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Record W4242378801 · doi:10.5383/ijtee.08.01.006

Experimental Study and Performance Analysis of Ceramic Packing Cooling Tower Using Taguchi Method

2014· article· en· W4242378801 on OpenAlexvenueno aff
R. Ramakrishnan, Ragupathy Arumugam

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsTaguchi methodsCooling towerOrthogonal arrayTowerTileVolumetric flow rateCeramicTurbochargerWater coolingInletEngineeringStructural engineeringMechanical engineeringMaterials scienceMechanicsComposite materialPhysicsTurbine

Abstract

fetched live from OpenAlex

Deterioration of the packing material is a major problem in cooling tower. In this experimental study, ceramic tile is used as packing material. The experimental study was conducted in a forced draft cooling tower. Cooling tower operating parameters were optimized using Taguchi approach. The application of Taguchi method is assessing maximum cooling tower effectiveness for the Forced draft counter flow cooling tower using ceramic packing. An experimental study has been carried out for Taguchi’s L9 orthogonal array. According to the Orthogonal array, the trail was performed under different inlet conditions of flow rate of water, air and Inlet water temperature. Signal-to-noise ratio (S/N) and regression were carried out in order to determine the effects of process parameters on cooling tower effectiveness. Finally, confirmation tests verified this reliability of Taguchi method for optimization of forced draft counter flow cooling tower performance with sufficient accuracy. Confirmation experiment was d o n e using optimum combination showed that cooling tower effectiveness was found by experiment is closer to the predicated value.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.399

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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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