Experimental Evaluation of a Heat Transport System for a High-Temperature Storage Unit
Bibliographic record
Abstract
The thermal storage system, developed by DLR, utilises the sensitive change in temperature and the phase change of the storage material to store energy. During charging, the metallic Phase Change Material (mPCM) is heating and melting, while during discharging, it is cooling and solidifying. Intelligent need-based charging can be carried out in parallel when charging the traction battery at the charging station. The heat released during the subsequent discharging can be made available to the interior of the car. \n \nA possible approach to make the stored thermal energy accessible is to thermally link the storage unit with the cooling cycle of the car. Due to high temperatures, which would evaporate the coolant, a flow through the storage unit is not possible. Instead, a concept for indirect coupling was developed to transport a certain amount of heat within a closed circuit between both systems. Within this concept, evaporation and condensation of a working fluid are the dominating transport phenomena. \n \nNumerical investigations were undertaken within the simulation software Dymola to estimate the performance of the developed concept. As a further step, the validation of the numerical analysis was targeted by an experimental investigation. Therefore, a test bench was built and experimental series were undertaken.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".