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Supersonic wind tunnels are an essential tool for high-speed aerodynamics research, supporting studies ranging from fundamental flow analysis to advancements in supersonic transport. Accurately predicting tunnel performance, however, requires precise mathematical modeling. Previous models have primarily focused on plenum pressure predictions, often assuming an adiabatic process and overlooking temperature dynamics. Temperature changes during a test affect velocity and Reynolds number, influencing experimental measurements and underscoring the need to improve temperature prediction capabilities. In this paper, we develop a new model introducing two key corrections: heat addition from the thermal mass of the wind tunnel and real gas effects, particularly the Joule–Thomson effect, allowing us to capture the critical influence of temperature. Additionally, we account for pressure losses within the piping system. Comparative analysis with experimental data shows that our model reduces temperature prediction errors to within 2%, a marked improvement over the base model’s 9–13% error range. Furthermore, pressure predictions are refined, yielding more accurate assessments of plenum, reservoir and valve inlet pressures. These findings underscore the model’s utility in enhancing control system development and its broader value in advancing experimental design and operational precision in supersonic wind tunnel research.
Emphasizing how and why machine learning algorithms work, this introductory textbook bridges the gap between the theoretical foundations of machine learning and its practical algorithmic and code-level implementation. Over 85 thorough worked examples, in both Matlab and Python, demonstrate how algorithms are implemented and applied whilst illustrating the end result. Over 75 end-of-chapter problems empower students to develop their own code to implement these algorithms, equipping them with hands-on experience. Matlab coding examples demonstrate how a mathematical idea is converted from equations to code, and provide a jumping off point for students, supported by in-depth coverage of essential mathematics including multivariable calculus, linear algebra, probability and statistics, numerical methods, and optimization. Accompanied online by instructor lecture slides, downloadable Python code and additional appendices, this is an excellent introduction to machine learning for senior undergraduate and graduate students in Engineering and Computer Science.
Design optimisation of hybrid airships consisting of multi-lobed configurations is being projected as the next paradigm shift in implementing sustainable flight operations within the aeronautical industry. To that end, this paper discusses the effect of varying the relative placement of side-lobes pertaining to a tri-lobed airship hull geometry with respect to the middle-lobe. The paper makes use of a validated OpenFOAM® solver to underscore the aerodynamic impact of shifting the side-lobes in upstream, downstream, upward and downward directions with respect to the middle-lobe while retaining the same volume. These tri-lobed airship hull variants called as skewed tri-lobed hulls, have been comprehensively investigated through the usage of numerical solver (Reynolds-averaged Navier-Stokes) at high Reynolds number flow across various angles of attack. The investigations delineate significant impact of skewed side-lobes on the overall aerodynamics of the tri-lobed airships. Skewing the side-lobes in fore direction leads to drag mitigation at the expense of degraded aerodynamic efficiency owing to lift reduction. Contrarily, aft-skewness amounts to an aerodynamic efficiency enhancement of $ \approx 17{\rm{\% }}$ and improved pitch stability while marginally increasing the pressure drag liability. Aerodynamic efficiency enhancement is attributed to increased lifting force. Skewed upward and downward variants present an overall aerodynamic efficiency reduction. The paper further made use of detailed flow-field visualistion as well as pressure-coefficient distribution plots to underscore the underlying flow-physics related to aforementioned aerodynamic trends. These investigations emphasised the presence of varying three-dimensional relieving effect, intermixing between the three lobes as well as diverse flow separation characteristics downstream of the maximum diameter region leading to the aerodynamic variations thereof. The paper enhances aerodynamic understanding related to tri-lobed geometry that will be crucial in implementing future design changes to the baseline model for improved aerodynamic performance. Amalgamation of these inferences with an optimisation scheme could be implemented in future aerodynamic investigations to optimise tri-lobed geometry for enhanced aerodynamic utility.
