LMS Virtual.Lab Designer Fatigue is an easy-to-use fatigue life prediction solution for predicting and improving Fatigue performance of a broad range of systems. It is applicable to a wide range of industry segments, including automotive, aerospace, white goods and other mechanical industries.
With LMS Virtual.Lab Designer Fatigue, designers and analysts start from static or dynamic stresses predicted using CATIA CAE models, apply varying load time histories to the component models, run the embedded fatigue life prediction solver and post-process the results, all in a completely geometry-associative process. This solution drastically improves simple stress analysis as it allows the identification of dynamic stress peaks and fatigue problems very early on in the design stage.
LMS Virtual.Lab Designer Fatigue offers different fatigue-life analysis capabilities, including assessment of low-cycle fatigue (strain-life approach), high-cycle fatigue (stress-life) and infinite life (Dang Van). Fatigue material parameters are estimated using the uniform material law or based on the embedded fatigue material parameter library. As the fatigue life prediction solver supports both proportional as non-proportional loading conditions real-life loading conditions can be simulated. Dedicated durability postprocessing functionalities including dynamic stress animation, hotspot detection and local time series analysis allow to quickly zoom in on the critical areas and to derive the root cause of fatigue problems.
The CATIA V5-based associativity concept of LMS Virtual.Lab Designer offers users to automatically execute consecutive simulation runs, which enable different design options and and/or load cases to seamlessly flow through the complete analysis process. Evaluating a new design only involves plugging in the modified structure, upon which the analysis is automatically restarted – avoiding tedious redefinitions of load input points, constraints and other parameters. These capabilities support design teams in their efforts to experiment with multiple design options and identify the best solutions before physical prototyping starts.
