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.

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.

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®.

scholarly journals Two-phase CFD simulation of the monodyspersed suspension hydraulic behaviour in the tank apparatus from a circulatory pipe

2008 ◽  
Vol 10 (1) ◽  
pp. 22-27 ◽  
Author(s):  
Roch Plewik ◽  
Piotr Synowiec ◽  
Janusz Wójcik
Keyword(s):  
Cfd Simulation ◽  
Fluidised Bed ◽  
Cfd Simulations ◽  
Two Phase ◽  
Hydraulic Behaviour ◽  
Hydraulic Conditions ◽  
The One ◽  
Cfd Method ◽  
Multi Phase ◽  
The Ideal

Two-phase CFD simulation of the monodyspersed suspension hydraulic behaviour in the tank apparatus from a circulatory pipe The hydrodynamics in fluidized-bed crystallizers is studied by CFD method. The simulations were performed by a commercial packet of computational fluid dynamics Fluent 6.x. For the one-phase modelling (15), a standard k-ε model was applied. In the case of the two-phase flows the Eulerian multi-phase model with a standard k-ε method, aided by the k-ε dispersed model for viscosity, has been used respectively. The collected data put a new light on the suspension flow behaviour in the annular zone of the fluidised bed crystallizer. From the presented here CFD simulations, it clearly issues that the real hydraulic conditions in the fluidised bed crystallizers are far from the ideal ones.

Weak Coupling Strategy for Multi-Physics CFD Simulation in Engineering Problems

2011 ◽  
Author(s):  
Makoto Yamamoto ◽  
Masaya Suzuki
Keyword(s):  
Strong Coupling ◽  
Time Scale ◽  
Weak Coupling ◽  
Particle Deposition ◽  
Cfd Simulation ◽  
The Other ◽  
Cfd Simulations ◽  
Sand Erosion ◽  
Coupling Strategy ◽  
The Difference

Multi-Physics CFD Simulation will be one of key technologies in various engineering fields. There are two strategies to simulate a multi-physics phenomenon. One is “Strong Coupling”, and the other is “Weak Coupling”. Each can be employed, based on time-scales of physics embedded in a problem. That is, when a time-scale of one physics is nearly same as that of the other physics, we have to use Strong Coupling to take into account the interaction between two physics. On the other hand, when one time-scale is quite different from the other one, Weak Coupling can be applied. Considering the present computer performance, Strong Coupling is difficult to be used in engineering design processes now. Therefore, we are focusing on Weak Coupling, and it has been applied to a number of multi-physics CFD simulations in engineering. We have successfully simulated sand erosion, ice accretion, particle deposition, electro-chemical machining and so on, with using Weak Coupling method. In the present study, the difference between strong and weak couplings is briefly described, and two examples of our multi-physics CFD simulations are expressed. The numerical results indicate that Weak Coupling strategy is promising in a lot of multi-physics CFD simulations.

The Impact of Predried Lignite Cofiring With Hard Coal in an Industrial Scale Pulverized Coal Fired Boiler

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Halina Pawlak-Kruczek ◽  
Robert Lewtak ◽  
Zbigniew Plutecki ◽  
Marcin Baranowski ◽  
Michal Ostrycharczyk ◽  
...  
Keyword(s):  
Combustion Chamber ◽  
Bituminous Coal ◽  
Cfd Simulation ◽  
Numerical Study ◽  
Fluid Dynamic ◽  
Industrial Scale ◽  
Hard Coal ◽  
Cfd Simulations ◽  
Reference Case ◽  
The Impact

The paper presents the experimental and numerical study on the behavior and performance of an industrial scale boiler during combustion of pulverized bituminous coal with various shares of predried lignite. The experimental measurements were carried out on a boiler WP120 located in CHP, Opole, Poland. Tests on the boiler were performed during low load operation and the lignite share reached over to 36% by mass. The predried lignite, kept in dedicated separate bunkers, was mixed with bituminous coal just before the coal mills. Computational fluid dynamic (CFD) simulation of a cofiring scenario of lignite with hard coal was also performed. Site measurements have proven that cofiring of a predried lignite is not detrimental to the boiler in terms of its overall efficiency, when compared with a corresponding reference case, with 100% of hard coal. Experiments demonstrated an improvement in the grindability that can be achieved during co-milling of lignite and hard coal in the same mill, for both wet and dry lignite. Moreover, performed tests delivered empirical evidence of the potential of lignite to decrease NOx emissions during cofiring, for both wet and dry lignite. Results of efficiency calculations and temperature measurements in the combustion chamber confirmed the need to predry lignite before cofiring. Performed measurements of temperature distribution in the combustion chamber confirmed trend that could be seen in the results of CFD. CFD simulations were performed for predried lignite and demonstrated flow patterns in the combustion chamber of the boiler, which could prove useful in case of any further improvements in the firing system. CFD simulations reached satisfactory agreement with the site measurements in terms of the prediction of emissions.

