Eulerian-Lagrangian Model Research Articles

Numerical investigation of tip vortex cavitation noise with an emphasis on environmental nucleation effect

The multiscale interactions between macroscale cavities and microscale nuclei influence the tip vortex cavitation (TVC) dynamics and its induced noise. To study the underlying mechanisms, we employ a two-way transition and coupling Euler–Lagrange model. Through acoustic analogy analysis, we identify the cavity volume fluctuations as the main vocal mechanism of cavitating tip vortex, highlighting two critical cavity dynamics, i.e., the breathing mode of surface waves and the cavity collapse of downstream end. Multiscale simulation results suggest that the merging of microscale bubbles into macroscale cavities can generate a more continuous tip vortex cavity and enlarge the cavity size. In addition, the high-frequency noise is significantly increased due to the nuclei injection, and the cavity collapse noise is reduced due to a more continuous cavity. Our investigation provides a foundation for understanding multiscale TVC dynamics and noise.

Effect of the Vortex on the Movement Law of Sand Particles in the Hump Region of Pump-Turbine

The pump turbine, as the core equipment of a pumped storage power plant, is most likely to operate in the hump zone between condition changes, which has a great impact on the stable operation of the power plant, and the high sedimentation of a natural river will lead to wear and tear in the overflow components of the equipment. Therefore, this paper is based on the Euler–Lagrange model, and seeks to investigate the distribution of vortices in the hump zone of the pump turbine and its effect on the movement of sand particles. The study shows that as the flow rate increases, the strip vortex in the straight cone section of the draft tube becomes elongated, and the cluster vortex in the elbow tube section gradually decreases. The strip vortex encourages the sand particles to move along its surface, while the cluster vortex hinders the movement of the sand particles. The accumulation areas of the sand particles in the straight cone section and the elbow tube section increase axially and laterally, respectively. The blade vortex in the runner gradually occupies the flow channel as the flow rate increases, and the blade vortex near the pressure surface encourages the sand particles to move towards the suction surface, resulting in the serious accumulation of sand particles on the suction surface. As the flow rate increases, the number of blades where sand particles accumulate increases and the accumulation area moves towards the cover plate and the outlet. The flow separation vortex in the double-row cascade decreases as the flow rate increases, which drives the sand movement in the middle and lower sections of the vanes. The area of sand accumulation in the stay vane decreases with increasing flow rate, but the area of sand accumulation between the guide vanes increases and then decreases. The vortex on the wall surface of the volute gradually decreases with the flow rate, and the vortex zone at the outlet first decreases, then disappears, and finally reappears. The vortex at the wall surface suppresses the sand movement, and its sand accumulation area changes from elongated to lumpy and finally to elongated due to the increase in flow. The results of the study provide an important theoretical reference for reducing the wear of pump turbine overflow components.

Numerical simulation and experimental analysis of aerosol deposition: Investigating nozzle effects on particle dynamics and deposition

Aerosol deposition (AD), also known as vacuum kinetic spray (VKS), is used to deposit dense ceramic films onto a substrate surface at room temperature. One predominant factor influencing the overall deposition process is the nozzle geometry, which significantly impacts the quality, shape, and thickness of the coating. To evaluate the effect of nozzle geometry in AD, particle trajectory and impact velocity were investigated via computational simulation. A Eulerian-Lagrangian model was used to simulate the gas flow, particle in-flight behavior, as well as particle deposition characteristics on a flat substrate. Three common nozzle geometries in AD were utilized: a converging-diverging nozzle with a slit cross-section (CD-Slit), a converging-barrel nozzle with a slit cross-section (CB-Slit), and a converging-diverging round (CD-Round) nozzle. Moreover, suitable drag coefficient and Nusselt number correlations were used to account for compressibility and rarefaction effects on particle dynamics and heat transfer. The simulation results were compared to the deposition pattern obtained experimentally. The results demonstrate that shape of the nozzle has profound effect on the particle impact velocity and deposition pattern. The CD-Round nozzle provides uniform, thinner coatings ideal for extensive surfaces at a slower deposition rate. In contrast, the CB-Slit nozzle is optimized for maximum coating thickness in narrow, thick linear patterns. The CD-Slit nozzle achieves high deposition rates with uniform coatings and a distinctive cat-ear shaped pattern.

