Bibliographic record
Abstract
An air hybrid vehicle is an alternative to the electric hybrid vehicle that stores the kinetic \nenergy of the vehicle during braking in the form of pressurized air. In this thesis, a novel \ncompression strategy for an air hybrid engine is developed that increases the efficiency of \nconventional air hybrid engines significantly. The new air hybrid engine utilizes a new \ncompression process in which two air tanks are used to increase the air pressure during the \nengine compressor mode. To develop the new engine, its mathematical model is derived and \nvalidated using GT-Power software. An experimental setup has also been designed to test the \nperformance of the proposed system. The experimental results show the superiority of the \nnew configuration over conventional single-tank system in storing energy. \nIn addition, a new switchable cam-based valvetrain and cylinder head is proposed to \neliminate the need for a fully flexible valve system in air hybrid engines. The cam-based \nvalvetrain can be used both for the conventional and the proposed double-tank air hybrid \nengines. To control the engine braking torque using this valvetrain, the same throttle that \ncontrols the traction torque is used. Model-based and model-free control methods are adopted \nto develop a controller for the engine braking torque. The new throttle-based air hybrid \nengine torque control is modeled and validated by simulation and experiments. The fuel \neconomy obtained in a drive cycle by a double-tank air hybrid vehicle is evaluated and \ncompared to that of a single-tank air hybrid vehicle.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".