Traditional Computational Fluid Dynamics Methods Research Articles

Physics-informed neural networks and higher-order high-resolution methods for resolving discontinuities and shocks: A comprehensive study

Addressing discontinuities in fluid flow problems is inherently difficult, especially when shocks arise due to the nonlinear nature of the flow. While handling discontinuities is a well-established practice in computational fluid dynamics (CFD), it remains a major challenge when applying physics-informed neural networks (PINNs). In this study, we compare the shock-resolving capabilities of traditional CFD methods with those of PINNs, highlighting the advantages of the latter. Our findings show that PINNs exhibit less dissipative behavior compared to conventional techniques. We evaluated the performance of both PINNs and traditional methods on linear and nonlinear test cases, demonstrating that PINNs offer superior shock-resolving properties. Notably, PINNs can accurately resolve inviscid shocks with just three grid points, whereas traditional methods require at least seven points. This suggests that PINNs are more effective at resolving shocks and discontinuities when using the same grid for both PINN and CFD simulations. However, it’s important to note that PINNs, in this context, are computationally more expensive than traditional methods on a given grid.

Degradation performance rapid prediction and multi-objective operation optimization of gas turbine blades

The performance degradation of high-temperature blades will affect the safety and efficiency of operation. In response to issues such as ignoring strength characteristics, slow degradation performance prediction, insufficient applicability and accuracy of traditional proxy models, and long operation optimization cycle, a degradation performance prediction model of turbine blades based on the back propagation neural network is established in this research. Furthermore, the efficiency-stress multi-objective operation optimization method based on genetic algorithm is proposed. The results show that the R-square value of the degradation performance neural network prediction model is greater than 0.9 for predicting characteristic parameters. The prediction speed is improved by 104 orders of magnitude compared to the traditional computational fluid dynamics method. The influence mechanisms of turbine outlet flow rate, cooling gas inlet total temperature, and cooling gas flow rate on the degradation performance are revealed. Under fouling extreme conditions and corrosion/wear extreme conditions, the operating parameters of turbine blades are optimized. The maximum values of the efficiency improvement, power increase and maximum von Mises stress reduction are 0.74 %, 2.13 MW and 43 MPa, respectively. This study can provide an effective and novel method for real-time degradation performance prediction and rapid operating condition optimization of turbine blades.

An innovative end-to-end PINN-based solution for rapidly simulating homogeneous heat flow problems: An adaptive universal physics-guided auto-solver

In contemporary heat flow computations, the widespread application of deep learning, specifically Physical Informed Neural Networks (PINN), has been noted. However, existing PINN methods often exhibit limited applicability to specific operational conditions and are hindered by prolonged training times, rendering them unsuitable for engineering scenarios requiring frequent changes in operational parameters. This study addresses the imperative of enhancing the computational efficiency of conventional PINN methodologies and introduces an innovative end-to-end PINN-based solution—Adaptive Universal Physics-Guided Auto-Solver (AUPgAS) for swift simulation of homogeneous heat flow problems. AUPgAS introduces the operational condition as a variable within the neural network, thereby enhancing the model's versatility in addressing homogeneous problems. Simultaneously, AUPgAS refines training accuracy through the incorporation of adaptive sampling methods. The efficacy of this method is demonstrated through validations on two application cases of laminar incompressible flow with viscosity, namely the flow around a cylinder and the flow around two cylinders. After training, the model demonstrates the capability to predict pressure and velocity fields of the flow around a single cylinder in different locations in case Ⅰ, as well as simulate the flow field when the distance between two cylinders rapidly changes in case Ⅱ. Comparative analyses with traditional computational fluid dynamics methods show that the AUPgAS greatly reduces the solution time, the average time taken by the trained AUPgAS to solve for each problem is 3.4 s, which is a significant improvement in efficiency compared to the average time taken by the traditional fluid dynamics methods, which is 910 s. Furthermore, the agreement of the results obtained by AUPgAS with the reference solution is commendable, with a minimum average error of 13.55%. This end-to-end approach presents an innovative and efficient solution for the rapid simulation of homogeneous heat flow problems, offering promising advancements in the realm of engineering computational efficiency.

