scholarly journals Damage Prediction and Evolution using Uncoupled Elastoplastic-Damage Lemaitre’s and Vaz’s Models

2021 ◽  
Vol 36 (2) ◽  
pp. 33-42
Author(s):  
Mohamed El-Shourbagy ◽  
Mohamed Attia ◽  
Ahmed El-Shafei
Keyword(s):  
Damage Prediction ◽  
Elastoplastic Damage

scholarly journals Development of Elastoplastic-Damage Model of AlFeSi Phase for Aluminum Alloy 6061

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 954
Author(s):  
Hailong Wang ◽  
Wenping Deng ◽  
Tao Zhang ◽  
Jianhua Yao ◽  
Sujuan Wang
Keyword(s):  
Aluminum Alloy ◽  
Constitutive Model ◽  
Material Properties ◽  
Damage Model ◽  
Scratch Test ◽  
Aluminum Alloy 6061 ◽  
Ultra Precision ◽  
Simulation Results ◽  
Alloy 6061 ◽  
Elastoplastic Damage

Material properties affect the surface finishing in ultra-precision diamond cutting (UPDC), especially for aluminum alloy 6061 (Al6061) in which the cutting-induced temperature rise generates different types of precipitates on the machined surface. The precipitates generation not only changes the material properties but also induces imperfections on the generated surface, therefore increasing surface roughness for Al6061 in UPDC. To investigate precipitate effect so as to make a more precise control for the surface quality of the diamond turned Al6061, it is necessary to confirm the compositions and material properties of the precipitates. Previous studies have indicated that the major precipitate that induces scratch marks on the diamond turned Al6061 is an AlFeSi phase with the composition of Al86.1Fe8.3Si5.6. Therefore, in this paper, to study the material properties of the AlFeSi phase and its influences on ultra-precision machining of Al6061, an elastoplastic-damage model is proposed to build an elastoplastic constitutive model and a damage failure constitutive model of Al86.1Fe8.3Si5.6. By integrating finite element (FE) simulation and JMatPro, an efficient method is proposed to confirm the physical and thermophysical properties, temperature-phase transition characteristics, as well as the stress–strain curves of Al86.1Fe8.3Si5.6. Based on the developed elastoplastic-damage parameters of Al86.1Fe8.3Si5.6, FE simulations of the scratch test for Al86.1Fe8.3Si5.6 are conducted to verify the developed elastoplastic-damage model. Al86.1Fe8.3Si5.6 is prepared and scratch test experiments are carried out to compare with the simulation results, which indicated that, the simulation results agree well with those from scratch tests and the deviation of the scratch force in X-axis direction is less than 6.5%.

Modeling of elastoplastic damage behavior of a claystone

2003 ◽  
Vol 19 (1) ◽  
pp. 23-45 ◽  
Author(s):  
A.S. Chiarelli ◽  
J.F. Shao ◽  
N. Hoteit
Keyword(s):  
Damage Behavior ◽  
Elastoplastic Damage

On real-time creep damage prediction for steam turbine

2022 ◽  
pp. 014233122110689
Author(s):  
Yongjian Sun ◽  
Bo Xu
Keyword(s):  
Finite Element ◽  
Steam Turbine ◽  
Real Time ◽  
Creep Damage ◽  
Element Model ◽  
Turbine Rotor ◽  
Theoretical Research ◽  
Element Analysis ◽  
Damage Prediction ◽  
Time Calculation

In this paper, in order to solve the calculation problem of creep damage of steam turbine rotor, a real-time calculation method based on finite element model is proposed. The temperature field and stress field of the turbine rotor are calculated using finite element analysis software. The temperature data and stress data of the crucial positions are extracted. The data of temperature, pressure, rotational speed, and stress relating to creep damage calculation are normalized. A real-time creep stress calculation model is established by multiple regression method. After that, the relation between stress and damage function is analyzed and fitted, and creep damage is calculated in real-time. A creep damage real-time calculation system is constructed for practical turbine engineering. Finally, a numerical simulation experiment is designed and carried out to verify the effectiveness of this novel approach. Contributions of present work are that a practical solution for real-time creep damage prediction of steam turbine is supplied. It relates the real-time creep damage prediction to process parameters of steam turbine, and it bridges the gap between the theoretical research works and practical engineering.

Seismic risk for commercial buildings in Memphis

1983 ◽  
Vol 73 (5) ◽  
pp. 1435-1450
Author(s):  
Andrzej S. Nowak ◽  
Elizabeth L. M. Rose
Keyword(s):  
Reinforced Concrete ◽  
Seismic Risk ◽  
Steel Structure ◽  
Structural Type ◽  
Commercial Buildings ◽  
Concrete Frames ◽  
Reinforced Concrete Frame ◽  
Damage Prediction ◽  
Concrete Frame

Abstract This paper deals with the evaluation of seismic risk for commercial buildings in Memphis, Tennessee. The seismicity of the area is summarized, and commercial buildings are divided into categories with regard to parameters such as number of stories, year of construction, assessed value, total floor area, and structural type. The distributions of these parameters are presented in the figures. During the study, over 15 buildings were examined on site by a team of experts to evaluate their seismic resistances. The quality of the design, materials, and construction was found to be surprisingly good, particularly in those structures built since 1900. Seismic resistance is analytically evaluated for five buildings: a four-story reinforced concrete frame; a four-story steel structure with vertical trusses; a 13-story stell frame; and two multi-story reinforced concrete frames. The loadings from four sources are considered: EI Centro and Taft earthquakes in California (1940 and 1952, respectively) and the forces specified in the 1979 UBC and 1981 BOCA codes. Ratios of load to capacity are calculated. For each building considered, the expected percentage of damage is evaluated for the two earthquakes. The damage prediction is extended to all commercial buildings in Memphis.

scholarly journals Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jongsung Kim ◽  
Donghyun Kim ◽  
Younghye Bae ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Prediction Model ◽  
Prediction Models ◽  
Meteorological Data ◽  
Heavy Rain ◽  
Machine Learning Techniques ◽  
Damage Prediction ◽  
Explanatory Variables ◽  
The Republic

Prediction models of heavy rain damage using machine learning based on big data were developed for the Seoul Capital Area in the Republic of Korea. We used data on the occurrence of heavy rain damage from 1994 to 2015 as dependent variables and weather big data as explanatory variables. The model was developed by applying machine learning techniques such as decision trees, bagging, random forests, and boosting. As a result of evaluating the prediction performance of each model, the AUC value of the boosting model using meteorological data from the past 1 to 4 days was the highest at 95.87% and was selected as the final model. By using the prediction model developed in this study to predict the occurrence of heavy rain damage for each administrative region, we can greatly reduce the damage through proactive disaster management.

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