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Understand and Deliver

The premise is simple; to engineer a solution one must first understand the problem.

For most structural problems the days of trial and error are long gone.  However, convoluted cross-purposed design objectives do create challenges to reaching a good design from first principles.

 

Correlated simulation provides the necessary understanding to refine a design against goal metrics, and employing sophisticated optimisation techniques will deliver the best solutions.

Structural Evaluation

Optimisation

Visualisation

Outreach

Demoreel 2021-06

Demoreel 2021-06

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Structural Evaluation

Structural evaluation is the structural analysis and/or testing used to support both pre-emptive design (virtual prototyping) and compliance verification, and is best achieved through employing a numerical method, such as the Finite Element Method, correlated against test data at material, component and assembly levels. 

 

Numerical methods are much more robust when used to interpolate between tested data points.  Extrapolating beyond the tested data range requires additional care and diligence, together with crosschecks employing independent recognised methods.

 

Extrapolation of a complex numerical model can be compared to a modern-day weather forecast, which as we all know can still be inaccurate or wrong.  Nonetheless, worst case scenarios can still be engineered for.

 

If no test data are obtained to correlate against then the structural evaluation of a complex structure will either be compromised, inaccurate or all together incorrect and misleading, with no way of telling which.  In such cases the principle of conservatism is used to ensure structural integrity (i.e. overdesign), and this may lead to significant weight penalties and operational limitations.

If the design is starting from a clean sheet of paper then optimal basic structural load paths within a system's volume can be evaluated using approaches such as using strain energy density paths as a guide.

Visualisation

 

A picture tells a thousand words.  Animation conveys much more.  Visualisation of data can be as much a science as the data itself.

 

Good data visualisation often requires more than just good presentation skills.  For example, good choice of data selected for visualisation combined with videography techniques, such as depth of field and ambient occlusion to focus on and highlight contextual areas, lead to significant improvements in understanding and engagement.

Outreach

 

Informing those unfamiliar with the subject matter has never been as important a task.  So, visualisation needs to take into account the prospective audiences to make sure:

  1. The science and data are informed and understandable.

  2. The trap of incorrect interpretation is avoided.

  3. The media are engaging throughout.

  4. The audience is left with agency over the subject matter.

Optimisation

 

Optimisation of a structure is simple in principle; it can be considered analogous to calculating a heading to a destination, or target, and then heading towards it until you reach it.  A common optimisation target is that of low weight while maintaining structural integrity.  A weight reduction target might be reached by reducing panel thickness.  Similarly, a structural improvement may be met in a mass-efficient manner by optimising the reinforcement.

 

A common optimisation example can be seen everyday on the roads for convertible car models that are derived from saloon or hatchback base models.  In such cases, the design of the base model body-in-white (BIW) has the roof removed, with the B and C pillars halved in height.  This has a drastic effect on almost all structural integrity metrics.  Optimisation can be used in this instance to recover structural integrity for minimal mass increase and minimal structural change for the convertible variant BIW.  The first step of such a task would be to address the impact on the car body torsional stiffness, as shown in this example.

Sometimes, the parameters or geometry changes to optimise on are not obvious, or parameter phased interdependencies mean that there are an unknown number of optimisation "recipes" to consider.  In such cases trial methods can be used to search for the best parameter combinations for optimisation.

The nature of optimisation in practice is often complex, especially in cases of convoluted and contradictory design objectives.  However, sophisticated optimisation strategies exist to cater for such situations.