Low-resolution Grid Research Articles

The role of vertical grid resolution and turbulent diffusion uncertainty on chemical transport modeling

Chemical transport models (CTM) tend to perform poorly in simulating pollution processes under weak turbulent diffusion conditions. In this study, we address this issue from the perspectives of vertical grid resolution and vertical mixing schemes. Three vertical grid resolution configurations (L4, L12, L40) with the CHIMERE model are evaluated during a winter episode, which includes a heavy pollution episode (PE) in the Paris region. The results emphasize the significance of vertical grid resolution, particularly noticeable during nighttime, and consequently impacts CHIMERE simulations under nocturnal stable conditions. Consistent improvement in CTM modeling is observed with refined vertical resolutions and the first layer height based on a simple linear vertical diffusion scheme defined as the initial Kz diffusion (IKD) scheme. Compared to the other two configurations, the finest configuration (referred to as L4-IKD) demonstrates an average improvement in root mean square error of 23.26 % and 25.09 % on regular days (RD) and 62 % and 129 % during PE, respectively. However, simulations using the 1.5-order turbulence kinetic energy (TKE) based eddy diffusivity closure scheme, named the new eddy diffusion (NED), are more sensitive to the first layer height setup. Excessively fine first-layer heights can lead to inaccurate TKE calculations. Generally, models with low vertical grid resolution can reasonably predict air quality on RD or during light pollution events but struggle with heavy PEs. One straightforward enhancement strategy involves adding an extra fine first layer height in CTM simulations, resulting in an average 50.10 % improvement from L4-IKD to L12-IKD during PE. Another strategy is enhancing the model's vertical diffusion scheme, which improved the CTM modeling by 26.67 % compared with IKD during PE under identical vertical grid resolution.

The role of the electricity grid in operation-induced greenhouse gas emissions by a residential building: A multi-year retrospective simulation study

In view of the growing shares of renewables in the electricity grid in combination with the electrification of hvac (heating, ventilation, air conditioning) systems in residential buildings, the grid intensity (in terms of carbon dioxide emissions per unit of electric energy) becomes increasingly sensitive to weather conditions, and synchronicity between weather and the grid becomes a more critical aspect in building performance assessment. Using building performance simulation techniques to seek robust building designs requires awareness about the uncertainties in circumstantial factors that affect performance. This 2016 – 2022 retrospective study highlights the effects of using low or high temporal resolution grid emissions intensity data on projected operation-induced carbon dioxide emissions for a terraced dwelling in the Netherlands. Building fabric quality, the occupant profile, and systems configurations (i.e., hvac and photovoltaics) are varied to investigate the effects of the applied grid model resolution. This study shows that ignoring high-resolution grid intensity data is getting increasingly problematic; applying low resolution (annual) instead of high-resolution (15-minute) grid intensity data leads to an increasingly unjustified optimistic assessment both for net and gross emissions (either or not allowing for carbon displacement by feeding locally generated electricity into the grid).

Hybrid covariance super-resolution data assimilation

AbstractThe super-resolution data assimilation (SRDA) enhances a low-resolution (LR) model with a Neural Network (NN) that has learned the differences between high and low-resolution models offline and performs data assimilation in high-resolution (HR). The method enhances the accuracy of the EnKF-LR system for a minor computational overhead. However, performance quickly saturates when the ensemble size is increased due to the error introduced by the NN. We therefore combine the SRDA with the mixed-resolution data assimilation method (MRDA) into a method called “Hybrid covariance super-resolution data assimilation” (Hybrid SRDA). The forecast step runs an ensemble at two resolutions (high and low). The assimilation is done in the HR space by performing super-resolution on the LR members with the NN. The assimilation uses the hybrid covariance that combines the emulated and dynamical HR members. The scheme is extensively tested with a quasi-geostrophic model in twin experiments, with the LR grid being twice coarser than the HR. The Hybrid SRDA outperforms the SRDA, the MRDA, and the EnKF-HR at a given computational cost. The benefit is the largest compared to the EnKF-HR for small ensembles. However, even with larger computational resources, using a mix of high and low-resolution members is worth it. Besides, the Hybrid SRDA, the EnKF-HR, and the SRDA, unlike the MRDA, prevent the smoothing of dynamical structures of the background error covariance matrix. The Hybrid SRDA method is also attractive because it is customizable to available resources.

