Design of a Latent Heat Energy Storage System Coupled with a Domestic Hot Water Solar Thermal System
Bibliographic record
Abstract
Solar domestic hot water (SDHW) can be used to reduce energy bills and greenhouse gas emissions associated with heating domestic water.However, one of the most significant barriers to further deployment of solar thermal applications is the space and weight required for storage of the energy collected.Phase change materials (PCMs) are advantageous for daily energy storage with SDHW due to their high storage density and isothermal operation during phase transitions, and would overcome these obstacles.The aim of this paper is to outline the initial steps in the development of a SDHW energy storage system using PCMs, with emphasis on the numerical and experimental studies used to access the phase change and thermal behaviour of the selected PCM.Lauric acid was selected as the PCM based on the melting temperature range which was targeted by studying solar data from an existing solar hot water system in Halifax, Nova Scotia, Canada.Due to the low thermal conductivity of PCMs, additional work is required to develop and validate a design to enhance heat transfer to the storage material using fins.The selected design will be built and installed in an existing large scale solar thermal system on an apartment building in Halifax.The system will be instrumented in order to acquire continuous data (temperatures, flow rates, pressures, etc.) to fully characterize the 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".