Multi-Scale, Heterogeneous CAD Representation for Metal Alloy Microstructures

2014 ◽  
Author(s):  
David W. Rosen
Keyword(s):  
Volume Fraction ◽  
Material Model ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Reconstruction Algorithms ◽  
Multi Scale ◽  
Inconel Alloys ◽  
Grain Structures ◽  
Specific Metal ◽  
Part Model

Most heterogeneous CAD representations in the literature represent materials using a volume fraction vector, which may not by physically realizable or meaningful. In contrast, the multi-scale, heterogeneous CAD representation presented here models materials using their microstructure. For the specific metal alloys of interest in this work, the material model is a probabilistic model of grain characteristics, represented as cumulative distribution functions. Several microstructure reconstruction algorithms are presented that enable different alloy grain structures to be reconstructed in a part model. Reconstructions can be performed at any desired size scale, illustrating the multi-scale capability of the representation. A part rendering algorithm is presented for displaying parts with their material microstructures. The multi-scale, heterogeneous CAD representation is demonstrated on two Inconel alloys and is shown to be capable of faithfully reconstructing part representations consistent with the probabilistic grain models.

Multiscale, Heterogeneous Computer Aided Design Representation for Metal Alloy Microstructures

2014 ◽  
Vol 14 (4) ◽  
Author(s):  
David W. Rosen
Keyword(s):  
Computer Aided Design ◽  
Volume Fraction ◽  
Material Model ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Reconstruction Algorithms ◽  
Design Representation ◽  
Inconel Alloys ◽  
Computer Aided ◽  
Aided Design

Most heterogeneous computer aided design (CAD) representations in the literature represent materials using a volume fraction vector, which may not by physically realizable or meaningful. In contrast, the multiscale, heterogeneous CAD representation presented here models materials using their microstructure. For the specific metal alloys of interest in this work, the material model is a probabilistic model of grain characteristics, represented as cumulative distribution functions (CDFs). Several microstructure reconstruction algorithms are presented that enable different alloy grain structures to be reconstructed in a part model. Reconstructions can be performed at any desired size scale, illustrating the multiscale capability of the representation. A part rendering algorithm is presented for displaying parts with their material microstructures. The multiscale, heterogeneous CAD representation is demonstrated on two Inconel alloys and is shown to be capable of faithfully reconstructing part representations consistent with the probabilistic grain models.

scholarly journals USE OF BENDING TESTS AND VISUAL INSPECTION FOR MULTI-SCALE EXPERIMENTAL EVALUATION OF CHESTNUT TIMBER BEAMS STIFFNESS

2015 ◽  
Vol 22 (6) ◽  
pp. 728-738 ◽  
Author(s):  
Hélder S. SOUSA ◽  
Jorge M. BRANCO ◽  
Paulo B. LOURENÇO
Keyword(s):  
Experimental Evaluation ◽  
Visual Inspection ◽  
Castanea Sativa ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Coefficient Of Determination ◽  
Visual Grading ◽  
Bending Tests ◽  
Multi Scale ◽  
Bending Modulus

The assessment of existing timber structures requires the determination of the mechanical properties of the individual timber members, which is often obtained by visual grading combined with information of small clear wood specimens. The purpose of this work is to present the results obtained in a multi-scale experimental evaluation of 20 old chestnut (Castanea sativa Mill.) beams, aimed at defining the correlations between bending modulus of elasticity (MOE) in different scales of timber members in combination with visual grading. The results of bending tests, according to EN 408:2010 (2010), were statistically analyzed to obtain correlations of bending MOE between and within different scales size. The results of visual inspection according to UNI 11119:2004 (2004) regarding the presence and distribution of defects were considered for variation analysis of MOE, evidencing a significant variance between samples of differ­ent visual strength grades. Strong correlations within the same phase (coefficient of determination r2 from 0.82 to 0.89) and moderate to high correlations for different phases (r2 from 0.68 to 0.71) were found for bending MOE. Cumulative distribution functions for global MOE remained similar throughout the experimental phases. Moreover, percentage of segments attributed to a specific visual grade is given for each member and compared between scales.

scholarly journals Material Allowable Generation and AM Process Parameters Effect on Porosity

