Velocity and temperature distributions are both crucial for modeling compressible wall-bounded turbulent flows. The compressible law of the wall for velocity has been extensively examined through velocity transformations. However, a well-established temperature transformation remains an open issue. We propose new Van Driest type (VD-type) and semi-local type (SL-type) temperature transformation for compressible turbulent channel flow. Our approach is based on an analysis of the momentum and energy balance equations in the overlap layer. It accounts for the influences of mixing length model, the work of the body force, and the turbulent kinetic energy (TKE) flux. The proposed transformations are evaluated using data from direct numerical simulations and wall-resolved large eddy simulations of compressible turbulent channel flow. The SL-type transformation provides better data collapse than the VD-type in the viscous sublayer and buffer layer. With a suitable mixing length model, the SL-type transformed temperature agrees well with the incompressible temperature profile or the extended law of the wall. For the isothermal wall, the integral mean error over the entire boundary layer remains below 2% for most cases, with root mean square value of about 1.7%. The results highlight the importance of mitigating the energy imbalance in the transformation. This work identifies the multi-layer structure of the turbulent TKE flux, which in turn enables approximate models and corresponding simplified yet effective temperature transformations. Applications of the proposed approach in near-wall modeling and inverse transformation, as well as its potential extension to more general configurations, are also discussed.

We introduce a hybrid approach utilising a quantum machine learning surrogate model to approximate the non-linear collision dynamics of the LBM. It effectively offloads the non-unitary operations that challenge pure quantum solvers. The expressivity of the surrogate is built on the ability of parameterised quantum circuits to implement partial Fourier series, with data re-uploading extending the spectrum of representable frequencies. Unlike previous approaches with a fixed relaxation parameter, the surrogate recovers the complete Bhatnagar-Gross-Krook (BGK) collision dynamics across the full physically admissible range of relaxation without retraining. We reassess the relevance of standard variational quantum circuit (VQC) metrics, including expressibility, entanglement, and effective dimension, by relating them directly to task-specific surrogate performance and identifying the key architectural parameters that determine approximation accuracy. The proposed surrogate is validated against the classical BGK collision operator using established benchmark problems, including the Taylor-Green vortex for evaluating energy dissipation and the double shear layer for assessing shear-driven instabilities and nonlinear flow evolution. Our results demonstrate that the hybrid model achieves high accuracy and generalisability while closely replicating classical solutions. These findings suggest that hybrid quantum-classical strategies offer a practical path toward realising the potential of quantum computing in fluid engineering.

The collapse of vapour-bubble clusters near solid boundaries generates highly localised pressure loads that can damage propulsion and hydraulic systems but can also be exploited in therapeutic ultrasound applications. Predicting these loads with high-resolution compressible-flow simulations is computationally expensive, particularly when variability in the initial bubble configuration must be considered. This work develops a variational heteroscedastic multi-fidelity Gaussian-process surrogate for the peak area-averaged pressure recorded by a fixed sensor at the wall centre. The model combines numerous inexpensive coarse-grid simulations with a limited number of higher-resolution simulations through a nonlinear autoregressive architecture. At each fidelity level, separate latent processes represent the mean prediction and input-dependent aleatoric variance. The framework is first evaluated using controlled analytical benchmarks and a single-bubble-collapse case before being applied to randomly generated bubble clusters near a rigid wall.Assessed by negative log predictive density, continuous ranked probability score, and predictive-interval coverage, the surrogate is significantly more accurate and better calibrated than a single-fidelity model trained on the same resolved data at a fixed high-fidelity budget, and it captures the configuration-dependent scatter of the peak wall load. By reporting calibrated predictive intervals rather than an accurate mean alone, it enables uncertainty-aware assessment of cavitation loads and provides an inexpensive predictor of the extreme wall loading that governs erosion in engineering flows and therapeutic effect in focused-ultrasound applications.

