NUMERICAL INVESTIGATION OF THE EFFECTS OF MODEL UNCERTAINTIES ON COMPONENT-BASED TRANSFER PATH ANALYSIS METHOD WITH DYNAMIC
Bibliographic record
Abstract
The hydraulic pumps are considered to have a major contribution to the structure borne noise generated inside aircrafts.Methods such as Component-Based Transfer Path Analysis (CB-TPA) are promising tools for aircraft manufacturers to build internal processes for the specification, design and validation of the impact of vibrating systems on new aircrafts.However, the experimental applicability of these methods remains limited due to some experimental difficulties.CB-TPA' formulation is based on dynamical quantities, which require the determination of terms related to rotational degrees of freedom.Indirect methods such as Virtual Point (VP) are commonly employed for this purpose, but it results in more cumbersome experimental set-ups and increased measurement uncertainties.The implementation of these methods can also be difficult in the case of a flat and thin receiving structure as commonly encountered in aircraft applications, since in-plane translational excitations have to be applied and the access to the vibrating system/structure interface points is limited.In this work, a decoupling procedure is used to overcome these issues.A numerical model is used to investigate the influence of model uncertainties on the CB-TPA' predictions, when the dynamical quantities are provided by VP method including the decoupling procedure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".