Soaring Aircraft Flight Path Adjustment Using Crowdsourced Lift Data
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Solution Overview
Problem
Soaring aircraft rely on meteorological conditions for lift, making optimal flight paths non-direct and distance-dependent, limiting their ability to fly long distances efficiently.
Innovation Solution
A novel system and method for flight path calculation using fine-grained weather forecasting and nowcasting, incorporating lift data from various sources, including thermal, ridge, wave, convergence, and dynamic lift, to adjust flight paths in real-time, allowing for continuous updates and sharing of data among aircraft and ground stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If soaring aircraft rely on meteorological conditions for lift, then the aircraft can gain altitude and extend flight duration, but the optimal flight path becomes non-direct and distance-dependent
Solution Approach 1:
The flight path is dynamically adjusted in real-time based on changing meteorological conditions. The system continuously updates the optimal path by integrating real-time weather data, lift location predictions, and aircraft state information, allowing the flight path to adapt dynamically rather than following a fixed predetermined route
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual weather conditions, comparing them with predicted conditions, and adjusting the flight path accordingly. Real-time data from weather sensors and predictions are fed back into the path optimization algorithm to refine the optimal route continuously during the flight
2Productivity
If real-time weather data and predictions are integrated for path optimization, then flight efficiency is improved, but computational requirements and system complexity increase
Solution Approach 1:
The computational task is segmented into discrete processing steps: weather data acquisition, lift location prediction, path candidate generation, and optimization. This segmentation allows the complex problem to be solved in manageable stages, reducing the computational burden at each step while maintaining overall optimization effectiveness
Solution Approach 2:
Weather data and lift locations are predicted in advance using numerical weather prediction models before the aircraft needs to use them for path planning. This preliminary action allows the system to have forecast information ready, reducing real-time computational requirements when actual path optimization is performed
3Measurement precision
If continuous updates of weather forecasts are used, then accuracy of flight path determination is improved, but data processing time and energy consumption increase
Solution Approach 1:
Weather forecasts are updated at periodic intervals rather than continuously, with the update frequency optimized to balance accuracy requirements against processing time constraints. The system determines appropriate update intervals based on the rate of change of meteorological conditions and the aircraft's flight characteristics
Solution Approach 2:
The system changes the temporal resolution of weather data processing by using different forecast intervals and update frequencies depending on flight conditions. When weather conditions are stable, less frequent updates are used to reduce processing time; when conditions change rapidly, update frequency increases to maintain 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
Enables aircraft to determine optimal flight paths based on precise weather data, enhancing flight efficiency and distance covered by adjusting for current and future lift locations and wind directions, thereby improving navigation and operational reliability.
Implementation Method 1
1) thermal data where air rises due to temperature
Implementation Method 2
2) ridge lift data where air is forced upwards by a slope
Implementation Method 3
3) wave lift data where a mountain produces a standing wave
Implementation Method 4
4) convergence lift data where two air masses meet
Implementation Method 5
5) a dynamic soaring lift data where differences in wind speeds at various altitudes is used
Data Source
AI summary
Disclosed is a novel system and method for adjusting a flight path of an aircraft. The method begins with computing a flight path of an aircraft from a starting point to an ending point which incorporates predicted weather effects at different points in space and time. An iterative loop is entered for the flight path. Each of the following steps are performed in the iterative loop. First lift data is accessed from a fine-grain weather model associated with a geographic region of interest. The lift data is data to calculate a force that directly opposes a weight of the aircraft. In addition, lift data is accessed from sensors coupled to the aircraft. The lift data is one or more of 1) thermal data, 2) ridge lift data, 3) wave lift data, 3) convergence lift data, and 4) a dynamic soaring lift data. Numerous embodiments are disclosed.