Large Eddy Simulation Model Research Articles

Large-eddy-model closure and simulation of turbulent flux patterns over oasis surface

Abstract. Large-eddy simulations (LESs) have been increasingly used for studying atmosphere and land surface interactions over heterogeneous areas. However, parameterizations based on Monin–Obukhov Similarity Theory (MOST) often violate the basic assumptions of the very theory, generate inconsistencies with the LES turbulence closures, and produce surface flux estimates depending on LES model resolutions. Here, we propose a novel scheme for turbulent flux estimates in LES models. It computes the fluxes locally using the LES subgrid closure, which is then constrained on the macroscopic scale using MOST. Compared with several other schemes, the new scheme performs better for the various types of land surfaces tested. We validate our scheme by comparing surface flux estimates with field measurements obtained over an oasis surface at various height levels. Additionally, we scrutinize other quantities related to the surface energy balance, including net radiation, ground heat flux, and surface skin temperature, all of which align well with observational data. Our sensitivity experiments, focusing on model horizontal resolution, underscore the robustness of our scheme, as it maintains its corrective efficacy despite changes in horizontal grid spacing. We find that the macroscopic constraint imposed by MOST on LES-estimated fluxes strengthens as the horizontal grid spacing decreases, with a more pronounced influence on sensible than latent heat fluxes. These findings collectively highlight the promise and adaptability of our scheme for improved surface flux estimates in LES models.

Application of deep learning in estimating the convective mixing induced by brine rejection

The open-water deep convection induced by brine rejection constitutes a primary process for deep water formation in high latitudes, playing a critical role in the global thermohaline circulation. Proper parameterization of subgrid-scale convective salt plumes arising from brine rejection is crucial for improving climate model simulations of ocean convection. Traditional physically driven parameterization schemes require numerous physical parameters. In this study, we aim to explore an objective, few-data-driven approach by using deep learning for subgrid scale salt plume parameterization. The Backpropagation Neural Network (BPNN) is trained using the output results of the Large Eddy Simulation (LES) model, which is well-suited for simulating the highly turbulent salt plumes. Results show that using the BPNN driven solely by salinity and latitude effectively captures essential information about the mixing coefficient induced by salt plumes. The application of deep learning provides a new perspective for proposing the subgrid scale parameterization of salt plumes.

Large eddy simulations fire modeling of JIS A 1310 façade calibration test with respect to sidewall

AbstractThe full‐scale façade standard test is widely employed as a comprehensive method to assess the façade fire spread. Within this approach, the calibration test without combustible façade decouples the intricate interaction between gas‐phase combustion and material pyrolysis, which simplifies diagnostics and provides an ideal scenario for model validation. This paper presents large eddy simulations (LES) accompanied by comparisons of calibration tests in accordance with JIS A 1310. The calibration tests were conducted to obtain the flame morphologies, gas‐phase temperature, and heat flux of over‐ventilated façade fires, and the LES modeling is complemented by the modified eddy dissipation model for combustion, the one‐equation model for the sub‐grid scale turbulence, and the discrete ordinate method with the gray mean absorption‐emission approach for thermal radiation. The accuracy of LES data is discussed by comparing with measurements, and the mesh resolution is optimized as 2.5 cm for achieving mesh independency with good qualitative agreement. Furthermore, simulations are conducted to investigate the impact of sidewall distances on façade flame spread. The results highlight that the enhancement of sidewall in façade flame spread occurs under external heat release rate, and the 0.2 m sidewall distance for the designated JIS test is identified as a critical threshold increasing façade thermal load.

