Data-driven Methods to Predict the Burst Strength of Corroded Line Pipelines Subjected to Internal Pressure

A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines,especially those served for a long time.Finite-element method and empirical formulas are thereby used for the strength prediction of such pipes with corrosion.However,it is time-consuming for finite-element method and there is a limited application range by using empirical formulas.In order to improve the prediction of strength,this paper investigates the burst pressure of line pipelines with a single corrosion defect subjected to internal pressure based on data-driven methods.Three supervised ML(machine learning)algorithms,including the ANN(artificial neural network),the SVM(support vector machine)and the LR(linear regression),are deployed to train models based on experimental data.Data analysis is first conducted to determine proper pipe features for training.Hyperparameter tuning to control the learning process is then performed to fit the best strength models for corroded pipelines.Among all the proposed data-driven models,the ANN model with three neural layers has the highest training accuracy,but also presents the largest variance.The SVM model provides both high training accuracy and high validation accuracy.The LR model has the best performance in terms of generalization ability.These models can be served as surrogate models by transfer learning with new coming data in future research,facilitating a sustainable and intelligent decision-making of corroded pipelines.

High-speed GeSn resonance cavity enhanced photodetectors for a 50 Gbps Si-based 2 μm band communication system

Expanding the optical communication band is one of the most effective methods of overcoming the nonlinear Shannon capacity limit of single fiber.In this study,GeSn resonance cavity enhanced(RCE)photodetectors(PDs)with an active layer Sn component of 9%–10.8%were designed and fabricated on an SOI substrate.The GeSn RCE PDs present a responsivity of 0.49 A/W at 2μm and a 3-dB bandwidth of approximately 40 GHz at 2μm.Consequently,Si-based 2μm band optical communication with a transmission rate of 50 Gbps was demonstrated by using a GeSn RCE detector.This work demonstrates the considerable potential of the Si-based 2μm band photonics in future high-speed and high-capacity optical communication.

980 nm Near-Infrared Light-Emitting Diode Using All-Inorganic Perovskite Nanocrystals Doped with Ytterbium Ions

All-inorganic perovskite(CsPbX3)nanocrystals(NCs)have recently been widely investigated as versatile solution-processable light-emitting materials.Due to its wide-bandgap nature,the all-inorganic perovskite NC Light-Emitting Diode(LED)is limited to the visible region(400-700 nm).A particularly difficult challenge lies in the practical application of perovskite NCs in the infrared-spectrum region.In this work,a 980 nm NIR all-inorganic perovskite NC LED is demonstrated,which is based on an efficient energy transfer from wide-bandgap materials(CsPbCl3 NCs)to ytterbium ions(Yb3+)as an NIR emitter doped in perovskite NCs.The optimized CsPbCl3 NC with 15 mol%Yb3+doping concentration has the strongest 980 nm photoluminescence(PL)peak,with a PL quantum yield of 63%.An inverted perovskite NC LED is fabricated with the structure of ITO/PEDOT:PSS/poly-TPD/CsPbCl3:15 mol%Yb3+NCs/TPBi/LiF/Al.The LED has an External Quantum Efficiency(EQE)of 0.2%,a Full Width at Half Maximum(FWHM)of 47 nm,and a maximum luminescence of 182 cd/m?.The introduction of Yb3+doping in perovskite NCs makes it possible to expand its working wavelength to near-infrared band for next-generation light sources and shows potential applications for optoelectronic integration.