High fidelity FEM based on deep learning for arbitrary composite material structure

Due to the outstanding performance, composite materials are widely used and analyzing their properties and designing them based on performance has become a crucial task in the field of many manufacturing industries. Composite materials possess complex multiscale structures, and traditional fine-scale finite element modeling and analysis may lead to severe computational resource challenges. To overcome this difficulty, breakthroughs in key technologies of multiscale accelerated analysis algorithms are required. In this study, an innovative approach based on artificial intelligence and multiscale finite element method is presented. This approach involves partitioning the entire composite material structure into coarse grids that resemble homogenous structures of similar size, providing results consistent to fine-grid finite element analysis. By utilizing CNN for image feature recognition and employing the CGAN adversarial method, coarse-grid equivalent stiffness matrices and multiscale shape functions from completely random microstructures of composite materials can be obtained. Consequently, this enables a rapid response process from microstructure to low-resolution grid to high-resolution physical field, with remarkably accurate physical field results. Moreover, compared to traditional fine-grid finite element methods, this approach significantly reduces memory usage and computation time. This method is applicable to composite materials with varying shaped inclusions, different component properties, and diverse geometric distributions, allowing these materials to perform high-fidelity finite element calculations on coarse grids and predict their mechanical behavior. Furthermore, this breakthrough opens avenues for accelerating the optimization design of composite materials with diverse mechanical functionalities, by employing a bottom-up approach.

Arbitrary-Scale Video Super-resolution Guided by Dynamic Context

We propose a Dynamic Context-Guided Upsampling (DCGU) module for video super-resolution (VSR) that leverages temporal context guidance to achieve efficient and effective arbitrary-scale VSR. While most VSR research focuses on backbone design, the importance of the upsampling part is often overlooked. Existing methods rely on pixelshuffle-based upsampling, which has limited capabilities in handling arbitrary upsampling scales. Recent attempts to replace pixelshuffle-based modules with implicit neural function-based and filter-based approaches suffer from slow inference speeds and limited representation capacity, respectively. To overcome these limitations, our DCGU module predicts non-local sampling locations and content-dependent filter weights, enabling efficient and effective arbitrary-scale VSR. Our proposed multi-granularity location search module efficiently identifies non-local sampling locations across the entire low-resolution grid, and the temporal bilateral filter modulation module integrates content information with the filter weight to enhance textual details. Extensive experiments demonstrate the superiority of our method in terms of performance and speed on arbitrary-scale VSR.

Stochastic subgrid-scale modeling for particles dispersion and deposition for a filtered DNS channel flow field

The effect of subgrid-scale velocity fluctuations on the deposition and dispersion of solid particles in a filtered DNS turbulent channel flow at Reτ=180, and for particles with Stokes numbers of St = 1, 2, 5, 24 was studied. First, the turbulent flow field was simulated using the DNS on a high-resolution grid. Then, the filtered DNS flow fields were generated using several low-resolution grids. Particle tracking was performed for the DNS and filtered DNS channel flow fields, and the effects of filtering on particle deposition and concentration were studied. It was shown that filtering significantly decreases the particle deposition velocities at lower Stokes numbers (St = 1, 2, 5). However, at higher Stokes numbers (St = 24), the deposition velocity was less affected. Next, the ability of a stochastic model to generate the correct particle deposition velocities and concentration profiles was studied. A Langevin stochastic equation for modeling the subgrid-scale (SGS) fluid velocity fluctuations as seen by particles was developed and tested. The two input parameters of the stochastic model are the variance of SGS fluctuations in the wall-normal direction and the Lagrangian SGS time-scale. It was seen that the model prediction is sensitive to the SGS time scale used in the Langevin equation. The model was capable of predicting the correct deposition velocities and concentration profiles of lower inertia particles when the proper time-scale was used. For higher inertia particles and when a larger amount of energy was removed by filtering, the model was unable to predict the deposition velocities.

