scholarly journals A simple method for high-precision evaluation of valve flow coefficient by computational fluid dynamics simulation

2017 ◽  
Vol 9 (7) ◽  
pp. 168781401771370 ◽  
Author(s):  
Xiao-Ming Zhou ◽  
Zhi-Kun Wang ◽  
Yi-Fang Zhang
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Pressure Drop ◽  
Adaptation Strategy ◽  
Flow Coefficient ◽  
Dynamics Simulation ◽  
Accurate Estimation ◽  
Simple Method ◽  
Pressure Losses ◽  
The Difference

Flow coefficient is an important performance index associated with the energy efficiency of a valve, and an effective method to evaluate valve flow coefficient is necessary for valve industry. However, theoretical estimation often results in poor accuracy, while experimental measurements involve significant costs in time and equipment. In this article, a computational fluid dynamics method is proposed to achieve simple and accurate evaluation of valve flow coefficient. For each valve, a computational fluid dynamics model is established containing a valve section, an upstream section, and a downstream section. A grid-adaptation strategy is then applied to improve the accuracy of simulation. To calculate flow coefficient, the most important issue is to determine the net pressure loss induced by valve (Δ Pv). Herein, the overall pressure drop (Δ Po) is obtained first, and the pipe-induced pressure drop (Δ Pp) is estimated by linear fitting. Then, Δ Pv is calculated as the difference between Δ Po and Δ Pp. To ensure accurate estimation of the pressure losses, a length of 26 times of pipe diameter is preferred for the upstream section. The experiments demonstrated that the presented method can accurately predict flow coefficient for various types of valves and thus has great potential to be widely used in the valve industry.

scholarly journals Computational Fluid Dynamics Simulation of Gas–Solid Hydrodynamics in a Bubbling Fluidized-Bed Reactor: Effects of Air Distributor, Viscous and Drag Models

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 524 ◽  
Author(s):  
Khezri ◽  
Ghani ◽  
Masoudi Soltani ◽  
Biak ◽  
RobiahYunus ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Pressure Drop ◽  
Turbulence Models ◽  
Solid Particles ◽  
Dynamics Simulation ◽  
Actual Process ◽  
Momentum Exchange ◽  
Pressure Drops ◽  
Drag Models

In this work, we employed a computational fluid dynamics (CFD)-based model with a Eulerian multiphase approach to simulate the fluidization hydrodynamics in biomass gasification processes. Air was used as the gasifying/fluidizing agent and entered the gasifier at the bottom which subsequently fluidized the solid particles inside the reactor column. The momentum exchange related to the gas-phase was simulated by considering various viscous models (i.e., laminar and turbulence models of the re-normalisation group (RNG), k-ε and k-ω). The pressure drop gradient obtained by employing each viscous model was plotted for different superficial velocities and compared with the experimental data for validation. The turbulent model of RNG k-Ɛ was found to best represent the actual process. We also studied the effect of air distributor plates with different pore diameters (2, 3 and 5 mm) on the momentum of the fluidizing fluid. The plate with 3-mm pores showed larger turbulent viscosities above the surface. The effects of drag models (Syamlal–O’Brien, Gidaspow and energy minimum multi-scale method (EMMS) on the bed’s pressure drop as well as on the volume fractions of the solid particles were investigated. The Syamlal–O’Brien model was found to forecast bed pressure drops most consistently, with the pressure drops recorded throughout the experimental process. The formation of bubbles and their motion along the gasifier height in the presence of the turbulent flow was seen to follow a different pattern from with the laminar flow.

A Parametric Computational Fluid Dynamics Analysis of the Valve Pocket Losses in Reciprocating Compressors

2014 ◽  
Vol 137 (1) ◽  
Author(s):  
Francesco Balduzzi ◽  
Giovanni Ferrara ◽  
Riccardo Maleci ◽  
Alberto Babbini ◽  
Guido Pratelli
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Response Surface ◽  
Cfd Simulation ◽  
Flow Coefficient ◽  
Reciprocating Compressor ◽  
Pressure Losses ◽  
Computational Fluid Dynamics Analysis ◽  
Efficiency Increase ◽  
Analytical Response

The reduction of pressure losses is one of the most important challenges for the efficiency increase of a reciprocating compressor. Since the absorbed power is strongly affected by the losses through pocket valves and cylinder ducts, an accurate prediction of these losses is essential. The use of computational fluid dynamics (CFD) simulation has shown great potential for the study of the entire reciprocating compressor, but is still limited by high computational costs. Recently, the authors have presented a simplified CFD approach: the actual valve geometry is replaced with an equivalent porous region, which has significantly increased the speed of calculation while ensuring accuracy as well. Based on this approach, a new methodology for the evaluation of pocket valve losses is presented. A set of CFD simulations using a parameterized geometry of the pocket valve was performed to evaluate the relationship between the losses of the pocket and its geometrical features. An analytical response surface (RS) was defined using the values of the geometrical dimensions as inputs and the pocket flow coefficient as output. Finally, the response surface was validated through a set of test cases performed on different geometries with the actual valve and the results have shown good predictability of the tool.

scholarly journals Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David R. Rutkowski ◽  
Alejandro Roldán-Alzate ◽  
Kevin M. Johnson
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Velocity Fields ◽  
Dynamics Simulation ◽  
Patient Specific ◽  
4D Flow Mri ◽  
4D Flow ◽  
Flow Phase ◽  
Flow Mri ◽  
Trained Network

AbstractBlood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.

scholarly journals Evaluation of Active Heat Sinks Design under Forced Convection—Effect of Geometric and Boundary Parameters

Materials ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2041
Author(s):  
Eva C. Silva ◽  
Álvaro M. Sampaio ◽  
António J. Pontes
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Additive Manufacturing ◽  
Pressure Drop ◽  
Forced Convection ◽  
Thermal Performance ◽  
Heat Sinks ◽  
Trapezoidal Shape ◽  
Computational Fluid Dynamics Simulations ◽  
Dynamics Simulations

This study shows the performance of heat sinks (HS) with different designs under forced convection, varying geometric and boundary parameters, via computational fluid dynamics simulations. Initially, a complete and detailed analysis of the thermal performance of various conventional HS designs was taken. Afterwards, HS designs were modified following some additive manufacturing approaches. The HS performance was compared by measuring their temperatures and pressure drop after 15 s. Smaller diameters/thicknesses and larger fins/pins spacing provided better results. For fins HS, the use of radial fins, with an inverted trapezoidal shape and with larger holes was advantageous. Regarding pins HS, the best option contemplated circular pins in combination with frontal holes in their structure. Additionally, lattice HS, only possible to be produced by additive manufacturing, was also studied. Lower temperatures were obtained with a hexagon unit cell. Lastly, a comparison between the best HS in each category showed a lower thermal resistance for lattice HS. Despite the increase of at least 38% in pressure drop, a consequence of its frontal area, the temperature was 26% and 56% lower when compared to conventional pins and fins HS, respectively, and 9% and 28% lower when compared to the best pins and best fins of this study.

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