CFD Simulation of Temperature Field Distribution of the Liquefied Hydrocarbon Spherical Tank Leaking

AbstractLiquefied hydrocarbon is normally stored under high pressure in overheating state in the spherical tank. Once leakage occurs, the liquefied hydrocarbon will quickly gasify and absorb a great deal of heat, making temperature of spherical tank decrease sharply. In order to investigate this process, physical model was established, and the Reynolds time averaged Navier-Stokes equation and k-ɛ turbulent model as the CFD simulation method were used in this study. The temperature distribution of the spherical tank and the environment after spherical tank pipeline leaking was analyzed. The influences of leakage location and leak area on the spherical tank temperature distribution were analyzed, and a meaningful conclusion was obtained. This study could provide theoretical basis and technical support for the safety control of liquefied hydrocarbon spherical tank leakage. © 2012 Published by Elsevier Ltd. Selection and/or peer-review (pre-review) under responsibility of the Capital University of Economics and Business, China Academy of Safety Science and Technology

Combating Unknown Bias with Effective Bias-Conflicting Scoring and Gradient Alignment

Models notoriously suffer from dataset biases which are detrimental to robustness and generalization. The identify-emphasize paradigm shows a promising effect in dealing with unknown biases. However, we find that it is still plagued by two challenges: A, the quality of the identified bias-conflicting samples is far from satisfactory; B, the emphasizing strategies just yield suboptimal performance. In this work, for challenge A, we propose an effective bias-conflicting scoring method to boost the identification accuracy with two practical strategies -- peer-picking and epoch-ensemble. For challenge B, we point out that the gradient contribution statistics can be a reliable indicator to inspect whether the optimization is dominated by bias-aligned samples. Then, we propose gradient alignment, which employs gradient statistics to balance the contributions of the mined bias-aligned and bias-conflicting samples dynamically throughout the learning process, forcing models to leverage intrinsic features to make fair decisions. Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can alleviate the impact of unknown biases and achieve state-of-the-art performance

An Analysis on the Effectiveness of 2 and 3 Terminal Capacitors in PDN Design

The Parasitic Inductance of a Capacitor Depends on its Physical Structure. Due to the Geometry of 3-Terminal Capacitors, They Boast a Lower Parasitic Inductance Compared to 2-Terminal Capacitors of the Same and Possibly Smaller Package Sizes. While the Parasitic Inductance of a Single 3-Terminal Capacitor May Be Lower, using Multiple 2-Terminal Capacitors May Result in Similar Performance. in This Work, the Inductance of 2-Terminal (0201, Nominal 2.2 UF) and 3-Terminal (0402, Nominal 4.3 UF) Capacitors is Extracted and Compared through Measurements. from Our De-Embedding Method and Characterized Capacitors, the Inductance of 2-Terminal Capacitors is Only About 20 PH Higher Than the Characterized 3terminal Capacitor. on a Power Net of a Real Product, 3-Terminal Capacitors of the Same Type as Characterized Were Replaced with 2-Terminal Capacitors of the Same Type as Characterized. from Measurement Results, the Measured Inductance at 100 MHz is Lower by Only About 3.45 PH, or 2.62%, When using 3-Terminal Capacitors

Adaptive Transmission Range Based Topology Control Scheme for Fast and Reliable Data Collection

