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A data-driven turbulence modeling method based on symbolic regression (SR) is proposed in this paper to enhance the prediction accuracy of the Spalart–Allmaras (SA) model for airfoil stall. Unlike traditional methods that rely on neural networks and lack physical interpretability, this paper utilizes SR to establish an analytic expression mapping local flow field variables to the SA model correction factor β. The training data are obtained through field inversion with the discrete adjoint method in the flow field of the S809 airfoil. Additionally, a relearning approach proposed in this paper is applied to the SR process to address the issue arising from the multi-solution nature of field inversion. The SA model embedded with β , referred to as the SA-SR model, can be integrated into computational fluid dynamics solvers with negligible computational cost. The generalization performance of the SA-SR model is tested under various conditions and airfoil types. The results indicate that the new model improves the predictive capability for airfoil stall without compromising the performance of the baseline SA model for attached flows. [ABSTRACT FROM AUTHOR]

In nonequilibrium slip and jump conditions, normal gas velocity and temperature gradients are used to calculate the gas slip velocity and temperature at the surface, respectively. Gökçen et al. (Computational fluid dynamics near the continuum limit, AIAA Paper No. 87-1115, 1987, and Gökçen and MacCormack, Nonequilibrium effects for hypersonic transitional flows using continuum approach, AIAA Paper No. 89-0461, 1989) stated that the tangential velocity and temperature of the gas molecules before a collision with the surface could be interpreted as the macroscopic tangential velocity and temperature of the gas molecules at the so-called near-wall distances auλ and aTλT away from the surface, respectively. The coefficients au and aT are the order of unity. In the present work, new forms of the slip and jump conditions are proposed by modifying the Gökçen slip and jump conditions to include the coefficients (au, aT). Numerical investigations are comprehensively conducted to determine the numerically proper values (au, aT) for the hypersonic rarefied gas flows. Cases such as the circular cylinder in cross-flow and sharp and blunted leading edge wedge are considered in the present work, with nitrogen as the working gas. The simulation results show the significant effects of the coefficients (au, aT) on the accuracy of the slip velocity and surface gas temperature predictions, and the values of au = 1.2 and aT = 1.1 show good agreement with the direct simulation Monte Carlo data. [ABSTRACT FROM AUTHOR]

Designing advanced combustors that operate at high temperatures and produce little pollution, especially in the absence of primary and dilution holes, is a difficult task that may bring significant challenges. In this regard, this paper introduces a Kriging surrogate model approach to optimize the outlet temperature distribution of the combustor to achieve such advanced low-pollution combustors. Building upon previous research, this study explores the effects of the swirler blade installation angle on the outlet temperature distribution of the combustor without primary or dilution holes. Traditional methods, such as the control variable method using computational fluid dynamics (CFD) for numerical simulation, are limited in application due to the complex coupling of flow, heat transfer, mass transfer, and combustion processes. In contrast, surrogate models, especially the Kriging model, offer a rapid and efficient alternative to extensive CFD simulations that provide accurate predictions and error estimates for the solution of the problem. In summary, this paper details the process of generating sample points through three-dimensional numerical simulations, develops a Kriging surrogate model through Latin hypercube sampling, and optimizes the model to identify the most uniform outlet temperature distribution achievable by adjusting the installation angle of the swirl blade. The optimal design parameters, which are quickly obtained through the Kriging model, showed a significant reduction in the overall temperature distribution function and the radial temperature distribution function by 21% and 27.14%, respectively. [ABSTRACT FROM AUTHOR]

The fuel rod bundles are the core part of pressurized water reactors (PWRs), and its heat transfer characteristics directly impact the safety of PWRs. A computational fluid dynamics (CFD) model of 5 × 5 fuel rod bundles with a spacer grid is established, and the numerical simulation results are in excellent agreement with the experimental results. Then, the effects of four turbulence models, namely shear stress transport model, standard k–ε model, re-normalization group k–ε model, and realizable k–ε model on the thermal-hydraulic characteristics of the 5 × 5 fuel rod bundles are systematically investigated. Furthermore, two data-driven methods, namely proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), are used to analyze the flow fields of the 5 × 5 fuel rod bundles. The two methods can extract key modes or features to enhance the comprehension and description of the dynamic behaviors within the flow fields of 5 × 5 fuel rod bundles. Finally, two reduced-order models (ROMs), called the POD-radial basis function neural network surrogate model and DMD method, are constructed, which can enable rapid prediction of the flow fields for 5 × 5 fuel rod bundles with high accuracy. The CFD simulation results presented in this paper can provide valuable insights for studying the thermal-hydraulic characteristics of the 5 × 5 fuel rod bundles. The two ROMs proposed in this paper can significantly reduce the computational costs associated with studying the thermal-hydraulic characteristics of 5 × 5 fuel rod bundles. [ABSTRACT FROM AUTHOR]

