scholarly journals Exact regularized point particle method for multiphase flows in the two-way coupling regime

2015 ◽  
Vol 773 ◽  
pp. 520-561 ◽  
Author(s):  
P. Gualtieri ◽  
F. Picano ◽  
G. Sardina ◽  
C. M. Casciola
Keyword(s):  
Multiphase Flows ◽  
Parallel Machines ◽  
High Efficiency ◽  
Computational Cost ◽  
Turbulent Shear ◽  
Rigid Sphere ◽  
Coupling Mechanism ◽  
Momentum Exchange ◽  
Carrier Fluid ◽  
Phase Momentum

Particulate flows have mainly been studied under the simplifying assumption of a one-way coupling regime where the disperse phase does not modify the carrier fluid. A more complete view of multiphase flows can be gained calling into play two-way coupling effects, i.e. by accounting for the inter-phase momentum exchange, which is certainly relevant at increasing mass loading. In this paper we present a new methodology rigorously designed to capture the inter-phase momentum exchange for particles smaller than the smallest hydrodynamical scale, e.g. the Kolmogorov scale in a turbulent flow. The momentum coupling mechanism exploits the unsteady Stokes flow around a small rigid sphere, where the transient disturbance produced by each particle is evaluated in a closed form. The particles are described as lumped point masses, which would lead to the appearance of singularities. A rigorous regularization procedure is conceived to extract the physically relevant interactions between the particles and the fluid which avoids any ‘ad hoc’ assumption. The approach is suited for high-efficiency implementation on massively parallel machines since the transient disturbance produced by the particles is strongly localized in space. We will show that hundreds of thousands of particles can be handled at an affordable computational cost, as demonstrated by a preliminary application to a particle-laden turbulent shear flow.

A Model Constraint for Polydisperse Solids in Multifluid Flows

2015 ◽  
Vol 137 (11) ◽  
Author(s):  
Michael P. Kinzel ◽  
Leonard Joel Peltier ◽  
Brigette Rosendall ◽  
Mallory Elbert ◽  
Andri Rizhakov ◽  
...  
Keyword(s):  
Model Development ◽  
Fluid Flows ◽  
Momentum Exchange ◽  
Carrier Fluid ◽  
Single Carrier ◽  
Cfd Models ◽  
Model Constraint ◽  
Level Model ◽  
Consistency Constraint ◽  
Multiphase Fluid

A method to assess computational fluid dynamics (CFD) models for polydisperse granular solids in a multifluid flow is developed. The proposed method evaluates a consistency constraint, or a condition that an Eulerian multiphase solution for a monodisperse material in a single carrier fluid is invariant to an arbitrary decomposition into a pseudo-polydisperse mixture of multiple, identical fluid phases. The intent of this condition is to develop tests to assist model development and testing for multiphase fluid flows. When applied to two common momentum exchange models, the constraint highlights model failures for polydisperse solids interacting with a multifluid flow. It is found that when inconsistency occurs at the algebraic level, model failure clearly extends to application. When the models are reformulated to satisfy the consistency constraint, simple tests and application-scale simulations no longer display consistency failure.

A Lightweight SE-YOLOv3 Network for Multi-Scale Object Detection in Remote Sensing Imagery

2021 ◽  
Author(s):  
Lifang Zhou ◽  
Guang Deng ◽  
Weisheng Li ◽  
Jianxun Mi ◽  
Bangjun Lei
Keyword(s):  
Remote Sensing ◽  
High Efficiency ◽  
Computational Cost ◽  
Image Data ◽  
Detection Accuracy ◽  
Data Sets ◽  
One Stage ◽  
Efficiency And Effectiveness ◽  
Spatial Pyramid Pooling ◽  
Feature Augmentation

