Optimization of a Gas Turbine Engine Rotor Disc Using Case-Based Reasoning and the GATE Genetic Algorithm
Bibliographic record
Abstract
The implementation of an automated decision support system in the field of structural design and optimization can give a significant advantage to any industry working on mechanical design. Such a system can reduce the project cycle time or allow more time to produce a better design by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work. This paper presents an approach to automating the process of designing a gas turbine engine rotor disc using case-based reasoning (CBR), combined with a new genetic algorithm, the Genetic Algorithm with Territorial core Evolution (GATE). GATE was specifically created to solve problems in the mechanical structural design field, and is essentially a real number genetic algorithm that prevents new individuals from being born too close to previously evaluated solutions. The restricted area becomes smaller or larger during optimization to allow global or local searches when necessary. The CBR process uses a databank filled with every known solution to similar design problems. The closest solutions to the current problem in terms of specifications are selected, along with an estimated solution from an artificial neural network. Each solution selected by the CBR is then used to initialize the population of a GATE island. Our results show that CBR may significantly upgrade the performance of an optimization algorithm when sufficient preliminary information is known about the design problem. It provides an average solution 5.0% lighter than the average solution found using random initialization. The results are compared to other results obtained for the same problems by four optimization algorithms from the I-SIGHT 3.5 software: the sequential quadratic programming algorithm (SQP), the insular genetic algorithm (GA), the Hookes & Jeeves generalized pattern search (HJ) and POINTER. Results show that GATE can be a very good candidate for automating and accelerating the structural design of a gas turbine engine rotor disc, providing an average disc 18.9% lighter than SQP, 11.2% lighter than HJ, 23.9% lighter than GA and 4.3% lighter than POINTER, even when starting with the same solution set.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".