CyberSec Research

Accurate and efficient predictions of three-dimensional (3D) turbulent flows are of significant importance in the fields of science and engineering. In the current work, we propose a hybrid U-Net and Fourier neural operator (HUFNO) method, tailored for mixed periodic and non-periodic boundary conditions which are often encountered in complex turbulence problems. The HUFNO model is tested in the large-eddy simulation (LES) of 3D periodic hill turbulence featuring strong flow separations. Compared to the original Fourier neural operator (FNO) and the convolutional neural network (CNN)-based U-Net framework, the HUFNO model has a higher accuracy in the predictions of the velocity field and Reynolds stresses. Further numerical experiments in the LES show that the HUFNO framework outperforms the traditional Smagorinsky (SMAG) model and the wall-adapted local eddy-viscosity (WALE) model in the predictions of the turbulence statistics, the energy spectrum, the wall stresses and the flow separation structures, with much lower computational cost. Importantly, the accuracy and efficiency are transferable to unseen initial conditions and hill shapes, underscoring its great potentials for the fast prediction of strongly separated turbulent flows over curved boundaries.

Computational Fluid Dynamics simulations are crucial in industrial applications but require extensive computational resources, particularly for extreme turbulent regimes. While classical digital approaches remain the standard, quantum computing promises a breakthrough by enabling a more efficient encoding of large-scale simulations with a limited number of qubits. This work presents a practical numerical assessment of a hybrid quantum-classical approach to CFD based on the Lattice Boltzmann Method (LBM). The inherently non-linear LBM equations are linearized via a Carleman expansion and solved using the quantum Harrow Hassidim Lloyd algorithm (HHL). We evaluate this method on three benchmark cases featuring different boundary conditions, periodic, bounceback, and moving wall, using statevector emulation on high-performance computing resources. Our results confirm the validity of the approach, achieving median error fidelities on the order of $10^{-3}$ and success probabilities sufficient for practical quantum state sampling. Notably, the spectral properties of small lattice systems closely approximate those of larger ones, suggesting a pathway to mitigate one of HHL's bottlenecks: eigenvalue pre-evaluation.

Shallow water waves are a striking example of nonlinear hydrodynamics, giving rise to phenomena such as tsunamis and undular waves. These dynamics are typically studied in hundreds-of-meter-long wave flumes. Here, we demonstrate a chip-scale, quantum-enabled wave flume. The wave flume exploits nanometer-thick superfluid helium films and optomechanical interactions to achieve nonlinearities surpassing those of extreme terrestrial flows. Measurements reveal wave steepening, shock fronts, and soliton fission -- nonlinear behaviors long predicted in superfluid helium but never previously directly observed. Our approach enables lithography-defined wave flume geometries, optomechanical control of hydrodynamic properties, and orders of magnitude faster measurements than terrestrial flumes. Together, this opens a new frontier in hydrodynamics, combining quantum fluids and nanophotonics to explore complex wave dynamics at microscale.

The present work extends the direct-forcing immersed boundary method introduced by Garc\'ia-Villalba et al. (2023), broadening its application from spherical to arbitrarily-shaped particles, while maintaining its capacity to address both neutrally-buoyant and light objects (down to a density ratio of 0.5). The proposed method offers a significant advantage over existing methods regarding its simplicity, in particular for the case of neutrally-buoyant particles. Three test cases from the literature are selected for validation: a neutrally-buoyant prolate spheroid in a shear flow; a settling oblate spheroid; and, finally, a rising oblate spheroid.

This paper presents a novel autonomous drone-based smoke plume tracking system capable of navigating and tracking plumes in highly unsteady atmospheric conditions. The system integrates advanced hardware and software and a comprehensive simulation environment to ensure robust performance in controlled and real-world settings. The quadrotor, equipped with a high-resolution imaging system and an advanced onboard computing unit, performs precise maneuvers while accurately detecting and tracking dynamic smoke plumes under fluctuating conditions. Our software implements a two-phase flight operation, i.e., descending into the smoke plume upon detection and continuously monitoring the smoke movement during in-plume tracking. Leveraging Proportional Integral-Derivative (PID) control and a Proximal Policy Optimization based Deep Reinforcement Learning (DRL) controller enables adaptation to plume dynamics. Unreal Engine simulation evaluates performance under various smoke-wind scenarios, from steady flow to complex, unsteady fluctuations, showing that while the PID controller performs adequately in simpler scenarios, the DRL-based controller excels in more challenging environments. Field tests corroborate these findings. This system opens new possibilities for drone-based monitoring in areas like wildfire management and air quality assessment. The successful integration of DRL for real-time decision-making advances autonomous drone control for dynamic environments.

Unmanned aerial vehicles (UAVs) are becoming more commonly used in populated areas, raising concerns about noise pollution generated from their propellers. This study investigates the acoustic performance of unconventional propeller designs, specifically toroidal and uneven-blade spaced propellers, for their potential in reducing psychoacoustic annoyance. Our experimental results show that these designs noticeably reduced acoustic characteristics associated with noise annoyance.

