CyberSec Research

Aerodynamic drag on flat-backed vehicles like vans and trucks is dominated by a low-pressure wake, whose control is critical for reducing fuel consumption. This paper presents an experimental study at $Re_W\approx 78,300$ on active flow control using four pulsed jets at the rear edges of a bluff body model. A hybrid genetic algorithm, combining a global search with a local gradient-based optimizer, was used to determine the optimal jet actuation parameters in an experiment-in-the-loop setup. The cost function was designed to achieve a net energy saving by simultaneously minimizing aerodynamic drag and penalizing the actuation's energy consumption. The optimization campaign successfully identified a control strategy that yields a drag reduction of approximately 10%. The optimal control law features a strong, low-frequency actuation from the bottom jet, which targets the main vortex shedding, while the top and lateral jets address higher-frequency, less energetic phenomena. Particle Image Velocimetry analysis reveals a significant upward shift and stabilization of the wake, leading to substantial pressure recovery on the model's lower base. Ultimately, this work demonstrates that a model-free optimization approach can successfully identify non-intuitive, multi-faceted actuation strategies that yield significant and energetically efficient drag reduction.

Rayleigh-Benard convection (RBC) is a canonical system for buoyancy-driven turbulence and heat transport, central to geophysical and industrial flows. Developing efficient control strategies remains challenging at high Rayleigh numbers, where fully resolved simulations are computationally expensive. We use a control framework that couples data-driven manifold dynamics (DManD) with reinforcement learning (RL) to suppress convective heat transfer. We find a coordinate transformation to a low-dimensional system using POD and autoencoders, and then learn an evolution equation for this low-dimensional state using neural ODEs. The reduced model reproduces key system features while enabling rapid policy training. Policies trained in the DManD environment and deployed in DNS achieve a 16-23 % reduction in the Nusselt number for both single- and dual-boundary actuation. Physically, the learned strategy modulates near-wall heat flux to stabilize and thicken the thermal boundary layer, weaken plume ejection, and damp the wall-driven instabilities that seed convective bursts. Crucially, the controller drives the flow toward a quasi-steady state characterized by suppressed temporal fluctuations and spatially steady heat-flux patterns. This work establishes DManD-RL as a physically interpretable, scalable approach for turbulence control in high-dimensional flows.

The use of dissipative particle dynamics (DPD) simulation to study the rheology of fluids under shear has always been of great interest to the research community. Despite being a powerful tool, a limitation of DPD is the need to use high shear rates to obtain viscosity results with a sufficiently high signal-to-noise ratio (SNR). This often leads to simulations with unrealistically large deformations that do not reflect typical stress conditions on the fluid. In this work, the transient time correlation function (TTCF) technique is used for a simple Newtonian DPD fluid to achieve high SNR results even at arbitrarily low shear rates. The applicability of the TTCF on DPD systems is assessed, and the modifications required by the nature of the DPD force field are discussed. The results showed that the standard error (SE) of viscosity values obtained with TTCF is consistently lower than that of the classic averaging procedure across all tested shear rates. Moreover, the SE resulted proportional to the shear rate, leading to a constant SNR that does not decrease at lower shear rates. Additionally, the effect of trajectory mapping on DPD is studied, and a TTCF approach that does not require mappings is consolidated. Remarkably, the absence of mappings has not reduced the precision of the method compared with the more common mapped approach.

Inspired by the spontaneous behaviour observed in filamentous layers -- where the balance between flow-induced drag and structural elasticity dictates the filaments' equilibrium streamlined posture -- we perform a series of large-eddy simulations to investigate how filament inclination affects turbulent shear flows developing both above and within a canopy of filaments. We examine six distinct filament inclination angles ranging from 0\deg to 90\deg. The in-plane solid fraction and filament length are chosen to achieve a fully dense canopy at zero inclination, and these parameters remain constant throughout our study. By setting a nominal bulk Reynolds number of 6000, we provide a detailed statistical characterisation of the turbulent flow. Our findings illustrate distinct changes in the flow regime with varying filament inclination. At lower angles, the canopy remains dense and significantly influences the flow, conforming to a classical canopy-flow regime. However, as the inclination approaches 90\deg, the intra-canopy region progressively becomes shielded from the outer flow. Remarkably, at 90\deg inclination, the flow drag reduces significantly, and the total drag becomes lower than that typically seen in an open, filament-free flow. We document this transition from a canopy-dominated regime to a scenario where the canopy becomes largely sheltered from the outer turbulent flow, highlighting key alterations in intra-canopy dynamics as filament inclination increases. Our observations are substantiated by an analysis of the velocity spectra, providing deeper insight into the interactions between the canopy and the developing turbulent boundary layer.

