Burst Capacity Evaluation of Corroded Pipelines under Internal Pressure and Internal Pressure Combined with Longitudinal Compression

Central to the design and integrity assessment of oil and gas transmission pipelines is to accurately evaluate their pressure containment capacities, i.e. burst capacities. Corrosion defects threaten the structural integrity of pipelines as they cause thinning of the pipe wall and therefore reduce the burst capacity. Corroded in-service pipelines may be subjected to longitudinal compression resulting from, for example, ground movement or formation of free spans, in addition to internal pressures. The main objective of the research reported in this thesis is to facilitate Fitness-For-Service (FFS) assessment of corroded pipelines. The first study investigates the conservatism associated with the rectangular and semi-ellipsoidal idealizations of corrosion defects of naturally-occurring corrosion defects by finite element analysis (FEA). The semi-ellipsoidal idealization of naturally-occurring corrosion defects in FEA is found to lead to more accurate predictions of the burst capacity than the rectangular idealization for defects that are less than 70% through the pipe wall thickness. The FEA results conducted with the semi-ellipsoidal-shaped defects indicate that the burst capacity in general increases as the defect width increases if the defect depth and length remain the same. The defect width effect is marked for deep, relatively short defects, and should therefore be taken into account accordingly in the empirical or semi-empirical burst capacity models. The second study proposes a new burst capacity model for corroded pipelines based on extensive parametric three-dimensional (3D) elasto-plastic FEA validated by full-scale burst tests. Based on the well-known NG-18 equation, the proposed model takes into account the beneficial effect of the defect width on the burst capacity and employs a new Folias factor that depends on both the defect depth and length. The flow stress in the proposed model is defined as a function of the strain hardening exponent and ultimate tensile strength of the pipe steel based on the analytical solution of the burst capacity of defect-free pipes. The accuracy of the proposed model is validated using extensive parametric FEA and shown to be higher than existing burst capacity models. The third study investigates the burst capacity of corroded pipelines under combined internal pressure and longitudinal compression based on extensive parametric 3D elastic-plastic FEA. It is observed that the longitudinal compressive stress can markedly reduce the burst capacity of corroded pipelines. The adverse effect of the compressive stress on the burst capacity is the strongest for wide, relatively shallow defects, and relatively insensitive to the defect length. Based on the parametric FEA results, an artificial neural network (ANN) model is developed in the open-source platform PYTHON to predict the burst capacity of pipelines under internal pressure only or combined loads. The ANN model is validated using FEA and full-scale burst tests conducted by DNV and the results indicate good accuracy of the ANN model. The fourth study develops a new semi-empirical burst capacity model for corroded oil and gas pipelines under combined internal pressure and longitudinal compression. The proposed model evaluates the burst capacity of a corroded pipeline under combined loads as the burst capacity of the pipeline under internal pressure only, which is proposed in the second study, multiplied by a correction factor to account for the effect of the longitudinal compression. Extensive parametric elastoplastic FEA are carried out, the results of which are used as the basis to develop the correction factor as a function of the corrosion defect sizes and magnitude of the longitudinal compressive stress. The proposed model is validated by a large set of parametric FEA and full-scale burst tests reported in the literature, and is shown to provide marked improvements over two existing models, the DNV and RPA-PLLC models, for corroded pipelines under combined loads. The fifth study investigates the interaction effect on the burst capacity of oil and gas pipelines containing closely-spaced corrosion defects under combined internal pressure and longitudinal compression by carrying out extensive parametric 3D elasto-plastic finite element analyses. The analysis results reveal that the interaction effects under combined loads are different from the interaction effects under internal pressure only. The interaction between circumferentially-aligned defects under combined loads is significant: the burst capacity corresponding to the two-defect case can be markedly lower than that corresponding to the single-defect case. On the other hand, the interaction between longitudinally-aligned defects under combined loads is negligible due to the so-called shielding effect