Accurate three-dimensional (3D) reconstruction of cardiac chamber motion from time-resolved medical imaging modalities is of growing interest in both the clinical and biomechanical fields. Despite recent advancement, the cardiac motion reconstruction process remains complex and prone to uncertainties. Moreover, traditional assessments often focus on static comparisons, lacking assurances of dynamic consistency and physical relevance. This work introduces a novel paradigm of flow-compatible motion reconstruction, integrating anatomical imaging with flow data to ensure adherence to fundamental physical principles, such as mass and momentum conservation. The approach is demonstrated in the context of right ventricular motion, utilizing diffeomorphic mappings and multi-slice MRI to achieve dynamically consistent and physically robust reconstructions. Results show that enforcing flow compatibility within the reconstruction process is feasible and enhances the physical realism of the resulting kinematics.

Lattice-Boltzmann methods are established mesoscopic numerical schemes for fluid flow, that recover the evolution of macroscopic quantities (viz., velocity and pressure fields) evolving under macroscopic target equations. The approximated target equations for fluid flows are typically parabolic and include a (weak) compressibility term. A number of Lattice-Boltzmann models targeting, or making use of, flow through porous media in the representative elementary volume, have been successfully developed. However, apart from two exceptions, the target equations are not reported, or the assumptions for and approximations of these equations are not fully clarified. Within this work, the underlying assumption underpinning parabolic equations for porous flow in the representative elementary volume, are discussed, clarified and listed. It is shown that the commonly-adopted assumption of negligible hydraulic dispersion is not justifiable by clear argument - and in fact, that by not adopting it, one can provide a qualitative and quantitative expression for the effective viscosity in the Brinkman correction of Darcy law. Finally, it is shown that, under certain conditions, it is possible to interpret porous models as Euler-Euler multiphase models wherein one phase is the solid matrix.

Weconsider Burgers equation on metric graphs for simplest topologies such as star, loops, and tree graphs. Exact traveling wave solutions are obtained for the vertex boundary conditions providing mass conservation and continuity of the solution at the nodes. Constraints for the nonlinearity coefficients ensuring integrability of the Burgers equation are derived. Numerical treatment of the soliton dynamics and their transmission through the graph vertex is presented.

This extensive investigation explores the influence of inclined magnetic fields and radiative non-linear heat flux on the behavior of dusty hybrid nanofluids over stretching/shrinking wedges. Employing $Cu$-$SiO_2$ as a hybrid nanoparticle composition and ethylene glycol $(EG)$ as the base liquid, the study investigates the fluid's response to a uniform magnetic field. The governing partial differential equations and associated boundary conditions are adeptly transformed into ordinary differential equations using appropriate transformations and then non-dimensionalized. Numerical simulations are executed using MATLAB and the bvp-4c solver. The outcomes offer a profound insight into thermofluid dynamics in industrial applications featuring intricate fluid flows, evaluating the influence of magnetic parameters on diverse fluid types, including nanofluids and dusty hybrid nanofluids. Furthermore, the investigation analyzes the impact of heat production and absorption on both vertical and horizontal plates, studying the significance of the velocity ratio factor in relation to the drag coefficient and local Nusselt number under thermal conditions of generation and absorption.

This article deals with the existence and scaling of an energy cascade in steady granular liquid flows between the scale at which the system is forced and the scale at which it dissipates energy. In particular, we examine the possible origins of a breaking of the Kolmogorov Universality class that applies to Newtonian liquids under similar conditions. In order to answer these questions, we build a generic field theory of granular liquid flows and, through a study of its symmetries, show that indeed the Kolmogorov scaling can be broken, although most of the symmetries of the Newtonian flows are preserved.

We consider the problem of a charged impurity exerting a weak, slowly decaying force on its surroundings, treating the latter as an ideal compressible fluid. In the semiclassical approximation, the ion is described by the Newton equation coupled to the Euler equation for the medium. After linearization, we obtain a simple closed formula for the effective mass of the impurity, depending on the interaction potential, the mean medium density, and sound velocity. Thus, once the interaction and the equation of state of the fluid is known, an estimate of the hydrodynamic effective mass can be quickly provided. Going beyond the classical case, we show that replacing the Newton with Schr\"{o}dinger equation can drastically change the behavior of the impurity. In particular, the scaling of the Fermi polaron effective mass with the medium density is opposite in quantum and classical scenario. Our results are relevant for experimental systems featuring low energy impurities in Fermi or Bose systems, such as ions immersed in neutral atomic gases.

Data-driven flow control has significant potential for industry, energy systems, and climate science. In this work, we study the effectiveness of Reinforcement Learning (RL) for reducing convective heat transfer in the 2D Rayleigh-B\'enard Convection (RBC) system under increasing turbulence. We investigate the generalizability of control across varying initial conditions and turbulence levels and introduce a reward shaping technique to accelerate the training. RL agents trained via single-agent Proximal Policy Optimization (PPO) are compared to linear proportional derivative (PD) controllers from classical control theory. The RL agents reduced convection, measured by the Nusselt Number, by up to 33% in moderately turbulent systems and 10% in highly turbulent settings, clearly outperforming PD control in all settings. The agents showed strong generalization performance across different initial conditions and to a significant extent, generalized to higher degrees of turbulence. The reward shaping improved sample efficiency and consistently stabilized the Nusselt Number to higher turbulence levels.