Current state-of-the-art detectors achieved impressive performance in detection accuracy with the use of deep learning. However, most of such detectors cannot detect objects in real time due to heavy computational cost, which limits their wide application. Although some one-stage detectors are designed to accelerate the detection speed, it is still not satisfied for task in high-resolution remote sensing images. To address this problem, a lightweight one-stage approach based on YOLOv3 is proposed in this paper, which is named Squeeze-and-Excitation YOLOv3 (SE-YOLOv3). The proposed algorithm maintains high efficiency and effectiveness simultaneously. With an aim to reduce the number of parameters and increase the ability of feature description, two customized modules, lightweight feature extraction and attention-aware feature augmentation, are embedded by utilizing global information and suppressing redundancy features, respectively. To meet the scale invariance, a spatial pyramid pooling method is used to aggregate local features. The evaluation experiments on two remote sensing image data sets, DOTA and NWPU VHR-10, reveal that the proposed approach achieves more competitive detection effect with less computational consumption.

scholarly journals A Kriging Surrogate Model for the Interference Reduction in the Settlement Surveillance Sensors of Steel Transmission Towers

2019 ◽  
Vol 9 (16) ◽  
pp. 3343 ◽  
Author(s):  
Jiajia Shi ◽  
Liu Chu ◽  
Eduardo Souza de Cursi
Keyword(s):  
Surrogate Model ◽  
High Efficiency ◽  
Computational Cost ◽  
Element Model ◽  
Kriging Surrogate Model ◽  
Transmission Tower ◽  
Modal Frequency ◽  
Interference Reduction ◽  
Transmission Towers ◽  
Tower Foundation

The utilization of modal frequency sensors is a feasible and effective way to monitor the settlement problem of the transmission tower foundation. However, the uncertainties and interference in the real operation environment of transmission towers highly affect the accuracy and identification of modal frequency sensors. In order to reduce the interference of modal frequency sensors for transmission towers, a Kriging surrogate model is proposed in this study. The finite element model of typical transmission towers is created and validated to provide the effective original database for the Kriging surrogate model. The prediction accuracy and convergences of the Kriging surrogate model are measured and confirmed. Besides the merits in computational cost and high-efficiency, the Kriging surrogate model is proven to have a satisfied and robust interference reduction capacity. Therefore, the Kriging surrogate model is feasible and competitive for interference filtration in the settlement surveillance sensors of steel transmission towers.

Temperature-Based Optimization of Film Cooling in Gas Turbine Hot Gas Path Components

2013 ◽  
Author(s):  
Felipe A. C. Viana ◽  
Jack Madelone ◽  
Niranjan Pai ◽  
Genghis Khan ◽  
Sanghum Baik
Keyword(s):  
Film Cooling ◽  
Gas Turbines ◽  
Optimization Design ◽  
Goal Attainment ◽  
High Efficiency ◽  
Cooling System ◽  
Computational Cost ◽  
Metal Temperature ◽  
Cooling Flow ◽  
Hot Gas

To achieve high efficiency, modern gas turbines operate at temperatures that exceed melting points of metal alloys used in turbine hot gas path parts. Parts exposed to hot gas are actively cooled with a portion of the compressor discharge air (e.g., through film cooling) to keep the metal temperature at levels needed to meet durability requirements. However, to preserve efficiency, it is important to optimize the cooling system to use the least amount of cooling flow. In this study, film cooling optimization is achieved by varying cooling hole diameters, hole to hole spacing, and film row placements so that the specified targets for maximum metal temperature are met while preserving (or saving) cooling flow. The computational cost of the high-fidelity physics models, the large number of design variables, the large number and nonlinearity of responses impose severe challenges to numerical optimization. Design of experiments and cheap-to-evaluate approximations (radial basis functions) are used to alleviate the computational burden. Then, the goal attainment method is used for optimizing of film cooling configuration. The results for a turbine blade design show significant improvements in temperature distribution while maintaining/reducing the amount of used cooling flow.

Numerical Prediction of Poly-Dispersed Condensation Using Quadrature Method of Moments and Multi-Fluid Model

2017 ◽  
Author(s):  
Anjaneyulu Lankadasu ◽  
Laurent Krumenacker ◽  
Anil Kumar ◽  
Amita Tripathi
Keyword(s):  
Method Of Moments ◽  
Laval Nozzle ◽  
High Efficiency ◽  
Fluid Model ◽  
Computational Cost ◽  
Droplet Size Distribution ◽  
General Purpose ◽  
Navier Stokes ◽  
Quadrature Method ◽  
Quadrature Method Of Moments

