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ESTECO modeFRONTIER v2014.1SSQ
modeFRONTIER  a framework for problem solving and multicriteria optimization criterion, working with various CAD, CAE, CFD and other software systems. The medium is possible to work in an automatic optimization of the design and manufacturing. Implemented processing and analyzing data using various methods
Main specifications:
Design of Experiments (DOE), the population distribution of the input variables, evaluation of forecast accuracy
User DOE; Random; Sobol; Full factorial; Cubicfacecentered; Taguchi; BoxBenken; Montecarlo; Reduced Factorial; Latin Square; Latin Hypercube;
DOptimal; The method of crossvalidation (cross validation); Constraint satisfaction problems (constraint satisfaction problem).
Decision making during the multicriteria optimization (MCDM):
Hurwitz criterion (Hurwicz criterion);
Linear algorithm;
GA algoriphm;
Minimax, savage mimimax regret criterion;
Algorithms optimization techniques:
DOE Sequence  direct search parameters;
MOGA II  genetic algorithm for multicriteria optimization;
ARMOGA  genetic algorithm based on MOGA;
NSGA II  nondominated sorting genetic algorithm for multicriteria optimization;
NASH  algorithm based on the theory of noncooperative games Nash (Nash), for multicriteria optimization;
BBFGS  gradient algorithm;
SIMPLEX  search for a solution without the use of derivatives on the NelderMead method;
LevenbergMarquardt (LevenbergMarquardt);
Simulated Annealing hardening modeling algorithm (simulated annealing method);
1P1ES  evolutionary strategy;
DES  an evolutionary strategy for criterion optimization with continuous variables;
MMES  evolutionary strategy for multicriteria optimization with discrete and continuous variables;
FMOGA II  version of the algorithm MOGA with improved convergence;
FSIMPLEX  Simplex version with better convergence and the possibility of making multicriteria problems;
MOSA  version of simulated annealing with the possibility of making multicriteria problems;
MACK  an algorithm for approximating the response surfaces;
NLPQLP  algorithm of sequential quadratic programming (SQP);
NLPQLPNBI  Normal Boundary Intersection method + NLPQLP (algorithm with the ability to solve multiobjective nonlinear problems);
MultiObjective Particle Swarm.
Metamodel (response surface approximation, RSM, approximate mathematical models), methods of construction:
KNearest (method Sheparda);
SVD (singular value decomposition);
Kriging (Kriging), a technique of regression analysis based on the work Daniel Krige;
Parametric surfaces, polynomial regression;
Gaussian Processes  approach to solving problems of regression analysis based on the work of Beziers (Bayesian);
Artificial neural networks, radial basis neural networks (radial basis function),
Means validation meta  models.
6 Sigma Quality Management, Design for Six Sigma (DFSS):
Quality Sigma (six sigma quality);
Failure modes and effects analysis of their (discards analysis);
Ishikawa diagram.