Effect of porous layer and initial salt gradient on improving SGSP stability: Transient numerical simulation and theoretical analysis

The salt gradient solar pond (SGSP) is a low-cost and large-scale solar energy capture and storage device. The stability of the non-convective zone (NCZ) is critical for the well-functioning and efficiency of SGSP. To understand when and how SGSP lost its effectiveness, the turbulent double-diffusive convection in solar ponds was first solved numerically with a Large Eddy Simulations (LES) model. The simulations show that attaching a porous layer or increasing the initial salt concentration effectively delays the destruction of NCZ. By increasing the initial salt concentration from 5.2% to 20.8%, the destruction time increases from 7 to 22 h. By attaching a porous layer, the overturn time can increase by about 8 h, resulting in a temperature rise from 46 °C to 73 °C in the storage layer. The flow field analysis shows that both interface erosion and turbulent mixing in NCZ cause the destruction of NCZ. The stability analysis shows that the turbulent mixing decreases the salinity gradient in NCZ, making it more susceptible to erosion. By inhibiting the turbulent mixing, the porous layer at the bottom can effectively delay the arrival of NCZ destruction. Thus, the heat storage capacity and stability of solar ponds are enhanced.

Modeling advanced air mobility aircraft in data-driven reduced order realistic urban winds

The concept of Advanced Air Mobility involves utilizing cutting-edge transportation platforms to transport passengers and cargo efficiently over short distances in urban and suburban areas. However, using simplified atmospheric models for aircraft simulations can prove insufficient for modeling large disturbances impacting low-altitude flight regimes. Due to the complexities of operating in urban environments, realistic wind modeling is necessary to ensure trajectory planning and control design can maintain high levels of safety. In this study, we simulate the dynamic response of a representative advanced air mobility platform operating in wing-borne flight through an urban wind field generated using Large Eddy Simulations (LES) and a wind field created using reduced-order models based on full-order computational solutions. Our findings show that the longitudinal response of the aircraft was not greatly affected by the fidelity of the LES models or if the spatial variation was considered while evaluating the full-order wind model. This is encouraging as it indicates that the full LES generation of the wind field may not be necessary, which decreases the complexity and time needed in this analysis. Differences are present when comparing the lateral response, owing to the differences in the asymmetric loading of the planform in the full and reduced order models. These differences seen in the lateral responses are expected to increase for planforms with smaller wing loadings, which could pose challenges. Additionally, the response of the aircraft to the mean wind field, the temporal average of the full order model, was misrepresentative in the longitudinal response and greatly under-predicted control surface activity, particularly in the lateral response.

Scale Effects Investigation in Physical Modeling of Recirculating Shallow Flow Using Large Eddy Simulation Technique

In this study, the Large Eddy Simulation (LES) model in OpenFOAM was used to investigate the scale effects in the physical modeling of recirculating shallow flow at low Froude numbers. A laboratory test of turbulent flow through a submerged conical island with a Reynolds number of 6,210 was selected. The lab prototype was scaled with factors of 3 and 10 for both undistorted and distorted models. Our study employed the Froude similarity as the gravitational force is more dominant than the others (viscous, drag, and cohesion forces). Because the fluid (water) used for the prototype and model is the same, it is impossible to match the Reynolds, Weber, and Froude numbers simultaneously, resulting in the scale effects. For a scale of 1:1, the LES model could simulate the experimental data by appropriately capturing the vortices behind the conical island. For the undistorted models with scales of 3 and 10, the numerical model captured weaker magnitudes of vortices than the 1:1 scale, indicated by the discrepancies in velocity. In fact, the magnitudes of vortices became weaker with the distorted models. We also observed a significant increment in energy loss behind the conical island (where recirculating flows exist) as the scale increased. However, no significant discrepancies in velocity were observed between the results of the 1:1 scale and the scaled models in front of the conical island, where vortices were absent. These results indicate that the scale effects due to the Froude similarity are quite significant provided that recirculating turbulent flow occurs.

Two-zone subgrid combustion model for large eddy simulations of buoyant diffusion flames

