Multi-level Modeling Methodology for Aircraft Thermal Architecture Design
Bibliographic record
Abstract
This paper proposes a new methodology to conduct thermal analysis in the conceptual phase of the aircraft development process. Traditionally, thermal analysis is conducted after the system architecture has already been defined. The aircraft system thermal environment evaluation may lead to late design changes that can have a significant impact on the development process. To reduce the risk of late design changes, thermal requirements need to be defined and validated in the conceptual design phase. This research paper introduces a novel multi-level modeling strategy based on a bottom-up approach. It proposes an automatic geometrical simplification procedure for Computational Fluid Dynamic (CFD) analysis, a methodology for the generation of analytical correlations based on highly detailed methods, and a thermal risk assessment approach based on dimensionless numbers. This methodology generates models with the right level of fidelity to conceptual and preliminary design, offers the possibility to assess thermal risk, and defines thermal requirements for the selection of aircraft and systems architectures. A proof-of-concept of the methodology for a simplified test case is presented to highlight the benefits of such multi-level capability.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".