Virtual Accelerometer Approach to Create Vibration Profile for Automotive Component Shake Test
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Vibration shaker testing is a great tool of validating the vibration fatigue performance of automotive components &amp; systems. However, the representative vibration schedule requires a pre-knowledge of the acceleration history for the test object, which usually is not available until the later development phase of a vehicle program when physical properties are available. Sometimes, a generic vibration schedule developed from the worst-case loading profiles are used with risk of lacking correlation with later full vehicle durability test such as Road Test Simulator (RTS) or Proving Ground (PG) road test due to the higher loading amplitude.</div><div class="htmlview paragraph">This paper proposes a virtual accelerometer approach to collect acceleration responses of a component from a virtual vehicle model. First, a multiple body dynamic model will be produced for virtual load calculation over a series of digitalized virtual proving ground road profiles. These loads will be then applied on the virtual vehicle CAE model with all representative components &amp; subsystems. Acceleration response for the system of interest is then extracted from modal transient dynamic analysis. Later, the acceleration history is converted to frequency domain, and the associated spectral damage calculation is conducted. The final vibration profile is developed based on the equivalent damage concept. The virtual acceleration acquisition and physical acceleration acquisition show good agreement in both time domain and frequency domain. The profile generated from virtual acceleration acquisition covers very well for the physical acceleration acquisition. Overall, virtual acceleration-based profile ensures the conservative shake test results for the example studied. Compared to the physical accelerometer, the virtual accelerometer can provide acceleration data economically and quickly in the early phase of the product development. The proposed approach will ensure the component shaker test is conducted using the meaningful and representative vibration load.</div></div>
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".