Performance and Emission Characteristics of CI Engine Operated on Madhuca Indica biodiesel using Multi-Objective Optimization by Applying Taguchi Grey Relational Analysis
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper’s analysis approach combines the orthogonal array design of experiments with grey relational analysis for optimization CI engine performance using blend of Madhuca Indica biodiesel as a fuel. Grey relational theory is adopted to determine the best input parameters that give lower emission and higher performance of CI engine. Five design parameters namely; compression ratio, injection pressure, injection nozzle geometry (no. of holes on nozzle of injector), additive (AA-93 <sup>TM</sup>) and fuel fraction were selected, and four levels for each factor. To reduce an experimental effort the experiments have been performed by employing <i>Taguchi's L<sub>16</sub></i> orthogonal array for various engine performance and emission related responses. Injection <i>nozzle geometry</i> was found to <i>most influencing factors</i>. The optimal combination so obtained was further confirmed through experimentation was suitable for optimizing the performance and emission parameters of diesel engine. The optimal combination of the input parameters in CI engine operated on Madhuca indica biodiesel blend is: <i>Compression ratio (CR) 18, fuel injection pressure (FIP) 310 bar, injection nozzle geometry (ING) 3H, fuel fraction (FF) 15% and without additive (ADD).</i></div></div>
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Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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