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.

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

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.

scholarly journals IMPROVING THE LOCAL GEOMETRIC GEOID MODEL OF FCT ABUJA ACCURACY BY FITTING A HIGHER ORDER/DEGREE POLYNOMIAL SURFACE

2020 ◽  
Vol 4 (3) ◽  
pp. 114-120
Author(s):  
Paul Dare Oluyori ◽  
Sylvester Okiemute Eteje
Keyword(s):  
Higher Order ◽  
Model Parameters ◽  
Degree Polynomial ◽  
Geoid Model ◽  
Data Set ◽  
Least Squares Adjustment ◽  
Order Degree ◽  
Best Fit ◽  
Polynomial Surface

The improvement of the accuracy of a local geometric geoid model using the same data set (geoid heights) requires the fitting of a higher degree polynomial surface to the data set. Consequently, this paper presents improving the local geometric geoid model of FCT, Abuja accuracy by fitting a higher order polynomial surface. A fifth degree polynomial surface was fit to the existing geoid heights of 24 points used previously for the determination of the geometric geoid model of the study area to improve its accuracy. The least squares adjustment technique was applied to compute the model parameters, as well as the fit. The RMSE index was applied to compute the accuracy of the model. The computed accuracy (0.081m) of the model was compared with those of the previously determined geoid models (Multiquadratic, 0.110m and Bicubic, 0.136m models) of the study area to determine which of the models best fit the study area, as well as has the highest resolution. The comparison result shows that the fifth degree polynomial surface best fit the study area.

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.

FIDDLE. Simultaneous Indexing and Structure Solution from Powder Diffraction Data using a Genetic Algorithm and Correlation Functions

2019 ◽  
Author(s):  
Carmen Guguta ◽  
Jan M.M. Smits ◽  
Rene de Gelder
Keyword(s):  
Genetic Algorithm ◽  
Powder Diffraction ◽  
Diffraction Data ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Simultaneous Optimization ◽  
Powder Diffraction Data ◽  
Structure Solution ◽  
Matching Technique

A method for the determination of crystal structures from powder diffraction data is presented that circumvents the difficulties associated with separate indexing. For the simultaneous optimization of the parameters that describe a crystal structure a genetic algorithm is used together with a pattern matching technique based on auto and cross correlation functions.<br>

scholarly journals Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

2021 ◽  
Vol 19 (1) ◽  
pp. 205-213
Author(s):  
Hany W. Darwish ◽  
Abdulrahman A. Al Majed ◽  
Ibrahim A. Al-Suwaidan ◽  
Ibrahim A. Darwish ◽  
Ahmed H. Bakheit ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Predictive Power ◽  
Principal Component Regression ◽  
Selection Procedure ◽  
Principal Component ◽  
Chemometric Methods ◽  
Full Spectrum ◽  
Azilsartan Medoxomil

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

scholarly journals Reactor rate modulation oscillation analysis with two detectors in Double Chooz

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
T. Abrahão ◽  
◽  
H. Almazan ◽  
J. C. dos Anjos ◽  
S. Appel ◽  
...  
Keyword(s):  
Reactor Power ◽  
Background Model ◽  
Fast Neutrons ◽  
Mixing Angle ◽  
Oscillation Analysis ◽  
Central Value ◽  
Double Chooz ◽  
Model Independent ◽  
Best Fit

Abstract A θ13 oscillation analysis based on the observed antineutrino rates at the Double Chooz far and near detectors for different reactor power conditions is presented. This approach provides a so far unique simultaneous determination of θ13 and the total background rates without relying on any assumptions on the specific background contributions. The analysis comprises 865 days of data collected in both detectors with at least one reactor in operation. The oscillation results are enhanced by the use of 24.06 days (12.74 days) of reactor-off data in the far (near) detector. The analysis considers the $$ {\overline{\nu}}_e $$ ν ¯ e interactions up to a visible energy of 8.5 MeV, using the events at higher energies to build a cosmogenic background model considering fast-neutrons interactions and 9Li decays. The background-model-independent determination of the mixing angle yields sin2(2θ13) = 0.094 ± 0.017, being the best-fit total background rates fully consistent with the cosmogenic background model. A second oscillation analysis is also performed constraining the total background rates to the cosmogenic background estimates. While the central value is not significantly modified due to the consistency between the reactor-off data and the background estimates, the addition of the background model reduces the uncertainty on θ13 to 0.015. Along with the oscillation results, the normalization of the anti-neutrino rate is measured with a precision of 0.86%, reducing the 1.43% uncertainty associated to the expectation.

Experience with the IMMa tyre test bench for the determination of tyre model parameters using genetic techniques

2005 ◽  
Vol 43 (sup1) ◽  
pp. 253-266 ◽  
Author(s):  
J. A. Cabrera ◽  
A. Ortiz ◽  
E. Carabias ◽  
A. Simón
Keyword(s):  
Test Bench ◽  
Model Parameters ◽  
Tyre Model ◽  
Genetic Techniques
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