An Adaptive Transmission Range Based Topology Control (ATRTC) scheme is proposed to reduce delay and improve reliability for data collection in delay and loss sensitive wireless sensor network. The core idea of the ATRTC scheme is to extend the transmission range to speed up data collection and improve the reliability of data collection.The main innovations of our work are as follows: (1) an adaptive transmission range adjustment method is proposed to improve data collection reliability and reduce data collection delay. The expansion of the transmission range will allow the data packet to be received by more receivers, thus improving the reliability of data transmission. On the other hand, by extending the transmission range, data packets can be transmitted to the sink with fewer hops.Thereby the delay of data collection is reduced and the reliability of data transmission is improved. Extending the transmission range will consume more energy. Fortunately, we found the imbalanced energy consumption of the network.There is a large amount of energy remains when the network died. ATRTC scheme proposed in this paper can make full use of the residual energy to extend the transmission range of nodes. Because of the expansion of transmission range, nodes in the network form multiple paths for data collection to the sink node.Therefore, the volume of data received and sent by the near-sink nodes is reduced, the energy consumption of the near-sink nodes is reduced, and the network lifetime is increased as well. (2)According to the analysis in this paper, compared with the CTPR scheme, the ATRTC scheme reduces the maximum energy consumption by 9%, increases the network lifetime by 10%, increases the data collection reliability by 7.3%, and reduces the network data collection time by 23%

Enhancing Graph Neural Networks with Structure-Based Prompt

Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently, a new paradigm "pre-train, prompt" has shown promising results in adapting GNNs to various tasks with less supervised data. The success of such paradigm can be attributed to the more consistent objectives of pre-training and task-oriented prompt tuning, where the pre-trained knowledge can be effectively transferred to downstream tasks. However, an overlooked issue of existing studies is that the structure information of graph is usually exploited during pre-training for learning node representations, while neglected in the prompt tuning stage for learning task-specific parameters. To bridge this gap, we propose a novel structure-based prompting method for GNNs, namely SAP, which consistently exploits structure information in both pre-training and prompt tuning stages. In particular, SAP 1) employs a dual-view contrastive learning to align the latent semantic spaces of node attributes and graph structure, and 2) incorporates structure information in prompted graph to elicit more pre-trained knowledge in prompt tuning. We conduct extensive experiments on node classification and graph classification tasks to show the effectiveness of SAP. Moreover, we show that SAP can lead to better performance in more challenging few-shot scenarios on both homophilous and heterophilous graphs

Development of accurate well models for numerical reservoir simulation

 Peaceman well model is widely used in numerical reservoir simulation. With the help of the defined equivalent radius, the bottom-hole inflow or outflow flux can be calculated and is proportional to the difference of the bottom-hole pressure and the well grid pressure. It is shown in this article, though the bottom-hole flux is calculated accurately in Peaceman well model, there are some significant errors of pressure near the well for the large value of the length-to-width ratio of the mesh. Two alternative methods, the source term compensation method and the pattern competition method, which are both based on the analytic solution induced by the source term, are proposed for homogeneous medium. In the source term compensation method, auxiliary pressure, which satisfies the Laplace equation strictly, is defined and solved instead of the original pressure variable, which actually satisfies the Poisson equation. In the pattern competition method, different flow patterns including the linear flow pattern and radial flow pattern are considered. Each flow pattern corresponds to a transmissibility of the adjacent two grids and all the transmissibilities are calculated respectively. The smallest transmissibility will outcompete, and be used for solving the discrete pressure equations. Numerical results show that for the two proposed methods, not only the bottom-hole flux but also the pressure fields can be calculated accurately.Cited as: Zhang, S., Liu, Z., Shi, A., Wang, X. Development of accurate well models for numerical reservoir simulation. Advances in Geo-Energy Research, 2019, 3(3): 250-257, doi: 10.26804/ager.2019.03.0

QoE-ensured Price Competition Model for Emerging Mobile Networks

The ubiquitous availability of devices such as smart phones, tablets, and other portable devices enables the collection of massive amounts of distributed data from the daily lives of citizens. These types of emerging mobile networks can provide new forms of valuable information that are currently not available on this scale via any traditional data collection methods. In such networks, price competition is the most important factor among the participants (mobile devices, Services Organizers and users) that highly affects their Quality-of-Experience (QoE). In this article, we first explain how a game theory model can depict social behavior, price competition and the evolutionary relationship among devices, Services Organizers (SOs) and users, and then we provide insights into understanding the price competition process of those participants in mobile networks. Finally, we outline several important open research directions