Here is a challenge for you: overall this blog has a readability index of 8.6 using the Flesch Kincaid Grades, which means it should be easily understood by 14-15 year olds. However, my editor didn’t understand the first draft of the post below and so I have revised it; but it still scores 15 using Flesch Kincaid! So, it might require the formation of some larger scale neuronal assemblies in your brain [see my post entitled ‘Digital Hive Mind‘ on November 30th, 2016].
I wrote a couple of weeks ago about guessing the weight of a reader. I used some national statistics and suggested how they could be updated using real data about readers’ weights with the help of Bayesian statistics [see my post entitled ‘Uncertainty about Bayesian statistics’ on July 5th, 2017]. It was an attempt to shed light on the topic of Bayesian statistics, which tends to be obscure or unknown. I was stimulated by our own research using Bayesian statistics to predict the likelihood of failure in damaged components manufactured using composite material, such as carbon-fibre laminates used in the aerospace industry. We are interested in the maximum load that can be carried by a carbon-fibre laminate after it has sustained some impact damage, such as might occur to an aircraft wing-skin that is hit by debris from the runway during take-off, which was the cause of the Concorde crash in Paris on July 25th, 2000. The maximum safe load of the carbon-fibre laminate varies with the energy of the impact, as well as with the discrepancies introduced during its manufacture. These multiple variables make our analysis more involved than I described for readers’ weights. However, we have shown that the remaining strength of a damage laminate can be more reliably predicted from measurements of the change in the strain pattern around the damage than from direct measurements of the damage for instance, using ultrasound.
This might seem to be a counter-intuitive result. However, it occurs because the failure of the laminate is driven by the energy available to create new surfaces as it fractures [see my blog on Griffith fracture on April 26th, 2017], and the strain pattern provides more information about the energy distribution than does the extent of the existing damage. Why is this important – well, it offers a potentially more reliable approach to inspecting aircraft that could reduce operating costs and increase safety.
If you have stayed with me to the end, then well done! If you want to read more, then see: Christian WJR, Patterson EA & DiazDelaO FA, Robust empirical predictions of residual performance of damaged composites with quantified uncertainties, J. Nondestruct. Eval. 36:36, 2017 (doi: 10.1007/s10921-017-0416-6).