Network modelling of dry-type transformer cooling systems

2018 ◽  
Vol 37 (3) ◽  
pp. 1039-1053 ◽  
Author(s):  
Andrea Cremasco ◽  
Wei Wu ◽  
Andreas Blaszczyk ◽  
Bogdan Cranganu-Cretu
Keyword(s):  
Network Model ◽  
Thermal Model ◽  
Cfd Simulation ◽  
Cooling System ◽  
Cfd Simulations ◽  
Network Modelling ◽  
Cooling Systems ◽  
Content Type ◽  
System Configurations ◽  
Dielectric Insulation

Purpose The application of dry-type transformers is growing in the market because the technology is non-flammable, safer and environmentally friendly. However, the unit dimensions are normally larger and material costs become higher, as no oil is present for dielectric insulation or cooling. At designing stage, a transformer thermal model used for predicting temperature rise is fundamental and the modelling of cooling system is particularly important. This paper aims to describe a thermal model used to compute dry transformers with different cooling system configurations. Design/methodology/approach The paper introduces a fast-calculating thermal and pressure network model for dry-transformer cooling systems, preliminarily verified by analytical methods and advanced CFD simulations, and finally validated with experimental results. Findings This paper provides an overview of the network model of dry-transformer cooling system, describing its topology and its main variants including natural or forced ventilation, with or without cooling duct in the core, enclosure with roof and floor ventilation openings and air barriers. Finally, it presents a formulation for the new heat exchanger element. Originality/value The network approach presented in this paper allows to model efficiently the cooling system of dry-type transformers. This model is based on physical principles rather than empirical assessments that are valid only for specific transformer technologies. In comparison with CFD simulation approach, the network model runs much faster and the accuracies still fall in acceptable range; therefore, one is able to utilize this method in optimization procedures included in transformer design systems.

scholarly journals Stochastic parcel tracking in an Euler-Lagrange compartment model for fast simulation of fermentation processes

2021 ◽  
Author(s):  
Cees Haringa ◽  
Wenjun Tang ◽  
Henk Noorman
Keyword(s):  
Cellular Response ◽  
Cfd Simulation ◽  
Mixing Time ◽  
Compartment Model ◽  
Reaction Model ◽  
Heterogeneous Environments ◽  
Cfd Simulations ◽  
Fermentation Processes ◽  
Spatially Resolved ◽  
Unit Operations

Compartment modeling (CM) is a well-known approach for computationally affordable, spatially-resolved hydrodynamic modeling of unit operations. Recent implementations use flow profiles based on CFD simulations, and several authors included microbial kinetics to simulate gradients in bioreactors. However, these studies relied on black-box kinetics, that do not account for intra-cellular changes and cell population dynamics in response to heterogeneous environments. In this paper, we report the implementation of a Lagrangian reaction model, where the microbial phase is tracked as a set of biomass-parcels, each linked with an intra-cellular composition vector and a structured reaction model describing their intra-cellular response to extracellular variations. A stochastic parcel tracking approach is adopted, in contrast to the resolved trajectories used in prior CFD implementations. A penicillin production process is used as a case-study. We show good performance of the model compared to full CFD simulations, both regarding the extra-cellular gradients and intra-cellular pool response, provided the mixing time in the CM matches the full CFD simulation; taking into account that the mixing time is sensitive to the number of compartments. The sensitivity of the model output towards some of the inputs is explored. The coarsest representative CM requires a few minutes to solve 80 hours of flow time, compared to approx. 2 weeks for a full Euler-Lagrange CFD simulation of the same case. This alleviates one of the major bottlenecks for the application of such CFD simulations towards analysis and optimization of industrial fermentation processes.

Prediction of Blood Flow Distribution in Liver Radioembolization Using Convolutional Neural Networks

2020 ◽  
Author(s):  
Amirtahà Taebi ◽  
Catherine T. Vu ◽  
Emilie Roncali
Keyword(s):  
Blood Flow ◽  
Cfd Simulation ◽  
Mean Squared Error ◽  
Flow Distribution ◽  
Homogeneous Distribution ◽  
Blood Flow Distribution ◽  
Cfd Simulations ◽  
Squared Error ◽  
Network Output ◽  
Yttrium 90

Abstract We have developed a new dosimetry approach, called CFDose, for liver cancer radioembolization based on computational fluid dynamics (CFD) simulation in the hepatic arterial tree. Although CFDose overcomes some of the limitations of the current dosimetry methods such as the unrealistic assumption of homogeneous distribution of yttrium-90 in the liver, it suffers from the expensive computational cost of CFD simulations. To accelerate CFDose, we introduce a deep learning model to predict the blood flow distribution between the liver segments in a patient with hepatocellular carcinoma. The model was trained with the results of CFD simulations under different outlet boundary conditions. The model consisted of convolutional, average pooling and transposed convolution layers. A regression layer with a mean-squared-error loss function was utilized at the network output to estimate the arterial outlet blood flow. The mean-squared error and prediction accuracy were calculated to measure model performance. Results showed that the average difference between the CFD results and predicted flow data was less than 2.45% for all the samples in the test dataset. The proposed model thus estimated the blood flow distribution with high accuracy significantly faster than a CFD simulation. The network output can be used to estimate the yttrium-90 dose distribution in the liver in future studies.

Probabilistic model predictive control for extended prediction horizons

2021 ◽  
Vol 69 (9) ◽  
pp. 759-770
Author(s):  
Tim Brüdigam ◽  
Johannes Teutsch ◽  
Dirk Wollherr ◽  
Marion Leibold ◽  
Martin Buss
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Prediction Models ◽  
Computational Effort ◽  
Probabilistic Constraints ◽  
Detailed Model ◽  
Control Approach ◽  
Proposed Model ◽  
Sampling Times

Abstract Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods are introduced before presenting the proposed Model Predictive Control approach. The advantages of the proposed method are shown in a mobile robot simulation example.

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