In marine environments, tidal currents exhibit periodic changes in both direction and velocity. Consequently, tidal turbines often operate under yawed conditions. While conventional horizontal-axis turbines show decreased performance and undergo periodic load fluctuations due to blade rotation when yawed, research on the effects of yaw on ducted turbines has been sparse, leaving the underlying impact mechanisms poorly understood. This paper presents a three-dimensional hydrodynamic model of a ducted turbine, developed using the computational fluid dynamics method and validated through flume experiments. The hydrodynamic characteristics of the ducted turbine when operating under yawed conditions are analyzed using large eddy simulation. The findings indicate that yaw does not alter the optimal rotational speed of the ducted turbine. The turbine performance remains superior to non-yawed conditions up to a yaw angle of approximately 7°, peaking with a 1% improvement at 5°, but deteriorates beyond this point, declining by 1.5% at a yaw angle of 10°. In addition, yaw causes a deflection in the wake of the ducted turbine. This deflection increases with the yaw angle, reaching its maximum at a yaw angle of 10° with an angle of about 3.4°, before diminishing. The duct structure significantly influences this deflection, while rotor rotation has a minimal impact on wake deflection. [ABSTRACT FROM AUTHOR]

Modeling and simulating the sudden depressurization of liquids inside nozzles is a significant challenge because of the plethora of the associated complex phenomena. This pressure drop together with the rapid phase change of the liquid is important characteristics of flash boiling. Computational fluid dynamics (CFD) multiscale simulations of flashing jets usually deploy additional models for modeling heat and mass transfer with long computational times. Intermediate steps such as volumetric meshing in mesh-based methods can also significantly increase the computational cost. This paper aims at providing academia and industry with a modeling tool to simulate and investigate the complex multi-facet phenomenon of flash-boiling atomization deploying a machine-learning method that could save thousand Central Processing Unit hours offering instantaneous CFD predictions. The presented machine-learning CFD method completely replaces the traditional CFD simulations workflow and requires little simulation expertise from the end-user. Notably, this is a novel model that couples for the first time the thermodynamic non-equilibrium with convolutional neural networks to simulate flashing liquid hydrogen jets thousand times faster than the standalone CFD solver. The accuracy of the novel approach is evaluated, demonstrating adequate accuracy compared to different unseen simulations and experiments. This work offers the groundwork for further accelerating CFD predictions in multiphase flow problems and could significantly improve testing flash-boiling scenarios in various industrial settings. [ABSTRACT FROM AUTHOR]

In this paper, we present a physics-informed approach to tailor the lift profile of an unsteady airfoil through the execution of an appropriate maneuver. In previous research, a low-order aerodynamic model based on the unsteady thin airfoil theory was developed for predicting the flowfield and loads on airfoils undergoing arbitrary motions. The theory was phenomenologically augmented using the concept of leading edge suction parameter (LESP) to incorporate the capability to predict intermittent leading edge vortex (LEV) shedding. The criticality of LESP was used to predict the onset and termination of LEV shedding and thus model the effect of LEVs on the flowfield and loads for a prescribed motion. In the current work, an inverse aerodynamic formulation is developed based on this framework for tackling the inverse problem: to obtain the motion kinematics required for generating a prescribed lift profile for an airfoil operating in the dynamic-stall regime. The LEV-modeling capability of the aerodynamic model enables the motion-design algorithm to take into account the effect of complex phenomena, such as dynamic stall and LEV shedding, which are not taken into account in previous research approaches. Several case studies are presented to demonstrate various scenarios such as lift tracking using pitching and heaving motions, lift cancellation during unsteady motion, and the generation of a given lift profile using two equivalent motions. The kinematic profiles generated by the inverse formulation are also simulated using a high-fidelity unsteady computational fluid dynamics solver to validate the predictions. [ABSTRACT FROM AUTHOR]

In this paper, we consider experimental data available for graphene-based nanolubricants to evaluate their convective heat transfer performance by means of computational fluid dynamics (CFD) simulations. Single-phase models with temperature-dependent properties are employed for this purpose. The base fluid is a polyalkylene glycol, and we show the effect of the addition of carbon nanohorns and graphene nanoplatelets (GNPs), in different volume fractions, on the convective heat transfer coefficient between two parallel plates. Then, an application to hydrodynamic lubrication is discussed. The extreme in-plane thermal conductivity of graphene allows a smaller temperature rise of the GNP-based nanolubricant, i.e., a more effective heat removal. To the best of our knowledge, this work represents the first application of single-phase nanofluid models to hydrodynamic lubrication. [ABSTRACT FROM AUTHOR]

