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Record W4252397751 · doi:10.32920/ryerson.14660997

Thermo-physical characteristics of building integrated phase change materials (PCM) and their applicability to energy efficient homes

2021· preprint· en· W4252397751 on OpenAlexaboutno aff
Omar Siddiqui

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsGypsumPhase-change materialMaterials scienceFlexural strengthThermal comfortThermal massThermalCompressive strengthComposite materialMass concreteLatent heatComposite numberPhase changeStructural engineeringEnvironmental scienceEngineeringThermodynamicsEngineering physics

Abstract

fetched live from OpenAlex

The applicability of utilizing a variety of thermal mass including phase change materials with commonly used building materials is investigated through the use of simulations and physical testing. The thermal performance and occupant comfort potential of a novel solid-solid phase change material, known as Dal HSM, is compared and contrasted to commonly available forms of thermal mass. Detailed experimentation is conducted to successfully integrate Dal HSM with gypsum and concrete. The measurement of physical characteristics such as compressive strength and modulus of rupture is conducted to ensure that the PCM-composite compound retains the structural integrity to be utilized in a typical building. The use of thermal mass in the Toronto Net Zero house was found to contribute to energy savings of 10-15% when different types of thermal mass were used. The comfort level of the indoor occupants was also found to increase. The performance of Dal HSM was found to be comparable to a commercially available PCM known as Micronal in the heating mode. The cooling mode revealed that Dal HSM provided slightly lower energy savings when compared to Micronal due to a lower phase transition temperature and latent heat. The performance of physical test revealed a decrease in the compressive strength as the concentration of Dal HSM was increased in the PCM-gypsum specimens. Tests were also performed to analyze the impact of increasing the PCM concentration on the flexural strength of PCM-gypsum composite.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.308
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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