On the determination of single-crystal plasticity parameters by diffraction: optimization of a polycrystalline plasticity model using a genetic algorithm

2012 ◽  
Vol 45 (4) ◽  
pp. 627-643 ◽  
Author(s):  
T. Skippon ◽  
C. Mareau ◽  
M. R. Daymond
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Model Parameters ◽  
Plasticity Model ◽  
Flow Curves ◽  
Lattice Strains ◽  
Tension And Compression ◽  
Single Crystal Plasticity ◽  
Selection Of

A genetic algorithm was implemented in order to optimize the selection of parameters within a polycrystalline plasticity model. Previously collected experimental data from tests performed on textured Zircaloy-2, consisting of macroscopic flow curves, lattice strains and Lankford coefficients, all measured in both tension and compression in three principle directions of a plate, were reproduced by the model. The results obtained were found to be comparable to prior attempts to optimize the model parameters manually.

EXTRACTION OF ILLUMINATED SOLAR CELL AND SCHOTTKY DIODE PARAMETERS USING A GENETIC ALGORITHM

2005 ◽  
Vol 16 (07) ◽  
pp. 1043-1050 ◽  
Author(s):  
A. SELLAI ◽  
Z. OUENNOUGHI
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Solar Cell ◽  
Schottky Diode ◽  
Initial Guess ◽  
Model Parameters ◽  
Model Calculations ◽  
Subsequent Determination ◽  
Critical Requirement

Details concerning the implementation of a versatile genetic algorithm are presented. Solar cell and Schottky diode model parameters are extracted based on the fitness of experimental data to theoretical curves simulated in the framework of certain physical processes and the use of this genetic algorithm. The method is shown to be a reliable alternative to conventional numerical techniques in fitting experimental data to model calculations and the subsequent determination of model related parameters. It is demonstrated, through two examples in particular, that some of the drawbacks associated with the conventional methods can be circumvented if a genetic algorithm is used instead. For instance, a good initial guess is not a critical requirement for convergence and an initial broad range for each of the fitting parameters is enough to achieve reasonably good fits.

scholarly journals Technical note: Monte Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets

2017 ◽  
Vol 17 (12) ◽  
pp. 8021-8029 ◽  
Author(s):  
Thomas Berkemeier ◽  
Markus Ammann ◽  
Ulrich K. Krieger ◽  
Thomas Peter ◽  
Peter Spichtinger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Monte Carlo ◽  
Kinetic Models ◽  
Reaction Rate ◽  
Rate Coefficients ◽  
Model Parameters ◽  
Data Sets ◽  
Model Input ◽  
Input Parameters

Abstract. We present a Monte Carlo genetic algorithm (MCGA) for efficient, automated, and unbiased global optimization of model input parameters by simultaneous fitting to multiple experimental data sets. The algorithm was developed to address the inverse modelling problems associated with fitting large sets of model input parameters encountered in state-of-the-art kinetic models for heterogeneous and multiphase atmospheric chemistry. The MCGA approach utilizes a sequence of optimization methods to find and characterize the solution of an optimization problem. It addresses an issue inherent to complex models whose extensive input parameter sets may not be uniquely determined from limited input data. Such ambiguity in the derived parameter values can be reliably detected using this new set of tools, allowing users to design experiments that should be particularly useful for constraining model parameters. We show that the MCGA has been used successfully to constrain parameters such as chemical reaction rate coefficients, diffusion coefficients, and Henry's law solubility coefficients in kinetic models of gas uptake and chemical transformation of aerosol particles as well as multiphase chemistry at the atmosphere–biosphere interface. While this study focuses on the processes outlined above, the MCGA approach should be portable to any numerical process model with similar computational expense and extent of the fitting parameter space.