Two-dimensional temperature field inversion of turbine blade based on physics-informed neural networks

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 R2−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 R2−Score reaches 0.99.

A Novel Hybrid Deep Learning Method for Predicting the Flow Fields of Biomimetic Flapping Wings.

The physics governing the fluid dynamics of bio-inspired flapping wings is effectively characterized by partial differential equations (PDEs). Nevertheless, the process of discretizing these equations at spatiotemporal scales is notably time consuming and resource intensive. Traditional PDE-based computations are constrained in their applicability, which is mainly due to the presence of numerous shape parameters and intricate flow patterns associated with bionic flapping wings. Consequently, there is a significant demand for a rapid and accurate solution to nonlinear PDEs, to facilitate the analysis of bionic flapping structures. Deep learning, especially physics-informed deep learning (PINN), offers an alternative due to its great nonlinear curve-fitting capability. In the present work, a hybrid coarse-data-driven physics-informed neural network model (HCDD-PINN) is proposed to improve the accuracy and reliability of predicting the time evolution of nonlinear PDEs solutions, by using an order-of-magnitude-coarser grid than traditional computational fluid dynamics (CFDs) require as internal training data. The architecture is devised to enforce the initial and boundary conditions, and incorporate the governing equations and the low-resolution spatiotemporal internal data into the loss function of the neural network, to drive the training. Compared to the original PINN with no internal data, the training and predicting dynamics of HCDD-PINN with different resolutions of coarse internal data are analyzed on the problem relevant to the two-dimensional unsteady flapping wing, which involves unsteady flow features and moving boundaries. Additionally, a hyper-parametrical study is conducted to obtain an optimal model for the problem under consideration, which is then utilized for investigating the effects of the snapshot and fraction of the coarse internal data on the HCDD-PINN's performances. The results show that the proposed framework has a sufficient stability and accuracy for solving the considered biomimetic flapping-wing problem, and its great potential means that it can be considered as an alternative to accelerate or replace traditional CFD solvers in the future. The interested variables of the flow field at any instant can be rapidly obtained by the trained HCDD-PINN model, which is superior to the traditional CFD method that usually needs to be re-run. For the three-dimensional and optimization problems of flapping wings, the advantages of the proposed method are supposedly even more apparent.

A finite element implementation of the incompressible Schrödinger flow method

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.

Efficient optimization design of flue deflectors through parametric surrogate modeling with physics-informed neural networks

In engineering applications, deflectors play a vital role in regulating the uniformity of flow field distribution in the selective catalytic reduction (SCR) system, and their optimal design is a topic of great concern. However, traditional optimal design methods often suffer from insufficient prediction accuracy or too high computational cost. This paper develops and verifies an efficient and robust parametric surrogate model for SCR systems based on the physics-informed neural networks (PINNs) framework. This study comprises three progressive steps. (1) We predicted the flow field distribution in the original flue based on the PINNs framework and compared the results qualitatively and quantitatively with the traditional computational fluid dynamics (CFD) method. The results show that the maximum relative error of velocity is 12.6%, and the relative error is within 5% in most areas. (2) For the optimal design of the deflector in the SCR system, a parametric surrogate model based on the PINNs framework is developed, and the model inputs include not only the coordinate variables but also the position parameters of the deflector. The accuracy and efficiency of this parametric surrogate model are also compared with the traditional CFD method. (3) Based on the parametric surrogate model developed above, the deflector optimal position for the research object of this study is found through two quantitative indicators (uniformity coefficient and flue gas energy loss). The results demonstrate that the parameterized model based on PINNs can reduce the computational time to about 14% compared to traditional methods. Finally, the sensitivity analysis of the deflector position parameters is carried out. Overall, the results of this study demonstrate that the parametric surrogate model based on the PINNs framework is an efficient and robust tool for system optimization, design, and autonomous control.