Understanding the detachment dynamics of droplets adhered to reservoir walls holds significant importance for the residual oil displacement process in high water-cut oilfields. Existing studies demonstrated that increasing mainstream shear by adjusting displacement flow and weakening wall adhesion by increasing contact angle can enhance droplet detachment. However, the complex physical and chemical oil–wall interactions, a crucial reservoir feature, cannot be solely relied on the macroscopic representation through contact angle. A deviation in understanding the process of crude oil droplet detachment would be resulted due to the change of wall adhesion. Considering the intricate physical and chemical interactions between oil and walls, in this paper, we employed the extended Derjaguin–Landau–Verwey–Overbeek (EDLVO) theory to establish an oil–wall interaction forces system, and coupling computational fluid dynamics method to further explore the detachment dynamics of sessile oil droplets in a microchannel under varying EDLVO forces. The findings showed that (1) by increasing the Capillary number, the droplets gradually occur in four typical dynamic states: static, sliding, detachment, and pinch-off. (2) Static droplets are more prone to experience sliding behavior when influenced by EDLVO forces. (3) For droplets undergoing detachment and pinch-off, EDLVO forces inhibit entrainment behavior. These results contribute to an enhanced comprehension of droplet detachment dynamics in reservoirs, offering fresh insights for enhanced oil recovery strategies. [ABSTRACT FROM AUTHOR]

Waves can induce motion in the floating platforms that support tidal turbines, affecting their hydrodynamic loads. To study the non-constant hydrodynamic of floating tidal turbines in a wave condition, this paper investigates the effect of pitch motion on the power coefficient (CP), thrust coefficient (CT), and wake flow of a tidal turbine using computational fluid dynamics. A pitch motion experiment is designed to verify the validity of the numerical method. The results show that the CP and CT exhibit periodic fluctuations under pitch motion, with the fluctuation period being consistent with the pitch period. Waves do not change the overall fluctuation trend of the CP and CT, but they do complicate the fluctuations and increase the likelihood of blade fatigue damage. Pitch motion reduces the mean power, with large-amplitude pitch motions particularly likely to result in severe power loss. The low-velocity region of the wake under pitch motion exhibits significant periodic oscillations. The wake is more susceptible to the pitch period than the pitch amplitude, and small-period pitch motions force the wake to deform earlier, accelerating wake vortex dissipation and velocity recovery. Increasing the immersion depth reduces the effect of waves on tidal turbine performance, but is not conducive to wake recovery. In summary, the rational design of the immersion depth and limiting the movement amplitude of the floating platform have the potential to prolong the working life of tidal turbines and increase their power output. [ABSTRACT FROM AUTHOR]

The idea of water wave control for ease of shipping or energy exploitation has been the subject of extensive research. Yet, most studies are limited to two-dimensional simulations focusing primarily on transformations of small-amplitude linear waves. This paper presents a theoretical and numerical investigation of the hydrodynamic performance of a type of shallow-water waveguides. The concept of an effective refractive index for water waves is proposed through analogy with electromagnetic waves, based on which the wave-controlling mechanism is explained. The precise wave field is revealed using computational fluid dynamics simulations. The numerical model is validated by comparing the numerical predictions with experimental data. Subsequently, the nonlinear wave fields around the waveguide are systematically studied by increasing the incident wave height. The numerical results confirm the significant increase in the wave height above the waveguide platform, as a result of wave refraction and reflection. Such an amplification effect slightly reduces as the nonlinearity of the incident wave increases. [ABSTRACT FROM AUTHOR]

This study investigates the impact of yaw conditions on the aerodynamic performance and flow field of three high-performance vehicle model configurations by means of wind tunnel testing and unsteady Reynolds-Averaged Navier–Stokes-based computational fluid dynamics simulations. While yaw effects on automotive vehicles have been explored, the effects on far more complex flow fields of high-performance vehicles remain insufficiently researched. This paper reveals that yaw conditions have a significant negative influence both downforce and drag performance. Spoiler and rear wing devices enhance downforce but increase the vehicle's sensitivity to yaw. Furthermore, yaw conditions significantly alter vortex structures and local flow velocities, affecting downstream flow behavior. Surface pressure measurements on the slant confirm these findings and highlight notable yaw effects and upstream effects from spoiler and rear wing devices. Wake analyses through total pressure measurements show that yaw induces a substantial deviation from straight-line wake characteristics, which become dominated by an inboard rotating vehicle body vortex. Overall, this research enhances the understanding of the effects of yaw conditions on high-performance vehicle aerodynamics and provides valuable data for future vehicle aerodynamics research in real-world operating conditions. [ABSTRACT FROM AUTHOR]