This study numerically investigates the interaction of planar shock waves with cavity-embedded n-dodecane fuel cylinders under transcritical thermodynamic conditions. Fourteen cases with Mach numbers ranging from 1.2 to 2.1 are simulated using the finite-volume compressible multi-component solver CAvitation Technical University of Munich (CATUM), incorporating an optimized WENO3 reconstruction scheme and a modified Peng-Robinson equation of state. The numerical approach is validated against reference data, and mesh convergence is confirmed through four levels of grid refinement. The analysis highlights the influence of Mach number on wave dynamics, structural deformation, vorticity deposition, circulation growth, and enstrophy evolution. Compared with full-cylinder configurations, cavity-embedded cylinders undergo earlier deformation, faster downstream displacement, and stronger vorticity generation, leading to enhanced fuel-nitrogen mixing, with the effect becoming more pronounced at higher Mach numbers. Quantitative comparisons demonstrate that the proposed modified Zhang-Zou (M-ZZ) model reliably predicts circulation deposition across all examined Mach numbers, with errors of less than 10%. To assess cavity effects, a transcritical-enstrophy model (TEM) is developed, which predicts enstrophy evolution with errors below 12%. In particular, at Mach 2.0 the enstrophy of the cavity case is about 20% higher than that of the non-cavity case, representing the most significant enhancement among all examined conditions. Cavity-induced mechanisms substantially enhance mixing efficiency, which is evidenced by an increase in enstrophy of more than 10% under stronger shocks. These findings provide insights into instability-driven mixing processes in transcritical environments.

Ultrasonic cleaning systems generate acoustic fields using high-frequency transducer vibrations. While transducer parameters can be tuned, a unified and efficient model for predicting field behavior across diverse cleaning conditions remains unavailable. In this work, we present Ultrasonic-Net, a Physics-Informed Neural Network (PINN) developed to predict ultrasonic fields, specifically targeting cleaning applications. The JAX-Fluids solver is used to generate ground truth for evaluation. The complex system, comprising the ultrasonic transducers (acoustic sources) and the target devices, is modeled using a sharp interface method. The design of Ultrasonic-Net is based on a synthesis of detailed physics and established knowledge. In the proposed architecture, Multi-scale Convolutional Neural Networks (Multi-Scale CNN) and Fast Kolmogorov-Arnold Networks (FastKAN) have been integrated into the Spline-based PINN. The Attention Fusion Layer integrates features from the multi-scale local and global branches by performing adaptive channel-wise recalibration on their concatenated representations. During training, the wave equation is employed as a physical constraint. The vibration frequency, along with the variations in transducers, and the configuration of the devices to be cleaned, is incorporated through initial and boundary condition (IBC) constraints. The decomposition method combined with vectorization is designed to facilitate the enforcement of these constraints. The combination is motivated by ultrasonic cleaning-specific physical requirements. Systematic ablation studies across seven PINN variants and multiple hyperparameter groups confirm that the proposed architecture and training configuration achieve the best accuracy-efficiency trade-off. Once trained, Ultrasonic-Net efficiently predicts the spatiotemporal evolution of acoustic waves under varying conditions, closely matching ground truth while significantly reducing computational cost. An extended experimental comparison is provided to further validate the model's performance. The model's practical utility is demonstrated through application to acoustically driven bubble dynamics. Finally, a systematic evaluation confirms its accuracy and generalization capacity across diverse conditions.

In this work, we present a data-driven high-order Godunov-type finite-volume scheme for machine-learned implicit large-eddy simulations (ML-ILES) of compressible homogeneous isotropic turbulence. For the simulation of compressible flows, many Godunov-type finite-volume schemes combine high-order shock-capturing schemes with approximate Riemann solvers. Here, we devise neural-network based reconstruction operators which are trained to best approximate turbulent subgrid-scales. In particular, we use separate reconstruction neural networks for each physical flow quantity and show that an optimal reconstruction for ILES may require different reconstruction strategies for different flow quantities. The neural networks used in the reconstruction operator are trained end-to-end, using the automatically differentiable JAX-Fluids CFD solver. The training data set comprises coarse-grained spatio-temporal trajectories of compressible temporally decaying homogeneous isotropic turbulence. Comparisons with established ILES discretizations show encouraging results.