Entrapment behavior of argon bubbles and non-metallic inclusions under combined magnetic fields in slab continuous casting

The entrapment of argon bubbles and non-metallic inclusions under combined applications of electromagnetic braking (EMBr) and electromagnetic stirring (EMS) is receiving increasing attention in slab continuous casting. A Eulerian-Lagrangian model, incorporating Archimedes electromagnetic force, was established to study the entrapment behavior of particles under multi-mode electromagnetic fields. Results showed that the novel EMBr device could enhance the mixing of liquid steel around the nozzle and significantly suppress inclusion entrapment. The optimal magnetic induction intensity ranged from 0.218 T to 0.272 T based on the number of entrapped particles beneath the slab surface. Furthermore, employing appropriate drag force coefficients and considering the Archimedes electromagnetic force were crucial for predicting the quantity and size of entrapment particles. Particles from the nozzle jet experienced a downward electromagnetic force induced by EMBr. EMS tended to drag particles toward the solidified shell, with a more pronounced effect on large-sized particles. More particles were entrapped by the solidified shell in numerical results. Further research should account for Archimedes electromagnetic force in quantitative examination of inclusion entrapment.

Insights into the characteristics of sheet/cloud cavitation and tip-leakage cavitation based on a compressible Euler-Lagrange model

Insights into the characteristics of sheet/cloud cavitation and tip-leakage cavitation based on a compressible Euler-Lagrange model

Fixed Switching Frequency Model Predictive Control and Passivity Based Control for DC-DC Converter

The DC-DC converter represents a crucial component in the renewable energy sources. The stability and dynamic capability enhancement of the DC/DC converter have emerged as a significant research topic in the current era. Model predictive control (MPC) is particularly prevalent due to its high dynamic response speed, simplicity of the controller design, and capacity for multi-objective optimization. However, the traditional finite control set model predictive control (FCS-MPC) method is suffer to variable switching frequency and vast computing. To improve the dynamic performance of the converter, a novel nonlinear control strategy named fixed switching frequency MPC and passivity-based control (PBC), named FSFPBMPC, is proposed, which could achieve fixed switching frequency and enhance the system's dynamic response speed. Firstly, the Euler-Lagrange (EL) model of the boost converter is established. Secondly, the relationship between duty cycle and MPC is established. Ultimately, the output voltage of PBC is incorporated into the cost function of the FCS-MPC. The characteristics of PBC power shaping and damping injection can enhance the system's immunity to interference, improve the system's dynamic response speed, and thus reinforce the system's stability. Then, depending on MATLAB, the simulation results proved that the proposed strategy has the same effect as we expected.

Multiscale modeling of cavitation around high-speed object based on Euler-Lagrange model and overset mesh

Abstract Cavitation is a common phenomenon, which usually occurs inside the high-speed liquid or at the liquid-solid interface. The unsteady behaviors of cavitating flow, such as shedding and collapse, can cause intense pressure fluctuations and reduce the stability of devices such as under-water high-speed vehicles. In addition, cavitating flow changes the dynamic characteristics of the liquid and causes vibration of the structure as well, making it extremely difficult in achieving precise control. Using OpenFOAM, the present work employs the large eddy simulation (LES) approach to investigate the characteristics of the unsteady cavitating flow around a high-speed under-water object with using the overset mesh technology. The volume of fluid (VOF) approach jointed with the Lagrangian discreate bubble model (DBM) is used for capturing the multiscale cavitation features. The Schnerr & Sauer model is applied for the mass transfer between water and vapor in macroscale, while the Reynolds-Pel equation is incorporated for the growing and collapsing of microscale bubbles. Due to the difficulty in incorporating the multi-scale method into the problems with mesh motion, the present work employs the overset mesh method. Compared with the published small-scale water tank experiment, the LES simulation results fit well with the experimental phenomena, and the DBM can capture the small bubbles accurately. On the basis of model verification, the mechanisms of cavitation forming and collapsing is investigated and the effect of cavitation shedding on the motion of the object is also studied. Moreover, the nucleation, growth, collapse of bubbles, and the interaction between discrete bubbles and large cavities can be well revealed. Further, the multi-scale Euler-Lagrange model is also applied in the rotating propeller, the generation of tip vortex cavitation which is hard to be captured by traditional models is well presented.

