A Practical Procedure to Predict AIS Inlet Noise Using CAE Simulation Tools
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The air induction system (AIS), which provides clean air to the engine for combustion, is very important for engine acoustics. A practical CAE procedure to predict AIS inlet noise is presented in this paper. GT-Power, a commercially available software program can be used to simulate the engine performance and predict air induction noise. The accuracy of GT-Power is dependent on many variables, such as: proper duct discretization size, proper number of flow splits to model the air box and the capturing of the correct resonator geometry for tuning frequency. Since GT-Power is based on a 1D assumption, several iterations need be performed to model the complex AIS components, such as, irregular shaped air box, resonator volume, porous ducts and perforated pipes. Because of this, the GT-Power AIS model needs to be correlated to test data using transmission loss data. But in the case when test data is not available for correlation, especially in early design stage when no test parts are available, a virtual ‘test’ method needs to be utilized. As an alternative, a 3D Finite Element Method (FEM) or Boundary Element Method (BEM), can be utilized as a virtual ‘test’ TL bench for GT-Power model correlation. In this paper, SYSNOISE, which has both a FEM and BEM solver, is utilized to compute the TL of an Air Induction System. The procedure is described as follows. First, a preliminary AIS GT-Power model is built based on the design geometry. Second, the corresponding SYSNOISE model for the same geometry is built and the TL is computed. Then, iterations are taken to update the GT-Power model in order to correlate the TL data with SYSNOISE TL results. Finally, the correlated GT-Power AIS model is attached to the full system GT-Power model and then the software computations are run yielding a predictive inlet noise curve. Several case studies are shown that demonstrate the validity of the proposed procedure.</div></div>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it