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Cheng, Yu

Publications and source records attributed to Cheng, Yu.

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Deep Learning for Subgrid‐Scale Turbulence Modeling in Large‐Eddy Simulations of the Convective Atmospheric Boundary Layer

Abstract In large‐eddy simulations, subgrid‐scale (SGS) processes are parameterized as a function of filtered grid‐scale variables. First‐order, algebraic SGS models are based on the eddy‐viscosity assumption, which does not always hold for turbulence. Here we apply supervised deep neural networks (DNNs) to learn SGS stresses from a set of neighboring coarse‐grained velocity from direct numerical simulations of the convective boundary layer at friction Reynolds numbers Re τ up to 1243 without invoking the eddy‐viscosity assumption. The DNN model was found to produce higher correlation between SGS stresses compared to the Smagorinsky model and the Smagorinsky‐Bardina mixed model in the surface and mixed layers and can be applied to different grid resolutions and various stability conditions ranging from near neutral to very unstable. The DNN model can capture key statistics of turbulence in a posteriori (online) tests when applied to large‐eddy simulations of the atmospheric boundary layer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Connection Between Mass Flux Transport and Eddy Diffusivity in Convective Atmospheric Boundary Layers

Turbulence parameterizations for convective boundary layer in coarse-scale atmospheric models usually consider a combination of the eddy-diffusive transport and a non-local transport, typically in the form of a mass flux term, such as the widely adopted eddy-diffusivity mass-flux (EDMF) approach. These two types of turbulent transport are generally considered to be independent of each other. Using results from large-eddy simulations, here, we show that a Taylor series expansion of the updraft and downdraft mass-flux transport can be used to approximate the eddy-diffusivity transport in the atmospheric surface layer and the lower part of the mixed layer, connecting both eddy-diffusivity and mass-flux transport theories in convective conditions, which also quantifies departure from the Monin-Obukhov similarity (MOS) in the surface layer. This study provides a theoretical support for a unified EDMF parameterization applied to both the surface layer and mixed layer and highlights important correction required for surface models relying on MOS

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

PCGF3 promotes the proliferation and migration of non-small cell lung cancer cells via the PI3K/AKT signaling pathway

Highlights: • PCGF3 is highly expressed in non-small cell lung cancer (NSCLC) tissues. • PCGF3 overexpression positively correlates with lymph node metastasis and p-TNM staging. • PCGF3 promotes NSCLC cells proliferation by regulating CyclinB1, CyclinD1, and CDK4. • PCGF3 promotes NSCLC cells migration by regulating RhoA, RhoC, and CDC42. • PCGF3 promotes malignant behavior in NSCLC by positively regulating the PI3K/AKT signaling pathway. The Polycomb Group Ring Finger 3 (PCGF3) protein has been reported to be significantly upregulated in pancreatic islet tumors and related to signal transduction; however, its detailed mechanisms and biological roles in other tumors, including non-small cell lung cancer (NSCLC), remain unclear. This study investigated the function of PCGF3 in NSCLC and further elucidated its mechanism of action. The immunohistochemical analysis of 86 selected lung cancer tissues revealed that PCGF3 was highly expressed in NSCLC tissues and positively correlated with lymph node metastasis and p-TNM staging. Additionally, PCGF3 promoted cell proliferation in lung cancer by regulating CyclinB1, CyclinD1, and CDK4 expression, and also promoting their migration by regulating RhoA, RhoC, and CDC42. Furthermore, PCGF3 affected both the proliferation and migration of lung cancer cells by regulating the PI3K/AKT pathway, as verified by inhibiting this pathway using LY294002. The findings of this study suggested that PCGF3 is associated with poor prognosis in patients with NSCLC and could therefore be an important biomarker for treating and preventing NSCLC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Logarithmic profile of temperature in sheared and unstably stratified atmospheric boundary layers

The impact of buoyancy on the mean velocity, temperature, and scalar concentration profiles in the lower atmosphere is typically investigated within the framework of Monin-Obukhov similarity theory (MOST). MOST is the theoretical foundation for parametrizing surface-atmosphere exchanges in nearly all weather, climate, and hydrological models.According to MOST, the classic logarithmic profiles of mean velocity and temperature break down as the buoyancy effects become important. However, recent studies on turbulent Rayleigh-Bénard convection and natural convection along vertical walls suggest that the mean temperature in the near-surface region still follows a logarithmic profile.Motivated by these new results, we study the mean potential temperature profile in sheared and unstably stratified atmospheric boundary layers using direct numerical simulations and field observations. We find that the mean potential temperature profile remains logarithmic across a wide range of stability parameters, which characterizes the relative importance of buoyancy versus shear effects. Compared to MOST, our results suggest that the buoyancy force does not modify the logarithmic nature of the mean potential temperature profile, but instead modulates its slope, which is no longer universal and differs from 1/κ,whereκisthe von Kármán constant. Furthermore, this study provides another perspective on scalar turbulence in the atmospheric boundary layer

58 GEOSCIENCES↗
DOE OSTI2020

Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV Based Random Access IoT Networks With NOMA

In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive p-persistent slotted Aloha protocol; and the solar-powered UAVs adopt Successive Interference Cancellation (SIC) to decode multiple received data from IoT devices to improve access efficiency. To enable an energy-sustainable capacity-optimal network, we study the joint problem of dynamic multi-UAV altitude control and multi-cell wireless channel access management of IoT devices as a stochastic control problem with multiple energy constraints. We first formulate this problem as a Constrained Markov Decision Process (CMDP), and propose an online model-free Constrained Deep Reinforcement Learning (CDRL) algorithm based on Lagrangian primal-dual policy optimization to solve the CMDP. Extensive simulations demonstrate that our proposed algorithm learns a cooperative policy in which the altitude of UAVs and channel access probability of IoT devices are dynamically controlled to attain the maximal long-term network capacity while ensuring energy sustainability of UAVs, outperforming baseline schemes. The proposed CDRL agent can be trained on a small network, yet the learned policy can efficiently manage networks with a massive number of IoT devices and varying initial states, which can amortize the cost of training the CDRL agent.

42 ENGINEERING↗
DOE OSTI2020

A Model for Turbulence Spectra in the Equilibrium Range of the Stable Atmospheric Boundary Layer

Stratification can cause turbulence spectra to deviate from Kolmogorov's isotropic -5/3 power law scaling in the universal equilibrium range at high Reynolds numbers. However, a consensus has not been reached with regard to the exact shape of the spectra. Here we propose a shape of the turbulent kinetic energy and temperature spectra in horizontal wavenumber for the equilibrium range that consists of three regimes at small Froude number: the buoyancy subrange, a transition region, and the isotropic inertial subrange through dimensional analysis and substantial revision of previous theoretical approximation. These spectral regimes are confirmed by various observations in the atmospheric boundary layer. The representation of the transition region in direct numerical simulations will require large-scale separation between the Dougherty-Ozmidov scale and the Kolmogorov scale for strongly stratified turbulence at high Reynolds numbers, which is still challenging computationally. In addition, we suggest that the failure of Monin-Obukhov similarity theory in the very stable atmospheric boundary layer is due to the fact that it does not consider the buoyancy scale that characterizes the transition region.

54 ENVIRONMENTAL SCIENCES↗