Numerical Study of the Targeted Energy Transfer Between the Euler-Bernoulli Beam and a Nonlinear Energy Sink
Bibliographic record
Abstract
Targeted energy transfer (TET) refers to the spatial transfer of energy between a primary structure of interest and isolated oscillators called the energy sink (ES). In this work, the primary structure of interest is a slender beam modeled by the Euler-Bernoulli theory, and the ES is a single-degree-of-freedom oscillator with either linear or cubic nonlinear stiffness property. The objective of this study is to characterize the TET and the effectiveness of ES under impact and periodic excitations. By using the scientific computation package, MATLAB, numerical simulations are carried out based on excitations of various strength and locations. Both time and frequency domain characterizations are used. For the impact excitation, the ES with the cubic nonlinear stiffness property is more superior to the linear oscillator in that larger percentage of the impact energy can be dissipated there. The main energy transfer was found to be due to a 3- to-1 frequency coupling between the first bending mode and the ES. For the periodic excitation, however, both linear and nonlinear ES exhibit generally poorer performance than the case with the impact excitation. Future works should focus on the frequency-energy relationship of the periodic solution of the underlying Hamiltonian, as well as using finite element model to verify the simulation results.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".