Grid Resolution Research Articles

A Parametric Kinetic Solver for Simulating Boundary-Dominated Turbulent Flow Phenomena

Boundary layer flow plays a very important role in shaping the entire flow feature near and behind obstacles inside fluids. Thus, boundary treatment methods are crucial for a physically consistent fluid simulation, especially when turbulence occurs at a high Reynolds number, in which accurately handling thin boundary layer becomes quite challenging. Traditional Navier-Stokes solvers usually construct multi-resolution body-fitted meshes to achieve high accuracy, often together with near-wall and sub-grid turbulence modeling. However, this could be time-consuming and computationally intensive even with GPU accelerations. An alternative and much faster approach is to switch to a kinetic solver, such as the lattice Boltzmann model, but boundary treatment has to be done in a cut-cell manner, sacrificing accuracy unless grid resolution is much increased. In this paper, we focus on simulating the boundary-dominated turbulent flow phenomena with an efficient kinetic solver. In order to significantly improve the cut-cell-based boundary treatment for higher accuracy without excessively increasing the simulation resolution, we propose a novel parametric boundary treatment model, including a semi-Lagrangian scheme at the wall for non-equilibrium distribution functions, together with a purely link-based near-wall analytical mesoscopic model by analogy with the macroscopic wall modeling approach, which is yet simple to compute. Such a new method is further extended to handle moving boundaries, showing increased accuracy. Comprehensive analyses are conducted, with a variety of simulation results that are both qualitatively and quantitatively validated with experiments and real life scenarios, and compared to existing methods, to indicate superiority of our method. We highlight that our method not only provides a more accurate way for boundary treatment, but also a valuable tool to control boundary layer behaviors. This has not been achieved and demonstrated before in computer graphics, which we believe will be very useful in practical engineering.

Damage Curves Derived from Hurricane Ike in the West of Galveston Bay Based on Insurance Claims and Hydrodynamic Simulations

Hurricane Ike, which struck the United States in September 2008, was the ninth most expensive hurricane in terms of damages. It caused nearly USD 30 billion in damage after making landfall on the Bolivar Peninsula, Texas. We used the Delft3d-FM/SWAN hydrodynamic and spectral wave model to simulate the storm surge inundation around Galveston Bay during Hurricane Ike. Damage curves were established through the relationship between eight hydrodynamic parameters (water depth, flow velocity, unit discharge, flow momentum flux, significant wave height, wave energy flux, total water depth (flow depth plus wave height), and total (flow plus wave) force) simulated by the model and National Flood Insurance Program (NFIP) insurance damage data. The NFIP insurance database contains a large amount of building damage data, building stories, and elevation, as well as other information from the Ike event. We found that the damage curves are sensitive to the model grid resolution, building elevation, and the number of stories. We also found that the resulting damage functions are steeper than those developed for residential structures in many other locations.

Assessment of the DDES-γ Model for the Simulation of a Highly Loaded Turbine Cascade

