Certification for Design Reshaping the Testing Pyramid

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Developing the scientific foundation that enables lighter, safer and more cost-efficient composite aerostructures.

 

This research project is to provide a route for lessening regulatory constraints, moving towards a more cost/performance optimised philosophy by reducing the multiple coupon level materials tests at the bottom of the test pyramid. It will create the scientific basis for a new culture of virtual certification that will enable significant mass savings and target reduction of design costs and development time.

 

The project is developing a new holistic approach to design at a hitherto unattainable level of fidelity. Bayesian techniques such as Monte-Carlo Markov Chains to quantify uncertainty and variability and Bayesian inference process will be used to minimise model uncertainty. Lock-In Digital Image Correlation and Thermoelastic Stress Analysis will be integrated and scaled up to provide a non-contact methodology to capture stress and strain in the vicinity of as-designed internal meso/micro features and in-service damage, and quantitatively identify the mechanisms that initiate damage and lead to failure.

  

 

 

Recent Updates 

 

Recently, we have successfully built a Gaussian Process based Data-Driven Emulator for Quantifying the defects/uncertainty of Composite structure (C-section), The most significant merits of the proposed scheme include: 1) It directly maps from input wrinkle with different parameters to full solution field displacement without time-consuming stiffness matrix assembling and governing equation solving in each single computational prediction. 2) unlike other machine learning algorithms requiring a huge amount of training data, this emulator is capable to provide the predication (expected value) and, from a statistical aspect, the corresponding confidence interval (standard deviation) for the testing input uncertainty at the same time, even the number of training data is limited

 

 

 

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