Aircraft Flight Path Planning Using Tail-Specific Weather Models
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Solution Overview
Problem
Existing flight path determination systems for aircraft rely on generic performance data, leading to inaccuracies and inefficiencies in fuel and time management due to lack of tail-specific data and real-time weather considerations.
Innovation Solution
A system and method utilizing a control unit with a machine learning model that incorporates tail-specific data and real-time weather conditions to simulate and determine the most efficient flight path, using a machine learning model to analyze flight paths and weather conditions dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic performance data is used for flight path determination, then the system is simpler to operate, but accuracy and efficiency in fuel and time management deteriorate
Solution Approach 1:
The system automatically collects, processes, and utilizes tail-specific data without requiring manual input from pilots or controllers. The machine learning model self-updates and self-optimizes flight paths based on real-time data, eliminating the need for complex manual calculations while maintaining high accuracy.
Solution Approach 2:
The patent replaces traditional manual flight path calculation methods with an automated machine learning-based system. The control unit with ML algorithms substitutes the mechanical process of manual route planning, providing accurate tail-specific performance calculations without requiring human intervention in the computational process.
2Productivity
If tail-specific data and real-time weather conditions are incorporated, then fuel and time optimization improves, but device complexity increases
Solution Approach 1:
The control unit serves multiple functions: it collects tail-specific performance data, processes real-time weather information, runs machine learning simulations, and generates optimized flight paths. This multi-functional approach consolidates what would otherwise require separate systems into a single integrated unit, managing complexity while delivering comprehensive optimization.
Solution Approach 2:
The machine learning model acts as an intermediary between raw data inputs (tail-specific performance parameters and weather conditions) and flight path decisions. The ML model processes complex multi-variable relationships and translates them into actionable route recommendations, simplifying the interface between data complexity and operational simplicity.
3Measurement precision
If machine learning models simulate numerous possible flight paths, then measurement precision improves, but computational time and power consumption increase
Solution Approach 1:
The system pre-processes and stores tail-specific performance data and historical flight information before actual flight planning. The machine learning model is pre-trained with extensive flight data, enabling it to quickly evaluate multiple flight path scenarios during actual operations without requiring time-consuming real-time computations for basic performance parameters.
Solution Approach 2:
The system dynamically adjusts the level of simulation detail based on flight conditions and available computational resources. The machine learning model can operate in different modes, balancing between comprehensive simulation accuracy and real-time computational constraints, allowing flexible adaptation to varying operational requirements.
Data Source
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AI summary
A system (100) and a method include an aircraft (102) including a user interface (112) having a display (114). A control unit (110) is in communication with the user interface (112). The control unit (110) is configured to determine a flight path (200) for a flight of the aircraft (102) based on tail-specific data (122) for the aircraft (102), and weather conditions.