We present a versatile and efficient quantum algorithm based on the Lattice Boltzmann method (LBM) approximate solution of the linear advection-diffusion equation (ADE). We emphasize that the LBM approximation modifies the diffusion term of the underlying exact ADE and leads to a modified equation (mADE). Due to its versatility in terms of operator splitting, the proposed quantum LBM algorithm for the mADE provides a building block for future quantum algorithms to solve the linearized Navier-Stokes equation on quantum computers. We split the algorithm into four operations: initialization, collision, streaming, and calculation of the macroscopic quantities. We propose general quantum building blocks for each operator, which adapt intrinsically from the general three-dimensional case to smaller dimensions and apply to arbitrary lattice-velocity sets. Based on (sub-linear) amplitude data encoding, we propose improved initialization and collision operations with reduced complexity and efficient sampling-based simulation. Quantum streaming algorithms are based on previous developments. The proposed quantum algorithm allows for the computation of successive time steps, requiring full state measurement and reinitialization after every time step. It is validated by comparison with a digital implementation and based on analytical solutions in one and two dimensions. Furthermore, we demonstrate the versatility of the quantum algorithm for two cases with non-uniform advection velocities in two and three dimensions. Various velocity sets are considered to further highlight the flexibility of the algorithm. We benchmark our optimized quantum algorithm against previous methods employed in sampling-based quantum simulators. We demonstrate sampling efficiency, with sampling accelerated convergence requiring fewer shots.

In high-speed propulsion systems, for instance, scramjets and ramjets, shock waves impacting the fuel-environment gas interfaces at near-critical thermodynamic states is a phenomenon that is frequently encountered. The effects of shock waves on fuel cylinders with the inclusion of real-fluid features remains a relatively underexplored area of research. In this study, we investigate the influence of re-shock on the shocked fuel cylinder at near-critical thermodynamic states. The spacing between the fuel cylinder and the wall is varied from 1.5R to 9R. The simulation employs compressible multi-component equations and real-fluid thermodynamic relationships to model the evolution of the cylinder and the dynamics of the surrounding gas flow. A hybrid numerical scheme based on finite volume is used to capture shocks and interfaces. The approach has been validated against reference data and shows excellent agreement. The effects of varying cylinder-wall distances on the evolution of the surface of the re-shocked fuel cylinder are thoroughly analyzed. The analysis presented covers intricate shock and re-shock impingement, cylinder deformation, cylinder displacements, and vortex development. A range of features at different stages of evolution are analyzed quantitatively and qualitatively. These include wave pattern evolution, pressure redistribution, shift properties (center-of-mass parameters), cylinder geometry deformation characteristics (cylinder width), baroclinic vorticity distribution, circulation histories, and enstrophy progression. The analysis also quantifies changes over time in the characteristic mixing degree between the fuel and ambient gas. Two additional parameters, the area and the mass fraction-weighted area of the mixing region, are included. Current studies could provide valuable insights for experimental design and industrial applications involving gas-fuel mixing processes at near-critical thermodynamic states.

We propose a quantum algorithm for the linear advection-diffusion equation (ADE) Lattice-Boltzmann method (LBM) that leverages dynamic circuits. Dynamic quantum circuits allow for an optimized collision-operator quantum algorithm, introducing partial measurements as an integral step. Efficient adaptation of the quantum circuit during execution based on digital information obtained through mid-circuit measurements is achieved. The proposed new collision algorithm is implemented as a fully unitary operator, which facilitates the computation of multiple time steps without state reinitialization. Unlike previous quantum collision operators that rely on linear combinations of unitaries, the proposed algorithm does not exhibit a probabilistic failure rate. Moreover, additional qubits no longer depend on the chosen velocity set, which reduces both qubit overhead and circuit complexity. Validation of the quantum collision algorithm is performed by comparing results with digital LBM in one and two dimensions, demonstrating excellent agreement. Performance analysis for multiple time steps highlights advantages compared to previous methods. As an additional variant, a hybrid quantum-digital approach is proposed, which reduces the number of mid-circuit measurements, therefore improving the efficiency of the quantum collision algorithm.

