On the Riemann Problem Simulation for the Drift-Flux Equations of Two-Phase Flows

2016 ◽  
Vol 13 (01) ◽  
pp. 1650009 ◽  
Author(s):  
S. Kuila ◽  
T. Raja Sekhar ◽  
D. Zeidan
Keyword(s):  
Riemann Problem ◽  
Analytical Techniques ◽  
Momentum Equation ◽  
Two Phase ◽  
Computational Framework ◽  
Drift Flux ◽  
Numerical Predictions ◽  
Godunov Methods ◽  
Model Equations ◽  
Two Phases

This work presents computational simulations and analytical techniques for solving the drift-flux two-phase flow model. The model equations are formulated to describe the exact solution of the Riemann problem. The solution is constructed by solving the conservation of mass for each phase and the mixture conservation momentum equation of the two phases under isothermal conditions. Particular attention is given to address the expressions for jump relationships and the Riemann invariants. The performance of the developed Riemann solver is assessed with respect to different test cases selected from the literature. Comparisons with Godunov methods of centred-type are provided to demonstrate the use of the proposed exact and computational framework. Excellent agreement is observed between analytical results and numerical predictions.

NUMERICAL SOLUTION FOR HYPERBOLIC CONSERVATIVE TWO-PHASE FLOW EQUATIONS

2007 ◽  
Vol 04 (02) ◽  
pp. 299-333 ◽  
Author(s):  
D. ZEIDAN ◽  
A. SLAOUTI ◽  
E. ROMENSKI ◽  
E. F. TORO
Keyword(s):  
Numerical Solution ◽  
Riemann Problem ◽  
Two Phase Flow ◽  
Numerical Dissipation ◽  
Phase Flow ◽  
Two Phase ◽  
Flow Equations ◽  
Flow Problems ◽  
Slope Limiter ◽  
Two Phases

We outline an approximate solution for the numerical simulation of two-phase fluid flows with a relative velocity between the two phases. A unified two-phase flow model is proposed for the description of the gas–liquid processes which leads to a system of hyperbolic differential equations in a conservative form. A numerical algorithm based on a splitting approach for the numerical solution of the model is proposed. The associated Riemann problem is solved numerically using Godunov methods of centered-type. Results show the importance of the Riemann problem and of centered schemes in the solution of the two-phase flow problems. In particular, it is demonstrated that the Slope Limiter Centered (SLIC) scheme gives a low numerical dissipation at the contact discontinuities, which makes it suitable for simulations of practical two-phase flow processes.

Computation of Turbulent Gas-Particle Flows Behind a Bluffbody

2001 ◽  
Author(s):  
X.-Q. Chen ◽  
M. Renksizbulut ◽  
X. Li
Keyword(s):  
Particle System ◽  
Interaction Model ◽  
Reynolds Stress Model ◽  
Particle Dispersion ◽  
Particulate Flows ◽  
Two Phase ◽  
Gaseous Flow ◽  
Numerical Predictions ◽  
Particle Flows ◽  
Two Phases

Abstract A numerical analysis of multisized glass particles interacting with a confined bluffbody flow was performed by combining the finite-volume method for the gaseous flow with the Lagrangian approach for the particulate flow. The second-moment Reynolds-stress model was used to predict the turbulent gaseous flow in a gas-particle system, where an improved eddy-interaction model was used to predict turbulence-induced particle dispersion. The interaction between the two phases was accounted for in terms of coupling sources. Numerical predictions of two-phase mean and fluctuating velocities for different particle sizes were compared with corresponding experimental data. Reasonably good agreement was achieved for the mean properties of both the gaseous and particulate flows.

Assessment of Cavitation Models for Flows in Diesel Injectors With Single- and Two-Fluid Approaches

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Kaushik Saha ◽  
Xianguo Li
Keyword(s):  
Fluid Model ◽  
Injection Pressure ◽  
Experimental Results ◽  
The Other ◽  
Computational Time ◽  
Two Phase ◽  
Numerical Predictions ◽  
Mass Flow Rates ◽  
Two Phases ◽  
Two Fluid

Several recent cavitation models for the analysis of two-phase flows in diesel injectors with single- and two-fluid modeling approaches have been evaluated, including the Saha–Abu-Ramandan–Li (SAL), Schnerr–Sauer (SS), and Zwart–Gerber–Belamri (ZGB) models. The SAL model is a single-fluid model, while the other two models have been implemented with both single- and two-fluid approaches. Numerical predictions are compared with experimental results available in literature, qualitatively with experimental images of two-phase flow in an optically accessible nozzle, and quantitatively with measured mass flow rates and velocity profiles. It is found that at low injection pressure differentials there can be considerable discrepancy in the predictions of the vapor distribution from the three models considered. This discrepancy is reduced as the injection pressure differential is increased. Implementation of the SS and ZGB models with single- and two- fluid approaches yields noticeable differences in the results because of the relative velocity between the two phases, with two-fluid approach providing better agreement with experimental results. The performance of the SS and ZGB models implemented with the two-fluid approach is comparable with the SAL single-fluid model, but with significantly more computational time. Overall, the SAL single-fluid model performs comparatively better with respect to the other two models.

