pSeven

pSeven provides a variety of tools for data and model analysis:

Design of Experiments allows exploring design space using as small number of observations as possible, enables reliable surrogate-based optimization and generates a training sample for building an accurate approximation model.

Design of Experiments includes the following techniques:

• Space Filling
  • Batch techniques (Random, Full-Factorial, Latin hypercube sampling, Optimal LHS)
  • Sequential techniques (Random, Halton, Sobol, Faure sequences)
• Optimal Designs for RSM
  • Composite, D-optimal, IV-optimal, Box Behnken
• Adaptive DoE with Uniform, Maximum Variance and Integrated Mean Squared Errors Gain - Maximum Variance criteria.

Design of Experiments allows controlling the process of surrogate modeling via adaptive sampling plan, which benefits the quality of approximation. As a result, it ensures time and resource saving on experiments and smarter decision-making based on the detailed knowledge of the design space.

Sensitivity and Dependence analysis are used to filter non-informative design parameters in the study, ranking the informative ones with respect to their influence on the given response function and selecting parameters that provide the best approximation. It is applied to better understand the variables affecting the design process.

Uncertainty Quantification capabilities in pSeven [6] are based on OpenTURNS library. They are used to improve the quality of the designed products, manage potential risks at the design, manufacturing and operating stages and to guarantee product reliability.

Dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.

Predictive modeling capabilities in pSeven are based on building, exploring and managing approximation models. pSeven incorporates several proprietary approximation techniques, including methods for ordered and structured data, allowing to understand behavior of user's system with minimal costs, replace expensive computations by approximation models (metamodels, RSM, surrogate models etc.) and make smarter decisions based on detailed knowledge of the design space.[7]

Optimization algorithms implemented in pSeven allow solving single and multi-objective constrained optimization problems as well as robust and reliability based design optimization problems. Users can solve both engineering optimization problems with cheap to evaluate semi-analytical models and the problems with expensive (in terms of CPU time) objective functions and constraints.[8][9] SmartSelection technique automatically and adaptively selects the most suitable optimization algorithm for a given optimization problem from a pool of optimization methods and algorithms in pSeven.

Process integration capabilities are used to capture the design process by automating single simulation, trade-off studies and design space exploration. For that, pSeven provides tools to build and automatically run the workflow, to configure and share workflows with other design team members, to distribute computation over different computing resources, including HPC. Main process integration tools of pSeven:

• Graphical user interface and command-line interface for advanced users
• Comprehensive library of workflow building blocks
• CAD/CAE integration adapters (SolidWorks, CATIA, NX, PTC Creo, KOMPAS-3D, ANSYS Workbench), CAE solvers and other engineering tools (ANSYS Mechanical, ANSYS CFD, FloEFD, CST Microwave Studio, ADAMS, Simulink, MATLAB, Scilab, Abaqus, Unified FEA, Nastran, LS-DYNA, Simcenter STAR-CCM+, OpenFOAM, etc.)
• High Performance Computing (HPC) capabilities (supported batch systems: SLURM, TORQUE, LSF)
• Functional Mock-up Interface (FMI) for model exchange and co-simulation