Visual data analysis, evaluation of the statistical significance of the data:
The probability density function (probability density function);
Investigation of the relationship between variables, scatterplot, line, bubble chart, trend lines;
The distribution data, histogram, "pie», cumilative plot;
Linear correlation analysis, correlation matrix (correlation matrix), matrix dispersion (scatter matrix), matrix effects
(effects matrix);
Determination of the main characteristics of the sample, "a box with a mustache» (boxwhiskers), a graph quantile (QuantileQuantile plot);
Calculation of the closeness of interaction parameters;
Working with data samples of large dimension, Student's t test, analysis of variance (test BonFerroni, ANOVA);
Test samples (distribution fitting);
Methods for cluster analysis:
 Partitive clustering
 Methods of hierarchical cluster analysis (hierarchical clustering)  averagelinkage, centroidlinkaga, completelinkaga, singlelinkage,
ward approach,
 Kmeans algorithm (KMeans Clustering), Forgy approach, Kaufman approach, Macqueen approach, random
 The algorithm selforganizing maps (SOMs),
 Dendrogram
Previous use in various areas:
 Optimization of the form inlets
 Optimization of the cooling system
 Optimization of the flow of air in the engine compartment
 Reduction of vibrations
 Aerospace
 Optimizing the form of a centrifugal compressor
 The task of optimizing the shape of the axial turbine and axial compressor
 General Engineering
 Optimization of injection molding process
 Optimization of the casting of metals
 Optimization of technology of hot stamping
 Marine Construction
 Optimizing the ship hull, reduced drag
 Optimal design of steering
 Financial markets
 The problem of optimizing the investment portfolio shares
 Decisionmaking in the financial market
In modeFRONTIER implemented to work with a number of software systems:
AMESim; AVL Boost; AVL Hydsim; Flowmaster; GTPower; KULI; Wave Aspen PLUS; CHEMKIN; eta / VPG; LSDYNA; MADYMO; RADIOSS; Mathematica;
Matlab; DEP; MS Excel; MySQL; OpenOffice; Winbatch; ADAMS; Carsim; Dymola; RecurDyn; SIMPACK; Virtual.Lab; CADFix; CATIA; SolidWorks; IDEAS;
UnigraphicsNX; Maxsurf; ProEngineer; JMAG; AVLFame; ICEMCFD; GID; Gridgen; MSC Patran; Paramesh; Sculptor; AdvantEdge; Cadmould; Magma COMSOL Multiphysiscs (FEMLAB); Simulink; ANSYS CFX; ANSYS TASCflow; FIDAP; FLUENT; GAMBIT; AVLFire; StarCD; StarCCM +; StarDesign; ABAQUS
ANSYS; ANSYS Workbench; AVLExcite; eta / VPG; MSC MARC; MSC NASTRAN; PERMAS; SAMCEF; STRAUS7; SYSNOISE Fieldview; Friendship; Icare;
NAPA4; NuSHALLO; RAPID; REVA; Shipflow Condor; GridEngine; IBM LoadLeveler; LSF; NQS
Extras. Info: Downloads and universal scrap for Windows (x86 + x64), Linux (x86 + 64) and UNIX.
Download link
modeFRONTIER  a framework for problem solving and multicriteria optimization criterion, working with various CAD, CAE, CFD and other software systems. The medium is possible to work in an automatic optimization of the design and manufacturing. Implemented processing and analyzing data using various methods
Main specifications:
Design of Experiments (DOE), the population distribution of the input variables, evaluation of forecast accuracy
User DOE; Random; Sobol; Full factorial; Cubicfacecentered; Taguchi; BoxBenken; Montecarlo; Reduced Factorial; Latin Square; Latin Hypercube;
DOptimal; The method of crossvalidation (cross validation); Constraint satisfaction problems (constraint satisfaction problem).
Decision making during the multicriteria optimization (MCDM):
Hurwitz criterion (Hurwicz criterion);
Linear algorithm;
GA algoriphm;
Minimax, savage mimimax regret criterion;
Algorithms optimization techniques:
DOE Sequence  direct search parameters;
MOGA II  genetic algorithm for multicriteria optimization;
ARMOGA  genetic algorithm based on MOGA;
NSGA II  nondominated sorting genetic algorithm for multicriteria optimization;
NASH  algorithm based on the theory of noncooperative games Nash (Nash), for multicriteria optimization;
BBFGS  gradient algorithm;
SIMPLEX  search for a solution without the use of derivatives on the NelderMead method;
LevenbergMarquardt (LevenbergMarquardt);
Simulated Annealing hardening modeling algorithm (simulated annealing method);
1P1ES  evolutionary strategy;
DES  an evolutionary strategy for criterion optimization with continuous variables;
MMES  evolutionary strategy for multicriteria optimization with discrete and continuous variables;
FMOGA II  version of the algorithm MOGA with improved convergence;
FSIMPLEX  Simplex version with better convergence and the possibility of making multicriteria problems;
MOSA  version of simulated annealing with the possibility of making multicriteria problems;
MACK  an algorithm for approximating the response surfaces;
NLPQLP  algorithm of sequential quadratic programming (SQP);
NLPQLPNBI  Normal Boundary Intersection method + NLPQLP (algorithm with the ability to solve multiobjective nonlinear problems);
MultiObjective Particle Swarm.