A virtual calibration chamber for cone penetration test based on deep-learning approaches

The interpretation of the cone penetration test (CPT) still relies largely on empirical correlations that have been predominantly developed in resource-intensive and time-consuming calibration chambers. This paper presents a CPT virtual calibration chamber using deep learning (DL) approaches, which allow for the consideration of depth-dependent cone resistance profiles through the implementation of two proposed strategies: (1) depth-resistance mapping using a multilayer perceptron (MLP) and (2) sequence-to-sequence training using a long short-term memory (LSTM) neural network. Two DL models are developed to predict cone resistance profiles (qc) under various soil states and testing conditions, where Bayesian optimization (BO) is adopted to identify the optimal hyperparameters. Subsequently, the BO-MLP and BO-LSTM networks are trained using the available data from published datasets. The results show that the models with BO can effectively improve the prediction accuracy and efficiency of neural networks compared to those without BO. The two training strategies yielded comparable results in the testing set, and both can be used to reproduce the whole cone resistance profile. An extended comparison and validation of the prediction results are carried out against numerical results obtained from a coupled Eulerian-Lagrangian (CEL) model, demonstrating a high degree of agreement between the DL and CEL models. Ultimately, to demonstrate the usability of this new virtual calibration chamber, the predicted qc is used to enhance the preceding correlations with the relative density (Dr) of the sand. The improved correlation with superior generalization has an R2 of 82% when considering all data, and 89.6% when examining the pure experimental data.

A Numerical Evaluation of Airborne Transmission Control through Saliva Modification

The present study explored the relationship between airborne transmission and the saliva fluid properties of a human sneeze. Specifically, we aimed to understand if altering the saliva and its relationship to droplet breakup and stability can affect its transmission characteristics. The study aimed to answer this question using computational fluid dynamics, specifically, a hybrid Eulerian–Lagrangian model with a Spalart–Allmaras, detached eddy simulation turbulence model. The effort focused on a scenario with a sneeze event within a ventilated room. The study found that for sneezes, secondary breakdown processes are important. Thicker saliva that increased the Ohnesorge number displayed a clear resistance to aerosolization due to stabilized secondary breakup, leading the bulk of the drops having high settling rates that are less likely to drive airborne transmission. For instance, the use of xanthum gum, which increased the saliva viscosity by 2000%, reduced the formation of aerosols. Additionally, another class of modifiers that reduce saliva content was studied, which was also effective in reducing airborne transmission drivers. Zingiber, which reduced the saliva content, reduced the formation of aerosols. However, when considering the overall reduction in droplet volume, saliva modifiers such as cornstarch, xanthum gum, and lozenges increased the mean droplet size by 50%, 25%, and 50%, respectively, while reducing the overall droplet volume by 71.6%, 71.2%, and 77.2%, respectively. Conversely, Zingiber reduced the mean droplet size by 50% but increased the overall droplet volume by 165.7%. Overall, for this type of respiratory event, this study provides insight into the potential for modifying saliva characteristics that may impact airborne transmission and could introduce new tools for reducing airborne pathogen transmission.

