Author
Guoming Chen
Bio: Guoming Chen is an academic researcher from China University of Petroleum. The author has contributed to research in topics: Subsea & Deepwater drilling. The author has an hindex of 20, co-authored 145 publications receiving 1426 citations.
Topics: Subsea, Deepwater drilling, Drilling riser, Capacitive sensing, Wellhead
Papers
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TL;DR: Wang et al. as discussed by the authors presented a risk-based accident model to conduct quantitative risk analysis (QRA) for leakage failure of submarine pipeline, which can provide a more case-specific and realistic analysis consequence compared to bow-tie method.
205 citations
TL;DR: In this paper, a coupled composite phase change material and liquid cooling thermal management system is proposed to improve the working performance of the lithium-ion battery pack in continuous operation under different ambient temperatures.
177 citations
TL;DR: In this article, a case study of hydrogen sulfide releases from a gas gathering station has been carried out using a three-dimensional FLUENT model and a combination of numerical simulation and dose-response model combined approach has been proposed for quantitative analysis of acute toxic gas exposure threats.
64 citations
TL;DR: A new methodology comprising of Hierarchical Bayesian analysis implemented with the Bayesian network is proposed to assess the risk of subsea pipelines leak, which makes use of a concept of fuzzy loss ratio to evaluate the consequence of different scenarios.
63 citations
TL;DR: A fatigue failure risk analysis approach based on dynamic Bayesian Networks, aiming to predict the fatigue failure probability of the wellhead during service life, which can use the previously accumulated fatigue of theWellhead to probabilistically predict the present failure risk under present dynamic conditions.
60 citations
Cited by
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
694 citations
TL;DR: In this article, a focus review summarising best metrological practice in the application of EIS to commercial Li-ion cells is presented, highlighting the benefits and drawbacks of the technique.
259 citations
TL;DR: The main principles, measurement and processing of MFL data, the identification of the leakage magnetic signal is discussed, and future developments in pipeline MFL detection are predicted.
Abstract: Magnetic flux leakage (MFL) detection is one of the most popular methods of pipeline inspection. It is a nondestructive testing technique which uses magnetic sensitive sensors to detect the magnetic leakage field of defects on both the internal and external surfaces of pipelines. This paper introduces the main principles, measurement and processing of MFL data. As the key point of a quantitative analysis of MFL detection, the identification of the leakage magnetic signal is also discussed. In addition, the advantages and disadvantages of different identification methods are analyzed. Then the paper briefly introduces the expert systems used. At the end of this paper, future developments in pipeline MFL detection are predicted.
227 citations
TL;DR: Wang et al. as discussed by the authors presented a risk-based accident model to conduct quantitative risk analysis (QRA) for leakage failure of submarine pipeline, which can provide a more case-specific and realistic analysis consequence compared to bow-tie method.
205 citations