TechnipFMC (Germany)

Abstract Technip Energies has recently completed a fit-for-service and life extension project for nearly 200 submarine pipelines with a total length of over 2,000 km in the Gulf of Thailand. One of the main challenges in this project was to accurately determine the remaining life and necessary intervention works for free spans, which are a typical concern for pipeline integrity with respect to local buckling and fatigue capacity. Initial assessments revealed that the conventional free span evaluation method of comparing observed free span lengths with analytically determined allowable lengths would lead to extensive intervention works. Additionally, ROV (Remotely Operated Vehicle) surveys showed a continuous increase in free span lengths due to scouring, and pipeline Inline Inspections (ILI) identified multiple active corrosion defects at span locations. These findings underscored the importance of developing an automated approach to enhance free span assessment accuracy with higher workflow efficiency. The automated approach developed integrates industry-standard design tools with tailored in-house programs to streamline the free span assessment process. The workflow begins with a detailed analysis of pipeline design data, ROV survey and inline inspection data. ROV survey data from multiple inspection campaigns are analyzed to determine free span lengths, gaps, and growth rates, while inline inspection data are used to identify major corrosion defects and their growth. An in-house program is then used to develop a global finite element (FE) model for the entire pipeline – either on a flat seabed with predefined free spans or on a more realistic undulated seabed profile to account for rotated boundary conditions. This FE model accurately captures the on-bottom condition of the free spans, including the effective axial force. Free span growth is either incorporated directly in the global model (through multiple analysis iterations) or its effect is considered separately through a mapping function. For spans affected by corrosion, a separate specialized program develops a localized 3D shell element finite element model featuring local pipe wall thickness reduction from corrosion to determine equivalent pipe stiffness for fatigue analysis and corroded bending moment capacity for local buckling assessment. Outputs extracted from the global FE models serve as inputs to pipeline local buckling assessment and Vortex-Induced-Vibration (VIV) fatigue analysis software for a comprehensive fatigue analysis. This paper presents the developed automated free span assessment methodology together with a case study of a typical free spanning pipeline with internal corrosion. The paper compares various modelling techniques and their effects on fatigue damage and local buckling assessment results. The findings provide valuable insights for the assessment of free spans in corroded pipelines in dynamic seabed environments, enhancing the service life of operating subsea pipelines while substantially reducing unnecessary intervention costs.

Increasing environmental concerns caused by vehicular emissions have intensified the search for the design and development of non-noble metal catalysts for catalytic converter devices as potential replacements for conventional Pt-, Pd-, and Rh-based noble metal catalysts. This research highlights the development and evaluation of an alternative to conventional catalysts through the synthesis of non-noble metal perovskite-based catalysts and the design modification of a catalytic converter. A non-noble metal catalyst, La 0 . 8 Sr 0 . 2 Co 0 . 8 Fe 0 . 2 O 3 (LSCF), was synthesized by co-precipitation, coated onto a ceramic monolith of a catalytic converter, and examined for effectiveness under petrol fuel laboratory test setup. The synthesized catalyst was also analyzed using SEM, XRD, and EDX to study surface morphology and confirm the crystal structure. The catalytic converter housing assembly was modified by integrating design modifications and analyzed through computational simulations to investigate velocity profile, pressure distribution, and reaction behavior. Among the three catalytic converter design configurations with diffuser cone angles of 8°, 10°, and 14°, the first was selected as it showed a favorable gas flow pattern, pressure distribution, and velocity profile. The entire module was then experimentally evaluated on a petrol fuel laboratory test setup to assess emission performance under varying loads and speeds. Experimental emission tests revealed a significant reduction in hydrocarbon (HC) and carbon monoxide (CO) emissions compared to engines without a catalytic converter. The results demonstrate that the synthesized La 0 . 8 Sr 0 . 2 Co 0 . 9 Fe 0 . 1 O 3 -δ non-noble metal catalyst, combined with the modified catalytic converter design, effectively reduces vehicular emissions and provides an alternative and practical approach to noble metal catalysts. A noticeable reduction in CO and HC exhaust emissions was achieved using the LSCF catalyst for an automotive catalytic converter.

