ESTECO modeFRONTIER 4.5.4 Multilingual | 2.67 GB
Main technical characteristics:
Planning of experiment (DOE), the population distribution of the input variables, the estimation accuracy of the forecast
User DOE; Random; Sobol; Full factorial; Cubic-face-centered; Taguchi; Box-Benken; Montecarlo; Reduced Factorial; Latin Square; Latin Hypercube;
D-Optimal; The method of cross-validation (cross validation); Constraint satisfaction problems (constraint satisfaction problem).
Decision-making during the multi-criteria 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 multiobjective optimization;
ARMOGA - genetic algorithm based on MOGA;
NSGA II - non-dominated sorting genetic algorithm for multiobjective optimization;
NASH - algorithm based on the theory of noncooperative games Nash (Nash), for multiobjective optimization;
B-BFGS - gradient algorithm;
SIMPLEX - search for a solution without the use of derivatives by the method of Nelder-Mead;
Levenberg-Marquardt (Levenberg-Marquardt);
Simulated Annealing- hardening modeling algorithm (method of simulated annealing);
1P1-ES - evolutionary strategy;
DES - evolutionary strategy for the optimization criterion with continuous variables;
MMES - evolutionary strategy for multi-criteria optimization with discrete and continuous variables;
FMOGA II - version of MOGA algorithm with improved convergence;
FSIMPLEX - Simplex version with improved convergence and possible solutions of multiobjective problems;
MOSA - version of simulated annealing with the possibility of solving multiobjective problems;
MACK - approximation algorithm for the response surfaces;
NLPQLP - algorithm of sequential quadratic programming (SQP);
NLPQLP-NBI - Normal Boundary Intersection method + NLPQLP (algorithm with the possibility of solving multiobjective nonlinear problems);
Multi-Objective Particle Swarm.
Metamodel (response surface approximation, RSM, approximate mathematical model), methods of construction:
K-Nearest (method Shepard-a);
SVD (singular value decomposition);
Kriging (Kriging), a technique of regression analysis based on the works of Daniel Krige;
Parametric surfaces, polynomial regression;
Gaussian Processes - approach to solving problems of regression analysis based on the works of Beziers (Bayesian);
Artificial neural networks, radial basis neural network (radial basis function),
Means of 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 analysis of data, 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, the trend line;
The distribution of the 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, "box and whisker» (box-whiskers), the quantile plot (Quantile-Quantile plot);
Calculation of the tightness of interaction parameters;
Working with data samples of high dimensionality, Student's t test, analysis of variance (test Bon-Ferroni, ANOVA);
Check sampling (distribution fitting);
Cluster analysis methods:
- Partitive clustering
- Methods of hierarchical cluster analysis (hierarchical clustering) - average-linkage, centroid-linkaga, complete-linkaga, single-linkage,
ward approach,
- K-means algorithm (K-Means Clustering), Forgy approach, Kaufman approach, Macqueen approach, random
- Algorithm for self-organizing maps (SOMs),
- dendrogram
Experience of use in various fields:
- Optimization of the form inlets
- Optimization of the cooling system
- Optimization of the flow of air in the engine compartment
- Reducing vibrations
- Aerospace
- Optimizing the shape of a centrifugal compressor
- Optimizing the shape of an axial turbine and axial compressor
- General Engineering
- Optimization of injection molding process
- Optimization of the casting of metals
- Optimization of the technology of hot stamping
- Marine Construction
- Optimizing the ship hull, reducing drag
- Optimal design steering
- Financial markets
- The problem of optimizing the investment portfolio of shares
- Decision-making in the financial market
In modeFRONTIER implemented to work with a number of 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
Extras. Information: Distributions and universal scrap for Windows (x86 + x64), Linux (x86 + 64), and UNIX. Just added to the sample files and Tutorials on modeFRONTIER.
Year / Release Date: 2014
Version: 4.5.4
Developer: ESTECO s.r.l
Developer website:
Код:
www.esteco.comCompatibility with Vista: complete
Compatible with Windows 7: complete
Language: Multilingual
Medicine: Present
OS: Win/Mac/Linux/UNIX
Код:
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