We model the formation and evolution of wrinkles in a floating elastic sheet under uniaxial compression. This is a canonical setup in the study of wrinkling, and whilst its static equilibrium configuration is well characterised, its dynamics are not. In this work, we focus on modelling the transition from early, inertia-dominated wrinkle growth to late-time gravity-moderated equilibrium. For an initial configuration in which the sheet is flat, an initial disturbance will first grow at the shortest available wavelengths, because this requires the least kinetic energy, but will subsequently transition to a longer preferred wavelength that minimises potential energy. We observe that the evolving wave pattern must be a spectrum, as opposed to a fundamental wrinkle mode whose wavelength evolves in time. Our results demonstrate that changes in the dominant wrinkle wavelength are coupled to a decay in the compressive force, which is to be expected from equilibrium theory.

The present study investigates the aerodynamic and aeroacoustic characteristics of a propeller operating under varying rotational speeds (RPM) and heights ("H" ), with a particular focus on the effects of upstream obstruction modelled as a tall building. Unlike previous studies that primarily examined rotor noise under axial inflow conditions, this work explores how vortex shedding and flow ingestion from different elevations influence rotor performance and noise emissions. Experiments were carried out in an anechoic wind tunnel, where a tall cylinder was positioned above the propeller to replicate real-world obstruction scenarios. Results revealed that lower propeller heights led to increased broadband noise due to intensified turbulence interactions and reduced aerodynamic efficiency, while higher positions improved thrust performance and mitigated noise effects under certain conditions. The findings contribute to understanding noise sources in eVTOL propulsion systems and provide insights for optimizing propeller placement to enhance aerodynamic efficiency and noise reduction in urban environments

Large offshore wind farm wakes in shallow atmospheric boundary layers (ABL) exhibit often an asymmetric behaviour when observed through Synthetic-Aperture-Radar or simulated through Large-Eddy Simulations (LES). In previous LES of wind farms in the northern hemisphere, the asymmetry manifests as a streak at the left side of the wake, looking downstream, where the turbulence kinetic energy (TKE) is greater than the surrounding flow. This work aims at clarifying the physical mechanism that leads to the formation of such a phenomenon. Identifying the Coriolis force as one possible source of asymmetry in the resolved physics, we simulate a real wind farm located in the German Bight operating under different ABLs: one representative of the northern hemisphere; one of the southern hemisphere; and three fictitious ABLs where the Coriolis effects on the inflow and wake, i.e. veer and the wake deflecting force, are removed individually or altogether. Our results show that the TKE streak appears on the opposite side of the wake, i.e. the right one, in the southern hemisphere, and it is primarily caused by veer in the incoming flow, a result of the Coriolis force in a marine ABL. The process involves a larger TKE production which originates from a larger vertical shear promoted where the undisturbed veer profile converges towards the wake in the top part of the ABL. We find that the TKE streak improves the farm wake recovery modestly. Finally, we compare the asymmetry modelled by LES with those observed in several on-field measurements, finding striking similarities.

