scholarly journals A study of computational methods for wake structure and base pressure prediction of a generic SUV model with fixed and rotating wheels

2017 ◽  
Vol 231 (9) ◽  
pp. 1222-1238 ◽  
Author(s):  
David Forbes ◽  
Gary Page ◽  
Martin Passmore ◽  
Adrian Gaylard
Keyword(s):  
Experimental Data ◽  
Computational Methods ◽  
Large Scale ◽  
Ground Plane ◽  
Computational Cost ◽  
Rear Wheel ◽  
Base Pressure ◽  
Detached Eddy Simulation ◽  
Wake Structure ◽  
Wake Interactions

This study is an evaluation of the computational methods in reproducing experimental data for a generic sports utility vehicle (SUV) geometry and an assessment on the influence of fixed and rotating wheels for this geometry. Initially, comparisons are made in the wake structure and base pressures between several CFD codes and experimental data. It was shown that steady-state RANS methods are unsuitable for this geometry due to a large scale unsteadiness in the wake caused by separation at the sharp trailing edge and rear wheel wake interactions. unsteady RANS (URANS) offered no improvements in wake prediction despite a significant increase in computational cost. The detached-eddy simulation (DES) and Lattice–Boltzmann methods showed the best agreement with the experimental results in both the wake structure and base pressure, with LBM running in approximately a fifth of the time for DES. The study then continues by analysing the influence of rotating wheels and a moving ground plane over a fixed wheel and ground plane arrangement. The introduction of wheel rotation and a moving ground was shown to increase the base pressure and reduce the drag acting on the vehicle when compared to the fixed case. However, when compared to the experimental standoff case, variations in drag and lift coefficients were minimal but misleading, as significant variations to the surface pressures were present.

scholarly journals The Role of Dimensionality in Understanding Granuloma Formation

Computation ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 58 ◽  
Author(s):  
Simeone Marino ◽  
Caitlin Hult ◽  
Paul Wolberg ◽  
Jennifer Linderman ◽  
Denise Kirschner
Keyword(s):  
Experimental Data ◽  
Large Scale ◽  
Bacterial Load ◽  
Computational Cost ◽  
Granuloma Formation ◽  
In Silico Studies ◽  
Cellular Recruitment ◽  
2D And 3D

Within the first 2–3 months of a Mycobacterium tuberculosis (Mtb) infection, 2–4 mm spherical structures called granulomas develop in the lungs of the infected hosts. These are the hallmark of tuberculosis (TB) infection in humans and non-human primates. A cascade of immunological events occurs in the first 3 months of granuloma formation that likely shapes the outcome of the infection. Understanding the main mechanisms driving granuloma development and function is key to generating treatments and vaccines. In vitro, in vivo, and in silico studies have been performed in the past decades to address the complexity of granuloma dynamics. This study builds on our previous 2D spatio-temporal hybrid computational model of granuloma formation in TB (GranSim) and presents for the first time a more realistic 3D implementation. We use uncertainty and sensitivity analysis techniques to calibrate the new 3D resolution to non-human primate (NHP) experimental data on bacterial levels per granuloma during the first 100 days post infection. Due to the large computational cost associated with running a 3D agent-based model, our major goal is to assess to what extent 2D and 3D simulations differ in predictions for TB granulomas and what can be learned in the context of 3D that is missed in 2D. Our findings suggest that in terms of major mechanisms driving bacterial burden, 2D and 3D models return very similar results. For example, Mtb growth rates and molecular regulation mechanisms are very important both in 2D and 3D, as are cellular movement and modulation of cell recruitment. The main difference we found was that the 3D model is less affected by crowding when cellular recruitment and movement of cells are increased. Overall, we conclude that the use of a 2D resolution in GranSim is warranted when large scale pilot runs are to be performed and if the goal is to determine major mechanisms driving infection outcome (e.g., bacterial load). To comprehensively compare the roles of model dimensionality, further tests and experimental data will be needed to expand our conclusions to molecular scale dynamics and multi-scale resolutions.