A Euler-lagrange Model of dynamic internal friction

A Euler-Lagrange model of dynamic internal friction is proposed and is shown to match the frequency and decay of oscillations in both simple extension (pull) and cantilever beam experiments. The proposed dynamic internal frictional stress, τij, is proportional to the rate of change of the engineering stress, σ˙ij. i.e.τij=μmσ˙ij,with μm the dynamic internal friction coefficient. A single value of the dynamic internal friction coefficient is shown to match the results of the experiments for a number of different geometries of the silicon rubber, Dragon SkinTM. Dragon SkinTM is used in skin effects for movies and in prosthetics and cushioning applications. It is chosen here because of its ease of preparation and relatively simple non-linear stress-strain response. Because of these characteristics, it provides a simple starting place for simulating more complicated synthetic rubber and biological materials, which are used in a myriad of commercial applications.

Measurement report: Source attribution and estimation of black carbon levels in an urban hotspot of the central Po Valley – an integrated approach combining high-resolution dispersion modelling and micro-aethalometers

Abstract. Understanding black carbon (BC) levels and its sources in urban environments is of paramount importance due to the far-reaching health, climate, and air quality implications. While several recent studies have assessed BC concentrations at specific fixed urban locations, there is a notable lack of knowledge in the existing literature on spatially resolved data alongside source estimation methods. This study aims to fill this gap by conducting a comprehensive investigation of BC levels and sources in Modena (Po Valley, Italy), which serves as a representative example of a medium-sized urban area in Europe. Using a combination of multi-wavelength micro-aethalometer measurements and a hybrid Eulerian–Lagrangian modelling system, we studied two consecutive winter seasons (February–March 2020 and December 2020–January 2021). Leveraging the multi-wavelength absorption analyser (MWAA) model, we differentiate sources (fossil fuel combustion, FF, and biomass burning, BB) and components (BC vs. brown carbon, BrC) from micro-aethalometer measurements. The analysis reveals consistent, minimal diurnal variability in BrC absorption, in contrast to FF-related sources that exhibit distinctive diurnal peaks during rush hours, while BB sources show less diurnal variation. The city itself contributes significantly to BC concentrations (52 ± 16 %), with BB and FF playing a prominent role (35 ± 15 % and 9 ± 4 %, respectively). Long-distance transport also influences BC concentrations, especially in the case of BB and FF emissions, with 28 ± 1 % and 15 ± 2 %, respectively. When analysing the traffic-related concentrations, Euro 4 diesel passenger cars considerably contribute to the exhaust emissions. These results provide valuable insights for policy makers and urban planners to manage BC levels in medium-sized urban areas, taking into account local and long-distance sources.

Influence of the shape and number of obstacles on the excitation effect of dust-containing gas explosion

To investigate the excitation effects of different shapes and quantities of obstacles in dust-containing gas explosions, this study utilized an Euler-Lagrangian model. This model simulated explosions involving dust-laden gas, applying various governing equations to address the roles of coal dust at different combustion stages and its dynamic forces within the flow field. The research examined a 9.5 % gas concentration explosion with 5 g/m3 coal dust in a straight pipeline, assessing the impacts of spherical, regular tetrahedral, and cuboidal obstacles, as well as combinations of 1–3 cuboidal obstacles and three differently shaped obstacles on the explosion's flow field structure, overpressure, and flame propagation speed. Key findings include: dust-containing gas explosions result in more complex flow field structures, and the excitation effects of coal dust and obstacles on flame propagation speed exhibit mutual inhibition. Obstacles increase the maximum overpressure locally without affecting the overall trend of increasing overpressure with distance in closed conduits. Cuboidal obstacles show the most pronounced excitation effects, followed by regular tetrahedral and spherical obstacles, respectively. The number of obstacles also progressively enhances their excitation effects. When three obstacles of different shapes are present, cuboidal and regular tetrahedral obstacles primarily affect the flow field structure, with flame propagation speed and maximum explosion overpressure slightly lower compared to the presence of three cuboidal obstacles alone. These findings offer valuable insights into the destructive effects of gas explosions and the excitation effects of obstacles, providing theoretical support for disaster prevention and recommendations for underground equipment design and layout.

