Automation by Parameter Variation and Optimisation
The DSHplus Parameter Variation Module, the DSHplus Batch Simulation Module and the DSHplus optimization Module are core modules of the DSHplus Virtual Engineering Lab’s preprocessing functionality. By means of these three modules the design engineer is able to automate the simulation in order to analyze different parameter configurations or to optimize complete system set-ups. The modules are suitable for background or overnight work whereas the design engineer merely checks the results after the automatic simulation is concluded.
The DSHplus Batch Simulation Module offers the possibility to calculate pre-defined parameter sets in a batch run. Using the DSHplus Parameter Variation Module it is possible to vary each design parameter of the simulation model in arbitrary or fixed steps. The variation is realized using DOE matrixes; obtained from statistical programs such as MiniTabTM, or by user inputs. Through this the effect of different component sizes, such as pump displacement or piston diameter, or even manufacturing tolerances, such as gap height or spring stiffness, onto the system’s dynamic can be examined. In combination with the DSHplus functionality to use parametric equations in order to calculate component parameters, which are in dependence of other model parameters, it is also possible to realize constraints between components.
A HTML-based simulation report can be generated that allows a quick visual analysis of the simulation runs. This report includes a separate page for each parameter set that includes a model image, the result graphics and a legend that lists boundary condition parameter values. For a subsequent more detailed post-processing all results can be stored.
Combined with the DSHplus Optimization Module, both modules are capable of performing an automatic design parameter optimization. Herewith a real virtual engineering of fluid power system within DSHplus is possible.
The DSHplus Optimization Module uses gradient or generic search algorithms, such as the Hookes-Jeeves search algorithm or an evolution strategy, to perform an automatic variation of system parameter in defined ranges. Goal is the minimization of a quality criterion that has to be defined by the design engineer. Possible applications are an automatic search for the optimal controller gain settings, the best design parameter values, such as diameter or spring stiffness, or parameters are simply automatically tuned until simulation results match measurement data.