Coatings ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 625
Author(s):  
Frank Abdi ◽  
Parviz Yavari ◽  
Vasyl Harik ◽  
Cody Godines
Keyword(s):  
Mechanical Properties ◽  
Volume Fraction ◽  
Material Model ◽  
Cumulative Distribution ◽  
Control Plan ◽  
Void Defect ◽  
3D Print ◽  
Characteristic Points ◽  
3D Printed ◽  
Computational Materials

Additive manufacturing (AM) process methods such as powder bed fusion (LPBF) of metal powder layers can produce layered material systems with designed microstructures, which may exhibit scatter in mechanical properties (e.g., lower yield and lower failure strain), corrosion due to porosity and print anomalies. This study shows the development of AM process simulation to predict As-built material characteristic and their scatter comparing with experimental test data. ICME (Integrated Computational Materials Engineering) was used to simulate yield, ultimate, strain, and reduction of the area of sample AM. The method was extended to predict oxidation and damage of as-built parts. The samples were fabricated horizontally and vertically in multiple and scatter directions to find the effect on the mechanical properties such as ultimate tensile strength (UTS) and yield strength (YS). The probabilistic sensitivities show that in order for the next-generation technology to improve the strength of 3D printed materials, they must control the void volume fraction (trapped gas) and orientation of voids. The studied 3D print modality processes: (a) LPBF of AlSi10Mg, and (b) Electron Beam (EBM) of Ti-6Al-4V materials are shown to be over 99.99% reliable. The statistics of 3D printed Ti-6Al-4V have been observed for room and high temperature (RT/HT). The ICME Material Characterization and Qualification (MCQ) software material model prediction capabilities were used to predict (a) Material Allowable, a variation in Stress Strain Curves Characteristic Points and Residual Stress due to air particle (void/defect) shape and size and orientation. The probabilistic simulation computes Cumulative Distribution Function (CDF) and probabilistic sensitivities for YS, UTS, and %Elongation as well as A and B basis allowable of the As-Built 3D printed material and; and (b) Fracture Control Plan fracture toughness determination, and fatigue crack growth vs. stress intensity.

scholarly journals Do galactic bars depend on environment?: an information theoretic analysis of Galaxy Zoo 2

2020 ◽  
Vol 501 (1) ◽  
pp. 994-1001
Author(s):  
Suman Sarkar ◽  
Biswajit Pandey ◽  
Snehasish Bhattacharjee
Keyword(s):  
Spatial Distribution ◽  
Mutual Information ◽  
Local Density ◽  
Statistical Significance ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Host Galaxy ◽  
Data Sets ◽  
Data Set ◽  
Information Theoretic

ABSTRACT We use an information theoretic framework to analyse data from the Galaxy Zoo 2 project and study if there are any statistically significant correlations between the presence of bars in spiral galaxies and their environment. We measure the mutual information between the barredness of galaxies and their environments in a volume limited sample (Mr ≤ −21) and compare it with the same in data sets where (i) the bar/unbar classifications are randomized and (ii) the spatial distribution of galaxies are shuffled on different length scales. We assess the statistical significance of the differences in the mutual information using a t-test and find that both randomization of morphological classifications and shuffling of spatial distribution do not alter the mutual information in a statistically significant way. The non-zero mutual information between the barredness and environment arises due to the finite and discrete nature of the data set that can be entirely explained by mock Poisson distributions. We also separately compare the cumulative distribution functions of the barred and unbarred galaxies as a function of their local density. Using a Kolmogorov–Smirnov test, we find that the null hypothesis cannot be rejected even at $75{{\ \rm per\ cent}}$ confidence level. Our analysis indicates that environments do not play a significant role in the formation of a bar, which is largely determined by the internal processes of the host galaxy.

scholarly journals Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations

2021 ◽  
Vol 13 (6) ◽  
pp. 1096
Author(s):  
Soi Ahn ◽  
Sung-Rae Chung ◽  
Hyun-Jong Oh ◽  
Chu-Yong Chung
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Northeast Asia ◽  
Low Earth Orbit ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Radiometric Calibration ◽  
Climate Data ◽  
Error Statistics ◽  
Spatiotemporal Resolution

