Numerical Analysis and Strength Evaluation of an Exposed River Crossing Pipeline with Casing Under Flood Load

Pipelines in service always experience complicated loadings induced by operational and environmental conditions. Flood is one of the common natural hazard threats for buried steel pipelines. One exposed river crossing X70 gas pipeline induced by flood erosion was used as a prototype for this study. A mechanical model was established considering the field loading conditions. Morison equations were adopted to calculate distributional hydrodynamic loads on spanning pipe caused by flood flow. Nonlinear soil constraint on pipe was considered using discrete nonlinear soil springs. An explicit solution of bending stiffness for pipe segment with casing was derived and applied to the numerical model. The von Mises yield criterion was used as failure criteria of the X70 pipe. Stress behavior of the pipe were analyzed by a rigorous finite element model established by the general-purpose Finite-Element package ABAQUS, with 3D pipe elements and pipe-soil interaction elements simulating pipe and soil constraints on pipe, respectively. Results show that, the pipe is safe at present, as the maximum von Mises stress in pipe with the field parameters is 185.57 MPa. The critical flow velocity of the pipe is 5.8 m/s with the present spanning length. The critical spanning length of the pipe is 467 m with the present flow velocity. The failure pipe sections locate at the connection point of the bare pipe and the pipe with casing or the supporting point of the bare pipe on riverbed

Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts

Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study presents a multistage machine learning (ML) approach to streamline the discovery and optimization of complex multimetallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then, the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we demonstrate an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially benchmark in electrocatalysts research

Safety assessment on girth welds of large-diameter X80 pipelines in water network area

As the soil in the water network area is soft and has large water content, it is very easy to settle, which applies an axial load to the pipeline, and the girth weld under the action of tensile load has a higher risk of failure. In order to ensure the intrinsic safety of the pipeline, a nonlinear finite element method was adopted to establish a numerical calculation model for the axial stress and strain of the pipeline under sudden settlement of soft soil based on the X80 pipeline with a diameter of 1 422 mm in the water network area, and thus the influence of different settlement displacements of soft soil on the size and distribution of the axial load of pipeline under the most unfavorable sudden displacement condition was studied. On this basis, the safety of pipeline welds at different locations in the soft soil settlement area was evaluated according to the Guide to Methods for Assessing the Acceptability of Flaws in Metallic Structures (BS 7910-2019). Meanwhile, the influence of external load conditions and weld misalignment was analyzed, which indicates that the increase of misalignment leads to an increase in the load ratio and toughness ratio used in the assessment, with the conservativeness reduced for the safety of pipeline. If the critical Crack Tip Opening Displacement (CTOD) is taken to be 0.25 mm in the current standard, and the level-1 assessment curve is adopted, the welds of X80 pipeline in the water network area are always in a safe state. If the level-2 assessment curve is used, the X80 pipeline weld is in a critical safe state when the critical CTOD is reduced to 0.08 mm and the settlement displacement takes the maximum value of 1 m

Dynamic structural evolution of iron catalysts involving competitive oxidation and carburization during CO2 hydrogenation

Identifying the dynamic structure of heterogeneous catalysts is crucial for the rational design of new ones. In this contribution, the structural evolution of Fe(0) catalysts during CO2 hydrogenation to hydrocarbons has been investigated by using several (quasi) in situ techniques. Upon initial reduction, Fe species are carburized to Fe3C and then to Fe5C2. The by-product of CO2 hydrogenation, H2O, oxidizes the iron carbide to Fe3O4. The formation of Fe3O4@(Fe5C2+Fe3O4) core-shell structure was observed at steady state, and the surface composition depends on the balance of oxidation and carburization, where water plays a key role in the oxidation. The performance of CO2 hydrogenation was also correlated with the dynamic surface structure. Theoretical calculations and controll experiments reveal the interdependence between the phase transition and reactive environment. We also suggest a practical way to tune the competitive reactions to maintain an Fe5C2-rich surface for a desired C2+ productivity

Uncertainty Quantification for Aerothermal Characteristics of HP Turbine Vanes Under Combined Hot-Streak and Turbulence Intensity Effects

This study presents a systematic framework for quantifying aerothermal uncertainties in high-pressure turbine nozzle guide vanes (NGV) under combustor-turbine interaction, focusing on the combined impacts of hot streak spatial variations and turbulence intensity fluctuations. By integrating parametric modeling of combustor-exit temperature fields, non-intrusive polynomial chaos expansion (PCE), and Sobol sensitivity analysis, the methodology enables probabilistic evaluation of aerothermal performance across arbitrary turbine locations. Conjugate heat transfer simulations were conducted to analyze the effect of stochastic parameters on the NGV metal temperature uncertainty. The findings reveal that cooled NGVs exhibit an 80% increase in mean total pressure loss and 42% higher fluctuation amplitudes, driven by enhanced midspan mixing and counter-rotating vortices. Localized metal temperature fluctuations reach 4.3% of inlet total temperature, concentrated in cooling transition zones and secondary flow paths. Turbulence intensity dominates uncertainty contributions, while hot streak circumferential variations show minimal influence. The PCE based framework, augmented by Hammersley sampling, achieves computational efficiency with 20 samples, demonstrating robust capability for cooling system design under realistic inflow uncertainties. This work advances probabilistic aerothermal analysis methodologies, offering critical insights for turbine architectures operating under lean-burn combustor conditions

Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for the Efficient Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts

Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study introduces a novel, multi-stage machine learning (ML) approach to streamline the discovery and optimization of complex multi-metallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we establish an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially new benchmark in electrocatalysts research.95 pages (main text 37 pages; supplementary materials 58 pages); 38 figures (main text 6 figures; supplementary materials 32 figures

Optimization of IMU-based bending strain solving algorithm and full-scale experimental validation

Objective The pipe bending strain identification technology based on an inertial mapping unit (IMU) in-line detector has seen extensive applications in China and abroad. IMUs obtain bending strains along entire pipelines through a solving process, using pitch angles and heading angles acquired by a gyroscope. However, deviations from the detection path may occur during the practical in-line detector operation, due to the sizes of IMUs and other factors, leading to inevitable errors between the bending strains identified by IMUs and the actual pipeline conditions. Methods A simulation model was developed for IMU in-line detectors based on their actual size, without abnormal vibrations and other interferences. In addition, a simulation database was created for pipelines with a diameter of 508 mm under varying bending strain conditions. Moreover, a solving algorithm optimized through an ANNExtraTree deep learning model was introduced. These tools were leveraged for error analysis and to delve into the bending strain solving algorithm. Furthermore, full-scale pulling experiments were conducted on pipeline bending strains, to verify the feasibility and accuracy of the optimized solving algorithm. Results This study revealed that as pipeline bending strain increased, the errors between IMU detections and true bending strains grew. The mean square error, error covariance and error standard deviation of the bending strain data derived from the optimized solving process decreased from 0.007 0, 0.004 9, 0.070 2 to 0.001 2, 0.001 6, 0.012 6 respectively, while the coefficient of determination, indicating correlation, rose from 0.443 to 0.981. Furthermore, the full-scale pulling experiments conducted to validate the algorithm revealed substantial reductions in errors between bending strains computed by the optimized solving algorithm and those detected by a strain gauge at 79.6%, 79.4%, and 76.0%, respectively. Conclusion The proposed optimized solving algorithm is verified feasible and accurate through finite element simulations and full-scale pulling experiments. The study findings provide technical support and guidance for accurately identifying bending strains along pipelines based on IMU

Reliability analysis of pipeline in fault area based on BP-MC method

Strike-slip fault is a common geologic hazard for buried pipelines. The fault-induced ground displacement often leads to excessive deformation and failure of pipelines, so it is of great significance to carry out reliability analysis of pipelines in fault areas for safety assessment of pipelines. Therefore, the finite element model of X80 steel pipeline crossing strike-slip fault was established based on the nonlinear finite element software ABAQUS, the design strain of the pipeline under the influence of many factors such as pipeline geometric dimensioning, surface displacement, inner pressure and soil type was calculated, and the finite element data of the pipeline design strain in the project was formed. Based on this database, the design strain prediction model of double hidden layered BP neural network was built, and the limit state equation based on strain criterion was established. Combined with MC (Monte Carlo) method, the reliability calculation of X80 pipeline in strike-slip fault area and the analysis of influencing factors of pipeline reliability were conducted. The calculation result based on BP-MC method is accurate and the running time cost is low, so it will be applicable to the reliability analysis of pipelines under faulting

Precise Drift Tracking for <i>In Situ</i> Transmission Electron Microscopy via a Thon-Ring Based Sample Position Measurement

Abstract Visualizing how a catalyst behaves during chemical reactions using in situ transmission electron microscopy (TEM) is crucial for understanding the activity origin and guiding performance optimization. However, the sample drifts as temperature changes during in situ reaction, which weakens the resolution and stability of TEM imaging, blocks insights into the dynamic details of catalytic reaction. Herein, a Thon-ring based sample position measurement (TSPM) was developed to track the sample height variation during in situ TEM observation. Drifting characteristics for three commercially available nanochips were studied, showing large biases in aspects of shifting modes, expansion heights, as well as the thermal conduction hysteresis during rapid heating. Particularly, utilizing the TSPM method, for the first time, the gas layer thickness inside a gas-cell nanoreactor was precisely determined, which varies with reaction temperature and gas pressure in a linear manner with coefficients of ~8 nm/°C and ~50 nm/mbar, respectively. Following drift prediction of TSPM, fast oxidation kinetics of a Ni particle was tracked in real time for 12 s at 500°C. This TSPM method is expected to facilitate the functionality of automatic target tracing for in situ microscopy applications when feedback to hardware control of the microscope.</jats:p

Strain Prediction for X80 Steel Pipeline Subjected to Strike-Slip Fault Under Compression Combined With Bending

Strike-slip fault is one main kind of PGD faced by long distance gas pipelines. Based on non-linear finite element method, a numerical model for buried pipeline under strike-slip fault was proposed. The model was proven to be reasonable by comparing the numerical results with previous researcher’s experiment results. By using the FE model, peak compressive strain of X80 steel pipeline subjected to strike-slip fault under compression combined with bending was studied. The sensitivities of the diameter, wall thickness, soil rigidity, fault displacement and crossing angle on the peak compressive strain of the pipeline are examined in detail. Furthermore, based on numerous numerical results, a regression equation for predicting peak compressive strain of X80 steel pipeline is proposed. The applicable range of the formula is given. 15 true design cases in the Second West to East pipeline Project in China were investigated to demonstrate the accuracy and applicability of the proposed methodology by comparing the predicting peak compressive strain results with FEM results. The proposed method can be referred in the strain-based and reliability-based design for X80 steel pipelines subjected to strike-slip fault.</jats:p