Damage-Based Assessment of the Fatigue Crack Initiation Site in High-Strength Steel Welded Joints Treated by HFMI

This study aimed to identify the fatigue crack initiation site of high-frequency mechanical impact (HFMI)-treated high-strength steel welded joints subjected to high peak stresses; the impact of HFMI treatment residual stress relaxation being of particular interest. First, the compressive residual stresses induced by HFMI treatment and their changes due to applied high peak stresses were quantified using advanced measurement techniques. Then, several features of crack initiation sites according to levels of applied peak stresses were identified through fracture surface observation of failed specimens. The relaxation behavior was simulated with finite element (FE) analyses incorporating the experimentally characterized residual stress field, load cycles including high peak load, improved weld geometry and non-linear material behavior. With local strain and local mean stress after relaxation, fatigue damage assessments along the surface of the HFMI groove were performed using the Smith–Watson–Topper (SWT) parameter to identify the critical location and compared with actual crack initiation sites. The obtained results demonstrate the shift of the crack initiation most prone position along the surface of the HFMI groove, resulting from a combination of stress concentration and residual stress relaxation effect.

Er-Doped Tapered Fiber Amplifier for High Peak Power Sub-ns Pulse Amplification

A tapered Er-doped fiber amplifier for high peak power pulses amplification has been developed and tested. The core diameter changed from 15.8 µm (mode field diameter (MFD) 14.5 µm) to 93 µm (MFD 40 µm) along 3.7 m maintaining single-mode performance at 1555 nm (according to the S2-method, the part of the power of high-order modes does not exceed 1.5%). The amplification of 0.9 ns pulses with spectral width below 0.04 nm up to a peak power above 200 kW (limited by self-phase modulation) with a slope pump-to-signal conversion efficiency of 15.6% was demonstrated.

An Efficient Method for Capturing the High Peak Concentrations of PM2.5 Using Gaussian-Filtered Deep Learning

This paper proposes a deep learning model that integrates a convolutional neural network with a gate circulation unit that captures patterns of high-peak PM2.5 concentrations. The purpose is to accurately predict high-peak PM2.5 concentration data that cannot be trained in general deep learning models. For the training of the proposed model, we used all available weather and air quality data for three years from 2015 to 2017 from 25 stations of the National Institute of Environmental Research (NIER) and the Korea Meteorological Administration (KMA) observatory in Seoul, South Korea. Our model trained three years of data and predicted high-peak PM2.5 concentrations for the year 2018. In addition, we propose a Gaussian filter algorithm as a preprocessing method for capturing high concentrations of PM2.5 in the Seoul area and predicting them more accurately. This model overcomes the limitations of conventional deep learning approaches that are unable to predict high peak PM2.5 concentrations. Comparing model measurements at each of the 25 monitoring sites in 2018, we found that the deep learning model with a Gaussian filter achieved an index of agreement of 0.73–0.89 and a proportion of correctness of 0.89–0.96, and compared to the conventional deep learning method (average POC = 0.85), the Gaussian filter algorithm (average POC = 0.94) improved the accuracy of high-concentration PM2.5 prediction by an average of about 9%. Applying this algorithm in the preprocessing stage could be updated to predict the risk of high PM2.5 concentrations in real time.