Aircraft Flight Optimization Algorithm Using Cost Models
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Solution Overview
Problem
Current aircraft control systems are inefficient in optimizing economic operations, as they rely on time-consuming and approximate methods for evaluating fuel consumption, maintenance costs, and downtime, lacking real-time and comprehensive analysis for pre-flight planning and in-flight optimization.
Innovation Solution
A system comprising a receiver and processor that runs an optimization algorithm using various cost models to determine vehicle operational parameters, including a usage-based lifting cost model, environmental cost model, and financial cost and revenue model, to optimize flight plans and strategies, with real-time output and automatic adjustments for autopilot operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If basic analysis of flight operations is performed to reduce fuel consumption, then fuel efficiency is improved, but the analysis process becomes time consuming and provides only approximate results
Solution Approach 1:
The system performs pre-flight planning and real-time optimization calculations before and during flight operations, establishing optimal flight parameters in advance and adjusting them dynamically based on actual conditions, thereby avoiding time-consuming post-flight analysis
Solution Approach 2:
The system continuously receives actual flight data during operation and compares it with planned parameters, providing real-time feedback for optimization adjustments, which enables precise control without requiring lengthy post-flight analysis periods
2Reliability
If historical operational data is analyzed to evaluate maintenance costs and downtime, then cost evaluation is performed, but the process is time consuming and generates only approximate results
Solution Approach 1:
The system continuously collects and processes actual operational data during flight, providing real-time feedback on component condition and predicted maintenance needs, thereby delivering accurate cost evaluations without relying on lengthy historical data analysis
Solution Approach 2:
The system performs predictive maintenance analysis by evaluating component wear and failure probability before actual maintenance events occur, allowing operators to plan maintenance activities in advance with accurate cost estimates rather than relying on post-event historical analysis
Data Source
AI summary
A system for a vehicle includes a receiver configured to receive input data, and a processor configured to run an optimization algorithm to evaluate the input data. The optimization algorithm includes at least one cost model configured to determine vehicle operational parameters to meet the input data. An output module is configured to output to the vehicle operational parameters that optimize operation of the vehicle in view of the input data.