Enhanced Global Convolution Networks for Defect Detection in Full Wavefield Imaging

Non-Destructive Evaluation (NDE) techniques based on ultrasonic guided waves can provide relevant information about the health status of an inspected structure. The analysis of the wavefields recorded through Scanning Laser Doppler Vibrometers (SLDVs) has been used successfully in NDE systems to detect damages in planar isotropic materials, such as interlaminar fractures in composites. However, the high spatial point resolution required to ensure an accurate localization and quantification of defects typically implies a time-consuming acquisition process, which limits the applicability of such approaches. In this work, we exploit the potential of convolutional neural networks (CNNs) to perform segmentation on full wavefield images, obtained by reconstruction from a low resolution grid having a spatial sampling rate below the Nyquist frequency, for the purpose of detecting and localising delaminations in carbon fibre reinforced plastic (CFRP) plates. In particular, we trained an improved version of Global Convolutional Networks (GCN) using a public dataset containing 475 simulated cases of full wavefield Lamb waves propagation in a CFRP plate, generated by an actuator with a carrier frequency of 50 kHz. We adopted channel and spatial attention mechanisms to improve the accuracy of the networks and applied our method on (i) the resulting image given by the root mean square value (RMS) in time for each spatial position and on (ii) the 3-dimensional animation representing the full wavefield propagation. Our networks show the ability to accurately locate target damages with a spatial resolution 8 times higher than the dimension of the adopted sampling grid, achieving an Intersection over Union score equal to 0.78 with a number of scanning point more than 60 times lower than the number of pixels in the output segmentation mask.

Intelligent path planning by an improved RRT algorithm with dual grid map

This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm that synergizes the A* algorithm with the Rapidly-exploring Random Tree (RRT) approach. This hybrid model significantly enhances route timeliness and reliability, particularly in obstacle avoidance scenarios for driverless vehicles. Our methodology employs a 'two-level map' approach, where a lower-resolution grid map is derived from a high-resolution map. Utilizing the A* algorithm on this framework, we ascertain a preliminary 'coarse path' for the navigation target. The RRT algorithm, modified to reduce the traditional redundancy associated with random uniform sampling, is then applied for probabilistic sampling within this defined area. ovel aspect of our approach is the simultaneous generation of two trees, originating from both the start and end points, guided by a target-biased strategy and dual-direction theory. This method probabilistically expands towards the node of the opposite tree, thereby enhancing both the generation speed and trajectory viability. Further refinements are made through a pruning process, optimizing the path, and employing Bezier curves for smoothing, ensuring compliance with the dynamic constraints of Ackerman chassis vehicles. Comparative analysis in complex environments demonstrates the superiority of our proposed algorithm. It outperforms traditional methods with a 400 % increase in planning speed relative to the RRT-Connect algorithm, and a 30 % reduction in average path length. Additionally, the mean curvature of routes generated by our algorithm is 19 % lower than traditional routes, underscoring significant advancements in both the timeliness and viability of the planned routes.

A Flow Field Super-resolution Strategy for Direct Numerical Simulation Based on Physics-informed Convolutional Neural Networks

In the computational fluid dynamics method, the discretization of the solution domain has an important impact on the calculation results. The higher resolution grid improves the solution accuracy and is accompanied by a significant increase in the calculation time. How to improve efficiency under the premise of ensuring accuracy is of great significance in engineering. To this end, we propose a super-resolution strategy for direct numerical simulation (DNS): take the numerical simulation results at low-resolution grid as the initial solution, construct a model for super-resolution utilizing the convolutional neural networks, and embed the flow governing equations in the model to modify the initial solution. The proposed method is verified in the engineering case of pipeline transportation of non-Newtonian fluids. The results show that this strategy can improve the solution accuracy and shorten the simulation time. The deviation between the high-resolution results reconstructed by the model and the high-resolution flow field simulated by DNS is 63.18% lower than that of the low-resolution one simulated by DNS, and the calculation time is saved by 84.65%.

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.

