Finite Element Modeling of a Slewing Non-linear Flexible Beam for Active Vibration Control with Arrays of Sensors and Actuators
Bibliographic record
Abstract
In this article, a new finite element model (FEM) of an Euler—Bernoulli beam, developed through an absolute nodal coordinate formulation (ANCF), is presented for simulation and analysis of the performance of surface-bonded piezoelectric actuators in suppressing non-linear transverse vibrations that are induced by very fast slewing. The elastic deformations experienced are an order of magnitude larger than cases considered to date, and the model employs a unique cubic spline approximation to the beam’s deformed elastic line that is in terms of node positions and curvatures. To ensure relevant commentary on the vibration suppression properties of the distributed piezoelectric actuators, a material damping model was introduced in the continuum equations to capture the non-linear damping of the very slender beam that is observed in experiments. Following the ANCF methodology, the constitutive damping moment is formulated in terms of the absolute nodal coordinates with care taken to ensure the calculation is singularity free. Galerkin’s method of weighted residuals is applied to discretize the revised equations of motion derived for the beam continuum. The FE beam model exploits a synergy between the twisted spline geometry and the lumped mass approximation to halve the size of the matrix equations that must be solved on each time step. However, this condensation of the matrix equations requires the use of interelement boundaries at the edges of the surface-bonded piezos. Using a single-link flexible manipulator as an example, a number of static and dynamic simulation examples that illustrate the validity of our FEM are presented, including comparisons to theoretical and other existing numerical solutions in literature. In addition, active vibration control examples are presented using proportional- and derivative-based hub motion and piezoelectric actuator controls in suppressing dramatic vibrations induced by fast slewing.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".