Aero structural modeling of a wing using CATIA V5 and XFLR5 software and experimental validation using the Price- Païdoussis wing tunnel
Bibliographic record
Abstract
During the structural study of an aircraft wing, it is difficult to accurately model the aerodynamic forces applied on the wing. To facilitate analysis, the lift of the wing is distributed on the main wing’s spar and its ribs. This method particularly works when the wing structure has a main beam. At this point; we design this spar so that it can withstand the lift of the wing on its own. This implies that the entire wing will be stronger than necessary so that the structure will not be fully optimized. To overcome this problem, we should be able to apply an overall aerodynamic distribution over the entire surface of the wing. By applying realistic embedment, we should be able to get much more reliable results. To achieve this we will therefore need to combine the software results of calculations about the aerodynamics of a wing with the software results for its design and structural analysis. In this project, the software used to calculate the coefficients of pressure on the wing is XFLR5 and the software for the design and structural analysis will be CATIA V5.The XFLR5 software allows quick analysis of a wing based on the analysis of its airfoils. This software computes airfoil performances as XFoil and lets you choose from three methods to calculate the performance of the wing (LLT, VLM and 3D Panels). To validate the results given by XFLR5, wind tunnel tests were performed on several different airfoils. Regarding the design and finite-element analysis of the structure, the CATIA V5 software is commonly used in the aerospace field. It is easier and faster to use than software like HyperMesh and gives very similar results. CATIA V5 also provides greater automation steps for wing design. Thus, in this project we will see how to automate the design process from the reading of the results obtained by XFLR5, specifically about the pressures around the wing, to the creation of the skin representing the surface of the wing. The goal to achieve, after a quick analysis on XFLR5 and a recording of values of the pressure coefficient, the user would only have to launch a CATIA V5 program to get the skin of the wing with the applied pressures. Once the skin is obtained, the user can create the inside structure of the wing and during the structural analysis, the deformation of the wing can realistically be visualized and thus optimized for the best possible structure.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".