Solve complex engineering problems with no specialized competence required: Design Exploration tools support the automatic selection of the best suitable technique.
Design Exploration capabilities empower users to explore various design alternatives and easily find optimal solutions. pSeven tools allow users to fully set up Design of Experiments studies, combine Design Optimization strategies, run Uncertainty Quantification and switch between techniques while solving design problems on the fly.
Design Exploration allows engineers to:
“Design Exploration (or Design Space Exploration) 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
Create a workflow describing your product or process
Apply Design Exploration tools
Make decisions based on numbers
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. DoE can be used to perform reliable Surrogate-Based Optimization (SBO) or to generate a training data sample for a building of an accurate predictive model.
Create a simulation model
Define design variables and responses
Run a series of model evaluations
Model behavior can be very different in dimensionality, size, smoothness, noisiness etc., and the available number of model evaluations is often limited. To explore such models faster and more effective pSeven offers a variety of techniques including well-known batch algorithms and in-house proprietary adaptive search.
Adaptive Design of Experiments (ADoE) considers model behavior before adding new points and takes into account linear and non-linear constraints of the model. ADoE supports 3 scenarios:
Feasible domain sampling:
Response surface improvement:
Search for designs with given objective function value:
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:
Create a simulation model
Define design variables and goals
pSeven offers a complete set of in-house developed methods and algorithms to conduct optimization of one or multiple model objective functions subject to various constraints. It allows to efficiently solve both design optimization problems with fast to evaluate analytical models and the problems where the key challenge is expensive in terms of computing resources simulations.
Analytical and simulation models used in engineering have a set of specific aspects that often blocks the using of open-source or academic optimization tool. Such aspects can be easily taken into account during the design optimization process in pSeven:
pSeven allows to handle optimization problems with:
pSeven supports any combination of:
pSeven supports parallel execution of optimization procedures allowing to reduce the computational time of resource-consuming problems solution drastically.
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, 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 a 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.
In real life, the values of the product design variables, like geometry, material properties, load magnitudes etc., always contain some uncertainty, that can be caused by manufacturing tolerances or natural variability. By default simulation is deterministic but the reality is always stochastic.
Solving engineering problems with uncertainties requires a stochastic approach:
pSeven provides a variety of tools to deal with uncertainties and to find robust designs in an automatic manner:
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