Accurate prediction of condensation plays an important role in the development of high efficiency turbo-machines working on condensable fluid. Therefore it demands modeling of poly-disperse characteristic of number distribution function while modeling condensation. Two such kind of models are considered in this work and they are namely, quadrature method of moments (QMOM) and multi-fluid method (MFM) models. The vital difference between these two models lies in the method of discretisation of the droplet size distribution. Further, their numerical aspects like ease of implementation in general purpose computational fluid dynamics solvers, accuracy and associated computational cost are discussed. In order to obtain accurate thermodynamic properties, the real gas formulations defined in IAPWS-IF97 are used. These algorithms are applied to the compressible Navier-Stokes solver of Fluidyn MP and tests are carried on Laval nozzle and compared with the experimental measurements.

scholarly journals Distance Based Multiple Kernel ELM: A Fast Multiple Kernel Learning Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Chengzhang Zhu ◽  
Xinwang Liu ◽  
Qiang Liu ◽  
Yuewei Ming ◽  
Jianping Yin
Keyword(s):  
Extreme Learning Machine ◽  
High Efficiency ◽  
Computational Cost ◽  
Multiple Kernel Learning ◽  
Superior Performance ◽  
Kernel Learning ◽  
Normal Vector ◽  
Learning Approach ◽  
Multiple Kernel ◽  
Learning Machine

We propose a distance based multiple kernel extreme learning machine (DBMK-ELM), which provides a two-stage multiple kernel learning approach with high efficiency. Specifically, DBMK-ELM first projects multiple kernels into a new space, in which new instances are reconstructed based on the distance of different sample labels. Subsequently, anl2-norm regularization least square, in which the normal vector corresponds to the kernel weights of a new kernel, is trained based on these new instances. After that, the new kernel is utilized to train and test extreme learning machine (ELM). Extensive experimental results demonstrate the superior performance of the proposed DBMK-ELM in terms of the accuracy and the computational cost.

scholarly journals An Efficient and High-Resolution Topology Optimization Method Based on Convolutional Neural Networks

2019 ◽  
Author(s):  
Liang Xue ◽  
Jie Liu ◽  
Guilin Wen ◽  
Hongxin Wang
Keyword(s):  
Topology Optimization ◽  
High Resolution ◽  
High Efficiency ◽  
Optimization Problems ◽  
Combined Treatment ◽  
Computational Cost ◽  
Optimization Method ◽  
Element Analysis ◽  
Arbitrary Boundary ◽  
Topology Optimization Method

Topology optimization is a pioneering design method that can provide various candidates with high mechanical properties. However, the high-resolution for the optimum structures is highly desired, normally in turn leading to computationally intractable puzzle, especially for the famous Solid Isotropic Material with Penalization (SIMP) method. In this paper, an efficient and high-resolution topology optimization method is proposed based on the Super-Resolution Convolutional Neural Network (SRCNN) technique in the framework of SIMP. The SRCNN includes four processes, i.e. refining, path extraction & representation, non-linear mapping, and reconstruction. The high computational efficiency is achieved by a pooling strategy, which can balance the number of finite element analysis (FEA) and the output mesh in optimization process. To further reduce the high computational cost of 3D topology optimization problems, a combined treatment method using 2D SRCNN is built as another speeding-up strategy. A number of typical examples justify that the high-resolution topology optimization method adopting SRCNN has excellent applicability and high efficiency for 2D and 3D problems with arbitrary boundary conditions, any design domain shape, and varied load.

Blade Arrangement Optimization for Mistuned Bladed Disk Based on Gaussian Process Regression and Genetic Algorithm

2020 ◽  
Vol 142 (2) ◽  
Author(s):  
Wangbai Pan ◽  
Meiyan Zhang ◽  
Guoan Tang
Keyword(s):  
Genetic Algorithm ◽  
Gaussian Process ◽  
High Efficiency ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Computational Cost ◽  
Combinatorial Problems ◽  
Optimal Arrangement ◽  
Bladed Disk ◽  
Gp Model