The rapid development of deep learning has significant implications for the advancement of computational fluid dynamics. Currently, most pixel-grid-based deep learning methods for flow field prediction exhibit significantly reduced accuracy in predicting boundary layer flows and poor adaptability to geometric shapes. Although graph neural network models for unstructured grid-based unsteady flow prediction have better geometric adaptability, these models suffer from error accumulation in long-term predictions of unsteady flows. More importantly, fully data-driven models often require extensive training time, greatly limiting the rapid update and iteration speed of deep learning models when facing more complex unsteady flows. Therefore, this paper aims to balance the demands for training overhead and prediction accuracy by integrating physical constraints based on the finite volume method into the loss function of the graph neural network. Additionally, it incorporates a twice-message aggregation mechanism inspired by the extended stencil method to enhance the unsteady flow prediction accuracy and geometric shape generalization ability of the graph neural network model on unstructured grids. We focus particularly on the model's predictive accuracy within the boundary layer. Compared to fully data-driven methods, our model achieves better predictive accuracy and geometric shape generalization ability in a shorter training time. [ABSTRACT FROM AUTHOR]

The escalating demand for enhanced cooling systems, particularly in high-power computer processors, necessitates innovative solutions within the spatial constraints of computing environments. This paper introduces a design modification that strategically diverts a portion of the main flow toward and through the porous fin region, creating a "cross flow." The novel approach aims to maximize heat transfer through the fins, harnessing both conduction and convection heat transfer mechanisms. The realization of these flow patterns is achieved through interdigitated blocking of the inlet and outlet reigns of the microchannel, coupled with the utilization of converging and diverging microchannel porous fins in distinct elbow and zigzag geometrical configurations. Numerical simulations using computational fluid dynamics are employed to assess the proposed microchannel heat sink's thermo-hydraulic performance. The proposed design innovation significantly enhances heat dissipation efficiency by augmenting the convection heat transfer mechanism within the porous fins, resulting in a remarkable 17% improvement in Nusselt number compared to conventional parallel microchannel heat sinks with porous fins. This study provides valuable insight into the thermal and hydraulic performance of the proposed design, offering promising prospects for addressing the escalating demand for enhanced cooling systems in high-power computing environments. [ABSTRACT FROM AUTHOR]

In this paper, the characteristic of the internal flow structure and the spatiotemporal evolution of the rotating stall cell for a centrifugal compressor are investigated through the computational fluid dynamics approach at low-flow rate conditions. The qualitative study like generation mechanisms of the instable internal flow, the movement trend of the stall, and the spatiotemporal characteristics of fluctuation in the impeller are mainly focused in the present work. The stall cells in the impeller move along the impeller rotating direction, and significantly influenced by the volute tongue. The traveling mechanism of rotating stall is closely related to the volute tongue, which easily deteriorates the obvious distortion of the flow structure. Furthermore, the transition law of the rotating stall is quantificationally described through the density and velocity fluctuations. The stall evolution on the circumferential monitoring points is demonstrated by the pressure fluctuation both time and frequency scale, which verifies the influence of the volute tongue on the fluid structure in different operating conditions. In addition, the damage of the volute tongue that performed on the impeller passage and blade is analyzed specifically. The mean value of the fluctuation in the passage and the load on the blade decreases about 2.5% and 3.3%, respectively, after the blade passes through the volute tongue. The spatiotemporal evolution provides a profound understanding of the flow characteristics of stall evolution within the centrifugal compressors. [ABSTRACT FROM AUTHOR]

Correct evaluation of the blades' surface temperature field is crucial to the structural design and operational safety of aero-engine turbine blades. Current high-temperature measurement technology can only measure the limited discrete points temperature of the hot-end turbine blades. If the boundary conditions are completely unknown, it is still difficult to solve equations through traditional computational fluid dynamics methods. It is an inverse problem to predict the temperature field of the turbine engine blade with unknown boundary conditions. This paper proposes a two-dimensional temperature field inversion of turbine blades based on physics-informed neural networks (PINN) and finite discrete temperature measurement points. The PINN is used to model the nonlinear mapping of input variables and output variables. Only a small amount of data is used to train the neural network. It can be found that adding the loss term of the physical governing equation during training can make the neural network better predict the temperature field of the turbine blade, which can also avoid anomalies such as large temperature errors in some areas that may occur when training with only a small amount of data. When the boundary conditions are completely unknown, the average relative error of PINN trained with only 62 points of temperature data on the test set is below 2%, and the R 2 − Score is above 0.95. When the training data reaches 122 points of temperature data, the average relative error of PINN on the test set is less than 1%, and the R 2 − Score reaches 0.99. [ABSTRACT FROM AUTHOR]

