Integrated Analysis and Design Environment for a Climate Compatible Air Transport System
Bibliographic record
Abstract
Aviation affects the Earth’s atmosphere and radiative balance through the emission of greenhouse gases, greenhouse gas precursors, aerosols, contrails and induced cirrus cloudiness. The resulting climate impact is a response of the complex interactions between the amount and type of emitted constituents, their geographical position, altitude and time of emission as well as the actual weather and climate situation. In 2005 aviation accounted for 3.5 % of the global anthropogenic radiative forcing (excluding the impact of contrail cirrus clouds). As the global air traffic is predicted to grow approx. 5% per year, the development of a climate compatible air transport system is of increasing importance for society and science. To achieve this goal, different technological and operational options can be applied to reduce the climate impact by air travel. The range of possibilities is wide, including new propulsion concepts such as open rotors or intercooler recuperative engine cycles, improved combustion chambers for low NOX and soot, novel aircraft configurations such as Blended Wing Bodies, innovative subsystem architectures for minimal engine cycle disturbance through secondary power off take and operational procedures such as multi-step operations and changed cruise altitudes for contrail avoidance. In order to provide a solid basis for decision and policy makers, the remaining uncertainties in climate modeling have to be reduced and the different options and their interrelations have to be assessed in a reliable way. To catch all relevant effects of the coupled disciplines, sophisticated numerical models for climate response, mission calculation, propulsion, aircraft subsystems and overall aircraft design are combined to an integrated simulation and assessment chain. In addition, further efforts are made to reduce remaining uncertainties in modeling emissions and their corresponding climate impact. This complex and multidisciplinary task further requires the contribution of experts from the included areas to ensure a secure evaluation of the obtained results. Here we present such an integrated approach as it is applied within the DLR project Climate compatible Air Transport System (CATS).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".