Recent advances in velocity and temperature transformations have enabled recovery of the law of the wall in compressible wall-bounded turbulent flows. Building on this foundation, a flux-controlled wall model (FCWM) for large eddy simulation (LES) is proposed. Unlike conventional wall-stress models that solve the turbulent boundary layer equations, FCWM formulates the near-wall modeling as a control problem applied directly to the outer LES solution. It consists of three components: (1) the compressible law of the wall, (2) a feedback flux-control strategy, and (3) a shifted boundary condition. The model adjusts the wall shear stress and heat flux based on discrepancies between the computed and target transformed velocity and temperature, respectively, at the matching location. The proposed wall model is evaluated using LES of turbulent channel flows across a broad range of conditions, including quasi-incompressible cases with bulk Mach number M-b=0.1 and friction Reynolds number Re-tau=180-10 000, and compressible cases with M-b=0.74-4.0 and bulk Reynolds number Reb=7667-34 000. The wall-modeled LES reproduces mean velocity and temperature profiles in agreement with direct numerical simulation data. For all tested cases with M-b <= 3, the wall model achieves relative errors of |epsilon(Cf)|<4.1%, |epsilon(Bq)|<2.7%, and |epsilon(Tc)|<2.7% in friction coefficient, non-dimensional heat flux, and centerline temperature, respectively. In the quasi-incompressible regime, the wall model achieves |epsilon Cf|<1%. Compared to the conventional equilibrium wall model, the proposed FCWM achieves higher accuracy in compressible turbulent channel flows without solving the boundary layer equations, thereby reducing computational cost.

The logarithmic velocity profile is a key feature of wall-bounded turbulence, typically observed in a narrow near-wall region. In this study, we revisit the mixing length (lm) foundation of the logarithmic law in turbulent channel flow and propose a method to extend the logarithmic velocity profile. The derivation follows a bottom-up approach without relying on the uniform shear stress assumption or Prandtl's linear mixing length model. We demonstrate that the logarithmic velocity profile emerges where Prandtl's parabolic model for lm aligns with its true value. Extending this overlap region for lm correspondingly broadens the logarithmic profile. Based on this principle, we propose an enhanced mixing length model with a wider overlap region compared to the existing models. When applied to conventional velocity transformations—such as the IC-type for incompressible flow, and the Van Driest (VD-type) and Trettel and Larsson (TL-type) transformations for compressible flow—enhanced versions of these transformations are obtained accordingly. The enhanced model and corresponding transformations are evaluated using a series of direct numerical simulation data, covering a wide range of Mach and Reynolds numbers. In both incompressible and compressible turbulent channel flows, the predicted mixing length closely follows the theoretical distribution throughout the outer layer. The extended logarithmic profile reaches the channel center, with the corresponding diagnostic function deviating by no more than ±7% from the reference value. Compared to conventional logarithmic and defect laws, the extended logarithmic profile provides a more consistent description of velocity distribution across the outer layer.