Numerical investigation on the tracer followability of nitrogen droplets over the airfoil in cryogenic wind tunnel

Cryogenic wind tunnels (CWT) achieve high Reynolds number aerodynamic performance measurements by cooling the medium with liquid nitrogen. Utilization of naturally-occurring liquid nitrogen droplets as tracer particles for non-intrusive measurement applications in CWT shows great potential in the previous work. However, the droplet motion characteristics over the airfoil remain to be investigated, and the followability evaluation approach is lacking. Accordingly, an Euler-Lagrange model for transonic particle flow over the NACA0012–64 airfoil was established. The effect of flow characteristics on the particle motion was explored. The results reveal that the flow separation and shock wave phenomenon over the airfoil causing velocity deviation between airflow and droplet. The shock wave is the main deviation driver, and flow separation causing trace blind zones. Droplets larger than 10 μm fail to effectively follow the variation of airflow. The blind zone enlarges with angle of attack (AoA), particularly when AoA exceeding 10 degrees. Moreover, Stokes number is found to be inadequate for characterizing the followability of tracer particles in turbulent flow. Consequently, an evaluation method based on the Basset-Boussinesq-Oseen equation was developed. This research provides theoretical and technical support for advancing the use of nitrogen droplets as tracer particles in CWT.

Evaluating the Performance of Protective Barriers against Debris Flows Using Coupled Eulerian Lagrangian and Finite Element Analyses

Protective structures are critical in mitigating the dangers posed by debris flows. However, evaluating their performance remains a challenge, especially considering boulder transport in complex 3D terrains. This study introduces a comprehensive methodology to appraise the effectiveness of protective structures under the impact of debris flows for real-world conditions along the Hobart Rivulet in Tasmania, Australia. The validation of the Coupled Eulerian-Lagrangian (CEL) model against experimental data demonstrates its high accuracy in predicting flow dynamics and impact forces, whereby flow velocities are estimated for subsequent Finite Element (FE) analyses. By simulating boulder-barrier interactions, weak points in I-beam post barriers are identified, with a broad investigation of the effects on the barrier performance under various conditions. The establishment of a 3D CEL model to assess the interactions between debris flow, boulders, and I-beam post barriers in a complex rivulet terrain is of particular significance. Through CEL and FE analyses, various aspects of debris flow-structure interactions are presented, including structural failure, impact force, and boulder velocity. The findings provide insights into the suitability of various numerical methods to assess the performance of protective measures in real-world scenarios.

Accurate machine-learning-based prediction of aerodynamic and heat transfer coefficients for cylindrical biomass particles

Cylindrical biomass (straw) particles play a crucial role in the numerical simulations of biomass co-firing in coal-fired boilers, wherein the aerodynamic and heat transfer coefficients of these particles are particularly important. Despite the existence of models proposed thus far for various specific conditions, developing a universal model for cylindrical particles remains challenging in multiphase flow systems. In this paper, a total of 1092 validated computational fluid dynamics (CFD) datasets of cylindrical drag, lift, torque coefficients and average Nusselt number are acquired based on direct numerical simulations of OpenFOAM body-fitted meshes, and a hybrid algorithmic framework of N+1 convolutional neural network (CNN) machine learning optimality is established. where multiple single-tree models (referred to as N models) are combined with an additional CNN layer (referred to as +1 model).The framework inherits the advantages of a single-tree models while significantly improving the overall algorithmic generalisation performance, with the aim of allowing efficient use of time and minimal expertise in the fluid modelling process. The mean relative absolute errors for drag, lift, torque coefficient, and the average Nusselt number was evaluated, and the results demonstrated a reduction of 75.19 %, 89.48 %, 89.53 %, and 85.70 %, respectively, in the mean relative absolute errors compared to the extremely randomised tree single-tree model. We extended the model scope to different aspect ratios (1 ≤ Ar ≤ 30), angles of incidence (0° ≤ θ ≤ 90°), and Reynolds numbers (1 ≤ Re ≤ 4000), and conducted random predictions. Compared with traditional mathematical relationships, the proposed framework reduced the average relative error to 0.99 %. The simultaneous application of models for the prediction of unknown parameters was validated against relevant early literature, which demonstrated that the average relative error of drag coefficients for the predicted values remained within 2.07 %. The N+1 (CNN) hybrid algorithm framework exhibited high accuracy and excellent adaptability, furnishing a substantial quantity of data to support the Euler–Lagrange model of non-spherical biomass particle multiphase flow in industrial applications.