Abstract The flow over the linear low-pressure turbine cascade MTU-T161 at Re = 90,000 is analyzed using delayed detached eddy simulations (DDES). At this operating point, the low Reynolds number and the high loading of the blade result in a separation bubble and a separation-induced transition of the flow over the suction side. The utilized DDES method is based on a vorticity-based formulation to calculate the subgrid length scales, and it incorporates the one-equation γ-transition model. The computational model of the MTU-T161 cascade consists of one blade passage, including the diverging viscous sidewalls. To reproduce realistic operating conditions and to mimic the experiments, synthetic turbulence is prescribed at the inlet of the computational domain. Several studies are performed to assess the accuracy and performance of the DDES one-equation γ-transition model against experimental data and a benchmark large eddy simulations (LES). The primary focus is on the prediction of the separation and the separation-induced transition mechanism. First of all, a systematic grid convergence study is conducted and grid criteria are derived in order to ensure a satisfactory agreement of the flow metrics, such as isentropic Mach number, friction coefficient distribution, and total pressure wake losses at mid-span with experimental data. Furthermore, a detailed analysis of the DDES model parameters, such as shielding function and subgrid length scale, is presented and the effect of these parameters on the prediction accuracy of the separation bubble region is analyzed. The analysis of the suction side boundary layer indicates that the turbulent kinetic energy should be resolved and modeled properly in order to represent the separation bubble correctly. In particular, the correct prediction of the separated shear layer above the separation bubble is of utmost importance. The results of the simulations reveal higher demands on grid resolution for such transitional flows than typically have been reported in the literature for turbulent boundary layers. This higher demand on grid resolution results in more expensive simulations than Reynolds-averaged Navier–Stokes (RANS). Nevertheless, DDES requires less computing time than wall-resolved LES. Additionally, the results of the transitional DDES model are compared to DDES without a transition model, an RANS eddy viscosity model, and a reference LES. The results show that the DDES approach needs to be coupled with a transition model, such as the one-equation γ-transition model, in order to capture the flow topology over a highly loaded turbine blade correctly. The benefit of the DDES one-equation γ-transition model becomes particularly evident when predicting the separated shear layer, the transition process, and the subsequent reattachment. The RANS eddy viscosity turbulence and transition models applied within our study are not able to predict the aforementioned mechanisms accurately. For highly loaded turbine blades in particular, the accurate prediction of flow separation and potential reattachment is crucial for the aerodynamic design of turbines, since large parts of the total pressure loss are generated in the separated region. For this reason, the DDES one-equation γ-transition model can be a good compromise in terms of predictive accuracy and computational costs.

Numerical Prediction of Aerodynamic Noise for a Propeller-wing Configuration and an Investigation of Pitch Effect

Driven by the sustainable and eco-friendly vision of aviation, Distributed Electric Propulsion (DEP) technology has received much attention for its high aerodynamic efficiency. Simultaneously, lower noise emissions from DEP configurations are desired and therefore it is important to investigate the aerodynamic noise features, which include but are not limited to the noise generation mechanism and the effect of propeller-propeller/wing interactions. This paper focuses on a numerical approach, which is suitable for industrial practice, for predicting the aerodynamic noise of propeller-wing configurations. A generic DEP configuration is employed, consisting of a Mejzlik 2-blade propeller installed on the leading edge of a NACA0018 airfoil. A hybrid method of computational aeroacoustics has been performed in the present simulations, where aeroacoustics in the near field is solved by coarse-grid compressible large-eddy simulations and then the acoustic far field is calculated by the Ffowcs-Williams Hawkings analogy. The influence of grid resolution is studied based on Pope's criterion M and satisfactory results are achieved at a moderate mesh count as compared to the experimental measurements. Further simulations are conducted to investigate the effect of propeller pitch on the aerodynamic performance, noise generation and propeller-wing interactions.

Downscaling of surface wind forecasts using convolutional neural networks

Abstract. Near-surface winds over complex terrain generally feature a large variability at the local scale. Forecasting these winds requires high-resolution numerical weather prediction (NWP) models, which drastically increase the duration of simulations and hinder them in running on a routine basis. Nevertheless, downscaling methods can help in forecasting such wind flows at limited numerical cost. In this study, we present a statistical downscaling of WRF (Weather Research and Forecasting) wind forecasts over southeastern France (including the southwestern part of the Alps) from its original 9 km resolution onto a 1 km resolution grid (1 km NWP model outputs are used to fit our statistical models). Downscaling is performed using convolutional neural networks (CNNs), which are the most powerful machine learning tool for processing images or any kind of gridded data, as demonstrated by recent studies dealing with wind forecast downscaling. The previous studies mostly focused on testing new model architectures. In this study, we aimed to extend these works by exploring different output variables and their associated loss function. We found that there is no one approach that outperforms the others in terms of both the direction and the speed at the same time. Finally, the best overall performance is obtained by combining two CNNs, one dedicated to the direction forecast based on the calculation of the normalized wind components using a customized mean squared error (MSE) loss function and the other dedicated to the speed forecast based on the calculation of the wind components and using another customized MSE loss function. Local-scale, topography-related wind features, which were poorly forecast at 9 km, are now well reproduced, both for speed (e.g., acceleration on the ridge, leeward deceleration, sheltering in valleys) and direction (deflection, valley channeling). There is a general improvement in the forecast, especially during the nighttime stable stratification period, which is the most difficult period to forecast. The result is that, after downscaling, the wind speed bias is reduced from −0.55 to −0.01 m s−1, the wind speed MAE is reduced from 1.02 to 0.69 m s−1 (32 % reduction) and the wind direction MAE is reduced from 25.9 to 15.5∘ (40 % reduction) in comparison with the 9 km resolution forecast.

