ENGOPT 17-19 September Lisboa




Oral presentation.




ASME 26-29 August Quebec




Oral presentation.

Paper.



ESREL 17-21 June Trondheim




Oral presentation.

Paper.



NAFEMS Nordic Conference 24-25 April Gothenburg




Oral presentation.


ASME IDETC/CIE International Design Engineering Technical Conferences & Computers & Information in Engineering Conference




Draft paper.


ECCOMAS: COMUS - Computational Modelling of Multi-Uncertainty and Multi-Scale Problems, 12-14 September, 2017.




The oral presentation.


12th World Congress of Structural and Multidisciplinary Optimisation




The oral presentation.


European LS-DYNA Conference 2017




Our paper.

The oral presentation.



MY PRESENTATION AT SAE BRAKE IN SEPTEMBER




The oral presentation.


MY PRESENTATION AT ASME IN AUGUST




The oral presentation.


A RBDO BENCHMARK WITH 100 VARIABLES AND 80 CONSTRAINTS




I have continued to improve my SORM-based RBDO algorithm. Yesterday I think solved a rather challenging benchmark with 100 variables and 80 constraints. The CPU-time was only 114 seconds and the accuracy extremely high.


THE BENCHMARK

This is a generalization of a benchmark with 10 variables and 8 constraints suggested by Cho and Lee. It is formulated for N times 10 variables and N times 8 constraints. The problem is then solved using a new algorithm for SORM-based RBDO with non-Gaussian variables and the performance is excellent.






THE SOLUTION

In fact, the solution generated with this algorithm (SSQP) is slightly better than presented previously ([14]) depending on the SORM corrections utilized in the algorithm. The solution is obtained within 11 CPU seconds for 10 variables and 8 constraints.






THE PERFORMANCE

The performance of the algorithm is excellent. The benchmark with [2,3,4,5,6,7,8,9,10] times 10 variables and [2,3,4,5,6,7,8,9,10] times 8 constraints are solved with high accuracy and extremely fast. The plot shows CPU-time and the optimal objective value as a function of number of variables N. The CPU-time is 114 seconds for 100 variables and 80 constraints. In addition, the optimal objective value is exactly 10 times the value obtained for 10 variables and 8 constraints.