Predictive modelling of drag, lift and torque coefficients of cylindrical biomass particles under artificial intelligence

The escalating threats of climate change and fossil fuel depletion have greatly spurred the widespread utilization of renewable energy sources. Biomass energy stands out as the most abundant renewable energy resource. Among all biomass conversion technologies, direct combustion of biomass for heating and power generation represents the most common and cost-effective approach. Suspended combustion technology is widely employed in existing biomass combustion power plants worldwide. However, conventional modeling procedures often fail to adequately address the differences between coal and biomass combustion, particularly the significant impact of large-sized and highly non-spherical cylindrical straw biomass particles on their trajectories. This study utilizes OpenFOAM-body-fitted mesh direct numerical simulation (DNS) to generate a Computational Fluid Dynamics (CFD) dataset consisting of 300 data points for cylindrical particles, aimed at constructing a prediction model based on machine learning (N+1) for key aerodynamic parameters (drag coefficient, lift coefficient, and torque coefficient). The proposed model enables precise prediction of aerodynamic parameters for a wider range of different cylindrical particles and flow conditions, thus extending the existing Euler-Lagrangian model for non-spherical biomass multiphase flow.

Coupled Eulerian–Lagrangian model prediction of neural tissue strain during microelectrode insertion

Objective.Implanted neural microelectrodes are an important tool for recording from and stimulating the cerebral cortex. The performance of chronically implanted devices, however, is often hindered by the development of a reactive tissue response. Previous computational models have investigated brain strain from micromotions of neural electrodes after they have been inserted, to investigate design parameters that might minimize triggers to the reactive tissue response. However, these models ignore tissue damage created during device insertion, an important contributing factor to the severity of inflammation. The objective of this study was to evaluate the effect of electrode geometry, insertion speed, and surface friction on brain tissue strain during insertion.Approach. Using a coupled Eulerian-Lagrangian approach, we developed a 3D finite element model (FEM) that simulates the dynamic insertion of a neural microelectrode in brain tissue. Geometry was varied to investigate tip bluntness, cross-sectional shape, and shank thickness. Insertion velocities were varied from 1 to 8 m s-1. Friction was varied from frictionless to 0.4. Tissue strain and potential microvasculature hemorrhage radius were evaluated for brain regions along the electrode shank and near its tip.Main results. Sharper tips resulted in higher mean max principal strains near the tip except for the bluntest tip on the square cross-section electrode, which exhibited high compressive strain values due to stress concentrations at the corners. The potential vascular damage radius around the electrode was primarily a function of the shank diameter, with smaller shank diameters resulting in smaller distributions of radial strain around the electrode. However, the square shank interaction with the tip taper length caused unique strain distributions that increased the damage radius in some cases. Faster insertion velocities created more strain near the tip but less strain along the shank. Increased friction between the brain and electrode created more strain near the electrode tip and along the shank, but frictionless interactions resulted in increased tearing of brain tissue near the tip.Significance. These results demonstrate the first dynamic FEM study of neural electrode insertion, identifying design factors that can reduce tissue strain and potentially mitigate initial reactive tissue responses due to traumatic microelectrode array insertion.

