No abstract
Natural gas hydrates (NGH) are prone to causing pipeline blockage in flow assurance, attracting considerable attention in the petroleum industry. This work reviews the significant progress in hydrate flow assurance research in China. Gas hydrate structures are briefly introduced to provide a basic understanding, while the application and development of hydrate management strategies in China are summarized. Subsequently, the development and improvement of hydrate phase equilibrium models are presented, which have been widely applied to the practical challenges of flow assurance. Moreover, kinetics research involving hydrate nucleation, growth, and decomposition are summarized, including nucleation mechanisms, induction time, memory effect, hydrate growth at different interfaces, hydrate growth at a microscopic level, and hydrate decomposition under different systems. The current research status of hydrate slurry flow is also analyzed in detail, covering the viscosity and flow resistance of hydrate slurry and the mechanisms of hydrate particle aggregation, deposition, and blockage. In addition, even though the numerical models of hydrate slurry multiphase flow have been sorted out, the accurate quantitative calculations and risk assessments are still in the initial stage, presenting significant room for improvement. Although substantial research progress has been made in China regarding gas hydrate flow assurance, considerable effort should be devoted to further understanding the intrinsic mechanism work to improve the applicability of various models. This review discusses the current developments, existing problems, and future prospects in the various basic hydrate flow assurance fields in China. It aims to provide readers with an overview of hydrate flow assurance research in China, hoping to provide a reference for developing this field.
Brain image registration transforms a pair of images into one system with the matched imaging contents, which is of essential importance for brain image analysis. This paper presents a novel framework for unsupervised 3D brain image registration by capturing the feature-level transformation relationships between the unaligned image and reference image. To achieve this, we develop a feature-level probabilistic model to provide the direct regularization to the hidden layers of two deep convolutional neural networks, which are constructed from two input images. This model design is developed into multiple layers of these two networks to capture the transformation relationships at different levels. We employ two common benchmark datasets for 3D brain image registration and perform various experiments to evaluate our method. Experimental results show that our method clearly outperforms state-of-the-art methods on both benchmark datasets by a large margin.
We give the first systematic investigation of the design space of worm defense system strategies. We accomplish this by providing a taxonomy of defense strategies by abstracting away implementation-dependent and approach-specific details and concentrating on the fundamental properties of each defense category. Our taxonomy and analysis reveals the key parameters for each strategy that determine its effectiveness. We provide a theoretical foundation for understanding how these parameters interact, as well as simulationbased analysis of how these strategies compare as worm defense systems. Finally, we offer recommendations based upon our taxonomy and analysis on which worm defense strategies are most likely to succeed. In particular, we show that a hybrid approach combining Proactive Protection and Reactive Antibody Defense is the most promising approach and can be effective even against the fastest worms such as hitlist worms. Thus, we are the first to demonstrate with theoretic and empirical models which defense strategies will work against the fastest worms such as hitlist worms.
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