Better thermal management options with heat storage systems for various applications: An Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it