Local Wind Variability Modeling for Low-Altitude UAV Missions
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Solution Overview
Problem
Unmanned aerial vehicles operating at low altitudes face challenges in accurately predicting wind conditions due to variability in wind flows within chaotic atmospheric layers, leading to potential violations of established limits during missions.
Innovation Solution
A system utilizing ground-based weather sensors distributed across a region to capture and process wind data, modeling wind conditions using Weibull distributions to predict wind variability and make mission decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wind conditions are determined using sensors at a single takeoff station, then the measurement setup is simple, but the prediction accuracy of wind conditions at different locations and times is poor
Solution Approach 1:
The patent divides the operational region into multiple grid cells and distributes weather sensors across different locations within the region. Each sensor captures wind data for its specific location, enabling localized wind condition predictions that account for spatial variability in the roughness sublayer.
Solution Approach 2:
The patent introduces a computer system that acts as an intermediary to receive wind data from multiple sensors, process the data through histogram analysis and Weibull distribution modeling, and generate predicted wind condition sets for different locations and times. This intermediary system integrates distributed measurements into comprehensive predictions.
2Reliability
If wind conditions are assumed to be uniform across the operational region, then the operational planning is simplified, but the reliability of aerial vehicle operations is compromised due to unaccounted local variability
Solution Approach 1:
The patent generates separate predicted wind condition sets for different grid cells within the operational region, allowing each location to have its own wind characteristics. This enables the system to account for local variations in wind conditions caused by different surface features and obstacles in the roughness sublayer.
Solution Approach 2:
The patent uses Weibull distribution parameters (shape parameter k and scale parameter c) to characterize wind conditions at different locations and times. By modeling wind speeds as random variables with location-specific and time-specific parameters, the system captures the variability and uncertainty of wind conditions without requiring overly complex physical models.
3Measurement precision
If wind data is collected continuously at high frequency, then the temporal resolution of wind predictions is improved, but the data processing burden and computational requirements increase
Solution Approach 1:
The patent performs preliminary data processing by generating histograms of wind speeds from raw sensor data and fitting Weibull distributions to these histograms. This preprocessing step organizes the data in a structured format that facilitates efficient prediction generation and reduces the computational burden during actual operational planning.
Solution Approach 2:
The patent collects wind data at regular time intervals and updates predicted wind condition sets periodically. This periodic data collection and processing approach balances temporal resolution with computational efficiency, allowing the system to capture wind variability without continuous high-frequency processing.
Data Source
AI summary
Data captured by wind sensors installed at various locations in a region is processed to predict a wind value representative of wind flows within the region. One or more histograms of wind flows represented in the data are generated, and continuous probability distributions (e.g., Weibull distributions) are derived based on the histograms. The wind value may represent wind flows represented in the histograms at which an aerial vehicle may be expected to encounter when performing a mission within the region. If the wind value exceeds a predetermined threshold, the aerial vehicle may be permitted to perform the mission. If the wind value does not exceed the predetermined threshold, however, the aerial vehicle may not be permitted to perform the mission.