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Operating principles of peristaltic pumping through a dense array of valves

Published online by Cambridge University Press:  29 July 2024

Aaron Winn*
Affiliation:
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
Eleni Katifori
Affiliation:
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
*
Email address for correspondence: winna@sas.upenn.edu

Abstract

Immersed nonlinear elements are prevalent in biological systems that require a preferential flow direction, such as the venous and the lymphatic system. We investigate here a certain class of models where the fluid is driven by peristaltic pumping and the nonlinear elements are ideal valves that completely suppress backflow. This highly nonlinear system produces discontinuous solutions that are difficult to study. We show that, as the density of valves increases, the pressure and flow are well approximated by a continuum of valves which can be analytically treated, and we demonstrate through numeric simulation that the approximation works well even for intermediate valve densities. We find that the induced flow is linear in the peristaltic amplitude for small peristaltic forces and, in the case of sinusoidal peristalsis, is independent of pumping direction. Despite the continuum approximation used, the physical valve density is accounted for by modifying the resistance of the fluid appropriately. The suppression of backflow causes a net benefit in adding valves when the valve density is low, but once the density is high enough, valves predominately suppress forward flow, suggesting there is an optimum number of valves per wavelength. The continuum model for peristaltic pumping through an array of valves presented in this work can eventually provide insights about the design and operating principles of complex flow networks with a broad class of nonlinear elements.

Information

Type
JFM Papers
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press.
Figure 0

Figure 1. Model for peristaltic pumping with a dense array of valves satisfying assumption (1.1). The imposed peristaltic force $f$ has wavelength $\lambda$ and speed $c$. The strength of the force at each point on the boundary is proportional to the length of the arrows. The vessel has rest radius $R_0$ and intervalve spacing $x_v$.

Figure 1

Figure 2. Demonstration of how the pressure and flow in a system of many randomly placed valves is approximated by the results in the valve continuum limit for various choices of the amplitude of peristalsis $\eta _P$ and the stiffness $\kappa$. The solid lines show numerical results from simulating a tube with $n_v=20$ valves per wavelength. Red solid line: normalized radius as a function of $\bar {x}$, the normalized location along the tube; blue solid line: normalized pressure; purple solid line: normalized flow. The dimensionless valve resistance was chosen to be $\bar {r}_v = 0.05$. The black dashed line shows the corresponding valve continuum prediction, and the grey dotted line shows the prediction for the valveless case. The valves in the shaded region are closed while the valves in the unshaded region are open. Parameters used for the discrete valve simulations are $(a)$$\kappa = 16, \eta _P=0.2$, $(b)$$\kappa = 16, \eta _P=2$, $(c)$$\kappa = 0.4, \eta _P=0.2$, $(d)$$\kappa = 0.4, \eta _P=2$. For the valve continuum and valveless cases, $\kappa$ and $\eta _P$ were divided by $1+\bar {r}_v n_v=2$, and $\bar {P}$ was multiplied by 2 as compared with the discrete valve case.

Figure 2

Figure 3. Exact numerical solutions to (2.9), (2.10), (2.11a,b) and (3.4) with $f(\bar {x},\bar {t})=\cos (2{\rm \pi} (\bar {x}-\bar {t}))$ in a region of many closed valves are shown with solid lines. The analytical predictions (3.13), (3.14), (3.15) are shown with dash-dotted lines, and appear to agree well with the exact solutions. The valve continuum solutions (3.16), (3.17) and (3.18) are shown with black dashed lines. Parameters used for this simulation are $n_v=20$, $\bar {r}_v=0$, $\kappa =0.5$ and $\eta _P=0.5$. Subtracting (or in the case of the flow, dividing) by a constant value eliminates the unknown parameters $\bar {R}_0$ and $\bar {P}_1$. In the case of the analytical solutions, the pressure and radius should be evaluated at the limit as $\bar {x}$ approaches one from below.

Figure 3

Figure 4. Example solutions to the valve continuum model which demonstrate the appropriate matching conditions. $(a)$ For a forward-propagating wave, closing occurs at $\max \bar {P}$ and $\min \bar {R}$, so it must also occur at $\max f$ by (2.11a,b). The origin is chosen to be $\max f$ for simplicity. However, opening occurs at $\min \bar {P}$ and $\min \bar {R}$, so the opening coordinate $\tilde {\xi }$ cannot be simply expressed in terms of $f$. $(b)$ By a similar argument, for a backward-propagating wave, closing occurs at $\min f$, but the opening coordinate $\tilde {\xi }$ cannot be simply expressed in terms of $f$. Note that opening is defined as the time at which the valve transitions from closed to open, and closing is defined as the time at which the valve transitions from open to closed. For a forward-propagating wave where $\xi =\bar {x}-\bar {t}$, one should read the plots from right to left when determining the opening and closing coordinates, but for a backward-propagating wave where $\xi =\bar {x}+\bar {t}$, one should read the plots from left to right when determining the opening and closing coordinates.

Figure 4

Figure 5. Results for forward-propagating peristalsis in a stiff tube with a continuum of valves, $f(\bar {x},\bar {t}) = \cos (2{\rm \pi} (\bar {x}-\bar {t}))$. $(a)$ Fraction of valves open in a stiff vessel for two different choices of large stiffness and varying radial amplitude $\eta _R$. The dashed line is the small-amplitude analytic result (4.30). $(b)$ Mean flow for two different choices of large stiffness and varying radial amplitude $\eta _R$. The dashed line is the analytic result (4.10).