Abstract Mistuning phenomena exist in the bladed disk due to the inevitable deviations among blades' properties, e.g., stiffness, mass, geometry, etc., leading to localization and response amplification. The dynamic performance of mistuned bladed disk is sensitive to the arrangement of blades. The blade arrangement optimization aims to obtain the optimal arrangement that minimizes the influence of mistuning. In this paper, a framework of high efficiency is raised to deal with the challenge of high computational cost this optimization. It comprehensively utilizes mixed-dimensional finite element model (MDFEM), Gaussian process (GP) regression, and genetic algorithm (GA). The MDFEM can perform mistuned modal analysis efficiently and provides the training set of GP regression rapidly. The GP model, as a surrogate model, predicts the desired dynamic performance directly without calculating the numerical model and can function as fitness function in optimization. GA has the capability to deal with combinatorial problems and is a good option for problems with large search domains and several local maxima/minima. The techniques and processes of three methods are illustrated in detail. Case studies, based on a real turbine, are concretely presented in a gradually progressive manner to test and verify the effectiveness, accuracy, and efficiency of methods and entire framework step by step. The results show the satisfactory optimal arrangement for a randomly chosen set of mistuned blades, and the influence of mistuning is reduced indeed. The time cost of the optimization has been reduced several orders of magnitude. This framework can be a promising approach for the blade arrangement optimization problem.

scholarly journals Droplets in homogeneous shear turbulence

2019 ◽  
Vol 876 ◽  
pp. 962-984 ◽  
Author(s):  
Marco E. Rosti ◽  
Zhouyang Ge ◽  
Suhas S. Jain ◽  
Michael S. Dodd ◽  
Luca Brandt
Keyword(s):  
Steady State ◽  
Production Rate ◽  
Droplet Size ◽  
Weber Number ◽  
Single Phase ◽  
Isotropic Turbulence ◽  
Turbulent Shear ◽  
Phase Flow ◽  
Carrier Fluid ◽  
The Mean

We simulate the flow of two immiscible and incompressible fluids separated by an interface in a homogeneous turbulent shear flow at a shear Reynolds number equal to 15 200. The viscosity and density of the two fluids are equal, and various surface tensions and initial droplet diameters are considered in the present study. We show that the two-phase flow reaches a statistically stationary turbulent state sustained by a non-zero mean turbulent production rate due to the presence of the mean shear. Compared to single-phase flow, we find that the resulting steady-state conditions exhibit reduced Taylor-microscale Reynolds numbers owing to the presence of the dispersed phase, which acts as a sink of turbulent kinetic energy for the carrier fluid. At steady state, the mean power of surface tension is zero and the turbulent production rate is in balance with the turbulent dissipation rate, with their values being larger than in the reference single-phase case. The interface modifies the energy spectrum by introducing energy at small scales, with the difference from the single-phase case reducing as the Weber number increases. This is caused by both the number of droplets in the domain and the total surface area increasing monotonically with the Weber number. This reflects also in the droplet size distribution, which changes with the Weber number, with the peak of the distribution moving to smaller sizes as the Weber number increases. We show that the Hinze estimate for the maximum droplet size, obtained considering break-up in homogeneous isotropic turbulence, provides an excellent estimate notwithstanding the action of significant coalescence and the presence of a mean shear.

Simulation of Structural Evolution Using Time-Dependent Density-Functional Based Tight-Binding Method

2020 ◽  
Vol 20 (11) ◽  
pp. 7206-7209
Author(s):  
Seung Mi Lee ◽  
Thomas A. Niehaus
Keyword(s):  
Density Functional ◽  
High Efficiency ◽  
Tight Binding ◽  
Computational Cost ◽  
Structural Evolution ◽  
Simulation Method ◽  
Time Dependent ◽  
Excitation Energies ◽  
Carbon Chains ◽  
Experimental Values

A faster and more efficient quantum mechanical simulation method for application to complicated issues of real systems beyond model cases has long been sought after. The density-functional based tight-binding (DFTB) method has successfully explained the atomistic and electronic properties of semiconductors, surfaces, and nanostructures. In addition, the time-dependent formalism implemented in DFTB showed high efficiency in terms of computational cost. In this study, we demonstrated the structural and electronic evolution of small molecules induced by a laser pulse using the time-dependent DFTB (TD-DFTB) method. We identified the critical fluence of the input laser for structural dissociations in carbon chains and fullerenes, which related to the structural stability. The excitation energies of several molecules calculated by TD-DFTB agreed with the experimental values.

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