Ground-Based Wake Turbulence Encounter Detection System
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Solution Overview
Problem
Current methods for collecting data on wake turbulence encounters by aircraft are inadequate, as they are often collected succinctly or after the event, resulting in piecemeal data that does not allow for a rigorous and complete study of flight behavior disturbances.
Innovation Solution
A ground-based system comprising a processor and memory with a wake turbulence analyzer that collects avionics data from in-flight aircraft and traffic data from other aircraft, determines proximity to wake turbulence, and generates reports based on severity of impact, using criteria such as uncommanded forces and wind signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collection is performed by pilots manually or after the event, then human judgment and flexibility are maintained, but data completeness and objectivity deteriorate resulting in piecemeal data
Solution Approach 1:
The patent introduces a ground-based server as an intermediary between aircraft sensors and data analysis. This server automatically collects avionics data, traffic data, and meteorological data, processes them through wake turbulence detection algorithms, and generates objective reports without human intervention, thereby improving data objectivity while managing system complexity through centralized processing
Solution Approach 2:
The system enables aircraft to automatically report their own state parameters (accelerations, wind signatures, flight behavior) without requiring pilot input. The ground-based server autonomously processes these self-reported data along with traffic and meteorological data to detect wake turbulence encounters, eliminating the need for manual data collection by pilots
2Reliability
If ground-based automated detection system is implemented, then data completeness and objectivity are improved, but system complexity and implementation cost increase
Solution Approach 1:
The ground-based server performs multiple functions: collecting avionics data from aircraft, acquiring traffic data from other aircraft, obtaining meteorological data, processing all these data streams, detecting wake turbulence encounters, and generating reports. This multi-functionality improves detection reliability while avoiding the need for separate specialized systems for each function
Solution Approach 2:
The system continuously monitors aircraft parameters and compares them against wake turbulence detection criteria. When wake turbulence is detected, the system generates reports that can be used to improve future detection algorithms and provide feedback to airlines and airports for operational adjustments, creating a closed-loop system that enhances reliability over time
3Measurement precision
If comprehensive data collection from multiple sources is performed, then wake turbulence detection accuracy is improved, but data processing complexity and time requirements increase
Solution Approach 1:
The system pre-processes and stores avionics data, traffic data, and meteorological data as they are collected from various sources. By having this data ready and organized before wake turbulence detection is needed, the system can quickly perform real-time analysis when an encounter occurs, improving detection accuracy without excessive processing delays
Solution Approach 2:
The patent divides the data collection and processing system into distinct modules: avionics data collection from the aircraft, traffic data collection from other aircraft, meteorological data collection, and a processing module that integrates these data streams. This segmentation allows each module to operate independently and efficiently, reducing overall processing complexity while maintaining comprehensive data analysis
Data Source
AI summary
A ground-based server is configured for identifying encounters of aircraft with wake turbulences generated by at least one other generating aircraft. The ground-based server is configured for: collecting avionics data for an in-flight aircraft and traffic data for one or more other aircraft in flight at a first flight time of the in-flight aircraft; determining that the in-flight aircraft was near a wake turbulence estimated, using the traffic data, to be generated by another aircraft; determining that the in-flight aircraft experienced at the first flight time a severe impact; determining that the in-flight aircraft experienced at the first flight time at least one of: one or more uncommanded forces and moments, and a wind signature indicative of wake turbulence; and in response, generating a report of a wake turbulence encounter for the in-flight aircraft at the first flight time.