Improved Eulerian-Lagrangian Spray Simulation by Using an Enhanced Momentum Coupling Model

Author(s):  
Sayop Kim ◽  
Sung Wook Park ◽  
Chang Sik Lee

This study describes a strategy for reducing grid-size dependency that mainly comes from inaccurate calculation of the droplet-gas interactions and droplet collision modeling. The present paper suggests an enhanced momentum coupling (EMC) model and introduces the improved collision models to obtain the goal of reducing grid dependency. For conventional CFD codes, due to the low computational cost and effort, the Eulerian-Lagrangian method is preferred in simulating the multiphase flow, for instance, liquid spray penetrating into gaseous phase. However, it is well known that the spray computations are highly dependent on the grid resolution because momentum gain from liquid droplet less or more transferred to unit gaseous mass according to the grid cell volume, resulting in inaccurate prediction of droplet-gas relative velocity. For this reason, the grid-size dependency leads to inaccurate prediction of spray tip penetration and mean droplet size. To overcome the problem, in the present study, enhanced sub-models for reducing the grid dependency are introduced and implemented in the three dimensional engine simulation code, KIVA-3V. In the EMC model, keeping the standard Eulerian-Lagrangian method, the momentum coupling term in the momentum conservation equation of the Eulerian phase is revised and lack of momentum transfer due to inadequate cell resolution is compensated regarding the gaseous volume receiving the effective spray momentum. Computations were conducted using the EMC model, the gas-velocity interpolation scheme, and grid-size independent collision model under the high-pressure diesel injection conditions. From the results, the improved model composed of the EMC model and the grid independent collision model give a dramatic decrease in grid dependency of spray tip penetration and overall droplet size.

2010 ◽  
Vol 132 (12) ◽  
Author(s):  
Y. Liu ◽  
W. Z. Li

The liquid droplet size distribution in gas-liquid vertical upward annular flow is investigated through a CFD (computational fluid dynamics)-PBM (population balance model) coupled model in this paper. Two-fluid Eulerian scheme is employed as the framework of this model and a population balance equation is used to obtain the dispersed liquid droplet diameter distribution, where three different coalescence and breakup kernels are investigated. The Sauter mean diameter d32 is used as a bridge between a two-fluid model and a PBM. The simulation results suggest that the original Luo–Luo kernel and the mixed kernel A (Luo’s coalescence kernel incorporated with Prince and Blanch’s breakup kernel) can only give reasonable predictions for large diameter droplets. Mixed kernel B (Saffman and Turner’s coalescence kernel incorporated with Lehr’s breakup kernel) can accurately capture the particle size distribution (PSD) of liquid droplets covering all droplet sizes, and is appropriate for the description of liquid droplet size distribution in gas-liquid annular flow.


2021 ◽  
Vol 104 (4) ◽  
Author(s):  
Davide D’Ambrosio ◽  
Xavier Zambrana-Puyalto ◽  
Marialuisa Capezzuto ◽  
Antonio Giorgini ◽  
Pietro Malara ◽  
...  

2021 ◽  
Author(s):  
Landon Conner ◽  
Clarence L. Worrell ◽  
James P. Spring ◽  
Jun Liao

Abstract The nuclear power industry is increasingly identifying applications of machine learning to reduce design, engineering, manufacturing, and operational costs. In some cases, applications have been deployed and are realizing value, in particular in the higher volume and data rich manufacturing areas of the nuclear industry. In this paper, we use machine learning to develop metamodel approximations of a computationally intense safety analysis code used to simulate a postulated loss-of-coolant accident (LOCA). The benefit of an accurate metamodel is that it runs at a fraction of the computational cost (milliseconds) compared to the LOCA analysis code. Metamodels can therefore support applications requiring a high volume of runs, for example optimization, uncertainty analysis, and probabilistic decision analysis, which would otherwise not be possible using the computationally intense code. We first generate training data by running the safety analysis code over a design of experiment. We then perform exploratory data analysis and an initial fitting of several model forms, including neighbor-based models, tree-based models, support vector machines, and artificial neural networks. We select neural network as the most promising candidate and perform hyperparameter optimization using a genetic algorithm. We discuss the resulting model, its potential applications, and areas for further research.


2017 ◽  
Vol 10 (1) ◽  
pp. 98-115 ◽  
Author(s):  
Jun Zhang ◽  
Rongliang Chen ◽  
Chengzhi Deng ◽  
Shengqian Wang

AbstractRecently, many variational models involving high order derivatives have been widely used in image processing, because they can reduce staircase effects during noise elimination. However, it is very challenging to construct efficient algorithms to obtain the minimizers of original high order functionals. In this paper, we propose a new linearized augmented Lagrangian method for Euler's elastica image denoising model. We detail the procedures of finding the saddle-points of the augmented Lagrangian functional. Instead of solving associated linear systems by FFT or linear iterative methods (e.g., the Gauss-Seidel method), we adopt a linearized strategy to get an iteration sequence so as to reduce computational cost. In addition, we give some simple complexity analysis for the proposed method. Experimental results with comparison to the previous method are supplied to demonstrate the efficiency of the proposed method, and indicate that such a linearized augmented Lagrangian method is more suitable to deal with large-sized images.


2020 ◽  
Vol 10 (17) ◽  
pp. 5893
Author(s):  
Maolin Lei ◽  
Ting Wang ◽  
Chen Yao ◽  
Huan Liu ◽  
Zhi Wang ◽  
...  

Self-collisions of a dual-arm robot system can cause severe damage to the robot. To deal with this problem, this paper presents a real-time algorithm for preventing self-collisions in dual-arm systems. Our first contribution in this work is a novel collision model built using discrete spherical bounding volumes with different radii. In addition, we propose a sensitivity index to measure the distance between spheres with different radii in real time. Next, according to the minimal sensitivity index between different spheres, the repulsive velocity is produced at the centers of the spheres (control points), which the robot uses to generate new motion based on the robot kinematic model. The proposed algorithm offers the additional benefits of a decrease in the number of bounding spheres, and a simple collision model that can effectively decrease the computational cost of the process. To demonstrate the validity of the algorithm, we performed simulations and experiments by an upper-body humanoid robot. Although the repulsive velocity acted on the control points, the results indicate that the algorithm can effectively achieve self-collision avoidance by using a simple collision model.


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