Misleading overestimation bias in methods to estimate wolf abundance that use spatial models.

Abstract 1. Population abundance is the main criterion used by agencies to manage and conserve species and it allows adaptive decision-making in response to impacts. Its estimation is particularly important for large mammals, especially carnivores that are notoriously difficult to monitor yet have high ecological and economic importance to humans. Reversal of their historic population decline is also vital to restoring ecosystem health through the ecosystem service of trophic interactions. 2. Because bias and precision (variance) are the independent yardsticks of the quality and reliability of population estimates, we preliminarily assessed new abundance estimators for wolves that non-traditionally use spatial models to estimate the area that an observation represents. Among many biases and problems identified by recent assessments, we identified suspected biases due to obvious violation of the closure assumption in occupancy modeling used to determine the area occupied by territorial pack members. Thus, we chose to simulate the effect of spatial resolution (grid size) on the direction and magnitude of that inherent bias in a recent method called iPOM used in Montana. We also examined the potential bias in their estimate of variance and confidence intervals. 3. We found that even the use of small grid cells (relative to wolf territory size), biased the total area occupied and the number of packs used to calculate abundance. The bias rapidly increases with increasing grid cell size. At the grid cell size used in iPOM for Montana (600 km 2 ) there was a severe overestimation bias of 150% that proliferated through the iPOM submodel structure and resulted in estimated wolf abundance 2.5 times larger than true abundance. 4. This bias alone when combined with a misapplication and underreporting of iPOM's estimate of variance (biased low) results in a precariously misleading situation for decision-makers that threatens wolf populations. Other identified biases that inflate abundance likely make this situation worse but they should be further examined and tested. 5. Due to these biases and the high sensitivity of iPOM's spatial models to estimate area, we suggest that such spatial models should not be used in population estimation methods or such methods, iPOM for example, should be improved and/or restructured with submodels robust to assumption violation thereby reducing bias. Given iPOM's current design, there is no ability to detect change let alone determine wolf population size. 6. We provide a comparative framework for testing and improvement and strongly suggest proper model-assisted sampling designs and hierarchical modeling such as is used in capture-recapture models, especially those that use non-invasive procedures that avoid costly capture and marking. We also recommend collaborative activities that lead to using the best available scientific methods to determine carnivore abundance.

On the application of principal component transport for compression ignition of lean fuel/air mixtures under engine relevant conditions