Dimensionality reduction for regularization of sparse data-driven RANS simulations

Data assimilation can reduce the model-form errors of RANS simulations. A spatially distributed corrective parameter field can be introduced into the closure model, whose optimal values can be efficiently found by an adjoint method and gradient-based optimization. When assimilating experimental data, which in most cases are sparsely distributed or based on a low-resolution grid, the inverse problem is highly underdetermined and thus ambiguous. Therefore, to reduce the ambiguity, regularization is required. Established regularization approaches such as total variation and Sobolev gradient methods can produce smooth and physically meaningful velocity fields in many cases. However, if the measurements are located close to walls, spiky and unphysical wall shear stress profiles can occur. A new regularization strategy based on a piecewise linear approximation of the corrective field is proposed. This method is shown to lead to a very accurate free stream velocity field and smooth wall shear stress profiles. The resulting skin friction drag error for the case of flow over periodic hills was around 1.38% which is seven times lower than the error obtained with the Sobolev gradient and two orders of magnitude lower than that obtained with the other two methods.

Self-adaptive turbulence eddy simulation of a premixed jet combustor

The self-adaptive turbulence eddy simulation (SATES) is employed to investigate the lean premixed methane/air turbulent flame in a single-nozzle model gas turbine combustor, in which the high axial momentum jet issuing from an off-center nozzle facilitates the development of a large-scale, dominant lateral recirculation zone that stabilizes the flame. For turbulence modeling, the SATES method can dynamically adjust the proportion of resolving and modeling of turbulent scales according to the local grid scale and turbulence length scales, thus depressing the gird-sensitivity in large eddy simulation (LES) calculation. For combustion modeling, the thickened flame model with a reduced chemistry mechanism is integrated into the SATES turbulence modeling framework to capture the unsteady flame dynamics. The accuracy of SATES results is assessed against experimental data, as well as the ones from LES and the detached eddy simulation (DES) of this burner with the same combustion model and grids. The predicted length of large-scale recirculation of the flow field by SATES is significantly better than that by LES and DES with low mesh resolution. Detailed comparisons show that the SATES adaptively improves the modeling degree of near-wall turbulence to improve the prediction accuracy with the low grid resolution. Similar to LES, the SATES method does not cause serious delay of shear layer instability and weakening of flame wrinkle observed in DES. The study demonstrates the suitability and accuracy of the hybrid turbulence modeling methods of SATES for complex turbulent flame simulations coupled with suitable combustion model.

A force partitioning method to model spherical particles in liquid flows with low grid resolution

We have extended the traditional point-force method for modeling suspended small particles to include a force partitioning approach for particles that are slightly larger than the grid size. This size range falls between the limits of applicability for both the point-force and immersed-boundary methods. In our approach, we partition the modified term of the momentum exchange between the solid and fluid phases into the grid cells based on the area and volume fraction of the solid particle contained in each cell. We demonstrate that, for particle Reynolds numbers less than 40 and using the relatively coarse grid resolution, our method yields resolution-consistent results for the flow field, which are in good agreement with those obtained using the highly resolved immersed boundary method. By incorporating our method with the immersed-boundary method to handle large-sized particles, we demonstrate the ability to efficiently model the segregation of mixed-sized particles in liquid flows.

Generating high-resolution climate maps from sparse and irregular observations using a novel hybrid RBF network

ABSTRACT Sparse and irregular climate observations in many developing countries are not enough to satisfy the need of assessing climate change risks and planning suitable mitigation strategies. The wide-used statistical downscaling model (SDSM) software tools use multi-linear regression to extract linear relations between large-scale and local climate variables and then produce high-resolution climate maps from sparse climate observations. The latest machine learning techniques (e.g. SRCNN, SRGAN) can extract nonlinear links, but they are only suitable for downscaling low-resolution grid data and cannot utilize the link to other climate variables to improve the downscaling performance. In this study, we proposed a novel hybrid RBF (Radial Basis Function) network by embedding several RBF networks into new RBF networks. Our model can well incorporate climate and topographical variables with different resolutions and extract their nonlinear relations for spatial downscaling. To test the performance of our model, we generated high-resolution precipitation, air temperature and humidity maps from 34 meteorological stations in Bangladesh. In terms of three statistical indicators, the accuracy of high-resolution climate maps generated by our hybrid RBF network clearly outperformed those using a multi-linear regression (MLR), Kriging interpolation or a pure RBF network.

