Influence of Ventilation Flow Rate and Gap Distance on the Radiative Heat Transfer in Aircraft Avionics Bays
Bibliographic record
Abstract
The feasibility of the future more-electric, hybrid-electric, and all-electric aircraft configurations will depend on a good understanding of thermal aspects early in the design. However, thermal analysis of aircraft equipment bays is typically performed at later design stages to validate if the design meets the minimal certification requirements rather than to optimize the cooling strategy. The presented work aims to provide new insight into thermal aspects in typical aircraft equipment bays. In particular, system thermal interactions, such as radiation, play a more significant role in tightly packaged bays, such as avionics bays. This paper investigates the influence of radiation on the overall system heat dissipation in two representative avionics bays. Using Computational Fluid Dynamics (CFD) simulation, combined with an analytical approach, the authors analyze the impact of several parameters, such as varying mass flow rates and distances between adjacent systems, on their thermal interaction. The results suggest that the radiative effects must be considered when the gap distance between the systems is larger than 0.1 m, the flow rate between two systems is not strong enough to have high convective heat exchanges, when the systems of interest are hidden by other systems from the ventilation sources, and when the system’s internal heat dissipation is significant. Overall, this paper’s results will contribute enhance conceptual design methods, such as the previously developed Thermal Risk Analysis, and help optimize thermal management strategies for future aircraft.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".