The faint gray curves in the charts below display the tight application of the same prediction formula, extrapolating the original curve more accurately than the simulation in white.įigure 2- Comparing an actual 24" drop (dark blue) to a 30" drop simulating 24” (white)įigure 3- Comparing an actual 30" drop (dark blue) to a 24" drop simulating 30” (white)
In terms of fragility levels, the more cautious option is to simulate the curve of a smaller drop height than the one sampled, or that of a thicker foam than sampled, rather than the reverse. The heavier the static loading, the more the predicted curve is likely to deviate from the source curve’s path of observed behavior.Īs illustrated in the charts below, the direction of simulated change in the predicted curve will affect the direction of the error. The cost of this flexibility is in precision. This includes predicting new curves that are not provided by the manufacturer, such as 4.75” thick foam cushioning, or a 48” drop height. One dynamic application of the same formula can return a ballpark of a given material’s static load range at any other drop height, foam thickness or fragility level fed into it. It appears to work equally well with open-cell foams relying on a structural component only, and with closed-cell foams that also involve air-displacement.įigure 1- A tight application of the proposed formula With this technique, the predictions interpolate the original curves with a remarkable line of fit and extrapolate with plausible growth rates. Each published ASTM D1596 generated curve of a given material is formulated individually in order to make off-chart predictions for that particular scenario. This method requires multiple calculators to represent the anticipated behavior of a single material in multiple scenarios, so it is advisable to create these calculators only as they are needed rather than attempting to populate a complete set all at once.
These two approaches to cushion curve prediction constitute the author's modification of an existing formula proposed nearly thirty years ago, dramatically improving its effectiveness in cushion calculation. Both methods feature transparent calculations that can be evaluated and modified with new data at any time after the setup. Two applications of such a formula are presented here, in which relative ease and precision have an inverse relationship. For anyone who has squinted at traditional dynamic cushion curves (static stress curves) supplied by material manufacturers and other sources, a versatile mathematical formula may sound like a welcome alternative.
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