Bench Test Method for Fuel Tank Vent Valve Noise Induced by EVAP System Pressure Pulsation
Bibliographic record
Abstract
In gasoline Powertrain systems, the evaporative emission control (EVAP) system canister purge valve (CPV) can be actuated by pulse-width modulated (PWM) signals. The CPV is an electronically actuated solenoid. The PWM controlled CPV, when actuated, creates pressure pulsations in the system. This pulsation is sent back to the rest of the EVAP system. Given the right conditions, the fill limit vent valve (FLVV) inside the fuel tank can be excited. The FLVV internal components can be excited and produce noise. This noise can be objectionable to the occupants. Additional components within the EVAP system may also be excited in a similar way. This paper presents a bench test method using parts from vehicle’s EVAP system and other key fuel system components. The test method achieves the following objectives: first, re-create the conditions that result in excitation; second, establish a controlled environment that provides insight into the noise phenomenon; and finally, set the ground work for further studies which may lead to further noise mitigation methods. The key system parameters controlled in this method are: frequency and duty cycle of the PWM signal, liquid level inside the fuel tank and tank grade/orientation. The output data includes: EVAP system purge flow rate, pressure signal inside the EVAP system (one near the CPV and one near the FLVV) and acceleration in Z (vertical) direction on the top tank surface near the FLVV mounting location.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".