arXiv subjects

Tetsuya Uchimoto

Publications and source records attributed to Tetsuya Uchimoto.

7 recordsLinked to original sources

arXiv2026

Turing patterns on non-fluctuating surfaces under mechanical stresses

This paper presents a numerical study of Turing patterns (TPs) governed by reaction diffusion equations for the activator $u$ and the inhibitor $v$ on two- and three-dimensional lattices without vertex fluctuations. In this framework, $u$ and $v$ are fixed at discrete spatial locations, as pigment cells on zebrafish skin or shell patterns. Mechanical effects are incorporated through the Finsler geometry modeling formulation, which introduces an internal degree of freedom, $\vec{\tau}$, representing the direction of mechanical stress. A tensile-stress formula based on the Gaussian bond potential is shown to be well defined on non-fluctuating lattices, enabling the entropy associated with stress relaxation to be evaluated in a manner analogous to that on fluctuating surfaces. The results indicate that biological TPs respond to external mechanical forces in much the same way as TPs on fluctuating membranes. Simulation codes are provided in the Supplementary Material.

nlin.PS↗

arXiv2023

Finsler Geometry Modeling and Monte Carlo Study on Geometrically Confined Skyrmions in Nanodots

Using the Finsler geometry modeling (FG) technique without spontaneous magnetic anisotropy, we numerically study the stability and morphology of geometrically confined skyrmions experimentally observed in nanodots. We find a confinement effect that stabilizes skyrmions for a low external magnetic field without mechanical stresses by decreasing the diameter of the cylindrical lattice and strain effects that cause the sky and vortex to emerge under the zero magnetic field. Moreover, the obtained MC data on the morphological changes are also consistent with the reported experimental data.

cond-mat.mes-hall↗

arXiv2023

Langevin and Navier-Stokes Simulation of Three-Dimensional Protoplasmic Streaming

In this paper, we report the numerical results obtained using the Langevin Navier-Stokes (LNS) simulation of the velocity distribution of three-dimensional (3D) protoplasmic streaming in plant cells, such as those of {\it Nitella flexilis}. The LNS simulations are performed on 3D cylinders discretized by regular cubes in which fluid velocities are activated by boundary velocities parallel and nonparallel to the longitudinal direction and a random Brownian force with strength $D$. We find that, for a finite $D$, the velocity distribution $h(V), V\!=\!|\vec{V}|$, has two different peaks at a small non-zero $V$ and a finite $V$, and the distribution $h(V_z)$ for $|V_z|$ along the longitudinal direction also has a peak at finite $V_z$. These results are in good agreement with the reported velocity distributions observed using laser Doppler velocimetry. Moreover, we study the effects of the Brownian force on biological material mixing and find that mixing along the $\vec{V}$ direction enhanced by the nonparallel circular motion is further improved by the Brownian force in the experimentally relevant region of $D$. In addition, the experimentally relevant $D$ is found to be consistent with the expectation from the fluctuation dissipation relation between the random stress and viscosity in the LNS equation of Landau and Lifschitz for incompressible fluids.

physics.flu-dyn↗

arXiv2023

Numerical study of anisotropic diffusion in Turing patterns based on Finsler geometry modeling

We numerically study the anisotropic Turing patterns (TPs) of an activator-inhibitor system, focusing on anisotropic diffusion by using the Finsler geometry (FG) modeling technique. In the FG modeling prescription, the diffusion coefficients are dynamically generated to be direction dependent owing to an internal degree of freedom (IDOF) and its interaction with the activator and inhibitor under the presence of thermal fluctuations. In this sense, FG modeling contrasts sharply with the standard numerical technique, where direction-dependent diffusion coefficients are assumed in the reaction-diffusion (RD) equations of Turing. To find the solution of the RD equations, we use a hybrid numerical technique as a combination of the metropolis Monte Carlo method for IDOF updates and discrete RD equations for steady-state configurations of activator-inhibitor variables. We find that the newly introduced IDOF and its interaction are one possible origin of spontaneously emergent anisotropic patterns on living organisms such as zebra and fishes. Moreover, the IDOF makes TPs controllable by external conditions if the IDOF is identified with lipids on cells or cell mobility.

