Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow

Fully connected neural networks(FCNNs)have been developed for the closure of subgrid-scale(SGS)stress and SGS heat flux in large-eddy simulations of compressible turbulent channel flow.The FCNNbased SGS model trained using data with Mach number Ma=3.0 and Reynolds number Re=3000 was applied to situations with different Mach numbers and Reynolds numbers.The input variables of the neural network model were the filtered velocity gradients and temperature gradients at a single spatial grid point.The a priori test showed that the FCNN model had a correlation coefficient larger than 0.91 and a relative error smaller than 0.43,with much better reconstructions of SGS unclosed terms than the dynamic Smagorinsky model(DSM).In a posteriori test,the behavior of the FCNN model was marginally better than that of the DSM in predicting the mean velocity profiles,mean temperature profiles,turbulent intensities,total Reynolds stress,total Reynolds heat flux,and mean SGS flux of kinetic energy,and outperformed the Smagorinsky model.

Machine learning in mechanics

Machine learning has attracted much attention in various fields of mechanics. It can represent high-dimensional complex nonlinear systems and has powerful optimization algorithms. So far, machine learning has achieved much success in various mechanical simulation problems, including reconstruction and reduced-order modeling of complex mechanical systems, turbulence modeling and simulation, aerodynamic optimization design for wings, flow control,etc.

Spatial artificial neural network model for subgrid-scale stress and heat flux of compressible turbulence

The subgrid-scale(SGS)stress and SGS heat flux are modeled by using an artificial neural network(ANN)for large eddy simulation(LES)of compressible turbulence.The input features of ANN model are based on the first-order and second-order derivatives of filtered velocity and temperature at different spatial locations.The proposed spatial artificial neural network(SANN)model gives much larger correlation coefficients and much smaller relative errors than the gradient model in an a priori analysis.In an a posteriori analysis,the SANN model performs better than the dynamic mixed model(DMM)in the prediction of spectra and statistical properties of velocity and temperature,and the instantaneous flow structures.

Constraine d large-e ddy simulation of turbulent flow over inhomogeneous rough surfaces

In this work we extend the method of the constrained large-eddy simulation(CLES)to simulate the tur-bulent flow over inhomogeneous rough walls.In the original concept of CLES,the subgrid-scale(SGS)stress is constrained so that the mean part and the fluctuation part of the SGS stress can be modelled separately to improve the accuracy of the simulation result.Here in the simulation of the rough-wall flows,we propose to interpret the extra stress terms in the CLES formulation as the roughness-induced stress so that the roughness inhomogeneity can be incorporated by modifying the formulation of the constrained SGS stress.This is examined with the simulations of the channel flow with the spanwise alternating high/low roughness strips.Then the CLES method is employed to investigate the temporal response of the turbulence to the change of the wall condition from rough to smooth.We demonstrate that the temporal development of the internal boundary layer is just similar to that in a spatial rough-to-smooth transition process,and the spanwise roughness inhomogeneity has little impact on the transition process.

Recent progress in compressible turbulence

In this paper, we review some recent studies on compressible turbulence conducted by the authors＇ group, which include fundamental studies on compressible isotropic turbulence （CIT） and applied studies on developing a con- strained large eddy simulation （CLES） for wall-bounded turbulence. In the first part, we begin with a newly pro- posed hybrid compact-weighted essentially nonoscillatory （WENO） scheme for a CIT simulation that has been used to construct a systematic database of CIT. Using this database various fundamental properties of compressible turbulence have been examined, including the statistics and scaling of compressible modes, the shocklet-turbulence interac- tion, the effect of local compressibility on small scales, the kinetic energy cascade, and some preliminary results from a Lagrangian point of view. In the second part, the idea and for- mulas of the CLES are reviewed, followed by the validations of CLES and some applications in compressible engineering problems.

Fourier neural operator approach to large eddy simulation of three-dimensional turbulence

Fourier neural operator(FNO)model is developed for large eddy simulation(LES)of three-dimensional(3D)turbulence.Velocity fields of isotropic turbulence generated by direct numerical simulation(DNS)are used for training the FNO model to predict the filtered velocity field at a given time.The input of the FNO model is the filtered velocity fields at the previous several time-nodes with large time lag.In the a posteriori study of LES,the FNO model performs better than the dynamic Smagorinsky model(DSM)and the dynamic mixed model(DMM)in the prediction of the velocity spectrum,probability density functions(PDFs)of vorticity and velocity increments,and the instantaneous flow structures.Moreover,the proposed model can significantly reduce the computational cost,and can be well generalized to LES of turbulence at higher Taylor-Reynolds numbers.