Coupled FSI Simulations of the Interaction of a Flexible Hydrofoil With Large Scale Unsteady Flows

2014 ◽  
Author(s):  
Abe H. Lee ◽  
Robert L. Campbell ◽  
Brent A. Craven ◽  
Stephen A. Hambric
Keyword(s):  
Large Scale ◽  
Flexible Structures ◽  
Computational Cost ◽  
Unsteady Flows ◽  
Turbulence Models ◽  
Vortical Flow ◽  
Detached Eddy Simulation ◽  
Fine Mesh ◽  
Hybrid Approaches ◽  
Tightly Coupled

Fluid-structure interaction (FSI) effects must be considered when flexible structures are subjected to unsteady flows. Large-scale unsteady flows can excite structural vibrations significantly and cause the fluid flow to be altered by the large amplitude vibrations. In this work, an in-house finite-element structural code FEANL is tightly coupled with the open-source computational-fluid dynamics (CFD) library package OpenFOAM to simulate the interaction of a backward-skewed, flexible hydrofoil with vortical flow structures shed from a large upstream rigid cylinder in the Penn State-ARL 12” water tunnel. To simulate the turbulent flow at a moderate computational cost, hybrid LES-RANS approaches, i.e. Delayed-Detached-Eddy-Simulation (DDES) and k–ω SST-SAS, are used. The hybrid approaches have been widely employed to simulate massively-separated flows at moderately high Reynolds numbers. Both of the turbulence models are used for a coarse mesh CFD-only case (no FSI effects by assuming a rigid structure) to test their capabilities, and the results of the two models are compared. DDES is chosen to simulate a fine mesh CFD-only case to conduct a mesh convergence study, and it is then used for final FSI simulations. The purpose of this work is focused on obtaining computational results; detailed comparisons against experimental data will be made in future work.

scholarly journals Reactor modelling assessment for urea-SNCR applications

2017 ◽  
Vol 27 (7) ◽  
pp. 1395-1411
Author(s):  
Oskar Finnerman ◽  
Narges Razmjoo ◽  
Ning Guo ◽  
Michael Strand ◽  
Henrik Ström
Keyword(s):  
Experimental Data ◽  
Catalytic Reduction ◽  
Computational Cost ◽  
Detached Eddy Simulation ◽  
Content Type ◽  
One Dimensional ◽  
Turbulent Fluctuations ◽  
Numerical Predictions ◽  
Dynamics Simulations ◽  
Reactor Models

Purpose This work aims to investigate the effects of neglecting, modelling or partly resolving turbulent fluctuations of velocity, temperature and concentrations on the predicted turbulence-chemistry interaction in urea-selective non-catalytic reduction (SNCR) systems. Design/methodology/approach Numerical predictions of the NO conversion efficiency in an industrial urea-SNCR system are compared to experimental data. Reactor models of varying complexity are assessed, ranging from one-dimensional ideal reactor models to state-of-the-art computational fluid dynamics simulations based on the detached-eddy simulation (DES) approach. The models use the same reaction mechanism but differ in the degree to which they resolve the turbulent fluctuations of the gas phase. A methodology for handling of unknown experimental data with regard to providing adequate boundary conditions is also proposed. Findings One-dimensional reactor models may be useful for a first quick assessment of urea-SNCR system performance. It is critical to account for heat losses, if present, due to the significant sensitivity of the overall process to temperature. The most comprehensive DES setup evaluated is associated with approximately two orders of magnitude higher computational cost than the conventional Reynolds-averaged Navier–Stokes-based simulations. For studies that require a large number of simulations (e.g. optimizations or handling of incomplete experimental data), the less costly approaches may be favored with a tolerable loss of accuracy. Originality/value Novel numerical and experimental results are presented to elucidate the role of turbulent fluctuations on the performance of a complex, turbulent, reacting multiphase flow.

Preliminary Validation of the Detached Eddy Simulation Model in CFD Code GASFLOW-MPI

2018 ◽  
Author(s):  
Zhang Han ◽  
Li Yabing ◽  
Xiao Jianjun ◽  
Thomas Jordan
Keyword(s):  
Experimental Data ◽  
Nuclear Power ◽  
Computational Cost ◽  
Thermal Hydraulics ◽  
Detached Eddy Simulation ◽  
Safety Issues ◽  
Eddy Simulation ◽  
First Case ◽  
Preliminary Validation ◽  
Des Model