A combined experimental and numerical investigation was conducted to examine the mechanisms of aerodynamic noise reduction for twisted hexagonal cylinders at Reynolds numbers ($ \textit{Re} = 2\times 10^4$–$10^5$) and twist angles per unit span $\gamma ^*\in \mathbb{R}[0,1/3]$. It reveals a non-monotonic dependence of noise reduction on $\gamma ^*$, optimised for $\gamma ^* = 1/6$, where a tonal noise reduction of 15 dB and a total sound reduction of 11 dB at $ \textit{Re} = 2\times 10^4$ were achieved. This was consistent across all Reynolds numbers tested. Additionally, dual tones were observed in the noise spectra for cases with $1/18\leqslant \gamma ^* \lt 1/6$, leading to the identification of two distinct flow patterns (Pattern I and II) based on the number of tones in the spectrum. Large-eddy simulations were performed at $ \textit{Re} = 2\times 10^4$ to support the acoustic measurements. Spanwise variations in flow separation gave rise to two distinct regimes: separation (RI) and reattachment (RII). For Pattern I ($1/5.4 \leqslant \gamma ^* \leqslant 1/3$), the spanwise variation of shear layer separation induced wavy vortex shedding, contributing to a moderate noise reduction. For Pattern II ($1/18 \leqslant \gamma ^* \leqslant 1/7.2$), differences in vortex shedding frequencies between RI and RII regimes led to vortex dislocation, forming C- or X-type vortex structures. The $\gamma ^* = 1/6$ configuration leads to a transitional pattern between Pattern I and II, where modulation was predominantly observed in the RI regime. The superior noise reduction of $\gamma ^* = 1/6$ stems from the combined effects of frequent vortex dislocation and modulation, which reduces spanwise coherency and increases wake three-dimensionality.
This study investigates the heat-flux enhancement of convection flows inside a fluid layer bounded from the top and bottom by two inhomogeneous porous layers. The porous matrix is made of solid materials with very high diffusivity. The numerical results reveal that, compared with the traditional convection system, the heat flux is greatly increased when the thickness of porous layer is large enough. At small Rayleigh numbers, the enhancement is the result of the increase in effective diffusivity in the fluid-saturated porous layers and the reduction in flow friction at the porous interface. For large Rayleigh numbers, the permeable motions across the interfaces generate strong convective flux, which greatly increases the total heat flux. For the latter parameter range, the exponent of the power-law scaling between the Nusselt number and the Rayleigh number exceeds 1/2, which is the value of the ultimate scaling. Our findings are not only of great potential in heat management in various industrial applications but also imply that, in many natural systems with imperfect boundaries, the global heat flux may be much stronger than the prediction by using a convection system with perfect boundaries.
This paper explores dispersive shock waves (DSWs) of gravity-capillary waves within the framework of the two-dimensional, fully nonlinear Euler equations. In this system, initial wave profiles characterised by a smooth step function evolve into modulated wavetrains that connect different constant states, a phenomenon arising from the interplay between nonlinear and dispersive effects. The Bond number, which quantifies the relative significance of gravity compared to surface tension, is crucial in determining the behaviour of the DSW solution. As the Bond number increases from zero, solutions traverse four distinct zones: the radiating DSW region, an unstable crossover region, the travelling DSW region, and the inverse radiating DSW region. The propagation velocities of DSWs can be estimated using the DSW fitting method alongside numerical results from travelling waves. Particular attention is given to travelling DSWs, which are characterised by a uniform wavetrain followed by an oscillatory decaying wavepacket. Notably, the high platform and its extended periodic wavetrain can be part of a specific type of gravity-capillary solitary wave that features an oscillatory pulse, with the number of oscillations at the core potentially increasing indefinitely. The Whitham modulation theory for the Euler equations is employed to describe the modulation parameters – such as wavenumber, amplitude and wave mean – in the travelling DSW region. Finally, we discuss the bifurcation mechanism of solitary waves with oscillatory pulses in the Euler equations, along with analyses of their stability. It is also demonstrated that for relatively small Bond numbers, a series of trapped bubbles can occur along the bifurcation curves, representing the limiting configuration of this type of solitary wave.
It is well known that the amount of damage caused by lightning strikes to protected composite airframe structures depends on the paint characteristics, often applied on the surface of composite structures to protect from environmental effects and to personalise a product. In this work, physically based models of the mechanical loads induced by lightning strikes are employed in the generation of the mechanical overpressure fields due to a simulated lightning strike, while accounting for the paint thickness. These fields are then implemented into a three-dimensional finite element framework and combined with a damage model to predict the effect of paint thickness on the mechanical damage in composite structures subjected to this type of events. These models accurately predict the increase of damage extent with the increase of paint thickness, which is corroborated by experimental observations from industry and by the experimentally observed trends reported in literature.
