A machine learning framework for LES closure terms

In the present work, we explore the capability of artificial neural networks (ANN) to predict the closure terms for large eddy simulations (LES) solely from coarse-scale data. To this end, we derive a consistent framework for LES closure models, with special emphasis laid upon the incorporation of implicit discretization-based filters and numerical approximation errors. We investigate implicit filter types, which are inspired by the solution representation of discontinuous Galerkin and finite volume schemes and mimic the behaviour of the discretization operator, and a global Fourier cutoff filter as a representative of a typical explicit LES filter. Within the perfect LES framework, we compute the exact closure terms for the different LES filter functions from direct numerical simulation results of decaying homogeneous isotropic turbulence. Multiple ANN with a multilayer perceptron (MLP) or a gated recurrent unit (GRU) architecture are trained to predict the computed closure terms solely from coarse-scale input data. For the given application, the GRU architecture clearly outperforms the MLP networks in terms of accuracy, whilst reaching up to 99.9% cross-correlation between the networks' predictions and the exact closure terms for all considered filter functions. The GRU networks are also shown to generalize well across different LES filters and resolutions. The present study can thus be seen as a starting point for the investigation of data-based modeling approaches for LES, which not only include the physical closure terms, but account for the discretization effects in implicitly filtered LES as well

Toward Discretization-Consistent Closure Schemes for Large Eddy Simulation Using Reinforcement Learning

This study proposes a novel method for developing discretization-consistent closure schemes for implicitly filtered Large Eddy Simulation (LES). Here, the induced filter kernel, and thus the closure terms, are determined by the properties of the grid and the discretization operator, leading to additional computational subgrid terms that are generally unknown in a priori analysis. In this work, the task of adapting the coefficients of LES closure models is thus framed as a Markov decision process and solved in an a posteriori manner with Reinforcement Learning (RL). This optimization framework is applied to both explicit and implicit closure models. The explicit model is based on an element-local eddy viscosity model. The optimized model is found to adapt its induced viscosity within discontinuous Galerkin (DG) methods to homogenize the dissipation within an element by adding more viscosity near its center. For the implicit modeling, RL is applied to identify an optimal blending strategy for a hybrid DG and Finite Volume (FV) scheme. The resulting optimized discretization yields more accurate results in LES than either the pure DG or FV method and renders itself as a viable modeling ansatz that could initiate a novel class of high-order schemes for compressible turbulence by combining turbulence modeling with shock capturing in a single framework. All newly derived models achieve accurate results that either match or outperform traditional models for different discretizations and resolutions. Overall, the results demonstrate that the proposed RL optimization can provide discretization-consistent closures that could reduce the uncertainty in implicitly filtered LES.Comment: 24 pages, 14 figures. Accepted Manuscript. This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Physics of Fluids 35 (2023) and may be found at https://doi.org/10.1063/5.017622

Investigating Model-Data Inconsistency in Data-Informed Turbulence Closure Terms

In the present work, we investigate the stability of turbulence closure predictions from neural network models and highlight the role of model-data-inconsistency during inference. We quantify this inconsistency by applying the Mahalanobis distance and demonstrate that the instability of the model predictions in practical large eddy simulations (LES) correlates with a deviation of the input data between the training dataset and actual simulation data. Moreover, the method of 'stability training' is applied to increase the robustness of recurrent artificial neural networks (ANN) against small perturbations in the input, which are typically unavoidable in any practical scenario. We show that this method can increase the stability of simulations with ANN-based closure term predictions significantly. The models also achieve good accuracy on the blind testing set in comparison to the baseline model trained without stability training. The work presented here can thus be seen as a building block towards long-term stable data-driven models for dynamical systems and highlights methods to detect and counter model-data-inconsistencies

Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows

Standard kernel methods for machine learning usually struggle when dealing with large datasets. We review a recently introduced Structured Deep Kernel Network (SDKN) approach that is capable of dealing with high-dimensional and huge datasets - and enjoys typical standard machine learning approximation properties. We extend the SDKN to combine it with standard machine learning modules and compare it with Neural Networks on the scientific challenge of data-driven prediction of closure terms of turbulent flows. We show experimentally that the SDKNs are capable of dealing with large datasets and achieve near-perfect accuracy on the given application

An Efficient Sliding Mesh Interface Method for High-Order Discontinuous Galerkin Schemes