Principal component transport-based data-driven reduced-order models (PC-transport ROM) are being increasingly adopted as a combustion model of turbulent reactive flows to mitigate the computational cost associated with incorporating detailed chemical kinetics. Previous studies were mainly limited to replicating relatively-simple chemistry in canonical configurations. The objective of the present study, therefore, is to further explore the accuracy of PC-transport ROM on more complex combustion phenomenon where, for example, large hydrocarbon fuel chemistry spanning a broad range of thermochemical space governs sequential multi-stage compression ignition processes. The cumulative error of PC-transport for this problem, and for others that depend upon sequential highly nonlinear physics, has to be minimal as the combustion phasing and heat release rate in internal combustion engines depends upon accurate predictions of minor ignition species whose concentrations start from ashes and grow orders of magnitude over the course of low- and high-temperature autoignition. Specifically, the PC-transport ROM is applied to predict the compression ignition characteristics of lean n-heptane/air and primary reference fuel (PRF)/air mixtures in a two-dimensional (2-D) constant volume computational domain initialized with a two-dimensional isotropic turbulence spectrum and temperature inhomogeneities. PCA is used to define the low-dimensional manifold that represents the original thermochemical state vector, and artificial neural network (ANN) models are adopted to tabulate chemical kinetics, transport, and thermodynamic properties. A series of 2-D pseudo-turbulent simulations are performed at engine pressures by varying the initial mean and r.m.s. of temperature, turbulence intensity, and the composition of fuel/air mixture. The results show that the PC-transport ROM accurately reproduces the instantaneous and statistical ignition characteristics of the fuel/air mixture, aided by pre-processing techniques including species subsetting, data clustering, and data transformation. It is found that PCs are not properly scaled with a power transformer if reactants are included in the species subset, which leads to a decrease in the accuracy of the PC-transport ROM. A separation of the reactants from the species subset ensures that the temporal evolution of the PCs starts from zero and spans orders of magnitude with time, and as such, this approach is found to effectively redistribute both PCs and their source terms with a power transformer. The computational speed-up factor of the PC-transport ROM ranges between 5.1 and 15.0 for the cases with n-heptane/air mixture and PRF/air mixture, respectively. Moreover, a potential further speed-up is anticipated through a combination of reduction in grid resolution requirements and in the stiffness of the chemical system. As an example, many of the pre-processing methods for inhomogeneous compression ignition may also apply to other complex intermittent combustion phenomena.Novelty and significance statement• The PCA-based reduced-order model (PC-transport ROM) has been applied to the multi-stage compression ignition of large hydrocarbon fuels under HCCI-relevant conditions. The present work presents a systematic procedure to accurately capture the two-stage ignition behavior of lean n-heptane/air or PRF50/air mixture.• The present work demonstrates an advantage of the PC-transport ROM in terms of computational speed-up. The computational speed-up factor for the ROM is up to 15, and moreover, a potential additional speed-up is anticipated through the reduction in the spatial and temporal resolution required.• A series of 2-D PC-transport ROMs are conducted to demonstrate the robustness of the ROM. A limitation of the ROM against different operating conditions is also discussed.

Comment on: “Insights on the tectonic styles of the Red Sea rift using gravity and magnetic data” by Saada et al. (2021)

Global grids of geophysical data or properties of the Earth are increasingly being used, perhaps because they are easy to access and make calculations on. Where original data are proprietary, researchers can sometimes obtain a form of those data within these grids, albeit at commonly lower resolution. Examples include gravity fields (Balmino et al., 2011; Sandwell et al., 2014), thicknesses of sediment overlying basement (Whittaker et al., 2013; Straume et al., 2019), Earth's topography (Smith and Sandwell, 1997; Becker et al., 2009; Ryan et al., 2009; Weatherall et al., 2015), crustal thickness (Laske et al., 2013), seafloor spreading history (Müller et al., 1997) and magnetic fields (Maus et al., 2009). However, depending on the type of study, when working with such data, we need to know the distribution of the original measurements that were used to compile those grids, hence which grid nodes contain interpolated values and which nodes are close to measurement sites. Ideally, we should be aware of the noise characteristics and resolution of such data also. The term “grid resolution” is commonly used in the literature but is misleading, as it refers to the spacing of nodes in the grid, not the resolution of the contributing data. Resolution can also be affected by processing steps used to create the grid. The article of Saada et al. (2021) describes important calculations and useful observations, though some parts of the study illustrate issues that can be encountered in analysing global grids. Given the increasing use of global grids, we provide this comment to help engender discussion.

