7 research outputs found
Corrosion Behavior of Metal Active Gas Welded Joints of a High-Strength Steel for Automotive Application
© 2017, ASM International. In this work, the corrosion behavior of metal active gas-welded joints of a high-strength steel with tensile yield strength of 900 MPa was investigated. The welded joints were obtained using two different heat inputs. The corrosion behavior has been studied in a 3.5 wt.% NaCl aqueous solution using electrochemical impedance spectroscopy and potentiodynamic polarization tests. Optical microscopy images, scanning electron microscopy and transmission electron microscopy with energy-dispersive x-ray revealed different microstructural features in the heat-affected zone (HAZ) and the weld metal (WM). Before and after the corrosion process, the sample was evaluated by confocal laser scanning microscopy to measure the depth difference between HAZ and WM. The results showed that the heat input did not play an important role on corrosion behavior of HSLA steel. The anodic and cathodic areas of the welded joints could be associated with depth differences. The HAZ was found to be the anodic area, while the WM was cathodic with respect to the HAZ. The corrosion behavior was related to the amount and orientation nature of carbides in the HAZ. The microstructure of the HAZ consisted of martensite and bainite, whereas acicular ferrite was observed in the weld metal
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Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments
The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources
Recommended from our members
Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments
The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources