A simulation-assisted complexity metric for design optimization of integrated architecture aero-engine structures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditional metrics for assessing system complexity are based on aspects such as number of and interaction among components. For functionally integrated structures, the application of such metrics can be difficult and/or limited due to the inseparability of the structure into components or sub-systems; a single monolithic structure satisfies all required functions. At the same time, complexity metrics are necessary for effective application of design optimization and systems engineering principles. Aero-engine static structures are typical examples of functionally integrated architecture. In this paper, we present a complexity metric for integrated architecture structures that can be included as an objective or constraint in design optimization problem formulations. The proposed metric is based on two existing metrics, one providing a system wide scheme for complexity calculations and the second, giving complexity for individual components. In order to account for its integrated architecture, different regions of the structure are considered. Interactions are estimated as load paths through the structure, identified by means of physical simulations. Complexity evaluations are demonstrated using two detail-designed aero-engine structures with similar functions but belonging to different engine designs. Despite the similarities, the structures differ in complexity. This enables quantitative comparison among different designs of integrated architecture structures based on physical arrangements and main functions. Moreover, the metric can be used to identify regions with most influence on complexity which in turn enables design improvements on those regions. Automated computation of the metric can result in rapid comparison and selection among a number of structure designs, and thus be used in optimization studies. Finally, a correlation of the metric with the development time or cost can be useful for future integrated architecture structure design optimization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it