Bibliographic record
Abstract
Heating and cooling of residential buildings account for 15% of the total energy use in Canada and produce 11% of the total GHG emissions, due to reliance on fossil fuels. Renewable thermal energy and usage of low-grade waste heat offer solutions for decarbonization of heating and cooling. Inherent intermittent nature of such energy resources makes integration of thermal energy storage (TES) systems inevitable. High energy storage density, low heat loss, and using non-toxic and non-polluting refrigerants make sorption TES (S-TES) more appealing and effective for heat/cold storage, compared to other thermal storage methods. This PhD research is set out to assess the performance of low-grade heat-driven S-TES systems for space heating and cooling. As such, the focus of this study is on the thermal and sorption characterization of the sorber bed, mathematical S-TES system modeling, and experimental testing of an S-TES prototype. An analytical model is developed for prediction of thermal conductivity and thermal resistance of packed bed sorbers. Thermal conductivity of packed bed sorber of AQSOA FAM-Z02 with different numbers of layers is measured by heat flow meter for the first time. The model, which is validated by the experimental data, provides a comprehensive platform for the design of packed bed S-TES to (i) predict thermal conductivity and thermal contact resistance of packed bed under the target operating condition and (ii) optimize the packed bed by finding the optimum particle size and arrangement. Small-scale characterizations and screening of sorbent candidates are performed by thermogravimetric analysis/differential scanning calorimetry. Moreover, comprehensive experimental studies are carried out on a custom-built lab-scale S-TES in our lab to study storage performance under various conditions, namely, i) coated vs loose grain sorbent configurations, ii) various heat storage durations, iii) adding high conductive additives in the sorbent material, iv) different operating temperatures, and v) different discharge-to-charge time ratios. A comprehensive transient resistance-capacitance lumped-parameter model is developed to assess the performance of a closed S-TES system. The model is proved to be accurate in comparison with the experimental data and offers a reliable platform for the design and optimization of an S-TES system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".