A novel study on forecasting the airfoil self-noise, using a hybrid model based on the combination of CatBoost and Arithmetic Optimization Algorithm

One of the important challenges in aircraft design is the light, inexpensive, and self-noise-free design of airfoil. Since the self-noise generated by rotor blades interacting with turbulent flow produced in their boundary layer can impact how well the system works. Predicting aerofoil self-noise can help shorten computational times and improve cost-effectiveness because traditional computational fluid dynamics methods flow has more time consumption. Therefore, this paper proposes a forecasting approach using the hybrid model of the CatBoost algorithm. In this way, the hyperparameters of the CatBoost are tuned by twelve optimizers to increase precision and speed up forecasting. The precision of the hybrid models is evaluated by statistical evaluation indices such as Coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MSE), and relative absolute error (MAE). The obtained results show that the CatBoost combination with the Arithmetic Optimization Algorithm better operates optimally and depicts lower error values compared to other optimizers. Also, the values of the coefficient of determination for training and testing data with 0.9999 and 0.9706, respectively, demonstrate that Arithmetic Optimizer is the most precise optimizer among studied metaheuristic algorithms.

Prediction of Aircraft Wake Vortices under Various Crosswind Velocities Based on Convolutional Neural Networks

In order to overcome the time-consuming computational drawback of using computational fluid dynamics (CFD) for the numerical simulation of aircraft wake vortex evolution under different crosswind velocities, this paper proposes a wake vortex prediction model based on a convolutional neural network (CNN) algorithm. The study focuses on the B737-800 aircraft, and employs CFD numerical simulations to obtain the evolutionary characteristics of wake vortex parameters under crosswind velocities ranging from 0 to 7 m/s. The wake vortex velocity and Q-criterion vorticity values are collected and partitioned into mutually exclusive training and testing datasets. A CNN model is constructed, and the training dataset is used to tune hyperparameters to minimize loss and achieve accurate predictions. After saving the trained model, the desired crosswind velocity value is input to obtain the predicted wake vortex velocity and Q-criterion vorticity values. The results indicate that the convolutional neural network model exhibits an average absolute percentage error of 1.5%, which is 2.3% lower than that of the fully connected neural network model. This suggests that convolutional neural networks can enhance the accuracy of wake vortex predictions, as demonstrated in this study. Compared to traditional CFD methods, the proposed model reduces the computation time by approximately 40 times, effectively improving computational efficiency and offering valuable insight for studies involving numerous numerical simulations, such as analyzing the safety separation between aircraft wake vortices during paired approach procedures.

Assessing the impact of architectural and behavioral interventions for controlling indoor COVID-19 infection risk: An agent-based approach

The COVID-19 pandemic changed our lives, forcing us to reconsider our built environment. In some buildings with high traffic flow, infected individuals release viral particles during movement. The complex interactions between humans, building, and viruses make it difficult to predict indoor infection risk by traditional computational fluid dynamics methods. The paper developed a spatially-explicit agent-based model to simulate indoor respiratory pathogen transmission for buildings with frequent movement of people. The social force model simulating pedestrian movement and a simple forcing method simulating indoor airflow were coupled in an agent-based modeling environment. The impact of architectural and behavioral interventions on the indoor infection risk was then compared by simulating a supermarket case. We found that wearing a mask was the most effective single intervention, with all people wearing masks reducing the percentage of infections to 0.08%. Among the combined interventions, the combination of customer control is the most effective and can reduce the percentage of infections to 0.04%. In addition, the extremely strict combination of all the interventions makes the supermarket free of new infections during its 8-hour operation. The approach can help architects, managers, or the government better understand the effect of nonpharmaceutical interventions to reduce the infection risk and improve the level of indoor safety.

Abstract P1048: Lattice-Boltzmann Method Achieves Feasible Hemodynamic Analysis In Carotid Atherosclerosis With Low Computational Cost

