A5023044903- Fatigue and fracture mechanics
- High Temperature Alloys and Creep
- Hydrogen embrittlement and corrosion behaviors in metals
- Nuclear Materials and Properties
- Probabilistic and Robust Engineering Design
- Non-Destructive Testing Techniques
- Material Properties and Failure Mechanisms
- High-Temperature Coating Behaviors
- Fusion materials and technologies
- Microstructure and Mechanical Properties of Steels
- Graphite, nuclear technology, radiation studies
- Nuclear reactor physics and engineering
Pusan National University
2020-2024
Universitas Gadjah Mada
2018
The growth of Al2O3 scale during the short-duration oxidation ferritic FeCrAl alloy containing a small amount reactive elements added as oxide dispersions was investigated. test specimens were oxidised isothermally for 100 s, 3 h or 24 at 1200°C under steam environment. time-dependent changes in microstructure and composition carefully observed. All covered by continuous α-Al2O3 without spallation scale. A comparison is also made to model with lower Cr content Y metallic form. In addition,...
In this study, the probabilistic fatigue life model for Ni-base alloys was developed based on Weibull distribution using statistical analysis of data reported in NUREG/CR-6909 and new Alloy 52M/152 82/182. The can consider right-censored (i.e., non-failed data) quantify improved safety (or reliability) level failure probability. overall margin current design limit (ASME curve + Fen model) is similar to that with a cumulative probability approximately 2.5%. demonstrated inconsistencies alloy...
Abstract In this study, we attempted to model the fatigue life of Ni alloys by employing probabilistic approaches, i.e., survival regressions. The selected models included Weibull AFT and RSF models. input variables for were strain amplitude, rate, temperature, concentration dissolved oxygen, material category (i.e., base or weld). dataset was divided into two subsets, training test sets. set used train models, while evaluate models’ predictions. Several performance metrics measure...
A machine learning (ML) approach is proposed to predict and understand the corrosion fatigue (CF) crack growth rate of austenitic stainless steels (SSs) in high temperature water. Some commonly used supervised ML algorithms were considered here have been shown perform reasonably well. The models are compared substantially outperform empirical models. Among trained models, categorical boosting (CB) model has best. Finally, CB explained/interpreted using Shapley Additive explanation (SHAP)...
A machine learning (ML) approach is proposed to predict and understand the corrosion fatigue (CF) crack growth rate of austenitic stainless steels (SSs) in high temperature water. Six commonly used supervised ML algorithms were considered here shown perform exceptionally well. Among models, categorical boosting (CB) model was best. The models also compared substantially outperform existing empirical models. Finally, CB model, which has accurately learned captured important hidden patterns...
<p class='IJASEITAbtract'>A study about Resource Renewable Boiling Water Reactor (RBWR) core, a reduced moderation boiling water reactor that features the breeding ratio larger than 1 was conducted. This focuses on neutronic performances of core and aims to investigate sustainability when using thorium as main fertile fuel. A fuel-self-sustaining with high burnup set design target. <sup>233</sup>U+Th were used initial fuel, impact fissile (<sup>233</sup>U) content in zone evaluated....