Validation of PANS and effects of ground and wheel motion on the aerodynamic behaviours of a square-back van

This paper presents a numerical investigation of the effects of the moving ground and rotating wheels on the turbulent flow around a 1/10 scaled square-back van model. A comprehensive comparison among the partially averaged Navier–Stokes (PANS), large eddy simulation (LES) and particle image velocimetry (PIV) involving the aerodynamic drag, the wake topology, the velocity and the Reynolds stress profiles in the wake region is conducted. The proper orthogonal decomposition (POD) and fast Fourier transform (FFT) are applied to the shear layers shedding from the trailing edges to comment on the coherent structures and their frequency content. The Reynolds number for both simulations and experiments is set to Re = 2.5 × 105 based on the inlet velocity ${U_{inf}} = 9\;\textrm{m}\;{\textrm{s}^{ - 1}}$ and the width of the model W = 0.17 m. The results show that PANS accurately predicts the flow field measured in experiments and predicted by a resolved LES, even with a low-resolution grid. The superiority of the PANS approach could provide good guidance for industrial research in predicting the turbulent flow around the square-back van model with affordable computational grids. The ground and wheel motion mechanism on the aerodynamic forces has been revealed by analysing the surface pressure distribution, the wheels’ surrounding flow, the underbody flow characteristics and the turbulent wake structures. The effects of the ground and wheel motion on the frequency, evolution and development characteristics of the wake shear layers are analysed, thus providing relevant insights for future experimental investigations of square-back van models.

Numerical Investigation of Wall-Cooling Effect on Aero-Optical Distortions for Hypersonic Boundary Layer

Compressible wall-modeled large-eddy simulations of Mach 8 turbulent boundary-layer flows over a flat plate were carried out for the conditions of the hypersonic wind tunnel at Sandia National Laboratories. The simulations provide new insight into the effect of wall cooling on the aero-optical path distortions for hypersonic turbulent boundary-layer flows. Four different wall-to-recovery temperature ratios, 0.3, 0.48, 0.71, and 0.89, are considered. Despite the much lower grid resolution, the mean velocity, temperature, and resolved Reynolds stress profiles from the simulation for a temperature ratio of 0.48 are in good agreement with those from a reference direct numerical simulation. The normalized root-mean-square optical path difference obtained from the present simulations is compared with that from reference direct numerical simulations, Sandia experiments, as well as predictions obtained with a semi-analytical model by Notre Dame University. The present analysis focuses on the effect of wall cooling on the wall-normal density correlations, on key underlying assumptions of the aforementioned model such as the strong Reynolds analogy, and on the elevation angle effect on the optical path difference. Wall cooling is found to increase the velocity fluctuations and decrease the density fluctuations, resulting in an overall reduction of the normalized optical path distortion. Compared to the simulations, the basic strong Reynolds analogy overpredicts the temperature fluctuations for cooled walls. Also different from the strong Reynolds analogy, the velocity and temperature fluctuations are not perfectly anticorrelated. Finally, as the wall temperature is raised, the density correlation length, away from the wall but inside the boundary layer, increases significantly for beam paths tilted in the downstream direction.

Cell2Grid: an efficient, spatial, and convolutional neural network-ready representation of cell segmentation data.

Cell segmentation algorithms are commonly used to analyze large histologic images as they facilitate interpretation, but on the other hand they complicate hypothesis-free spatial analysis. Therefore, many applications train convolutional neural networks (CNNs) on high-resolution images that resolve individual cells instead, but their practical application is severely limited by computational resources. In this work, we propose and investigate an alternative spatial data representation based on cell segmentation data for direct training of CNNs. We introduce and analyze the properties of Cell2Grid, an algorithm that generates compact images from cell segmentation data by placing individual cells into a low-resolution grid and resolves possible cell conflicts. For evaluation, we present a case study on colorectal cancer relapse prediction using fluorescent multiplex immunohistochemistry images. We could generate Cell2Grid images at resolution that were 100 times smaller than the original ones. Cell features, such as phenotype counts and nearest-neighbor cell distances, remain similar to those of original cell segmentation tables ( ). These images could be directly fed to a CNN for predicting colon cancer relapse. Our experiments showed that test set error rate was reduced by 25% compared with CNNs trained on images rescaled to with bilinear interpolation. Compared with images at resolution (bilinear rescaling), our method reduced CNN training time by 85%. Cell2Grid is an efficient spatial data representation algorithm that enables the use of conventional CNNs on cell segmentation data. Its cell-based representation additionally opens a door for simplified model interpretation and synthetic image generation.

