Introduction to 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.

Agenda

  • Basic stochastic: random variables and vectors, correlation, covariance
  • Random fields: definitions, specific properties, discretization, decomposition and series expansion, simulation
  • SoS: 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

Details

Date:
to be announced

Map

Please register not later than 14 days prior to the event date.

Time:
9am - 5pm

Fee: 
600 Euro (excluding VAT) per participant in Weimar or Vienna
2000 Euro (excluding VAT) per on-site training including maximum 12 participants

The participation includes a 1-month test licence of SoS

Discount:50% for students30% for university members / Ph.D. students