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Record W3202829697 · doi:10.1002/est2.269

Accumulated and transient exergy analyses of pneumatic systems with isochoric and isobaric compressed air storage tanks

2021· article· en· W3202829697 on OpenAlexaff
Zhengren Tong, Hu Wang, Wei Xiong, David S.‐K. Ting, Rupp Carriveau, Zhiwen Wang

Bibliographic record

VenueEnergy Storage · 2021
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsUniversity of Windsor
FundersNational Natural Science Foundation of China
KeywordsIsobaric processExergyIsochoric processCompressed air energy storageStorage tankExergy efficiencyGas compressorEnvironmental scienceTransient (computer programming)Process engineeringEnergy storageWaste managementThermodynamicsEngineeringComputer scienceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Low energy efficiency is one of the most significant shortcomings of industrial pneumatic systems. The isobaric air storage tank is a new concept for improving the energy efficiency of pneumatic systems. In this study, comprehensive accumulated and transient exergy analyses are conducted to provide insight into the energy‐saving mechanism of isobaric air storage tanks. The results show that the system exergy efficiency can be improved by replacing the isochoric tank with an isobaric one. There is negligible exergy destruction in both storage tanks. However, the storage tank can bring about significant influences on other components. The isobaric tank can effectively improve the exergy efficiencies of the compressor, cooler, and regulator. Besides, there is a huge potential to improve system exergy efficiency by taking advantage of the wasted exergy from the pneumatic cylinder.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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 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

Citations11
Published2021
Admission routes1
Has abstractyes

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