Examining the influence of shock waves on cylinders and droplets at near -critical conditions, especially when accounting for real fluid effects, represents a relatively unexplored frontier. This research gap becomes even more relevant when extending the investigation to three-dimensional scenarios. The underlying evolution mechanisms at these conditions remain elusive, with limited existing literature. In this study, we present a thorough exploration employing two-dimensional and three-dimensional numerical simulations of a droplet with an embedded gas cavity subjected to a normal shock wave at near -critical conditions. Our approach involves modeling the cylinder/droplet and the surrounding gas flow using the compressible multicomponent equations, incorporating real fluid thermodynamic relationships, and implementing a finite -volume -based hybrid numerical framework capable of capturing shocks and interfaces. To establish the reliability of our approach, we validate it against reference data, demonstrating excellent agreement. We also conduct mesh independence studies, both qualitatively and quantitatively. Our analysis is comprehensive, considering the intricacies of shock impingement, the morphological deformation of the cylinder/droplet and cavity, and the development of vortices. We discuss and analyze various phenomena, including the evolution of wave patterns, jet formation, sheet formation, hole appearance, the emergence of petal -shaped structures or lobes, ligament formation, shear -induced entrainment, and internal cavity (bubble) breakup. We compare the results obtained from the cylinder/droplet with a cavity to those from a planar shock wave impacting a pure cylinder/droplet. We provide a holistic view of the two-dimensional cylinder and three-dimensional droplet's evolution before and after the impact of a shock wave, accompanied by quantitative data regarding the positions of characteristic points along the column over time. Our analysis further scrutinizes the geometrical characteristics of the cylinder and the trends in the distribution of baroclinic vorticity at various stages. Our findings reveal that the presence of a gas cavity plays a pivotal role in shaping the shock wave, which, in turn, influences the generation and distribution of baroclinic vorticity. This leads to a transformation in the unstable evolution process of both the cylinder and the droplet. Importantly, shock waves impacting the evolving interfaces of the cylinder/droplet and the internal gas cavity generate baroclinic vorticity, which subsequently affects the transport and distribution of vorticity, thereby influencing the evolution of the cylinder/droplet interface. In the case of three-dimensional droplets, baroclinic vorticity induces complex, intricate three-dimensional structure transformations.

We investigate turbulent duct flows with a square cross section using well-resolved large-eddy simulations (LES). A physically consistent subgrid-scale turbulence model based on the Adaptive Local Deconvolution Method (ALDM) for implicit LES is used. The wall shear stress is artificially modified at one of the four walls to analyse the influence on the secondary flow. A direct numerical simulation (DNS) of a symmetrical duct flow is used as reference to assess the influence of a modified wall shear stress. The modification results in an asymmetrical distribution of the secondary flow source terms, affecting the momentum distribution. Further, the anisotropy of the Reynolds stress tensor, which induces the secondary flow vortices is considerably affected by the wall shear stress modulation.

For internal combustion engine, the determination of combustion characteristics and subsequent emissions formation relies heavily on the fuel injection process. With the increasing demand for enhanced fuel efficiency and reduced emissions, it becomes vital to develop fundamental understanding of physical process involved in the fuel injection process. In this study, an optimal numerical approach to predict high pressure liquid injection process in the context of industrial computations has been investigated. In particular, this study focuses on the respective performance of the Partially-Averaged Navier-Stokes and Large Eddy Simulation models to predict turbulent igniting sprays. Both approaches are coupled with widely used Lagrangian Discrete Droplet Method for spray modelling. The results are validated against well established ECN Spray A case in reactive and non reactive conditions. For reacting conditions, Flamelet Genrated Manifold (FGM) combustion model is employed in the present work. Comparative study and validation against experimental data showed that PANS turbulence model allows for coarser grids while still maintaining accurate results.

Conventional WENO3 methods are known to be highly dissipative at lower resolutions, introducing significant errors in the pre-asymptotic regime. In this paper, we employ a rational neural network to accurately estimate the local smoothness of the solution, dynamically adapting the stencil weights based on local solution features. As rational neural networks can represent fast transitions between smooth and sharp regimes, this approach achieves a granular reconstruction with significantly reduced dissipation, improving the accuracy of the simulation. The network is trained offline on a carefully chosen dataset of analytical functions, bypassing the need for differentiable solvers. We also propose a robust model selection criterion based on estimates of the interpolation's convergence order on a set of test functions, which correlates better with the model performance in downstream tasks. We demonstrate the effectiveness of our approach on several one-, two-, and three-dimensional fluid flow problems: our scheme generalizes across grid resolutions while handling smooth and discontinuous solutions. In most cases, our rational network-based scheme achieves higher accuracy than conventional WENO3 with the same stencil size, and in a few of them, it achieves accuracy comparable to WENO5, which uses a larger stencil.

