Physical experimental static testing and structural design optimisation for a composite wind turbine blade

Fagan, Edward; Flanagan, Michael; Leen, Seán B.; Flanagan, Tomás; Doyle, Adrian; Goggins, Jamie

doi:10.1016/j.compstruct.2016.12.037

Cited by 41 publications

(29 citation statements)

References 29 publications

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“…The wind speeds of a normal operating wind turbine range from 3.5-25m/s with a nominal wind speed of 14 m/s. at the nominal wind speed constant power production must be maintained hence the turbine uses a pitch control system [23]. In selecting the test loading on the blade a wind speed was used in the range of as low as 2.94m/s to an extreme gust of 12.75m/s.…”

Section: Experimental Testmentioning

confidence: 99%

Stress validation of finite element model of a small-scale wind turbine blade

Babawarun

Ngwangwa

2019

J. energy South. Afr.

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Wind turbine blades are the first mechanical part of a wind turbine that interacts with the wind and hence play a key role in wind power generation. It is important that the wind turbine blade is tested for structural integrity in accordance to design code IEC 61400-23 such as strain limits, fatigue life, blade tip clearance limit, and surface stress. This paper focuses on the calculation and validation of static bending stresses in the blade; it presents the experimental and simulated stress analysis of a small-scale wind turbine blade. The simulation and 3D design software ANSYS, version 19.0 is used in the finite element analysis (FEA). By using FEA, we aim to capture the stress generated on the blade geometry under static loading and unloading conditions. As a first step towards this, the finite element results were validated against experimental results on a kestrel E230i turbine blade. The blade was fixed at one end, loaded, and unloaded statically at three selected points. The finite element results are calculated within a 25% error margin of the experimental results. A reverse engineering procedure was used to determine the appropriate ANSYS model blade properties that were used, as the exact material properties were not available from the manufacturer.

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Section: Experimental Testmentioning

confidence: 99%

Stress validation of finite element model of a small-scale wind turbine blade

Babawarun

Ngwangwa

2019

J. energy South. Afr.

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“…The FE models are generated using a Python code developed in-house and previously used to model concept tidal turbine blades (Fagan et al, 2016a) and validated against 13 m long wind turbine blade tests (Fagan et al, 2016b). The code requires geometric, material and structural input data such as that outlined in the previous section (and summarised in Table 4).…”

Section: Finite Element Modellingmentioning

confidence: 99%

Experimental investigation, numerical modelling and multi-objective optimisation of composite wind turbine blades

Fagan

Leen

Torre

2017

Journal of Structural Integrity and Maintenance

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Additional thanks are given to the technical staff at NUI Galway and the group of mechanical engineering Masters students for their efforts on the testing of the wind turbine blades. Thanks also to Dr Jonathan Byrne for his guidance on the implementation of the NSGA-II algorithm. Experimental investigation, numerical modelling and multi-objective optimisation of composite wind turbine blades Static and modal testing of two blades from a 15 kW wind turbine is presented. The two blades are made from glass-fibre reinforced polypropylene, one of which has been reinforced with additional carbon-fibre plies. Static testing is performed with a Whiffle tree test rig to determine the structural response of the blades. Blade mass, deflections, strains and natural frequencies are reported. The following objectives are undertaken: (i) evaluate and compare the test results of the two wind turbine blade designs, (ii) use the results to validate finite element models of the blades and (iii) utilise the validated models in a design optimisation study. Parametric blade models are generated using the Python programming language and are based on manufacturing specifications for the blades. The models show good correspondence with the experimental results. The goal of the optimisation study is to maximise the stiffness and reduce the mass of the glassfibre blade. A multi-objective genetic algorithm is used to determine the optimum laminate thicknesses along the length of the blades. The optimisation study produced a set of Pareto efficient blade designs with up to 17% improvement in stiffness or 30% reduction in mass for the glass-fibre blade design.

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“…Many studies have been conducted regarding blade structural testing. For example, Fagan et al [2] presented an experimental testing on a 13-m long wind turbine blade and used the test results to calibrate finite element models, and then the materials used in the blade construction and manufacturing costs were reduced by optimization design using a genetic algorithm. Yang et al [3] tested the limit loads in full-scale static testing of a wind turbine blade and the deformation situation of the blade under the limit loads.…”

Section: Introductionmentioning

confidence: 99%

GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades

Liu

Wang³

et al. 2019

Energies

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This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades’ health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades.

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Physical experimental static testing and structural design optimisation for a composite wind turbine blade

Cited by 41 publications

References 29 publications

Stress validation of finite element model of a small-scale wind turbine blade

Stress validation of finite element model of a small-scale wind turbine blade

Experimental investigation, numerical modelling and multi-objective optimisation of composite wind turbine blades

GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades

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