Modelling the HIC
Our approach is to model the deceleration curve with a function. We recognise that the shape of the crash data graph is basically like the curve:
(This similar to the 'bell-shaped curve' in statistics.)
Let's look at the curve using LiveMath:
Model for the Acceleration
By modifying the function above, we can get curves very close to the Mercedes experimental data.
[This model was obtained by observing 2 peaks in the deceleration graph, centred at 68 ms and 93 ms. By adding the 2 bell-like curves, we get a model very close to the required graph.]
The computer time to calculate the HIC expression is high, so we need to simplify things a bit...
We can simplify that part of the HIC formula in {} for different values of d = t2 − t1. We define a family of curves:
We then vary the value of d. The value of the highest peak of the family of curves obtained gives us the HIC.
HIC without Airbag
We now use the model for a(t) from above and maximise the value of H for different values of d.
We see that the highest peak occurs when d = 50 and reading from the graph we see that the HIC is approximately 725. This is reasonably close to the Mercedes Benz data. (Other values for d are shown, but not all, of course. The value d = 50 did in fact give the highest curve.)
HIC with Airbag
The model is simpler for the airbag case, as the deceleration is smoother and is almost bell-shaped. Some modelling achieves the following expression for the acceleration:
The graph is as follows (drawn with the same vertical scale as the non-airbag case):
Now to apply the formula for H again. We get a family of curves and once again, d = 50 gives us the maximimum value, hence the HIC.
The HIC for the airbag case is around 310, close to the Mercedes Benz data.
Our main aim here is to reduce the effects of rapid deceleration. If the curve is low and flat, injury to the head is reduced.
Let's get LiveMath to draw these models for us:
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