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Most turbulence models used in Reynolds-averaged Navier-Stokes (RANS) simulations are partial differential equations (PDE) that describe the transport of turbulent quantities. Such quantities include turbulent kinetic energy for eddy viscosity models and the Reynolds stress tensor (or its anisotropy) in differential stress models. However, such models all have limitations in their robustness and accuracy. Inspired by the successes of machine learning in other scientific fields, researchers have developed data-driven turbulence models. Recently, a nonlocal vector-cloud neural network with embedded invariance was proposed, with its capability demonstrated in emulating passive tracer transport in laminar flows. Building upon this success, we use nonlocal neural network mapping to model the transport physics in the k-epsilon model and couple it to RANS solvers, leading to a PDE-free eddy-viscosity model. We demonstrate the robustness and stability of the RANS solver with a neural network-based turbulence model on flows over periodic hills of parameterized geometries. Our work serves as a proof of concept for using a vector-cloud neural network as an alternative to traditional turbulence models in coupled RANS simulations. The success of the coupling paves the way for neural network-based emulation of Reynolds stress transport models.

In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, represented as a tensor basis neural network, from velocity data. Data-driven turbulence models have emerged as a promising alternative to traditional models for providing closure mapping from the mean velocities to Reynolds stresses. Most data-driven models in this category need full-field Reynolds stress data for training, which not only places stringent demand on the data generation but also makes the trained model ill-conditioned and lacks robustness. This difficulty can be alleviated by incorporating the Reynolds-averaged Navier–Stokes (RANS) solver in the training process. However, this would necessitate developing adjoint solvers of the RANS model, which requires extra effort in code development and maintenance. Given this difficulty, we present an ensemble Kalman method with an adaptive step size to train a neural-network-based turbulence model by using indirect observation data. To our knowledge, this is the first such attempt in turbulence modelling. The ensemble method is first verified on the flow in a square duct, where it correctly learns the underlying turbulence models from velocity data. Then the generalizability of the learned model is evaluated on a family of separated flows over periodic hills. It is demonstrated that the turbulence model learned in one flow can predict flows in similar configurations with varying slopes.

Large-eddy simulation (LES) is able to capture key boundary-layer (BL) turbulence and cloud processes. Yet, large-scale forcing and surface turbulent fluxes of sensible and latent heat are often poorly constrained for LES simulations. We derive these quantities from measurements and reanalysis obtained for two cold air outbreak (CAO) events during Phase I of the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) in February-March 2020. We study the two contrasting CAO cases by performing LES simulations and test the sensitivity of BL structure and clouds to large-scale forcings and turbulent heat fluxes. Profiles of atmospheric state and large-scale divergence and surface turbulent heat fluxes obtained from the reanalysis data ERA5 agree reasonably well with those derived from ACTIVATE field measurements for both cases at the sampling time and location. Therefore, we adopt the time evolving heat fluxes, wind and advective tendencies profiles from ERA5 reanalysis data to drive the LES simulations. We find that large-scale thermodynamic advective tendencies and wind relaxations are important for the LES to capture the evolving observed BL meteorological states characterized by the hourly ERA5 reanalysis data and validated by the observations. We show that the divergence (or vertical advection) is important in regulating the BL growth driven by surface heat fluxes in LES simulations. The evolution of liquid water path is largely affected by the evolution of surface heat fluxes. The liquid water path simulated in LES agrees reasonably well with the ACTIVATE measurements. This study paves the path to investigate aerosol-cloud-meteorology interactions using LES informed and evaluated by ACTIVATE field measurements.