Introduction: Atherosclerosis is a common cardiovascular pathophysiology that can lead to plaque-related complications, including stroke and myocardial infarction. Non-invasive hemodynamic analysis can provide valuable insights into the plaque's rupture vulnerability and growth progression. Computational fluid dynamics is widely studied to understand hemodynamic pathophysiology further; however, traditional computational fluid dynamics methods often require daily-based analysis time to visualize hemodynamic parameters. This study investigated the feasibility of using the lattice Boltzmann method (LBM) for hemodynamic analysis of atherosclerosis lesions. Methods: From ten carotid artery stenosis patients, 20 carotid artery models were generated and analyzed using LBM. Hemodynamic parameters, including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT), were compared between the stenotic lesion and contralateral control sides. The patient-specific boundary conditions were applied. Their cardiac cycle was subdivided into three periods: systolic acceleration, systolic deceleration, and diastolic, and analyzed to investigate their relationship with the hemodynamic parameters. Results: LBM effectively captured the hemodynamic parameters associated with carotid atherosclerosis with an hourly-based computational cost (2.765 [2.435-3.233] hours). The analysis visualized high TAWSS and low RRT distribution at stenotic areas during the systolic deceleration period and low TAWSS and high RRT distribution at the post-stenotic distal lesion during the diastolic period. These findings were reproducible among the ten patients. Conclusion: LBM-based hemodynamic analysis has the potential to serve as a non-invasive diagnostic tool that can help understand atherosclerosis pathophysiology with low computational cost.

Flow-field reconstruction in rotating detonation combustor based on physics-informed neural network

The flow-field reconstruction of a rotating detonation combustor (RDC) is essential to understand the stability mechanism and performance of rotating detonation engines. This study embeds a reduced-order model of an RDC into a neural network (NN) to construct a physics-informed neural network (PINN) to achieve the full-dimensional high-resolution reconstruction of the combustor flow field based on partially observed data. Additionally, the unobserved physical fields are extrapolated through the NN-embedded physical model. The influence of the residual point sampling strategy and observation point spatial-temporal sampling resolution on the reconstruction results are studied. As a surrogate model of the RDC, the PINN fills the gap that traditional computational fluid dynamics methods have difficulty solving, such as inverse problems, and has engineering value for the flow-field reconstruction of RDCs.

A novel prediction method for unsteady aerodynamic force on three-dimensional folding wing aircraft

The aerodynamic characteristics of morphing aircraft frequently exhibit extremely unsteady and nonlinear features during morphing process, making it challenging to obtain accurate aerodynamic parameters fast based on traditional computational fluid dynamics methods. In this paper, a data driven model Multi-Task Cross network, which is based on deep learning methods of cross network and Multi-Task Learning, is proposed to predict the unsteady aerodynamic parameters of a three-dimensional folding wing aircraft at any angle of attack. The Multi-Task Cross model seeks to effectively fuse external flow field and aircraft properties while making full use of sparse input features and limited training data. More accurate results can be obtained by using the Multi-Task Cross model to predict different aerodynamic parameters of folding wing aircraft. The model takes three inputs: angle of attack, time and unfolding angle of wing. It outputs the unsteady forces and moments coefficients through multi-layer nonlinear mapping. The training and testing data used for the Multi-Task Cross model are calculated by computational fluid dynamics method. With the proposed prediction model, the unsteady aerodynamic parameters at any angle of attack and any morphing state can be obtained in seconds. The testing results indicate that the Multi-Task Cross model predicts aerodynamic parameters quickly and accurately. The proposed Multi-Task Cross model achieves higher prediction accuracy when compared to prediction models based solely on Multilayer Perceptron, Multi-Task Learning method, and Cross method.

Fast sparse flow field prediction around airfoils via multi-head perceptron based deep learning architecture

In order to obtain the information about flow field, traditional computational fluid dynamics methods need to solve the Navier-Stokes equations on the mesh with boundary conditions, which is a time-consuming task. In this work, a data-driven method based on convolutional neural network and multi-head perceptron is used to predict the incompressible laminar steady sparse flow field around the airfoils. Firstly, we use convolutional neural network to extract the geometry parameters of the airfoil from the input gray scale image. Secondly, the extracted geometric parameters together with Reynolds number, angle of attack and flow field coordinates are used as the input of the multi-layer perceptron and the multi-head perceptron. The proposed multi-head neural network architecture can predict the aerodynamic coefficients of the airfoil in seconds. Furthermore, the experimental results show that for sparse flow field, multi-head perceptron can achieve better prediction results than multi-layer perceptron.

