Flutter analysis of laminated fiber-reinforced magnetorheological elastomer sandwich plate resting on an elastic foundation using an improved first-order shear deformation theory
Bibliographic record
Abstract
Abstract Magnetorheological elastomers (MREs) are polymers with viscoelastic properties that can be adjusted by manipulating the magnetic field. When MREs are combined with reinforcing fabrics, a new category of materials known as MRE composites (MRECs) can be created, which not only possess the characteristics of MREs but also enhance their rigidity. This study focuses on investigating the supersonic aeroelastic instability of a rectangular sandwich plate with a laminated MREC core layer and functionally graded materials with porosities as face layers. Additionally, the sandwich plate is supported by an elastic foundation and subjected to supersonic airflow. This investigation presents an improved first-order shear deformation theory, postulating a parabolic distribution of shear stresses. Consequently, the transverse shear stresses are rendered as zero at the surface of every individual layer; thus, the requirement for shear correction in this theory is eliminated. In addition, 8-node elements are implemented to circumvent the necessity for distinct handling of shear-locking. The aeroelastic pressure acting on the structure is considered using first-order piston theory. Micromechanical approaches, such as Halpin‐Tsai and rule of mixture approaches, are employed to determine the effective mechanical properties of the core and face layers. The dynamic equations of the structure are derived using Hamilton’s principle and the finite element method. The study also examines the impact of different magnetic fields, fiber volume fraction, elastic foundation factors, layering angles, geometry, and boundary conditions on flutter frequency.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".