Developing robust constitutive models is a fundamental and longstanding problem for accelerating the simulation of complicated physics. Machine learning provides promising tools to construct constitutive models based on various calibration data. In this work, we propose a neural operator to develop nonlocal constitutive models for tensorial quantities through a vector-cloud neural network with equivariance (VCNN-e). The VCNN-e respects all the invariance properties desired by constitutive models, faithfully reflects the region of influence in physics, and is applicable to different spatial resolutions. By design, the model guarantees that the predicted tensor is invariant to the frame translation and ordering (permutation) of the neighboring points. Furthermore, it is equivariant to the frame rotation, i.e., the output tensor co-rotates with the coordinate frame. We evaluate the VCNN-e by using it to emulate the Reynolds stress transport model for turbulent flows, which directly computes the Reynolds stress tensor to close the Reynolds-averaged Navier--Stokes (RANS) equations. The evaluation is performed in two situations: (1) emulating the Reynolds stress model through synthetic data generated from the Reynolds stress transport equations with closure models, and (2) predicting the Reynolds stress by learning from data generated from direct numerical simulations. Such a priori evaluations of the proposed network pave the way for developing and calibrating robust and nonlocal, non-equilibrium closure models for the RANS equations.

The Reynolds-averaged Navier–Stokes (RANS)-based method is a practical tool to provide rapid assessment of jet noise-reduction concepts. However, the RANS-based method requires modeling assumptions to represent noise generation and propagation, which often reduces the predictive accuracy due to the model-form uncertainties. In this work, the ensemble Kalman filter-based acoustic inversion method is introduced to reduce uncertainties in the turbulent kinetic energy and dissipation rate based on the far-field noise and the axial centerline velocity data. The results show that jet noise data are more effective from which to infer turbulent kinetic energy and dissipation rate compared to velocity data. Moreover, the inferred noise source is able to improve the estimation of the turbulent flowfield and the far-field noise at unobserved locations. Further, the noise model parameters are also considered uncertain quantities, demonstrating the ability of the proposed framework to reduce uncertainties in both the RANS and noise models. Finally, one realistic case with experimental data is investigated to show the practicality of the proposed framework. The method opens up the possibility for the inverse modeling of jet noise sources by incorporating far-field noise data that are relatively straightforward to be measured compared to the velocity field. Accepted version

Oxide metallurgy technology can improve the microstructure of a coarse-grained heat-affected zone (CGHAZ) but introduces extra inclusions. Local corrosion behavior of the CGHAZ of a Zr–Ti–Al–RE deoxidized steel was investigated in this work using theoretical calculations and experimental verification. The modified inclusions have a (Zr–Mg–Al–Ca–RE)Ox core claded by a CaS and TiN shell. CaS dissolves first, followed by the oxide core, leaving TiN parts. This confirms that the addition of rare earth can reduce lattice distortion and prevent a galvanic couple between the inclusions and the matrix, while the chemical dissolution of CaS causes localized acidification, resulting in the pitting corrosion initiation.

Background: Intraductal papillary neoplasm of the bile duct (IPNB) is a rare biliary benign tumor with atypical clinical features and is frequently misdiagnosed. Its treatment is limited and surgical resection is thought to be the only therapeutic option in patients with IPNB. With the aim of increasing the early diagnosis rate of IPNB and providing more therapeutic options for surgeons, we innovatively put forward the concept of combined utilization of SpyGlass and endoscopic endoluminal radiofrequency ablation (ERFA) in the diagnosis and treatment of IPNB.Case Presentation: An 85-year-old woman was referred to our hospital due to right upper quadrant abdominal pain. The image examinations indicated suspicious filling defects at the upper common bile duct. Further evaluation of SpyGlass cholangioscopy showed multiple reddish villous lesions at the left hepatic duct, and SpyBite biopsy under direct visualization demonstrated papillary low-grade dysplasia. In consideration of the advanced age and preference of the patient, the novel ERFA therapy was performed. The procedure was successful without periprocedural complications; the patient recovered uneventfully and was discharged 2 days after the operation. Upon follow-up, the patient was asymptomatic and in good physical condition at 8 months postoperatively.Conclusion: Preliminarily, we demonstrate that the strategy of a combination of SpyGlass and ERAF seems to be a promising, feasible, well-tolerated, and safe management for patients with IPNB. However, more data with larger patient volumes are needed to evaluate its outcomes further.