This study presents an innovative method for in situ measurements of electrical conductivity at microwave frequencies using a dielectric resonator (DR)-based probe. The DR, excited in the transverse electric mode with azimuthal index 0, radial index 1, and an open (non-integer) axial field variation denoted by δ (TE01δ mode) by a ridged waveguide, interacts with metallic layers as small as 5 × 5 mm2 on a dielectric substrate, enabling precise conductivity measurements through reflection coefficient analysis and electromagnetic simulations. Validation is performed by comparing the probe’s conductivity extraction results at 13.28 GHz with a conventional resonant cavity method at 10 GHz, demonstrating strong agreement. The used samples are therefore much smaller than what other comparable methods would require. The method is further applied to additively manufactured metallic deposits produced via a micro-dispensing technique, providing insights into their high-frequency (HF) conductivity and the effects of surface roughness. Additionally, a second-generation contact-based probe is developed to extend the characterization capabilities to larger samples and perform HF surface conductivity mapping. This advancement enables localized evaluations of surface properties and correlations with roughness, offering a valuable tool for optimizing additively manufactured components for HF applications.
We report an experimental study on the effects of polymer additives in the dissipative-scale flow field properties in turbulent Rayleigh–Bénard convection. The experiments were conducted in a cylindrical convection cell with a minute amount of polyacrylamide long-chain polymer. The local velocity gradient tensor was measured using an integrated home-made measurement system (J. Fluid Mech., 2024, vol. 984, p. A8). Although the single-roll large-scale circulation persists (owing to the slight tilt of the convection cell), polymers induce an anisotropic suppression of the dissipative-scale flow properties. The normal velocity gradient components are suppressed more than the shear components. The mean energy dissipation rate in both centre and side regions decreases, then levels off with increasing polymer concentration and the final reduction ratio exceeds 50 % in each region. In the side region, adding polymers has a stronger stabilising effect on the strain rate than the rotation. The anisotropic suppression of the velocity gradient tensor affects dissipation-rotation co-occurrence probability, velocity gradient triple decomposition and local streamline topology. Adding polymers also induces a deceleration effect and increases the contribution of local buoyancy in driving the flow. These results reveal that the addition of polymers can non-trivially manipulate dissipative-scale turbulence fields and energy cascades.
This research presents the design and development of a microwave sensor capable of detecting and distinguishing hydroxylated organic compounds (HOCs), namely water, methanol, ethanol, and propanol. The work analyzes the physical mechanisms that govern the sensitivity and detectability of these liquids in the microwave range. Differences in sensor response are linked to variations in molecular characteristics such as dipole moment, microwave absorption, and refractive index. Unlike approaches that rely solely on experimentation, this study connects microwave behavior to fundamental molecular properties, enabling a predictive, physics-based understanding of HOC detection. Molecular polarization and relaxation models were combined with experimental observations to explain how these compounds interact with microwave fields. A metamaterial-based sensing cell was designed, simulated, and experimentally validated. Results demonstrate that the sensor effectively identifies hydroxyl compounds with high sensitivity. Water produced the highest resonance-frequency shift (0.76 GHz), followed by methanol (0.7 GHz), while ethanol and propanol showed similar shifts around 0.35–0.37 GHz. Propanol achieved a quality factor of 10.82 in the 12–17 GHz range. The sensor also reached a frequency detection resolution of 6.75 MHz and showed strong amplitude sensitivity, highest for water at 10.66 dB.
Predicting unsteady loads on plate-like objects during unsteady motion is important in many applications, such as ship manoeuvring, flight and biological propulsion. The drag force on a starting plate that moves normal to its surface can be severely underestimated during the acceleration phase when conventional methods are used to incorporate the effects of acceleration. These methods often introduce an inviscid added mass force that has its origin in potential flow. However, the flow field around a starting plate quickly diverges from potential flow after the start of the motion due to the continuous creation of vorticity at the plate surface. Following the concept of drag by Burgers (1921 Proc. K. Ned. Akad. Wet. 23, 774–782), we propose a model to predict the creation of vorticity on the plate surface and its advection into the vortex loop at the plate edges, based on Stokes’ first problem. This model shows that the acceleration drag force is a history force, in contrast to the inviscid added mass force that is proportional to the instantaneous acceleration of the plate. We perform experiments on starting plates over a large range of accelerations, velocities, fluid viscosities and plate geometries for which the model gives accurate predictions for the drag force during acceleration and during the relaxation phase immediately after the acceleration ceases. This model is extended to also predict the drag forces on accelerating plates during a starting motion with a non-constant acceleration.