Metamodel (response surface approximation, RSM, approximate mathematical models), methods of construction:
KNearest (method Sheparda);
SVD (singular value decomposition);
Kriging (Kriging), a technique of regression analysis based on the work Daniel Krige;
Parametric surfaces, polynomial regression;
Gaussian Processes  approach to solving problems of regression analysis based on the work of Beziers (Bayesian);
Artificial neural networks, radial basis neural networks (radial basis function),
Means validation meta  models.
6 Sigma Quality Management, Design for Six Sigma (DFSS):
Quality Sigma (six sigma quality);
Failure modes and effects analysis of their (discards analysis);
Ishikawa diagram.
Visual data analysis, evaluation of the statistical significance of the data:
The probability density function (probability density function);
Investigation of the relationship between variables, scatterplot, line, bubble chart, trend lines;
The distribution data, histogram, "pie», cumilative plot;
Linear correlation analysis, correlation matrix (correlation matrix), matrix dispersion (scatter matrix), matrix effects
(effects matrix);
Determination of the main characteristics of the sample, "a box with a mustache» (boxwhiskers), a graph quantile (QuantileQuantile plot);
Calculation of the closeness of interaction parameters;
Working with data samples of large dimension, Student's t test, analysis of variance (test BonFerroni, ANOVA);
Test samples (distribution fitting);
Methods for cluster analysis:
 Partitive clustering
 Methods of hierarchical cluster analysis (hierarchical clustering)  averagelinkage, centroidlinkaga, completelinkaga, singlelinkage,
ward approach,
 Kmeans algorithm (KMeans Clustering), Forgy approach, Kaufman approach, Macqueen approach, random
 The algorithm selforganizing maps (SOMs),
 Dendrogram
Previous use in various areas:
 Optimization of the form inlets
 Optimization of the cooling system
 Optimization of the flow of air in the engine compartment
 Reduction of vibrations
 Aerospace
 Optimizing the form of a centrifugal compressor
 The task of optimizing the shape of the axial turbine and axial compressor
 General Engineering
 Optimization of injection molding process
 Optimization of the casting of metals
 Optimization of technology of hot stamping
 Marine Construction
 Optimizing the ship hull, reduced drag
 Optimal design of steering
 Financial markets
 The problem of optimizing the investment portfolio shares
 Decisionmaking in the financial market
In modeFRONTIER implemented to work with a number of software systems:
AMESim; AVL Boost; AVL Hydsim; Flowmaster; GTPower; KULI; Wave Aspen PLUS; CHEMKIN; eta / VPG; LSDYNA; MADYMO; RADIOSS; Mathematica;
Matlab; DEP; MS Excel; MySQL; OpenOffice; Winbatch; ADAMS; Carsim; Dymola; RecurDyn; SIMPACK; Virtual.Lab; CADFix; CATIA; SolidWorks; IDEAS;
UnigraphicsNX; Maxsurf; ProEngineer; JMAG; AVLFame; ICEMCFD; GID; Gridgen; MSC Patran; Paramesh; Sculptor; AdvantEdge; Cadmould; Magma COMSOL Multiphysiscs (FEMLAB); Simulink; ANSYS CFX; ANSYS TASCflow; FIDAP; FLUENT; GAMBIT; AVLFire; StarCD; StarCCM +; StarDesign; ABAQUS
ANSYS; ANSYS Workbench; AVLExcite; eta / VPG; MSC MARC; MSC NASTRAN; PERMAS; SAMCEF; STRAUS7; SYSNOISE Fieldview; Friendship; Icare;
NAPA4; NuSHALLO; RAPID; REVA; Shipflow Condor; GridEngine; IBM LoadLeveler; LSF; NQS
Extras. Info: Downloads and universal scrap for Windows (x86 + x64), Linux (x86 + 64) and UNIX.
Download link
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