Eulerian incompressible smoothed particle hydrodynamics on multiple GPUs

Recent advances in the development of Eulerian incompressible smoothed particle hydrodynamics (EISPH), such as high-order convergence and natural coupling with Lagrangian formulations, demonstrate its potential as a meshless alternative to traditional computational fluid dynamics (CFD) methods. This work aims to address one of the major outstanding limitations of EISPH, its relatively high computational cost, by providing an implementation that can be deployed on multiple graphics processing units (GPUs). To this end, a pre-existing multi-GPU version of the open-source Lagrangian weakly-compressible code DualSPHysics is converted to an EISPH formulation and integrated with an open-source multi-GPU multigrid solver (AmgX) to treat the pressure Poisson equation implicitly. The integration of AmgX within DualSPHysics presents a significant challenge, since AmgX is designed for distributed systems and therefore conflicts with the single-node shared memory design of the multi-GPU DualSPHysics code. The present implementation is validated against well-known test cases, showing excellent agreement with benchmark solutions and demonstrating second-order convergence. A detailed profiling and performance testing is also presented to investigate memory consumption and scaling characteristics. The results show approximately 87%–95% strong scaling efficiency and 92%–94% weak scaling efficiency in both 2D and 3D on up to four GPUs. Large spikes in memory consumption during the initialisation of the linear solver library are found to impede full utilisation of the device memory. Nevertheless, the present implementation is shown to permit problem sizes on the order of 69.3 million (2D) and 20.8 million (3D) particles on four GPUs (128 GB total device memory), which is beyond what has previously been reported for incompressible SPH on GPUs, where the PPE is treated implicitly, and demonstrates its potential as an alternative to traditional CFD methods.

Analysis of flow field and hemolysis index in axial flow blood pump by computational fluid dynamics-discrete element method.

To fully study the relationship between the internal flow field and hemolysis index in an axial flow blood pump, a computational fluid dynamics-discrete element method coupled calculation method was used. Through numerical analysis under conditions of 6000, 8000, and 10,000 r/min, it was found that there was flow separation of blood cell particles in the tip of the impeller and the guide vane behind the impeller. The flow field has a larger pressure gradient distribution, which reduces the lift ratio of the blood pump and easily causes blood cell damage. The study shows that the hemolysis index obtained by the computational fluid dynamics-discrete element method is 4.75% higher than that from the traditional computational fluid dynamics method, which indicates the impact of microcollision between erythrocyte particles and walls on hemolysis index and also further verifies the validity of the computational fluid dynamics-discrete element coupling method. Through the hydraulic and particle image velocimetry experiments of the blood pump, the coincidence between numerical calculation and experiment is analyzed from macro and micro aspects, which shows that the numerical calculation method is feasible.

Flow-Simulation Model Improves Analysis of Perforated-Rock Cleanup and Productivity

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 190113, “Advanced Analysis of Cleanup and Productivity From Perforated Rocks Using Computational Fluid Dynamics,” by Rajani Satti, SPE, Stephen N. Zuklic, and Derek Bale, SPE, Baker Hughes, a GE company, and Nils Koliha, Andrew Fager, SPE, Gana Balasubramanian, Bernd Crouse, SPE, and David Freed, SPE, EXA, prepared for the 2018 SPE Western Regional Meeting, Garden Grove, California, USA, 22–27 April. The paper has not been peer reviewed. Because of inherent complexities, understanding the characteristics of perforations in downhole environments is a significant challenge. Perforation-flow laboratories have been used to provide insight into cleanup and productivity mechanisms around perforation tunnels. In contrast to previous studies, the model presented in this work uses the perforation-flow laboratory, micro-computed-tomography (CT) and conventional CT imaging, and, most importantly, an advanced simulation approach to provide an accurate assessment of cleanup techniques and productivity. This full-scale 3D flow model accounts for realistic aspects of tunnel geometry, perforation damage, and blockages that impede flow. Introduction In recent years, numerical tools increasingly have been used in conjunction with experiments to provide better insight into the flow characteristics of perforated cores and perforated well-scale formations. Several numerical studies on perforation fluid flow have been conducted for core scale. However, comprehensive details relating to the modeling of perforation-zone damage and thickness, flow directionality, debris mechanisms, and implications for cleanup have not been studied in detail. In addition, most computational fluid dynamics (CFD) studies have used traditional Navier-Stokes-based solvers. In this study, the authors have used a commercially available flow-simulation software based on the lattice Boltzmann method (LBM) to calculate the complex flow and cleanup mechanisms around the perforation tunnel. Lattice-based methods, an alternative to traditional CFD methods, track the advection and collisions of fluid particles on a computational grid. Because the average number of particles per grid cell far exceeds the computing power required to track them individually, the particles are grouped into an integer number of discrete velocities. LBM has been well validated and is in common use for many flow applications. The flow solver used here includes a turbulence model that is similar conceptually to the large-eddy simulation approach. The flow solver also includes a porous-media model that is used, in this study, to model the rock. The porous-media model invokes a Darcy-type pressure loss by applying a flow resistance determined from the known permeability of the rock. The resistance can include a viscous (linear) and an inertial (quadratic) term with respect to local velocity and can be made to vary spatially to model rock heterogeneity.

