Characterization of Biomimetic Peristaltic Pumping System Based on Flexible Silicone Soft Robotic Actuators as an Alternative for Technical Pumps

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Abstract

In nature and technology fluids are often transported directionally via pumping systems. Technical pumps often show signs of wear caused by abrasion of moving parts, erosion and fluid impurities, which can result in damage and excessive noise in the system. Pumping systems for electric cars (in e.g. cooling systems) should emit little noise as combustion engine noise, which normally ‘masks’ the noise caused by pumping systems, is missing. The biological peristaltic pumping principle was identified as having the highest biomimetic potential in terms of space requirements and transport capabilities (flow rate, pressure and transported media) for a transfer into silent and safe pumping systems. The peristaltic pumping of the esophagus directionally transports various media in a simple, silent and secure way and was therefore used as a biological role model for abstraction and technical implementation. For the present study, a biomimetic tubular pump was developed allowing for a simple, quiet and safe transport of a wide variety of Newtonian and non-Newtonian fluids with variable viscosity. The system is actuated by eight silicone-based pneumatic ring actuators with an elliptical inner conduit longitudinal diameter of 2 cm. The influence of actuation frequency and varying peristaltic actuation patterns on the flow rate achieved were investigated. The results indicate that the developed flexible and elastic silicone-based self-priming peristaltic pump achieves sufficient flow rates over 250 l/h and can serve as an alternative to conventional technical pumps in the field of electro-mobility.

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Esser, F., Krüger, F., Masselter, T., & Speck, T. (2019). Characterization of Biomimetic Peristaltic Pumping System Based on Flexible Silicone Soft Robotic Actuators as an Alternative for Technical Pumps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11556 LNAI, pp. 101–113). Springer Verlag. https://doi.org/10.1007/978-3-030-24741-6_9

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