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How to Choose a SOLIDWORKS Simulation Solver

Understanding SOLIDWORKS Simulation Solvers

SOLIDWORKS Simulation offers several solver options, each with its strengths and weaknesses. 

 

 Selecting the right solver can significantly impact the accuracy, speed, and resource consumption of your simulation. This article will break down the different types of solvers and when to use them.    

Types of Solvers in SOLIDWORKS Simulation

There are primarily two categories of solvers in SOLIDWORKS Simulation: Direct and Iterative.

   

Direct Solvers 

 

Direct solvers provide exact solutions to linear equations. They are generally faster for smaller problems and offer higher accuracy. However, their memory requirements can grow rapidly with increasing problem size.    

    • Direct Sparse: This solver is suitable for small to medium-sized models with sufficient available RAM. It offers good accuracy and speed but can become resource-intensive for larger models.   
    • Large Problem Direct Sparse (LPDS): Designed for models with millions of degrees of freedom, LPDS optimizes memory usage and can handle larger problems than Direct Sparse. However, it might be slower than Direct Sparse for smaller models.    

 

Iterative Solvers 

 

Iterative solvers approximate solutions through successive refinements. They are generally more memory-efficient than direct solvers, making them suitable for large models. However, they can converge slowly or fail to converge in certain cases.    

    • FFEPlus: This iterative solver is often the default choice due to its balance of speed, accuracy, and memory efficiency. It works well for a wide range of problems but might struggle with highly nonlinear or unstable models. 

When to Use Which Solver

Choosing the right solver depends on several factors, including model size, complexity, available system resources, and desired accuracy. 

 

    • Small to Medium-Sized Models:
        • Direct Sparse: Ideal for linear and nonlinear problems where accuracy is critical and sufficient RAM is available.
    • Large Models:
        • FFEPlus: Generally a good starting point due to its robustness and memory efficiency.
        • Large Problem Direct Sparse: Consider using LPDS if FFEPlus fails to converge or is too slow, and you have sufficient memory.
    • Nonlinear Problems:
        • Direct Sparse: Often preferred for its accuracy, but can be computationally expensive.
        • FFEPlus: Can be used as an alternative, but convergence might be slower.
    • Memory Constraints:
        • FFEPlus: Generally more memory-efficient than direct solvers.   
        • Large Problem Direct Sparse: Designed for large models with limited memory.   
    • Additional Considerations
        • Model Preparation: Good mesh quality and appropriate boundary conditions can significantly impact solver performance. 

 

It’s important to note that the best solver choice can vary depending on the specific problem and available resources. Experimentation and analysis of results are crucial for determining the optimal solver for a given simulation. 

 

By understanding the characteristics of different solvers and considering the factors outlined above, you can make informed decisions to improve the efficiency and accuracy of your SOLIDWORKS simulations. 

 

Additional information is available in this SOLIDWORKS Help Article. 

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Last Updated On
byEdward Kennedy
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