Powerful tools for design optimization
Analysis tools: understanding intrinsic system behavior
Analysis tools, such as Fast Fourier Transform (FFT), spectral map, linear analysis order tracking and activity index, help explain system behavior, highlighting main dynamics so that the user can simplify the modeling process with a managed level of accuracy. Linear analysis can also help users obtain sufficient intrinsic system behavior. AMESim provides a comprehensive set of methods: eigenvalues, modal shapes, root locus, and transfer function representation thanks to Bode, Nichols and Nyquist plots.
LMS Imagine.Lab OPTIMUS
Exploring the parameter space and optimizing the design
Thanks to the multi-objective state-of-the-art optimization methods adapted from Noesis OPTIMUS, another LMS technology, LMS Imagine.Lab OPTIMUS can find the best-possible set of design variables to satisfy all constraints and provide optimal values for established objective functionality.
To assess and optimize design responses taking into account the variability present in the LMS Imagine.Lab AMESim design input parameters, LMS Imagine.Lab OPTIMUS deploys robustness and reliability engineering methodologies as well.
With a sole single processor, design exploration or batch runs can take some time. In this case, it is best to use more than one processor. LMS Imagine.Lab AMESim can use the MPICH standard for parallel processing. This option is a much more convenient method to handle robust design runs. It uses separate networked processors.
Discrete partitioning is a technique that can lead to extremely large run-time reductions for certain types of hydraulic systems, such as fuel injection, automatic gearbox command, and ABS. Discrete partitioning uses genuine physical discrete communication to produce a model that is suitable for co-simulation without accuracy loss. It takes advantage of wave propagation physics in hydraulic lines.
LMS Imagine.Lab AMESim offers an extensive tool set to optimize not only the design, but vital aspects of the entire design process. Besides its excellent design optimization capabilities, users can analyze multi-domain systems and graphical renditions of simulation results and generate customizable HTML reports. Pre-processing and post-processing tools include AMETable, a table editor that can handle N-dimension tables and create a 3D representation of the N-dimension table, and AMEPlot, a tool that displays complex plots.
AMEAnimation: create a 3D animation
AMEAnimation is a convenient way to create a 3D animation of any AMESim simulation. Users can simply create and link objects in AMEAnimation to the simulation itself. Users can easily visualize the physical component behavior according to set parameters to demonstrate the final results of the simulation.
Analysis tools: understanding intrinsic system behavior
Analysis tools, such as Fast Fourier Transform (FFT), spectral map, linear analysis order tracking and activity index, help explain system behavior, highlighting main dynamics so that the user can simplify the modeling process with a managed level of accuracy. Linear analysis can also help users obtain sufficient intrinsic system behavior. AMESim provides a comprehensive set of methods: eigenvalues, modal shapes, root locus, and transfer function representation thanks to Bode, Nichols and Nyquist plots.
LMS Imagine.Lab OPTIMUS
Exploring the parameter space and optimizing the design
In the world of multi-domain system simulation, the realm of 1D is probably the place where design exploration techniques are the most effective. Whether used for design or validation, system models can provide global parameter access that directly influences design decisions. LMS Imagine.Lab OPTIMUS is a design optimization software application that captures and manages LMS Imagine.Lab AMESim engineering simulations. Users can quickly explore the design space with Design of Experiments (DOE) and Response Surface Modeling (RSM) techniques to gain critical insight in possible design alternatives.
Design of Experiments (DOE) permits parameter screening and helps define an optimal set of experiments in the design space to obtain maximum information with the highest accuracy level at the lowest cost.
Thanks to the multi-objective state-of-the-art optimization methods adapted from Noesis OPTIMUS, another LMS technology, LMS Imagine.Lab OPTIMUS can find the best-possible set of design variables to satisfy all constraints and provide optimal values for established objective functionality.
To assess and optimize design responses taking into account the variability present in the LMS Imagine.Lab AMESim design input parameters, LMS Imagine.Lab OPTIMUS deploys robustness and reliability engineering methodologies as well.
Parallel Processing: CPU maximization
With a sole single processor, design exploration or batch runs can take some time. In this case, it is best to use more than one processor. LMS Imagine.Lab AMESim can use the MPICH standard for parallel processing. This option is a much more convenient method to handle robust design runs. It uses separate networked processors.
Discrete Partitioning: Simulations beyond your wildest dreams
Discrete partitioning is a technique that can lead to extremely large run-time reductions for certain types of hydraulic systems, such as fuel injection, automatic gearbox command, and ABS. Discrete partitioning uses genuine physical discrete communication to produce a model that is suitable for co-simulation without accuracy loss. It takes advantage of wave propagation physics in hydraulic lines.
