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Record W7038095034

Heat Exchangers for Waste Heat Recovery

2021· dissertation· W7038095034 on OpenAlexaboutno aff

Bibliographic record

VenueTSpace · 2021
Typedissertation
Language
FieldAgricultural and Biological Sciences
TopicPlant and Biological Electrophysiology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHeat exchangerHeat recovery ventilationWaste heat recovery unitCopper in heat exchangersHeat transfer coefficientHeat transferWaste heatEnergy recoveryEnergy consumption
DOInot available

Abstract

fetched live from OpenAlex

To reduce energy consumption and water heating requirements, the Ontario Building Code mandates every new residential building must install a drain water heat recovery unit to recover energy from drain water. A two-stage waste heat recovery unit is designed and tested with conformance to the Ontario Building Code. A novel leak detection system was implemented by measuring the electrical resistance of a coating. Thermal spray coating processes are used to create surfaces with enhanced performance. Flame spray and wire-arc spray produced two and three samples respectively. Each sample is tested to find the best heat transfer occurred with 0.125” diameter pillars, staggered, spaced 0.188” with enhanced surface area of 45.8% while increasing the convective heat transfer coefficient by 26.1% - 64.4% depending on the Reynolds number between 281 and 2745. The coatings are recommended to be utilized in a flat plate design with reduced complexity, size, and cost.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.009

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 head0.035
GPT teacher head0.291
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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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