Large Eddy Simulation Research Articles (Page 1)

Large eddy simulation on the flow and thermal fluctuation of non-isothermal water mixing in reactor upper plenum

Studies of vegetation effect on turbulence dynamics in an urban canopy layer using large eddy simulation

A reversible pump turbine is a large-scale commercial energy storage device that serves as the core component of a pumped storage power station. Under pump operating conditions, the reversible pump turbine often exhibits hump characteristics on the head–discharge performance curve, leading to operational instability and limiting both regulation performance and safety. However, the mechanism underlying flow instability in the hump region is not adequately understood. Existing analysis methods are limited in scope, and they struggle to identify key flow structures accurately in application. In this study, large eddy simulation (LES) is used to investigate unsteady flow characteristics under typical hump conditions. Proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) methods are applied to a transient flow field to extract dominant modal structures and analyze their dynamic behaviors. The results show that rotating stalls in the guide vane and runner regions are the primary factors creating hump characteristics. Both the POD and DMD methods effectively capture major vortex structures and their changes, demonstrating their suitability for analyzing complex flow dynamics. These findings provide theoretical insight into the flow instability mechanism in the hump region and offer guidance for improving the operational performance of pump turbines.

Analysis of entrainment and mixing in a supersonic air ejector using Large-Eddy Simulation

Assessing the influence of rooftop vegetation on pollution dispersion in urban canyons through Large-Eddy Simulations

Analyzing the impact of inclined single and multi-inlet configurations on the turbulent flow field in cyclone separators using large-eddy simulation

Large eddy simulation of turbulent partially premixed dimethyl ether jet flame by the extended direct moment closure model coupled with acceleration algorithms

Large eddy simulation of turbulent premixed combustion with a data-driven filtered density function model

High-fidelity large eddy simulation of transonic wet steam flows through a steam turbine cascade

Accelerating Large Eddy Simulations of Urban Airflow with Generative Adversarial Networks

A semi-stochastic approach for point-particle dispersion in Wall-Modeled Large Eddy Simulation of particle-laden turbulent flows

Evaluation of the gradient diffusion hypothesis for jet into crossflow field: A prior analysis via large eddy simulation of flat plate film cooling

Large eddy simulation of unsteady flame dynamics in a sidewall quenching configuration

Wall-modeled large-eddy simulations of shock-turbulent boundary layer interactions with wall heating and cooling

Large-eddy simulation of turbulent spray flames: Effects of scalar correlation and enthalpy reduction in flamelet modeling

Characterising the force exerted on obstacles by non-Boussinesq gravity currents using implicit large-eddy simulations

Abstract Bispectral retrievals of the droplet effective radius ( r e ) from instruments such as MODIS are widely utilized to study cloud microphysics in marine boundary layer clouds. These retrievals are known to have systematic errors due to cloud heterogeneity. Here, we develop a neural network regression to retrieve cloud‐top r e at a solar zenith angle of 30° and nadir viewing using MODIS. The neural network regression is trained on 3D radiative transfer simulations of quasi‐adiabatic stochastically generated clouds and corrects relative errors in r e with respect to cloud‐top with an r 2 of 0.88. The neural network regression produces unbiased retrievals of cloud‐top r e against large eddy simulation cloud fields where the bispectral retrieval has biases reaching +100%. The neural network regression reduces retrieval biases against airborne observations of cumulus from CAMP 2 Ex from +100% to +40%, and marginally improves already good consistency against stratocumulus sampled during VOCALS. A cross‐comparison technique for assessing statistical remote sensing retrievals is introduced. The neural network regression explains 63% and 91% of the variance in the differences between MODIS 1.6 and 2.1 μm retrievals for Overcast and partially cloudy pixels (PCL) and 42% and 76% for the 2.1 and 3.7 μm differences, respectively. Residual spectral inconsistency is partially attributed to precipitation‐sized particles using radar observations. Regional averages of the operational MODIS r e exceed the cloud‐top r e predicted by the neural network by +50% for Overcast pixels in the tropics and a consistent +70% for PCL pixels. Errors in bispectral retrievals due to heterogeneity are nonrandom at both the cloud and climate scale.

