Choosing the Right Solver: Linear vs Nonlinear in Simulation
Imagine being able to predict how your design will perform without spending any money on creating a prototype. That’s the magic of Simulation technology. A key element in achieving accurate results from your simulations is selecting the appropriate tool for the job. This blog post aims to help you determine whether a linear or nonlinear solver is the best fit for your analysis, ensuring you gain reliable insights and make informed decisions for your projects.
Linear Solvers vs Nonlinear Solvers
As a general guideline, if your simulation doesn’t exhibit nonlinear effects, opting for a linear solver is more efficient, as these are typically quicker to set up and run. Tools like Simulation are ideal for many straightforward problems.
However, nonlinear solvers come into play when your analysis involves more complex behaviors such as materials that permanently deform or large displacements. Both Simulation and Simulia Structural offer nonlinear solvers, with Simulia providing a comprehensive solution.
When are Nonlinear Solvers Necessary?
Let’s examine three key scenarios where nonlinear solvers are essential:
- Material Nonlinearity: This occurs if, for instance, you’re analyzing a metal part that deforms beyond its elastic limit or working with inherently nonlinear materials like rubber. For example, consider the difference in stress-strain behavior between metals and rubbers. If your metal analysis point is below the yield point, you don’t need a nonlinear solver. However, for rubber, where no linear portion exists, a nonlinear solver is mandatory for accurate results.
- Geometric Nonlinearity (Large Deformations): This nonlinearity is evident when parts significantly change shape under stress. If looking at the part from a distance reveals a noticeable deformation, you’re possibly dealing with large displacements, which require a nonlinear solver. Beyond mere deformation, the gradual return to the original shape when stress is removed still classifies as geometric nonlinearity.
- Boundary Condition Nonlinearity: Changes in contacts, forces, or fixtures during a simulation can signal this type of nonlinearity. Multi-step simulations, where results from one stage affect the next, also fall into this category. An example is a paper clip bending; as the load changes, the input boundary conditions reflect nonlinearity. A similar principle applies to snap-fit buckles, where contact locations change throughout the analysis.
Recap of Nonlinearities
To summarize, the three types of nonlinearities are:
- Material Nonlinearity: Occurs when materials surpass their elastic limit or involve materials without a linear response like rubber.
- Geometric Nonlinearity: Emerges when components undergo significant shape changes.
- Boundary Condition Nonlinearity: Involves dynamic changes in simulation inputs such as contacts and forces.
Choosing the Right Solver and Future Considerations
If your analysis doesn’t involve any nonlinear effects, a linear solver offers the fastest setup, with Simulation excelling in this area. For problems with mild nonlinearities, Simulation also provides a capable nonlinear solver. If a nonlinear solver is necessary, consider whether an implicit or explicit solver best suits your needs.
Conclusion
From basic linear challenges to intricate nonlinear analyses, choosing the right solver marks just the start of your journey. Mastering these tools requires understanding their optimal use. With MLC, whether for Simulation or Simulia Structural, our training programs can guide you in selecting the right solver, optimizing simulations, and refining decision-making for your projects.
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About the author
Jon Duffin is a SOLIDWORKS Technical Support Expert based in the Atlanta area. Prior to his career at MLC CAD Systems, he worked in the defense industry improving the manufacturing processes for Solid Fuel Rocket Motors. He immensely enjoys helping others improve their SOLIDWORKS productivity because it combines his love of engineering with the desire to help other people.