This work presents the development of a novel two-zone subgrid combustion model (SCM) recently designed to incorporate the finite-rate kinetics in the large eddy simulations of buoyant turbulent diffusion flames in which turbulence might not be fully developed. The reaction rate is evaluated as the mass-weighted average of contributions by the higher-temperature flamelet zone and the lower-temperature surrounding. The volume fraction of the flamelet zone and the time scale responsible for the inter-zone transfer are determined by the expected thickness of the laminar diffusion flamelet existing in the grid cell, and by wrinkling its surface by the unresolved turbulent eddies. This approach is applicable to an arbitrary level of turbulence (quantified by the subgrid Reynolds number) thereby overcoming the limitation of the conventional eddy dissipation concept formulated for the high-Re flames. Advancing the previous study [1], the flamelet status at every time step is evaluated by solving transient equations of heat and mass transfer between the subgrid zones. Effective kinetic parameters for the simplified global reaction of fuel oxidation are derived from experimental data for autoignition delay in methane-air and n-heptane-air mixtures and are shown to capture transition from the low- to high-temperature measurement datasets including the NTC (negative temperature coefficient) interval. With these kinetic parameters, the SCM is validated against the measurements in the unconfined methane-air flame produced by the UMD line burner and then applied to predict the development and extinction of the heptane-air flame in an under-ventilated enclosure. In the first case, flame extinction is predicted at the oxygen concentrations consistent with the experimental values. In the second scenario, global low-frequency oscillations of the heat release rate are predicted prior to flame extinction.

Using graph neural networks for wall modeling in compressible anisothermal flows

Abstract Compressible anisothermal flows, which are commonly found in industrial settings such as combustion chambers and heat exchangers, are characterized by significant variations in density, viscosity, and heat conductivity with temperature. These variations lead to a strong interaction between the temperature and velocity fields that impacts the near-wall profiles of both quantities. Wall-modeled large-eddy simulations (LESs) rely on a wall model to provide a boundary condition, for example, the shear stress and the heat flux that accurately represents this interaction despite the use of coarse cells near the wall, and thereby achieve a good balance between computational cost and accuracy. In this article, the use of graph neural networks for wall modeling in LES is assessed for compressible anisothermal flow. Graph neural networks are a type of machine learning model that can learn from data and operate directly on complex unstructured meshes. Previous work has shown the effectiveness of graph neural network wall modeling for isothermal incompressible flows. This article develops the graph neural network architecture and training to extend their applicability to compressible anisothermal flows. The model is trained and tested a priori using a database of both incompressible isothermal and compressible anisothermal flows. The model is finally tested a posteriori for the wall-modeled LES of a channel flow and a turbine blade, both of which were not seen during training.

On the effectiveness of Reynolds-averaged and subgrid scale models in predicting flows inside car cabins

The aim of the present study is to analyze the performances of unsteady Reynolds-averaged Navier–Stokes (URANS) and large eddy simulation (LES) approaches in predicting the airflow patterns inside car cabins and to give insight in the design of computational fluid dynamics simulations of a real car cabin. For this purpose, one eddy viscosity-based turbulence model (shear stress transport k–ω) and two subgrid scale models (wall-adapting local eddy-viscosity and dynamic kinetic energy) were tested, and numerical results were compared with particle image velocimetry measurements carried out on a commercial car. The URANS model exhibited great accuracy in predicting the mean flow behavior and was appreciably outperformed by the LES models only far from the inlet sections. For this reason, it was deemed suitable for conducting further analyses, aimed at characterizing the airflow patterns in winter and summer conditions and performing a thermal comfort analysis. The thermal regime was found to have a very little effect on the air flow patterns, once the quasi-steady state regime is achieved; in fact, both in winter and in summer, the temperature field is fairly uniform within the car cabin, making the contribution of buoyancy negligible and velocity fields to be very similar in the two seasons. Findings also reveal that thermal comfort sensation can be different for passengers sharing the same car but sitting on different seats; this aspect should be considered when designing and operating the ventilation system, since the minimum comfort requirements should be met for all the occupants.

Detailed modeling of the radiative feedback in the large eddy simulation of a lab-scale heptane pool fire

