Multiobjective Optimization
Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. Example problems include analyzing design tradeoffs, selecting optimal product or process designs, or any other application where you need an optimal solution with tradeoffs between two or more conflicting objectives. Our Engineers save time and improve products by optimizing them against performance or cost metrics through statistical methods, such as Design of Experiments (DOE) or Design for Six Sigma. Virtual prototyping is necessary for cost efficiency.Test cycles are reduced and placed late in the product development.
CAE-based optimization and CAE-based robustness evaluation becomes more and more important in virtual prototyping. Enteknograte engineering team use advanced algorithmic for sensitivity analysis, optimization, robustness evaluation, reliability analysis and robust design optimization.
- Optimization is introduced into virtual prototyping
- Robustness evaluation is the key methodology for safe, reliable and robust products
- The combination of optimizations and robustness evaluation will lead to robust design optimization strategies
Sensitivity analysis
Sensitivity analysis scans the design/random space and measures the sensitivity of the inputs with statistical measures. Application as pre-investigation of an optimization procedure or as part of an uncertainty analysis. Results of a global sensitivity study are:
- Sensitivities of inputs with respect to important responses
- Estimate the variation of responses
- Estimate the noise of an underlying numerical model
- Better understanding and verification of dependencies between input and response variation
Design of Experiments (DOE):
- Central Composite
- Data File
- Full Factorial
- Fractional-Factorial
- Box-Behnken
- Latin Hypercube
- Optimal Latin Hypercube
- Orthogonal Array
- Dependent Variable Sampling and Parameter Study
Optimization Algorithms :
- Gradient:NLPQL, MMFD
- LSGRG2
- Pattern: Hooke-Jeeves, Downhill Simplex, Adaptive Simulated Annealing
- Mixed Integer/Real: MISQP, MOST
- Genetic Algorithms: Evolution, Multi-Island GA
- Multi-Objective: AMGA, NSGA II, NCGA, Particle Swarm
- Other: Stress-Ratio Method, Pointer I & II Automatic Optimizer, Multi-objective approximation Loop
Response surface modeling (RSM)
- Orthogonal polynomial models
- Radial or Ellliptic Basis Function methods
- Shape functions and smoothing
- Kriging method with Exponential, Gaussian and Matern correlation functions
Monte Carlo Analysis
- Simple random sampling
- Descriptive sampling
- Eight standard distributions
- Distribution truncation
Six Sigma
Probabilistic analysis to measure the quality of a design given uncertainty or randomness of a product or process
- Perform reliability analysis with the mean value method, FORM and SORM reliability method
- Importance sampling
- Sobol sampling
- DOE sample
- Monte Carlo Analysis
Taguchi Method
Improve the quality of a product or process by striving to achieve performance targets and minimizing performance variation
- Taguchi analysis for static, dynamic, and dynamic-standardized system types