Finite strain elastoplastic J2-flow equations are established toward the purpose of automatically, accurately simulating pseudo-elastic effects of SMAs. The uniaxial responses derived from these equations in each loading-unloading cycle exactly produce a closed stress-strain curve of any given shape. Then, any given test data for pseudo-elastic hysteresis loops of SMAs may be accurately fitted by means of a new technique for combining linear spline functions into a unified, smooth interpolating function, in a sense with no need to identify any unknown parameters.

In view of the long stagnation in traditional turbulence modeling, researchers have attempted using machine learning to augment turbulence models. This paper presents some of the recent progresses in our group on augmenting turbulence models with physics-informed machine learning. We also discuss our works on ensemble-based field inversion to provide training data for constructing machine learning models. Future and on-going research efforts are introduced.

Purpose A direct and unified approach is proposed toward simultaneously simulating large strain elastic behaviors of gellan gels with different gellan polymer concentrations. The purpose of this paper is to construct an elastic potential with certain parameters of direct physical meanings, based on well-designed invariants of Hencky’s logarithmic strain. Design/methodology/approach For each given value of the concentration, the values of the parameters incorporated may be determined in the sense of achieving accurate agreement with large strain uniaxial extension and compression data. By means of a new interpolating technique, each parameter as a function of the concentration is then obtained from a given set of parameter values for certain concentration values. Findings Then, the effects of gellan polymer concentrations on large strain elastic behaviors of gellan gels are studied in demonstrating how each parameter relies on the concentration. Plane-strain (simple shear) responses are also presented for gellan gels with different polymer concentrations. Originality/value A direct, unified approach was proposed toward achieving a simultaneous simulation of large elastic strain behaviors of gellan gels for different gellan polymer concentrations. Each parameter incorporated in the proposed elastic potential will be derived as a function of the polymer concentration in an explicit form, in the very sense of simultaneously simulating large strain data for different concentrations.

Deformable micro-continua of highly localized nature are found to exactly exhibit all quantum effects commonly known for quantum entities at microscopic scale. At every instant, the spatial configuration of each such micro-continuum is prescribed by four spatial distributions of the mass, the velocity, the internal stress, and the intrinsic angular momentum. The deformability features of such micro-continua in response to all configuration changes are identified with a constitutive equation that specifies how the internal stress responds to the mass density field. It is shown that these microcontinua are endowed with the following unique response features: (i) the coupled system of the nonlinear field equations governing their dynamic responses to any given force and torque fields is exactly reducible to a linear dynamic equation governing a complex field variable; (ii) this fundamental dynamic equation and this complex field variable are just the Schrodinger equation and the complex wave function in quantum theory; and, accordingly, (iii) the latter two and all quantum effects known for quantum entities are in a natural and unified manner incorporated as the inherent response features of the micro-continua discovered, thus following objective and deterministic response patterns for quantum entities, in which the physical origins and meanings of the wave function and the Schrodinger equation become self-evident and, in particular, any probabilistic indeterminacy becomes irrelevant.

A new approach is proposed to characterize the work-hardening behavior of metals based on the stress–strain data from uniaxial extension testing. With this new approach, the yield strength as a function of the plastic work can be determined by directly fitting a well-chosen single-variable shape function to any given uniaxial data from the initial yielding up to the strength limit, in an explicit sense with no need to carry out the usual tedious trial-and-error procedures in treating nonlinear elastoplastic rate equations toward identifying numerous unknown parameters. Numerical examples show that the simulation results with the new approach are in accurate agreement with the test data.

