Revision 6 of LMS Virtual.Lab Released

LMS have announced the release of LMS Virtual.Lab Rev 6. This new release introduces an advanced and unified modeling environment, integrating all the required model creation and simulation tools to perform accurate system-level performance analyses. This new solution eliminates the tremendous effort to create individual models for separate disciplines, and allows easy and smooth cross-attribute analyses. Rev 6 provides multiple innovative technologies and 64-bit data processing support, making it more practical to handle complex models with high accuracy, while significantly reducing calculation times. Rev 6 also marks the introduction of a new value-based licensing system, giving users flexible access to the full portfolio of LMS Virtual.Lab applications. Furthermore, LMS Virtual.Lab Rev 6 offers new and completed applications for structural analysis, acoustics simulation, noise and vibration prediction, system dynamics analysis and durability simulation, and extended automation and customization capabilities.

With Rev 6 of Virtual.Lab, LMS delivers a unified modeling environment, which integrates all the required model creation tools and multidisciplinary simulation capabilities to accurately assess system-level performance for a complete car, a full aircraft or any other complex mechanical assembly. The new modeling and assembly solution offers extensive capabilities to flexibly build full-system simulation models starting from multiple component and sub-system models. Working from a single data model, users can efficiently analyze the system’s behavior in multiple performance aspects like dynamic stiffness, strength, noise and vibration, durability, vehicle ride & handling or the dynamic performance of mechanical systems. This new solution eliminates the tremendous effort to create individual models for separate disciplines, saving tremendous time and avoiding the accumulation of modeling errors and inaccuracies. In addition, the unified modeling solution allows easy and smooth cross-attribute analyses.

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