Dynamics of spheroidal particle migration within the elasto-inertial square duct flow of Giesekus viscoelastic fluids were studied by using the direct forcing/fictitious domain method. The results show rich migration behaviours, a spheroidal particle gradually transitions from the corner (CO), channel centreline (CC), inertial rotational (IR), diagonal line and cross-section midline equilibrium positions with a decrease in the elastic number, depending on the initial particle position, initial particle orientation and fluid elasticity. From the effect of secondary flow, the IR equilibrium position is reported when the fluid inertia is relatively strong. Six (five) kinds of rotational behaviours are observed for the elasto-inertial migration of prolate (oblate) spheroids. Moreover, the critical elastic number is determined for the migration of spheroidal particles in Giesekus fluids. Near the critical elastic number, oblate and prolate spheroids can simultaneously maintain the CC, CO and IR equilibrium positions, and the initial orientation of particles affects their final rotational modes and equilibrium positions. Through comprehensive analysis, empirical formulas governing the ability of oblate and prolate spheroids to maintain the CC equilibrium position are proposed as $\textit{Wi} = 0.055\,\textit{Re}{-0.1}$ and Wi = 0.045 Re−0.35 when n = 0.5, 0.01 ≤ Wi ≤ 1. Due to the different directions of the pressure forces acting on the particles and the forces from the first normal stress difference and the second normal stress difference, the equilibrium position in Giesekus fluids is rapidly increased by increasing the secondary flow at higher elastic numbers, which is contrary to the phenomenon observed in the Oldroyd-B fluid.
Rough walls are commonly encountered in engineering applications. However, existing understanding of combustion in the turbulent boundary layer over rough walls is lacking. This study investigates turbulent boundary layer premixed flame flashback over rough walls using direct numerical simulations for the first time. The features of boundary layer flashback over walls with various roughness are explored in terms of flame morphology and flashback speed. It is found that the flame in rough-wall cases is more wrinkled compared with the smooth-wall case, particularly in the near-wall region, due to the presence of more small-scale vortical structures. Wall roughness reduces the flame flashback speed, which is attributed to the higher flow velocity at the leading edge of the flame front in rough-wall cases. The effects of wall roughness and combustion on boundary layer turbulence are revealed through two-point correlations of fluctuating velocity and wall resistance. The results show that, under non-reacting conditions, wall roughness reduces the streamwise and wall-normal extents of near-wall hairpin packets of boundary layer turbulence while increasing their inclination angles. Under reacting conditions, combustion further increases the inclination angle, with a more pronounced effect in rough-wall cases. Wall roughness influences wall resistance, primarily through its pressure component. Flame/wall interactions are also scrutinised, revealing higher wall heat loss in rough-wall cases, which is is mainly attributed to the increased wall surface area. A negative correlation between the quenching distance and the alignment of flame normal and wall normal is observed in rough-wall cases, which is weaker in smooth-wall cases.
This paper presents an actively controllable nonreciprocal metasurface based on a ferrite–patch structure with PIN diodes for dynamic control. Two activation methods are investigated: (a) phase control, which enables a 30° transmission-phase shift while maintaining nonreciprocal behavior, and (b) ON–OFF control, which switches the response by altering the propagation path. The phase-control metasurface is analyzed using transmission-line theory, full-wave simulation, and experiments, showing good agreement across methods. The ON–OFF design is optimized to suppress bidirectional transmission when ON. Experimental results confirm strong nonreciprocity, though slight frequency shifts arise from FR4 variability, and a back-fitted simulation improves consistency. The proposed dual-control framework provides a compact and low-cost approach to reconfigurable nonreciprocal surfaces that retain the use of permanent magnets for ferrite bias and are applicable to microwave wireless systems, including adaptive isolation, interference control, and tunable shielding. The results demonstrate the feasibility of compact, reconfigurable nonreciprocal metasurfaces using simple biasing circuits and offer design insights for frequency-stable implementations.