We study the stability of plane Poiseuille flow (PPF) and plane Couette flow (PCF) subject to streamwise system rotation using linear stability analysis and direct numerical simulations. The linear stability analysis reveals two asymptotic regimes depending on the non-dimensional rotation rate ($Ro$): a low-$Ro$ and a high-$Ro$ regime. In the low-$Ro$ regime, the critical Reynolds number $Re_c$ and critical streamwise wavenumber $\alpha_c$ are proportional to $Ro$, while the critical spanwise wavenumber $\beta_c$ is constant. In the high-$Ro$ regime, as $Ro \rightarrow \infty$, we find $Re_c = 66.45$ and $\beta_c = 2.459$ for streamwise rotating PPF, and $Re_c = 20.66$ and $\beta_c = 1.558$ for streamwise rotating PCF, with $\alpha_c\propto 1/Ro$. Our results for streamwise rotating PPF match previous findings by Masuda et al. (2008). Interestingly, the critical values of $\beta_c$ and $Re_c$ at $Ro \rightarrow \infty$ in streamwise rotating PPF and PCF coincide with the minimum $Re_c$ reported by Lezius & Johnston (1976) and Wall & Nagata (2006) for spanwise rotating PPF at $Ro=0.3366$ and PCF at $Ro=0.5$. We explain this similarity through an analysis of the perturbation equations. Consequently, the linear stability of streamwise rotating PCF at large $Ro$ is closely related to that of spanwise rotating PCF and Rayleigh-Benard convection, with $Re_c = \sqrt{Ra_c}/2$, where $Ra_c$ is the critical Rayleigh number. To explore the potential for subcritical transitions, direct numerical simulations were performed. At low $Ro$, a subcritical transition regime emerges, characterized by large-scale turbulent-laminar patterns in streamwise rotating PPF and PCF. However, at higher $Ro$, subcritical transitions do not occur and the flow relaminarizes for $Re < Re_c$. Furthermore, we identify a narrow $Ro$-range where turbulent-laminar patterns develop under supercritical conditions.

The inclusion of convection in stellar evolution models lacks realism, especially near convective-radiative interfaces. Furthermore, the interaction of convection with oscillations prevent us from accurately predicting seismic frequencies, and therefore from fully exploiting the asteroseismic data of low-mass stars. We aim to develop a new formalism to model the one-point statistics of stellar convection, to implement it in a new numerical code, and to validate this implementation against benchmark cases. This new formalism is based on Lagrangian Probability Density Function (PDF) methods, where a Fokker-Planck equation for the PDF of particle-based turbulent properties is integrated in time. We then develop a Monte-Carlo implementation of this method, where the flow is represented by a large number of notional particles acting as realisations of the PDF. Notional particles interact with each other through the time- and space-dependent mean flow, which is estimated from the particle realisations through a scheme similar to Smoothed Particle Hydrodynamics. We establish a model for the evolution of turbulent properties along Lagrangian trajectories applicable to stellar turbulent convection, with only a minimal number of physical assumptions necessary to close the system. In particular, no closure is needed for the non-linear advection terms, which are included exactly through the Lagrangian nature of formalism. The numerical implementation of this new formalism allows us to extract time-dependent maps of the statistical properties of turbulent convection in a way which is not possible in grid-based large-eddy simulations, in particular the turbulent pressure, Reynolds stress tensor, internal energy variance and convective flux.

Reactive flows in confined spaces involve complex flame-wall interaction (FWI). This work aims to gain more insights into the physics of the premixed near-wall flame and the wall heat flux as an important engineering relevant quantity. Two different flame configurations have been studied, including the normal flushing flame and inclined sweeping flame. By introducing the skin friction vector defined second-order tensor, direct numerical simulation (DNS) results of these two configurations show consistently that larger flame curvatures are associated with small vorticity magnitude under the influence of the vortex pair structure. Correlation of both the flame normal and tangential strain rates with the flame curvature has also been quantified. Alignment of the progress variable gradient with the most compressive eigenvector on the wall is similar to the boundary free behavior. To characterize the flame ordered structure, especially in the near-wall region, a species alignment index is proposed. The big difference in this index for flames in different regions suggests distinct flame structures. Building upon these fundamental insights, a predictive model for wall heat flux is proposed. For the purpose of applicability, realistic turbulent combustion situations need to be taken into account, for instance, flames with finite thickness, complex chemical kinetics, non-negligible near-wall reactions, and variable flame orientation relative to the wall. The model is first tested in an one-dimensional laminar flame and then validated against DNS datasets, justifying the model performance with satisfying agreement.

The relationship between the spatiotemporal distribution of oxygen transport and cellular flow dynamics is of fundamental importance for understanding microcirculation systems. Three-dimensional (3D) modeling is indispensable for addressing complex oxygen transport and cellular behaviors in capillary networks; however, the computational approach is formidable for enforcing interface (or jump) conditions on largely moving and deforming interfaces. In this paper, we propose a diffusive interface approach for the oxygen transport using a mixture formulation. We formulate oxygen transport using an advection-diffusion-reaction equation and rewrite all governing equations in mixture forms using phase indicator functions, where all the interface conditions are included in the governing equations. This innovation avoids the complexity associated with discontinuities for largely moving interfaces in highly dense red blood cell (RBC) conditions. We model cellular flow as a fluid-membrane interaction problem using the immersed boundary method (IBM). The method allows the seamless calculation of coupling problems for cellular flows and oxygen transports in the cytoplasm (internal fluid) of the RBC, plasma (external fluid), and tissue regions using a fixed Cartesian coordinate mesh. The proposed method accurately captures the analytical solution for spherically symmetric diffusion, and successfully demonstrates oxygen transport in both straight capillaries and their networks.

