Production of heat-resistant EP220 and EP929 alloys by high-temperature treatment of melt

Analysis of samples of EP220 and EP929 alloys in the liquid and solid state permits the determination of the parameters for high-temperature melt treatment in their production. On heating to specific temperatures, the structure of the liquid alloys moves closer to equilibrium. In the solidification of such melt, the cast metal formed is characterized by finer grain structure, greater dispersity of the dendrites, and greater density and microhardness of the matrix. Industrial adoption of high-temperature melt treatment will improve plasticity, increase the long-term strength, and boost the product yield. The proposed technology does not fully utilize the potential of the alloy structure obtained after high-temperature melt treatment. The effect may be amplified by more prolonged holding of the melt at 1650°C and by optimization of the vacuum-arc heating, deformation, and heat treatment, in the light of the structural changes in the experimental samples of solid metal. © 2013 Allerton Press, Inc

Seismic risk mapping for Germany

International audienceThe aim of this study is to assess and map the seismic risk for Germany, restricted to the expected losses of damage to residential buildings. There are several earthquake prone regions in the country which have produced Mw magnitudes above 6 and up to 6.7 corresponding to observed ground shaking intensity up to VIII?IX (EMS-98). Combined with the fact that some of the earthquake prone areas are densely populated and highly industrialized and where therefore the hazard coincides with high concentration of exposed assets, the damaging implications from earthquakes must be taken seriously. In this study a methodology is presented and pursued to calculate the seismic risk from (1) intensity based probabilistic seismic hazard, (2) vulnerability composition models, which are based on the distribution of residential buildings of various structural types in representative communities and (3) the distribution of assets in terms of replacement costs for residential buildings. The estimates of the risk are treated as primary economic losses due to structural damage to residential buildings. The obtained results are presented as maps of the damage and risk distributions. For a probability level of 90% non-exceedence in 50 years (corresponding to a mean return period of 475 years) the mean damage ratio is up to 20% and the risk up to hundreds of millions of euro in the most endangered communities. The developed models have been calibrated with observed data from several damaging earthquakes in Germany and the nearby area in the past 30 years

Modeling of surface dust concentration in snow cover at industrial area using neural networks and kriging

Modeling of spatial distribution of pollutants in the urbanized territories is difficult, especially if there are multiple emission sources. When monitoring such territories, it is often impossible to arrange the necessary detailed sampling. Because of this, the usual methods of analysis and forecasting based on geostatistics are often less effective. Approaches based on artificial neural networks (ANNs) demonstrate the best results under these circumstances. This study compares two models based on ANNs, which are multilayer perceptron (MLP) and generalized regression neural networks (GRNNs) with the base geostatistical method-kriging. Models of the spatial dust distribution in the snow cover around the existing copper quarry and in the area of emissions of a nickel factory were created. To assess the effectiveness of the models three indices were used: the mean absolute error (MAE), the root-mean-square error (RMSE), and the relative root-mean-square error (RRMSE). Taking into account all indices the model of GRNN proved to be the most accurate which included coordinates of the sampling points and the distance to the likely emission source as input parameters for the modeling. Maps of spatial dust distribution in the snow cover were created in the study area. It has been shown that the models based on ANNs were more accurate than the kriging, particularly in the context of a limited data set. © 2017 Author(s)

Approximating heat resistance of nickel-based superalloys by a sigmoid

The nickel-based superalloys are unique materials with complex doping applied to manufacturing the gas turbine engine parts. The alloys show resistance to mechanical and chemical degradation under high pressure, high temperature, and long-term isothermal exposures. One of the main alloys' service properties is the heat resistance. Numerically, it is expressed in the tensile strength values (MPa). Simulation of the heat resistance behavior is an important engineering task, which would significantly simplify the analysis of existing and designing the new alloys. In this paper, we use results of the heat resistance simulation by an artificial neural network, as well as, experimental data for approximating the changes in the heat resistance vs isothermal exposures expressed in the complex Larson-Miller parameter by a sigmoidal function. © 2020 American Institute of Physics Inc.. All rights reserved

Modeling the heat resistance of nickel-based superalloys by a deep learning neural network

The nickel-based superalloys are unique materials with complex alloying used in the manufacture of gas turbine engines. The alloys exhibit excellent resistance to mechanical and chemical degradation under the high loads and long-term isothermal exposures. The main service property of the alloy is its heat resistance, which is expressed by the tensile strength. Simulation of changes in the heat resistance is an important engineering problem, which would significantly simplify the design of new alloys. In this paper, we apply a deep learning neural network to predict the tensile strength values and to compare the predictive ability of the proposed approach. Also, the results are presented of the feed-forward neural network accounting changes in heat resistance vs isothermal exposures that are expressed in the complex Larson-Miller parameter. © 2020 American Institute of Physics Inc.. All rights reserved