Design Space Exploration capabilities empower users to explore various design alternatives and easily find optimal solutions. pSeven’s tools allow users to fully set up optimization and design of experiments techniques, combine strategies and switch between techniques when solving design problems.
Design Space Exploration allows engineers to:
“Design Space Exploration (DSE) is both a class of quantitative methods and a category of software tools for systematically and automatically exploring very large numbers of design alternatives and identifying optimal performance parameters.”
B. Jenkins, Ora Research
Apply Design Space Exploration
Make smart decisions
Design of Experiments (DoE) is a selection of inputs at which outputs of the model are measured to explore design space or to get as much information as possible about the model behavior using a small number of observations as possible.
Run Design of Experiments
Model behavior can be very different in dimensionality, size, smoothness, noisiness, etc. and the available number of model computations is often limited. To explore such models faster and more effective pSeven offers a variety of techniques for Design of Experiments including batch, sequential and adaptive sampling. Sequential sampling is a uniform filling of white regions of the model step by step, while adaptive sampling considers model behavior before adding new points. Also, DoE can be used to perform reliable surrogate-based optimization or to generate a training data sample for a building of an accurate approximation model.
DoE techniques available in pSeven:
Design optimization is a process of finding the values of input parameters, which lead to the best performance of analytical or simulation model of a product or a manufacturing process under investigation. Ultimately, it answers the following questions:
Define variables and goals
Key advantages of Optimization in pSeven:
With pSeven, the user has to simply set the basic properties of the model (if known), such as model evaluation expensiveness, smoothness of model responses, etc., instead of tedious tuning of optimization technique internal parameters. After that automatic and adaptive choice of specific optimization technique(s) based on this information is provided by SmartSelection technique.
Along with the automatic technique's selection convenient for the users, full control over the whole optimization process is available for expert-level users, making optimization capabilities of pSeven highly customizable.
pSeven provides full control over external data and rich post-processing capabilities. Visualize and reuse engineering results with a comprehensive set of interactive and customizable tools, including all kinds of tables and statistics, correlations, dependencies, parallel coordinates and 2D/3D visualization.
Parameters correlation analysis
Parameters dependency analysis
Design points in parallel coordinates
Specialists from a wide spectrum of industries face the need to evaluate the influence of uncertain parameters of a product, like material properties or operating conditions, on its technical and operational characteristics. Uncertainty Quantification (UQ) in pSeven addresses this need and allows engineers to significantly improve quality and reliability of designed products and manage potential risks at early design, manufacturing and operating stages.
Set input distributions
Evaluate model responses
Identify output distribution
Estimate failure probability
UQ is used to assess model design point taking into account all possible deviations of input parameters and their influence on the output. Uncertainties of the input parameters are described with distributions, based on experimental data, production constraints, best practices or engineering judgment. The most important part of UQ process is defining the model assessment criteria, for example, failure conditions. As a result of UQ user obtains a distribution of these criteria, including mean and dispersion values which allow to evaluate the model reliability and make better engineering decisions.
Sometimes model input parameters are hard or impossible to determine, for example, damping or scattering coefficient. Running an experiment may help, but if these parameters can’t be found directly, a more advanced research is required.
Bad fit = model parameters unknown
Good fit = model parameters identified!
In such cases, model identification (or data matching) in pSeven can be used. The idea is to collect output data of the experiment and create simulation or analytical model of the product or manufacturing process with unknown input parameters. After that, an optimization process with residual check between predicted and experimental data is set up to identify unknown input parameters. This approach provides less expensive research and grants more reliable simulation.