A Novel DoE based Front-End Airflow Target Setting Approach for Optimum HVAC Cool Down Performance
Bibliographic record
Abstract
The front-end air flow conditions have a substantial impact on the cool down performance of a vehicle Heating, Ventilation and Air-Conditioning (HVAC) system. The performance of a mobile HVAC system is analyzed by conducting tests on the vehicle in a drive cell, subjecting it to different drive cycles. This now can be done virtually using system level simulation or one-dimensional (1D) tools. Target values for condenser air inlet velocity and temperature for these HVAC performance focused drive cycles needs to be established during the development phase to meet the cool down functional objectives of the vehicle. Thus, in the early stages of development, 1D tools play a major role. Condenser air flow should be sufficient and the temperature should be as low as possible at different vehicle operating conditions to have good air-conditioning (AC) performance. This paper focuses on a Design of Experiments (DoE) approach to investigate the effect of condenser air inlet conditions on overall AC performance and introduces an effective target setting strategy to help move towards an optimal front-end module design. First, a Central Composite DoE (CCD) study is conducted to show how condenser air inlet conditions: air inlet velocity and temperature at different vehicle speeds affect the cool down functional objectives. Second, after identifying the most contributing factors, a novel target setting strategy is proposed to accurately identify the required condenser air inlet velocity and temperature conditions for optimum AC performance. This new strategy can guide the engineers to design more efficient front-end cooling modules to meet all the requirements of AC system and passenger comfort. The simulations and DoE are both done using Siemens’ LMS Imagine. Lab AMESim 15.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".