We present an enhanced immersed interface method for simulating incompressible fluid flows in thin gaps between closely spaced immersed boundaries. This regime, common in engineered structures such as including tribological interfaces and bearing assemblies, poses significant computational challenges because of limitations in grid resolution and the prohibitive cost of mesh refinement near contact. The immersed interface method imposes jump conditions that capture stress discontinuities generated by forces that are concentrated along immersed boundaries. Our approach introduces a bilinear velocity interpolation operator that incorporates jump conditions from multiple nearby interfaces when they occupy the same interpolation stencil. Numerical results demonstrate substantial improvements in both interface and Eulerian velocity accuracy compared to lubrication-based immersed boundary and immersed interface methods. The proposed method improves upon previous interpolation schemes, and eliminates the need for prior knowledge of interface orientation or geometry. This makes it broadly applicable to a wide range of fluid--structure interaction problems involving near-contact dynamics.

Slicks are thin viscous films that can be found at the air--water interface of water bodies such as lakes, rivers and oceans. These micro-layers are enriched in surfactants, organic matter, and microorganisms, and exhibit steep physical and chemical gradients across only tens to hundreds of micrometers. In such geometrically confined environments, the hydrodynamics and transport of nutrients, pollutants, and microorganisms are constrained, yet they collectively sustain key biogenic processes. It remains however largely unexplored how the hydrodynamic flows and transport are affected by the vertical extent of slicks relative to the size of microbial colonies. Here, we study this question by combining analytical and numerical approaches to model a microbial colony as an active carpet: a two-dimensional distribution of micro-swimmers exerting dipolar forces. We show that there exists a ratio between the carpet size and the confinement height that is optimal for the enhancement of particle transport toward the colony edges through advective flows that recirculate in 3D vortex-ring-like patterns with a characteristic length comparable to the confinement height. Our results demonstrate that finite, coherent vortex-ring-like structures can arise solely from the geometrical confinement ratio of slick thickness to microbial colony size. These findings shed light on the interplay between collective activity and out-of-equilibrium transport, and on how microbial communities form, spread, and persist in geometrically constrained environments such as surface slicks.

To effectively handle flows characterized by strong backflow and multiple open boundaries within particle-based frameworks, this study introduces three enhancements to improve the consistency, independence, and accuracy of the buffer-based open boundary condition in SPHinXsys. First, to improve the buffer consistency, the continuum hypothesis is introduced to prevent the excessive particle addition induced by strong backflow. Secondly, the independence of the bidirectional buffer is enhanced through region-constrained and independent labeling schemes, which effectively eliminate buffer interference and erroneous particle deletion in complex open-boundary flows. Thirdly, the original zeroth-order consistent pressure boundary condition is upgraded to first-order consistency by introducing a mirror boundary treatment for the correction matrix. The implementation is based on the rigorously validated weakly compressible smoothed particle hydrodynamics coupled with Reynolds-averaged Navier-Stokes (WCSPH-RANS) method, and both laminar and turbulent flow simulations are performed. Four test cases, including straight and U-shaped channel flows, a plane jet, and the flow in a 3D self-rotational micro-mixer, are conducted to comprehensively validate the proposed improvements. Among these cases, the turbulent plane jet is successfully simulated at a moderate resolution within a very compact computational domain involving strong backflow, a condition that is usually challenging for mesh-based methods. The three improvements require only minor modifications to the code framework, yet they yield significant performance gains.

