From post-processing and visualization to simulation result exploration

We all know post-processing because it is part of CAE.

All textbooks teach you how important the post-processing is. For most Generation Y engineers, post-processing (and maybe even the whole CAE) almost equal to visualization as discussed in this post.

Yes, post-processing is important: it gives you the chance to justify your one-month (or one-day, or one-week, or one-year) work; it shows you something you want or something unexpected.

If you are lucky, you can even show how your simulation matches the experimental results. If you don’t have any experimental data (unfortunately, nowadays, this is not uncommon any more), you can still justify your results against common sense.

Of course, “right” or “accurate” results are not the goal of simulation.  The purposes of simulation usually  include:

• to get some data or insights that are impossible, or impractical, or too expensive, from experiments or physical tests (so that the process or system or physics can be better understood; for PhD students, more colorful figures in papers and thesis) ;
• to diagnose the problems encountered of a system (or a component) in real-world operation (so that the possible causes or scapegoats can be identified);
• to predicate the performance of a system (usually not manufactured yet) in ideal or real-world conditions (so that design can be improved before or during the prototyping stage);

In the past, simulation, especially CFD simulation, is costly: you need to spend at least a few days to prepare the geometry and mesh; then you need to wait at least another a few days for the calculation. Of course, you also need time to do the post-processing. So, once you finish the project, you are exhausted.

Thanks to the advancement in computing hardware, and improvements in the efficiency of parallel computing, the calculation time can significantly be shorted although the problem (e.g., the mesh) is getting bigger and bigger.

In addition, because most of the meshing tools now can import geometry from CAD directly, are more tolerant to dirty geometries, and are more automated, the pre-processing is no longer a bottleneck for most CAE projects. For FEA, this can be even transparent to the users: users just define loads, contacts and other constraints on CAD entities, then click the solve button.

Such advancements mean more simulations can be done in the same time frame, and therefore it is practical to incorporate or integrate simulations into the design and even the product lifecycle management.

This means at least two sets of tools are needed: 1) Simulation data management; 2). Simulation data exploration.

For simulation data management (SDM), or simulation lifecycle management (SLM), most CAE or PLM vendors have some sorts of such tools. For example. MSC has SimManager; Dassault has SIMULIA SLM; ANSYS has EKM; Siemens has Teamcenter for Simulation. Chad Jackson wrote several posts with detailed analysis on some of these tools on Engineering-matters.com (now moved to Engineering.com).

However, for simulation results exploration, it is quite disappointing. Most  SLM/SDM tools simply are incapable of exploration: they mainly can manage the simulation data and extract some metadata. Probably a few can provide some sorts of simulation template for new engineers.

Last year, Tecplot launched the Tecplot Chorus, which is a quite impressive and interesting product for CFD. But as an independent (from solver vendors) tool, it is impossible to directly compose a test case for immediate verification of the explored results. I haven’t seen any other similar products on the market.

Simulation data exploration is an extension of post-processing: deriving valuable correlations among independent variables or designs from results of multiple (often independent)  simulations.  This is the real value of having piles of simulation data.  Without simulation data exploration, SLM is merely a simple file repository for bunches of unrelated simulation files on the storage server.

Essentially, simulation data exploration is different from design optimization, a hot topic in the CAE industry. Exploration is trying to discover the knowledge buried upon the massive amounts of simulation data. This is essential for improving the efficiency of simulation in an organization and can be an indispensable part of the IP portfolio.  Currently, most organizations don’t have such knowledge bases, and therefore have to rely on “experiences” of some “old” engineers to train “new” staffs or to build some so-called best-practice templates. This is one of the major reasons why simulation has not realized its potential in most organizations.

In short, simulation results exploration is a necessity, not a luxury.