Urban Flight Wind Metrics Combining Vector Differences and Gradients
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Solution Overview
Problem
Existing technologies fail to account for individual wind components in urban environments, particularly near obstacles like buildings, which can pose significant hazards to urban aviation operations.
Innovation Solution
A system that translates horizontal and vertical wind measurements into potential hazard metrics, focusing on turbulence (Shake) and wind gradients (Sharp), and combines these data sets to provide a graphical representation for urban air mobility operations, including drone and air taxi flight planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing wind hazard derivation technologies are used, then general meteorological threats can be identified, but individual wind components in the vicinity of urban obstacles cannot be recognized
Solution Approach 1:
The patent segments the wind hazard assessment into distinct components: shear turbulence, mechanical turbulence, and building wake effects. Each component is analyzed separately using specific meteorological parameters and models, allowing precise measurement of individual wind components while maintaining manageable system complexity through modular analysis approaches.
Solution Approach 2:
The patent transitions from two-dimensional wind speed measurements to three-dimensional wind vector analysis by incorporating vertical wind shear, horizontal wind gradients, and building wake flow patterns. This dimensional expansion enables recognition of individual wind components in the vicinity of obstacles while using advanced but systematic measurement and modeling techniques.
2Reliability
If comprehensive wind hazard analysis is performed, then urban aviation safety is improved, but real-time processing capability is reduced
Solution Approach 1:
The patent performs preliminary analysis of wind hazard patterns using historical meteorological data and building geometry information to pre-identify high-risk zones and typical hazard patterns. This preliminary action enables faster real-time processing by comparing current conditions against pre-computed reference patterns, thus maintaining both comprehensive safety analysis and real-time processing capability.
Solution Approach 2:
The system uses self-service approaches by automatically selecting appropriate hazard analysis models based on environmental conditions and building characteristics without requiring manual intervention. The system autonomously processes meteorological data, applies relevant models, and generates hazard assessments, improving real-time processing efficiency while maintaining comprehensive safety analysis.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for utilizing non-RAM memory to implement calculating a first wind metric over a geographic area, wherein the first wind metric reflects total wind velocity vector differences. The method further calculates a second wind metric over the geographic area, wherein the second wind metric reflects a horizontal wind gradient near obstacles. The method further generates a first data set of the first wind metric and a second data set of the second wind metric and combines the first data set and the second data set into a combined data set representing a combination of the first wind metric and the second wind metric. The combined data set represents wind hazards in an urban environment with buildings and is used to identify or generate low altitude flighted vehicle navigation paths for urban flighted vehicles, such as drones or air taxis.