Aircraft Component Selection via Multi-Objective Optimization
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Solution Overview
Problem
The complexity and time-consuming nature of manually selecting aircraft system components, which often results in overlooked combinations and increased complexity, while relying on engineer expertise and trial-and-error methods, despite regulatory standards for reliability and operational events.
Innovation Solution
A computer-implemented method utilizing a reliability evaluation function, complexity evaluation function, and multi-objective optimization to select candidate components based on reliability and complexity scores, employing genetic algorithms to identify optimal sets that meet specified conditions, thereby reducing complexity and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual component selection by engineers is used, then expertise and experience can be applied, but the process becomes time-consuming and complex combinations may be overlooked
Solution Approach 1:
The patent replaces the manual mechanical process of engineer review and selection with an automated computer-implemented system. The system uses algorithms to evaluate candidate components against reliability conditions and operational events, automatically identifying optimal component sets without human intervention in the evaluation process.
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a bridge between component specifications and engineering decisions. This intermediary system processes reliability data, evaluates operational events, and presents optimized component selections, mediating between raw data and final decisions.
2Reliability
If manual component selection is used, then engineer judgment can be applied, but the complexity of installation and servicing increases
Solution Approach 1:
The patent changes the parameters used to evaluate component selections by introducing quantitative reliability scores and complexity metrics. Instead of relying solely on qualitative engineer judgment, the system uses measurable parameters such as reliability conditions, operational event probabilities, and installation complexity scores to objectively compare component options.
Solution Approach 2:
The patent performs preliminary evaluation of component complexity and reliability before final selection. By assessing installation and servicing complexity in advance through the computational system, potential issues are identified early in the design process, allowing engineers to make informed decisions before committing to specific component configurations.
3Productivity
If traditional design methods are used, then standard procedures can be followed, but the rate of identifying optimal component selections is limited
Solution Approach 1:
The patent implements periodic evaluation cycles where the computational system automatically assesses multiple candidate component sets against defined reliability conditions and operational events. This systematic periodic evaluation accelerates the identification of optimal configurations by continuously iterating through possibilities rather than relying on sequential manual review.
Solution Approach 2:
The patent maintains continuous computational evaluation of component options throughout the design process. The system continuously processes reliability data, evaluates operational events, and updates optimal component selections as new information becomes available, ensuring the design process benefits from ongoing analysis rather than discrete manual assessments.
Data Source
AI summary
Computer-implemented methods and systems for selecting components to be used in an aircraft system including: a reliability evaluation function, to produce a reliability score, and a complexity evaluation function, to produce a complexity score, are provided; and a multi-objective optimisation function is performed to determine at least one set of candidate components for the aircraft system that satisfy one or more conditions. The multi-objective optimisation function includes selecting a new set of candidate components, performing at least one of the reliability evaluation function and the complexity evaluation function, and evaluating at least one of the reliability score and the complexity score according to the conditions. The new set of candidate components are stored in associated with an indication of the outcome of the evaluation. An aircraft comprising a set of components that have been selected from one or more of the stored sets of components is also provided.