scholarly journals DEVELOPMENT OF A MESH CLUSTERING ALGORITHM AIMED AT REDUCING THE COMPUTATIONAL EFFORT OF GEARBOXES’ CFD SIMULATIONS

2021 ◽  
Author(s):  
MARCO NICOLA MASTRONE ◽  
FRANCO CONCLI
Keyword(s):  
Clustering Algorithm ◽  
Computational Effort ◽  
Cfd Simulations

Comparison of Transient Blade Row Methods for the CFD Analysis of a High-Pressure Turbine

2014 ◽  
Author(s):  
Dirk Witteck ◽  
Derek Micallef ◽  
Ronald Mailach
Keyword(s):  
Cfd Simulation ◽  
Computational Cost ◽  
Computational Effort ◽  
Cfd Simulations ◽  
Computational Costs ◽  
Ansys Cfx ◽  
Numerical Effort ◽  
Blade Row ◽  
Flow Phenomena ◽  
Temporal Periodicity

Usually, in a turbine an uneven number of blades are selected for vane and blade rows to reduce the level of interaction forces. To consider all unsteady flow phenomena within a turbine the computation of the full annulus is required causing considerable computational cost. Transient blade row methods using few passages reduce the numerical effort significantly. Nevertheless, those approaches provide accurate results. This contribution presents three different unsteady approaches to compare the accuracy and the computational effort, using a full annulus unsteady CFD simulation as a reference. The first approach modifies the blade-to-blade ratio whereas the second method scales the circumferential flow pattern to reach spatial and temporal periodicity. Third approach is based on time-inclining method to overcome unequal blade pitches with less numerical effort. All unsteady CFD simulations are carried out for the transonic test turbine VKI BRITE EURAM using the commercial CFD solver ANSYS CFX 14.5. The resulting unsteady pressure disturbances and blade forces of the different transient blade row methods are compared to each other as well as to experimental data. Finally, the accuracy and the computational costs are discussed in more detail.

An Evolutionary Clustering Approach to Pareto Solutions in Multiobjective Optimization

2002 ◽  
Author(s):  
Min Joong Jeong ◽  
Sinobu Yoshimura
Keyword(s):  
Multiobjective Optimization ◽  
Engineering Design ◽  
Function Space ◽  
Clustering Algorithm ◽  
Computational Effort ◽  
Local Optimum ◽  
Pareto Solutions ◽  
Evolutionary Clustering ◽  
Interpretation Process ◽  
Clustering Approach

Pareto solutions in multiobjective optimization are very problematic to measuring the characteristics of solutions for engineering design because of their considerable variety in function space and parameter space. To overcome these situations, a clustering-based interpretation process for Pareto solutions is considered. For better competitive clustering algorithm, we propose an evolutionary clustering algorithm — ECA. The ECA requires less computational effort, and overcomes local optimum of the K-means clustering algorithm and its related algorithms. Effectiveness of the method is examined in detail through the comparison with other algorithms.

Predicting the Powering Performance of Different Vessel Types using an Open-Source CFD Propulsion Framework

2021 ◽  
Author(s):  
Björn Windén
Keyword(s):  
Energy Saving ◽  
Open Source ◽  
Fuel Efficiency ◽  
Computational Effort ◽  
The Body ◽  
Cfd Simulations ◽  
Local Flow ◽  
Flow Solver ◽  
Twin Screw ◽  
Coupled Solvers

CFD is a useful tool for ship designers looking for accurate predictions of the fuel efficiency achieved by a certain combination of hull, propeller and Energy Saving Devices (ESDs). Such predictions are key to meeting ever-increasing demands for reductions in emissions. However, CFD simulations of propeller-hull interaction can be very costly in terms of computational effort due to the need to resolve the unsteady flow around the rotating propeller. A popular approach to alleviate this cost, that has seen much practical use in industry, is the use of body forces (momentum sources) to represent the rotating propeller. There are many ways to describe the body force distribution in the fluid for a certain propeller and there are many options for what flow solver to use. In a previous meeting of the Society, an open-source framework for easily creating coupled solvers using an arbitrary combination of models was presented. Here, one of these coupled solvers is used to predict the local flow behind the propeller, as well as integral coefficients indicating performance, of four different vessels: a bulk carrier fitted with an Energy Saving Device, a fast container ship, a tanker and a fully appended twin-screw navy destroyer. All simulations are compared to available experimental data. Conclusions are drawn based on the success of the coupled solver to predict the local flow behind the propeller for each individual hull and how this relates to the vessel type and the local stern geometry.

