High Fidelity and Efficient Computations of Dynamic Loads for Multidisciplinary Design Optimization of Flexible Transport Aircraft
Bibliographic record
Abstract
The current state-of-the-art design optimization of airframes is tightly wounded to its loads analyses as the process is usually conducted employing a deterministic set of critical load cases.The sheer number of scenarios required to estimate the critical loading conditions prevent these two processes from integrating, obstructing the development of a Multidisciplinary Design Optimization (MDO) framework.In this thesis, the problem of high fidelity and efficient estimation of critical dynamic aeroelastic loads is addressed, as a first step, towards the development of an integrated MDO platform for airframes at preliminary and detailed design stages.The method is based on the Kriging metamodeling technique along with the Latin Hypercube scheme for initial sampling and the expected improvement function for subsequent selection of sample points, known formally as the Efficient Global Optimization (EGO) algorithm.Furthermore, different inexpensive metrics, based on the concept of modal contribution factors, are investigated to serve as indicators to determine if a substantial change in the loads has occurred during the design optimization cycle, triggering the requirement for the re-exploration of the loads design space.A case study is presented to evaluate the performance of the proposed methodology versus a full factorial search.A reduction of 84% was achieved in the total time of execution employing the proposed methodology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".