nlin.PS↗

arXiv2022

Langevin Navier-Stokes simulation of protoplasmic streaming by 2D MAC method

We study protoplasmic streaming in plant cells such as chara brauni by simplifying the flow field to a two-dimensional Couette flow with Brownian random motion inside parallel plates. Protoplasmic streaming is receiving a lot of attention in many areas, such as agriculture-technology and biotechnology. The plant size depends on the velocity of streaming and the driving force originating in molecular motors. Therefore, it is interesting to study detailed information on the velocity of streaming. Recently, experimentally observed peaks in the velocity distribution have been simulated by a 2D Langevin Navier-Stokes (LNS) equation for vortex and flow function. However, to simulate actual 3D flows, we have to use the NS equation for velocity, which, in the case of 2D flows, is not always equivalent to that for vorticity and stream function. In this paper, we report that a 2D LNS equation for velocity and pressure successfully simulates protoplasmic streaming by comparing the results with the experimental data and those obtained by 2D LNS simulations for vortex and flow function. Moreover, a dimensional analysis clarifies the dependence of numerical results on the strength $D$ of Brownian random force and physical parameters such as kinematic viscosity and cell size. We find from this analysis how the peak position in normalized velocity distribution moves depending on these parameters.

physics.flu-dyn↗

arXiv2022

The stability of 3D skyrmions under mechanical stress studied via Monte Carlo calculations

Using Monte Carlo (MC) simulations, we study the skyrmion stability/instability as a response to uniaxial mechanical stresses. Skyrmions emerge in chiral magnetic materials as a stable spin configuration under external magnetic field $\vec{B}$ with the competition of ferromagnetic interaction and Dzyaloshinskii-Moriya interaction (DMI) at low temperature $T$. Skyrmion configurations are also known to be stable (unstable) under a compressive stress applied parallel (perpendicular) to $\vec{B}$. To understand the origin of such experimentally confirmed stability/instability, we use the Finsler geometry modeling technique with a new degree of freedom for strains, which plays an essential role in DMI being anisotropic. We find from MC data that the area of the skyrmion state on the $B$-$T$ phase diagram increases (decreases) depending on the direction of applied stresses, in agreement with reported experimental results. This change in the area of the skyrmion state indicates that skyrmions become more (less) stable if the tensile strain direction is parallel (perpendicular) to $\vec{B}$. From the numerical data in this paper, we find that the so-called magneto-elastic effect is suitably implemented in the effective DMI theory with the strain degree of freedom without complex magneto-elastic coupling terms for chiral magnetic materials. This result confirms that experimentally-observed skyrmion stability and instability are caused by DMI anisotropy.

cond-mat.str-el↗

arXiv2020

Stochastic Fluid Dynamics Simulations of the Velocity Distribution in Protoplasmic Streaming

Protoplasmic streaming in plant cells is directly visible in the cases of \textit{Chara corallina} and \textit{Nitella flexilis}, and this streaming is understood to play a role in the transport of biological materials. For this reason, related studies have focused on molecular transportation from a fluid mechanics viewpoint. However, the experimentally observed distribution of the velocity along the flow direction $x$, which exhibits two peaks at $V_x\!=\!0$ and at a finite $V_x(\not=\!0)$, remains to be studied. In this paper, we numerically study whether this behavior of the flow field can be simulated by a 2D stochastic Navier-Stokes (NS) equation for Couette flow, in which random Brownian force is assumed. We present the first numerical evidence that these peaks are reproduced by the stochastic NS equation, which implies that the Brownian motion of the fluid particles plays an essential role in the emergence of these peaks in the velocity distribution. We also find that the position of the peak at $V_x(\not=\!0)$ moves with the variation in the strength $D$ of the random Brownian force, which also changes depending on physical parameters such as the kinematic viscosity, boundary velocity and diameter of the plant cells.

cond-mat.stat-mech↗