arXiv subjects

Guohui Hu

Publications and source records attributed to Guohui Hu.

6 recordsLinked to original sources

arXiv2026

Fluid-Structure Interaction and Scaling Laws for Deterministic Encapsulation of Hyperelastic Cells in Microfluidic Droplets

The precise encapsulation of deformable particles in multiphase flows involves complex transient Fluid-Structure Interactions (FSI) and topological interfacial changes. In the context of single-cell analysis, a numerical framework that couples the Cahn-Hilliard phase-field model with the Arbitrary Lagrangian-Eulerian (ALE) method is employed to investigate the dynamics of deformable cell encapsulation in flow-focusing microchannels. By resolving the coupling between the hyperelastic cell, carrier fluid, and evolving interface, we propose a unified dimensionless scaling law to predict the operational spatial window for the deterministic encapsulation quantitatively. Furthermore, the physical presence of cells modulates the droplet generation flow regime via a "geometric blockage effect", shifting the transition boundary from the squeezing to the dripping regime toward lower flow-rate ratios. The droplet generation period demonstrates a non-monotonic dependence on the cell blockage ratio $\Gamma$, which induces a competitive mechanism between shear enhancement and hydraulic resistance penalty, and consequently leads to an optimal hydrodynamic balance at $\Gamma \approx 0.32$. Finally, we find that while droplet periodicity is robust to variations in cell stiffness, the transient stress field within the cell is highly sensitive, particularly during the capillary pinch-off singularity. This work clarifies the fundamental interaction between hyperelastic cells and multiphase flows, and provides a quantitative framework for optimizing damage-free cell encapsulation systems.

physics.flu-dyn↗

arXiv2026

Quantum computing of the nonlinear Schr\"odinger equation via measurement-induced potential reconstruction

The nonlinear Schr\"odinger equation (NLSE) is a fundamental model that describes diverse complex phenomena in nature. However, simulating the NLSE on a quantum computer is inherently challenging due to the presence of the nonlinear term. We propose a hybrid quantum-classical framework for simulating the NLSE based on the split-step Fourier method. During the linear propagation step, we apply the kinetic evolution operator to generate an intermediate quantum state. Subsequently, the Hadamard test is employed to measure the Fourier components of low-wavenumber modes, enabling the efficient reconstruction of nonlinear potentials. The phase transformation corresponding to the reconstructed potential is then implemented via a quantum circuit using the phase kickback technique. To validate the efficacy of the proposed algorithm, we numerically simulate the evolution of a Gaussian wave packet, a soliton wave, and the wake flow past a cylinder. The simulation results demonstrate excellent agreement with the corresponding classical solutions. This work provide a concrete basis for analyzing accuracy-cost trade-offs in quantum-classical simulations of nonlinear dispersive wave dynamics.

quant-ph↗

arXiv2025

Piezo1 Decodes Mechanical Forces via Allosteric Network Reprogramming

Understanding how molecular machines transduce mechanical force into chemical signals is a central goal in chemistry. The mechanosensitive ion channel Piezo1 is an archetypal nanoscale mechanotransducer, but the molecular principles by which it decodes distinct mechanical stimuli remain elusive. Here, we combine large-scale molecular dynamics simulations with time-series causal inference to elucidate the dynamic allosteric communication networks within Piezo1 under both quasi-static membrane tension and shockwave-induced cavitation. Under tangential tension, Piezo1 employs the lever-like pathway, a linear, feed-forward pathway propagating the signal from peripheral mechanophores to the central pore. In contrast, a shockwave impulse in the normal direction triggers a two-stage gating mechanism based on the dynamic reprogramming of the allosteric network. An initial compression phase activates an apical shortcut pathway originating from the cap domain. A subsequent tension phase utilizes a rewired network with complex feedback loops to drive the channel to a fully open state. These findings reveal that the allosteric wiring of a molecular machine is not static but can be dynamically reconfigured by the nature of the physical input. This principle of force-dependent pathway selection offers a new framework for understanding mechanochemistry and for designing programmable, stimuli-responsive molecular systems.