We use a large‐eddy simulation model with a nested domain configuration (297 and 120 km wide) and an interactive land surface parameterization to simulate the complex population of shallow clouds observed on 30 August 2016 during the Holistic Interactions of Shallow Clouds, Aerosols, and Land‐Ecosystems campaign conducted in north‐central Oklahoma. Shallow convective clouds first formed over southeast Oklahoma and then spread toward the northwest into southern Kansas. By the early afternoon, the relatively uniform shallow cloud field became more complex in which some regions became nearly cloud free and in other regions larger shallow clouds developed with some transitioning to deeper, precipitating convection. We show that the model reproduces the observed heterogeneity in the cloud populations only when realistic variations in soil moisture are used to initialize the model. While more variable soil moisture and to a lesser extent cool lake temperatures drive the initial spatial heterogeneity in the cloud populations, precipitation‐driven cold pools become an important factor after 1300 CST. When smoother soil moisture variations are used in the model, more uniform shallow cloud populations are predicted with far fewer clouds that transition to deeper, precipitating convection that produce cold pools. An algorithm that tracks thousands of individual cumulus show that the more realistic soil moisture distributions produces clouds that are larger and have a longer lifetime. The results suggest that shallow and deep convection parameterizations used by mesoscale models need to account for the effects of variable land‐atmosphere interactions and cold pools.

This study was carried out to study the effects of selenylation on physicochemical and biological properties of polysaccharide (SPP) extracted from Sargassum pallidum. The selenized derivative of SPP (Se-SPP) with the selenium content of 2419 μg/g was synthesized by sodium selenite/dilute nitric acid method. Physicochemical characterization indicated that selenylation modification resulted in some changes in chemical composition, monosaccharide composition, molecular weight and surface morphology of polysaccharides. FT-IR spectroscopy showed that a new absorption peak appeared at 675 cm−1 in Se-SPP probably due to the substitution of selenyl groups. Bioactivity assay showed that Se-SPP exhibited higher scavenging radical activities and ferrous ion chelating activities than native SPP. Compared with SPP and acarbose, Se-SPP showed more significantly inhibitory effect on α-glucosidase activity in a noncompetitive inhibition type. The IC50 values of SPP, Se-SPP and acarbose were determined as 1.579, 0.896 and 2.742 mg/mL, respectively. These results suggest that Se-SPP can be used to develop a new selenium-complementary ingredient in functional foods.

Shallow convective clouds are common, occurring over many areas of the world, and are an important component in the atmospheric radiation budget. In addition to synoptic and mesoscale meteorological conditions, land–atmosphere interactions and aerosol–radiation–cloud interactions can influence the formation of shallow clouds and their properties. These processes exhibit large spatial and temporal variability and occur at the subgrid scale for all current climate, operational forecast, and cloud-system-resolving models; therefore, they must be represented by parameterizations. Uncertainties in shallow cloud parameterization predictions arise from many sources, including insufficient coincident data needed to adequately represent the coupling of cloud macrophysical and microphysical properties with inhomogeneity in the surface-layer, boundary layer, and aerosol properties. Predictions of the transition of shallow to deep convection and the onset of precipitation are also affected by errors in simulated shallow clouds. Coincident data are a key factor needed to achieve a more complete understanding of the life cycle of shallow convective clouds and to develop improved model parameterizations. To address these issues, the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the Atmospheric Radiation Measurement (ARM) Southern Great Plains site in north-central Oklahoma during the spring and summer of 2016. We describe the scientific objectives of HI-SCALE as well as the experimental approach, overall weather conditions during the campaign, and preliminary findings from the measurements. Finally, we discuss scientific gaps in our understanding of shallow clouds that can be addressed by analysis and modeling studies that use HI-SCALE data.