Analysis of Debris Flow Protective Barriers Using the Coupled Eulerian Lagrangian Method

Protective structures play a vital role in mitigating the risks associated with debris flows, yet assessing their performance poses crucial challenges for their real-world effectiveness. This study proposes a comprehensive procedure for evaluating the performance of protective structures exposed to impacts from media transported by large debris flow events. The method combines numerical modelling with site conditions for existing structures along the Hobart Rivulet in Tasmania, Australia. The Coupled Eulerian Lagrangian (CEL) model was validated by comparing simulation results with experimental data, demonstrating high agreement. Utilising three-dimensional modelling of debris flow–boulder interactions over the Hobart Rivulet terrain, boulder velocities were estimated for subsequent finite element analyses. Importantly, a model of interaction between boulders and I-beam posts was established, facilitating a comparative assessment of five distinct I-beam barrier systems defined as Type A to E, which are currently in use at the site. Simulation results reveal larger boulders display a slower increase in their velocities over the 3D terrain. Introducing a key metric, the failure ratio, enable a mechanism for comparative assessments of these barrier systems. Notably, the Type E barriers demonstrate superior performance due to fewer weak points within the structure. The combined CEL and FE assessments allow for multiple aspects of the interactions between debris flows, boulders, and structures to be considered, including structural failure and deformability, to enhance the understanding of debris flow risk mitigation in Tasmania.

Geometric factors affecting CO2 separation in a supersonic separator

The current work evaluates a potential carbon separation approach with swirl flows and spontaneous condensation. Euler-Euler model is established to depict the spontaneous condensation. By combining Euler-Lagrange model and Euler-Euler model, the droplet motion behavior is predicted, and the influence of separator structure including swirl generator and gap length on the separation efficiency of CO2 is quantified. The flue gas expands under pressure to create a non-equilibrium system, which leads to the emergence of CO2 condensation nuclei. On the surface of nuclei, metastable molecules aggregate and promote the growth of CO2 droplets. Based on the force on droplets, the droplet behavior can be separated into three categories: impacting the straight tube wall, entering the collector and entering the diffuser. When the angle of swirl generator and gap length rises by 315° and 3.75 mm respectively, the separation efficiency rises by 60.3 % and 48.6 %, respectively.

Effects of the droplet size and engine size on two-phase kerosene/air rotating detonation engines in flight operation conditions

The numerical and experimental research of rotating detonation engines is usually conducted in ground environmental conditions. Although many breakthroughs have been made, there are still deficiencies in understanding engines operating in real flight conditions. In order to reveal the effect of engine size and initial kerosene droplet size on the rotating detonation engines in flight conditions, the Eulerian-Lagrangian model is adopted, and a series of two-phase kerosene/air rotating detonation cases are simulated in the conditions of Mach 5 and 24 km altitude. When 5 μm and 10 μm droplets are adopted, the RDE behaves like gaseous rotating detonations with clear cellular structures. When the 20 μm and 30 μm droplets are adopted, the rotating detonation waves tend to be divided into two layers and form the λ-shaped shock structure. The results indicate that droplet size and engine size influence detonation wave propagation and flow field mainly through droplet heat absorption and evaporation height. Increasing the engine sizes can promote the two-phase rotating detonation and broaden the initial diameter range capable of obtaining rotating detonation waves. However, as droplet size increases, the fresh mixture layer becomes stratified, with an upper layer predominantly comprising fuel vapor and a lower layer of fuel droplets, which results in an λ-shaped shock structure at the detonation front. The flight operation condition brings in differences in features such as detonation wave height and velocity deficit. The detonation height in flight conditions is higher than that in ground conditions. The most velocity deficit in flight conditions observed in our study is below 15 %, which is lower than the results (22.5 %) in ground operating conditions. These results indicate that the optimal design of the rotating detonation engine must consider the real operation conditions and the effect of engine sizes and droplet sizes.