q-bio.MN↗

arXiv2021

BubbleNet: Inferring micro-bubble dynamics with semi-physics-informed deep learning

Micro-bubbles and bubbly flows are widely observed and applied in chemical engineering, medicine, involves deformation, rupture, and collision of bubbles, phase mixture, etc. We study bubble dynamics by setting up two numerical simulation cases: bubbly flow with a single bubble and multiple bubbles, both confined in the microchannel, with parameters corresponding to their medical backgrounds. Both the cases have their medical background applications. Multiphase flow simulation requires high computation accuracy due to possible component losses that may be caused by sparse meshing during the computation. Hence, data-driven methods can be adopted as an useful tool. Based on physics-informed neural networks (PINNs), we propose a novel deep learning framework BubbleNet, which entails three main parts: deep neural networks (DNN) with sub nets for predicting different physics fields; the semi-physics-informed part, with only the fluid continuum condition and the pressure Poisson equation $\mathcal{P}$ encoded within; the time discretized normalizer (TDN), an algorithm to normalize field data per time step before training. We apply the traditional DNN and our BubbleNet to train the coarsened simulation data and predict the physics fields of both the two bubbly flow cases. The BubbleNets are trained for both with and without $\mathcal{P}$, from which we conclude that the 'physics-informed' part can serve as inner supervision. Results indicate our framework can predict the physics fields more accurately, estimating the prediction absolute errors. Our deep learning predictions outperform traditional numerical methods computed with similar data density meshing. The proposed network can potentially be applied to many other engineering fields.

physics.flu-dyn↗

arXiv2013

Hydrodynamic flow in the vicinity of a nanopore induced by an applied voltage

Continuum simulation is employed to study ion transport and fluid flow through a nanopore in a solid-state membrane under an applied potential drop. Results show the existence of concentration polarization layers on the surfaces of the membrane. The nonuniformity of the ionic distribution gives rise to an electric pressure that drives vortical motion in the fluid. There is also a net hydrodynamic flow through the nanopore due to an asymmetry induced by the membrane surface charge. The qualitative behavior is similar to that observed in a previous study using molecular dynamic simulations. The current--voltage characteristics show some nonlinear features but are not greatly affected by the hydrodynamic flow in the parameter regime studied. In the limit of thin Debye layers, the electric resistance of the system can be characterized using an equivalent circuit with lumped parameters. Generation of vorticity can be understood qualitatively from elementary considerations of the Maxwell stresses. However, the flow strength is a strongly nonlinear function of the applied field. Combination of electrophoretic and hydrodynamic effects can lead to ion selectivity in terms of valences and this could have some practical applications in separations.

physics.flu-dyn↗

arXiv2013

Ion transport through a graphene nanopore

Molecular dynamics simulation is utilized to investigate the ionic transport of NaCl in solution through a graphene nanopore under an applied electric field. Results show the formation of concentration polarization layers in the vicinity of the graphene sheet. The non-uniformity of the ion distribution gives rise to an electric pressure which drives vortical motions in the fluid if the electric field is sufficiently strong to overcome the influence of viscosity and thermal fluctuations. The relative importance of hydrodynamic transport and thermal fluctuations in determining the pore conductivity is investigated. A second important effect that is observed is the mass transport of water through the nanopore, with an average velocity proportional to the applied voltage and independent of the pore diameter. The flux arises as a consequence of the asymmetry in the ion distribution with respect to reflection about the plane of the graphene sheet. The accumulation of liquid molecules in the vicinity of the nanopore due to reorientation of the water dipoles by the local electric field is seen to result in a local increasein the liquid density. Results confirm that the electric conductance is proportional to the nanopore diameter for the parameter regimes that we simulated. The occurrence of fluid vortices is found to result in an increase in the effective electrical conductance.

physics.bio-ph↗