Lagrangian modelling of oil concentrations at sea: A sensitivity analysis to the grid resolution and number of Lagrangian elements

This paper presents a novel method to select the optimal combination of grid resolution and number of Lagrangian elements (LEs) required in numerical modelling of oil concentrations at sea. A sensitivity analysis in terms of grid resolution and the number of LEs, was carried out to understand the uncertainty that these user-dependent parameters introduce in the numerical results. A dataset of 211,200 simulations performed under 400 metocean patterns, 6 initial volumes, 11 grid resolutions, and different numbers of LEs (100 to 500,000), was used to analyze the sensitivity of the model along different Thresholds of Concern. Results show the importance of a correct selection of the number of LEs and the grid resolution in Lagrangian modelling of surface oil concentrations. The method proposed will allow selecting the optimal combination of these parameters to find an optimal balance between the accuracy and the computational cost of the simulation.

Modeling the Air Pollution and Aerosol‐PBL Interactions Over China Using a Variable‐Resolution Global Model

AbstractModeling air quality has always been a challenge in global models constrained by coarse grids. Here, the variable‐resolution Community Atmosphere Model with full chemistry based on the scalable spectral element (SE) dynamical core (MUSICAv0) is applied in simulating air pollution with a finer grid resolution of ∼0.25° over East Asia (SE_VR), in contrast to the same model with a uniform resolution of ∼1.0° (SE_UR). Two nudging experiments and four free‐running experiments are conducted to investigate the capabilities of SE_VR in modeling the air pollution and aerosol‐planetary boundary layer (PBL) interactions over China. Results show the regional refinement in SE_VR is essential for simulating haze events over complex terrain areas attributed to its better performance in representing the local vertical and horizontal dispersion conditions. SE_VR shows prominent advantages over SE_UR in simulating surface ozone because of better resolving spatial segregation of NOx and volatile organic compounds (VOC) chemical regimes and subsequently representing more detailed chemical processes related to ozone formation, although the model generally overestimates surface ozone over China. Further analysis of SE_VR shows the daytime radiative effect of black carbon (BC) aerosols lowers PBL height by 12.0% (17.9%), and leads to an increase of PM2.5 by 14.5% (10.8%) under the moderate (severe) air pollution conditions over Sichuan Basin. However, SE_UR has deficiencies in simulating BC‐PBL interactions due to its inability to reproduce the strong inverse temperature structure caused by BC aerosols in the lower atmosphere layer. Our results highlight the value of variable‐resolution global models for simulating air pollution and its interactions with climate.

Wind field and model resolution optimization and testing source terms for the prediction of cyclone induced waves in the Bay of Bengal

The present study examines the impact of wind data resolution on wave climate prediction using the wind-wave model, WAM using different spatial resolutions of wind data such as 1/5°, 1/10°, 1/20°, and 1/40°, obtained from the Weather Research and Forecasting (WRF) model. It focuses on the prediction of extreme waves caused by three cyclones (Phailin, Hudhud, and Vardah) that hit the Bay of Bengal region of the North Indian Ocean in 2013, 2014, and 2016, respectively. These cyclones had a significant impact on the east coast of India and have generated significant wave height (Hs) of more than 10 m. To accurately predict these high-impact waves, it is necessary to have suitable wind resolution and a stable wave model grid resolution. We also investigate the effect of different spatial resolutions (1/5°, 1/10°, 1/20°, and 1/40°) on wave prediction. There is no significant difference in Hs prediction accuracy for wind data and wave model resolutions better than 1/10°. Additionally, the WAM model is tested with Ardhuin's input and dissipation source terms included in the model which doubles the source terms computational time required by the model although providing better predictions in the nearshore locations. An optimal model resolution of 1/10°, along with Ardhuin's source terms, is recommended for better wave prediction.

