Impact of Active-Grille Shutter Position on Vehicle Air-Conditioning System Performance and Energy Consumption in Real World Conditions
Bibliographic record
Abstract
Active grille shutter (AGS) in a vehicle provides aerodynamic benefit at high vehicle speed by closing the front-end grille opening. At the same time this causes lesser air flow through the cooling module which includes the condenser. This results in higher refrigerant pressure at the compressor outlet. Higher head pressure causes the compressor to work more, thereby possibly negating the aerodynamic benefits towards vehicle power consumption. This paper uses a numerical method to quantify the compressor power consumed in different scenarios and assesses the impact of AGS closure on total vehicle energy consumption. The goal is to analyze the trade-off between the aerodynamic performance and the compressor power consumption at high vehicle speeds and mid-ambient conditions. These so called real world conditions represent highway driving at mid-ambient temperatures where the air-conditioning (AC) load is not heavy. AC system is modeled using 1D methodology and its performance simulated at system level. Net power consumed by the vehicle is computed for different scenarios using a robust comparison methodology. System model is validated against vehicle drive cell test data. Tests are conducted on a mid-size sport utility vehicle (SUV) equipped with a full face AGS. Simulations are then performed with the validated model using a Design for Six Sigma (DFSS) methodology to compute net power consumed by the vehicle by varying the blower setting, vehicle speed and ambient temperature. This paper provides guidelines regarding when to have the AGS closed or open for different noise factors considered.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".