This article proposes a detailed modeling of gas/soot radiation for large eddy simulation (LES) of sooting pool fires with a focus on the radiative heat feedback through the fuel vapor dome. The numerical model combines a non-adiabatic steady laminar flamelet (SLF) model, a two-equation acetylene/benzene-based soot production model, the Rank-Correlated Full-Spectrum K (RCFSK) and a presumed filtered density function (FDF) approach to model the subgrid-scale (SGS) interactions between turbulence, chemistry, soot production and radiation. This LES model is applied to simulate 15 cm heptane pool fire. The reliability of the RCSFK and the acetylene/benzene soot model for heptane pool fire scenarios is investigated through a priori analysis. First, decoupled radiative heat transfer calculations along lines of sight, representative of heptane pool fires and covering a wide range of optical thicknesses, demonstrate that the RCFSK computes the absorption of the fuel vapor dome with a similar accuracy as the narrow-band correlated-k (NBCK) model. Second, simulations of laminar coflow diffusion flame are used to extend the acetylene/benzene-based soot production model, previously validated for C1-C3 hydrocarbons, to n-heptane flames. LES of the 15 cm heptane pool fire reproduces with fidelity the flame structure in terms of mean and fluctuating temperatures and mean soot volume fraction (SVF). Simulations with and without considering heptane vapor radiation evidences its effects on the radiative outputs and the heat feedback. The radiative contribution of heptane vapor noticeably enhances the radiative loss to the surroundings and the peak of axial radiative flux. On the hand, the heat feedback to the pool surface is predicted within the experimental uncertainty when this contribution is taken into account with the radiative component being reduced by more than 10 %.

Responses of Simulated Arctic Mixed-Phase Clouds to Parameterized Ice Particle Shape

Abstract In simulations of Arctic mixed-phase clouds, cloud persistence and the liquid water path (LWP) are sensitive to ice particle number concentrations. Here, we explore sensitivities of cloud microphysical properties to the dominant ice particle shape (dendrites, plates, columns, or spheres) using the SCALE-AMPS large-eddy simulation model. AMPS is a bin microphysics scheme that predicts particle shapes based on the inherent growth ratio (IGR) of spheroids, which determines vapor depositional growth rates along the a and c axes, and the rimed and aggregate mass fractions. We examine the impacts of various IGR values on simulations of clouds observed during the M-PACE and SHEBA experiments. Under M-PACE (SHEBA) conditions, LWP varies between 49 (1.1) and 230 (6.7) g m−2, and the ice water path (IWP) varies between 3 (0.03) and 40 (0.12) g m−2, depending on the ice shape. The lowest LWP and the highest IWP are obtained when columnar particles dominate because their low terminal velocities and large capacitance and collisional area result in large vapor deposition and riming rates, whereas the highest LWP and lowest IWP are obtained when spherical particles dominate because their vapor deposition and riming rates are low. Because ice particle shape significantly influences simulated Arctic mixed-phase clouds, reliable simulations require accurately estimated IGR values under various atmospheric conditions. Finally, comparisons between the simulation results and observations show that the size distribution larger than 2000 μm is better reproduced when the increase in rimed mass that causes ice particles to become spherical is suppressed. Significance Statement Atmospheric models have difficulties in reproducing Arctic mixed-phase clouds because of uncertainties in the parameterization of microphysical processes. This is the first study to use a large-eddy simulation model implemented with a habit-predicting bin microphysics scheme to demonstrate the important role of ice particle shape on the microphysical properties of both heavy-riming and no-riming mixed-phase clouds. We found the vapor deposition and riming rates to be greatly influenced by ice particle shape. By comparing the ice particle size distribution, mass–diameter relationship, and area ratio between simulation results and observations, we show that a hexagonal ice shape model and a riming model that simply converts ice crystals to graupel may not accurately reproduce actual heavy-riming clouds.

NUMERICAL INVESTIGATION OF THERMAL STRATIFICATION EFFECTS ON WIND TURBINE WAKE FLOW

Numerical investigation of thermal stratification effects on wind turbine wake flow with and without yawed conditions was conducted in this paper. The three-dimensional wind turbine model from National Renewable Energy Laboratory (NREL) Phase VI experiment was adopted in all the simulations. Model validation was done by using three commonly used turbulence models: realizable <i>k-ε</i> model, shear-stress transport (SST) <i>k-ω</i> model, and large eddy simulation (LES) model. Simulation results were compared with experimental data in the literature. The realizable <i>k-ε</i> model was selected for further simulation as it showed a good balance in achieving accurate results and lowering computational cost. Followed by the validation, wind turbine wake flow with and without yawed effects under different thermal stratification conditions were simulated. Wake flow parameters were discussed and analyzed, followed by suggestions of wind turbine layouts based on different operation conditions to efficiently harness wind energy.

