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Modeling and Global Sensitivity Analysis of a Solar Powered Air Cooling System Using Liquid Desiccant

2013· article· en· W2038443331 on OpenAlexvenueno aff
Hery Tiana Rakotondramiarana, Sambatra Andrianomena, Ando Ludovic Andriamamonjy

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2013
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsSensitivity (control systems)Liquid desiccantDesiccantAir conditioningWork (physics)Environmental scienceCoefficient of performanceSolar energyComputer scienceSimulationMeteorologyAutomotive engineeringProcess engineeringEngineeringMechanical engineeringElectronic engineeringElectrical engineeringHeat pumpPhysics

Abstract

fetched live from OpenAlex

Solar energy technology is an option for energy saving in building air conditioning. A theoretical investigation of an open cycle solar air cooling system using aqueous Lithium Chloride solution as liquid desiccant is presented in this paper. The purpose of this work is to analyze the influences of both internal loads and external forcing, on the studied system by developing a computational code related to its mathematical model. The simulation results justify the choice of the system design. Indeed, it was highlighted that the higher is the outdoor temperature; the better is the coefficient of performance (COP) of the system. Furthermore, a global sensitivity analysis of the system model, achieved using Fourier Amplitude Sensitivity Test method, allowed us to identify the most influential factors that were ranked in a decreasing order of their influence degree on the system COP. Hence, key factors to be controlled for improving the system overall performance are specified.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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