Unmanned Aerial Vehicles (UAVs) are increasingly populating urban areas for delivery and surveillance purposes. In this work, we develop an optimal navigation strategy based on Deep Reinforcement Learning. The environment is represented by a three-dimensional high-fidelity simulation of an urban flow, characterized by turbulence and recirculation zones. The algorithm presented here is a flow-aware Proximal Policy Optimization (PPO) combined with a Gated Transformer eXtra Large (GTrXL) architecture, giving the agent richer information about the turbulent flow field in which it navigates. The results are compared with a PPO+GTrXL without the secondary prediction tasks, a PPO combined with Long Short Term Memory (LSTM) cells and a traditional navigation algorithm. The obtained results show a significant increase in the success rate (SR) and a lower crash rate (CR) compared to a PPO+LSTM, PPO+GTrXL and the classical Zermelo's navigation algorithm, paving the way to a completely reimagined UAV landscape in complex urban environments.

Capillary heterogeneity is increasingly recognized as a first-order control on gas plume migration and trapping in aquifers and storage formations. We show that spatial variability in the water-methane contact angle, determined by mineralogy and salinity, alters capillary entry pressures and migration pathways. Using molecular dynamics simulations, we estimate contact angles on quartz and kaolinite under fresh and saline conditions and incorporate these results into continuum-scale multiphase flow simulations via a contact-angle-informed Leverett J function, mapping wettability directly onto continuum-scale flow properties. Accounting for contact angle heterogeneity affects methane behavior: mobile and residually trapped methane in aquifers decrease by up to 10 percent, while leakage to the atmosphere increases by as much as 20 percent. The magnitude of this effect depends on permeability contrast, leakage rate, salinity, and facies proportions. By coupling molecular-scale wettability to continuum-scale flow and transport, this cross-scale framework provides a physically grounded basis for groundwater protection and risk assessment and yields more reliable emissions estimates. The approach can be generalized to other subsurface gas transport problems, including hydrogen and carbon dioxide storage, as well as natural releases such as methane from permafrost thaw.

In this work, we introduce HybriNet an innovative and robust framework capable of enhancing spatial resolution, generating fluid dynamics databases for specific flow parameters, and predicting their temporal evolution. The methodology is based on the development of a reduced-order model (ROM) by integrating high-order singular value decomposition (HOSVD) with machine learning (ML) and deep learning (DL) techniques. The ROM enables the generation of multi-parametric fluid dynamics databases concerning varying flow conditions, increases the spatial resolution, and predicts the behaviour of the fluid dynamics problem in terms of time. This helps to accelerate numerical simulations and generate new data efficiently. The performance of the proposed approach has been validated using a collection of 30 two-dimensional laminar flow simulations over a square cylinder at different Reynolds numbers and angles of attack. The databases reconstructed using the proposed methodology exhibited a relative root mean square error below 2% when compared to ground-truth high-resolution data, demonstrating the robustness, accuracy, and efficiency of the proposed framework.

Ammonia (NH3) is a zero-carbon fuel that has been receiving increasing attention for power generation and even transportation. Compared to H2, NH3's volumetric energy density is higher, is not as explosive, and has well established transport and storage technologies. Yet, NH3 has poor flammability and flame stability characteristics and more reactive nitrogen (RN: NOx, N2O) emissions than hydrocarbon fuels, at least with traditional combustion processes. Partially cracking NH3 (into a NH3-H2-N2 mixture, AHN) addresses its flammability and stability issues. RN emissions remain a challenge, and mechanisms of their emissions are fundamentally different in NH3 and hydrocarbon combustion. While rich-quench-lean NH3 combustion strategies have shown promise, the largest contributions to RN emissions are the unrelaxed emissions in the fuel-rich stage due to overshoot of thermodynamic equilibrium within the reaction zone of premixed flames coupled with finite residence times available for relaxation to equilibrium. This work introduces a rush-to-equilibrium concept for AHN combustion, which aims to reduce the unrelaxed RN emissions in finite residence times by accelerating the approach to equilibrium. In the concept, a flow particle is subjected to a decaying mixing rate as it transits the premixed flame. This mitigates the mixing effects that prevents the particle approach to equilibrium, and promotes the chemistry effects to push the particle toward equilibrium, all while considering finite residence times. Evaluated with a state-of-the-art combustion model at gas turbine conditions, the concept shows the potential to reduce RN emissions by an order of magnitude, and that works irrespective of cracking extent, pressure, temperature, etc. A brief discussion of possible practical implementation reveals reasonable geometric and flow parameters characteristic of modern gas turbine combustors.