Non-zonal detached eddy simulation coupled with a steady RANS solver in the wall region

Xiao and Jenny (2012) proposed an interesting hybrid LES/RANS method in which they use two solvers and solve the RANS and LES equations in the entire computational domain. In the present work this method is simplified and used as a hybrid RANS-LES method, a wall-modeled LES. The two solvers are employed in the entire domain. Near the walls, the flow is governed by the steady RANS solver; drift terms are added to the DES equations to ensure that the time-averaged DES fields agree with the steady RANS field. Away from the walls, the flow is governed by the DES solver; in this region, the RANS field is set to the time-averaged LES field. The disadvantage of traditional DES models is that the RANS models in the near-wall region – which originally were developed and tuned for steady RANS – are used as URANS models where a large part of the turbulence is resolved. In the present method – where steady RANS is used in the near-wall region – the RANS turbulence models are used in a context for which they were developed. In standard DES methods, the near-wall accuracy can be degraded by the unsteady agitation coming from the LES region. It may in the present method be worth while to use an accurate, advanced RANS model. The EARSM model is used in the steady RANS solver. The new method is called NZ S-DES. It is found to substantially improve the predicting capability of the standard DES. A great advantage of the new model is that it is insensitive to the location of the RANS-LES interface

Using Machine Learning for formulating new wall functions for Large Eddy Simulation: A First Attempt

Machine learning is used for developing wall functions for Large Eddy\ua0Simulations (LES). I use Direct Numerical Simulation (DNS) of fully-developed channel flow at frictional Reynolds number of 800 to create a database. This\ua0database is using as a training set for the machine learning method (support vector regression). The input data (i.e. the influence parameters) are the\ua0local Reynolds number, the non-dimensional velocity gradient and the timeaveraged y\ua0+ value. The machine learning method is trained to predict the\ua0wall shear stress.\ua0The support vector regression methods in Python are used. The trained\ua0machine learning model is saved to disk and it is subsequently uploaded into the Python CFD code pyCALC-LES (Davidson, 2021). LES is carried out\ua0on coarse – and semi-course – near-wall meshes and the wall-shear stress is\ua0predicted using the developed machine learning models

Detached Eddy Simulation coupled with steady RANS in the wall region

Xiao & Jenny (2012) proposed an interesting hybrid LES/RANS method in which they use two solvers and solve the RANS and LES equations in the entire computational domain. In the present work this method is simplified and used as a hybrid RANS-LES method, a wall-modeled LES.The two solvers are employed in the entire domain. Near the walls, the flow is governed by the steady RANS solver; drift terms are added to the DESequations to ensure that the time-integrated DES fields agree with the steady RANS field. Away from the walls, the flow is governed by the DESsolver; in this region, the RANS field is set to the time-integrated LES field. The disadvantage of traditional DES models is that the RANS modelsin the near-wall region – which originally were developed and tuned for steady RANS – are used as URANS models where a large part of the turbulence is resolved. In the present method – where steady RANS is used in the near-wall region – the RANS turbulence models are used in a context for which they were developed. In this method, it may be worth while to use an accurate, advanced RANS model. The EARSM model is used in the steady RANS solver. The new method is called NZ S-DES . It is found to substantially improve the predicting capability of the standard DES. A great advantage of the new model is that it is insensitive to the location of the RANS-LES interface

Large eddy simulation: A study of clearings in forest and their effect on wind turbines

In this article, the Ryningsnas site in Sweden is investigated using large eddy simulation with three different clearing setups: a homogeneous forest, that is, no clearing, the current clearing, that is, the existing clearing at the location, and an extended clearing. Neutral stratification is simulated, and the wind turbines are modelled by a two-way-coupled actuator line model. From the simulations, the electrical generator power was found to be the highest for the current clearing. But the fatigue loads were both higher and lower than the homogeneous forest depending on which part of the wind turbine that was investigated. The extended clearing nearly always had the lowest fatigue loads but unfortunately also the lowest electrical generator power. Further optimization of the clearings and the wind turbine locations in relation to them is needed to find the sweet spot where the fatigue loads are lower and the electrical generator power is higher

2D CFD simulations of flow and reaction during carbon dioxide methanation: A spatially resolved channel plate reactor study

Carbon dioxide methanation is a way of storing excess electrical energy as grid compatible gas. Spatially resolved channel plate reactor experiments were used to validate competing reactor (1D, 2Dx-z) models. Parallel exothermic carbon dioxide methanation and endothermic reverse water gas shift reactions were considered. The kinetic model, where the rate determining step is between an oxygenated complex (HCOO*) and an active site (*), was used in 2Dx-z CFD simulations for six laminar inflow conditions and variations in pressure, temperature, H2/CO2 ratio, methane, and steam co-feeds. The performance is improved by decreasing flowrate, and increasing H2/CO2 ratio, pressure, and temperature. Co-feeding methane has a negligible effect on reactor performance. However, co-feeding steam significantly reduces performance. At relatively high conversions, differential rates are obtained. This is due to the negligible dependence of the rate of carbon dioxide conversion with the equilibrium term of the reverse water gas shift reaction. With these studies, a link between the reaction mechanism and reactor performance is established at conditions relevant to power-to-gas applications

Detached Eddy Simulations: Analysis of a limit on the dissipation term for reducing spectral energy transfer at cut-off

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