The growing demand for sustainable energy storage technologies, particularly batteries, is driving research on their safety and performance [1]. Thermal runaway (TR) events occur when exothermic reactions rapidly release heat and flammable gases, potentially leading to fires, ignitions, and explosions [2]. While many studies have focused on TR behavior at the module or system level, the need for robust tools capable of predicting and mitigating TR events at the cell level has become more pressing. Although most existing models emphasize module-scale phenomena, the present work introduces a high-fidelity numerical framework to resolve heat release, gas venting, and flame propagation processes within individual cells. Large Eddy Simulations (LES) were performed using OpenFOAM, following a configuration inspired by the experimental setup and reduced-order modeling approach proposed by Cellier (2023) [3]. The model captures reactive gas dynamics, pressure evolution, and anisotropic heat transfer during TR events while also accounting for the effects of geometric confinement on flame behavior, as emphasized in the studies by Illacanchi et al. (2023) [4] and Liberman et al. (2022) [5]. The definition of boundary conditions and thermochemical parameters draws on experimental observations by Golubkov et al. (2015) [2], particularly concerning the influence of cathode composition on gas release and ignition thresholds. Additionally, key design variables, such as vent hole size and casing thermal conductivity, were explored to evaluate their impact on containment and heat propagation. Our framework establishes a foundation for identifying critical failure modes and supports future design improvements in thermal protection systems and sensor placement. By resolving cell-level processes with greater physical detail, the model addresses limitations in conventional battery safety simulations and contributes to developing more predictive tools for next-generation batteries.

A data assimilation (DA) strategy based on an ensemble Kalman filter (EnKF) is used to enhance the predictive capabilities of scale-resolving numerical tools for the analysis of flows exhibiting cyclic behaviour. More precisely, an ensemble of numerical runs using large-eddy simulations (LES) for a compressible intake flow rig is augmented via the integration of high-fidelity data. This observation is in the form of instantaneous velocity measurements, which are sampled at localised sensors in the physical domain. Two objectives are targeted. The first objective is the calibration of an unsteady inlet condition suitable to capture the cyclic flow investigated. The second objective is the analysis of the synchronisation of the LES velocity field with the available observations. In order to reduce the computational costs required for this analysis, a hyper-localisation procedure (HLEnKF) is proposed and integrated in the library CONES, tailored to perform fast online DA. The proposed strategy performs a satisfactory calibration of the inlet conditions, and its robustness is assessed using two different prior distributions for the free parameters optimised in this task. The DA state estimation is efficient in obtaining accurate local synchronisation of the inferred velocity fields with the observed data. The modal analysis of the kinetic energy field provides additional insight into the improved reconstruction quality of the velocity field. Thus, the HLEnKF shows promising features for the calibration and synchronisation of scale-resolved turbulent flows, opening perspectives of applications for complex phenomena using advanced tools such as digital twins.

Abstract. Many natural ecosystems are composed of heterogeneous patches differentiated by wetness levels and vegetation composition, resulting in fine-scale flux patterns across the different landcovers that can be challenging to quantify. Here, we present a case study at Stordalen Mire in subarctic Sweden, where we conducted Uncrewed Aerial Vehicle (UAV) measurements of CO2 mole fractions and combine them with a large-eddy simulation (LES) model through a site-level inversion method to differentiate the flux rate signatures from different patch types. We use the LES model EULAG (EUlerian LAGrangian) to simulate high-resolution flow patterns and benchmark the spatial variability of modelled concentrations with data from UAV-based grid surveys of CO2 mixing ratio. Coupling the inversion results with eddy-covariance (EC) flux measurements for the time of the UAV flight allows quantifying net CO2 fluxes for the individual landcover types. Model evaluation showed an R2 up to 0.70, with model uncertainties mostly related to the transport model uncertainty and the UAV sampling footprint that does not evenly sample landcover types. The inversion fluxes were subsequently compared to patch-level chamber measurements of carbon dioxide from palsa, bog, and fen, and showed a good agreement in flux patterns across those patch types dominating the UAV-sampled footprint. Different landcover classification schemes were considered, and results showed a consistent improvement in the model performance when further representing the ecological and hydrological heterogeneities. Our novel technique shows promising results in estimating landcover-type flux heterogeneity within eddy-covariance tower footprints, thus providing a basis for upscaling of EC fluxes to a larger domain.