The cavitation phenomenon can induce non-uniformity in the fluid, impacting fluid dynamic performance. This paper focuses on the cavitation shedding of the Delft Twist 11 hydrofoil. First, the reliability of numerical simulations is verified by computational fluid dynamics results. Utilizing the variational mode decomposition method, the cavitation signals on two cavitation paths are decomposed. Finally, the cavitation pulsation tracking network method is proposed to extensively investigate the spatial propagation patterns of cavitation signals at various sections above the twisted hydrofoil. The results reveal that typical frequencies at different monitoring planes are 30, 58, and 88 Hz. The corresponding amplitude analysis at these frequencies provides insight into the spatial propagation and attenuation process of cavitation vortices shedding. This study offers a novel perspective for a deeper understanding of cavitation mechanisms. Simultaneously, this provides references for enhancing the performance of mechanical engineering systems, reducing energy consumption, and improving structural reliability. [ABSTRACT FROM AUTHOR]

The separator is a key component of coal pneumatic conveying systems, which plays an important role in improving particle collection efficiency and reducing dust pollution. In this paper, a combined rotary separator was designed. Based on the traditional cyclone separator, the rotor blades were installed and matched with the guide vanes to increase the material separation and collection performance. The influence of rotor speed on the characteristics of the separator was studied by computational fluid dynamics simulation, and the flow field velocity and pressure distribution and the particle trajectory and separation degree were obtained. The results showed that the flow field tangential velocity plays a dominant role in the separation process and is approximately symmetrically distributed with the rotor axis as the center. The velocity of the flow field in the inner rotor is approximately positively correlated with the rotor speed, and the tangential velocity gradually decreases with the increase in the flow field height. The static pressure of the flow field is approximately axisymmetric along the rotor axis, and there is a pressure gradient from the outer separation cone to the rotor axis. The particles in the separator show a separation phenomenon based on the different sizes, and the change trend of the separation degree under different rotor speeds is similar. When the rotor speed is 160 rpm, the particles maintain the highest integrity. The rotor speed of 320 rpm has a protective effect on coarse particles above 1000 µm. [ABSTRACT FROM AUTHOR]

To reduce the drag of underwater vehicles during navigation, this paper proposes a skin imbricated with bionic placoid scale based on micro-Stewart mechanism. The skin is composed of bionic shark placoid scales and Stewart structure with multi-dimensional motion characteristics, which can well simulate the multi-dimensional oscillation motion of shark scales during swimming. A co-simulation platform of computational fluid dynamics and multi-body dynamics is established to investigate the impact of oscillating parameters (heave and pitch) on the drag reduction performance of the skin. The novel skin shows a remarkable drag reduction performance, with a relative drag reduction rate over 20% (up to 33%) in the range of Re = 105 ∼ 106. It is found that the oscillation motion generated by the placoid scales can cause the fluid inside the skin to spray upward, which can increase the thickness of the fluid boundary layer, revealing the drag reduction mechanism of the skin to some extent. Moreover, the pitching motion of the placoid scale is more effective in drag reduction than the heaving motion in the condition of Re = 105. It is expected that applying this skin to underwater vehicles can achieve satisfactory drag reduction effects. [ABSTRACT FROM AUTHOR]

Due to the complex structure of a double-suction pump's suction chamber, the flow in the pump's cavity is often ignored in numerical simulations because of difficulties in structured hexahedral meshing. However, the wear ring clearance interlinking the pump chamber leads the fluid at the impeller inlet directly to the impeller area. This significantly impacts the pump's internal flow field, so the influence of the clearance on the internal flow of a double-suction pump cannot be ignored. This paper develops four three-dimensional double-suction pump models with different wear ring clearances to investigate their influence on pump performance, and structured hexahedral meshes were used for all the computational domains. The clearances varied from 0.2 to 0.5 mm in 0.1 mm increments. The influences of the clearance on the energy loss, external characteristics, and internal flow field distribution of the pump were simulated via a verified computational fluid dynamics method. The results show that the wear ring leakage decreases with the flow rate and increases with wear ring clearance. The increase in backflow leads to an internal flow disorder inside the impeller, resulting in a decreased head and efficiency. Energy loss is mainly caused by increasing the turbulence entropy production with an increasing wear ring clearance. Also, the low-pressure region in the pump cavity expands to the volute with increasing clearance, and the impeller outlet pressure decreases. This study's research on wear ring clearance provides a reference for the design and application of double-suction centrifugal pumps. [ABSTRACT FROM AUTHOR]