Abstract This paper presents a case study for a deep-water, HPHT, short flowline evaluated using a probabilistic lateral buckling design approach that aims to predict the pipeline end loads. The lateral buckling assessment of short flowlines, with fixed pipe ends and length of 1000m, may show an acceptable lateral buckling result, not requiring a controlled lateral buckling strategy, i.e., no engineered triggers, to ensure the pipe integrity. Although the integrity of the pipeline is ensured, it is recommended to introduce at least one engineered trigger in the pipeline to reduce the end loads stably by creating a lateral buckle to release the compressive axial force in the pipeline. The end loads, affected by uncertain parameters, are evaluated based on a probabilistic approach to have a robust and demonstrably efficient design. It is expected that short flowlines have small, lateral out-of-straightness features (which are inherent to the pipeline installation process). The uncertainty in the out-of-straightness features along the line can influence the critical buckling force and therefore the end loads due to cyclic thermal loads. The proposed approach is useful for predicting the pipeline end loads for the anchor structures design at early or detailed phase in the presence of uncertain parameters. This probabilistic approach requires more simulation time, compared with the deterministic approach, but it helps to predict a more robust range for the lateral buckling response and lower anchor load, which may result in project cost savings.

This paper discusses the complete absorption of microwaves by conductive plates and its application in heating technology. Numerical calculations were conducted to determine the conditions for complete absorption of 2.45 GHz microwaves propagating through a rectangular waveguide tube with an inner dimension of 5.5×10.9 cm2. Based on the calculated results, we developed a resonating absorber with a three-layered structure of single-crystalline silicon (c-Si) which consisted of a 0.7-cm-thick semi-insulating c-Si substrate (DP) with a resistivity over 1000 Ωcm, a 0.49-cm-thick c-Si substrate (CP1) with 19-22 Ωcm, and a 0.05-cm-thick c-Si substrate (CP2) with 0.001 Ωcm or less. The absorber achieved complete microwave absorption with a measurement accuracy of 2%. Complete absorption was again observed when a half-wavelength (λ/2)-long air gap was introduced between the DP and CP1 layers. A radiation thermometer detected a temperature increase of 75.5 K in CP2 under 500 W microwave irradiation for 10 s, which was higher than the 43 K increase observed without the air gap. This demonstrates that the λ/2-air gap effectively provides thermal isolation between the DP and CP1 layers.

X1-Wind has developed an innovative platform concept specifically for floating offshore environments in deep waters that combines the advantages of a semi-submersible and Tension Leg Platform (TLP) through its single-point mooring (SPM, called PivotBuoy®). The result is a downwind tripod-like platform that self-aligns passively with the wind allowing an efficient structural design and also eliminating the traditional tower (which suffers high bending moments at the tower base) and the active yaw, substantially lighter and more scalable design than the SPAR, semi or barge existing technologies. The tripod structure supporting the Rotor and Nacelle Assembly (RNA) is made of three masts each of which work in mostly tension or compression to connect a column to an element just below the RNA. Intrinsically very stiff, the tripod has a low natural period away from the 1P and 3P excitation range of the turbine. Furthermore, the tripod structure's natural period is almost not affected by the presence of the floater (frequency/period shift). This allows an easier design as it can be done independently and in parallel to that of the floater. Finally, this kind of support structure is well suited for big turbines considered for actual development (up to 15 MW) and planned for the future (20 MW and more). A comparison of tower and tripod structures is presented in this paper for different sizes of turbines (6, 15 and 20 MW). The comparison covers the natural periods, sensitivity to the presence of the floater, stress level and weight.

One of the crucial sectors of Indian livelihood is agriculture. The development and stability of the country have been vastly backed by the farming sector. Thus, ultramodern inventions and technologies can support the testing of new approaches and practices in the farming sector. Artificial intelligence (AI) is one of the most important, useful, and current technologies. Due to its capacity to acquire dependable interpretations from images, Deep learning (DL) in particular has numerous uses. The most common DL architecture for image categorization is Convolutional Neural Networks (CNN). Classifying fruits and vegetables using deep learning is the main emphasis of this work. The model is also compared to some ML classifiers like Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Decision Tree (DT) whereas ResNet the Pretrained Model, Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP) in deep literacy. It was derived that SVM and MLP both showed an accuracy of97.36 on the fruit dataset.