Stochastic Risk Analyzer

Design confidently to the limit

Stochastic risk analyzer for holistic performance reviews

A stochastic risk analysis gives you insights into your design’s performance in real-world conditions at the concept stage, even before manufacturing prototypes or testing. Consider fluctuations of load conditions and material properties and understand the sum effect of these uncertainties on your design’s performance. Detect critical areas and quantify your design’s in-service robustness against static and dynamic failures, even in off-design scenarios.

Understand your design’s limits and avoid failure locations

A stochastic risk analysis allows you to expose your design to your planned load conditions as well as expected off-design scenarios. By accounting for all this uncertainty at the same time, you can know your worst-case scenario – automatically.

Quantify your performance limits with the Conditional Value at Risk of the von Mises stress, which shows the expected stress everywhere in your part for the 10% most dangerous load cases.

Stochastic risk analyses reveal worst-case stress scenarios in-service.

Detect hidden failure points

Locations of maximum stress variability do not always coincide with maximum variability of stress locations.

Detect hidden dynamic failure locations which do not necessarily coincide with the highest static stress areas. The stochastic risk analysis reveals areas of your design which exhibit a high degree of displacement variability in response to small fluctuations in applied loads.

This analysis goes beyond a classical sensitivity analysis, giving you quantified insights into design features which are prone to fatigue failure during in-service operations.

Reverse compute your required material property quality assurance limits in production

Rafinex’s Stochastic Risk Analyzer is the automatic tool to simulate your designs under real-world variabilities of load conditions and material properties.

It reveals deep insights across your entire design, allowing uniquely informed design changes and enabling you to set adequate material quality control limits as well as to manufacture confidently, even for off-design scenarios.

The influence of material quality control limits on final in-service can be deduced from a stochastic risk analysis.