Assessment of Different Cavitation Models in Mixture and Eulerian Framework for Two-Phase Flow in Diesel Injectors

2013 ◽  
Author(s):  
Kaushik Saha ◽  
Xianguo Li
Keyword(s):  
Two Phase Flow ◽  
Operating Conditions ◽  
Phase Flow ◽  
Axisymmetric Nozzle ◽  
Two Phase ◽  
Numerical Predictions ◽  
Gradient Calculation ◽  
Two Phases ◽  
Coupling Algorithms ◽  
Relaxation Factors

A comparative study using different cavitation models in mixture and Eulerian framework is carried out for the analysis of two-phase flows in diesel injectors. Three different cavitation models are investigated here: recently developed modified single-fluid, Schnerr-Sauer and Zwart-Gerber-Belamri models. The last two models have been implemented in both mixture and Eulerian framework. The numerical predictions are compared both qualitatively and quantitatively with experimental results available in the literature. Qualitative assessments have been carried out with experimental images of two-phase flow in an optically accessible nozzle. Quantitative comparisons have been done with measured mass flow rates and velocity profiles. It appears that at low pressure differentials there can be considerable discrepancy in the predictions of the vapour distribution from the three models considered. The modified single-fluid approach turns out to be comparatively better with respect to the other two models. Implementation in mixture and Eulerian framework yields noticeable differences in the results because of the relative velocity of the two phases. Numerical experiments have been carried out with different two-phase turbulence modelling approaches, pressure-velocity coupling algorithms, gradient calculation methods and under-relaxation factors to assess the robustness of the models. Additionally comparisons have been carried out for conditions under high inlet pressure in an axisymmetric nozzle to understand the performance of the models under realistic operating conditions.

scholarly journals A fifth order WENO scheme for numerical simulation of shallow granular two-phase flow model

2021 ◽  
Vol 13 (9) ◽  
pp. 168781402110452
Author(s):  
Omar Rabbani ◽  
Saqib Zia ◽  
Asad Rehman
Keyword(s):  
Numerical Technique ◽  
Momentum Equation ◽  
Weno Scheme ◽  
Two Phase ◽  
Contact Discontinuities ◽  
Two Phases ◽  
Solid Liquid ◽  
Fifth Order ◽  
The Mathematical Model ◽  
Derivatives Of

In this article, a weighted essentially non-oscillatory (WENO) scheme is implemented to simulate two-phase shallow granular flow (TPSF) model. The flow is assumed to be incompressible and it is regarded as shallow layer of granular and liquid material. The mathematical model consists of two phases, that is, solid and liquid. Each phase has its continuity and momentum equation. The presence of the equations are coupled together involving the derivatives of unknowns which make it more challenging to solve. An efficient numerical technique is needed to tackle the numerical complexities. Our main intrigue is the numerical approximation of the above-mentioned solid-liquid model. The weighted essentially non-oscillatory (WENO) scheme of order 5 is utilized to handle the shock waves and contact discontinuities appear in the solution. The results are compared with the results already available in the literature by conservation element and solution element (CESE) scheme. It is observed the WENO scheme produces less errors as compared to CESE scheme and also effectively handle the shocks.

A Lightweight Formalism for Reference Lifetimes and Borrowing in Rust

2021 ◽  
Vol 43 (1) ◽  
pp. 1-73
Author(s):  
David J. Pearce
Keyword(s):  
Memory Management ◽  
Programming Model ◽  
Type System ◽  
Control Flow ◽  
Two Phase ◽  
Type Checking ◽  
Strong Focus ◽  
Reference Implementation ◽  
Two Phases ◽  
Complex Features

Rust is a relatively new programming language that has gained significant traction since its v1.0 release in 2015. Rust aims to be a systems language that competes with C/C++. A claimed advantage of Rust is a strong focus on memory safety without garbage collection. This is primarily achieved through two concepts, namely, reference lifetimes and borrowing . Both of these are well-known ideas stemming from the literature on region-based memory management and linearity / uniqueness . Rust brings both of these ideas together to form a coherent programming model. Furthermore, Rust has a strong focus on stack-allocated data and, like C/C++ but unlike Java, permits references to local variables. Type checking in Rust can be viewed as a two-phase process: First, a traditional type checker operates in a flow-insensitive fashion; second, a borrow checker enforces an ownership invariant using a flow-sensitive analysis. In this article, we present a lightweight formalism that captures these two phases using a flow-sensitive type system that enforces “ type and borrow safety .” In particular, programs that are type and borrow safe will not attempt to dereference dangling pointers. Our calculus core captures many aspects of Rust, including copy- and move-semantics, mutable borrowing, reborrowing, partial moves, and lifetimes. In particular, it remains sufficiently lightweight to be easily digested and understood and, we argue, still captures the salient aspects of reference lifetimes and borrowing. Furthermore, extensions to the core can easily add more complex features (e.g., control-flow, tuples, method invocation). We provide a soundness proof to verify our key claims of the calculus. We also provide a reference implementation in Java with which we have model checked our calculus using over 500B input programs. We have also fuzz tested the Rust compiler using our calculus against 2B programs and, to date, found one confirmed compiler bug and several other possible issues.

scholarly journals XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Vishu Madaan ◽  
Aditya Roy ◽  
Charu Gupta ◽  
Prateek Agrawal ◽  
Anand Sharma ◽  
...  
Keyword(s):  
Image Classification ◽  
Second Phase ◽  
Two Phase ◽  
X Ray ◽  
The Neural Network ◽  
Rapid Spread ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Two Phases ◽  
Active Patients

AbstractCOVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

Sign in / Sign up

Export Citation Format

Share Document