Pure ammonia flames with high thermal intensities through fuel and air staging under extreme rich-to-lean conditions

Ammonia combustion has gained significant attention due to its high hydrogen content and zero carbon emissions. It poses a challenge for stabilization due to their low energy content and limited flammability range, with the added concern of fuel NOx emissions. In the current study, a novel burner design equipped with four series of reactors is proposed to achieve stable pure ammonia-air flames with reduced NOx emissions. The impact of thermal intensity (∼0.7 MW/m3 to ∼4 MW/m3), number of stages, equivalence ratio (0.5–1.2), and fuel staging on flame stabilization and emissions were investigated in the proposed burner. Comprehensive emissions analysis is performed at various burner levels. Numerical simulations incorporating Large Eddy Simulation (LES) modeling are employed to enhance understanding of the impact of thermal intensity and equivalence ratios on ammonia dissociation, mixedness, and NO emissions. The results indicated that the present burner design improved reactant mixing, and flame stability, reduced NH3 emissions (∼ 0 PPM), and lowered NOx levels in non-premixed ammonia-air flames. The computational and experimental results demonstrated that the implementation of fuel staging is crucial for reducing NOx emissions for the flames with lean global equivalence ratios. Lower NO emissions were identified at a global equivalence ratio of 1.1 for all the considered flames in the range of 0.5–1.2. In the four-stage rich-lean combustion strategy employed in this study; it was observed that higher thermal intensity (4 MW/m3) with fuel staging resulted in lower NOx emissions per kW of energy input compared to lower thermal intensities 0.7 MW/m3. This finding underscores the significance of achieving uniform mixtures and ensuring local flame in rich conditions for achieving low NOx emissions in ammonia combustion. Exhaust gas analysis is conducted at three stages of the burner to enhance the understanding of emissions at various levels of the burner. The proposed combustor design has achieved a substantial reduction in NOx to ∼1 PPM/kW and ∼2.8 PPM/kW with and without fuel staging, respectively. This impressive outcome is attributed to the controlled ammonia consumption facilitated by uniform mixing generated through the use of tangential air inlets.

Augmenting filtered flame front displacement models for LES using machine learning with a posteriori simulations

The Flame Surface Density (FSD) model is an affordable combustion model that has been widely used in simulating turbulent premixed flames. In Large Eddy Simulations (LES) with FSD, the combined effect of reaction and diffusion is governed by the Filtered Flame Front Displacement (FFFD) term. While the existing modelling approaches for this term are computationally cost-effective, their predictions still show inconsistencies in certain cases. This study aims to address these inconsistencies by generating Machine Learning (ML) models for the FFFD and FSD terms using the DNS data of a turbulent premixed jet flame. With this approach, the relevance of certain input parameters as well as certain modelling assumptions used for the FFFD term are assessed. Overall, it is found that the resolved curvature term is the most important input parameter to consider and that the resolved progress variable should also be considered in the models. It is shown that the ML models perform significantly better than legacy, algebraic formulations using a priori testing. To further assess the performance of ML, one of the ML models is employed in a a posteriori LES and compared against simulations with the algebraic model. The ML simulation is stable and yields encouraging improvements on key physical parameters regarding the flame length and the FFFD distribution.Novelty and Significance Statement: This research is of importance because it answers fundamental and practical questions related to the use of combustion modelling approaches, specifically the Flame Surface Density (FSD) and the Filtered Flame Front Displacement (FFFD) models, by means of Machine Learning (ML) algorithms. From a fundamental aspect, we show that two features which are typically not considered as inputs in combustion models, i.e., the progress variable and the resolved curvature, are key to consider for improved predictions of the model, more so than features which are typically used in FSD modelling, i.e., uΔ′,Δ, and |∇c¯|. From a practical standpoint, we demonstrate a framework to use the developed ML combustion model a posteriori in a LES, without any stability issues. Overall, these findings are key to guide further traditional and ML improvement efforts on combustion models.

