Aircraft Design Optimization via Flight Network Simulation
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Solution Overview
Problem
Current aircraft design methods often result in aircraft being dimensioned beyond market requirements due to subjective customer consultations and expert opinions, leading to high development costs and potential commercial failure.
Innovation Solution
A method and system that define an initial catalog of requirements, optimize aircraft design based on anticipated operating costs, simulate a total flight network, and adapt requirements iteratively to achieve optimal total flight network efficiency, ensuring the configuration point is optimized for both cost and market alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If customer focus groups and expert opinions are used to define configuration parameters, then the design process is simplified and faster, but the aircraft may be dimensioned beyond market requirements leading to commercial failure
Solution Approach 1:
The patent implements a feedback mechanism where simulation results of total flight network efficiency are fed back into the requirement definition process. The system simulates different aircraft designs against a predefined flight network, evaluates efficiency metrics, and uses this feedback to iteratively refine the configuration parameters, ensuring market alignment without extending development time significantly
Solution Approach 2:
The patent performs preliminary simulation and evaluation of total flight network efficiency before finalizing the aircraft design. By pre-defining the flight network and performing simulations during the design phase rather than after, the system identifies potential market misalignment early and allows for corrections before committing to full development
2Ease of manufacture
If iterative optimization of aircraft design is performed based on initial requirements, then cost efficiency is improved, but the initial requirements themselves may be suboptimal leading to overall design suboptimality
Solution Approach 1:
The patent makes the requirement specifications dynamic rather than static. The initial requirements are not fixed but are adjusted iteratively based on simulation results of total flight network efficiency. This dynamic approach allows the design to be optimized for both cost and overall network efficiency, preventing suboptimality from propagating through the development process
Solution Approach 2:
The system uses feedback from total flight network efficiency simulations to refine the requirement specifications. Each iteration of the synthesis method incorporates lessons learned from previous simulations, adjusting requirements to better align with actual market needs and flight network characteristics, thereby improving both cost efficiency and design accuracy
3Adaptability or versatility
If subjective expert opinions are used to define configuration parameters, then the design process is more flexible and adaptable, but the risk of selecting incorrect configuration points increases
Solution Approach 1:
The patent introduces an intermediary computational system that acts as a mediator between subjective expert opinions and final design decisions. Expert inputs are processed through simulation models and optimization algorithms that objectively evaluate total flight network efficiency, translating subjective judgments into quantifiable, accurate configuration parameters
Solution Approach 2:
The system systematically varies configuration parameters based on simulation feedback to identify optimal settings. By changing parameters iteratively and evaluating their impact on total flight network efficiency, the system transforms subjective expert preferences into precisely optimized configuration points with quantifiable performance metrics
Data Source
AI summary
A method for designing an aircraft includes defining an initial catalog of requirements for at least one aircraft design. An optimization of the at least one aircraft design is carried out based on the catalog of requirements in terms of anticipated operating costs. A predefined total flight network is simulated with the at least one aircraft design and a total flight network efficiency is determined. It is then checked as to whether the determined total flight network efficiency constitutes an optimum. The catalog of requirements is adapted and an iteration is performed upon a determination that the determined total flight network efficiency does not constitute the optimum.