Clarifying the evolutionary mechanism of the start-up process of a large axial-flow pump that includes cutoff facilities is of great significance for ensuring the safe and stable operation of such a system. However, the three-dimensional dynamic characteristics of the start-up process of a prototype axial-flow pump system (PAPS), considering cutoff facilities, are still unknown. In this paper, a method combining motor starting characteristic experiment, computational fluid dynamics, and field test of prototype pump system is proposed to study the starting process of PAPS. The results indicate that flow interruption facilities will significantly affect the start-up process of the pump system. High-speed forward fluid particles collide with reverse fluid particles in the guide vane channel during the acceleration of the pump owing to the influence of the cutoff facilities. A large number of wall vortex structures block the guide vane channel. This leads to a brief plateau period during the increase in instantaneous head. As the start-up process progresses, a clear horseshoe-shaped vortex structure is formed at the trailing edge of the guide vane, which subsequently falls back. The accelerated shedding of horseshoe-shaped vortical structures at the trailing edge of the guide vane induces high-frequency pulsating components, leading to a high-energy region of the pressure fluctuation signal at the outlet of the guide vane, which gradually expands toward higher frequencies. During this transition process, the flow field near the cutoff facility also exhibited significant unstable flow behavior. After the interaction between the outflow at the flap gate and the outflow at the gate, entrainment occurs, forming a region of circulating motion, and a large number of vortices with a jet-like structure are formed at the exit of the flap gate. [ABSTRACT FROM AUTHOR]

The mode analysis of parallel twin cylinders is conducted in this paper using two data-driven methods: proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). First, a high-fidelity computational fluid dynamics (CFD) model of parallel twin cylinders is established, and numerical simulations of the model are carried out. Subsequently, the fundamental principles of the POD and DMD algorithms are systematically introduced. Utilizing snapshots obtained from the high-fidelity CFD model, the POD and DMD methods are employed to extract the dominant flow structures. Furthermore, a comparison between the two data-driven methods is conducted by analyzing modal frequencies, pressure distribution, and the reconstruction errors of pressure fields. Finally, the pressure fields of non-sample points are predicted based on the POD–backpropagation neural network (BPNN) surrogate model and the DMD method, and the predicted results are compared with the CFD simulation results. It found that (i) the DMD method is capable of extracting the main coherent structures of the pressure fields, directly obtaining flow modes and their corresponding frequencies, and assessing the stability of flow modes; (ii) the DMD method can capture the main flow features of the pressure fields in both spatial and temporal dimensions, while the POD method is primarily efficient at capturing the spatial features of the pressure fields; (iii) in contrast to the frequency-ranked DMD method, the energy-ranked POD method can reconstruct the pressure fields using a smaller number of modes, indicating that the POD method has an advantage in terms of mode reduction; (iv) in contrast to the energy-ranked POD method, the frequency-ranked DMD method has a wider applicability to the range of flow types and has more advantages in stability analysis of complex dynamic systems; (v) the predicted pressure fields around the cylinder using the first five-order POD modes or DMD modes closely align with CFD calculation results. Additionally, the evolution of pressure fields predicted by the POD–BPNN surrogate model with the first five-order POD modes or the DMD method with the first 200-order DMD modes significantly agrees with CFD simulation results; (vi) the combined use of the POD–BPNN surrogate model and DMD methods allows efficient interpolation and extrapolation of samples, delivering exceptional predictive performance. This study offers insight into the coherent structures in parallel twin cylinders. [ABSTRACT FROM AUTHOR]

This study focuses on the flow-induced rotational characteristics of an asymmetrically confined square cylinder in a channel for various engineering applications. We consider the classical, fully developed channel flow problem to numerically study the flow-induced rotation (FIR) of a square cylinder in a two-dimensional laminar flow regime. We employ our in-house computational fluid dynamics solver, based on the hybrid Lagrangian–Eulerian method, for the present study. For the very first time, we identify seven distinct rotational modes by investigating FIR for a wide range of Reynolds number Re (1 − 150) and eccentricity ε (0, 1/3, 2/3). This paper reports the combined effects of Re and ε on the FIR characteristics in terms of rotational modes, accompanying flow structures, and pertinent engineering parameters. At low Re, the shear-induced torque dominates over the pressure-induced torque, whereas with an increase in Re, the pressure-induced torque gradually outweighs the shear-induced torque. Thus, we observe a smooth transition in the direction of rotation from clockwise to counterclockwise with increasing Re. This study involves a detailed discussion of the flow physics and moment-generating mechanisms for four intriguing rotational modes, including Autorotation, Mono-harmonic Oscillation, Bi-harmonic Oscillation, and Reversal Autorotation. The current study has applications in micro energy-harvesting, vortex generation, and microfluidic mixing. [ABSTRACT FROM AUTHOR]