We present a numerical scheme valid in the range of highly to weakly compressible flows using a single -fluid four equation approach together with multi -component thermodynamic models. The approach can easily be included into existing finite volume methods on compact stencils and enables handling of compressibility of all involved phases including surface tension, cavitation and viscous effects. The mass fraction (indicator function) is sharpened in the two-phase interface region using the algebraic interface sharpening technique Tangent of Hyperbola for INterface Capturing (THINC). The cell face reconstruction procedure for mass fractions switches between an upwind -biased and a THINC-based scheme, along with proper slope limiters and a suitable compression coefficient, respectively. For additional sub -grid turbulence modeling, a fourth order central scheme is included into the switching process, along with a modified discontinuity sensor. The proposed "All -Mach" Riemann solver consistently merges the thermodynamic relationship of the components into the reconstructed thermodynamic variables (like density, internal energy), wherefore we call them All -Mach THINC-based Thermodynamic -Dependent Update (All -Mach THINC-TDU) method. Both, liquid -gas and liquid -vapor interfaces can be sharpened. Surface tension effects are taken into account by using a Continuum Surface Force (CSF) model. In order to reduce spurious oscillations at interfaces we decouple the computation of the interface curvature from the computation of the gradient of the Heaviside function. An explicit, four stage low storage RungeKutta method is used for time integration. The proposed methodology is validated against a series of reference cases, such as bubble oscillation/advection/deformation, shock -bubble interaction, a vapor/gas bubble collapse and a multi -component shear flow. The results of a near -critical shock/droplet interaction case are superior to those obtained by WENO3 and OWENO3 schemes and support that the proposed methodology works well with various thermodynamic relations, like the Peng-Robinson equation of state. Finally, the approach is applied to simulate the three-dimensional primary break-up of a turbulent diesel jet in a nitrogen/methane mixture including surface tension effects under typical dual -fuel conditions. The obtained results demonstrate that the methodology enables robust and accurate simulations of compressible multi-phase/multi-component flows on compact computational stencils without excessive spurious oscillations or significant numerical diffusion/ dissipation.

In this study, we investigate the impact of gas cavity size and eccentricity on the interaction of shockwaves with a cavity-embedded fuel-liquid cylinder under near-critical conditions. We analyze a range of scenarios involving both eccentric and concentric cavities, varying cavity radii (0-0.875R), eccentricity angles (0°–180°), and distances (0R-0.45R). Our methodology entails modeling the evolution of the fuel cylinder and surrounding gas flow using compressible multi-component equations, employing a finite-volume-based hybrid numerical framework capable of accurately capturing shocks and interfaces. Additionally, real-fluid thermodynamic relationships are employed, validated against reference data, showing excellent agreement. Mesh independence studies are provided. We analyze the shock impingement characteristics, deformation of the cylinder and cavity, and the formation of vortices. Various phenomena at different evolution stages are explored, including wave pattern evolution, jet formation, cavity breakup, baroclinic vorticity distribution, and circulation histories. Size and eccentricity of the cavity determine time intervals between wave contact with the cylinder and with the cavity, thereby influencing the evolution of wave patterns and interface deformation. We propose an analytical model for deposited circulation, obtained by appropriately combining the Yang, Kubota, and Zukoski (YKZ) and the Zhang and Zou (ZZ) models, which agrees well with numerical findings for cases involving smaller cavities. However, for larger cavities, as the cavity gradually reaches the cylinder surface, induced coupling effects invalidate the model. Furthermore, we introduce four predictive fits for the center-of-mass position of the shocked cylinder under near-critical conditions. These fits—the Time-Size Polynomial Prediction Fit, the Time-Eccentricity Polynomial Prediction Fit, the Time-Eccentricity Distance Polynomial Prediction Fit, and the Connecting Rod Prediction Fit—are tailored for cases involving cavities of varying sizes, eccentricity angles, and distances. Demonstrating good predictive performance, these fits offer valuable insights into the mixing behavior of liquid fuel sprays in a diverse range of near-critical environments and high-speed propulsion systems.

