A Unified View of Engineering Creep Parameters

2008 ◽  
Author(s):  
D. R. Eno ◽  
G. A. Young ◽  
T.-L. Sham
Keyword(s):  
General Framework ◽  
Likelihood Estimation ◽  
Material Model ◽  
Efficient Estimation ◽  
Upper And Lower Bounds ◽  
Model Parameters ◽  
Creep Data ◽  
Special Cases ◽  
Temperature Interaction ◽  
Model Recognition

Creep data are often analyzed using derived engineering parameters to correlate creep life (either time to rupture, or time to a specified strain) to applied stress and temperature. Commonly used formulations include Larson-Miller, Orr-Sherby-Dorn, Manson-Haferd, and Manson-Succop parameterizations. In this paper, it is shown that these parameterizations are all special cases of a common general framework based on a linear statistical model. Recognition of this fact allows for statistically efficient estimation of material model parameters and quantitative statistical comparisons among the various parameterizations in terms of their ability to fit a material database, including assessment of a stress-temperature interaction in creep behavior. This provides a rational basis for choosing the best parameterization to describe a particular material. Furthermore, using the technique of maximum likelihood estimation to estimate model parameters allows for a statistically proper treatment of runouts in a test database via censored data analysis methods, and for construction of probabilistically interpretable upper and lower bounds on creep rate. Comparisons are made to a generalization of the commonly used Larson-Miller parameterization (namely, the Mendelson-Roberts-Manson parameterization), which is comparable in complexity to the Manson-Haferd parameter, but utilizes a reciprocal temperature dependence. The general framework for analysis of creep data is illustrated with analysis of Alloy 617 and HAYNES® 230® alloy (Alloy 230) test data.

Latent class and finite mixture models for multilevel data sets

2008 ◽  
Vol 17 (1) ◽  
pp. 33-51 ◽  
Author(s):  
Jeroen K Vermunt
Keyword(s):  
Mixture Models ◽  
Random Effects ◽  
Latent Class ◽  
Finite Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Likelihood Estimation ◽  
Finite Mixture ◽  
Model Parameters ◽  
Data Sets ◽  
Special Cases

An extension of latent class (LC) and finite mixture models is described for the analysis of hierarchical data sets. As is typical in multilevel analysis, the dependence between lower-level units within higher-level units is dealt with by assuming that certain model parameters differ randomly across higher-level observations. One of the special cases is an LC model in which group-level differences in the logit of belonging to a particular LC are captured with continuous random effects. Other variants are obtained by including random effects in the model for the response variables rather than for the LCs. The variant that receives most attention in this article is an LC model with discrete random effects: higher-level units are clustered based on the likelihood of their members belonging to the various LCs. This yields a model with mixture distributions at two levels, namely at the group and the subject level. This model is illustrated with three rather different empirical examples. The appendix describes an adapted version of the expectation—maximization algorithm that can be used for maximum likelihood estimation, as well as providing setups for estimating the multilevel LC model with generally available software.

scholarly journals A New Class of Generalized Modified Weibull Distribution with Applications

2015 ◽  
Vol 44 (3) ◽  
pp. 45-68
Author(s):  
Broderick Oluyede ◽  
Shujiao Huang ◽  
Tiantian Yang
Keyword(s):  
Maximum Likelihood ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Estimation Technique ◽  
Exponential Distributions ◽  
New Class ◽  
Mathematical Properties ◽  
Special Cases ◽  
Generalized Modified Weibull ◽  
Modified Weibull

A new five parameter gamma-generalized modified Weibull (GGMW) distribution which includes exponential, Rayleigh, modified Weibull, Weibull, gamma-modified Weibull, gamma-modified Rayleigh, gamma-modified exponential, gamma-Weibull, gamma-Rayleigh, and gamma-exponential distributions as special cases is proposed and studied. Some mathematical properties of the new class of distributions including moments, distribution of the order statistics, and Renyi entropy are presented. Maximum likelihood estimation technique is used to estimate the model parameters and applications to a real datasets to illustrates the usefulness of the proposed class of models are presented.

scholarly journals Analytical Investigation of Viscoelastic Stagnation-Point Flows with Regard to Their Singularity

2021 ◽  
Vol 11 (15) ◽  
pp. 6931
Author(s):  
Jie Liu ◽  
Martin Oberlack ◽  
Yongqi Wang
Keyword(s):  
Stress Distribution ◽  
Stress Field ◽  
Stagnation Point ◽  
Analytical Solutions ◽  
Wide Spectrum ◽  
Momentum Conservation ◽  
Stagnation Point Flow ◽  
Model Parameters ◽  
Viscoelastic Models ◽  
Special Cases

