Every simulation team eventually settles into a favorite solver, and there’s a good reason for that: licensing costs, team familiarity, and existing model libraries all push toward standardizing on one platform. That’s a reasonable operational decision. It’s a less reasonable technical one, because LS-DYNA, PAM-CRASH, Nastran and Abaqus were built to answer different kinds of questions well, and defaulting to whichever one you have open can quietly shape the analysis itself.
Where Solver Choice Actually Matters
Explicit solvers like LS-DYNA and PAM-CRASH are built for short-duration, highly nonlinear events, crash, impact, drop testing, where large deformations and contact happen in milliseconds. Implicit solvers like Nastran and Abaqus are built for the opposite: linear and nonlinear structural analysis, static and dynamic loading, modal and fatigue studies, where the physics play out over a longer time scale and stability matters more than raw speed per step. Running a crash event through an implicit solver, or a slow structural fatigue study through an explicit one, is technically possible and practically painful, you’ll fight the solver instead of answering the engineering question.
Where It Doesn’t Matter as Much
For a lot of mainstream linear structural analysis, Nastran and Abaqus produce comparable results, and the choice comes down to team familiarity, existing model libraries, or downstream reporting requirements rather than any real technical edge. This is exactly the kind of decision where “whichever license we already have” is a perfectly fine answer. The mistake isn’t picking a default solver, it’s never questioning the default when the problem in front of you doesn’t fit it.
Why We Stay Solver-Agnostic
Being solver-agnostic isn’t a marketing position, it changes how a project gets scoped from day one. Instead of asking “how do we make this work in the solver we have,” the first question is “what does this physics actually need.” Most of the time that lands on a familiar answer. Occasionally it doesn’t, and that’s precisely the case where solver flexibility saves a project from a bad result delivered on time rather than a good one delivered slightly later.
Not sure which solver fits your next project? See our Multi-Solver Simulation service or talk to us about the specifics.