Wind Flow Model Using Terrain Exposure Coefficients
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Solution Overview
Problem
Current wind flow models, both linear and computational fluid dynamics (CFD), face significant errors in predicting wind speed and direction, especially in complex terrain, with linear models producing substantial errors in slopes over 20 degrees and CFD models requiring extensive resources and knowledge, lacking consistency in accuracy across validation studies.
Innovation Solution
A method utilizing multiple meteorological towers to generate site-specific wind flow models based on terrain exposure differences, employing coefficients (mUW and mDW) derived from the Navier-Stokes theory, which simplifies wind conditions to estimate wind speed changes solely due to terrain variations, and uses self-learning algorithms to minimize prediction errors, focusing on terrain complexity and direction sectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If linear models are used to predict wind flow, then the model is simple and quick to produce estimates, but the prediction accuracy deteriorates significantly in complex terrain with slopes over 20 degrees
Solution Approach 1:
The patent transforms the wind flow prediction problem by changing the fundamental parameters from direct CFD simulation to a physics-based analytical approach using modified momentum conservation equations. This involves parameterizing terrain effects through exposure coefficients (mUW, mDW) and using digital elevation data to calculate terrain-induced wind speed changes, thereby achieving high accuracy without CFD-level complexity
Solution Approach 2:
The patent replaces the complex numerical mechanical system of CFD with a simplified physics-based analytical model rooted in momentum conservation principles. By substituting the full Navier-Stokes numerical solution with an analytical approach that accounts for terrain exposure and elevation changes, the model achieves comparable accuracy to CFD while being computationally efficient and easier to implement
2Measurement precision
If CFD models are used to predict wind flow, then the prediction accuracy improves, but the computational resources and expertise required increase substantially
Solution Approach 1:
The patent extracts and isolates the dominant physical mechanisms affecting wind flow in complex terrain—specifically terrain exposure effects and elevation-driven pressure gradients—separating them from the full CFD system. By focusing only on these critical factors through momentum conservation equations, the model captures essential terrain effects without requiring the computational overhead of complete CFD simulations
Solution Approach 2:
The patent segments the wind flow prediction problem into distinct components: base wind climatology from met towers, terrain exposure effects through upwind/downwind coefficients, and elevation-driven pressure gradient effects. This segmentation allows each component to be modeled independently using appropriate physics, achieving high accuracy while avoiding the monolithic complexity of CFD
3Measurement precision
If wind flow models use terrain exposure coefficients derived from Navier-Stokes theory, then the prediction accuracy in complex terrain improves, but the model complexity increases compared to traditional linear models
Solution Approach 1:
The patent introduces terrain exposure coefficients (mUW, mDW) as intermediary parameters that bridge the gap between simple linear models and complex CFD simulations. These coefficients act as mediators that capture the essential physics of terrain exposure effects without requiring full CFD computation, enabling accurate prediction through a middle-ground analytical approach
Solution Approach 2:
The patent performs preliminary calculations of terrain exposure and elevation effects using digital elevation data and met tower measurements before conducting the main wind flow prediction. By pre-characterizing terrain effects through exposure coefficients and base climatology development, the model reduces the complexity of the final prediction step while maintaining high accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides accurate wind speed and energy production estimates across a wind farm site by weighting predictions based on terrain similarity and RMS error, achieving low RMS error and high accuracy in wind speed predictions, outperforming existing models in complex terrain scenarios.
Implementation Method 1
the invention utilizes the theory of conservation of momentum (Navier-Stokes) and wind data measured at two or more meteorological (met) towers to develop a wind flow model
Data Source
AI summary
A method of modeling the spatial variation in wind resource at a prospective wind farm site. The method involves a simplified analysis of the Navier-Stokes equation and utilizes data from all of the met sites simultaneously to develop site-calibrated models. The model coefficients, mUW and mDW, describe the sensitivity of the wind speed to changes in the upwind and downwind terrain exposure and are defined for downhill and uphill flow. The coefficients are a function of terrain complexity and, since terrain complexity can change across an area, the estimates are performed in a stepwise fashion where a path of nodes with a gradual change in complexity is found between each pair of sites. Also, coefficients are defined for each wind direction sector and estimates are performed on a sectorwise basis. The site-calibrated models are created by cross-predicting between each pair of met sites and, through a self-learning technique, the model coefficients that yield the minimum met cross-prediction error are found.