SIMULATING SPRAY DYNAMICS WITH A FINITE ELEMENT METHOD FOR INTERNAL COMBUSTION ENGINES USING LARGE EDDY SIMULATIONS

Spray dynamics in an internal combustion engine is comprised of complex phenomena while interacting with unsteady turbulence. Physics requires detailed modeling of the dynamics for spray and carrier gases to accurately predict a spray's fate. Large eddy simulation (LES) turbulence modeling approaches are capable predictors of the turbulent processes and of dynamically modeling sub-grid scales, therefore enabling calculation of model coefficients for the smallest resolved scale. Dynamic LES methods are also well suited for unsteady flows associated with spray injection and engine fluid dynamics. In this study, for the first time, a dynamic Verman LES scheme employed in a stabilized a finite element (FE) framework is used to model the spray process with emphasis on injected fuels for simulating internal combustion engines. Spray modeling often comprises a coupled Eulerian-Lagrangian approach to capture the droplet/particle dynamics, where the droplets are modeled in the Lagrangian frame, interacting with the gas. The momentum and heat exchange between the fluid gases and the evaporating and atomizing spray droplets are modeled in a two-way coupling system as described in this paper. Direct injected liquid is modeled in this paper as a spherical ligament of fuel and ligament breakup to atomization use our version of the Kelvin Helmholtz break-up scheme. Discussed are models and methods of the whole system in some detail. The method for simulation of the fluid's momentum, heat transfer, and turbulence are discussed as is the system to evaluate droplet or ligament properties. Validation or results of the modeling are presented on test cases as determined by the Engine Combustion Network, base cases G along with some benchmarking cases. We demonstrate in the paper by using the FE method combined with the Verman LES model, and our version of the Kelvin Helmholtz-Rayleigh Taylor spray break-up model combined with two-way coupling is quite capable of modeling sprays, having distinct and significant advantages over the more traditional finite volume discretization framework using similar spray modeling methods.

LES Modeling of High-Pressure Nitrogen Cross-Flow Jet Impact on Hydrogen-Air Deflagration

The Dynamic Thickened Flame (DTF) model has been widely used for Large Eddy Simulations (LES) of reacting flows over the past decades. This numerical approach was developed for simple flammable mixtures, assuming a constant amount of inert gas, if present. As a result, it is not possible to accurately compute premixed flames with local variations of inert species mass fraction using the standard DTF-LES model. The present paper addresses this limitation, and its purpose is twofold: (1) to propose and validate the extension of the DTF-LES model on a 3D LES deflagration of a hydrogen/air mixture at sub-atmospheric pressure, with a pressurized transverse injection of N2 (Pjet/P0=20) and, (2) to provide a comprehensive analysis of the key physical mechanisms responsible for the strong Flame Acceleration (FA). First, LES adequately replicates the experiment used as reference and shows that the extension of the DTF-LES model introduced in this work is necessary for such scenarios. Then, three mechanisms are identified as the main contributors to FA. At initial instants, the flame is sufficiently far from the jet exit to propagate unperturbed and, thus, it follows the laminar finger flame theory. Then, the flame-induced piston effect pushes fresh gases, as well as dilution gases, resulting in a delayed effect of the supersonic jet on the FA. This is further illustrated by simulating a premixed jet instead of N2, showing no differences in absolute flame speed during this phase. In the next phase, hydrodynamically-induced flow contraction in the jet impingement zone is responsible for the sudden FA observed in the simulation. Finally, flame-turbulence interaction leads to a sudden rise in flame surface area, revealing a transition towards the turbulent combustion regime and, only then, dilution effects start to lower the flame propagation speed when compared to a fully premixed jet.

