A study on the potential of skin heat exchangers for hybrid-electric aircraft
Bibliographic record
Abstract
This study investigates the viability of Skin Heat Exchangers (SHXs) as an effective thermal management solution for hybrid-electric aircraft, with a focus on their aerodynamic and heat transfer performance. Using two-dimensional Computational Fluid Dynamics (CFD) simulations, the work evaluates various SHX configurations across different flight phases of a conceptual regional hybrid-electric aircraft (FutPrInt50), analysing the effects of surface heating on lift, drag, and convective heat transfer. A Gaussian Process Regression surrogate model is developed to predict SHX performance under varying atmospheric and operational conditions. At cruise, results reveal that heating the lower surface of the aerofoil enhances both aerodynamic efficiency and heat transfer capacity compared to an all-heated configuration by 10.9% and 9.9%, respectively, while a heated upper patch had the best average heat transfer potential by 7%, it suffered significantly in aerodynamic performance by -14.2%, compared to the next best configuration. However, SHX performance proved to be highly sensitive, with diminished cooling capacity of up to 54.3% during high-demand phases such as take-off and climb compared to cruise. The findings underscore SHXs as a promising, though insufficient stand-alone, thermal solution for hybrid-electric propulsion systems, indicating the need for complementary technologies in future aircraft designs. • Heating lower surface improves aerodynamic and heat transfer performance in cruise. • Heating upper surfaces improves heat transfer but hinders aerodynamic efficiency. • Skin heat exchangers can contribute to heat rejection in hybrid-electric aircraft.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".