Singularities in the stress field of the stagnation-point flow of a viscoelastic fluid have been studied for various viscoelastic constitutive models. Analyzing the analytical solutions of these models is the most effective way to study this problem. In this paper, exact analytical solutions of two-dimensional steady wall-free stagnation-point flows for the generic Oldroyd 8-constant model are obtained for the stress field using different material parameter relations. For all solutions, compatibility with the conservation of momentum is considered in our analysis. The resulting solutions usually contain arbitrary functions, whose choice has a crucial effect on the stress distribution. The corresponding singularities are discussed in detail according to the choices of the arbitrary functions. The results can be used to analyze the stress distribution and singularity behavior of a wide spectrum of viscoelastic models derived from the Oldroyd 8-constant model. Many previous results obtained for simple viscoelastic models are reproduced as special cases. Some previous conclusions are amended and new conclusions are drawn. In particular, we find that all models have singularities near the stagnation point and most of them can be avoided by appropriately choosing the model parameters and free functions. In addition, the analytical solution for the stress tensor of a near-wall stagnation-point flow for the Oldroyd-B model is also obtained. Its compatibility with the momentum conservation is discussed and the parameters are identified, which allow for a non-singular solution.

scholarly journals Flexible models for overdispersed and underdispersed count data

2021 ◽  
Author(s):  
Dexter Cahoy ◽  
Elvira Di Nardo ◽  
Federico Polito
Keyword(s):  
Poisson Distribution ◽  
Count Data ◽  
Hypergeometric Functions ◽  
Natural Generalization ◽  
Model Parameters ◽  
Probability Models ◽  
Limiting Behavior ◽  
Poisson Models ◽  
Special Cases ◽  
Flexible Models

AbstractWithin the framework of probability models for overdispersed count data, we propose the generalized fractional Poisson distribution (gfPd), which is a natural generalization of the fractional Poisson distribution (fPd), and the standard Poisson distribution. We derive some properties of gfPd and more specifically we study moments, limiting behavior and other features of fPd. The skewness suggests that fPd can be left-skewed, right-skewed or symmetric; this makes the model flexible and appealing in practice. We apply the model to real big count data and estimate the model parameters using maximum likelihood. Then, we turn to the very general class of weighted Poisson distributions (WPD’s) to allow both overdispersion and underdispersion. Similarly to Kemp’s generalized hypergeometric probability distribution, which is based on hypergeometric functions, we analyze a class of WPD’s related to a generalization of Mittag–Leffler functions. The proposed class of distributions includes the well-known COM-Poisson and the hyper-Poisson models. We characterize conditions on the parameters allowing for overdispersion and underdispersion, and analyze two special cases of interest which have not yet appeared in the literature.

scholarly journals Variation of Passive Biomechanical Properties of the Small Intestine along Its Length: Microstructure-Based Characterization

2021 ◽  
Vol 8 (3) ◽  
pp. 32
Author(s):  
Dimitrios P. Sokolis
Keyword(s):  
Small Intestine ◽  
Orientation Angle ◽  
Axial Direction ◽  
Material Model ◽  
Biomechanical Properties ◽  
Intestinal Wall ◽  
Model Parameters ◽  
Material Models ◽  
Hyper Elastic ◽  
Rat Tissue

Multiaxial testing of the small intestinal wall is critical for understanding its biomechanical properties and defining material models, but limited data and material models are available. The aim of the present study was to develop a microstructure-based material model for the small intestine and test whether there was a significant variation in the passive biomechanical properties along the length of the organ. Rat tissue was cut into eight segments that underwent inflation/extension testing, and their nonlinearly hyper-elastic and anisotropic response was characterized by a fiber-reinforced model. Extensive parametric analysis showed a non-significant contribution to the model of the isotropic matrix and circumferential-fiber family, leading also to severe over-parameterization. Such issues were not apparent with the reduced neo-Hookean and (axial and diagonal)-fiber family model, that provided equally accurate fitting results. Absence from the model of either the axial or diagonal-fiber families led to ill representations of the force- and pressure-diameter data, respectively. The primary direction of anisotropy, designated by the estimated orientation angle of diagonal-fiber families, was about 35° to the axial direction, corroborating prior microscopic observations of submucosal collagen-fiber orientation. The estimated model parameters varied across and within the duodenum, jejunum, and ileum, corroborating histologically assessed segmental differences in layer thicknesses.

scholarly journals Flexural behavior of composite structural insulated panels with magnesium oxide board facings

2020 ◽  
Vol 20 (4) ◽  
Author(s):  
Łukasz Smakosz ◽  
Ireneusz Kreja ◽  
Zbigniew Pozorski
Keyword(s):  
Magnesium Oxide ◽  
Material Model ◽  
Expanded Polystyrene ◽  
Flexural Behavior ◽  
Joint Analysis ◽  
Model Parameters ◽  
Fe Model ◽  
Computational Results ◽  
Four Point Bending ◽  
Proposed Model

