A Finite Similitude Approach to Scaled Impact Mechanics

The response characteristics of large-scale structures subjected to impact loading can in principle be determined by scaled experiments. Unfortunately, scaling suffers from scale effects and for impact mechanics, the non-scalability of strain rate and strain hardening can diminish the effectiveness of scaled trials. To resolve this difficulty, a new scaling method has recently appeared in the open literature called finite similitude. The theory is founded on the metaphysical concept of space scaling, where the idea is that by expanding or contracting space, changes in the governing mechanics can be assessed. In this paper the finite-similitude theory is further developed, where it is demonstrated how the constraints imposed by dimensional analysis can be broken. A new form of similarity is introduced but at the cost of requiring two scaled experiments at distinct scales. It is shown however, how the theory is able to combine the information from the two scaled trials to predict outcomes that can be markedly superior to what can be achieved with experiments at a single scale. All scale dependencies are accounted by the theory and consequently the new formulation attempts to capture scale effects, so provides a more realistic approach to scaled experimentation. Unlike dimensional analysis, the new first-order finite similitude theory can simultaneously target two independent physical properties of common dimension (e.g. initial-yield stress and linear strain hardening). The advantage offered by this feature is demonstrated analytically and numerically in the paper with a focus on axisymmetrical tube buckling and energy absorption. The analytical model serves to expound the theory and the numerical highlights its capabilities and the kinds of accuracy achievable with the new approach.

Automated summary

The finite similitude theory is a new scaling method that aims to capture scale effects by conducting two scaled experiments at different scales, providing a more realistic approach to scaled experimentation. By combining information from two experiments, the theory can predict outcomes that may be superior to experiments at a single scale.