Real-Time Flight Path Optimization for Fuel and Payload Planning
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Solution Overview
Problem
Conventional flight planning systems rely on static models, leading to undesired fuel usage, payload restrictions, flight delays, and additional costs due to their inability to dynamically adjust operations during pre-flight, in-flight, and post-flight phases of an aircraft.
Innovation Solution
A flight optimization system that includes an onboard aircraft system with a flight management system and network server, capable of real-time data processing and analytics to dynamically calculate updated payload capacity and fuel bias values, and an external network allocation system that generates optimized flight plans and tail assignment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional static flight planning models are used, then flight plans can be generated quickly and easily, but fuel usage is excessive and payload capacity is restricted
Solution Approach 1:
The system transitions from static flight planning models to dynamic models that continuously adapt to real-time conditions. The flight management system receives live weather data, aircraft performance data, and operational information to dynamically recalculate optimal flight paths, climb profiles, and fuel management strategies during all phases of flight, thereby reducing fuel consumption while maintaining quick plan generation.
Solution Approach 2:
The system changes key operational parameters dynamically rather than using fixed values. It adjusts flight path coordinates, altitude profiles, speed parameters, and fuel flow rates based on real-time inputs from sensors, weather stations, and aircraft systems. This parameter adaptation enables optimized fuel burn while preserving efficient plan generation capabilities.
2Ease of operation
If conventional static flight planning models are used, then operational simplicity is maintained, but flight delays occur and additional costs are incurred
Solution Approach 1:
The system implements continuous feedback loops where real-time data from aircraft sensors, weather systems, and operational sources is fed back to the flight management system. This feedback enables automatic adjustments to flight plans without requiring complex manual interventions, maintaining operational simplicity while preventing delays through proactive response to changing conditions.
Solution Approach 2:
The flight management system performs self-optimization by automatically processing real-time data and generating updated flight plans without requiring constant pilot or dispatcher intervention. The system independently adjusts navigation parameters, fuel management, and performance settings, reducing operational complexity while eliminating delays caused by static planning limitations.
3Loss of energy
If real-time data processing and dynamic optimization are implemented, then fuel consumption is reduced and flight performance is improved, but system complexity increases
Solution Approach 1:
The flight management system is designed as a multi-functional platform that handles diverse tasks including real-time data acquisition from multiple sources, weather analysis, performance optimization, navigation management, and communication with ground systems. By consolidating these functions into a single universal system rather than separate specialized systems, the patent reduces overall system complexity while achieving dynamic fuel optimization.
Solution Approach 2:
The system employs intermediary components such as standardized data interfaces, protocol translators, and modular software architecture that facilitate integration between diverse real-time data sources and the core optimization algorithms. These intermediaries simplify the complexity of handling multiple data formats and sources, enabling real-time processing without proportionally increasing system complexity.
4Productivity
If dynamic recalculation of payload capacity and fuel bias is performed, then payload management is optimized and operational costs are reduced, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary calculations of payload capacity and fuel bias during the pre-flight planning phase using forecasted conditions. This preliminary action establishes baseline values that are then quickly adjusted during flight based on actual measured conditions, rather than performing complete recalculations from scratch. This approach optimizes payload management while minimizing additional processing time during critical flight phases.
Solution Approach 2:
The system implements selective recalculation of payload and fuel parameters, updating only those values that have changed significantly since the last calculation rather than performing complete recalculations. This partial action approach maintains payload management optimization while reducing computational overhead and processing time by focusing resources on the most critical and changed parameters.
Data Source
AI summary
Provided is a flight optimization system and a method of flight optimization that includes generating flight data via an onboard aircraft system, performing a pre-flight cycle by determining an updated tail assignment plan before departure based on the flight data received in real-time from the onboard aircraft system, performing an in-flight cycle by collating and processing in-flight data and external data via an onboard network server, and transmitting the processed data to an electronic flight bag in real-time and performing flight path optimization via a path optimizer application of the electronic flight bag to be accessed by flight personnel, and performing a post-flight cycle by transmitting post-flight data to an event measurement system along with operational data to be processed and sent to a fleet support system and a maintenance system for generating updated flight plans and maintenance plans which are data-driven.