Plugging and diverting fracturing is a promising technology that aims to enhance the heat extraction efficiency in hot dry rock. The key to the success of this technique is the formation of effective plugging zones in existing fractures. However, given the high temperature and high stress of hot dry rock, the migration and sealing mechanisms of temporary plugging agents in such reservoirs are quite different from those in conventional tight reservoirs. Using the computational fluid dynamics/discrete element method coupled method, this paper numerically investigates the migration mechanism of temporary plugging agents in rough fractures of hot dry rock. First, we construct a model of a rough fracture surface in hot dry rock by performing computerized tomography scanning. Second, we adopt the well-established theory of the joint roughness coefficient to describe the fracture surface roughness. Then a discrete phase model that considers the effect of temperature is constructed to characterize the interparticle interaction of temporary plugging agents. A bidirectional coupling algorithm between the fluid flow in the fracture and the migration of temporary plugging agent particles is adopted. Finally, the effects of key factors such as fracture wall temperature, fracture roughness, injection angle, and injection location on the migration mechanism of granular temporary plugging agents in rough fractures are analyzed in detail. The results show that fracture roughness and temperature have a significant impact on the migration process in hydraulic fractures. When the fracture surface roughness increases by 10.44 as measured by the joint roughness coefficient, the particle force and particle temperature increase by 12.0% and 37.8%, respectively. When the fracture surface temperature increases by 200 K, the particle force and particle temperature increase by 88.2% and 14.4%, respectively. [ABSTRACT FROM AUTHOR]

A ventilated vehicle exiting water in a wave environment is a complex nonlinear process, and the mechanism by which the wave conditions influence this process remains poorly understood. This paper describes realistic simulations of a ventilated vehicle exiting a water body under various wave conditions. Comprehensive analysis is conducted for a range of distinct wave scenarios, and a machine learning-based method is developed for the rapid forecasting of vehicle-related parameters. A three-layer backpropagation neural network is constructed, and its prediction performance is verified. Subsequently, predictive and optimization procedures are employed to determine the optimal wave phase for the water exit of the vehicle. Different wave conditions are shown to significantly affect the evolution of the ventilated cavity as well as the kinematic and loading characteristics of the vehicle. The pitch angular velocity and angle at the moment when the head of the vehicle reaches the free surface exhibit a positive cosine trend under different wave conditions. No regularity of the pitch angular velocity at the moment when the tail reaches the free surface is evident. The neural network exhibits exceptional proficiency in predicting the motion parameters and load characteristics of the vehicle. The optimal point for the vehicle to exit the water is determined to be at a wave phase of 0.125π, while the most hazardous point occurs when the wave phase is 1.1875π. [ABSTRACT FROM AUTHOR]

Flapping wing rotorcrafts (FWRs) combine both the motion characteristics of flapping and rotary wings, exhibiting high aerodynamic efficiency at low Reynolds numbers. In this paper, the ceiling effect of FWRs has been studied through numerical and experimental methods to further investigate the aerodynamic performance of FWRs operating under a ceiling and to explore the feasibility of enhancing the flight efficiency of FWRs via ceiling-effect-based perching locomotion. Based on the momentum theory and blade element methods, a theoretical model is first established to predict the additional thrust generated by the FWR operating under the ceiling. Additionally, to uncover the detailed aerodynamic mechanisms of FWRs' ceiling effect, the computational fluid dynamics (CFD) simulations were conducted to analyze the changes in force production and flow field around the FWR at 75–115 mm distances from the ceiling. Furthermore, experimental methods were employed to validate the theoretical model and CFD simulation. The results demonstrate a continuous increase in the thrust from 19.18 to 22.15 gf as the ceiling distance decreases, while the total energy consumption remains relatively constant. Leveraging the ceiling effect, the tested FWR could achieve an additional lift force of up to 9.5% at 75 mm ceiling height with a 33 Hz flapping frequency. Finally, a ceiling perching demonstration was conducted to validate the feasibility of achieving FWRs' energy-efficient locomotion based on ceiling effects. Our study highlights the positive influence of ceiling effect on FWRs, showing a promising way to further improve the flight efficiency of FWRs. [ABSTRACT FROM AUTHOR]

