scholarly journals MicroHH 1.0: a computational fluid dynamics code for direct numerical simulation and large-eddy simulation of atmospheric boundary layer flows

2017 ◽  
Vol 10 (8) ◽  
pp. 3145-3165 ◽  
Author(s):  
Chiel C. van Heerwaarden ◽  
Bart J. H. van Stratum ◽  
Thijs Heus ◽  
Jeremy A. Gibbs ◽  
Evgeni Fedorovich ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Numerical Simulation ◽  
Computational Fluid Dynamics ◽  
Large Eddy Simulation ◽  
Direct Numerical Simulation ◽  
Turbulent Flows ◽  
Processing Unit ◽  
Moist Convection ◽  
Eddy Simulation ◽  
Large Eddy

Abstract. This paper describes MicroHH 1.0, a new and open-source (www.microhh.org) computational fluid dynamics code for the simulation of turbulent flows in the atmosphere. It is primarily made for direct numerical simulation but also supports large-eddy simulation (LES). The paper covers the description of the governing equations, their numerical implementation, and the parameterizations included in the code. Furthermore, the paper presents the validation of the dynamical core in the form of convergence and conservation tests, and comparison of simulations of channel flows and slope flows against well-established test cases. The full numerical model, including the associated parameterizations for LES, has been tested for a set of cases under stable and unstable conditions, under the Boussinesq and anelastic approximations, and with dry and moist convection under stationary and time-varying boundary conditions. The paper presents performance tests showing good scaling from 256 to 32 768 processes. The graphical processing unit (GPU)-enabled version of the code can reach a speedup of more than an order of magnitude for simulations that fit in the memory of a single GPU.

scholarly journals MicroHH 1.0: a computational fluid dynamics code for direct numerical simulation and large-eddy simulation of atmospheric boundary layer flows

2017 ◽  
Author(s):  
Chiel C. van Heerwaarden ◽  
Bart J. H. van Stratum ◽  
Thijs Heus ◽  
Jeremy A. Gibbs ◽  
Evgeni Fedorovich ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Numerical Simulation ◽  
Computational Fluid Dynamics ◽  
Large Eddy Simulation ◽  
Direct Numerical Simulation ◽  
Turbulent Flows ◽  
Processing Unit ◽  
Moist Convection ◽  
Eddy Simulation ◽  
Large Eddy

Abstract. This paper describes MicroHH 1.0, a new and open source (www.microhh.org) computational fluid dynamics code for the simulation of turbulent flows in the atmosphere. It is primarily made for direct numerical simulation, but also supports large-eddy simulation (LES). The paper covers the description of the governing equations, their numerical implementation, and the parametrizations included in the code. Furthermore, the paper presents the validation of the dynamical core in the form of convergence and conservation tests, and comparison of simulations of channel flows and slope flows against well-established test cases. The full numerical model, including the associated parametrizations for LES, has been tested for a set of cases under stable and unstable conditions, under the Boussinesq and anelastic approximation, and with dry and moist convection under stationary and time-varying boundary conditions. The paper presents performance tests showing good scaling from 256 to 32,768 processes. The Graphical Processing Unit-enabled version of the code reaches speedups of more than an order of magnitude with respect to the conventional code for a variety of cases.

Large Eddy Simulation

1996 ◽  
Author(s):  
Joel H. Ferziger
Keyword(s):  
Numerical Simulation ◽  
Large Eddy Simulation ◽  
Direct Numerical Simulation ◽  
Turbulent Flow ◽  
Turbulent Flows ◽  
Flow Simulation ◽  
Simulation Methods ◽  
Eddy Simulation ◽  
Simulation Techniques ◽  
Large Eddy

Over a decade ago, the author (Ferziger, 1983) wrote a review of the then state-of-the-art in direct numerical simulation (DNS) and large eddy simulation (LES). Shortly thereafter, a second review was written by Rogallo and Moin (1984). In those relatively early days of turbulent flow simulation, it was possible to write comprehensive reviews of what had been accomplished. Since then, the widespread availability of supercomputers has led to an explosion in this field so, although the subject is undoubtedly overdue for another review, it is not clear that the task can be accomplished in anything less than a monograph. The author therefore apologizes in advance for omissions (there must be many) and for any bias toward the accomplishments of people on the west coast of North America. In the earlier review, the author listed six approaches to the prediction of turbulent flow behavior. The list included: correlations, integral methods, single-point Reynolds-averaged closures, two-point closures, large eddy simulation and direct numerical simulation. Even then the distinction between these methods was not always clear; if anything, it is less clear today. It was possible in the earlier review to give a relatively complete overview of what had been accomplished with simulation methods. Since then, simulation techniques have been applied to an ever expanding range of flows so a thorough review of simulation results is no longer possible in the space available here. Simulation techniques have become well established as a means of studying turbulent flows and the results of simulations are best presented in combination with experimental data for the same flow. There is also a danger that the success of simulation methods will lead to attempts to apply them too soon to flows which the models and techniques are not ready to handle. To some extent, this is already happening. Direct numerical simulation (DNS) is a method in which all of the scales of motion of a turbulent flow are computed. A DNS must include everything from the large energy-containing or integral scales to the dissipative scales; the latter is usually taken to be the viscous or Kolmogoroff scales.

