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

Fagan, Edward; Leen, Seán B.; Torre, Oscar de la

doi:10.1080/24705314.2017.1318043

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…Post-processing of the results is also conducted using Python. The code has been previously used in the analysis of smallscale wind turbine blades [30] and concept tidal turbine blades [14] and is well suited for parametric studies. The loads are applied to the blade using a form of multi-point constraint (MPC).…”

Section: Fe Model Set-upmentioning

confidence: 99%

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

Fagan

Flanagan

Leen

et al. 2017

Composite Structures

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This study presents experimental testing on a 13 m long glass-fibre epoxy composite wind turbine blade. The results of the test were used to calibrate finite element models. A design optimisation study was then performed using a genetic algorithm. The goal of the optimisation was to minimise the material used in blade construction and, thereby, reduce the manufacturing costs. The thickness distribution of the composite materials and the internal structural layout of the blade were considered for optimisation. Constraints were placed on the objective based on the stiffness of the blade and the blade surface stresses. A variable penalty function was used with limits derived from the blade test and the structural layout of the turbine. The model shows good correspondence to the test results (blade mass within 6% and deflection within 9%) and the differences between test and model are discussed in detail. The genetic algorithm resulted in five optimal blade designs, showing a reduction in mass up to 24%. Structural modelling in combination with numerical search algorithms provide a powerful tool for designers and demonstrates that the reader can have confidence in the claimed potential savings when the reference blade models are calibrated against physical test data.

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Section: Fe Model Set-upmentioning

confidence: 99%

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

Fagan

Flanagan

Leen

et al. 2017

Composite Structures

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show abstract

“…For further details of the operation of the algorithm (including crossover, mutation and repair strategies) refer to Fagan et al [21].…”

Section: Genetic Algorithm Parametersmentioning

confidence: 99%

“…The Twintex plies were cut to size so they overlapped slightly on the compression side of the blade. Table 5 shows the mass and tip deflection values for the original GFPP and CFPP test blades from the previous study [21], and the bespoke FE blade design. This blade provided the initial basis for a design document issued to the blade manufacturers [23].…”

Section: Bespoke Blade Construction and Modificationsmentioning

confidence: 99%

“…The optimisation technique used is based on the nondominated sorting genetic algorithm (NSGA-II) [19] and the GA objectives for each blade design are determined using the finite element software package Abaqus [20]. A previous design study [21] was performed on the composite blades, which identified several concept designs for a glass fibre-reinforced blade. In the previous study the multi-objective GA was used to optimise the structural design of the blade using the mass and tip deflection as the two objectives.…”

Section: Introductionmentioning

confidence: 99%

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Validation of the multi-objective structural optimisation of a composite wind turbine blade

Fagan

Torre

Leen

et al. 2018

Composite Structures

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Structural optimisation of a wind turbine blade is presented in this work. The optimisation was performed using a multi-objective genetic algorithm and finite element modelling to determine the optimal structural design for a glass fibre-reinforced polypropylene composite blade. A candidate blade design from the Pareto efficient set was manufactured and tested for a range of structural characteristics. Static testing was carried out using a Whiffletree test rig and a laser scanner was used to determine the deflection of the blade to a high degree of accuracy. The finite element model results for the custom-made design are compared to the measured blade response. The FE model predictions for strains, mass and natural frequencies are in general agreement with the test results; however, notable deviations in the deflections predictions are attributed to modifications to the blade for manufacture and the shell-based modelling approach. The differences are discussed in detail and recommendations for future design work are outlined. The test results of the bespoke blade are also compared to two additional designs to determine the level of improvement afforded by the genetic algorithm approach. The bespoke glass fibre blade demonstrated an improvement in tip deflection of 16% relative to the original blade design.

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“…A variety of modal identification techniques either in time or frequency domain have been proposed, including stochastic subspace identification (SSI), eigensystem realization algorithm (ERA), and frequency domain decomposition (FDD) . The obtained modal properties are further utilized in various structural health monitoring applications, such as finite element model updating, damage detection, and cable tension estimation . As such, modal analysis is a key component in many civil engineering applications that require the dynamic characteristics of a structure.…”

Section: Introductionmentioning

confidence: 99%

Automated peak picking using region‐based convolutional neural network for operational modal analysis

Kim

Sim

2019

Struct Control Health Monit

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Peak picking is used to determine locations of salient peaks in a graphical representation of a physical quantity. It is often used to extract possible natural frequencies from the frequency domain representation of structural responses.One of the challenges in peak picking is to establish a method to automatically distinguish peaks from data containing noise peaks. As selecting peaks intrinsically depends on human perception, algorithms for automated peak picking have exhibited only partial success to date. This paper presents an automated peak picking method that utilizes a region-based convolutional neural network with possible object locations to accurately identify the peaks. A tailored deep learning architecture is proposed, which is subsequently trained using only the peaks numerically generated by computer software. Laboratory and field tests are conducted using acceleration responses obtained from three test structures of beam, truss, and stay cable to verify the identification performance of the proposed peak detector. The proposed peak detector is shown to successfully identify most of the salient peaks with high accuracy.parameters of the physical modes are consistently found regardless of the provided system orders. 15 The modal parameters calculated from SSI or ERA with different system orders are grouped based on their similarity to each other, from which the frequently obtained are selected as the physical modes. [16][17][18][19] Neu et al. 20 and Cabboi et al. 21 further improved the approach in terms of automation by minimizing user-defined parameters. In addition, Afshar and Khodaygan 22 presented an enhanced stabilization chart for automated modal analysis. However, the identification performance is still highly dependent on the maximum system order, of which an optimal value should be provided as a priori information.Peak picking associated with frequency domain approaches has also been used to carry out automated modal analysis. [23][24][25] Peak picking on the frequency domain representation is considered as an intuitive way for operational modal analysis, as natural modes generally have salient peaks. A general approach for automated peak picking in the literature is to select maximum values of frequency domain representations of the structural responses (e.g., Fourier transform and power spectrum) from each of the user-defined subfrequency ranges, which are believed to include peaks. 26,27 This method is valid only when the subfrequency ranges are known; changes in peak frequencies due to structural damage or environmental effects can lead to the selection of undesired peaks. A user-defined threshold value was additionally introduced to filter out less important peaks in each subfrequency range. [28][29][30][31] This approach ignores peaks that are lower than the user-defined threshold and thus reduces the number of false positives; however, the imposition of thresholds for the peak amplitude and subfrequency range critically limits the practical applications of the approach to different ...

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

Cited by 6 publications

References 20 publications

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

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

Validation of the multi-objective structural optimisation of a composite wind turbine blade

Automated peak picking using region‐based convolutional neural network for operational modal analysis

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