A Joint Model and Data Driven Method for Distributed Estimation

This paper considers the problem of distributed estimation in wireless sensor networks (WSN), which is anticipated to support a wide range of applications such as the environmental monitoring, weather forecasting, and location estimation. To this end, we propose a joint model and data driven distributed estimation method by designing the optimal quantizers and fusion center (FC) based on the Bayesian and minimum mean square error (MMSE) criterions. First, universal mean square error (MSE) lower bound for the quantization-based distributed estimation is derived and adopted as the design metric for the quantizers. Then, the optimality of the mean-fusion operation for the FC with MMSE criterion is proved. Next, by exploiting different levels of the statistic information of the desired parameter and observation noise, a joint model and data driven method is proposed to train parts of the quantizer and FC modules as deep neural networks (DNNs), and two loss functions derived from the MMSE criterion are adopted for the sequential training scheme. Furthermore, we extend the above results to the case with multi-bit quantizers, considering both the parallel and one-hot quantization schemes. Finally, simulation results reveal that the proposed method outperforms the state-of-the-art schemes in typical scenarios.Comment: in IEEE Internet of Things Journa

Insights into the role of nucleotide methylation in metabolic-associated fatty liver disease

Metabolic-associated fatty liver disease (MAFLD) is a chronic liver disease characterized by fatty infiltration of the liver. In recent years, the MAFLD incidence rate has risen and emerged as a serious public health concern. MAFLD typically progresses from the initial hepatocyte steatosis to steatohepatitis and then gradually advances to liver fibrosis, which may ultimately lead to cirrhosis and carcinogenesis. However, the potential evolutionary mechanisms still need to be clarified. Recent studies have shown that nucleotide methylation, which was directly associated with MAFLD’s inflammatory grading, lipid synthesis, and oxidative stress, plays a crucial role in the occurrence and progression of MAFLD. In this review, we highlight the regulatory function and associated mechanisms of nucleotide methylation modification in the progress of MAFLD, with a particular emphasis on its regulatory role in the inflammation of MAFLD, including the regulation of inflammation-related immune and metabolic microenvironment. Additionally, we summarize the potential value of nucleotide methylation in the diagnosis and treatment of MAFLD, intending to provide references for the future investigation of MAFLD

Improved whale swarm algorithm for solving material emergency dispatching problem with changing road conditions

To overcome the problem of easily falling into local extreme values of the whale swarm algorithm to solve the material emergency dispatching problem with changing road conditions, an improved whale swarm algorithm is proposed. First, an improved scan and Clarke-Wright algorithm is used to obtain the optimal vehicle path at the initial time. Then, the group movement strategy is designed to generate offspring individuals with an improved quality for refining the updating ability of individuals in the population. Finally, in order to maintain population diversity, a different weights strategy is used to expand individual search spaces, which can prevent individuals from prematurely gathering in a certain area. The experimental results show that the performance of the improved whale swarm algorithm is better than that of the ant colony system and the adaptive chaotic genetic algorithm, which can minimize the cost of material distribution and effectively eliminate the adverse effects caused by the change of road conditions

Denervation as a Common Mechanism Underlying Different Pulmonary Vein Isolation Strategies for Paroxysmal Atrial Fibrillation: Evidenced by Heart Rate Variability after Ablation

Backgrounds. Segmental and circumferential pulmonary vein isolations (SPVI and CPVI) have been demonstrated to be effective therapies for paroxysmal atrial fibrillation (PAF). PVI is well established as the endpoint of different ablation techniques, whereas it may not completely account for the long-term success. Methods. 181 drug-refractory symptomatic PAF patients were referred for segmental or circumferential PVI (SPVI = 67; CPVI = 114). Heart rate variability (HRV) was assessed before and after the final ablation. Results. After following up for 62.23±12.75 months, patients underwent 1.41±0.68 procedures in average, and the success rates in SPVI and CPVI groups were comparable. 119 patients were free from AF recurrence (SPVI-S, n=43; CPVI-S, n=76). 56 patients had recurrent episodes (SPVI-R, n=21; CPVI-R, n=35). Either ablation technique decreased HRV significantly. Postablation SDNN and rMSSD were significantly lower in SPVI-S and CPVI-S subgroups than in SPVI-R and CPVI-R subgroups (SPVI-S versus SPVI-R: SDNN 91.8±32.6 versus 111.5±36.2 ms, rMSSD 47.4±32.3 versus 55.2±35.2 ms; CPVI-S versus CPVI-R: SDNN 83.0±35.6 versus 101.0±40.7 ms, rMSSD 41.1±22.9 versus 59.2±44.8 ms; all P<0.05). Attenuation of SDNN and rMSSD remained for 12 months in SPVI-S and CPVI-S subgroups, whereas it recovered earlier in SPVI-R and CPVI-R subgroups. Multivariate logistic regression analysis identified SDNN as the only predictor of long-term success. Conclusions. Beyond PVI, denervation may be a common mechanism underlying different ablation strategies for PAF