Schooling fish exhibit captivating biological behaviors that have garnered interest from both biologists and engineers. While the undulatory motion of fish in schools has been extensively studied, rapid turning within these schools remains underexplored. This paper presents a three-dimensional computational fluid dynamics (CFD) model of a fast-start scenario involving two fish to investigate the influence of spatial and phase differences on swimming performance. Our findings indicate that, by leveraging the wake of the upstream fish, a downstream fish can enhance its travel distance by up to 34.4% and its rotational angle by up to 41.6%. The optimal travel distance is achieved when the starting jet of the front fish aligns with the center of mass (COM) of the downstream fish. Conversely, the largest rotational angle is observed when this jet precedes the center of mass. The research further identifies a vortex phase matching phenomenon in fish schools during rapid turns. The phase difference, or the downstream fish's delayed response, allows it to harness the wake for improved transitional and rotational movement. However, this approach might decrease the average angular velocity, which represents the rotational angle divided by the total duration of turning and waiting. Our study concludes that, with a specific phase difference, this delayed action facilitates faster fish turns by utilizing the wake created by the neighboring fish. [ABSTRACT FROM AUTHOR]

As first proposed by Madelung in 1926, the analogy between quantum mechanics and hydrodynamics has been known for a long time; however, its potentialities and the possibility of using the characteristic equations of quantum mechanics to simulate the behavior of inviscid fluids have not been thoroughly investigated in the past. In this methodology, the incompressible Euler equations are thus substituted by the Schrödinger equation, turning a quasi-linear Partial Differential Equation into a linear one, an algorithm known in the literature as Incompressible Schrödinger Flow. Previous works on the subject used the Fast Fourier Transform method to solve this problem, obtaining promising results, especially in predicting vortex dynamics; this paper aims to implement this novel approach into a Finite Element framework to find a more general formulation better suited for future application on complex geometries and on test cases closer to real-world applications. Simple case studies are presented in this work to analyze the potentialities of this method: the results obtained confirm that this method could potentially have some advantages over traditional Computational Fluid Dynamics method, especially for what concerns computational savings related to the required time discretization, whilst also introducing new aspects of the algorithm, mainly related to boundary conditions, not addressed in previous works. [ABSTRACT FROM AUTHOR]

Understanding the aerodynamic hysteresis loop phenomenon is essential when assessing aerodynamic performance and designing aircraft control systems. This phenomenon is a result of time delay effects in unsteady flow. Traditional methods of predicting unsteady aerodynamic forces using computational fluid dynamics have drawbacks, such as long cycles and low efficiency. In this paper, we focus on predicting the aerodynamic hysteresis loop of the NACA (National Advisory Committee for Aeronautics) 0012 airfoil in transonic flow using a new model called LIDNN (Latin hypercube sample input deep neural network). This model integrates input signals and optimization methods to improve upon traditional neural network models. Based on the example validation, the LIDNN model is authenticated as an accurate and efficient method in predicting the unsteady aerodynamic hysteresis loop of the NACA 0012 airfoil in transonic flow, and another significant advantage of the proposed model is its ability to solve multivariable problems effectively, even under varying Mach numbers. [ABSTRACT FROM AUTHOR]

This paper concerns analyses of virus droplet dynamics resulting from coughing events within a confined environment using, as an example, a typical cruiser's cabin. It is of paramount importance to be able to comprehend and predict droplet dispersion patterns within enclosed spaces under varying conditions. Numerical simulations are expensive and difficult to perform in real-time situations. Unsupervised machine learning methods are proposed to study droplet dispersion patterns. Data from multi-phase computational fluid dynamics simulations of coughing events at different flow rates are utilized with an unsupervised learning algorithm to identify prevailing trends based on the distance traveled by the droplets and their sizes. The algorithm determines optimal clustering by introducing novel metrics such as the Clustering Dominance Index and Uncertainty. Our analysis revealed the existence of three distinct stages for droplet dispersion during a coughing event, irrespective of the underlying flow rates. An initial stage where all droplets disperse homogeneously, an intermediate stage where larger droplets overtake the smaller ones, and a final stage where the smaller droplets overtake the larger ones. This is the first time computational fluid dynamics is coupled with unsupervised learning to study particles' dispersion and understand their dynamic behavior. [ABSTRACT FROM AUTHOR]

The sharp density down-ramp injection (shock injection) mechanism produces the quasi-monoenergetic electron beam with a bunch duration of tens of femtoseconds via laser wakefield acceleration. The stability of the accelerated electron beam strongly depends on the stability of the laser beam and the shock structure produced by the supersonic gas nozzle. In this paper, we report the study of a newly designed modular supersonic nozzle with a flexible stilling chamber and a converging–diverging structure. The performance of the nozzle is studied both numerically and experimentally with the computational fluid dynamics simulation and the Mach–Zehnder interferometry method. The simulation results and the experimental measurements are well consistent, and both prove the effectiveness of the stilling chamber in stabilizing the gas flow. [ABSTRACT FROM AUTHOR]