Determination of Logistic Model Parameters From AM2 Model for Methane Production for Batch Bioreactors

Context. Membership analyses of the DANCe and Tycho + DANCe data sets provide the largest and least contaminated sample of Pleiades candidate members to date. Aims. We aim at reassessing the different proposals for the number surface density of the Pleiades in the light of the new and most complete list of candidate members, and inferring the parameters of the most adequate model. Methods. We compute the Bayesian evidence and Bayes Factors for variations of the classical radial models. These include elliptical symmetry, and luminosity segregation. As a by-product of the model comparison, we obtain posterior distributions for each set of model parameters. Results. We find that the model comparison results depend on the spatial extent of the region used for the analysis. For a circle of 11.5 parsecs around the cluster centre (the most homogeneous and complete region), we find no compelling reason to abandon King’s model, although the Generalised King model introduced here has slightly better fitting properties. Furthermore, we find strong evidence against radially symmetric models when compared to the elliptic extensions. Finally, we find that including mass segregation in the form of luminosity segregation in the J band is strongly supported in all our models. Conclusions. We have put the question of the projected spatial distribution of the Pleiades cluster on a solid probabilistic framework, and inferred its properties using the most exhaustive and least contaminated list of Pleiades candidate members available to date. Our results suggest however that this sample may still lack about 20% of the expected number of cluster members. Therefore, this study should be revised when the completeness and homogeneity of the data can be extended beyond the 11.5 parsecs limit. Such a study will allow for more precise determination of the Pleiades spatial distribution, its tidal radius, ellipticity, number of objects and total mass.

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Junction Parameter Extraction for Electronic Device Characterization

Active and Passive Electronic Components ◽

10.1080/1042015031000073805 ◽

2004 ◽

Vol 27 (2) ◽

pp. 61-67

Author(s):

S. Dib ◽

C. Salame ◽

N. Toufik ◽

A. Khoury ◽

F. Pélanchon ◽

...

Keyword(s):

Electronic Device ◽

Control Process ◽

Parameter Extraction ◽

Exponential Model ◽

Voltage Characteristic ◽

Model Parameters ◽

Current Voltage Characteristic ◽

Device Characterization ◽

Current Voltage

A new method for the extraction of junction parameters from a description of the current–voltage characteristic is developed. A simulation is performed and a high accuracy is obtained for the determination of the singleexponential model parameters. The method is easy to implement in a control process for device characterization. An application, achieved to observe the degradation of the emitter–base junction of a bipolar transistor during an aging experiment, shows that the evolutions of the single exponential model parameters versus time introduce a means for degradation quantification.

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Device Modeling of a-Si:H Alloy Solar Cells: Calibration Procedure for Determination of Model Input Parameters

MRS Proceedings ◽

10.1557/proc-507-409 ◽

1998 ◽

Vol 507 ◽

Cited By ~ 13

Author(s):

M. Zeman ◽

R.A.C.M.M. Van Swaaij ◽

E. Schroten ◽

L.L.A. Vosteen ◽

J.W. Metselaar

Keyword(s):

Solar Cell ◽

Material Properties ◽

Calibration Procedure ◽

Model Parameters ◽

Model Input ◽

A Value ◽

Input Parameters ◽

Simulated Material ◽

Good Agreement

ABSTRACTA calibration procedure for determining the model input parameters of standard a-Si:H layers, which comprise a single junction a-Si:H solar cell, is presented. The calibration procedure consists of: i) deposition of the separate layers, ii) measurement of the material properties, iii) fitting the model parameters to match the measured properties, iv) simulation of test devices and comparison with experimental results. The inverse modeling procedure was used to extract values of the most influential model parameters by fitting the simulated material properties to the measured ones. In case of doped layers the extracted values of the characteristic energies of exponentially decaying tail states are much higher than the values reported in literature. Using the extracted values of model parameters a good agreement between the measured and calculated characteristics of a reference solar cell was reached. The presented procedure could not solve directly an important issue concerning a value of the mobility gap in a-Si:H alloys.

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On the Determination of Hydraulic Model Parameters Using the Adjoint State Formulation

Modeling Marine Systems ◽

10.1201/9781351074698-2 ◽

2018 ◽

pp. 5-18 ◽

Cited By ~ 5

Author(s):

Vijay G. Panchang ◽

James J. O'Brien

Keyword(s):

Hydraulic Model ◽

Model Parameters ◽

Adjoint State

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Determination of GTN Model Parameters Based on Artificial Neutral Network for a Ductile Failure

Journal of Mechanical, Civil and Industrial Engineering ◽

10.32996/jmcie.2021.2.1.1 ◽

2021 ◽

Vol 2 (1) ◽

pp. 01-05

Author(s):

YASSINE CHAHBOUB ◽

SZAVAI Szabolcs

Keyword(s):

Ductile Failure ◽

Compact Tension ◽

Model Parameters ◽

Single Edge ◽

Direct Identification ◽

Simulation Data ◽

Short Period ◽

Artificial Neural Network Ann ◽

Excellent Tool

The Gurson – Tvergaard – Needleman (GTN) mechanical model is widely used to predict the failure of materials based on laboratory specimens, direct identification of Gurson – Tvergaard – Needleman parameters is not easy and time-consuming, and the most used method to determine them is the combination between the experimental results and those of the finite elements, the process consists of repeating the simulations several times until the simulation data matches the experimental data obtained at the specimen level.This article aims to find GTN parameters for the Compact Tension (CT) and Single Edge Tensile Test (SENT) specimen based on the Notch Specimen (NT) using the Artificial Neural Network (ANN) approach. . This work presents how the ANN could help us determine the parameters of GTN in a very short period of time. The results obtained show that ANN is an excellent tool for determining GTN parameters.

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