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Concept Constraint Analysis - A Proposed Computational Implementation

An inevitable aspect of concept constraint analysis is that its fidelity is in keeping with its place in the design process: a stage where we often do not have a detailed geometry yet, hence nor do we have access to serious numerical analysis. Calculations at this point are based on empirical models and simple physics instead - first-order guesses for a first-order design iteration.

The reader may have a pet tool that may be wielded to code up these models and to perform the constraint analysis but, if not, there is a great variety to choose from (basically any sophisticated programmable platform with a ready plotting capability could be made to work - MATLAB, Python, etc.). Our own preference is for the Jupyter[1] web application, the power of which lies in its ability to create a parametric notebook - a “living” text document that is capable of updating its (code driven - specifically, in our case, Python code driven) calculations and graphs in response to changes in the inputs. From the point of view of the design process, this is a very neat way of performing concept design calculations, because the result is a document that, once complete, can simply be slotted into the design audit trail. The constraint analysis notebook is available with this book, and in Section 10.4 we have included a sample output document. The results of this model[2] will serve as the starting point of a more sophisticated (but narrower in scope) subsequent design iteration, based on spreadsheets and set out in the next chapter.

  • [1] http://www.jupyter.org.
  • [2] https://aircraftgeometrycodes.wordpress.com/airconics-uav/.
 
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