Modelling and Experimental Evaluation of a Sand-Based Seasonal Storage System
Bibliographic record
Abstract
In 2016, space heating (SH) and domestic hot water (DHW) heating for Canadian residential buildings accounted for 11% of Canada's total greenhouse gas (GHG) emissions. To drastically decrease the GHG emissions from the residential sector, solar thermal energy can be used to fulfill the SH and DHW needs of residential buildings. A major challenge for solar energy use in Canada is the timing mismatch between the solar radiation received and the SH needs of homes. A seasonal thermal energy store bridges this gap by storing thermal energy and discharging it when the demand arises. Sand can be used as the medium in thermal energy storage, but no research has yet been published demonstrating the feasibility or efficiency of a sand-based seasonal thermal energy store (SSTES) for a residential building. A full-scale research house with a solar combisystem and an SSTES has been built in Ottawa, Canada, to evaluate this novel technology. This thesis documents and analyzes a year-long experiment that was performed at the research house to assess whether the solar combisystem and SSTES can fulfill over 90% of the house’s annual SH and DHW heating needs with solar energy. It also presents the development of a 3D, conduction-only numerical model of the heat transfer in and around the SSTES that is validated with the data from the year-long experiment. Validated numerical models allow various design scenarios to be tested and optimized with less time and economic investment. The experimental results show that solar energy fulfilled 70% of the house’s annual SH and DHW heating loads (i.e. solar fraction=70%). The principal reason for the lower-than-expected solar fraction was the SSTES’s high rate of heat loss (twice the expected rate). Its storage efficiency (i.e. heat extraction/heat injection) was only 20%. Analysis of the experimental data shows one side of the SSTES loses heat faster than the other sides. It is hypothesized that the SSTES’s insulation has become partially degraded; the numerical model was modified to reflect this. The final model has been validated through comparison to the experimental data using several key metrics that are presented and analyzed in this work.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".