Experimental Analysis of Combustion Noise Reduction with Performance Optimization in 110cc CVT Scooter Engine
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Indian two wheeler market is one of the largest and highly competitive in the world. Indian scooter segment grows at a pace of around 30% YOY. The stiff competition among OEM’s to increase the market share with fuel efficient and high performance products pushes development and calibration engineers to burn the midnight oil to concoct innovative methods to design technology boosted product. Customer expectations are always high in terms of fuel economy, drivability and NVH. Due to higher level of complexity involved in CVT (Continuously Varying Transmission) engine, it is difficult to optimize for achieving best of NVH characteristics along with Fuel Economy, drivability and reduced exhaust emission. This paper describes the experiment conducted during the development of 110cc CVT four stroke scooter engine. The development and calibration of this scooter was mainly based on real world usage pattern (RWUP).</div><div class="htmlview paragraph">In order to obtain best performance from engine, ignition timing, fuel metering and CVT were optimized to achieve Maximum Brake Torque. With the ignition timing which can provide maximum braking torque the performance characteristics of the engine was meeting the PALS/FI target but severe combustion noise was observed which restricted the use of MBT ignition Timing. This technical paper describes the optimization methodology of this four stroke scooter engine on which the combustion noise (Noise generated due to rapid combustion of charge) is substantially reduced within acceptable noise levels without compromise in engine performance. The experiment comprise of noise source identification, analyzing the factors which affects combustion noise and optimization of these parameters to reduce engine combustion noise. Engine combustion parameters like peak cylinder pressure, pressure rise rate, and mass fraction burned along with heat release rate were analyzed by varying ignition timing and carburetion.</div><div class="htmlview paragraph">The Engine ignition timing being dual curve, ignition timing varies with engine speed and operating condition (namely Partially Open Throttle, POT and Wide Open Throttle, WOT). Combustion noise was observed to be higher at both POT and WOT condition. Ignition timing was optimized at specific operating zones where combustion noise was observed to be predominantly higher, the loss in engine performance due to change in ignition timing was substantiated with optimization of carburetor venturi size, air filter connecting tube effective diameter and CVT ratio. Experimental results shows significant reduction in combustion noise up to 5dB(A), without any compromise in engine performance, Fuel economy and drivability.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".