AI-Generated Aircraft Propulsion Structural Architecture

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Solution Overview

Problem

Existing design processes for aircraft propulsion systems are limited by the need for substantial manual intervention and lack of efficient methods to integrate geometric and operational parameters, leading to suboptimal design outcomes.

Innovation Solution

Utilizing an artificial intelligence (AI) model trained on historical geometry and operational data to generate a preliminary design of a propulsion system structural architecture, incorporating a selected combination of geometric parameters that satisfy both technical and customer constraints.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional manual design processes are used for aircraft propulsion systems, then design flexibility and adaptability are maintained, but design time and complexity increase substantially

Engineering Contradiction:

Improvedesign speedVSAvoiddesign process complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical design processes with an AI-based automated system. The AI model processes geometric and operational parameters to generate preliminary designs, substituting human designers' manual iterations with automated computational processes. This reduces design time while managing complexity through algorithmic approaches.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The AI model performs self-learning and self-optimization by training on historical design data and operational parameters. The system automatically identifies optimal geometric parameters and structural configurations without requiring continuous manual intervention, enabling the design process to serve itself through automated feedback loops.

Inventive Principle:

Principle #25Self-service

2Reliability

If comprehensive geometric and operational parameters are integrated in manual processes, then design quality improves, but manual effort and time requirements increase

Engineering Contradiction:

Improvedesign qualityVSAvoiddesign iteration time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent transforms multiple geometric and operational parameters into a unified AI training dataset. The system processes parameters such as engine mounting hardware dimensions, carcass loads, rotor blade tip clearances, and thrust characteristics simultaneously, allowing comprehensive parameter integration to occur through automated computational processing rather than manual analysis.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The AI model performs preliminary design generation by pre-processing historical geometry and operational data during the training phase. This preliminary action establishes optimal parameter combinations before actual design needs arise, enabling rapid deployment of high-quality designs without repeated manual iterations.

Inventive Principle:

Principle #10Preliminary action

3Manufacturing precision

If multiple design constraints are manually evaluated, then design accuracy improves, but processing time and resource requirements increase

Engineering Contradiction:

Improvedesign accuracyVSAvoiddesign throughput

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The AI model serves multiple design evaluation functions simultaneously through a single integrated system. It processes technical constraints (carcass loads, inertial loads, bending moments) and customer constraints (mounting positions, envelope limits, weight limits) together, providing comprehensive design accuracy assessment without requiring separate manual evaluation processes for each constraint type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250291964A1System and method for generating a preliminary design of a structural architecture for an aircraft propulsion system

Publication Date: 2025.09.18 PRATT & WHITNEY CANADA CORP

AI summary

A method for generating a preliminary design of a propulsion system structural architecture for an aircraft propulsion system includes generating, with an artificial intelligence (AI) model at a computer system, one or both of at least one two-dimensional image or at least one three-dimensional model of the preliminary design including a selected combination of geometric parameters. The AI model has been trained for both a propulsion system geometry using the geometric parameters extracted from historical geometry data and a propulsion system structural design using the geometric parameters and operational parameters of historical operational data. The operational parameters are associated with the geometric parameters. Training the AI model included identifying the selected combination of geometric parameters of the preliminary design, with the AI model, by determining a plurality of combinations of the extracted geometric parameters and the associated operational parameters which satisfy each of at least one technical constraint and at least one customer constraint and selecting the selected combination of geometric parameters from the plurality of combinations of the extracted geometric parameters using the associated operational parameters.