Generalized wall-modeled large eddy simulation model for industrial applications

In this work, a generalized wall-modeled large eddy simulation model (GWMLES) is presented. An extended formulation of the classical WMLES approach is proposed that also enables the modeling of the entire log-layer by using a Reynolds-averaged Navier–Stokes (RANS) model. GWMLES is validated against direct numerical simulations, large eddy simulations (LES), WMLES, hybrid RANS/LES, unsteady RANS (URANS), and experimental data of test cases featuring industrial flows. It is demonstrated that GWMLES does not share the main shortcoming of current WMLES models. When the entire log-layer is solved with a RANS model, GWMLES gives a level of accuracy similar to recent LES results, as well as computational cost savings that are proportional to the Reynolds number in wall-bounded flows. The model shows superior performance than URANS even when the resolved portion of the energy spectrum is reduced. Motivated by the different time scales of the flow and RANS variables, it requires approximately 30% lower computational costs than the detached eddy simulation family models in the turbulent flows considered. These features represent significant advancements in the simulation of wall-bounded flows at high Reynolds numbers, particularly in industrial applications.

COMPUTATIONAL STUDY OF HIGH-PRESSURE LIQUID INJECTION PROCESS BY MEANS OF LES AND PANS APPROACHES

For internal combustion engine, the determination of combustion characteristics and subsequent emissions formation relies heavily on the fuel injection process. With the increasing demand for enhanced fuel efficiency and reduced emissions, it becomes vital to develop fundamental understanding of physical process involved in the fuel injection process. In this study, an optimal numerical approach to predict high pressure liquid injection process in the context of industrial computations has been investigated. In particular, this study focuses on the respective performance of the Partially-Averaged Navier-Stokes and Large Eddy Simulation models to predict turbulent igniting sprays. Both approaches are coupled with widely used Lagrangian Discrete Droplet Method for spray modelling. The results are validated against well established ECN Spray A case in reactive and non reactive conditions. For reacting conditions, Flamelet Genrated Manifold (FGM) combustion model is employed in the present work. Comparative study and validation against experimental data showed that PANS turbulence model allows for coarser grids while still maintaining accurate results.

Assessment of a Flow-dependent Subgrid Characteristic Length for Large-Eddy Simulation on Anisotropic Grids

This paper presents the latest results of a long track development activity in the context of low-dissipative finite volume method for compressible flows. Specifically, here we focus our attention on the Large-Eddy Simulation (LES) approach which can be considered a good candidate for turbulent flow simulations over the next decades. One of the key ingredients of LES models is the subgrid length scale which is typically evaluated based on the local mesh size. This standard approach suffers from loss of accuracy on anisotropic grids that are commonly employed to obtain sufficient wall-normal resolution, whilst keeping the total cell count to a minimum. In order to avoid this issue, we assess the effectiveness of a velocity-gradient-based length scale, referred to as least square length (LSQ) [1]. In this paper, we present for the first time results obtained with the LSQ length scale in the context of compressible LES. The superiority of the LSQ approach over the standard cubic-root length scale is demonstrated in terms of accuracy and overall time to solution.

A subgrid-scale model to account for thermo-diffusive effects in artificially thickened LES models for lean turbulent premixed ammonia/hydrogen flames

With the introduction of carbon-free fuels and their rapid implementation, accurate combustion models are needed to design decarbonized engines, gas turbines or burners. Recent experimental and numerical work have highlighted the high burning rate of lean hydrogen and hydrogen/ammonia flames and their deviation from flamelet assumptions, pointing out the need to revise classical combustion models for Reynolds Averaged Navier–Stokes (RANS) or Large-Eddy Simulations (LES). In this work, a novel subgrid-scale model accounting for stretch effects for lean ammonia/hydrogen (NH3/H2) flames in the Thickened-Flame LES framework (TFLES) is proposed as an extension to the standard Charlette model Charlette et al. (2002). This model, called ESE−NH3, is based on 1-D premixed strained flames and validated a priori with Direct Numerical Simulations (DNS) and used to perform LES simulation of a turbulent slot burner configuration. To assess the performance of the novel subgrid model, LES predictions are compared a posteriori to the DNS and to LES employing the standard Charlette model. The comparison shows that the LES computation with the novel subgrid-scale model accounting for stretch effects predicts flame lengths closer to the DNS. This shows that thermo-diffusive effects can be integrated simply in the TFLES framework. Moreover, results show that these effects are significant in all lean NH3/H2 flames even at low H2 contents.