scholarly journals Investigation of the velocity field downstream of a benchmark vent using numerical simulation and hot-wire anemometry

2019 ◽  
Vol 213 ◽  
pp. 02076
Author(s):  
Jan Sip ◽  
Frantisek Lizal ◽  
Jakub Elcner ◽  
Jan Pokorny ◽  
Miroslav Jicha
Keyword(s):  
Fluid Dynamics ◽  
Numerical Simulation ◽  
Velocity Field ◽  
Flow Field ◽  
Large Eddy Simulation ◽  
Turbulence Models ◽  
Hot Wire ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Precise Prediction

The velocity field in the area behind the automotive vent was measured by hot-wire anenemometry in detail and intensity of turbulence was calculated. Numerical simulation of the same flow field was performed using Computational fluid dynamics in commecial software STAR-CCM+. Several turbulence models were tested and compared with Large Eddy Simulation. The influence of turbulence model on the results of air flow from the vent was investigated. The comparison of simulations and experimental results showed that most precise prediction of flow field was provided by Spalart-Allmaras model. Large eddy simulation did not provide results in quality that would compensate for the increased computing cost.

Reactive computational fluid dynamics modelling methane–hydrogen admixtures in internal combustion engines part II: Large eddy simulation

2020 ◽  
pp. 146808742091034
Author(s):  
Jann Koch ◽  
Christian Schürch ◽  
Yuri M Wright ◽  
Konstantinos Boulouchos
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Large Eddy Simulation ◽  
Combustion Process ◽  
Flame Speed ◽  
Hydrogen Addition ◽  
Eddy Simulation ◽  
Modelling Framework ◽  
Large Eddy ◽  
Dynamics Modelling

Fuels based on admixtures of methane/natural gas and hydrogen are a promising way to reduce CO2 emissions of spark ignition engines and increase their efficiency. A lot of work was conducted experimentally, whereas only limited numerical work is available in the context of three-dimensional modelling of the full engine cycle. This work addresses this fact by proposing a reactive computational fluid dynamics modelling framework to consider the effects of hydrogen addition on the combustion process. Part I of this two-part study focuses on the modelling and crucial considerations in order to predict the mean cycle based on the G-equation combustion model using the Reynolds-averaged Navier–Stokes equations. There, the effect of increased burning speed was globally captured by increasing the flame speed coefficient A, appearing in the considered flame speed closure. The proposed simplified modelling of the early flame stage proved to be robust for the conducted hydrogen variation from 0 to 50 vol% H2 for stoichiometric and lean operation. Scope of this work, Part II, are cyclic fluctuations and the hydrogen influence thereon using large eddy simulation and the proposed modelling framework. The model is probed towards its capabilities to predict the fluctuation of the combustion process for 0 and 50 vol% H2 and correlations influencing the observed peak pressure of the individual cycle are presented. It is shown that the considered approach is capable to reproduce the cyclic fluctuations of the combustion process under the influence of hydrogen addition as well as lean operation. The importance of the early flame phase with respect to arising fluctuations is highlighted as well as the contribution of the resolved scales in terms of the flame front wrinkling.

scholarly journals Stably Stratified Flows: A Model with No Ri(cr)*

2008 ◽  
Vol 65 (7) ◽  
pp. 2437-2447 ◽  
Author(s):  
V. M. Canuto ◽  
Y. Cheng ◽  
A. M. Howard ◽  
I. N. Esau
Keyword(s):  
Numerical Simulation ◽  
Large Eddy Simulation ◽  
Direct Numerical Simulation ◽  
Turbulent Mixing ◽  
Stratified Flows ◽  
The Other ◽  
Large Set ◽  
Eddy Simulation ◽  
Stably Stratified Flows ◽  
Large Eddy

Abstract A large set of laboratory, direct numerical simulation (DNS), and large eddy simulation (LES) data indicates that in stably stratified flows turbulent mixing exists up to Ri ∼ O(100), meaning that there is practically no Ri(cr). On the other hand, traditional local second-order closure (SOC) models entail a critical Ri(cr) ∼ O(1) above which turbulence ceases to exist and are therefore unable to explain the above data. The authors suggest how to modify the recent SOC model of Cheng et al. to reproduce the above data for arbitrary Ri.

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