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Uncertainty Analysis in Aircraft Structures

Uncertainty Analysis in Aircraft Structures. Air Frame Finite Element Modeling for Uncertainty Analysis and Large-Scale Numerical Simulation Validation. Jason Gruenwald University of Illinois-Urbana/Champaign Dr. Mark Brandyberry MSSC, CSAR. Goal. Create a Methodology

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Uncertainty Analysis in Aircraft Structures

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  1. Uncertainty Analysis in Aircraft Structures Air Frame Finite Element Modeling for Uncertainty Analysis and Large-Scale Numerical Simulation Validation Jason Gruenwald University of Illinois-Urbana/Champaign Dr. Mark Brandyberry MSSC, CSAR

  2. Goal • Create a Methodology • Better Predicts Performance of aircraft structure • Using uncertain input variables • Minimizes computation expense (number of runs) • Need to be able to answer probabilistic questions • 99% confident satisfies requirement rather than use safety factor • Predictive Analysis: • Reduce experiments needed • Reduce the number of Prototypes built • Increase Cost effectiveness

  3. Uncertainty Analysis • Variation of the structure’s response due to collective variation of input parameters • i.e. Aircraft wing • Better understand change in response • Apply methodology used for Computational Fluid Dynamics in rockets

  4. Overview of Methodology Determine Input Uncertainties and probability distributions Create Sample Sets using sampling method Create Surrogate Model Simulate a few specific sample sets Create Clusters of Similar Predictions Predict output trends quickly Cumulative Probabilities of Output Variables Interpolate results over entire range

  5. Wing Box Model • Modeled in ABAQUS • Solid Mechanics Finite Element Program • Chosen for simplicity • Short Simulation time • Material assumptions: • Entire model is 7075-T6 Al • Behaves linearly

  6. Input Parameters & Sample Sets • Young’s Modulus • 10400 ksi ± 5% • Normal Distribution • Poisson’s Ratio • 0.33 ± 5% • Normal Distribution • Load Reference Case • FALSTAFF Spectrum • Assumed loads change in phase • Latin Hypercube Sampling • Samples values from extremes • 50 sample sets

  7. Set Prediction 1 1.327 2 0.779 3 0.746 48 1.223 49 0.921 50 1.06 Surrogate Model Wing Box Cluster 1 Cluster 2 Cluster 9 Cluster 10

  8. Clusters and Simulation Front Spar Max Stress Prediction 1.00 Prediction Cluster 10 Cluster 4 Cumulative Probability 0.50 Cluster 3 Cluster 1 0.00 0 60000 120000 Max Stress (psi)

  9. Cumulative Probability Maximum Stress (psi) Interpolation ABAQUS Sims Results Tensile Yield Tensile Yield Ultimate Yield Ultimate Yield

  10. Conclusion • Cluster Methodology accurately predicts performance • Engineers ability to answer probabilistic questions • Minimal computational expense • Predictive Analysis: • Reduce the number of Prototypes built • Reduce experiments needed • Cost effective

  11. Future Work • Investigate techniques to validate computational model • Compare uncertain simulation with uncertain experiments • Multiple points of comparison • Weighted comparisons • Multi-Attribute Decision Tree Methods • Incorporate other uncertainties • i.e. Geometric tolerances, Friction, Boundary Conditions Uncertainties • Apply to entire aircraft wing

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