取代环己酮还原立体选择性的过渡态理论解释

对于取代环已酮还原产物的立体选择性,仅通过还原试剂的动力学条件和生成产物的热力学稳定性进行预测是不准确的,需要考虑反应过程中存在的其他因素。采用过渡态理论可以对取代环已酮还原产物的立体选择性进行更全面、更准确的预测。一方面,还原试剂进攻羰基形成过渡态时,表现出决定立体选择性的空间位阻效应和电子效应;另一方面,反应物优势构象,即形成过渡态时的扭转应变和相对于标准对交叉构象偏转角的大小对产物的立体选择性也不容忽视。本文从过渡态形成过程中所体现的空间位阻、电子效应和构象三方面进行讨论,阐述了取代环已酮还原产物的立体选择性。

Effect of Multi-parameter Environmental Factors on Cucumber Leaf Surface Wetness

In this study, artificial leaf resistance was used to simulate leaf wetness. Specific to the solar greenhouse environment in Tianjin, microclimate monitoring equipment was installed for the collection of temperature group and humidity group data, as well as solar radiation and leaf wetness in the greenhouse. In order to reduce the complexity of multivariate factor prediction and ensure the richness of selected data types, correlation analysis was made to the 2 groups of data, screening 5 000 groups of data, including the humidity group data RH, RH_(20), RH_(40), temperature group data T, T_(20), T_(40), and solar radiation W. The data were then analyzed by principal component analysis, screening out 4 groups of principal components to show the leaf wetness index.

Sarmentosin Induces Autophagy-dependent Apoptosis via Activation of Nrf2 in Hepatocellular Carcinoma

Background and Aims:Hepatocellular carcinoma(HCC)is a common and deadly cancer.Accumulating evidence supports modulation of autophagy as a novel approach for determining cancer cell fate.The aim of this study to evaluate the effectiveness of sarmentosin,a natural compound,on HCC in vitro and in vivo and elucidated the underlying mechanisms.Methods:Cell functions and signaling pathways were analyzed in HepG2 cells using western blotting,real-time PCR,siRNA,transmission electron microscopy and flow cytometry.BALB/c nude mice were injected with HepG2 cells to produce a xenograft tumour nude mouse model for in vivo assessments and their tumors,hearts,lungs and kidneys were isolated.Results:We found that autophagy was induced by sarmentosin in a concentration-and timedependent manner in human HCC HepG2 cells by western blot assays and scanning electron microscopy.Sarmentosin-induced autophagy was abolished by the autophagy inhibitors 3-methyladenine,chloroquine,and bafilomycin A1.Sarmentosin activated Nrf2 in HepG2 cells,as shown by increased nuclear translocation and upregulated expression of Nrf2 target genes.Phosphorylation of mTOR was also inhibited by sarmentosin.Sarmentosin stimulated caspasedependent apoptosis in HepG2 cells,which was impaired by silencing Nrf2 or chloroquine or knocking down ATG7.Finally,sarmentosin effectively repressed HCC growth in xenograft nude mice and activated autophagy and apoptosis in HCC tissues.Conclusions:This study showed sarmentosin stimulated autophagic and caspase-dependent apoptosis in HCC,which required activation of Nrf2 and inhibition of mTOR.Our research supports Nrf2 as a therapeutic target for HCC and sarmentosin as a promising candidate for HCC chemotherapy.

Deconvolutional artificial-neural-network framework for subfilter-scale models of compressible turbulence

We establish a deconvolutional artificial-neural-network(D-ANN)approach in large-eddy simulation(LES)of compressible turbulent flow.Filtered variables in the neighboring locations are taken as the inputs of D-ANN to recover original(unfiltered)variables,including density,momentum and pressure.The scale-similarity form is adopted to reconstruct subfilter-scale(SFS)terms.The proposed D-ANN models can give better a priori predictions of the sub-filter stress and heat flux than the classical approximate-deconvolution method(ADM)and the velocity-gradient model(VGM).The predicted SFS terms with the D-ANN models have correlation coefficients larger than 98.4%and relative errors smaller than 18%.In the a posteriori analysis,the D-ANN model compares against the implicit LES(ILES),the dynamic-Smagorinsky model(DSM),and the dynamic-mixed model(DMM).The D-ANN model predicts better than these classical models for velocity spectra,statistical properties of SFS kinetic energy flux and velocity increments.The turbulence statistics and transient velocity divergence are also accurately reconstructed.The type of explicit filter and the impact of compressibility do not significantly affect a posteriori accuracy of the D-ANN model.Results showthat the proposed D-ANN approach has a great potential in developing highly accurate SFS models for large-eddy simulation of complex compressible turbulent flow.