We present a quantum algorithm for computational fluid dynamics based on the Lattice-Boltzmann method. Our approach involves a novel encoding strategy and a modified collision operator, assuming full relaxation to the local equilibrium within a single time step. Our quantum algorithm enables the computation of multiple time steps in the linearized case, specifically for solving the advection-diffusion equation, before necessitating a full state measurement. Moreover, our formulation can be extended to compute the non-linear equilibrium distribution function for a single time step prior to measurement, utilizing the measurement as an essential algorithmic step. However, in the non-linear case, a classical postprocessing step is necessary for computing the moments of the distribution function. We validate our algorithm by solving the one dimensional advection-diffusion of a Gaussian hill. Our results demonstrate that our quantum algorithm captures non-linearity.

This study is dedicated to improving the efficiency of the flamelet-generated manifold (FGM) tabulated chemistry combustion modeling approach for predicting the combustion process in diesel-ignited internal combustion (IC) engines. The primary focus is on reducing table generation time and memory requirements. To accurately predict dual-fuel combustion processes, it is important to model both premixed and non-premixed combustion regimes. However, attempting to include both regimes in a single FGM lookup table leads to significant increases in the table size and generation time. In response, this work proposes a dual-table configuration, with each table dedicated to a specific regime. The solution is then interpolated from these tables based on the calculated combustion regime indicator during the computational fluid dynamics (CFD) simulation. This approach optimizes computational efficiency while ensuring an accurate representation of dual-fuel combustion. Additionally, to establish a cost-effective and accurate 3D CFD simulation workflow, the dual-table FGM methodology is coupled with the partially averaged Navier–Stokes (PANS) turbulence model. The feasibility of the proposed FGM methodology is tested utilizing six chemical kinetics mechanisms with different levels of detail. The results of this study demonstrated that the dual-table approach significantly accelerates table generation time and reduces memory requirements compared to a single table that includes both combustion regimes. Furthermore, 3D CFD simulation results of the dual-fuel combustion process are validated against available experimental data for three engine operating points. The in-cylinder pressure traces and rate of heat release obtained from the 3D CFD simulations employing the FGM PANS methodology show good agreement with experimental measurements, confirming the accuracy and reliability of this modeling approach.

Cavitating vapor bubbles, occur in variety of engineering applications, employing complex fluids as operating medium. Especially in biomechanics, biomedical applications as well as in polymer processing, fluids exhibit viscoelastic properties and fundamentally differ-ent behavior than Newtonian fluids. To explain the influence of viscoelasticity on cavita-tion a detailed understanding of the viscoelastic influence on bubble dynamics and of the underlying mechanisms is essential. With this study we provide in-depth numerical in-vestigations of the spherical vapor bubble collapse in viscoelastic fluids. A compressible, density-based, 3D finite-volume solver with explicit time integration is used together with a conservative, compressible formulation for the constitutive equations of three different viscoelastic models, namely the upper convected Maxwell model, the Oldroyd-B model and the simplified linear Phan-Thien Tanner model. 3D simulations of the collapse dynamics are carried out to show the viscous and viscoelastic stress development during the col-lapse, and its relation to the occurring deformations. Collapse behavior is investigated for various elasticity, viscosity and constitutive models. It is demonstrated that viscoelastic-ity fundamentally alters the collapse behavior and evolution of stresses. It is observed that viscoelastic stresses develop with a time delay proportional to elasticity and show different spatial distributions as opposed to Newtonian stresses. Viscoelasticity introduces isotropic stress components even though the spherical collapse leads to purely deviatoric (elonga-tional) deformation. Furthermore, the distinct influence of constitutive models is illustrated and the influence of viscoelastic models with solvent contribution is explained. For the up-per convected Maxwell model, we show that for increased elasticity shock wave emission can be observed depending on the applied grid resolution.& COPY; 2023 Published by Elsevier Inc.