A large LNG tank technology system “CGTank®” of CNOOC and its engineering application

AbstractLNG tanks are complex in design and building process and high in costs, so LNG tank technology is one of the most advanced ones in the field of energy, which has been monopolized by foreign companies for a long time. In order to work out LNG tank technology domestically, China National Offshore Oil Corporation (CNOOC for short), the largest LNG importer in China, develops a LNG tank technology system “CGTank®” successfully in reference to the design and construction experience of domestic and foreign companies, after years of scientific research in tackling difficult problems. This system presents four traits as follows. First, a set of calculation software is developed independently by CNOOC, and the tanks in all operating conditions are calculated after 3D hologram and multi-point contact model of fluid-solid coupling effect is built up. Second, earthquake effect research and inner tank check research are improved innovatively by means of response spectrum analysis after European standards are introduced. Third, it is put forward for the first time that the stress strength discrimination standard is based on the principal stress which is obtained by means of the maximum shearing failure theory. And fourth, a large LNG full-capacity tank technology package with completely independent intellectual property right is established. The “CGTank®” system was first applied in the Tianjin LNG demonstration project, which has passed all indicator tests and is now in operation smoothly. The project is provided with the core tank design technology by CNOOC Gas and Power Group and with the EPC by CNOOC Engineering Co., Ltd. The independent LNG tank technology can be applied in a wide scope and it is favorable for impelling domestic production of LNG industry completely

A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images

Fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with computed tomography (CT) is considered the primary solution for detecting some cancers, such as lung cancer and melanoma. Automatic segmentation of tumors in PET/CT images can help reduce doctors' workload, thereby improving diagnostic quality. However, precise tumor segmentation is challenging due to the small size of many tumors and the similarity of high-uptake normal areas to the tumor regions. To address these issues, this paper proposes a localization-to-segmentation framework (L2SNet) for precise tumor segmentation. L2SNet first localizes the possible lesions in the lesion localization phase and then uses the location cues to shape the segmentation results in the lesion segmentation phase. To further improve the segmentation performance of L2SNet, we design an adaptive threshold scheme that takes the segmentation results of the two phases into consideration. The experiments with the MICCAI 2023 Automated Lesion Segmentation in Whole-Body FDG-PET/CT challenge dataset show that our method achieved a competitive result and was ranked in the top 7 methods on the preliminary test set. Our work is available at: https://github.com/MedCAI/L2SNet.Comment: 7 pages,3 figure

Polysaccharide–dextrin thickened fluids for individuals with dysphagia:Recent advances in flow behaviors and swallowing assessment methods

The global aging population has brought about a pressing health concern: dysphagia. To effectively address this issue, we must develop specialized diets, such as thickened fluids made with polysaccharide–dextrin (e.g., water, milk, juices, and soups), which are crucial for managing swallowing-related problems like aspiration and choking for people with dysphagia. Understanding the flow behaviors of these thickened fluids is paramount, and it enables us to establish methods for evaluating their suitability for individuals with dysphagia. This review focuses on the shear and extensional flow properties (e.g., viscosity, yield stress, and viscoelasticity) and tribology (e.g., coefficient of friction) of polysaccharide–dextrin-based thickened fluids and highlights how dextrin inclusion influences fluid flow behaviors considering molecular interactions and chain dynamics. The flow behaviors can be integrated into the development of diverse evaluation methods that assess aspects such as flow velocity, risk of aspiration, and remaining fluid volume. In this context, the key in-vivo (e.g., clinical examination and animal model), in-vitro (e.g., the Cambridge Throat), and in-silico (e.g., Hamiltonian moving particles semi-implicit) evaluation methods are summarized. In addition, we explore the potential for establishing realistic assessment methods to evaluate the swallowing performance of thickened fluids, offering promising prospects for the future

Experimental Study of Granular Clogging in Two-Dimensional Hopper

We experimentally investigate the clogging process of granular materials in a two-dimensional hopper, and present a self-consistent physical mechanism of clogging based on preformed dynamic chain structures in the flow. We found that these chain structures follow a specific modified restricted random walk, and clogging occurs when they are mechanically stable enough to withstand the flow fluctuations, resulting in the formation of an arch at the outlet. We introduce a simple model which can explain the clogging probability by incorporating an analytical expression for chain formation and its transition into an arch. Our results provide insight into the microscopic mechanism of clogging in hopper flow.Comment: 22 pages, 8 figure

Aggregate Model of District Heating Network for Integrated Energy Dispatch: A Physically Informed Data-Driven Approach

The district heating network (DHN) is essential in enhancing the operational flexibility of integrated energy systems (IES). Yet, it is hard to obtain an accurate and concise DHN model for the operation owing to complicated network features and imperfect measurement. Considering this, this paper proposes a physically informed data-driven aggregate model (AGM) for DHN, providing a concise description of the source-load relationship of DHN without exposing network details. First, we derive the analytical relationship between the state variables of the source and load nodes of DHN, offering a physical fundament for the AGM. Second, we propose a physics-informed estimator for AGM that is robust to low-quality measurement, in which the physical constraints associated with the parameter normalization and sparsity are embedded to improve the accuracy and robustness. Finally, we propose a physics-enhanced algorithm to solve the nonlinear estimator with non-closed constraints efficiently. Simulation results verify the effectiveness of the proposed method

OmniAvatar: Geometry-Guided Controllable 3D Head Synthesis

We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D heads with compelling dynamic details under full disentangled control over camera poses, facial expressions, head shapes, articulated neck and jaw poses. To achieve such high level of disentangled control, we first explicitly define a novel semantic signed distance function (SDF) around a head geometry (FLAME) conditioned on the control parameters. This semantic SDF allows us to build a differentiable volumetric correspondence map from the observation space to a disentangled canonical space from all the control parameters. We then leverage the 3D-aware GAN framework (EG3D) to synthesize detailed shape and appearance of 3D full heads in the canonical space, followed by a volume rendering step guided by the volumetric correspondence map to output into the observation space. To ensure the control accuracy on the synthesized head shapes and expressions, we introduce a geometry prior loss to conform to head SDF and a control loss to conform to the expression code. Further, we enhance the temporal realism with dynamic details conditioned upon varying expressions and joint poses. Our model can synthesize more preferable identity-preserved 3D heads with compelling dynamic details compared to the state-of-the-art methods both qualitatively and quantitatively. We also provide an ablation study to justify many of our system design choices