碘氧化铋光催化剂的合成、改性及净化一氧化氮

光催化技术因其节能、高效、无二次污染等特点,在低浓度一氧化氮(NO)污染治理方面展现出了巨大潜力。在众多半导体材料中,碘氧化铋(BiOI)光催化剂具有窄带隙和独特的层状结构,有利于可见光吸收和电子空穴对分离,展现出了良好的光催化活性和稳定性,近十几年来备受关注。本文综述了BiOI半导体材料光催化净化NO的最新研究进展,阐述了BiOI晶体形貌与晶面调控对其光催化性能的影响;重点介绍了各类改性方法如表面修饰、离子掺杂、异质结构筑等对BiOI光催化活性的提升机制,并提出了该研究方向所面临的挑战与应用前景,旨在为设计高活性BiOI基光催化材料以及高效处理低浓度NO污染提供理论借鉴与技术支撑。

Microfluidic Device： A Miniaturized Platform for Chemical Reactions

Microfluidic devices, as a new miniaturized platform stemming from the field of micro-electromechanical sys-tems, have been used in many disciplines. In the field of chemical reactions, microfluidic device-based microreac-tors have shown great promise in building new chemical technologies and processes with increased speed and reli- ability and reduced sample consumption and cost. This technology has also become a new and effective tool for precise, high-throughput, and automatic analysis of chemical synthesis processes. Compared with conventional chemical laboratory batch methodologies, microfluidic reactors have a number of features, such as high mixing ef- ficiency, short reaction time, high heat-transfer coefficient, small reactant volume, controllable residence time, and high surface-to-volume ratio, among others. Combined with recent advances in microfluidic devices for chemical reactions, this review aims to give an overview of the features and applications of microfluidic devices in the field of chemical synthesis. It also aims to stimulate the development of microfluidic device applications in the field of chemical reactions.

A Hybrid Numerical Simulation of Supersonic IsotropicTurbulence

This paper presents an extension work of the hybrid scheme proposed by Wang et al.[J.Comput.Phys.229(2010)169-180]for numerical simulation of sub-sonic isotropic turbulence to supersonic turbulence regime.The scheme still utilizes an 8th-order compact scheme with built-in hyperviscosity for smooth regions and a 7th-order WENO scheme for highly compression regions,but now both in their con-servation formulations and for the latter with the Roe type characteristic-wise recon-struction.To enhance the robustness of the WENO scheme without compromising its high-resolution and accuracy,the recursive-order-reduction procedure is adopted,where a new type of reconstruction-failure-detection criterion is constructed from the idea of positivity-preserving.In addition,a new form of cooling function is proposed,which is proved also to be positivity-preserving.With a combination of these techniques,the new scheme not only inherits the good properties of the original one but also extends largely the computable range of turbulent Mach number,which has been further confirmed by numerical results.

Curvature estimate of steepest descents of 2-dimensional maximal space-like hyper surfaces on space forms

For the maximal space-like hypersurface defined on 2-dimensional space forms,based on the regularity and the strict convexity of the level sets,the steepest descents are well defined.In this paper,we come to estimate the curvature of its steepest descents by deriving a differential equality.

Dynamic nonlinear algebraic models with scale-similarity dynamic procedure for large-eddy simulation of turbulence

A dynamic nonlinear algebraic model with scale-similarity dynamic procedure(DNAM-SSD)is proposed for subgrid-scale(SGS)stress in large-eddy simulation of turbulence.The model coefficients of the DNAM-SSD model are adaptively calculated through the scale-similarity relation,which greatly simplifies the conventional Germano-identity based dynamic procedure(GID).The a priori study shows that the DNAM-SSD model predicts the SGS stress considerably better than the conventional velocity gradient model(VGM),dynamic Smagorinsky model(DSM),dynamic mixed model(DMM)and DNAM-GID model at a variety of filter widths ranging from inertial to viscous ranges.The correlation coefficients of the SGS stress predicted by the DNAM-SSD model can be larger than 95%with the relative errors lower than 30%.In the a posteriori testings of LES,the DNAM-SSD model outperforms the implicit LES(ILES),DSM,DMM and DNAM-GID models without increasing computational costs,which only takes up half the time of the DNAM-GID model.The DNAM-SSD model accurately predicts plenty of turbulent statistics and instantaneous spatial structures in reasonable agreement with the filtered DNS data.These results indicate that the current DNAM-SSD model is attractive for the development of highly accurate SGS models for LES of turbulence.