Vertical Axis Wind Turbine Array Configuration Using Potential Flow
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Solution Overview
Problem
Current methods for siting wind turbines lack efficiency in configuring arrays to optimize power output and accommodate various physical constraints such as discrete locations, spacing, wind directions, and topography, leading to suboptimal performance.
Innovation Solution
A computer-implemented method using low-order potential flow elements to model and configure arrays of vertical axis wind turbines, incorporating variables for potential flow, vortex, blockage effect, energy sink, and turbulence, with a fitness function to optimize turbine placement based on desired attributes like power output and environmental impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If traditional methods are used for siting wind turbines, then the configuration process is simpler, but the power output optimization is insufficient and physical constraints cannot be properly accommodated
Solution Approach 1:
The patent replaces traditional manual or simple heuristic methods for turbine siting with a computer-implemented optimization system that uses potential flow theory and genetic algorithms. This substitution enables automated, constraint-aware optimization of turbine configurations to maximize power output while accounting for complex physical constraints like discrete locations, spacing requirements, wind directions, and topography.
Solution Approach 2:
The system optimizes multiple parameters simultaneously including turbine locations, orientations, and array configurations. By varying these parameters within defined constraints (discrete locations, spacing, wind directions, topography), the system finds configurations that maximize power output per unit land area while satisfying all physical constraints.
2Productivity
If turbine arrays are configured to maximize power output, then energy generation increases, but environmental and visual impacts may increase
Solution Approach 1:
The optimization system incorporates multiple objective functions that simultaneously consider power output and environmental/visual impact metrics. By adjusting configuration parameters (turbine spacing, arrangement patterns, site selection) within the genetic algorithm, the system identifies solutions that achieve acceptable power generation while minimizing adverse environmental and visual effects.
Solution Approach 2:
The system evaluates different local configurations and site characteristics to determine optimal turbine placements. By analyzing specific local conditions (topography, wind patterns, environmental sensitivity), the system can configure arrays that adapt to local constraints, thereby reducing environmental impact in sensitive areas while maintaining overall productivity.
3Power
If more turbines are placed in an array to increase power output, then energy generation improves, but the use of land area increases
Solution Approach 1:
The patent employs potential flow theory, which models the wind field as an inviscid, incompressible fluid. This theoretical framework allows for efficient calculation of flow patterns and power extraction without requiring detailed viscous flow simulations, enabling rapid evaluation of multiple configurations to maximize power density on available land.
Solution Approach 2:
The system optimizes the density and arrangement of turbines by varying configuration parameters within the genetic algorithm. It evaluates different spacing and layout patterns to achieve maximum power output per unit land area, effectively increasing power density while minimizing land use through intelligent spatial arrangement.
4Measurement precision
If complex optimization algorithms are used to configure turbine arrays, then configuration accuracy improves, but computational time increases
Solution Approach 1:
The patent replaces computationally intensive traditional CFD (Computational Fluid Dynamics) simulations with a simplified potential flow model combined with genetic algorithms. This substitution maintains sufficient accuracy for array configuration while dramatically reducing computational time, as potential flow theory provides closed-form solutions that are much faster to evaluate than full Navier-Stokes simulations.
Solution Approach 2:
The optimization process is segmented into distinct stages: (1) generating candidate configurations using genetic algorithms, (2) evaluating each configuration using potential flow theory, and (3) selecting the optimal solution. This segmentation allows for efficient exploration of the design space with accurate enough evaluations to identify optimal configurations without requiring exhaustive high-fidelity simulations of every candidate.
Data Source
AI summary
A method for providing potential flow elements including a vortex to capture rotation of a turbine, a dipole to capture a blockage effect of the turbine, a sink to capture extracted energy from wind by the turbine, and a source to capture recovery of flow due to inflow from around the turbine is described. Methods for providing configurations of a VAWT array based on a desired attribute of the array, using a low-order model when the array is subject to physical constraints are also described.