scholarly journals PARAMETERIZATION OF ENVIRONMENTAL INFLUENCES BY AUTOMATED CHARACTERISTIC DIAGRAMS FOR THE DECOUPLED FLUID AND STRUCTURAL-MECHANICAL SIMULATIONS

2019 ◽  
Vol 19 (1) ◽  
pp. 98-113
Author(s):  
Janine GLÄNZEL ◽  
Tharun Suresh  KUMAR ◽  
Christian NAUMANN ◽  
Matthias PUTZ
Keyword(s):  
Heat Transfer ◽  
Machine Tool ◽  
Boundary Data ◽  
Clustering Algorithm ◽  
Environmental Influences ◽  
Ambient Air ◽  
Cfd Simulations ◽  
Suitable Alternative ◽  
Single Output ◽  
Positioning Errors

Thermo-elastic effects contribute the most to positioning errors in machine tools especially in operations where high precision machining is involved. When a machine tool is subjected to changes in environmental influences such as ambient air temperature, velocity or direction, then flow (CFD) simulations are necessary to effectively quantify the thermal behaviour between the machine tool surface and the surrounding air (fluid). Heat transfer coefficient (HTC) values effectively represent this solid-fluid heat transfer and it serves as the boundary data for thermo-elastic simulations. Thereby, deformation results can be obtained. This two-step simulation procedure involving fluid and thermo-structural simulations is highly complex and time-consuming. A suitable alternative for the above process can be obtained by introducing a clustering algorithm (CA) and characteristic diagrams (CDs) in the workflow. CDs are continuous maps of a set of input variables onto a single output variable, which are trained using data from a limited number of CFD simulations which is optimized using the clustering technique involving genetic algorithm (GA) and radial basis function (RBF) interpolation. The parameterized environmental influences are mapped directly onto corresponding HTC values in each CD. Thus, CDs serve as look-up tables which provide boundary data (HTC values along with nodal information) under several load cases (combinations of environmental influences) for thermo-elastic simulations. Ultimately, a decoupled fluid-structural simulation system is obtained where boundary (convection) data for thermo-mechanical simulations can be directly obtained from CDs and would no longer require fluid simulations to be carried out again. Thus, a novel approach for the correction of thermo-elastic deformations on a machine tool is obtained.

scholarly journals A New Integrated Approach for the Prediction of the Load Independent Power Losses of Gears: Development of a Mesh-Handling Algorithm to Reduce the CFD Simulation Time

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
F. Concli ◽  
A. Della Torre ◽  
C. Gorla ◽  
G. Montenegro
Keyword(s):  
Prediction Models ◽  
Cfd Simulation ◽  
Integrated Approach ◽  
Numerical Approach ◽  
Computational Effort ◽  
Power Losses ◽  
Cfd Simulations ◽  
Numerical Calculation Method ◽  
Simulation Time ◽  
Potential Tool

To improve the efficiency of geared transmissions, prediction models are required. Literature provides only simplified models that often do not take into account the influence of many parameters on the power losses. Recently some works based on CFD simulations have been presented. The drawback of this technique is the time demand needed for the computation. In this work a less time-consuming numerical calculation method based on some specific mesh-handling techniques was extensively applied. With this approach the windage phenomena were simulated and compared with experimental data in terms of power loss. The comparison shows the capability of the numerical approach to capture the phenomena that can be observed experimentally. The powerful capabilities of this approach in terms of both prediction accuracy and computational effort efficiency make it a potential tool for an advanced design of gearboxes as well as a powerful tool for further comprehension of the physics behind the gearbox lubrication.

