Training: Statistics on Structures - SoS
Statistics on Structures (SoS) is a tool for visualization, analysis and assessment of spatially random properties of structures as well as the analysis of the nonlinear correlations between the inputs and the results of a simulated system with random properties. The one-day seminar provides an overview of the theoretical background of random fields. The possibilities of SoS to analyse spatially random properties of structures are demonstrated and trained with the help of practical examples.
Engineering structures or parts are naturally subject to random scatter: manufacturing tolerances cause random structural properties, random loads cause scatter of stresses and strains. Typically, such scatter cannot be characterized by single parameters, but it is rather spatially distributed on the structure. Analysis of the spatial characteristics of scatter and of the statistical relations of the spatial distribution to the random input parameters gives important insight to the robustness of the structure and enables the proof of product quality and fulfillment of code requirements within the product development process.
- Basic stochastic: random variables and vectors, correlation, covariance
- Random fields: definitions, specific properties, discretization, decomposition and series expansion, simulation
- SoS 3: data organization, GUI, visualization
- SoS training project: sheet metal forming
- Analysis of spatially scattered data:
descriptive statistics, hot spots, eroded elements (cracks)
- Random field scatter shapes, analysis of input / output correlations by optiSLang / MOP
Basic knowledge on statistics and robustness assessment is recommended to the participants, as, e.g. obtained by our seminar “Robust Design and Reliability Analysis”.
Dr.-Ing. Veit Bayer
Dipl.-Ing. Dr. techn. Sebastian Wolff
Mar 16, 2017 in Vienna
Mar 17, 2017 in Weimar
May 31, 2017 in Weimar (Special-Training WOST 2017 >>)
1 Day seminar from 9am-5pm
min. 5 to max. 8 participants
500 Euro (excluding VAT)
50% for students
30% for university members / Ph.D. students
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