The adsorption and lubrication of salivary pellicle is closely associated with saliva secretion, while the relationship between saliva flow rate and salivary pellicle lubrication is still unclear. In this study, the adsorption kinetics and lubrication behavior of salivary pellicle on human tooth enamel surface has been investigated in vitro at different salivary flow rates using quartz crystal microbalance with dissipation (QCM-D) and nano-indentation/scratch technique. Human whole saliva was collected from healthy donors. Regardless of flow rates, salivary protein adsorption on the enamel surface experiences initially rapid saturated adsorption to form a stable multi-layer structure of salivary pellicle and then slow oversaturated adsorption of some proteins with weak bonding. With the flow rate increasing, the saturated adsorption process is expedited, but the oversaturated adsorption is decelerated. Hence, the pellicle has decreased roughness, thickness, viscoelasticity, and normal load-bearing capability at high flow rates than at low flow rates. Its anti-wear properties are slightly weakened but its friction-reducing properties are enhanced. In sum, there exists a coupling effect of flow rate and adsorption time on the adsorption of salivary proteins. Saliva lubrication mainly depends on the saturated adsorption of salivary proteins on enamel surface.

The relationship between the microstructure and tribological behavior of mammalian tooth enamel has not been fully understood. In this paper, the microstructure, mechanical properties, and tribological behavior of canine (carnivore) and bovine (herbivore) enamel are studied using scanning electronic microscopy and nano-indentation/scratch technique, aiming to reveal the contribution of enamel microstructure to its mechanical and tribological properties. Canine enamel has a microstructure of hard keyhole-like rods embedded in soft inter-rod enamel, and its surface exhibits high resistance against both micro-crack initiation and crack-induced delamination during friction and wear process. Bovine enamel with the microstructure consisting of the hydroxyapatite (HAP) nano-fibers in decussation has higher surface hardness and better capabilities of resisting wear and encumbering crack propagation, as compared to canine enamel. In sum, the soft inter-rod enamel in the canine enamel contributes to high load tolerance and then protects enamel surface from brittle damage, while the staggered arrangement of HAP nano-fibers benefits hard bovine enamel in crack propagation resistance and then help resist wear and fatigue. The findings suggest that there exists a self-adaptation in enamel microstructure and tribological performance of mammals with their feeding habits, which will promote and assist the bionic design of high-performance materials.

Due to its applications in cloud computing, research on reversible data hiding in encrypted images (RDHEI) is becoming more and more important. This paper proposes a reversible data hiding scheme for encrypted images that utilizes an all-permutation technique to embed data into encrypted images. The proposed scheme follows a block-wise data hiding process. Message extraction and image restoration are performed by the receiver using the trained vector quantization (VQ) codebook. This scheme can provide a high embedding rate and reduce the hardware burden on the receiver.

Background: Breast cancer is a commonplace carcinoma in females. Recurrence and metastasis are the main problems affecting the survival rate of patients. The fundamental reason is the lack of understanding of the mechanism of breast cancer metastasis. This study aims to deliberate on the efficaciousness of Nuclear protein 1 (NUPR1)-mediated autophagy on breast cancer metastasis.Methods: The proliferation, migration and invasion ability of breast cancer cells were appraised by CCK-8, wound healing, and colony formation, as well as transwell assay. The relationship between NUPR1 and Translocation factor E3 (TFE3) was appraised by qPCR, western blot and ChIP. Migration-invasion-related proteins and autophagy-related proteins were appraised by western blot. The effects of NUPR1 on malignancy formation and metastasis were studied in vivo.Results: NUPR1 was upregulated in breast cancer cells and tissues. NUPR1 knockdown restrained the proliferation, migration and invasion of ZR-75-30 cells. Moreover, NUPR1 knockdown restrained malignancy formation and metastasis in vivo. Mechanically, NUPR1 promoted autophagy through activation of TFE3 transcription, thereby regulating the process of breast cancer metastasis.Conclusion: This paper elucidates the molecular mechanism of NUPR1 promoting breast cancer metastasis by activating autophagy through TFE3 signaling pathway, which provided biological basis for intervention of blocking distant metastasis.

