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Investigation on the thermal performance of the diaphragm wall in deep buried engineering: a simulation study

2019· article· en· W2981925887 on OpenAlexaff
Chao Zeng, Yanping Yuan, Fariborz Haghighat, Xiaoling Cao, Bo Xiang

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsDiaphragm (acoustics)Heat transferThermalHeat exchangerMaterials scienceEngineeringMechanical engineeringMechanicsMeteorologyElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Ground source heat pump technology is widely used in buildings’ to meet the cooling and heating demand. As a special form of the buried pipe, diaphragm wall has received increasing interests due to high energy-efficiency and relatively low costs. Previous studies investigated the thermal behavior of diaphragm wall in underground tunnels or underground parking while very few studies were carried out on deep-buried engineering with the air-conditioned adjacent indoor environment. In this study, the effects of buried pipes on the heat transfer regulation of the diaphragm wall and the indoor load are analyzed. Simulation results indicated that average energy exchange through the pipe in the diaphragm wall is 78.1% compared with that of the conventional buried pipe for the ground heat pump. The heat exchange capacity of the buried pipe in the diaphragm wall in intermittent mode is 1.2 times of that in the non-intermittent mode in 14h. For the underground engineering boundary with pipes buried in the concrete layer, heat transfer through the inner surface reduced 3.8W/m 2 , which would consequently add to the indoor cooling load. In order to reduce the heat transfer back to the indoor environment through the inner surface, insulation of the diaphragm wall are analyzed. This study can provide a reference for the analysis of the feasibility and application of diaphragm wall in deep-buried engineering.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.302

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 head0.018
GPT teacher head0.197
Teacher spread0.178 · 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 designSimulation or modeling
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

Citations1
Published2019
Admission routes1
Has abstractyes

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