Getting Started

Info

The info (main) panel has fields for entering problem description and author information.

Strategy

In the Strategy Panel you may choose the sampling strategies for tasks like metamodel-based optimization and RBDO.

Single Stage: Sampling points are chosen only once and the whole design space is taken as the region of interest. Therefore no iteration is needed.
Sequential: Sampling is done sequentially, only a small number of points are chosen for each iteration.
Sequential Strategy with Domain Reduction: It is the same as the Sequential Strategy, but in each iteration the domain reduction strategy is used to reduce the size of the region of interest.

Choose a strategy to apply in the task.
For the latter two strategies, choose a tolerance strategy for termination and specify the tolerance values.

Solvers

The Solver Panel allows the user to define:

Pre-Processor Package
- Change of input files by a parametric Pre-Processor controlled by design variables from LS-OPT (ANSA, HyperMorph, DEP-Morpher, TrueGrid, User-Defined).
Solver Package
- Solver Command: LS-DYNA or others (User-Defined)
- Input File: Will be appended to the Command in the Command Line
Post-Processor Package

Interface to METAPost for result extraction

Push the category chooser to define a Pre-Processor Package Name.
Define a solver with Solver Package Name.
Define the solver Command, can be in the path ($path) or can be specified with the full path name.
Type in the name of the Input File or browse the direction to the file.
Enter a label for the Analysis Case.
Push the Add button to create an analysis case.

Dist

The probabilistic component of a design variable is described using a probabilistic distribution. The distributions are created without referring to a variable. Many design variables can refer to a single distribution. As statistical distribution you can choose between these types:

Normal, Uniform, Lognormal, Weibull, Beta, User PDF (probability density function), User CDF (cumulative distribution function), Binomial

Add a Distribution

Select from the category chooser the statistical distribution Type, e.g. the type Normal, which will create a normal distribution that is symmetric and centered about the mean with a standard deviation.
Type in the appropriate values to describe the distribution, e.g. here enter a Mean value and Standard Deviation value.
Type in a Distribution Name or accept the default NORMAL3.
Push the Add button to create the distribution.
Select the generated distributions for a prewiew.

Var.

In the Variables Panel you can define or modify variables and choose between different types:

Variable, Constant, Dependent, Noise Variable, Discrete Variable

Add a Variable

Variable means a standard design variable which will be integrated in the optimization process.

From the Type category chooser select Variable.
Enter a variable Name (label), e.g. area_1.
Enter a Starting value.
Set the range: enter a Minimum (lower bound).
Enter a Maximum (upper bound).

Add a Constant

Constants are used:

to define constant values in the input file as π, e or any other constant that may relate to the optimization problem, e.g. initial velocity, integration limits, etc.

to convert a variable to a constant. The number of optimization variables can be reduced without interfering with the template files.

To add a new variable push the Add a Variable button.
To create a constant switch the type of an existing variable from Variable to Constant.

If a variable is no longer needed you can delete it with the Delete a Variable button.

Sampling

In the sampling panel you can choose between these metamodels:

Polynomial, Sensitivity, Feedforward Neural Network, Radial Basis Function Network

Furthermore, several options and point selections can be selected, e.g. "Compute Global Sensitivities" and "Approximate Histories" (calculation of metamodels for curve data).

Select a METAMODEL type, e.g. Radial Basis Function Network.
In the POINT SELECTION the default is Space Filling (usefull in conjunction with nonparametric models such as neural networks).

Histories

In the Histories panel you can specify time history curves to be extracted from the solver database.

Select the LS-DYNA database for the history from the list.
Choose further options for the selected history.
Enter a name for the history or accept the default.
To add a history push the button Add.

Responses

Create a response in the Responses Panel. A response can be a result of a mathematical operation applied to a time history curve, or can be directly extracted using the standard LS-DYNA interface or a user-defined interface.

Select the dataset for the response from the list.
Specify the dataset response options.
Enter a name for the response or accept the default.
To add a response push the button Add.

Objective

Specification of the objective (function to minimize) for the optimization problem.

Objectives can be specified in terms of composite functions and/or Response functions.
More than one objective can be defined (multi objective). A Weight might be applied to each objective.

Constraints

Soecitication of constraints (functions to satisfy) of the optimization problem.

Select the response (defined in the Responses Panel) out of the list.
Set the lower bound of the constraint.
Set the upper bound of the constraint.

Algorithms

In the Algorithms panel you can specify an optimization algorithms for the task. There are three core solver and two hybrid algorithms to choose.

LFOP: Leap Frog Optimizer
GA: Genetic Algorithm
Hybrid GA: Hybrid Genetic Algorithm
ASA: Adaptive Simulated Annealing
Hybrid ASA: Hybrid Adaptive Simulated Annealing

Specify an optimization algorithm.

Run

In the Run panel you can start the optimization, specify the number of concurrent jobs or adjust the convergence criteria. The state of completion is monitored.

Enter the Number of Iterations for the optimization problem.
Start the optimization with the Run button.

View

The View panel starts the LS-OPT Viewer for the visualization of the optimization results. For example Metamodel Surface, Sensitivity Analysis, Optimization History....

Metamodel Surface

You can plot the metamodel surface you have built. The plot can be zoomed and rotated. You may also add the sampling points (both feasible and infeasible) and the optimum points (predicted and computed) to the plot. This is a visual way to check the results.

Sensitivity

Which variable is the most important for a response? The sensitivity analysis shows the influence of all the variables on the selected response. There are two analysis methods to choose.

Linear ANOVA (Analysis of Variance)
GSA/Sobol (Sobol's Global Sensitivity Analysis)

Optimization History

Progress of a variable, a response or other entities during the optimization process. If the strategy Sequential with Domain Reduction is applied, the blue curves show the shrink of the domain of a variable.

Stats

In the DYNA stats panel you can compute various statistics of the LS-DYNA d3plot results and LS-OPT history data for viewing in LS-PREPOST on the FE model. These statistics show:

The variation of the LS-DYNA results due to the variation of the design parameters.
The variation of the LS-DYNA results due to bifurcations and other stochastic process events.

Select the Fringe Plot.
Select a D3Plot Components, e.g. D3Plot Components→Ndv→z-displacement.
Choose a Case.
Go to the next panel.
Select what to plot.
Choose a Statistic value, e.g. Mean or Standard Deviation of the z-displacement.
Select the Task, e.g. Monte Carlo.
Go to the next panel.
Select the Iteration, e.g. the first iteration.
Give the plot a Name.
Finish the setting.

Fringe Plot of the z-displacement of a steel tube being crushed

Sections

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