research

The Design & Uncertainty Quantification group at The University of Iowa, led by Professor Sharif Rahman, is looking for new Ph.D. students, who are capable of and interested in performing high-quality research on uncertainty quantification and stochastic design optimization. The research, supported by U.S. National Science Foundation, requires building a solid mathematical foundation, devising efficient numerical algorithms, and developing practical computational tools, all associated with stochastic analysis and design of complex materials and structures.

A very interesting recent paper by Dalvi et al. has demonstrated convincingly with adhesion experiments of a soft material with a hard rough material that the simple energy idea of Persson and Tosatti works reasonably well, namely the reduction in apparent work of adhesion is equal to the energy required to achieve conformal contact. We demonstrate here that, in terms of a stickiness criterion, this is extremely close to a criterion we derive from BAM (Bearing Area Model) of Ciavarella, and not very far from that of Violano et al.

https://doi.org/10.1088/1361-651X/ab3b62

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Abstract

Published in Extreme Mechanics Letters: https://doi.org/10.1016/j.eml.2019.100507. For more than half a century, physicists rejected the existence of two-dimensional (2D) materials since they theoretically underestimated the stability. However, the discovery of one-atom-thick graphene proved the inapplicability of this theory.

One of our studies on linear and nonlinear non-Hermitian metamaterials has been published on the recent special issue of the Journal of the Acoustical Society of America: Non-Reciprocal and Topological Wave Phenomena in Acoustics.

Abstract

Data-driven system identification procedures have recently enabled the reconstruction of governing differential equations from vibration signal recordings. In this contribution, the sparse identification of nonlinear dynamics is applied to structural dynamics of a geometrically nonlinear system. First, the methodology is validated against the forced Duffing oscillator to evaluate its robustness against noise and limited data.