Parameter Identification of a Nonlinear Vertical Axis Rotating Machine through Reduced Order Modeling and Data Assimilation
Bibliographic record
Abstract
One challenge in modeling nonlinear dynamic systems involves the uncertainty associated with certain parameters that cannot be directly measured or estimated, along with the complexity of incorporating all relevant physical phenomena into a mathematical model without increasing computational cost. A hybrid twin represents an advanced modeling approach that combines the system's physics-based mathematical model with the empirical data collected from the real-world system, using data assimilation. This strategy enhances the accuracy and reliability of both estimating the system's unknown parameters and predicting its overall behavior. Further improvements are achieved by using a reduced-order model, which significantly lowers the computational burden of the entire procedure. In this study, we construct a surrogate model for a Vertical Axis Rotating Machine (VARM) by deploying sparse Proper Generalized Decomposition (sPGD). The model is parametrized in terms of the machine's unidentified parameters, and we apply the Harmonic-Modal Hybrid (HMH) Frequency Approach to solve for sparse scenarios creating the library of solutions. This is then combined with the Levenberg-Marquardt optimization technique to identify the unknown parameters using the measured shaft displacements of an experimental rig of the machine. The results demonstrate that this method is effective for parameter estimation in complex nonlinear systems and allows for fast computations, whether the unknown force function is specified explicitly or presumed. Precisely estimating a system's parameters can serve as a crucial indicator for scheduling maintenance or predicting failures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 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".