Aerodynamic Shape Optimization of a Vertical-Axis Wind Turbine Using Differential Evolution

Carrigan, Travis; Dennis, Brian H.; Han, Zhen Xue; Wang, Bo

doi:10.1201/b16587-6

Cited by 15 publications

(24 citation statements)

References 13 publications

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“…In this communication, we target optimization problems under strict function evaluation budget constraints, which often occur in the context of industrial optimization problems which involve the simulation responses of complex dynamic systems. Industrial applications of such problems are for example: simulation based crashworthiness optimization of vehicle structures [9,10,11], and Computational Fluid Dynamics (CFD) based optimization [7,8]. The optimization problems of such complex system responses are often characterized by: a large number of design variables, the absence of analytical gradient information, highly non-linear system responses, and computationally expensive function evaluations resulting in a limited function evaluation budget.…”

Section: Introductionmentioning

confidence: 99%

“…In the context of budget limited optimization problems in structural and multidisciplinary optimization, DE was identified and recommended as an efficient algorithm for car-body optimization problems involving computationally expensive crashworthiness responses [9,10,11,12]. DE algorithms are also used for optimization of aircraft engines [7], wind turbines [8], and many other applications [22,23]. Optimization problems in the "expensive" function evaluation setting can also benefit from meta-modelling or surrogate model based optimization techniques such as e.g.…”

Section: Introductionmentioning

confidence: 99%

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SQG-Differential Evolution for Difficult Optimization Problems under a Tight Function Evaluation Budget

Sala

Baldanzini

Pierini

2017

Lecture Notes in Computer Science

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Abstract. In the context of industrial engineering it is important to integrate efficient computational optimization methods in the product development process. Some of the most challenging simulation based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradient information, highly non-linear objectives and a limited function evaluation budget. Although a huge variety of different optimization algorithms is available, the development and selection of efficient algorithms for problems with these industrial relevant characteristics, remains a challenge. In this communication a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics. The performance of the resulting method is compared with other state-of-the-art DE variants on 25 commonly used test functions, under tight function evaluation budget constraints of 1000 evaluations. The experimental results indicate that the proposed method performs particularly good on the "difficult" (high dimensional, multi-modal, inseparable) test functions. The operations used in the proposed mutation scheme, are computationally inexpensive, and can be easily implemented in existing differential evolution or other optimization algorithms by a few lines of program code as an non-invasive optional setting. Besides the applicability of the presented algorithm by itself, the described concepts can serve as a useful and interesting addition to the algorithmic operators in the frameworks of heuristics and evolutionary optimization and computing.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SQG-Differential Evolution for Difficult Optimization Problems under a Tight Function Evaluation Budget

Sala

Baldanzini

Pierini

2017

Lecture Notes in Computer Science

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“…We use a standard hybrid mesh for the present investigation, with a structured mesh for resolving the boundary layer and an unstructured mesh for the rest of the domain as shown in Figures and . Such hybrid meshes are known to be able to provide accurate results for wind turbine flows . The unsteady compressible Navier‐Stokes equations are discretised using a cell‐centered, finite‐volume flow solver.…”

Section: Aeroelastic Modelling For Downwind Wind Turbinesmentioning

confidence: 99%

“…Such hybrid meshes are known to be able to provide accurate results for wind turbine flows. 41,42 The unsteady compressible Navier-Stokes equations are discretised using a cell-centered, finite-volume flow solver. A second-order upwind scheme 43 is used for spatial discretisation, and temporal terms are discretised with a second-order implicit time-stepping scheme with dual-time sub-iteration.…”

Section: Mesh Generation and Fluid Solvermentioning

confidence: 99%

Aeroelastic validation and Bayesian updating of a downwind wind turbine

2020

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Summary Downwind wind turbine blades are subjected to tower wake forcing at every rotation, which can lead to structural fatigue. Accurate characterisation of the unsteady aeroelastic forces in the blade design phase requires detailed representation of the aerodynamics, leading to computationally expensive simulation codes, which lead to intractable uncertainty analysis and Bayesian updating. In this paper, a framework is developed to tackle this problem. Full, detailed aeroelastic model of an experimental wind turbine system based on 3‐D Reynolds‐averaged Navier‐Stokes is developed, considering all structural components including nacelle and tower. This model is validated against experimental measurements of rotating blades, and a detailed aeroelastic characterisation is presented. Aerodynamic forces from prescribed forced‐motion simulations are used to train a time‐domain autoregressive with exogenous input (ARX) model with a localised forcing term, which provides accurate and cheap aeroelastic forces. Employing ARX, prior uncertainties in the structural and rotational parameters of the wind turbine are introduced and propagated to obtain probabilistic estimates of the aeroelastic characteristics. Finally, the experimental validation data are used in a Bayesian framework to update the structural and rotational parameters of the system and thereby reduce uncertainty in the aeroelastic characteristics.

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“…The efficiency and robustness of the DE method directly depend on the settings of the control parameters such as population size, selection method, differentiation factor, and the crossover probability constant which controls the number of generated solutions for each individual through generations. As DE is easy to implement, not computationally expensive and it is highly efficient solving optimization problems, it has been used in many real-world applications such as text summarization [24], design of reconfigurable antenna arrays [25], job shop scheduling problem [26], blade design of wind turbines [27], and in the parameter estimation for a human immunodeficiency virus (HIV) [28]. …”

Section: Introductionmentioning

confidence: 99%

Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation

Cruz-Aceves

Aviña-Cervantes

López-Hernández

et al. 2013

Computational and Mathematical Methods in Medicine

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This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation.

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Aerodynamic Shape Optimization of a Vertical-Axis Wind Turbine Using Differential Evolution

Cited by 15 publications

References 13 publications

SQG-Differential Evolution for Difficult Optimization Problems under a Tight Function Evaluation Budget

SQG-Differential Evolution for Difficult Optimization Problems under a Tight Function Evaluation Budget

Aeroelastic validation and Bayesian updating of a downwind wind turbine

Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation

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