Flight Planning System Using Four-Dimensional Node Search
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Solution Overview
Problem
Current flight planning methods are computationally intensive and lack a simple, efficient approach to determine optimal flight paths, fuel logistics, and payload parameters, especially considering the varying weight of fuel and additional parameters like ground track, altitude, and speed.
Innovation Solution
An optimization system that simplifies trip planning by segmenting routes into nodes, associating multidimensional functions with each node, repeatedly selecting and expanding route segments based on acceptability measures, and selecting a preferred route based on these criteria, effectively reducing the complexity of six-dimensional optimization to a manageable three or four-dimensional search space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional flight planning methods are used to account for varying fuel weight, then accurate fuel and range estimation is achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The flight path is divided into multiple segments with intermediate nodes, where each segment has pre-calculated fuel consumption and time values. This segmentation allows the system to break down the complex continuous optimization problem into discrete, manageable segments that can be evaluated and combined efficiently, reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The system performs preliminary calculations of fuel consumption, time, and other parameters for each route segment and intermediate node before the actual flight planning. These pre-computed values are stored and reused during the optimization process, eliminating the need to recalculate them repeatedly and significantly reducing real-time computational requirements.
2Adaptability or versatility
If multiple optimization parameters (fuel, time, altitude, speed, ground track) are considered simultaneously, then comprehensive flight optimization is achieved, but the search space dimensionality increases to six dimensions
Solution Approach 1:
The patent transforms the six-dimensional continuous optimization problem into a lower-dimensional discrete search problem by introducing intermediate nodes and segments. Each node represents a specific state in the flight path, and the search proceeds through a graph structure rather than continuous space. This dimensionality reduction makes the multi-parameter optimization computationally tractable while still considering all six parameters (fuel, time, altitude, speed, ground track, and payload).
3Measurement precision
If iterative approaches are used to estimate flight characteristics, then realistic fuel and range values are obtained, but computational time and processing cost increase
Solution Approach 1:
The system pre-calculates and stores fuel consumption, time, and performance parameters for each route segment and intermediate node before the actual flight planning query. These preliminary calculations replace the need for repeated iterative computations during runtime, providing accurate realistic values while dramatically reducing computational time and processing costs.
Solution Approach 2:
Instead of performing repeated iterative calculations, the system creates a copy of the pre-computed flight characteristics data structure that can be quickly queried and manipulated. This data structure contains all necessary information about fuel consumption, time, and performance parameters for different flight conditions, allowing rapid retrieval and combination without recalculation.
Data Source
AI summary
A system and method for flight planning determines an optimal route by setting an initial departure weight or range of such weights for an aircraft traveling from a departure airport to a destination airport, defining a network of nodes for potentially legal routes, labeling each node with a graph of fuel needed to reach that node either as a function of flight duration or as a function of flight duration and departure weight, selecting or discarding routes when conditions at a node of that route are favorable or violated, selecting a preferred route, departure weight and duration for the desired payload and minimizing fuel for the desired route and payload.