Preliminary Assessment of the Possibilities of the Particle Finite Element Method in the Numerical Simulation of Bird Impact on Aeronautical Structures

As well known, in the analysis of bird impact events the bird is often reduced, even experimentally, to a surrogate projectile modeled as a weakly compressible fluid (typically a mixture of water and air) [15]. From a numerical standpoint, the presence of a free surface and the strong interaction with the aircraft structures represent a limit for traditional computational fluid dynamics methods based on an Eulerian grid. On the other hand, classical Lagrangian methods cannot cope with the extremely large deformations experienced by the projectile during the impact. The Particle Finite Element Method (PFEM) [6,11] is a Lagrangian particle method that can account for very large deformations, preserving the robustness and generality of the finite element method, and thus owning a key advantage over other approaches, e.g. Smoothed Particle Hydrodynamics (SPH) [9], usually cursed with consistency and stability issues [1,10]. To assess the possibilities of the method in the context of bird impact, theoretical analyses are initially performed based on the impact of a water jet on a rigid surface. Then, the influence of the geometry of a more realistic projectile is analyzed and the capability of the method to take into account separation and fragmentation is highlighted.

Sloshing impact simulation with material point method and its experimental validations

Liquid sloshing is usually associated with violent changes and breakups of free surfaces and strong fluid structure interactions. These phenomena present unique challenges for traditional computational fluid dynamics methods. In this paper, the material point method (MPM) is extended to solve the dynamic behavior of sloshing liquids in a moving container and a numerical scheme is developed to calculate impact pressure based on a contact algorithm over background grids. Moreover, a weakly compressible equation of state which employs an artificial sound speed is incorporated into the MPM to compute the pressure field of the liquid phase and a special scheme is employed to apply harmonic excitation to the particle-discretized container. The performance of the improved MPM in prediction of liquid impact pressure is verified by modeling a water block dropping test onto an aluminum plate. To further validate the proposed scheme, liquid sloshing experiments in a partially filled tank are conducted. The slosh-induced impact pressures on the vertical walls of the tank obtained from the MPM simulation are in good agreement with the experimental results.

Numerical prediction on erosion damage caused by wind-blown sand movement

This study quantitatively investigated the erosion damage on the metal material caused by wind-blown sand under different environmental conditions (e.g. height and wind velocity) in aeolian processes. Wind-blown sand causes destructive impacts on buildings, abrasion of crops and many other damages in arid regions. Sand transport caused by wind often appears in the desert, open-pit mines and coastal dune fields, which is a special case of two-phase flow of gas and solids. Due to its special dynamic characteristics, the sand–wind system near the bed surface is complex and difficult to be solved by traditional computational fluid dynamics method. However, the steady-state saltation model is effective for describing the wind-blown sand movement. Therefore, this paper combines the solid particles erosion model with the steady-state saltation model to predict the erosion damages caused by blown sand. Correctness of the algorithm has been verified through the comparison of simulated results and experimental data of the sand flow structure. An early conclusion that the kinetic energy is crucial to erosion has been proved in this paper. The results show that the erosion rate varies with different variables, e.g. saltating velocity, saltating height, length, etc. The quantity of erosion shows stratification pattern as well as the mass flux of the sand flow. These results quantitatively show the major features of the erosion caused by wind-blown sand.