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Sep 18 2019

ESTECO modeFRONTIER 2019 R1 x64-SSQ

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ESTECO modeFRONTIER 2019 R1 x64-SSQ | 476 MB

Information:

modeFRONTIER is an environment for solving criteria-based and multi-criteria optimization problems, working with various CAD, CAE, CFD and other software systems. In the environment there is the possibility of working in automatic design and optimization of products. Implemented data processing and analysis using various methods

Main technical specifications:
Experiment Planning (DOE), distribution of the input population of variables, prediction accuracy assessment
User DOE; Random Sobol; Full factorial; Cubic-face-centered; Taguchi; Box-Benken Montecarlo; Reduced Factorial; Latin Square Latin Hypercube;
D-Optimal; Cross validation method; Constraint satisfaction problem.
Decision making during multicriteria optimization (MCDM):
Hurwitz criterion;
Linear algorithm;
GA algoriphm;
Minimax, savage mimimax regret criterion;
Algorithms, optimization methods:
DOE Sequence – direct enumeration of parameters;
MOGA II – genetic algorithm for multi-criteria optimization;
ARMOGA – a genetic algorithm based on MOGA;
NSGA II – a non-dominant sorting genetic algorithm for multi-criteria optimization;
NASH – an algorithm based on the theory of non-cooperative games Nash (Nash), for multi-criteria optimization;
B-BFGS – gradient algorithm;
SIMPLEX – search for a solution without using derivatives using the Nelder-Mead method;
Levenberg-Marquardt (Levenberg-Marquardt);
Simulated Annealing- model hardening algorithm (simulated annealing method);
1P1-ES – evolutionary strategy;
DES is an evolutionary strategy for carrying out criterial optimization with continuous variables;
MMES is an evolutionary strategy for multicriteria optimization with discrete and continuous variables;
FMOGA II – version of the MOGA algorithm with improved convergence;
FSIMPLEX – Simplex version with improved convergence and the ability to solve multi-criteria problems;
MOSA – version of simulated annealing with the ability to solve multi-criteria problems;
MACK – an algorithm for approximating response surfaces;
NLPQLP – sequential quadratic programming algorithm (SQP);
NLPQLP-NBI – Normal Boundary Intersection method + NLPQLP (algorithm with the ability to solve multicriteria nonlinear problems);
Multi-Objective Particle Swarm.

Metamodels (approximation of the response surface, RSM, approximate mathematical models), construction methods:
K-Nearest (Shepard-a method);
SVD (singular value decomposition);
Kriging, a regression analysis technique based on the work of Daniel Krige;
Parametric surfaces, polynomial regression;
Gaussian Processes – an approach to solving the problems of regression analysis based on the work of Bayesian;
Artificial neural networks, radial basis neural networks (radial basis function),
Means of checking the correctness of meta – models.
6 sigma, quality management, Design for Six Sigma (DFSS):

Sigma quality (six sigma quality);
Types of failures and analysis of their impact (discards analysis);
Ishikawa diagram.
Visual analysis of data, assessment of statistical significance of data:
Probability density function;
Study of the relationship between variables, scatter chart, line, bubble chart, trend lines;
Data distribution, histogram, pie, cumilative plot;
Linear correlation analysis, correlation matrix, scatter matrix, effects matrix
(effects matrix);
Definition of the main characteristics of the samples, “box with whiskers” (box-whiskers), quantile plot (Quantile-Quantile plot);
Calculation of the tightness of interaction parameters;
Work with large-sized data samples, Student’s test, analysis of variance (Bon-Ferroni test, ANOVA);
Sampling check (distribution fitting);
Cluster analysis methods:
– partitive clustering
– hierarchical clustering methods – average-linkage, centroid-linkaga, complete-linkaga, single-linkage,
ward approach
– K-Means Clustering algorithm, Forgy approach, Kaufman approach, Macqueen approach, random
– self-organizing card algorithm (SOMs),
– dendrograms
Experience in use in various fields:
– Optimization of the shape of the inlet pipes
– Optimization of the cooling system
– Optimization of the flow of air in the engine compartment
– vibration reduction
– Aerospace industry
– The task of optimizing the shape of a centrifugal compressor
– The task of optimizing the shape of the axial turbine and axial compressor
– General engineering
– Optimization of the injection molding process
– Optimization of the metal casting process
– Optimization of hot stamping technology
– Marine construction
– The task of optimizing the contours of the vessel, reducing the hydrodynamic resistance
– Optimum steering design
– Financial markets
– The task of optimizing the investment portfolio of shares
– Making decisions in the financial market
ModeFRONTIER implements work with many software systems:
AMESim; AVL Boost; AVL Hydsim; Flowmaster GT-Power KULI; Wave Aspen PLUS; CHEMKIN; eta / VPG; LS-DYNA; MADYMO; RADIOSS; Mathematica
Matlab; DEP MS Excel; MySQL OpenOffice Winbatch ADAMS Carsim; Dymola; RecurDyn; SIMPACK; Virtual.Lab; CADFix; CATIA SolidWorks I-DEAS;
UnigraphicsNX; Maxsurf; ProEngineer; JMAG; AVL-Fame; ICEMCFD; GID Gridgen; MSC Patran; Paramesh; Sculptor; AdvantEdge Cadmould; Magma COMSOL Multiphysiscs (FEMLAB); Simulink ANSYS CFX; ANSYS TASCflow; FIDAP FLUENT; GAMBIT; AVL-Fire; Star-CD; Star-CCM +; Star-Design; ABAQUS
ANSYS; ANSYS Workbench; AVL-Excite; eta / VPG; MSC MARC; MSC NASTRAN; PERMAS; SAMCEF; STRAUS7; SYSNOISE Fieldview; Friendship Icare;
NAPA4; Nu-shallo; RAPID REVA; Shipflow Condor; GridEngine IBM LoadLeveler LSF; Nqs

Year / Release Date: 2019
Version: 2019 R1
Developer: ESTECO s.r.l
Developer’s site: http://www.esteco.com
Bit: 64bit
Interface language: English
Tabletka: Present (TeAM SolidSQUAD-SSQ)
System Requirements: Microsoft Windows 7 / 8.1 / 10 64-bit

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