With the development of the computational capability, Computational Fluid Dynamics (CFD) is used more and more for design and safety issues related to reactor thermal hydraulics. A series of CFD codes have been developed and utilized to analyze the reactor thermal hydraulics behavior. GASFLOW-MPI is a well-validated parallel scalable CFD software and has been widely utilized to predict the complex physical phenomena in nuclear power plant containment. Furthermore, the simulation results are well accepted by the nuclear authorities in several countries all over the world. Turbulent mixing is a basic but important issue for thermal hydraulics safety analysis. In order to capture more unsteady turbulent information under the acceptable computational cost, the LES and RANS hybrid turbulent model Detached Eddy Simulation (DES) model has been developed in the CFD code GASFLOW-MPI. In this paper, two typical turbulent cases are simulated and compared with the experimental data to validate the DES model. The first case is the turbulent backward-facing step flow which is a well-known turbulent benchmark. The second case is a hydrogen turbulent jet which is a typical turbulent flow for hydrogen combustion risk evaluation in nuclear containment. The numerical results show that the averaged flow profiles agree well with the experimental data.

scholarly journals Performance of k-ω and k-ε Model for Blood Flow Simulation in Stenosed Artery

2021 ◽  
Vol 40 (2) ◽  
pp. 111-125
Author(s):  
Md Alamgir Kabir ◽  
Kausari Sultana ◽  
Md Ashraf Uddin
Keyword(s):  
Experimental Data ◽  
Blood Flow ◽  
Large Scale ◽  
Flow Behavior ◽  
Computational Cost ◽  
Flow Simulation ◽  
Turbulence Models ◽  
Arterial Stenosis ◽  
Blood Flow Simulation ◽  
Stenosed Artery

Blood flow through arterial stenosis can play a crucial role at the post stenotic flow regions. This produces a disturbance in the normal flow path. The intensity of the flow disturbance (i.e. laminar, transitional and turbulent flow characteristics) depends not only on the severity of the stenosis but also on the pattern of the geometrical model. In that case, the turbulence model plays vital role to measure these flow disturbances. However, it is very important to choose a proper flow simulation model that can predict the flow behavior of fluid accurately and efficiently with less computational cost and time. Thus, this study aims to analyze the results of two turbulence models i.e. k-ω and k-ε for blood flow simulation to compare their performance for the prediction of the flow behavior. Simulations have been performed with 75% area reductions in the arteries. The results of simulation show that, the flow parameters obtained from the k-ε model exhibits lack of fits with the experimental data. On the other hand, k-ω model can capture large scale gradient in the different parameters of blood flow and has a good agreement with the experimental data. This study suggests that, k-ω model has the better performance comparing to k-ε model to predict the behavior of blood flow in stenosed artery. GANIT J. Bangladesh Math. Soc. 40.2 (2020) 111-125

scholarly journals Renormalization group theory of molecular dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daiji Ichishima ◽  
Yuya Matsumura
Keyword(s):  
Molecular Dynamics ◽  
Renormalization Group ◽  
Critical Exponent ◽  
Computational Efficiency ◽  
Large Scale ◽  
Dissipative Particle Dynamics ◽  
Computational Cost ◽  
Coarse Graining ◽  
Spatial Dimension ◽  
Deborah Number

AbstractLarge scale computation by molecular dynamics (MD) method is often challenging or even impractical due to its computational cost, in spite of its wide applications in a variety of fields. Although the recent advancement in parallel computing and introduction of coarse-graining methods have enabled large scale calculations, macroscopic analyses are still not realizable. Here, we present renormalized molecular dynamics (RMD), a renormalization group of MD in thermal equilibrium derived by using the Migdal–Kadanoff approximation. The RMD method improves the computational efficiency drastically while retaining the advantage of MD. The computational efficiency is improved by a factor of $$2^{n(D+1)}$$ 2 n ( D + 1 ) over conventional MD where D is the spatial dimension and n is the number of applied renormalization transforms. We verify RMD by conducting two simulations; melting of an aluminum slab and collision of aluminum spheres. Both problems show that the expectation values of physical quantities are in good agreement after the renormalization, whereas the consumption time is reduced as expected. To observe behavior of RMD near the critical point, the critical exponent of the Lennard-Jones potential is extracted by calculating specific heat on the mesoscale. The critical exponent is obtained as $$\nu =0.63\pm 0.01$$ ν = 0.63 ± 0.01 . In addition, the renormalization group of dissipative particle dynamics (DPD) is derived. Renormalized DPD is equivalent to RMD in isothermal systems under the condition such that Deborah number $$De\ll 1$$ D e ≪ 1 .

scholarly journals Three-Dimensional Structures of Carbohydrates and Where to Find Them

2020 ◽  
Vol 21 (20) ◽  
pp. 7702 ◽  
Author(s):  
Sofya I. Scherbinina ◽  
Philip V. Toukach
Keyword(s):  
Experimental Data ◽  
Molecular Modeling ◽  
Computational Methods ◽  
Three Dimensional ◽  
Structural Diversity ◽  
Structural Data ◽  
Structural Features ◽  
Data Validation ◽  
Data Generation ◽  
Efficient Treatment

Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.

