Application of the Fourier Amplitude Sensitivity Test (FAST) to Analyze Thermal Performance of Vehicle Underbody Components
Bibliographic record
Abstract
This paper describes the application of the Fourier Amplitude Sensitivity Test (FAST) method [1] to investigate the effect of uncertainty in design parameters on the thermal system performance of vehicle underbody components. The results from this study will pinpoint the design parameters which offer the greatest opportunity for improvement of thermal system performance and reliability. In turn, this method can save engineering time and resources. An analytical model was developed for a vehicle underbody system consisting of a muffler, heat shield, and spare tire tub. The output from this model was defined as the temperature of the spare tire tub. The majority of the input parameters in this model deviate from their nominal values due to environmental factors, wear and ageing, and/or variation in the manufacturing process. Using MATLAB software package, the model was simulated with input parameters which were simultaneously and sinusoidally varied at distinct frequencies over their respective uncertainty ranges. Finally, the Fourier transform was applied to the output of the model to convert the response into the frequency domain to allow the amplitude of each of the distinct frequencies to be recognized. The amplitude of each frequency was used as an indication of the effect of the corresponding parameter's variation on the temperature of the spare tire tub. Reducing the amount of variation in the most influential parameters and/or reducing the system sensitivity to these parameters will have the greatest improvement on the overall thermal performance of the system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".