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Record W4414548532 · doi:10.3390/en18195118

Analyzing the Transient Heat Transfer Characteristics of a Drain Water Heat Recovery Device

2025· article· en· W4414548532 on OpenAlexafffund
Ezra Ovadia, Allan R. Willms, Mostafa H. Sharqawy

Bibliographic record

VenueEnergies · 2025
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransient (computer programming)ThermalInertiaHeat transferThermal inertiaSteady state (chemistry)Heat recovery ventilationTransient response

Abstract

fetched live from OpenAlex

This paper investigates the transient behavior of a drain water heat recovery (DWHR) device, which recovers heat from warm grey water in buildings. Experimental and numerical investigations were conducted to study the thermal performance of the device under transient conditions. Thermal performance measurements were carried out, and a mathematical model was developed that considered the thermal inertia of the hot and cold-water streams as well as the device’s material. The experimental data were used to validate the model, and good agreement was observed between the two. Under transient operating conditions, the device’s performance was measured in terms of its effectiveness, and an actual effectiveness model was used to capture the transient effect on the system’s overall performance. The experimental results show that during short hot water usage, the actual device effectiveness is significantly reduced as it does not reach a steady state condition. An economic analysis indicates that considering the device’s transient performance leads to a 27.2% reduction in annual energy savings in a typical domestic installation with regular daily usage of hot water appliances. The presented model and analysis offer valuable insights for the development of improved DWHR devices with the potential to contribute to sustainable engineering and building practices.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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.

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
Published2025
Admission routes2
Has abstractyes

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