Numerical simulations of gas-liquid two-phase flow induced forces on 90-degere elbow structures

90-degree elbow structures such as pipelines with bends and rigid jumpers, are commonly used for flow transportation in subsea systems. The produced flow from the subsea wells is normally a multiphase flow which is a mixture of oil, water, gas polymer, and even rocks. The gas-liquid two-phase flow is a typical kind of multiphase flow conveyed in subsea systems, which has several flow regimes depending on different gas and liquid velocities. The transportation of gas-liquid two-phase flow in subsea pipeline systems can be a challenge because slug flow will occur with a specific combination of the gas and liquid velocities. For a slug flow, the gas phase coalesces into large-scale bubbles named Taylor bubbles and accumulates at the elbow sections, which will lead to large pressure fluctuation at these sections. With the peak of flow-induced forces acting on these sections, the pipelines will begin vibrating which may lead to even fatigue damage to the structures. In this thesis, the gas-liquid two-phase flow-induced forces on 90-degree elbow structures are numerically investigated by a one-way coupling method based on computational fluid dynamics (CFD) simulation and finite element analysis. The Reynolds numbers in the present simulations are in the range of 2.4×105 ~ 3.2×105. The mesh convergence studies are performed to determine the optimal computational grid resolution. Subsequently, the validation studies are conducted, and compared with published experimental results. Then, the numerical simulations in the open-source CFD package OpenFOAM and open-source finite-element program CodeAster are carried out to study the gas-liquid two-phase flow in pipeline structures. The studied pipeline structures in the present study cover pipe with one or two 90-degree elbows, uniplanar jumper, and multiplanar jumper. It is concluded that the flow-induced forces mainly peak at the sections where the slugging phenomena happen or the Taylor bubbles are frequently formed. For the pipe with one or two elbows, increasing the number of fixed supports can effectively reduce the reaction forces fluctuation of fixed supports. Moreover, the maximum deformation occurs at the top of the jumpers, with an evident sinking of the middle component, where the material tensile capacity should be considered in the design of subsea jumpers. Additionally, specific descriptions of the multiphase flow field are provided including volume fraction contour, iso-surface plot, and secondary flow

Pygopus 2 promotes kidney cancer OS-RC-2 cells proliferation and invasion in vitro and in vivo

AbstractObjectiveHuman Pygopus 2 (Pygo2) was recently discovered to be a component of the Wnt signaling pathway required for β-catenin/Tcf-mediated transcription. But the role of Pygo2 in malignant cell proliferation and invasion has not yet been determined.MethodsLentivirus-mediated small interfering RNA (siRNA) and vector-based overexpression were used to study the function of Pygo2 in OS-RC-2 cells. The resulted cells were subject to Western blotting assay, MTT assay, colony formation and cell invasion assays. Furthermore, renal cell carcinoma (RCC) models were established in BALB/c nude mice inoculated with OS-RC-2 cells. Immunohistochemistry (IHC) staining of matrix metalloproteinase-7 (MMP-7), matrix metalloproteinase-9 (MMP-9) and vascular endothelial growth factor (VEGF) was performed in tumor tissue.ResultsPygo2 gene was successful knocked down and overexpressed in RCC OS-RC-2 cells by using an shRNA and overexpressing vector, respectively. Overexpression of Pygo2 effectively promoted cell proliferation, colony formation and invasion in vitro. Knockdown of Pygo2 obviously inhibited xenograft tumor growth in nude mice. In addition, overexpression of Pygo2 increased the levels of MMP-7, MMP-9 and VEGF in the xenograft tumors.ConclusionPygo2 has a role in promoting cell proliferation, invasion and metastasis, and may regulate angiogenesis via the Wnt/β-catenin signaling pathway

Open Knowledge Base Canonicalization with Multi-task Unlearning

The construction of large open knowledge bases (OKBs) is integral to many applications in the field of mobile computing. Noun phrases and relational phrases in OKBs often suffer from redundancy and ambiguity, which calls for the investigation on OKB canonicalization. However, in order to meet the requirements of some privacy protection regulations and to ensure the timeliness of the data, the canonicalized OKB often needs to remove some sensitive information or outdated data. The machine unlearning in OKB canonicalization is an excellent solution to the above problem. Current solutions address OKB canonicalization by devising advanced clustering algorithms and using knowledge graph embedding (KGE) to further facilitate the canonicalization process. Effective schemes are urgently needed to fully synergise machine unlearning with clustering and KGE learning. To this end, we put forward a multi-task unlearning framework, namely MulCanon, to tackle machine unlearning problem in OKB canonicalization. Specifically, the noise characteristics in the diffusion model are utilized to achieve the effect of machine unlearning for data in OKB. MulCanon unifies the learning objectives of diffusion model, KGE and clustering algorithms, and adopts a two-step multi-task learning paradigm for training. A thorough experimental study on popular OKB canonicalization datasets validates that MulCanon achieves advanced machine unlearning effects

Collective Entity Alignment via Adaptive Features

Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration. When generating EA results, current solutions treat entities independently and fail to take into account the interdependence between entities. To fill this gap, we propose a collective EA framework. We first employ three representative features, i.e., structural, semantic and string signals, which are adapted to capture different aspects of the similarity between entities in heterogeneous KGs. In order to make collective EA decisions, we formulate EA as the classical stable matching problem, which is further effectively solved by deferred acceptance algorithm. Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks against state-of-the-art solutions, and the empirical results verify its effectiveness and superiority.Comment: ICDE2