Single-Pass Adaptive Convergence...

The Single-Pass Adaptive (SPA) convergence criteria are setup using the Analysis Definition data form. Setting up SPA convergence is very simple; just select the SPA option on the convergence tab and your done. This convergence method is easy to setup and generally runs much faster than the MPA convergence method. This method should be used for most of the design iteration process when a good solution is sufficient but a verifiable or know accuracy is not required.



This algorithm is not a convergence process at all. This algorithm performs only two solution passes. For the first pass all the elements edges are set to a uniform polynomial level (P-level) 3 or third order. After the first solution pass the algorithm performs error estimation throughout the model. It estimates the maximum P-level required everywhere in the model to obtain a good solution. The element P-levels are set to these values and the final or second solution pass is made.



The user has no control over the accuracy of the solution, so, how does it work and why should you use it (or not)? If you return to the FEA Basics section and note that each element calculates its own stress field based on the deformed shape of each element you will have a good basis for understanding how the error estimation is made. Since each element calculates the stress field independently from the others, the stress field across the entire mesh is not continuous. Stresses along common edges between elements will each calculate a different stress. If the solution is good, the stresses should be close. If the solution is not so good, the stresses will be much different. This is used as a basis for estimating solution error locally in the model. Also, stress components normal to the free unconstrained surfaces should be zero. Due to solution error they never are. How close they are to zero is another basis for the error estimation. These errors and other criteria are compared with the overall RMS stress in the model and, along with empirically derived fudge factors, are used to determine the P-level for the final pass. The algorithm was optimized with the goal of obtaining as good or better result with this algorithm as would be obtained using the Multi-Pass Adaptive (MPA) convergence algorithm setup with the default criteria.



The advantage to this algorithm is that it has been shown to be as much as 30 times faster than the MPA algorithm. The disadvantage is that you have no control over accuracy and very little feedback on the level of accuracy achieved.

For modal analysis SPA convergence is based on the stresses calculated for the normalized deformed mode shapes. For a thermal analysis the SPA uses Heat Flux rather than stress for error estimation.

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