Exploration of snowmobile noise: Conventional models and an electric snowmobile
Bibliographic record
Abstract
Snowmobile noise is a major issue affecting the perception and regulation of the snowmobile industry and community. Snowmobile producers and original equipment manufacturers have invested in the design, testing and implementation of noise reducing technologies. Other groups have studied snowmobile noise and its effects to expand the understanding of this subject.Electric vehicles (EVs) are slowly becoming more popular in the automotive space. Along with the rise of EV automobiles, some groups have successfully added electric powertrains to other traditionally internal combustion (IC) engine propelled vehicles including snowmobiles. An example of this type of vehicle modification is an EV snowmobile built for utility purposes at a glacial arctic research station.For many IC vehicles , the IC components (the engine, intake and exhaust for example) represent a significant source of noise. A number of researchers have studied the acoustic differences between IC and EV or hybrid EV automobiles and have found EVs significantly quieter than IC automobiles in a number of scenarios. To date, this research has not yet extended to off road electric vehicles. This thesis examines the potential noise-related benefits of electrification of the snowmobile powertrain by comparing a converted EV snowmobile with the IC snowmobile model it was converted from. Testing was done using a variation of the constant speed snowmobile pass-by noise measurement standard (SAE J1161). Additionally, this thesis aims to consider the effect of ground hardness on this standard based on previous research on the subject. To accomplish this goal, these vehicles were tested on both hard and soft snow types. This thesis also examines reductions in snowmobile noise over the past decade. The most popular snowmobile of 2003 in Quebec and the most popular snowmobile of the 2014-2015 winter season were tested concurrently using the SAE J1161 standard and a full throttle pass-by noise measurement standard (SAE J192).Testing on these vehicles demonstrated that the EV snowmobile was quieter than the IC snowmobile by close to 7 dB on soft snow but that this number reduced on harder snow types. In testing of the most popular snowmobiles of 2003 and winter 2014-2015, the newer snowmobile was considerably quieter than the older snowmobile (greater than 10 dB quieter) for both full speed and constant speed acceleration tests though the vehicles were only tested on soft snow. Although the results obtained with the EV snowmobile were not remarkable compared to the old versus new comparison, further testing is required to fully understand the possibilities of an EV snowmobile.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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".