Abstract The current report is devoted to the flexural analysis of a composite structural insulated panel (CSIP) with magnesium oxide board facings and expanded polystyrene (EPS) core, that was recently introduced to the building industry. An advanced nonlinear FE model was created in the ABAQUS environment, able to simulate the CSIP’s flexural behavior in great detail. An original custom code procedure was developed, which allowed to include material bimodularity to significantly improve the accuracy of computational results and failure mode predictions. Material model parameters describing the nonlinear range were identified in a joint analysis of laboratory tests and their numerical simulations performed on CSIP beams of three different lengths subjected to three- and four-point bending. The model was validated by confronting computational results with experimental results for natural scale panels; a good correlation between the two results proved that the proposed model could effectively support the CSIP design process.

scholarly journals Maximum Likelihood Estimation of the VAR(1) Model Parameters with Missing Observations

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Helena Mouriño ◽  
Maria Isabel Barão
Keyword(s):  
Stochastic Process ◽  
Missing Data ◽  
Maximum Likelihood ◽  
Moving Average ◽  
Practical Importance ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Missing Observations ◽  
Data Set ◽  
The Impact

Missing-data problems are extremely common in practice. To achieve reliable inferential results, we need to take into account this feature of the data. Suppose that the univariate data set under analysis has missing observations. This paper examines the impact of selecting an auxiliary complete data set—whose underlying stochastic process is to some extent interdependent with the former—to improve the efficiency of the estimators for the relevant parameters of the model. The Vector AutoRegressive (VAR) Model has revealed to be an extremely useful tool in capturing the dynamics of bivariate time series. We propose maximum likelihood estimators for the parameters of the VAR(1) Model based on monotone missing data pattern. Estimators’ precision is also derived. Afterwards, we compare the bivariate modelling scheme with its univariate counterpart. More precisely, the univariate data set with missing observations will be modelled by an AutoRegressive Moving Average (ARMA(2,1)) Model. We will also analyse the behaviour of the AutoRegressive Model of order one, AR(1), due to its practical importance. We focus on the mean value of the main stochastic process. By simulation studies, we conclude that the estimator based on the VAR(1) Model is preferable to those derived from the univariate context.

scholarly journals Considering multiple process observables to determine material model parameters for FE-cutting simulations

2021 ◽  
Author(s):  
Marvin Hardt ◽  
Thomas Bergs
Keyword(s):  
Chip Thickness ◽  
Material Model ◽  
Experimental Investigations ◽  
Model Parameters ◽  
Inverse Approach ◽  
Local Material ◽  
Novel Approach ◽  
Validation Parameters ◽  
Multiple Process ◽  
Cutting Simulations

AbstractAnalyzing the chip formation process by means of the finite element method (FEM) is an established procedure to understand the cutting process. For a realistic simulation, different input models are required, among which the material model is crucial. To determine the underlying material model parameters, inverse methods have found an increasing acceptance within the last decade. The calculated model parameters exhibit good validity within the domain of investigation, but suffer from their non-uniqueness. To overcome the drawback of the non-uniqueness, the literature suggests either to enlarge the domain of experimental investigations or to use more process observables as validation parameters. This paper presents a novel approach merging both suggestions: a fully automatized procedure in conjunction with the use of multiple process observables is utilized to investigate the non-uniqueness of material model parameters for the domain of cutting simulations. The underlying approach is two-fold: Firstly, the accuracy of the evaluated process observables from FE simulations is enhanced by establishing an automatized routine. Secondly, the number of process observables that are considered in the inverse approach is increased. For this purpose, the cutting force, cutting normal force, chip temperature, chip thickness, and chip radius are taken into account. It was shown that multiple parameter sets of the material model can result in almost identical simulation results in terms of the simulated process observables and the local material loads.

Tunable Membrane for Electromagnetic Devices Using Dielectric Elastomers

2008 ◽  
Vol 61 ◽  
pp. 141-146
Author(s):  
Christian Bolzmacher ◽  
Karin Bauer ◽  
Ulrich Schmid ◽  
Helmut Seidel ◽  
Moustapha Hafez
Keyword(s):  
Material Model ◽  
Silicone Membrane ◽  
Creep Data ◽  
Electromagnetic Actuators ◽  
Membrane Behavior ◽  
Electromagnetic Devices ◽  
Dynamic Experiments ◽  
Experimental Creep ◽  
Strain Relationship ◽  
Tunable Stiffness

The amplitudes of miniaturized electromagnetic actuators are clearly enhanced if the eigenfrequencies of the membrane are used for actuation. However, the bandwidth for such operation is very limited. This can be overcome to some extent by the employment of membranes with electrically tunable stiffness. In this context we investigated membranes of dielectric elastomer materials and present experimental results on the ability to change their pre-strain to shift the eigenmodes to lower frequencies upon activation. Furthermore, the viscoelastic properties of an acrylic and a silicone membrane are investigated and compared to dynamic experiments. The parameters for the stiffness and viscoelasticity are derived from the experimental creep data and incorporated in a hyperelastic material model. Using this adapted stress-strain relationship the membrane behavior over time can be evaluated for different loading as well as pre-strain conditions.

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