Optimal Performance Evaluation of Energy Efficient Residential Air Conditioning System with Nanofluid-based Intercooler using Taguchi-based Response Surface Methodology
Bibliographic record
Abstract
Air conditioning is viewed as a major energy consuming area in domestic and industrial applications. However energy conservation is effectively possible in air conditioners by employing an intercooler with nanofluids, which can consequently reduce the compressor load. This paper presents an investigative report on the performance of proposed energy efficient air conditioning system. A binary mixture of ethylene glycol (C2H6O2) and water is used as the base fluid in the shell and coil type heat exchanger (SCHE). The volume concentration of C2H6O2 in base fluid, type of suspended nano particles (Al2O3 and MgO), flow rate of nanofluid at shell side of intercooler and the volume fraction of nano particles are chosen for experimentation designed using Taguchi L18 orthogonal array. The coefficient of performance (COP) of the nanofluid-based domestic air conditioner is estimated as the performance index (response). Quadratic model and response surface plots are generated to observe the effects of inputs on the COP. The nano particles of MgO (0.75%v/v), suspended in a binary mixture with 28.65% C2H6O2 is found to improve the system performance (COP) at a nanofluid flow rate of 2.42 LPM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".