Dimensionality reduction method based on energy order distribution for multi-nonlinearity-coupled rotor-bearing system
Bibliographic record
Abstract
Gas turbine rotors are complex dynamic systems with high-dimensional, discrete, and multi-source nonlinear coupling characteristics. Significant amounts of resources and time are spent during the process of solving dynamic characteristics. Therefore, it is necessary to design a low-dimensional model that can well reflect the dynamic characteristics of high-dimensional system. To build such a low-dimensional model, this study developed a dimensionality reduction method considering global order energy distribution by modifying the proper orthogonal decomposition theory.First, sensitivity analysis of key dimensionality reduction parameters to the energy distribution was conducted. Then a high-dimensional rotor-bearing system considering the nonlinear stiffness and oil film force was reduced, and the accuracy and the reusability of the low-dimensional model under different operating conditions were examined. Finally, the response results of a multi-disk rotor-bearing test bench were reduced using the proposed method, and spectrum results were then compared experimentally. Numerical and experimental results demonstrate that, during the dimensionality reduction process, the solution period of dynamic response results has the most significant influence on the accuracy of energy preservation. The transient signal in the transformation matrix mainly affects the high-order energy distribution of the rotor system. The larger the proportion of steady-state signals is, the closer the energy tends to accumulate towards lower orders. The low-dimensional rotor model accurately reflects the frequency response characteristics of the original high-dimensional system with an accuracy of up to 98%. The proposed dimensionality reduction method exhibits significant application potential in the dynamic analysis of high-dimensional systems coupled with strong nonlinearities under variable operating conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".