Energy integration on multi‐periods for vehicle thermal powertrains
Bibliographic record
Abstract
Abstract Internal combustion engines are the most applied energy converters in passenger cars. In order to increase their efficiency, energy integration techniques are used to recover waste heat. An adapted methodology is required to design the organic Rankine cycle (ORC) as a waste heat recovery technology and to test the influence of the external temperature on its efficiency. The idea is to cluster the external temperature profile on typical external temperature multi‐periods. The energy system design is then tested on these typical multi‐periods. In this article the methodology is applied on a vehicle with a diesel engine, in order to define the energy integrated configuration of the vehicle and to estimate the cost of the additional equipment. The performance indicators of the energy integration technology of the internal combustion engine is done and discussed, according to the multi‐periods. The energy recovery potential of a single stage ORC for a diesel engine is assessed for different temperature profiles. The ORC equipment is simultaneously pre‐sized and its cost is estimated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".