LS-OPT 4.0 released
Examples
This section demonstrates LS-OPT capabilities by means of a series of examples.
Crashworthiness OptimizationThe problem is of a simplified vehicle moving at a constant velocity and crashing into a pole. It illustrates the following features: a simple single iteration optimization, dealing with an infeasible design formulation, use of composite functions, specifying an appended file for the simulation input, trade-off study, use of the LS-DYNA result interface, updating response surface approximations using the Repair feature, and discrete-continuous optimization. |
Parameter IdentificationThe material parameters of a foam material must be determined from experimental results, namely the resultant reaction force exerted by a cubic sample on a rigid base. The example illustrates the following features: how to do parameter identification (point-based Mean Squared Error composite function, history-based Mean Squared Error composite function) and multiple simulation models in the same optimization problem (multi-case). |
Reliability AnalysisThis example is a Monte Carlo analysis of a steel tube being crushed. The effect of both a variation in material thickness and a variation in the plastic stress-strain curve is investigated. The response is compared to the crush distance of the selected nominal design. This example demonstrates: Reliability Based Design Optimization (RBDO), Monte Carlo Analysis and Metamodel-based Monte Carlo Analysis. |
RBDOReliability Based Design Optimization (RBDO) includes the variation of the design variables into the design problem. The problem is of a simple two-bar truss. It has two variables: the thickness of the bars and the leg width. The example demonstrates: Reliability Based Design Optimization (RBDO), creating statistical distributions and assigning them to design variables and probabilistic constraints. |
Robust Parameter DesignRobust Parameter Design selects designs insensitive to the variation of given parameters. This is the two-bar truss as considered previously. The bar thicknesses are noise variables while the leg widths are adjusted (control variables) to minimize the effect of the variation of the bar thicknesses. The tutorial illustrates the feature Robust Parameter Design. |
Coupling ANSAThis example presents the coupling of LS-OPT with a pre-processor (ANSA). A front rail will be tested in crash simulation. The target is to find the best arrangement of its embosses in order to minimize the acceleration that appears in the test. Following features are illustrated: model definition (morphing boxes/parameters), Task Manager sequence definition and the LS-OPT setup. |
User Defined FunctionHere we have a function that contains two variables. It was designed with the aim of checking the solutions through LS-OPT. These variables x and y are in the range of [-1,3]. Within that range we attempt to obtain the optima. In order to provide a solver for this function that can be used in LS-OPT we will write a little script in Perl. |
