The Method of Generalized Influence functions - Adjoint Sensitivity Analysis for the Effective and Efficient Numerical Design of Structures with special Application for Reliability and Uncertainty Modelling, 2nd research period

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Sensitivity analysis has not been implemented into everyday structural engineering. Great potentials for the numerical design of structures remain unexploited. Combining sensitivity analysis with uncertainty modeling may double the effect. Appropriate methods of sensitivity have the potential to boost engineering effectiveness and efficiency and may be rated as major candidates for innovation in the classical field of structural analysis. Research is necessary.The general goals of the project are carried forward for the second research period. We will serve with sensitivity information and all the systematic insight which may be derived from that. The theoretical basis is to efficiently determine 1st and 2nd order sensitivity analysis by the generalized influence function method. We will develop a complete set of methodologic tools and their underlying theory to (i) determine, (ii) visualize and (iii) process sensitivity information of first and second order. We will serve with interfaces to support applications for uncertainty quantification as well as for many other fields, e.g. robust design, structural optimization or the evaluation of parameter variations in all stages (from early to final) of design and production. This includes additive manufacturing as a prominent example of actual importance. In total, we will serve with data and methodologies to further push digital and innovative technologies for digital design and production. The project combines scientific challenge with practical relevance. The foundation of our further plans are the substantial results of the first research period in a broad range of subjects. The main goals of the second period are:• Extending the method of generalized influence functions to second order, adjoint sensitivity analysis• Developing various methods for a broad range of practical relevant engineering applications:o Derivative-based global sensitivity measures for uncertainty analysiso Forward and inverse problemso Further development of singular value decomposition for design updateo Design update procedures, e.g. for structural optimization• Investigating applications of engineering relevanceo Very large scale structural optimization o Robust design by including second order sensitivity informationo Systematic parameter variations in the digital design processo Parameter identification