Altair HyperStudy

Altair HyperStudy is a design exploration tool for engineers and designers. It automatically creates smart design variants, manages races, and collects data. Users are oriented to understand data trends, conduct trade-off studies, and optimize project performance and reliability.

Category: CAD, GIS E 3D
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What is Altair HyperStudy?

Altair HyperStudy is a design exploration tool for engineers and designers. It automatically creates smart design variants, manages races, and collects data. Users are oriented to understand data trends, conduct trade-off studies, and optimize project performance and reliability.

HyperStudy enables users to explore, understand, and improve their projects using methods such as design experiments, response surface modeling, and optimization. The results of these studies can be easily analyzed and interpreted using HyperStudy's advanced post-processing and data mining capabilities. HyperStudy's intuitive user interface combined with its seamless integration with HyperWorks for direct model parameterization and CAE result readers simplify study configuration.

Benefits

Improve design performance and quality

HyperStudy includes innovative, cutting-edge optimization, design experiments, and stochastic methods for rapid evaluation and improvement of design performance and quality.

Conduct trade-off studies

HyperStudy's ability to adjust allows users to create response surface models. These efficient substitutes can then be used to conduct trade-off studies. They can also be exported as spreadsheets for use by field engineers.

Reduce development time and costs

HyperStudy helps engineers reduce trial-and-error iterations and therefore helps reduce project development and testing time.

Increased productivity through the easy-to-use environment

The HyperStudy step-by-step process guides the user in setting up and conducting design studies. Its open architecture allows easy integration with third-party solvers.

Powerful analytics of datasets

A comprehensive set of post-processing and data mining methods simplifies and helps an engineer's work analyze and understand large sets of simulation data.

Improve simulation correlation

HyperStudy optimization features can be applied to improve the correlation of analysis models with test results or with other models.

Resources

Experience Project

Experience Design (DOE) methods in HyperStudy include:

  • Full factorial
  • Plackett Burman
  • Central composite design
  • Modified Extendable Mesh Sequence (MELS)
  • Hammersley
  • D-Optimal
  • Fractional factorial
  • Behnken Box
  • Latin hypercube
  • User-defined and direct input from the external execution matrix
  • Taguchi

The study matrix may consist of continuous or discrete variables that can be controlled or uncontrolled. DOE studies can be performed using exact simulation or the adjustment model.

Response surface method (adjustment)

The available response surface methods are:

  • Least squares regression
  • Hyperkriging
  • Moving least squares
  • Radial base functions.

Response surfaces can be used to perform trade-off, DOE, optimization, and stochastic studies.

Optimization

HyperStudy's comprehensive optimization methods solve different types of design problems, including multi-objective design optimization and reliability/robustness. These methods are:

  • Adaptive Response Surface Method (ARSM)
  • Sequential quadratic programming
  • Genetic algorithm
  • System Reliability Optimization (SRO)
  • Sequential optimization and reliability analysis (SORA)
  • Single loop approach
  • Viable Directions Method (MFD)
  • Global Response Surface Method (GRSM)
  • Multi-purpose genetic algorithm
  • ARSM-based SORA
  • User-defined optimizer

Optimization studies can be performed using exact simulation or adjustment model. In addition, HyperStudy provides an API to incorporate external optimization algorithms.

Stochastic

The stochastic approach in HyperStudy enables engineers to assess the reliability and robustness of projects and provide qualitative guidance to improve and optimize based on these assessments. HyperStudy sampling methods are:

  • Simple random
  • Hammersley
  • Latin hypercube
  • Modified Extendable Mesh Sequence (MELS)

Stochastic studies can be performed using exact simulation or the adjustment model.

Post-processing and data mining

HyperStudy helps engineers gain a deeper understanding of a project through extensive post-processing and data mining capabilities. This significantly simplifies the task of studying, classifying, and analyzing results. The results of the study can be post-processed as statistical data, correlation matrices, scatter plots, box plots, interaction effects plots, histograms and parallel coordinates, among others. In addition, HyperStudy guides the user in selecting the post-processing methods to use, based on project objectives.

System requirements:

CPU: 1.8 GHz dual core processor

RAM: 2 GB

HDD / SSD: 50 GB of free space on the main drive

Operating system: Microsoft Windows 8 32-bit, Microsoft Windows 7 32-bit and Microsoft Windows Vista 32-bit

Resolution: 1024×768

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