This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various spaceborne imaging sensors capturing visible and infrared spectra. Nevertheless, differences in satellite-based retrieval algorithms, spatiotemporal resolution, sampling, radiometric calibration, and cloud-screening procedures create significant variability among AOD products. Satellite products, however, can be complementary in terms of their accuracy and spatiotemporal comprehensiveness. Thus, composite AOD products were derived for Northeast Asia based on data from four sensors: Advanced Himawari Imager (AHI), Geostationary Ocean Color Imager (GOCI), Moderate Infrared Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Cumulative distribution functions were employed to estimate error statistics using measurements from the Aerosol Robotic Network (AERONET). In order to apply the AERONET point-specific error, coefficients of each satellite were calculated using inverse distance weighting. Finally, the root mean square error (RMSE) for each satellite AOD product was calculated based on the inverse composite weighting (ICW). Hourly AOD composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to AERONET measurements, and the results showed that the correlation coefficient and RMSE values of composite were close to those of the low earth orbit satellite products (MODIS and VIIRS). The methodology and the resulting dataset derived here are relevant for the demonstrated successful merging of multi-sensor retrievals to produce long-term satellite-based climate data records.

Probabilistic finite element analysis in heat transfer to a nuclear fuel rod bumper support

2004 ◽  
Vol 218 (12) ◽  
pp. 1499-1505
Author(s):  
Rama Subba Reddy Gorla
Keyword(s):  
Heat Transfer ◽  
Finite Element ◽  
Nuclear Fuel ◽  
Cost Effective ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Element Analysis ◽  
Transfer Rates ◽  
Fuel Rod ◽  
Design Variables

Heat transfer from a nuclear fuel rod bumper support was computationally simulated by a finite element method and probabilistically evaluated in view of the several uncertainties in the performance parameters. Cumulative distribution functions and sensitivity factors were computed for overall heat transfer rates due to the thermodynamic random variables. These results can be used to identify quickly the most critical design variables in order to optimize the design and to make it cost effective. The analysis leads to the selection of the appropriate measurements to be used in heat transfer and to the identification of both the most critical measurements and the parameters.

scholarly journals Some new Simpson-type inequalities for generalized p-convex function on fractal sets with applications

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Thabet Abdeljawad ◽  
Saima Rashid ◽  
Zakia Hammouch ◽  
İmdat İşcan ◽  
Yu-Ming Chu
Keyword(s):  
Convex Functions ◽  
Fractional Derivatives ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Auxiliary Result ◽  
Fractal Sets ◽  
Different Types ◽  
Novel Applications ◽  
Class Of Functions ◽  
Harmonically Convex Functions

Abstract The present article addresses the concept of p-convex functions on fractal sets. We are able to prove a novel auxiliary result. In the application aspect, the fidelity of the local fractional is used to establish the generalization of Simpson-type inequalities for the class of functions whose local fractional derivatives in absolute values at certain powers are p-convex. The method we present is an alternative in showing the classical variants associated with generalized p-convex functions. Some parts of our results cover the classical convex functions and classical harmonically convex functions. Some novel applications in random variables, cumulative distribution functions and generalized bivariate means are obtained to ensure the correctness of the present results. The present approach is efficient, reliable, and it can be used as an alternative to establishing new solutions for different types of fractals in computer graphics.

scholarly journals Probabilistic downscaling of precipitation data in a subtropical mountain area: a two-step approach

2011 ◽  
Vol 18 (2) ◽  
pp. 223-234 ◽  
Author(s):  
R. Haas ◽  
K. Born
Keyword(s):  
Complex Terrain ◽  
Large Scale ◽  
Distribution Functions ◽  
Data Availability ◽  
Cumulative Distribution ◽  
Second Step ◽  
Mountain Area ◽  
Investigation Area ◽  
Distribution Parameters ◽  
Mountain Environment

Abstract. In this study, a two-step probabilistic downscaling approach is introduced and evaluated. The method is exemplarily applied on precipitation observations in the subtropical mountain environment of the High Atlas in Morocco. The challenge is to deal with a complex terrain, heavily skewed precipitation distributions and a sparse amount of data, both spatial and temporal. In the first step of the approach, a transfer function between distributions of large-scale predictors and of local observations is derived. The aim is to forecast cumulative distribution functions with parameters from known data. In order to interpolate between sites, the second step applies multiple linear regression on distribution parameters of observed data using local topographic information. By combining both steps, a prediction at every point of the investigation area is achieved. Both steps and their combination are assessed by cross-validation and by splitting the available dataset into a trainings- and a validation-subset. Due to the estimated quantiles and probabilities of zero daily precipitation, this approach is found to be adequate for application even in areas with difficult topographic circumstances and low data availability.

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