Evaluation of Geometric Symmetry Condition in Numerical Simulations of Thermal Process of Packed Liquid Food by Computational Fluid Dynamics (CFD)

2010 ◽  
Vol 6 (5) ◽  
Author(s):  
Pedro Esteves Duarte Augusto ◽  
Marcelo Cristianini
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Numerical Simulations ◽  
Thermal Process ◽  
Computational Effort ◽  
Food Preservation ◽  
Liquid Food ◽  
Cfd Simulations ◽  
Geometric Symmetry ◽  
Computational Fluid Dynamics Cfd

The growing demand for safer food and better nutritional and sensory quality creates the need for a better understanding of the processes involved in food production. Computational fluid dynamics (CFD) has been used by several authors for a better understanding of liquid food thermal process, one of the safest and most frequently used methods for food preservation. This study evaluated the condition of geometric symmetry used in numerical simulations of thermal process of packed liquid food via CFD. The analysis of temperature profiles obtained experimentally and through models built on 1/1, 1/4, 1/8, 1/16 and 1/32 of package geometry showed good correlation with the CFD simulations. The results indicate that the axial symmetry of the bottles allows the use of smaller models, which saves computational effort.

scholarly journals CFD simulations of gearboxes: implementation of a mesh clustering algorithm for efficient simulations of complex system’s architectures

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Marco Nicola Mastrone ◽  
Franco Concli
Keyword(s):  
Clustering Algorithm ◽  
Computational Domain ◽  
Power Losses ◽  
Cfd Simulations ◽  
Bevel Gears ◽  
Engineering Problems ◽  
Virtual Prototypes ◽  
System Configurations ◽  
Determinant Factor ◽  
Simulation Time

AbstractIn the last decade, computer-aided engineering (CAE) tools have become a determinant factor in the analysis of engineering problems. In fact, they bring a clear reduction of time in the design phase of a new product thanks to parametrical studies based on virtual prototypes. The application of such tools to gearboxes allowed engineers to study the efficiency and lubrication inside transmissions. However, the difficulties of handling the computational domain are still a concern for complex system configurations. For this reason, the authors maintain that it is fundamental to introduce time efficient algorithms that enable the effective study of any kind of gear, e.g., helical and bevel configurations. In this work, a new mesh handling strategy specifically suited for this kind of studies is presented. The methodology is based on the Global Remeshing Approach with Mesh Clustering (GRAMC) process that drastically reduces the simulation time by minimizing the effort for updating the grids. This procedure was tested on spur, helical, and bevel gears, thus demonstrating the flexibility of the approach. The comparison with experimentally measured power losses highlighted the good accuracy of the strategy. The algorithm was implemented in the opensource software OpenFOAM®.

Structure Information From Electron Diffraction Intensities

1990 ◽  
Vol 48 (2) ◽  
pp. 516-517
Author(s):  
J. Gjønnes ◽  
N. Bøe ◽  
K. Gjønnes
Keyword(s):  
Computational Effort ◽  
Unit Cell ◽  
Small Unit ◽  
Structure Information ◽  
Dispersion Surface ◽  
Integrated Intensity ◽  
The One ◽  
Image Position ◽  
Convergent Beam ◽  
Beam Patterns

Structure information of high precision can be extracted from intentsity details in convergent beam patterns like the one reproduced in Fig 1. From low order reflections for small unit cell crystals,bonding charges, ionicities and atomic parameters can be derived, (Zuo, Spence and O’Keefe, 1988; Zuo, Spence and Høier 1989; Gjønnes, Matsuhata and Taftø, 1989) , but extension to larger unit cell ma seem difficult. The disks must then be reduced in order to avoid overlap calculations will become more complex and intensity features often less distinct Several avenues may be then explored: increased computational effort in order to handle the necessary many-parameter dynamical calculations; use of zone axis intensities at symmetry positions within the CBED disks, as in Figure 2 measurement of integrated intensity across K-line segments. In the last case measurable quantities which are well defined also from a theoretical viewpoint can be related to a two-beam like expression for the intensity profile:With as an effective Fourier potential equated to a gap at the dispersion surface, this intensity can be integrated across the line, with kinematical and dynamical limits proportional to and at low and high thickness respctively (Blackman, 1939).

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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