Addressing and predicting degenerative phenomena in domains such as health care and engineering, two fundamental fields of vital importance for society, offers valuable insights into early warning steps and critical event forecasting, leading to far-reaching implications for safety and resource allocation. By harnessing the power of data-driven insights, prognostics becomes the principal component of predicting such phenomena. Developing clustering techniques as feature extractors acts as an intermediate step between the raw incoming data and prognostics and provides the opportunity to unveil hidden relationships within complex datasets. However, when limited, noisy, and multimodal data are available in a label-free format, extensive preprocessing, and unreliable, complicated models are required for extracting meaningful features. This prohibits the development of adaptable methods in diverse domains that are in favor of robustness and interpretability. In this regard, this study introduces a novel unsupervised deep clustering model for feature extraction in degenerative phenomena. The model innovatively extracts prognostic-related features from raw data via clustering analysis, characterized by an increasing monotonic behavior representing system deterioration. This monotonicity is partial rather than complete, to incorporate the potential occurrence of oscillations in the degradation trajectory of the system or noise-related data, reflecting real-world scenarios. Its performance, robustness, generalizability, and interpretability are evaluated across diverse domains utilizing three datasets from health care and engineering featuring limited, noisy, high-dimensional, and multimodal raw signals. Results show that the model extracts meaningful prognostic-related features in both domains and all datasets, without a significant alteration in its architecture and independently of the chosen prognostic algorithm.
The mixing mechanism within a single vortex has been a theoretical focus for decades, while it remains unclear especially under the variable-density (VD) scenario. This study investigates canonical single-vortex VD mixing in shock–bubble interactions (SBI) through high-resolution numerical simulations. Special attention is paid to examining the stretching dynamics and its impact on VD mixing within a single vortex, and this problem is investigated by quantitatively characterising the scalar dissipation rate (SDR), namely the mixing rate, and its time integral, referred to as mixedness. To study VD mixing, we first examine single-vortex passive-scalar (PS) mixing with the absence of a density difference. Mixing originates from diffusion and is further enhanced by the stretching dynamics. Under the axisymmetry and zero diffusion assumptions, the single-vortex stretching rate illustrates an algebraic growth of the length of scalar strips over time. By incorporating the diffusion process through the solution of the advection–diffusion equation along these stretched scalar strips, a PS mixing model for SDR is proposed based on the single-vortex algebraic stretching characteristic. Within this framework, density-gradient effects from two perspectives of the stretching dynamics and diffusion process are discovered to challenge the extension of the PS mixing model to VD mixing. First, the secondary baroclinic effect increases the VD stretching rate by the additional secondary baroclinic principal strain, while the algebraic stretching characteristic is still retained. Second, the density source effect, originating from the intrinsic nature of the density difference in the multi-component transport equation, suppresses the diffusion process. By accounting for both the secondary baroclinic effect on stretching and the density source effect on diffusion, a VD mixing model for SBI is further modified. This model establishes a quantitative relationship between the stretching dynamics and the evolution of the mixing rate and mixedness for single-vortex VD mixing over a broad range of Mach numbers. Furthermore, the essential role of the stretching dynamics on the mixing rate is demonstrated by the derived dependence of the time-averaged mixing rate $\overline {\langle \chi \rangle }$ on the Péclet number ${\textit{Pe}}$, which scales as $\overline {\langle \chi \rangle } \sim {\textit{Pe}}^{{2}/{3}}$.
In the fully developed region of a plane turbulent wall jet, the key jet parameters, including the jet velocity Um, jet half-width z1/2 and wall shear stress $ \tau_{0}$, follow the classical power-law scaling with the streamwise distance x: Um$v$/M0 ∼ (xM0/$v$2)−α, z1/2M0/$v$2 ∼ (xM0/$v$2)β and $ \tau_{0}$$v$2/(ρ$M_{0}^{2}$) ∼ (xM0/$v$2)−χ, where M0 is the source kinematic momentum flux, $v$ is the coefficient of kinematic viscosity of fluid, ρ is the mass density of fluid and α, β and χ are the positive scaling exponents. We present a theoretical framework to determine these exponents. Our framework reveals that each jet parameter exhibits a scaling transition. This transition is driven by a shift in the scaling law of the skin-friction coefficient as the Reynolds number Rem = Umzm/$v$ changes over from Rem < 8000 to Rem > 10 000, where zm is the wall-normal location corresponding to the jet velocity. Specifically, α transitions from 4(1 + γ)/(9 − γ) to 13(1 + γ)/[2(14 − γ)], β from 8/(9 − γ) to 13/(14 − γ) and χ from (9 + 7γ)/(9 − γ) to (14 + 12γ)/(14 − γ), where γ ≈ 0.05 is a parameter determined from experiments. We validate the theoretical predictions against extensive experimental datasets from the literature.