A simple hybrid statistical–dynamical downscaling method for emulating regional climate models over Western Europe. Evaluation, application, and role of added value?

A hybrid statistical dynamical downscaling method intended to emulate regional climate models is described and applied to Western Europe. The method is based on a constructed analogues algorithm, already used for statistical downscaling. For emulation, the statistical downscaling relationship is not derived from observations but from climate projections at low and high resolution. The hybrid approach therefore does not rely on the stationarity assumption inherent to conventional statistical downscaling. Within a perfect model framework, and using a large number of regional projections, the hybrid method is shown to reproduce climate change signals very well and to outperform a conventional statistical downscaling method also based on constructed analogues. The hybrid approach remains skillful even when applied to very low resolution climate data. In practice, two emulation modes exist. In the GCM/RCM mode, the downscaling relationship is built between a RCM and its forcing GCM. In the RCM/RCM mode, the relationship is built between a RCM and the same RCM after aggregation of its results to a low resolution grid. The large-scale climate change signal of the downscaled GCM is generally retained with the RCM/RCM mode, but not with the GCM /RCM mode. Additionally, the choice of the GCM/RCM pair used for learning leads to large differences in downscaling results at large scale (i.e. at low resolution) with the GCM /RCM mode, but not with the RCM/RCM mode. These results are explained by the differences that generally exist at large scale between projected changes by current RCMs and their forcing GCMs. Whether these differences are a testimony of a real added value of RCMs at large scale in the climate change context, or whether they have other causes, is therefore a crucial question.

Magnetic grid resolution enhancement using machine learning: A case study from the Eastern Goldfields Superterrane

Densely sampled geophysical surveys are a key driver for mineral exploration, but sample density, and therefore grid resolution, is limited by survey cost. Consequently, computational methods are resorted to for upsampling, or ‘super-resolving’, of gridded geophysical survey data. However, existing approaches such as interpolation filters do not leverage high-resolution detail from pre-existing geophysical surveys. Through the application of state-of-the-art deep learning super-resolution architectures, accurate high-resolution grids are predicted from corresponding low-resolution grid priors. Specifically, the RDN and ESRGAN+ neural network architectures, which were originally developed for enhancing images, are applied to enhance magnetic surveys and trained with high-resolution and low-resolution magnetic grids of the same extent. 80 m cell size magnetic grids are upsampled to 20 m cell size using this method, and predicted value and structural accuracy comparisons to the corresponding ground truth 20 m grids are presented over two test sites in the Eastern Goldfields Superterrane, Western Australia. The method based on RDN achieves 53 % lower error compared to Bicubic interpolation. The case study demonstrates that the deep learning approach can improve the resolution and frequency content of geophysical surveys without requiring additional sampling expense, while remaining accurate against known ground truth surveys. Super-resolution may assist in the interpretation of low-resolution survey grids, however these upsampled grids cannot perfectly recreate the accuracy of highly sampled surveys gridded at their optimal cell size.

Low-Grid-Resolution-RANS-Based Data Assimilation of Time-Averaged Separated Flow Obtained by LES

ABSTRACT The objective of this study is to obtain accurate flow field analysis results in a short computational time by using data assimilation, which increases the accuracy of Reynolds averaged Navier-Stokes (RANS) simulations with low grid resolution. The large-eddy simulation (LES) results are assimilated into RANS simulations. In those simulations, the turbulence-model parameters are optimised by an ensemble Kalman filter with a proposed method for adaptive hyperparameter optimisation. The target of calculations is the flow field around a square cylinder of the Reynolds number of approximately . Only the surface pressure of the square cylinder is used as an observation variable. For this shape, the assimilated RANS flow field is similar to that given by the LES analysis, and the drag coefficient reproducibility is improved by . The turbulence-model parameters are also used in the analyses of different cross-sectional shape and are found to improve the reproducibility of the flow field.