scholarly journals An inverse problem for the characterization of dynamic material model parameters from a single SHPB test

2011 ◽  
Vol 10 ◽  
pp. 1603-1608 ◽  
Author(s):  
C. Hernandez ◽  
A. Maranon ◽  
I.A. Ashcroft ◽  
J.P. Casas-Rodriguez
Keyword(s):  
Inverse Problem ◽  
Material Model ◽  
Model Parameters ◽  
Dynamic Material Model ◽  
Shpb Test

scholarly journals Hot Deformation Treatment of Grain-Modified Mg–Li Alloy

Materials ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 4557
Author(s):  
Mariusz Król ◽  
Przemysław Snopiński ◽  
Marek Pagáč ◽  
Jiří Hajnyš ◽  
Jana Petrů
Keyword(s):  
Hot Deformation ◽  
Cast Alloy ◽  
Material Model ◽  
Strain Rate Range ◽  
Al Alloy ◽  
Grain Refiner ◽  
Systematic Analysis ◽  
Dynamic Material Model ◽  
Instability Map

In this work, a systematic analysis of the hot deformation mechanism and a microstructure characterization of an as-cast single α-phase Mg–4.5 Li–1.5 Al alloy modified with 0.2% TiB addition, as a grain refiner, is presented. The optimized constitutive model and hot working terms of the Mg–Li alloy were also determined. The hot compression procedure of the Mg–4.5 Li–1.5 Al + 0.2 TiB alloy was performed using a DIL 805 A/D dilatometer at deformation temperatures from 250 °C to 400 °C and with strain rates of 0.01–1 s−1. The processing map adapted from a dynamic material model (DMM) of the as-cast alloy was developed through the superposition of the established instability map and power dissipation map. By considering the processing maps and microstructure characteristics, the processing window for the Mg–Li alloy were determined to be at the deformation temperature of 590 K–670 K and with a strain rate range of 0.01–0.02 s−1.

Characterization of Thermoplastic Elastomers for Design Efforts

2014 ◽  
Vol 905 ◽  
pp. 161-166
Author(s):  
Zoltan Major ◽  
Matei C. Miron ◽  
Umut D. Cakmak
Keyword(s):  
Experimental Data ◽  
Thermoplastic Elastomer ◽  
Material Model ◽  
Thermoplastic Elastomers ◽  
Failure Behavior ◽  
Model Parameters ◽  
Material Models ◽  
Fitting Procedures ◽  
Relevant Material

Different grades of several thermoplastic elastomer types were selected and are investigated over a wide frequency/time, temperature and loading range in a research project of the authors. Relevant material models are selected for different loading situations and based on these experimental data the material model parameters were determined either directly or by applying fitting procedures. These models along with the proper data were used for modeling the deformation and the failure behavior of typical engineering thermoplastic elastomer components. Furthermore, based on the modeling of various elastomers under different service relevant loading situation several design proposals were formulated.

scholarly journals Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1865
Author(s):  
Gabriel Calassou ◽  
Pierre-Yves Foucher ◽  
Jean-François Léon
Keyword(s):  
Matched Filter ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Sequential Method ◽  
Modal Radius ◽  
Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

scholarly journals Non-Stationary Model of Cerebral Oxygen Transport with Unknown Sources

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 910
Author(s):  
Andrey Kovtanyuk ◽  
Alexander Chebotarev ◽  
Varvara Turova ◽  
Irina Sidorenko ◽  
Renée Lampe
Keyword(s):  
Inverse Problem ◽  
Oxygen Transport ◽  
Model Parameters ◽  
Cerebral Oxygen ◽  
System Of Equations ◽  
Blood Oxygen ◽  
Stationary Model ◽  
Average Oxygen Concentration ◽  
The Right ◽  
The Brain

An inverse problem for a system of equations modeling oxygen transport in the brain is studied. The problem consists of finding the right-hand side of the equation for the blood oxygen transport, which is a linear combination of given functionals describing the average oxygen concentration in the neighborhoods of the ends of arterioles and venules. The overdetermination condition is determined by the values of these functionals evaluated on the solution. The unique solvability of the problem is proven without any smallness assumptions on the model parameters.

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 Bayesian Reconstruction through Adaptive Image Notion

Proceedings ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 21
Author(s):  
Fabrizia Guglielmetti ◽  
Eric Villard ◽  
Ed Fomalont
Keyword(s):  
Inverse Problem ◽  
Image Plane ◽  
Source Distribution ◽  
Bayesian Probability ◽  
Model Parameters ◽  
Uncertain Information ◽  
Source Detection ◽  
Component Mixture ◽  
Ill Posed ◽  
Image Planes

A stable and unique solution to the ill-posed inverse problem in radio synthesis image analysis is sought employing Bayesian probability theory combined with a probabilistic two-component mixture model. The solution of the ill-posed inverse problem is given by inferring the values of model parameters defined to describe completely the physical system arised by the data. The analysed data are calibrated visibilities, Fourier transformed from the ( u , v ) to image planes. Adaptive splines are explored to model the cumbersome background model corrupted by the largely varying dirty beam in the image plane. The de-convolution process of the dirty image from the dirty beam is tackled in probability space. Probability maps in source detection at several resolution values quantify the acquired knowledge on the celestial source distribution from a given state of information. The information available are data constrains, prior knowledge and uncertain information. The novel algorithm has the aim to provide an alternative imaging task for the use of the Atacama Large Millimeter/Submillimeter Array (ALMA) in support of the widely used Common Astronomy Software Applications (CASA) enhancing the capabilities in source detection.

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