VLE Determination of Carbon Dioxide Loaded Aqueous Alkanolamine Mixtures Using Modified Kent Eisenberg Model

2017 ◽  
Vol 231 (11-12) ◽  
Author(s):  
Humbul Suleman ◽  
Abdulhalim Shah Maulud ◽  
Zakaria Man
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide ◽  
Model Parameters ◽  
Thermodynamic Models ◽  
Carbon Dioxide Absorption ◽  
Proposed Model ◽  
Wide Range ◽  
Experimental Values ◽  
Good Agreement

AbstractA computationally simple thermodynamic framework has been presented to correlate the vapour-liquid equilibria of carbon dioxide absorption in five representative types of alkanolamine mixtures. The proposed model is an extension of modified Kent Eisenberg model for the carbon dioxide loaded aqueous alkanolamine mixtures. The model parameters are regressed on a large experimental data pool of carbon dioxide solubility in aqueous alkanolamine mixtures. The model is applicable to a wide range of temperature (298–393 K), pressure (0.1–6000 kPa) and alkanolamine concentration (0.3–5 M). The correlated results are compared to the experimental values and found to be in good agreement with the average deviations ranging between 6% and 20%. The model results are comparable to other thermodynamic models.

Determination of ZIP Load Model Parameters based on Synchrophasor Data by Genetic Algorithm

2020 ◽  
Author(s):  
Kriengsak Fungyai ◽  
Natcha Sangmeg ◽  
Achara Pichetjamroen ◽  
Sanchai Dechanupaprittha ◽  
Natthawuth Somakettarin
Keyword(s):  
Genetic Algorithm ◽  
Model Parameters ◽  
Load Model ◽  
Zip Load

New parameters controlling the effect of temperature on enzyme activity

2007 ◽  
Vol 35 (6) ◽  
pp. 1543-1546 ◽  
Author(s):  
R.M. Daniel ◽  
M.J. Danson ◽  
R. Eisenthal ◽  
C.K. Lee ◽  
M.E. Peterson
Keyword(s):  
Experimental Data ◽  
Enzyme Activity ◽  
Thermal Behaviour ◽  
Equilibrium Model ◽  
Thermal Inactivation ◽  
Effect Of Temperature ◽  
Model Parameters ◽  
Effects Of Temperature ◽  
Activity Loss

Arising from careful measurements of the thermal behaviour of enzymes, a new model, the Equilibrium Model, has been developed to explain more fully the effects of temperature on enzymes. The model describes the effect of temperature on enzyme activity in terms of a rapidly reversible active–inactive (but not denatured) transition, revealing an additional and reversible mechanism for enzyme activity loss in addition to irreversible thermal inactivation at high temperatures. Two new thermal parameters, Teq and ΔHeq, describe the active–inactive transition, and enable a complete description of the effect of temperature on enzyme activity. We describe here the Model and its fit to experimental data, methods for the determination of the Equilibrium Model parameters, and the implications of the Model for the environmental adaptation and evolution of enzymes, and for biotechnology.

scholarly journals Radiative Transfer Simulations of Infrared Dark Clouds

2010 ◽  
Vol 6 (S270) ◽  
pp. 455-458
Author(s):  
Yaroslav Pavlyuchenkov ◽  
Dmitry Wiebe ◽  
Anna Fateeva ◽  
Tatiana Vasyunina
Keyword(s):  
Genetic Algorithm ◽  
Radiative Transfer ◽  
Radiation Transfer ◽  
Model Parameters ◽  
Dark Clouds ◽  
Heating Source ◽  
Temperature Distributions ◽  
Infrared Dark Clouds ◽  
Best Fit

AbstractThe determination of prestellar core structure is often based on observations of (sub)millimeter dust continuum. However, recently the Spitzer Space Telescope provided us with IR images of many objects not only in emission but also in absorption. We developed a technique to reconstruct the density and temperature distributions of protostellar objects based on radiation transfer (RT) simulations both in mm and IR wavelengths. Best-fit model parameters are obtained with the genetic algorithm. We apply the method to two cores of Infrared Dark Clouds and show that their observations are better reproduced by a model with an embedded heating source despite the lack of 70 μm emission in one of these cores. Thus, the starless nature of massive cores can only be established with the careful case-by-case RT modeling.

