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Record W3084435274 · doi:10.32393/csme.2020.1155

Exergy and Energy Analysis of Solar-driven Dual-loop ORC with Nano Organic Fluid

2020· article· en· W3084435274 on OpenAlexaff
Seyedeh Elnaz Mirazimzadeh, Ofelia A. Jianu

Bibliographic record

VenueProgress in Canadian Mechanical Engineering. Volume 3 · 2020
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsExergyNano-Solar energyDual (grammatical number)Solar poweredLoop (graph theory)Exergy efficiencyDual purposeProcess engineeringEnergy (signal processing)Computer scienceEnvironmental scienceMechanical engineeringMathematicsEngineeringChemical engineeringElectrical engineeringStatistics

Abstract

fetched live from OpenAlex

A thermodynamic analysis of a regenerative solar-driven combined cooling, heating and power system of dual-loop organic rankine cycle (DORC) with flat plate solar collectors is conducted to determine the energy and exergy efficiency of the system, exergy destructions and losses along with their magnitudes and exact locations. The DORC consists of a high-temperature and a low-temperature loop, and nanofluid is used in the solar thermal sub-system to improve the heat absorption from solar collectors. In this study two recuperators are added to the DORC to improve the performance of the regenerative organic Rankine cycle and its effect on the overall produced power. The impact of connecting cooling by adding an ejector refrigeration cycle in the hightemperature loop is explored using an exergy analysis. The performance of the regenerative DORC using nanofluid in comparison to the regular DORC counterpart is discussed in terms of energy and exergy efficiencies. Energy and exergy analysis is presented to determine the irreversibilities within the systems, and based on the location of the irreversibilities, improvements are implemented. It was found that implementing recuperators in the proposad configuration increased the thermal energy efficiency and exergy efficiency by 5% and 4%, respectively.

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 categoriesMeta-epidemiology (narrow)
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 score0.724
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.206
Teacher spread0.199 · 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.

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueProgress in Canadian Mechanical Engineering. Volume 3Same topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207