Design Space Exploration

Why Do Design Space Exploration?

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:

  • Develop trust in their models
  • Explore design alternatives
  • Perform trade-off studies
  • Discover bottlenecks
  • Identify models
  • Set goals

“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

Model

Create model

arrow_forward
DSE

Apply Design Space Exploration

arrow_forward
Analytics

Make smart decisions

Design of Experiments

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.

Model

Create model

arrow_forward
Parameters

Define variables

arrow_forward
Design of Experiments

Run Design of Experiments

arrow_forward
Analytics

Analyze results

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:

Batch:

  • Latin Hypercube
  • Optimized Latin Hypercube
  • Full Factorial
  • Fractional Factorial
  • Parametric Study
  • Orthogonal Array
  • Box-Behnken Design
  • D-Optimality, I-Optimality

Sequential:

  • Random Sampling
  • Halton Sequence
  • Sobol Sequence
  • Faure Sequence

Adaptive:

  • Uniform
  • Maximum Variance
  • Integrated Mean Square Error Gain - Maximum Variance
  • Adaptive Design

Design Optimization

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:

  • How to improve product or process characteristics?
  • Which combination of input parameters is the best?
  • How to decrease the effect of input parameters variability on the overall product or process behavior?
Model

Create model

arrow_forward
Parameters

Define variables and goals

arrow_forward
Optimization

Run optimization

Key advantages of Optimization in pSeven:

  • Efficient solution of complex engineering problems with up to ten objective functions, hundreds of design variables and constraints with a small computation budget.
  • A wide range of easy-to-use proprietary and in-house developed optimization algorithms with a minimum setup required.
  • Automatic technique selection that allows users with no specialized competence to solve optimization problems easier.
  • Robustness of optimization process to random noise in model responses, as well as to undefined model behavior.
  • Parallel execution of optimization procedures allowing to reduce computational time of resource-consuming problems solution drastically.
Learn more >

SmartSelection

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.

Learn morenavigate_next

dsx smartselection

Results & External Data Analysis

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 correlation analysis

Parameters dependency analysis

Parameters dependency analysis

Design points in parallel coordinates

Design points in parallel coordinates

Uncertainty Quantification

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.

Multiple Distributions

Set input distributions

arrow_forward
Model

Evaluate model responses

arrow_forward
Single Distribution

Identify output distribution

arrow_forward
Failure Conditions

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.

Learn more >

Model Identification

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.

Model

Bad fit = model parameters unknown

arrow_forward
Experiment
arrow_forward
Data

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.

Interested in the solution?

Click to request a free 30-day demo.

Request demo