Figure 5

Figure 6. Results for forward-propagating peristalsis with a continuum of valves, $f(\bar {x},\bar {t}) = \cos (2{\rm \pi} (\bar {x}-\bar {t}))$. $(a)$ The fraction of valves per wavelength which are open at any given time is calculated numerically (points) as a function of $\kappa$ and compared with the small-amplitude result (4.21) (dashed line). $(b)$ The mean flow divided by $\eta _P$ is calculated numerically as a function of $\kappa$ and compared with the small-amplitude result (4.25). $(c)$ The mean flow is calculated as a function of $\eta _P$. For small $\eta _P$, the scaling is linear for each $\kappa$, as demonstrated by the dashed line.

Figure 6

Figure 7. Results for backward-propagating peristalsis with a continuum of valves, $f(\bar {x},\bar {t}) = -\cos (2{\rm \pi} (\bar {x}+\bar {t}))$. The dashed lines in (a,b) correspond to the small-amplitude analytic expressions (5.17) and (5.18), which are identical to (4.21) and (4.25) for the forward-propagating wave.

Figure 7

Figure 8. Large-amplitude peristalsis against a continuum of valves with $f(\bar {x}, \bar {t}) = - \cos (2{\rm \pi} (\bar {x}+\bar {t}))$. $(a)$ Numerical solutions for the cross-sectional area and flow using $\eta _P = 25, \kappa = 0.5$ are shown with solid lines and compared with the analytic solution (5.20) with $\bar {R}(0)^2$ given by (5.22) shown with dotted lines. $(b)$ Deviation of the mean flow from the fully occluded limit. The dots show numerical solutions for two different choices of $\kappa$, and the dashed line is the analytical large-amplitude prediction (5.23).

Figure 8

Figure 9. Mean flow as a function of the number of equally spaced valves per wavelength $n_v$. As the density of valves increases, the mean flow approaches the valve continuum result (lines). In all cases, $\eta _P=0.1$. $(a)$ Forward-propagating peristaltic wave with zero valve resistance. $(b)$ Backward-propagating peristaltic wave with zero valve resistance. $(c)$ Forward-propagating peristaltic wave with non-zero valve resistance. $(d)$ Backward-propagating peristaltic wave with non-zero valve resistance.

Figure 9

Figure 10. The value of $n_v$ which maximizes $\langle \bar {Q} \rangle$, denoted $n_v^*$, as a function of valve resistance $\bar {r}_v$. Valves were spaced equally, and all integer values of $n_v$ between 0 and 25 were tested. $(a)$ Forward-propagating peristaltic wave. $(b)$ Backward-propagating peristaltic wave.

Figure 10

Table 1. Experimental values of physical parameters in the lymphatic system.

Figure 11

Figure 11. Results when a small bending term is introduced such that the force-balance equation takes the form (A1) with $\alpha = 10^{-7}$. Other parameters used in this simulation are $\eta _P=0.25$, $\kappa =0.2$ and $\bar {r}_v=0$. $(a)$ Radius, pressure and flow induced by a forward-propagating sinusoidal peristaltic wave. The dotted line is the valveless solution, the dashed line is the solution with five equally spaced valves, and the solid line is the valve continuum result. $(b)$ Radius, pressure and flow induced by a backward-propagating sinusoidal peristaltic wave.

Figure 12

Figure 12. Summary of results for a Gaussian forcing with width parameter $l=0.1$. $(a)$ Each column represents a different peristaltic Gaussian wave train. In order from left to right, $f$ takes the form of a forward-propagating bell curve $f(\bar {x},\bar {t}) = \sum _m \exp (-(\bar {x}-\bar {t}-m)^2/2l^2) / \sqrt {2{\rm \pi} l^2} - 1$, a backward-propagating bell curve $f(\bar {x},\bar {t}) = \sum _m \exp (-(\bar {x}+\bar {t}-m- \frac {1}{2})^2/2l^2) / \sqrt {2{\rm \pi} l^2} - 1$, a forward-propagating inverted bell curve $f(\bar {x},\bar {t}) = 1 - \sum _m \exp (-(\bar {x}-\bar {t}-m - \frac {1}{2})^2/2l^2) / \sqrt {2{\rm \pi} l^2}$ and a backward-propagating inverted bell curve $f(\bar {x},\bar {t}) = 1 - \sum _m \exp (-(\bar {x}+\bar {t}-m)^2/2l^2) / \sqrt {2{\rm \pi} l^2}$. The radius, pressure and flow are displayed for each. Parameters used for these simulations are $\kappa =8$, $\eta _P=0.1$ and $\bar {r}_v=0$. Notice that, when $f$ takes the form of a bell curve such that $\bar {R}$ is an inverted bell curve, the flow is larger for a forward-propagating wave, but when $f$ takes the form of an inverted bell curve such that $\bar {R}$ is a bell curve, the flow is larger for a backward-propagating wave. $(b)$ Mean flow as a function of amplitude for each of the cases in $(a)$.

Figure 13

Figure 13. Mean flow as a function of the number of equally spaced valves per wavelength $n_v$ when driven by a $(a)$ forward-propagating and a $(b)$ backward-propagating Gaussian wave train. In both cases, $\bar {r}_v=0$, $\eta _P=0.01$ and $\kappa = 1$. The results for a discrete number of valves are shown with points for different choices of $l$ and compared with the valve continuum result shown with a dotted line. Smaller values of $l$ require more valves to reach the valve continuum.

Figure 14

Table 2. Parameters used in the paper.