Sliding meshes are a powerful method to treat deformed domains in computational fluid dynamics, where different parts of the domain are in relative motion. In this paper, we present an efficient implementation of a sliding mesh method into a discontinuous Galerkin compressible Navier-Stokes solver and its application to a large eddy simulation of a 1-1/2 stage turbine. The method is based on the mortar method and is high-order accurate. It can handle three-dimensional sliding mesh interfaces with various interface shapes. For plane interfaces, which are the most common case, conservativity and free-stream preservation are ensured. We put an emphasis on efficient parallel implementation. Our implementation generates little computational and storage overhead. Inter-node communication via MPI in a dynamically changing mesh topology is reduced to a bare minimum by ensuring a priori information about communication partners and data sorting. We provide performance and scaling results showing the capability of the implementation strategy. Apart from analytical validation computations and convergence results, we present a wall-resolved implicit LES of the 1-1/2 stage Aachen turbine test case as a large scale practical application example

Towards Exascale CFD Simulations Using the Discontinuous Galerkin Solver FLEXI

Modern high-order discretizations bear considerable potential for the exascale era due to their high fidelity and the high, local computational load that allows for computational efficiency in massively parallel simulations. To this end, the discontinuous Galerkin (DG) framework FLEXI was selected to demonstrate exascale readiness within the Center of Excellence for Exascale CFD (CEEC) by simulating shock buffet on a three-dimensional wing segment at transsonic flight conditions. This paper summarizes the recent progress made to enable the simulation of this challenging exascale problem. For this, it is first demonstrated that FLEXI scales excellently to over 500 000 CPU cores on HAWK at the HLRS. To tackle the considerable resolution requirements near the wall, a novel wall model is proposed that takes compressibility effects into account and yields decent results for the simulation of a NACA 64A-110 airfoil. To address the shocks in the domain, a finite-volume-based shock capturing method was implemented in FLEXI, which is validated here using the simulation of a linear compressor cascade at supersonic flow conditions, where the method is demonstrated to yield efficient, robust and accurate results. Lastly, we present the TensorFlow-Fortran-Binding (TFFB) as an easy-to-use library to deploy trained machine learning models in Fortran solvers such as FLEXI.Comment: 15 pages, 5 figure

Impact of pre-hospital handling and initial time to cranial computed tomography on outcome in aneurysmal subarachnoid hemorrhage patients with out-of-hospital sudden cardiac arrest—a retrospective bi-centric study

BackgroundAneurysmal subarachnoid hemorrhage (SAH) presents occasionally with cardiac arrest (CA). The impact of pre-hospital and emergency room (ER) treatment on outcome remains unclear. Therefore, we investigated the impact of pre-hospital treatment, focusing on lay cardiopulmonary resuscitation (CPR), and ER handling on the outcome of SAH patients with out-of-hospital CA (OHCA).MethodsIn this bi-centric retrospective analysis, we reviewed SAH databases for OHCA and CPR from January 2011 to June 2021. Patients were analyzed for general clinical and epidemiological parameters. CPR data were obtained from ambulance reports and information on ER handling from the medical records. Data were correlated with patient survival at hospital discharge as a predefined outcome parameter.ResultsOf 1,120 patients with SAH, 45 (4.0%) were identified with OHCA and CPR, 38 of whom provided all required information and were included in this study. Time to resuscitation was significantly shorter with lay resuscitation (5.3 ± 5.2 min vs. 0.3 ± 1.2 min, p = 0.003). Nineteen patients were not initially scheduled for cranial computed tomography (CCT), resulting in a significantly longer time interval to first CCT (mean ± SD: 154 ± 217 min vs. 40 ± 23 min; p < 0.001). Overall survival to discharge was 31.6%. Pre-hospital lay CPR was not associated with higher survival (p = 0.632). However, we observed a shorter time to first CCT in surviving patients (p = 0.065)ConclusionsOHCA in SAH patients is not uncommon. Besides high-quality CPR, time to diagnosis of SAH appears to play an important role. We therefore recommend considering CCT diagnostics as part of the diagnostic algorithm in patients with OHCA

Prediction of underlying atrial fibrillation in patients with a cryptogenic stroke: results from the NOR-FIB Study

