Better thermal management options with heat storage systems for various applications: An Evaluation
Bibliographic record
Abstract
Abstract With increasing worldwide population and rising standards of living, the global energy consumption is increasing at significant rates. Together with climate change concerns and negative impacts of fossil fuel use, the need for clean and smart energy systems is becoming more obvious. Clean and smart systems provide energy to all types of end‐use applications in an environmentally friendly, affordable, reliable, and efficient manner. Heat losses are recognized as some of the most significant causes of efficiency degradation in energy systems. Therefore, this study overviews and investigates current and future thermal management options for different end‐use purposes for a more sustainable future. In this study, a smart approach is taken when evaluating existing and emerging thermal management systems and smart targets are introduced for better thermal management systems. In addition, some novel thermal management systems for various applications such as electric/hybrid vehicles, power systems, and industrial processes are introduced as case studies and the energy and exergy efficiencies of these case studies are compared. In addition, some key future directions are provided in terms of better thermal management options for a sustainable future. The case study results of this study show that with better thermal management strategies, it is possible to reach energy and exergy efficiencies up to 60% and 50%, respectively in hybrid vehicles, industrial processes, and robotic applications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".