Sensitivity Analysis of an Unsteady Flow Around a Pitching Airfoil

Various ways to control the loads and thus the output of the wind turbine by pitching the blades and by controlling the rotational speed already exist. However, adverse pressure gradient leading to flow separation at the trailing edge, with relatively high angles of attack (AOA) due to blade-pitching motion, largely affects the airfoil aerodynamic performance. This work presents the results of a numerical study of the unsteady flow around a pitching FFA-W3-301 airfoil at a Reynolds 1.6 × 106 using OpenFOAM®, as obtained by first performing 2D Unsteady Reynolds-Averaged Navier- Stokes (URANS) simulations whereby the flow characteristics are simulated by the shear stress transport (SST) k − ω model and Spalart-Allmaras (SA). The influence of various parameters on the numerical results is investigated, namely y+, computational grid resolution, dynamic mesh technique, time step and turbulence model. Integral aerodynamic forces and detailed flow patterns are compared with experimental measurements presented in the literature. A range of angles of attack, including early stalls, are examined. For best-performing parameters, an adequate refinement close to the wall was imperative in order to match experiments, especially during the upstroke motion of the airfoil, while for the downstroke phase, some differences still appeared. The agreement was greatly improved by using a 2.5D hybrid RANS-LES approach with enhanced delayed-Detached Eddy Simulation (DES) capabilities.

High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Seasonal climate variability determines crop productivity in Ethiopia, where rainfed smallholder farming systems dominate in the agriculture production. Under such conditions, a functional and granular spatial yield forecasting system could provide risk management options for farmers and agricultural and policy experts, leading to greater economic and social benefits under highly variable environmental conditions. Yet, there are currently only a few forecasting systems to support early decision making for smallholder agriculture in developing countries such as Ethiopia. To address this challenge, a study was conducted to evaluate a seasonal crop yield forecast methodology implemented in the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). CRAFT is a software platform that can run pre-installed crop models and use the Climate Predictability Tool (CPT) to produce probabilistic crop yield forecasts with various lead times. Here we present data inputs, model calibration, evaluation, and yield forecast results, as well as limitations and assumptions made during forecasting maize yield. Simulations were conducted on a 0.083° or ∼ 10 km resolution grid using spatially variable soil, weather, maize hybrids, and crop management data as inputs for the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). CRAFT combines gridded crop simulations and a multivariate statistical model to integrate the seasonal climate forecast for the crop yield forecasting. A statistical model was trained using 29 years (1991–2019) data on the Nino-3.4 Sea surface temperature anomalies (SSTA) as gridded predictors field and simulated maize yields as the predictand. After model calibration the regional aggregated hindcast simulation from 2015 to 2019 performed well (RMSE = 164 kg/ha). The yield forecasts in both the absolute and relative to the normal yield values were conducted for the 2020 season using different predictor fields and lead times from a grid cell to the national level. Yield forecast uncertainties were presented in terms of cumulative probability distributions. With reliable data and rigorous calibration, the study successfully demonstrated CRAFT’s ability and applicability in forecasting maize yield for smallholder farming systems. Future studies should re-evaluate and address the importance of the size of agricultural areas while comparing aggregated simulated yields with yield data collected from a fraction of the target area.

Assessment of Tidal Current Energy Resources in the Pearl River Estuary Using a Numerical Method

In this paper, a numerical method is employed to assess tidal current energy resources in the Pearl River Estuary, China. The numerical model for tidal current simulation in the estuary is developed based on the MIKE 21 model, which enables numerical simulations in estuaries, coastal areas, and oceans. The model has a grid resolution that varies from about 2500 m at the open boundary to 500–1000 m inside the estuary. Extensive model validation is performed by comparing the model predictions with field observations of tidal level and velocity at various stations in the Pearl River Estuary. The tidal characteristics are thoroughly analyzed. Energy fluxes and power densities are calculated along selected cross sections to evaluate the feasibility of tidal energy development in the Pearl River Estuary. The results indicate that the distribution of annual average tidal current power density in the Pearl River Estuary generally aligns with the spatial distribution of tidal currents. The annual average power density of tidal energy is typically below 0.10 kW/m2. The theoretical potential of tidal current energy resources in the Pearl River Estuary is assessed to be approximately 11,000 kW.

