A multidisciplinary method for simulation guided design of mechanical structures
Bibliographic record
Abstract
Composite sandwich panels are often used in aerospace industry for their high bending stiffness to weight ratio and high vibration damping capacity. Use of sustainable materials for skins and core of sandwich panels have recently attracted researchers’ attention since structural, vibrational, environmental, and economic aspects should be considered in the design process. This work presents a multidisciplinary method for simulation guided design of mechanical structures by mainly focusing on vibro-acoustic performance of sandwich floor slabs used in aircrafts. Vibration behaviour of the slab strongly depends on the geometry, material properties (Young’s modulus, density, damping), and boundary conditions. Python programming language is used to fully parameterize the structure, to automate the multiphysics analysis, and to develop a user interface for practical use. It allows combining the computer aided design scripting, finite element software scripting, numerical calculations and postprocessing features. Numerical simulation is performed to estimate the natural frequencies and mode shapes of the complex floor slab geometry. Harmonic analysis is performed to determine the forced vibration response. Acoustic radiated power is deduced from the velocity continuity of the elastic surface and the acoustic particles on the surface. The acoustic radiated power and acoustic intensity of the slab geometry are computed using the elementary radiators method. Numerical simulation is also performed to estimate the structural stresses and deflections. The introduced multidisciplinary and parameterized approach allows to determine the main design parameters to guide the engineers with minimum prototyping efforts. The method allows quick sensitivity analysis of design parameters (material, geometry, joint and fixing locations, etc.) as well as the modification of multiple parameters at once. A good preliminary design can significantly reduce the time and cost spent in the structural design process. By considering all major aspects of the design (structural, vibration, environmental, cost, etc.) from the beginning of the design, exchanges between different departments and unexpected surprises during a project can be minimized. Furthermore, once the model, analyses and postprocessing are automated behind a user-friendly interface, the introduced physics-based tool can be used by design engineers who are not necessarily experts in simulations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".