다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Nanofluids are potential heat transfer fluids with enhanced thermophysical properties and heat transfer performance can be applied in many devices for better performances (i.e. energy, heat transfer and other performances). In this paper, a comprehensive literature on the applications and challenges of nanofluids have been compiled and reviewed. Latest up to date literatures on the applications and challenges in terms of PhD and Master thesis, journal articles, conference proceedings, reports and web materials have been reviewed and reported. Recent researches have indicated that substitution of conventional coolants by nanofluids appears promising. Specific application of nanofluids in engine cooling, solar water heating, cooling of electronics, cooling of transformer oil, improving diesel generator efficiency, cooling of heat exchanging devices, improving heat transfer efficiency of chillers, domestic refrigerator-freezers, cooling in machining, in nuclear reactor and defense and space have been reviewed and presented. Authors also critically analyzed some of the applications and identified research gaps for further research. Moreover, challenges and future directions of applications of nanofluids have been reviewed and presented in this paper. Based on results available in the literatures, it has been found nanofluids have a much higher and strongly temperature-dependent thermal conductivity at very low particle concentrations than conventional fluids. This can be considered as one of the key parameters for enhanced performances for many of the applications of nanofluids. Because of its superior thermal performances, latest up to date literatures on this property have been summarized and presented in this paper as well. However, few barriers and challenges that have been identified in this review must be addressed carefully before it can be fully implemented in the industrial applications.

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A review on applications and challenges of nanofluids

The validity of the cubic law for laminar flow of fluids through open fractures consisting of parallel planar plates has been established by others over a wide range of conditions with apertures ranging down to a minimum of 0.2 µm. The law may be given in simplified form by Q/Δh = C(2b)3, where Q is the flow rate, Δh is the difference in hydraulic head, C is a constant that depends on the flow geometry and fluid properties, and 2b is the fracture aperture. The validity of this law for flow in a closed fracture where the surfaces are in contact and the aperture is being decreased under stress has been investigated at room temperature by using homogeneous samples of granite, basalt, and marble. Tension fractures were artificially induced, and the laboratory setup used radial as well as straight flow geometries. Apertures ranged from 250 down to 4µm, which was the minimum size that could be attained under a normal stress of 20 MPa. The cubic law was found to be valid whether the fracture surfaces were held open or were being closed under stress, and the results are not dependent on rock type. Permeability was uniquely defined by fracture aperture and was independent of the stress history used in these investigations. The effects of deviations from the ideal parallel plate concept only cause an apparent reduction in flow and may be incorporated into the cubic law by replacing C by C/ƒ. The factor ƒ varied from 1.04 to 1.65 in these investigations. The model of a fracture that is being closed under normal stress is visualized as being controlled by the strength of the asperities that are in contact. These contact areas are able to withstand significant stresses while maintaining space for fluids to continue to flow as the fracture aperture decreases. The controlling factor is the magnitude of the aperture, and since flow depends on (2b)3, a slight change in aperture evidently can easily dominate any other change in the geometry of the flow field. Thus one does not see any noticeable shift in the correlations of our experimental results in passing from a condition where the fracture surfaces were held open to one where the surfaces were being closed under stress.

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VALIDITY OF CUBIC LAW FOR FLUID FLOW IN A DEFORMABLE ROCK FRACTURE - eScholarship

A transfer learning framework for well placement optimization based on denoising autoencoder

Recent years, Generative Adversarial Networks (GANs) have achieved tremendous success in image synthesis, which usually employ the convolutional operation to extract image features. However, most existing convolutional GANs only extract features in a local neighborhood at a time, which may often cause a lack of non-local information resulting in generating the wrong semantic object in the wrong position. In this paper, we propose a Graph Convolutional Architecture (GCA) for GANs to tackle this problem. GCA constructs a pixel-level graph structure between image regions through an attention mechanism and leverages Graph Convolutional Networks (GCNs) to extract non-local features. GCA extracts the connections between different regions of the image through GCNs, which is a more effective method of using relationship information than directly adding long-range dependencies to the model. We implement the GCA into Deep Convolutional Generative Adversarial Networks (DCGAN), Self-Attention Generative Adversarial Networks (SAGAN), and Concurrent-Single-Image-GAN (ConSinGAN). Extensive experiments are conducted to verify the performance of GCA. The results demonstrate that the GCA can significantly boost the quality of the generated image with more non-local features.

Leveraging GANs via Non-local Features.

High-temperature wear and oxidation behavior of (CrNbTaMoVW)N high entropy films deposited by reactive magnetron sputtering

Self-healing with the capability to be self-adhesive, which can recover from physical damage, is essential for space applications. However, regulatable adhesion under extreme space conditions has only been realized in low-dimensional materials and still poses a challenge on the discovery of suitable materials. Under an ultrahigh vacuum of 10−7 Pa, we found a strong adhesion between bulk Cu46Zr46Al8 metallic glasses with a maximum adhesion strength of 32.8 kPa, which is two orders of magnitude higher than that of the corresponding crystalline. This adhesion is suggested to be induced by a liquid-like layer on a bulk metallic glass surface, which has a high diffusion coefficient of 6.9 × 10−11 m2⋅s−1, even at a relatively low temperature of 263 K. By investigating the dynamics for this liquid-like layer, a special fractional Stokes–Einstein relationship was found. Inspired by this strong adhesion, metallic glasses can be proposed as one of the promising self-healing materials for future space applications. 

Strong adhesion induced by liquid-like surface of metallic glasses

The efficiency of reservoir simulation is important for automated history matching (AHM) and production optimization, etc. The fast marching marching method (FMM) has been used for efficient reservoir simulation. FMM can be regarded as a generalised streamline method but without the need to construct streamlines. In FMM reservoir simulation, the Eikonal equation for the diffusive time-of-flight (DTOF) is solved by FMM and then the governing equations are computed on the 1D DTOF coordinate. Standard FMM solves the Eikonal equation using a 4-stencil algorithm on 2D Cartesian grids, ignoring the diagonal neighbouring cells. In the current study, we build a 8-stencil algorithm considering all neighbouring cells, and use local analytical propagation speeds. In addition, a local path-correction coefficient is introduced to further increase the accuracy of DTOF solution. Next, a discretisation scheme is built on the 1D DTOF coordinate for efficient reservoir simulation. The algorithm is validated on homogeneous and heterogeneous test cases, and its potential for efficient forward simulation in AHM is demonstrated by two examples of dimensions 2 and 6.

Kai Zhang

Papers

A transfer learning framework for well placement optimization based on denoising autoencoder

Leveraging GANs via Non-local Features.

High-temperature wear and oxidation behavior of (CrNbTaMoVW)N high entropy films deposited by reactive magnetron sputtering

Strong adhesion induced by liquid-like surface of metallic glasses

A Multi-Stencil Fast Marching Method with Path Correction for Efficient Reservoir Simulation and Automated History Matching