Advantages of Computational Engineering
Computational Engineering offers several advantages over traditional engineering.
Design Flexibility
Because computational models offer the full breadth of capabilities of modern programming languages, parametrization can be done on a completely different level. Highly sophisticated designs can self-adapt to different input parameters and create vastly different geometries as a result. This moves the engineer to a more explorative mode, as they no longer have to fear the tedium of recreating CAD models from scratch.
In the above example, this highly complex injector head for a rocket combustion chamber self-adapts to different manufacturing constraints (here, the ability of an industrial 3D printer to print unsupported overhangs). See our case study for more information.
Sophistication
In traditional engineering, the sophistication and complexity of an object is dependent on the amount of manual work an engineer invests in a project. Very often, more refinement would be desirable, but there is simply not enough time or budget to make it happen.
In Computational Engineering, the engineers invest their effort in a reusable model, which can be refined over time, creating more sophisticated output with each execution. Work invested in engineering is not lost, but contributes directly to the improvement of each new generation of objects.
Furthermore, even simple algorithms can create complex structures, that would be hard or impossible to design through traditional visual CAD-based engineering. Routing of cooling channels, creation of manifolds, highly complex surface structures for cooling systems, all things we have automated using our computational models.
Since these code modules are reusable, the engineer can incorporate existing solutions into new projects, combining a base module for manifold creation with the routing of cooling channels in a rocket combustion chamber, for example.
Experimentation
Innovation depends on trying out new things, on experimenting, on going past reasonable boundaries and then stepping back until something emerges, that has not existed before.
In traditional engineering, experimentation is prohibitively expensive, because each redesign takes considerable manual effort.
In Computational Engineering, experimentation is a question of recomputing a model with different input parameters, which, depending on the complexity of the model and the geometry kernel used, can take seconds, minutes, rarely hours. Experimentation is just a question of computing power and collecting the right feedback.
When numerical simulation is coupled with Computational Engineering, a vast number of different designs can be evaluated in fully automatic feedback loops.
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