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In this paper, we numerically investigate drop impact on a micro-well substrate to understand the phenomena of non-wettability. The simulation is carried out by solving three-dimensional incompressible Navier–Stokes equations using a density projection method and an adaptive grid refinement algorithm. A very sharp interface reconstruction algorithm, known as the moment-of-fluid method, is utilized to identify the multi-materials and multi-phases present in the computation domain. Our simulations predicted that a micro-well with a deep cavity can significantly reduce a solid–liquid contact in the event of drop impact. The results from the drop impact on the micro-well substrate are compared with results from drop impact on a flat substrate. Significant differences are observed between these two cases in terms of wetted area, spreading ratio, and kinetic energy. Our simulation shows that under the same conditions, a drop is more apt to jump from a micro-well substrate than from a flat surface, resulting in smaller wetted area and shorter contact time. Based on the simulation results, we draw a drop jumping region map. The micro-well substrate has a larger region than the flat surface substrate. Finally, we present a comparative analysis between a flat substrate and a substrate constructed with a dense array of micro-wells and, therefore, show that the array of micro-wells outperforms the smooth substrate with regard to non-wettability and drop wicking capability.
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ContactNets: Learning of Discontinuous Contact Dynamics with Smooth, Implicit Representations
Common methods for learning robot dynamics assume motion is continuous, causing unrealistic model predictions for systems undergoing discontinuous impact and stiction behavior. In this work, we resolve this conflict with a smooth, implicit encoding of the structure inherent to contact-induced discontinuities. Our method, ContactNets, learns parameterizations of inter-body signed distance and contact-frame Jacobians, a representation that is compatible with many simulation, control, and planning environments for robotics. We furthermore circumvent the need to differentiate through stiff or non-smooth dynamics with a novel loss function inspired by the principles of complementarity and maximum dissipation. Our method can predict realistic impact, non-penetration, and stiction when trained on 60 seconds of real world data.
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- Award ID(s):
- 1830218
- PAR ID:
- 10273128
- Date Published:
- Journal Name:
- Conference on Robot Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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