The dynamics of a fluid flow about its limit cycle can be analysed through phase reduction analysis – an approach that distils a high-dimensional dynamical system to its scalar phase dynamics. This technique provides insights into phase sensitivity, revealing the mechanisms that advance or delay phase dynamics. The phase-based reduced-order model derived from this approach serves as a foundation for identifying lock-on conditions and designing flow control techniques. Recent work by Sumanasiri et al. (J. Fluid Mech. vol. 1020, 2025, R4) applied phase reduction analysis to the fluid–structure interaction problem of aerofoil flutter in a free stream. Their analysis systematically changed the stiffness of the structural dynamics to decipher the phase dynamics mechanism of flutter. Moreover, they considered the use of optimised heaving motion to suppress the emergence of flutter. Their approach opens new avenues for modifying flow physics through innovative modifications of material properties and structural dynamics.
Maintenance procedures are critically important for preserving the structural integrity, maintaining the functionality and ensuring the operational safety of aircraft. Traditional inspection techniques used in aircraft are often costly, time-consuming and prone to human mistake. Today, the opportunities provided by digitalisation and automation in aircraft maintenance and inspection processes are paving the way for innovative approaches. In this context, the use of inspection systems supported by image processing technologies has the potential to bring about a significant transformation in aircraft maintenance. Visual inspection methods integrated with unmanned aerial vehicles (UAVs) enable the rapid, accurate and repeatable detection of defects such as corrosion and cracks on the external surfaces of aircraft. This study focuses on the automatic detection and classification of defects on the external surfaces of aircraft, based on tests and analyses carried out by artificial intelligence algorithms using high-resolution data. The model developed in this study was implemented in Python in the Google Colab environment and supported by AI algorithms trained on visual data. The main objective is to investigate the feasibility of UAV-based systems for aircraft visual inspection and to provide concrete evidence of their practical applicability. In this regard, the UAV platform selected for image acquisition is intended to comprehensively scan the target areas and capture images with sufficient resolution for processing by artificial intelligence algorithms. A review of the literature reveals that UAV- and AI-based integrated approaches have been explored in only a limited number of studies related to aircraft maintenance. In this context, the present study proposes a system that enables the rapid and accurate detection of structural defects such as corrosion and cracks on the external surfaces of aircraft.
We developed a two-phase lattice Boltzmann model by coupling the entropic multiple-relaxation-time (EMRT or KBC) collision operator enabling low fluid viscosity, with a source term (Wang et al. 2022, Phys. Rev. E vol. 105, no 4) to independently adjust surface tension. The coupling is implemented via the exact difference method (EDM), which allows full consideration of external-force effects on the entropic stabiliser in KBC, in contrast to the recent work of Wang et al. (2022 Phys. Rev. E vol. 105) and Xu et al. (2024 Comput. Math. Appl. vol. 159, 92–101). More importantly, we address a major drawback of the EDM by explicitly demonstrating how its high-order error terms influence the pressure tensor and surface tension. Using the developed model, we investigated droplet impact and splashing on a thin liquid film at a remarkably high Weber number of ${\textit{We}} = 5000$ and Reynolds number of ${\textit{Re}} = 5000$. Droplet impact and splashing on flat surfaces and mesh structures at very high ${\textit{Re}}$ (15 200) and ${\textit{We}}$ (1020) are also studied after validating four representative cases against experiments. For droplet impact on flat surfaces, hydrophobicity promotes the growth of peripheral instabilities, leading to fingering splashing. Corona splashing transitions to fingering splashing as the liquid–gas viscosity ratio increases. For droplet impact on mesh structures, large openings promote liquid penetration, whereas small openings enhance spreading. As the solid ratio increases, the maximum spreading ratio increases monotonically but nonlinearly, whereas the maximum penetrated liquid pillar length first rises and then drops. These simulations demonstrate the proposed model offers significant advantages for accurately capturing and elucidating complex droplet impact and splashing dynamics at high ${\textit{Re}}$ and ${\textit{We}}$.