scholarly journals A mathematical and computational model of the calcium dynamics inCaenorhabditis elegansASH sensory neuro

2017 ◽  
Author(s):  
Ehsan Mirzakhalili ◽  
Bogdan Epureanu ◽  
Eleni Gourgou
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Endoplasmic Reticulum ◽  
Computational Model ◽  
Kinetic Equations ◽  
Model Parameters ◽  
C Elegans ◽  
Biochemical Pathways ◽  
Starting Point ◽  
First Time

AbstractWe propose a mathematical and computational model that captures the stimulus-generated Ca2+transients in theC. elegansASH sensory neuron. The model is built based on biophysical events and molecular cascades known to unfold as part of neurons’ Ca2+homeostasis mechanism, as well as on Ca2+signaling events. The state of ion channels is described by their probability of being activated or inactivated, and the remaining molecular states are based on biochemically defined kinetic equations with phenomenological adjustments. We estimate the parameters of the model using experimental data of hyperosmotic stimulus-evoked Ca2+transients detected with a FRET sensor in young and aged worms, unstressed and exposed to oxidative stress. We use a hybrid optimization method composed of a multi-objective genetic algorithm and nonlinear least-squares to estimate the model parameters. We first obtain the model parameters for young unstressed worms. Next, we use these values of the parameters as a starting point to identify the model parameters for stressed and aged worms. We show that the model, in combination with experimental data, corroborates literature results. In addition, we demonstrate that our model can be used to predict ASH response to complex combinations of stimulation pulses. The proposed model includes for the first time the ASH Ca2+dynamics observed during both "on" and "off" responses. This mathematical and computational effort is the first to propose a dynamic model of the Ca2+transients’ mechanism inC. elegansneurons, based on biochemical pathways of the cell’s Ca2+homeostasis machinery.Significance StatementC. elegansis widely used as a model system for monitoring neuronal Ca2+transients. The ASH neuron is the subject of several such studies, primarily due to its key importance as a polymodal nociceptor. However, despite its pivotal role inC. elegansbiology, and the special characteristics of its stimulus-evoked Ca2+transients (e.g., the "off" response), no mathematical or computational model has been developed to include special features of ASH Ca2+dynamics, i.e. the "off" response. The model includes for the first time the ASH Ca2+dynamics observed during both "on" and "off" responses, and is the first to propose a dynamical model of theC. elegansCa2+transients’ mechanism based on biochemical pathways of the cell’s Ca2+homeostasis machinery.AbbreviationsERendoplasmic reticulumPMCAplasma membrane Ca2+ATPaseSERCAsarco-endoplasmic reticulum Ca2+-transport ATPaseTRPVtransient receptor potential-vallinoidVGCCvoltage gated Ca2+channelsIP33-phopsho inositolIPRIP3receptorsROSreactive oxygen speciesGAgenetic algorithmESextracellular space

Determination of Gurson–Tvergaard–Needleman Model Parameters for Failure of a Polymeric Material

2010 ◽  
Vol 21 (1) ◽  
pp. 3-25 ◽  
Author(s):  
Alpay Oral ◽  
Gunay Anlas ◽  
John Lambros
Keyword(s):  
Carbon Monoxide ◽  
Polymeric Material ◽  
Simplex Method ◽  
Polymeric Materials ◽  
Numerical Results ◽  
Model Parameters ◽  
Metallic Materials ◽  
Selection Of ◽  
Fracture Specimens

In this work, the Gurson–Tvergaard–Needleman model, commonly used for metallic materials, is applied to the failure of a polymeric material – specifically a polyethylene carbon monoxide copolymer, which is an enhanced photodegradable material. Gurson–Tvergaard–Needleman model parameters for this material are obtained using the Nelder–Mead simplex method when correlating experimental and numerical results of both tensile and fracture specimens. Results show that the Gurson–Tvergaard–Needleman model can also be used for polymeric materials with selection of proper parameters that are quite different from the ones proposed for metallic materials.

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