Background - Atrial fibrillation (AF) detection and treatment are key elements to reduce recurrence risk in cryptogenic stroke (CS) with underlying arrhythmia. The purpose of the present study was to assess the predictors of AF in CS and the utility of existing AF-predicting scores in The Nordic Atrial Fibrillation and Stroke (NOR-FIB) Study. Method - The NOR-FIB study was an international prospective observational multicenter study designed to detect and quantify AF in CS and cryptogenic transient ischaemic attack (TIA) patients monitored by the insertable cardiac monitor (ICM), and to identify AF-predicting biomarkers. The utility of the following AF-predicting scores was tested: AS5F, Brown ESUS-AF, CHA2DS2-VASc, CHASE-LESS, HATCH, HAVOC, STAF and SURF. Results - In univariate analyses increasing age, hypertension, left ventricle hypertrophy, dyslipidaemia, antiarrhythmic drugs usage, valvular heart disease, and neuroimaging findings of stroke due to intracranial vessel occlusions and previous ischemic lesions were associated with a higher likelihood of detected AF. In multivariate analysis, age was the only independent predictor of AF. All the AF-predicting scores showed significantly higher score levels for AF than non-AF patients. The STAF and the SURF scores provided the highest sensitivity and negative predictive values, while the AS5F and SURF reached an area under the receiver operating curve (AUC) > 0.7. Conclusion - Clinical risk scores may guide a personalized evaluation approach in CS patients. Increasing awareness of the usage of available AF-predicting scores may optimize the arrhythmia detection pathway in stroke units

How the microbiome can help detect precancerous lesions and prevent anal cancer

This is a summary of: Serrano-Villar, S. et al. Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer. Nat. Med. https://doi.org/10.1038/s41591-023-02407-3 (2023).[EN] This study reveals that the production of cobalamin and succinyl-CoA is increased in the anal microbiome of patients with precancerous anal lesions. Testing for these two metabolites significantly improves diagnostic accuracy over standard cytology screening, which suggests potential for enhanced screening strategies for anal cancer.Peer reviewe

Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer

Human papillomavirus can cause preinvasive, high-grade squamous intraepithelial lesions (HSILs) as precursors to cancer in the anogenital area, and the microbiome is suggested to be a contributing factor. Men who have sex with men (MSM) living with human immunodeficiency virus (HIV) have a high risk of anal cancer, but current screening strategies for HSIL detection lack specificity. Here, we investigated the anal microbiome to improve HSIL screening. We enrolled participants living with HIV, divided into a discovery (n = 167) and validation cohort (n = 46), and who were predominantly (93.9%) cisgender MSM undergoing HSIL screening with high-resolution anoscopy and anal biopsies. We identified no microbiome composition signatures associated with HSILs, but elevated levels of microbiome-encoded proteins producing succinyl coenzyme A and cobalamin were significantly associated with HSILs in both cohorts. Measurement of these candidate biomarkers alone in anal cytobrushes outperformed anal cytology as a diagnostic indicator for HSILs, increasing the sensitivity from 91.2% to 96.6%, the specificity from 34.1% to 81.8%, and reclassifying 82% of false-positive results as true negatives. We propose that these two microbiome-derived biomarkers may improve the current strategy of anal cancer screening. © 2023, The Author(s), under exclusive licence to Springer Nature America, Inc.This work was supported by the ERANET TRANSCAN-2 program, JTC 2016 (SCRAtCH project, grant agreement no. 643638), funded by Instituto de Salud Carlos III (project AC17/00019), AECC (grant TRNSC17002SER), Lombardy Foundation for Biomedical Research, Italy (SCRAtCH project, grant agreement no. 643638); Federal Ministry of Education and Research, Germany (SCRAtCH project, grant agreement no. 643638); and the Research Council of Norway and Norwegian Cancer Society, Norway (SCRAtCH project, grant agreement no. 643638). The work was also supported by grants PI18/00154, ICI20/00058 and PI21/00141, funded by Instituto de Salud Carlos III and cofounded by the European Union, and grants PID2020-112758RB-I00 and PDC2021-121534-I00 funded by the Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación (AEI) (https://doi.org/10.13039/501100011033) and the European Union (‘NextGenerationEU’). The authors thank all of the study participants and their families and the staff involved in this study for their commitment to clinical research.The data used for these analyses are available as supporting material at https://github.com/sajanraju/SCRAtCH-Codes. All of the sequences are publicly available in the European Nucleotide Archive database under accession number PRJEB58898. The mass spectrometry proteomics data have been deposited with the ProteomeXchange Consortium via the PRIDE partner repository70 with the dataset identifier PXD037268.Peer reviewe