An Efficient Preconditioner for Linear System Solution in Multi-Domain Modeling of the Circulatory System

2013 ◽  
Author(s):  
Mahdi Esmaily Moghadam ◽  
Yuri Bazilevs ◽  
Tain-Yen Hsia ◽  
Alison Marsden
Keyword(s):  
Strong Coupling ◽  
Large Scale ◽  
Computational Cost ◽  
Circulatory System ◽  
Flow Simulation ◽  
Global Dynamics ◽  
Domain Modeling ◽  
Computationally Efficient ◽  
Lumped Parameter ◽  
System Solution

A closed-loop lumped parameter network (LPN) coupled to a 3D domain is a powerful tool that can be used to model the global dynamics of the circulatory system. Coupling a 0D LPN to a 3D CFD domain is a numerically challenging problem, often associated with instabilities, extra computational cost, and loss of modularity. A computationally efficient finite element framework has been recently proposed that achieves numerical stability without sacrificing modularity [1]. This type of coupling introduces new challenges in the linear algebraic equation solver (LS), producing an strong coupling between flow and pressure that leads to an ill-conditioned tangent matrix. In this paper we exploit this strong coupling to obtain a novel and efficient algorithm for the linear solver (LS). We illustrate the efficiency of this method on several large-scale cardiovascular blood flow simulation problems.

Numerical Investigation of Intermittent Corner Separation in a Linear Compressor Cascade

2016 ◽  
Author(s):  
Wei Ma ◽  
Feng Gao ◽  
Xavier Ottavy ◽  
Lipeng Lu ◽  
A. J. Wang
Keyword(s):  
Flow Field ◽  
Large Scale ◽  
Leading Edge ◽  
Suction Side ◽  
Detached Eddy Simulation ◽  
Compressor Cascade ◽  
Linear Compressor ◽  
Corner Separation ◽  
Eddy Simulation ◽  
Linear Compressor Cascade

Recently bimodal phenomenon in corner separation has been found by Ma et al. (Experiments in Fluids, 2013, doi:10.1007/s00348-013-1546-y). Through detailed and accurate experimental results of the velocity flow field in a linear compressor cascade, they discovered two aperiodic modes exist in the corner separation of the compressor cascade. This phenomenon reflects the flow in corner separation is high intermittent, and large-scale coherent structures corresponding to two modes exist in the flow field of corner separation. However the generation mechanism of the bimodal phenomenon in corner separation is still unclear and thus needs to be studied further. In order to obtain instantaneous flow field with different unsteadiness and thus to analyse the mechanisms of bimodal phenomenon in corner separation, in this paper detached-eddy simulation (DES) is used to simulate the flow field in the linear compressor cascade where bimodal phenomenon has been found in previous experiment. DES in this paper successfully captures the bimodal phenomenon in the linear compressor cascade found in experiment, including the locations of bimodal points and the development of bimodal points along a line that normal to the blade suction side. We infer that the bimodal phenomenon in the corner separation is induced by the strong interaction between the following two facts. The first is the unsteady upstream flow nearby the leading edge whose angle and magnitude fluctuate simultaneously and significantly. The second is the high unsteady separation in the corner region.

scholarly journals Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics

2006 ◽  
Vol 18 (12) ◽  
pp. 2959-2993 ◽  
Author(s):  
Eduardo Ros ◽  
Richard Carrillo ◽  
Eva M. Ortigosa ◽  
Boris Barbour ◽  
Rodrigo Agís
Keyword(s):  
Synaptic Plasticity ◽  
High Speed ◽  
Large Scale ◽  
Computational Cost ◽  
Brain Structures ◽  
Spike Timing ◽  
Neuronal Dynamics ◽  
Event Driven ◽  
Synaptic Dynamics

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.

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