Bibliographic record
Abstract
In order to increase the overall efficiency of energy use in a community, excess thermal energy from inefficient processes can be stored and used for heating applications. A one-dimensional analytical conduction model is therefore developed for sizing of phase change material thermal energy storage systems. The model addresses rectangular channels of phase change material separated by flow channels for the addition and removal of thermal energy. The analytical model assumes a planar melt front and linear temperature profiles throughout the thermal storage cell. Heat flux and interface temperatures are calculated at various melt fractions based on a quasi-steady electrical analogue analysis of the instant in question. Compensation is made for the sensible energy change between melt fractions by adding this energy at the calculated heat flux. A two dimensional, conduction only computational fluid dynamics model is used to compare the response of the analytical model to changes in the input parameters and shows good agreement. A test apparatus and a three dimensional computational fluid dynamics model are also created and melt-time results compared to analytical model predictions. These comparisons also show good agreement. Finally, a thermal storage system is sized for a specific application, H2Green Energy Corporation's Distributed Storage System, with sizing based on the heat load requirements of McMaster Innovation Park during the winter months. Technical feasibility of this system is shown with analysis also included on economic feasibility. It is determined that the analytical model is sufficient for initial assessment of phase change material thermal energy storage systems where detailed geometry is unavailable. Recommendations are made for further validation of the model and the development of a phase change material properties database. Suggestions are also presented on additional sources of revenue for the H2Green Distributed Storage System that will increase its economic feasibility.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".