Trading off sound pressure level and average power production for wind farm layout optimization

Tingey, Eric; Ning, Andrew

doi:10.1016/j.renene.2017.07.057

Cited by 27 publications

(12 citation statements)

References 25 publications

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“…However, GA is still actively used in various fields owing to its versatility. The mathematical programming method includes mixed-integer programming [34][35][36][37] and gradient-based optimization [38][39][40][41]. Unlike heuristics methods, it has the advantage of ensuring global optimization.…”

Section: Introductionmentioning

confidence: 99%

Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

Yang

Cho

2019

Energies

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The optimal layout of wind turbines is an important factor in the wind farm design process, and various attempts have been made to derive optimal deployment results. For this purpose, many approaches to optimize the layout of turbines using various optimization algorithms have been developed and applied across various studies. Among these methods, the most widely used optimization approach is the genetic algorithm, but the genetic algorithm handles many independent variables and requires a large amount of computation time. A simulated annealing algorithm is also a representative optimization algorithm, and the simulation process is similar to the wind turbine layout process. However, despite its usefulness, it has not been widely applied to the wind farm layout optimization problem. In this study, a wind farm layout optimization method was developed based on simulated annealing, and the performance of the algorithm was evaluated by comparing it to those of previous studies under three wind scenarios; likewise, the applicability was examined. A regular layout and optimal number of wind turbines, never before observed in previous studies, were obtained and they demonstrated the best fitness values for all the three considered scenarios. The results indicate that the simulated annealing (SA) algorithm can be successfully applied to the wind farm layout optimization problem.

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Section: Introductionmentioning

confidence: 99%

Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

Yang

Cho

2019

Energies

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“…While tip speed captures the magnitude change in noise for a set number of propellers, it is not enough to enable comparison of noise between aircraft designs with different numbers of propellers and the associated blade design changes. In this study we model propeller noise using code ‖ developed and validated by Tingey [33], which uses acoustic modeling techniques by Brooks, Pope, and Marcolini (referred to as the BPM equations) [34,35]. Figure 3 shows a graphical representation of the A-weighted noise profile for an example 8 propeller case in climb at 50 ft with respect to a ground observer.…”

Section: B Propeller Noisementioning

confidence: 99%

Takeoff and Performance Trade-Offs of Retrofit Distributed Electric Propulsion for Urban Transport

Moore

Ning

2019

Journal of Aircraft

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While vertical takeoff and landing aircraft have shown promise for urban air transport, distributed electric propulsion on existing aircraft may offer immediately implementable alternatives. Distributed electric propulsion could potentially decrease takeoff distances enough to enable thousands of potential inter-city runways. This conceptual study explores the effects of a retrofit of open-bladed electric propulsion units. To model and explore the design space we use blade element momentum method, vortex lattice method, linear-beam finite element analysis, classical laminate theory, composite failure, empirically-based blade noise modeling, motor and motor-controller mass models, and gradient-based optimization. With liftoff time of seconds and the safe total field length for this aircraft type undefined, we focused on the minimum conceptual takeoff distance. We found that 16 propellers could reduce the takeoff distance by over 50% compared to the optimal 2 propeller case. This resulted in a conceptual minimum takeoff distance of 20.5 meters to clear a 50 ft (15.24 m) obstacle. We also found that when decreasing the allowable noise by approximately 10 dBa, the 8 propeller case performed the best with a 43% reduction in takeoff distance compared to the optimal 2 propeller case. This resulted in a noise-restricted conceptual minimum takeoff distance of 95 meters.

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“…Any form of bin-packing problem or spatial arrangement problem [25] is an obvious use case. Additionally any problem that requires maintaining separation of a set of bodies is also directly applicable, such as maintaining a minimum separation distance between all aircraft within an airspace, all spacecraft in a set of intersecting orbits, or all wind turbines within a wind farm [26,27].…”

Section: Table 3 Notional Optimization Problem Formulation That Is Sumentioning

confidence: 99%

Using Graph Coloring To Compute Total Derivatives More Efficiently in OpenMDAO

Gray

Hearn

Naylor³

2019

AIAA Aviation 2019 Forum

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When they are applicable, gradient based optimization algorithms are the most efficient way to solve design optimization problems. Although gradient based methods are generally efficient, they can be made significantly more so through the usage of analytic techniques to compute the necessary total derivatives. The traditional forward (direct) and reverse (adjoint) analytic techniques have computational costs that scale linearly with the number of design variables and the number of constraints, respectively. In this work, we present an application of a graph coloring algorithm to the analytic techniques for computing total derivative Jacobians in order to achieve much better computational scaling than the pure analytic methods can provide alone. A detailed theoretical explanation of how coloring algorithms interact with analytic derivative methods is presented that illustrates specific types of sparsity patterns that must be present in total derivative Jacobians in order for this coloring technique to be effective. The new technique has been implemented as a feature in the OpenMDAO framework and the implementation is demonstrated on two example problems. The performance on the example problems up to 50% reduction in compute cost for optimizations with bi-directional coloring compared to traditional constraint aggregation. Additionally, the results show how coloring technique alleviates some of the numerical difficulties that constraint aggregation can cause, leading to the ability to solve larger problems. It is expected that the new method will have wide applicability to multidisciplinary optimization problems, and that its availability in OpenMDAO will offer significant computational savings for users without the need for them to implement the coloring algorithm themselves.

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Trading off sound pressure level and average power production for wind farm layout optimization

Cited by 27 publications

References 25 publications

Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

Takeoff and Performance Trade-Offs of Retrofit Distributed Electric Propulsion for Urban Transport

Using Graph Coloring To Compute Total Derivatives More Efficiently in OpenMDAO

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