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. 2022 Feb 8;18(2):1227-1240.
doi: 10.1021/acs.jctc.1c01020. Epub 2022 Jan 10.

Dissipative Particle Dynamics Simulation of Ultrasound Propagation through Liquid Water

Affiliations

Dissipative Particle Dynamics Simulation of Ultrasound Propagation through Liquid Water

Petra Papež et al. J Chem Theory Comput. .

Abstract

Ultrasound is widely used as a noninvasive method in therapeutic and diagnostic applications. These can be further optimized by computational approaches, as they allow for controlled testing and rational optimization of the ultrasound parameters, such as frequency and amplitude. Usually, continuum numerical methods are used to simulate ultrasound propagating through different tissue types. In contrast, ultrasound simulations using particle description are less common, as the implementation is challenging. In this work, a dissipative particle dynamics model is used to perform ultrasound simulations in liquid water. The effects of frequency and thermostat parameters are studied and discussed. We show that frequency and thermostat parameters affect not only the attenuation but also the computed speed of sound. The present study paves the way for development and optimization of a virtual ultrasound machine for large-scale biomolecular simulations.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Schematic representations of the setups used in the OBMD to simulate the propagation of an ultrasound wave through a DPD water.
Figure 2
Figure 2
Schematic representation of the momentum-flux-exchanging setup used in the OBMD to simulate the propagation of an ultrasound wave through the high resolution water described by the SPC water model.
Figure 3
Figure 3
Computed temperature profiles through the simulation box (setup 1) for the ultrasound wave with a frequency of ν = 1.84τDPD–1 and two different amplitudes. Colored crosses indicate average temperature, while error bars denote the associated standard deviation.
Figure 4
Figure 4
Comparison of the computed speed of sound with associated standard deviations, represented with error bars, for ultrasound waves of different frequencies and two different amplitudes (setup 1).
Figure 5
Figure 5
Comparison of the computed attenuation coefficients with associated standard deviations, represented with error bars, for ultrasound waves of different frequencies and two different amplitudes (setup 1). Crosses indicate results calculated from simulations using OBMD, while the black line corresponds to the quadratic dependence of the attenuation coefficients on the frequency for ultrasound waves with an amplitude of 0.50Pext, where a = (0.0372 ± 0.0007)NpτDPD2/Rc and b = (0.038 ± 0.001)NpτDPD2/Rc.
Figure 6
Figure 6
Computed density signal through the ROI for the ultrasound wave with a frequency of 1.84τDPD–1 and an amplitude of 0.50Pext at time t = t0 (setup 1). Blue crosses indicate results calculated from simulation using OBMD, error bars represent the associated standard error, and the black line corresponds to the analytical solution.
Figure 7
Figure 7
Comparison of the computed speed of sound with respect to the friction coefficients γ∥,ROI used for simulation of ultrasound waves of different frequencies and two different amplitudes (setup 2).
Figure 8
Figure 8
Comparison of the computed attenuation coefficients (setup 2).
Figure 9
Figure 9
Computed density signals through the ROI for the ultrasound wave with a frequency of 1.84τDPD–1 and an amplitude of 0.50Pext at time t = t0 and for three different friction coefficients γ∥,ROI used (setup 2).
Figure 10
Figure 10
Computed density signal through the ROI for the ultrasound wave with a frequency of 1.84τDPD–1 and an amplitude of 0.25Pext at time t = t0 (setup 3).
Figure 11
Figure 11
Comparison of the computed speed of sound with respect to the setups used for ultrasound waves of different frequencies and an amplitude of 0.25Pext.
Figure 12
Figure 12
Comparison of the computed attenuation coefficients for different setups.

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