Suspensions of motile microorganisms can spontaneously give rise to large scale fluid motion, known as bioconvection, which is characterized by dense, cell-rich downwelling plumes interspersed with broad upwelling regions. In this study, we investigate bioconvection in shallow suspensions of Chlamydomonas reinhardtii cells confined within spiral-shaped boundaries, combining detailed experimental observations with 3D simulations. Under open liquid-air interface conditions, cells accumulate near the surface due to negative gravitaxis, forming spiral shaped density patterns that subsequently fragment into lattice-like structures and give rise to downwelling plumes. Space-time analyses reveal coherent rotational dynamics, with inward-moving patterns near the spiral core and bidirectional motion farther from the center. Introducing confinement by sealing the top boundary with an air-impermeable transparent wall triggers striking transitions in the bioconvection patterns, driven by oxygen depletion: initially stable structures reorganize into new patterns with reduced characteristic wavelengths. Complementary 3D simulations, based on the incompressible Navier-Stokes equations and incorporating negative buoyancy and active stress from swimming cells, capture the initial pattern formation and its subsequent instability, reproducing the fragmentation of spiral-shaped accumulations into downwelling plumes and the emergence of strong vortical flows, nearly an order of magnitude faster than individual cell swimming speeds. However, these models do not capture the oxygen-driven pattern transitions observed experimentally. Our findings reveal that confinement geometry, oxygen dynamics, and metabolic transitions critically govern bioconvection pattern evolution, offering new strategies to control microbial self-organization and flow through environmental and geometric design.

Among cardiovascular diseases, atherosclerosis is a primary cause of stenosis, involving the accumulation of plaques in the inner lining of an artery. Inspired by drug delivery applications, the proposed study aims to examine the numerical modeling of a two-dimensional, axisymmetric, and time-dependent hybrid nanofluid composed of copper $(Cu)$, alumina $(Al_{2}O_{3})$ nanoparticles, and blood as base fluid. Blood, modeled by the non-Newtonian Casson model, flows through an elliptical stenotic artery. The pulsatile nature of the pressure gradient and magnetic field impact with the Hall current parameter are also taken into account in this study. A finite difference technique, forward in time and central in space (FTCS), is deployed to numerically discretize the transformed dimensionless model using MATLAB. Comprehensive visualization of the effects of hemodynamic, geometric, and nanoscale parameters on transport characteristics, and extensive graphical results for blood flow characteristics are provided. A comparison is made among blood, regular nanofluid, and hybrid nanofluid to analyze their properties in relation to fluid flow and heat transfer. An augmentation in the non-Newtonian parameter results in an amplification of velocity and in a reduction of the temperature profile. Incorporating $Cu$ and $Al_2O_3$ nanoparticles into the fluid results in a decrease of velocity and an increase of temperature. These findings possess significant practical implications for applications where efficient heat transfer is essential, such as in drug delivery systems and the thermal management of biomedical devices. However, the observed reduction in velocity may necessitate modifications to flow conditions to ensure optimal operational performance in these contexts.

This work proposes a method to identify and isolate the physical mechanisms that are responsible for linear energy amplification in fluid flows. This is achieved by applying a sparsity-promoting methodology to the resolvent form of the governing equations, solving an optimization problem that balances retaining the amplification properties of the original operator with minimizing the number of terms retained in the simplified sparse model. This results in simplified operators that often have very similar pseudospectral properties as the original equations. The method is demonstrated on both incompressible and compressible wall-bounded parallel shear flows, where the results obtained from the proposed method appear to be consistent with known mechanisms and simplifying assumptions, such as the lift-up mechanism, and (for the compressible case) Morkovin's hypothesis and the strong Reynolds analogy. This provides a framework for the application of this method to problems for which knowledge of pertinent amplification mechanisms is less established.

We introduce a novel method for bounding high-order multi-dimensional polynomials in finite element approximations. The method involves precomputing optimal piecewise-linear bounding boxes for polynomial basis functions, which can then be used to locally bound any combination of these basis functions. This approach can be applied to any element/basis type at any approximation order, can provide local (i.e., subcell) extremum bounds to a desired level of accuracy, and can be evaluated efficiently on-the-fly in simulations. Furthermore, we show that this approach generally yields more accurate bounds in comparison to traditional methods based on convex hull properties (e.g., Bernstein polynomials). The efficacy of this technique is shown in applications such as mesh validity checks and optimization for high-order curved meshes, where positivity of the element Jacobian determinant can be ensured throughout the entire element, and continuously bounds-preserving limiters for hyperbolic systems, which can enforce maximum principle bounds across the entire solution polynomial.

Here we show that passive scalars possess a hidden scaling symmetry when considering suitably rescaled fields. Such a symmetry implies (i) universal probability distribution for scalar multipliers and (ii) Perron-Frobenius scenario for the anomalous scaling of structure functions. We verify these predictions with high resolution simulations of a passive scalar advected by a 2D turbulent flow in inverse cascade.