simulation in the second approach generates more accurate results than the 3D RANS single blade simulation with periodic boundary conditions; (2) the second approach of the framework can automatically produce the blade geometry that satisfies the optimization objective, while the first approach is less desirable as the 3D stall delay is not prominent enough to be fruitfully exploited for this particular case study. | en_US |
| dc.identifier.citation | Sagimbayev, S., Kylyshbek, Y., Batay, S., Zhao, Y., Fok, S., & Soo Lee, T. (2021). 3D Multidisciplinary Automated Design Optimization Toolbox for Wind Turbine Blades. Processes, 9(4), 581. https://doi.org/10.3390/pr9040581 | en_US |
| dc.identifier.issn | 2227-9717 | |
| dc.identifier.uri | https://doi.org/10.3390/pr9040581 | |
| dc.identifier.uri | https://www.mdpi.com/2227-9717/9/4/581 | |
| dc.identifier.uri | http://nur.nu.edu.kz/handle/123456789/5699 | |
| dc.language.iso | en | en_US |
| dc.publisher | MDPI AG | en_US |
| dc.relation.ispartofseries | Processes;2021, 9(4), 581; https://doi.org/10.3390/pr9040581 | |
| dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
| dc.subject | Type of access: Open Access | en_US |
| dc.subject | 3D RANS solver | en_US |
| dc.subject | BEM | en_US |
| dc.subject | Design optimization | en_US |
| dc.subject | IBEM | en_US |
| dc.subject | NREL | en_US |
| dc.subject | Parametric modeling | en_US |
| dc.subject | Toolbox | en_US |
| dc.subject | Wind turbine blade | en_US |
| dc.title | 3D MULTIDISCIPLINARY AUTOMATED DESIGN OPTIMIZATION TOOLBOX FOR WIND TURBINE BLADES | en_US |
| dc.type | Article | en_US |
| workflow.import.source | science |