Multi-Stage Axial Compressor Disk Design for Stress-Weight Balance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for optimizing multi-stage axial compressor disks in gas turbine engines are computationally inefficient, oversimplify geometries, and fail to accurately account for complex boundary conditions, leading to inefficiencies and potential structural failures due to excessive mass and thermal stress.
Innovation Solution
A computational framework integrating finite element analysis (FEA), multi-objective genetic algorithms (MOGA), and Design of Experiments (DOE) to optimize compressor disk geometry, ensuring precise material distribution and structural reliability under realistic operating conditions, reducing weight and enhancing durability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional analytical calculations or simplified geometries are used for compressor disk design, then the design process is simpler and faster, but the accuracy and precision of the optimization are insufficient
Solution Approach 1:
The patent creates a digital copy of the compressor disk geometry through parameterized modeling, allowing virtual optimization without physical prototypes. The finite element model serves as a computational copy that accurately replicates the physical structure's behavior under various loading conditions, enabling precise optimization before manufacturing.
Solution Approach 2:
The patent performs preliminary optimization calculations using finite element analysis and genetic algorithms to determine optimal geometric parameters before actual manufacturing. This preliminary computational action identifies the best design configuration, avoiding iterative physical prototyping and accelerating the overall design process while maintaining high precision.
2Reliability
If detailed evaluation of complex compressor disk geometries under realistic operating conditions is performed, then the accuracy and reliability of the design improve, but the computational time and resource requirements increase
Solution Approach 1:
The patent performs preliminary identification of critical design parameters and boundary conditions to focus computational resources on the most influential factors. By pre-defining realistic operating conditions and material properties, the system achieves high reliability without exhaustive computational analysis of all possible variables.
Solution Approach 2:
The patent applies finite element analysis selectively to critical regions of the compressor disk where stress and deformation are most significant, rather than uniformly analyzing the entire geometry. This partial analysis approach maintains design reliability while reducing overall computational time and resource requirements.
3Adaptability or versatility
If manual optimization processes are used for compressor disk design, then flexibility in handling complex geometries is maintained, but the time consumption and labor intensity increase significantly
Solution Approach 1:
The patent implements an automated optimization system where the genetic algorithm independently evaluates multiple design configurations, performs finite element analysis, and selects optimal parameters without continuous manual intervention. The system serves itself by automatically generating, testing, and refining design solutions, maintaining flexibility while dramatically reducing time consumption and labor intensity.
Solution Approach 2:
The patent replaces manual mechanical optimization processes with computational algorithms. The genetic algorithm computationally explores the design space, substituting human engineers' manual iterations with automated computational search, thereby preserving design flexibility while eliminating the time and labor associated with manual optimization.
4Strength
If high-density traditional materials are used for compressor disks, then structural strength is sufficient, but the mass increases leading to higher rotational loads and reduced fuel efficiency
Solution Approach 1:
The patent optimizes the compressor disk geometry to achieve non-uniform material distribution, concentrating material in high-stress regions and reducing it in low-stress areas. This local quality optimization maintains structural strength where needed while minimizing overall mass, thereby reducing rotational loads and improving fuel efficiency without compromising strength.
Solution Approach 2:
The patent systematically varies geometric parameters such as disk thickness, rim height, and web thickness to find the optimal balance between strength and weight. By changing these dimensional parameters through automated optimization, the design achieves minimum mass while satisfying all strength and stiffness requirements under operational loading conditions.
Data Source
AI summary
A systematic framework for optimizing multi-stage axial compressor disk designs in gas turbine engines. By combining finite element analysis (FEA), Design of Experiments (DOE), and optimization algorithms of multi-objective genetic algorithm (MOGA), the method balances stress, deformation, and mass to enhance structural performance. The six-step process includes blade modeling, parameterizing disk geometry, structural analysis using FEA, developing functional relationships, applying optimization algorithms, and generating manufacturable 3D disk models. This approach reduces weight, improves fuel efficiency, and adapts to various compressor designs and materials, enhancing the overall performance of gas turbine engines.