3D hydrodynamics simulations of a 3 M⊙ core helium burning star

ABSTRACT The inner structure of core helium burning (CHeB) stars remains uncertain due to the yet unknown nature of mixing at the boundary of their cores. Large convective cores beyond a bare Schwarzschild model are favoured both from theoretical arguments and from asteroseismological constraints. However, the exact nature of this extra mixing, and in particular the possible presence of semiconvective layers, is still debated. In this work, we approach this problem through a new avenue by performing the first full-sphere 3D hydrodynamics simulations of the interiors of CHeB stars. We use the ppmstar explicit gas dynamics code to simulate the inner 0.45$\, {\rm M}_{\odot }$ of a 3 M⊙ CHeB star. Simulations are performed using different Cartesian grid resolutions (7683, 11523, and 17283) and heating rates. We use two different initial states, one based on mesas's predictive mixing scheme (which significantly extends the core beyond the Schwarzschild boundary) and one based on the convective premixing approach (which exhibits a semiconvective interface). The general behaviour of the flow in the convective core and in the stable envelope (where internal gravity waves are observed) is consistent with our recent simulations of core convection in massive main-sequence stars, and so are the various luminosity scaling relations. The semiconvective layers are dominated by strong internal gravity waves that do not produce measurable species mixing, but overshooting motions from the convective core gradually homogenize the semiconvective interface. This process can possibly completely erase the semiconvective layers, which would imply that CHeB stars do not harbour a semiconvection zone.

EUTERPE: A global gyrokinetic code for stellarator geometry

The current state of the EUTERPE code is described with emphasis on the implemented models and their numerical implementation. The code solves the multi-species electromagnetic gyrokinetic equations in the full volume of a three-dimensional domain. Noise reduction of the particle-in-cell method is achieved by using a δf-method and Fourier filters. The field equations are discretized with B-splines and the resulting system of equations is solved iteratively. For linear simulations a phase-factor transformation is applied in order to strongly reduce the necessary grid resolution. Apart from the full gyrokinetic model, other numerically less expensive hybrid models are also implemented. They are mainly tailored for comparison with fluid theory and for studying the interaction of the bulk plasma with fast particles. The code is parallelized for CPUs by particle and domain decomposition. Good scalability up to several thousand nodes is demonstrated.

Fine scale simulation of sea salt aerosol under strong wind: WRF sensitivity test on PBL schemes and LES under Typhoon Hagibis

Identifying which factors among the planetary boundary layer (PBL) scheme, grid resolution, and source or sink terms dominantly influence the sea salt aerosol (SSA) spatial distribution is necessary. The dependence of SSA transport on PBL processes was investigated using a numerical weather prediction (NWP) model with a grid scale of ∼143 m. Size-segregated SSA transport during Typhoon Hagibis, which made landfall in Japan in 2019, was simulated using the Weather Research and Forecasting (WRF) model. The PBL schemes of Yonsei University (YSU), scale-aware YSU (Shin and Hong, 2015's scheme, SH) and Mellor-Yamada Nakanishi-Niino (MYNN), and large-eddy simulation (LES) subjected to turbulence transition enhancement via a cell perturbation method were tested to evaluate the reproducibility of observed values of wind speed, surface pressure, relative humidity, and temperature at meteorology masts. The results showed that wind speed was most influenced by the selection of the PBL scheme and LES. The distance from the coastline, as determined by the wind direction during the most intense typhoon stage, was correlated with a decrease in the time averaged SSA concentration when the storm moved inland. Despite the simulated SSA emission scheme's high sensitivity to wind speed (nearly 3rd power), the near-surface average concentration was less sensitive to the PBL schemes and LES selection than wind speed. However, the maximum arrival of the SSA differed by ∼20 % during the most vigorous stage of the typhoon. Budget analysis of the SSA transport equation highlighted that the advection term and gravity settling were most dependent on the PBL schemes and LES. The impacts of both factors on SSA transport were not limited to near-surface tendencies but were in a broader range over the atmosphere, up to 1–2 km. These results indicated that the vertical structure of the SSA field over the boundary layer depended on the PBL schemes and LES, thereby modulating the inland arrival of the SSA.