Chronic pancreatitis (CP) is a chronic inflammatory and fibrotic disease of the pancreas, and activated pancreatic stellate cells (PSCs) play a vital role in the progression of pancreatic fibrosis in CP. It has been reported that long non-coding RNA small nucleolar RNA host gene 11 (SNHG11) is highly expressed in chronic pancreatitis (CP) patients. However, the role of SNHG11 in CP progression is unclear. The purport of the study was to survey the role of SNHG11 in CP. We employed transforming growth factor (TGF)-beta1 (TGF-β1) to activate human pancreatic stellate cells (PSCs). Expression of SNHG11 was assessed with qRT-PCR. Loss-of-function experiments were executed to evaluate the effects of SNHG11 on the proliferation and migration of TGF-β1-treated PSCs. Some protein levels were detected by western blotting. The regulatory mechanism of SNHG11 was verified by the dual-luciferase reporter and RIP assays. As a result, SNHG11 was upregulated in plasma of CP patients and TGF-β1-treated PSCs. Also, SNHG11 inhibition reduced TGF-β1-induced proliferation, migration, and ECM accumulation in PSCs. Mechanistically, SNHG11 regulated leukemia inhibitory factor (LIF) expression by sponging miR-34b. Furthermore, miR-34b inhibitor abolished SNHG11 silencing-mediated effects on TGF-β1-treated PSC proliferation, migration, and ECM accumulation. LIF overexpression counteracted the repressive influence of miR-34b mimic on proliferation, migration, and ECM accumulation of TGF-β1-treated PSCs. In conclusion, SNHG11 knockdown reduced TGF-β1-induced PSC proliferation, migration, and ECM accumulation by the miR-34b/LIF axis.

Intravoxel incoherent motion (IVIM) theory in MRI was proposed to account for the effect of vessel/capillary perfusion on the aggregate diffusion weighted MR signal. The prevalent IVIM modeling is based on equation-1: SI(b)/SI(0)= (1 -PF) × exp(-b× Dslow) + PF × exp(-b× Dfast) [1] where SI(b)and SI(0)denote the signal intensity of images acquired with the b-factor value ofbandb=0 s/mm2, respectively. We recently reported that, for the liver and likely for other organs as well, IVIM modeling of the perfusion component is constrained by the diffusion component, with a reducedDslowmeasure leading to artificially higher PF andDfastmeasures. With higher b-value associated lower image signal of the targeted tissue, Euqation-1 is focused on describing the signal decay pattern along increasingly higherb-values by three IVIM parameters. Signal intensity at eachb-value (i.e., SI(b)) is normalised by the signal intensity ofb=0 image (i.e., SI(0)). We noted an apparent problem for Euqation-1. For example, if we want to compare the IVIM parameters of the normal liver parenchyma and a liver tumor, following Euqation-1 we will take the assumption that the SI(0)of the normal parenchyma and the tumor are the same and considered equally as 1 (or 100) for the biexponential decay modelling. However, this assumption is invalid for many scenarios. From our liver IVIM database of 27 healthy female subjects, we chose six of the youngest subjects (20-27 yrs) and six of the oldest subjects (58-71 yrs) and measured the signals of the liver and left erector spinae muscle onb=0 and 2 s/mm2images. The results show, while there was no apparent difference of left erector spinae muscle signal among the young and elderly groups, the elderly group’s liver SI(0)is approximately 20 % lower than that of young group. This difference skewed the ratios of various SI(b)/SI(0)and the followed IVIM parameter determination. The general trend is that lower liver SI(0)is associated with lowerDslowand higher PF andDfast. If IVIM bi-exponential decay fitting starts from a very low non-zero b images (such asb=2 s/mm2images), this problem persists. We performed an additional analysis of our IVIM database of five cirrhotic livers and the results show SI(b=2)of cirrhotic right liver is positively associatedDslow(Pearson r=0.687), and negatively associated with PF (Pearsonr=-0.733). Though the examples we used in this letter are on liver aging and liver fibrosis, the points discussed are expected to be generalisable to other pathologies and to other organs.