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Record W2152170048 · doi:10.1002/cjce.22263

Numerical simulation of multiple polysilicon CVD reactors connected in series using CFD method

2015· article· en· W2152170048 on OpenAlexvenueno aff
Zheqing Huang, Siyuan Qie, Xiaoyu Quan, Kai Guo, Chunjiang Liu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsComputational fluid dynamicsChemical vapor depositionProcess (computing)Materials scienceDeposition (geology)ChlorineNuclear engineeringProcess engineeringAnalytical Chemistry (journal)Chemical engineeringMechanicsChemistryComputer scienceOptoelectronicsEngineeringPhysicsChromatography

Abstract

fetched live from OpenAlex

This study proposes a novel process in which multiple polysilicon chemical vapour deposition (CVD) reactors are connected in series, and this setup is investigated using computational fluid dynamics (CFD). A three‐dimensional CFD theoretical model is proposed to describe the various transport phenomena in the reactor, and this model is validated by comparing the reactor outlet temperatures of the simulation results and the data obtained using an industrial polysilicon CVD reactor. This model was then used for analyzing the production cost and growth rate of the traditional and proposed CVD processes. The simulation results show that the polysilicon production cost in the traditional and proposed processes is lowest for 0.091 mol/mol (9.1 mol %); the maximum deposition rate is achieved for 0.25 mol/mol (25 mol%). On the other hand, if the operation pressure increases from 101 to 607 kPa, the average production cost can be reduced from 86.26 to 39.88 $/kg; and the average growth rate can be raised from 1.68 to 4.18 µm · min −1 . In addition, compared with the 1‐reactor process, the cost of the 2‐reactor process is 21.9 % lower, and the 3‐reactor process is 26.9 % lower. Finally, in the novel process, changing the chlorine hydride of the feed in the reactor 2 from 0.1 g/g (10 wt%) to 0.02 g/g (2 wt%), the average production cost can be reduced by 35.5 %.

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)0.001
metaresearch head score (Gemma)0.004
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.030
GPT teacher head0.271
Teacher spread0.241 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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