CIOFC1.0: a common parallel input/output framework based on C-Coupler2.0

Abstract. As earth system modeling develops ever finer grid resolutions, the inputting and outputting (I/O) of the increasingly large data fields becomes a processing bottleneck. Many models developed in China, as well as the community coupler (C-Coupler), do not fully benefit from existing parallel I/O supports. This paper reports the design and implementation of a common parallel input/output framework (CIOFC1.0) based on C-Coupler2.0. The CIOFC1.0 framework can accelerate the I/O of large data fields by parallelizing data read/write operations among processes. The framework also allows convenient specification by users of the I/O settings, e.g., the data fields for I/O, the time series of the data files for I/O, and the data grids in the files. The framework can also adaptively input data fields from a time series dataset during model integration, automatically interpolate data when necessary, and output fields either periodically or irregularly. CIOFC1.0 demonstrates the cooperative development of an I/O framework and coupler, and thus enables convenient and simultaneous use of a coupler and an I/O framework.

PHI-SMFE: spatial multi-scale feature extract neural network based on physical heterogeneous interaction for solving passive scalar advection in a 2-D unsteady flow

Fluid dynamic calculations play a crucial role in understanding marine biochemical dynamic processes, impacting the behavior, interactions, and distribution of biochemical components in aquatic environments. The numerical simulation of fluid dynamics is a challenging task, particularly in real-world scenarios where fluid motion is highly complex. Traditional numerical simulation methods enhance accuracy by increasing the resolution of the computational grid. However, this approach comes with a higher computational demand. Recent advancements have introduced an alternative by leveraging deep learning techniques for fluid dynamic simulations. These methods utilize discretized learned coefficients to achieve high-precision solutions on low-resolution grids, effectively reducing the computational burden while maintaining accuracy. Yet, existing fluid numerical simulation methods based on deep learning are limited by their single-scale analysis of spatially correlated physical fields, which fails to capture the diverse scale characteristics inherent in flow fields governed by complex laws in different physical space. Additionally, these models lack an effective approach to enhance correlation interactions among dynamic fields within the same system. To tackle these challenges, we propose the Spatial Multi-Scale Feature Extract Neural Network based on Physical Heterogeneous Interaction (PHI-SMFE). The PHI module is designed to extract heterogeneity and interaction information from diverse dynamic fields, while the SMFE module focuses on capturing multi-scale features in fluid dynamic fields. We utilize channel-biased convolution to implement a separation strategy, reducing the processing of redundant feature information. Furthermore, the traditional solution module based on the finite volume method is integrated into the network to facilitate the numerical solution of the discretized dynamic field in subsequent time steps. Comparative analysis with the current state-of-the-art model reveals that our proposed method offers a 41% increase in simulation accuracy and a 12.7% decrease in inference time during the iterative evolution of unsteady flow. These results underscore the superior performance of our model in terms of both simulation accuracy and computational speedup, establishing it as a state-of-the-art solution.

Urban WRF-Chem evaluation over a high-altitude tropical city

Morphology and grid resolution are important aspects that need to be considered in urban modeling applications, since together with buildings they induce a direct effect on wind and dispersion of pollutants over urban areas. In this study, we evaluate high-resolution simulations of a multi-layer urban canopy model (UCM) based on a local climate zone (LCZ) classification coupled to the Weather Research and Forecasting model with Chemistry (WRF-Chem), in the local meteorological conditions and air quality pollutants of a highly urbanized megacity. This modeling system, known as Building Effect Parameterization (BEP) considers the effects of buildings’ vertical and horizontal surfaces on the momentum that considerably impacts the lower part of the urban boundary layer (UBL). Simulations of the urbanized model (WRFu) were compared against a Noah land surface model (Noah LSM) with no urban physics (WRF) for the same period. It was observed that the LCZ classification and urban parameterization coupled to the model have a direct influence in meteorological parameters and pollutant concentrations. Urban simulations of temperature and wind speed showed higher sensitivity to initial and boundary conditions, increasing the correlation with observations and reducing the bias error. An important observation is that emissions drive air quality concentrations despite the improvements in local meteorology.