HyperStudy®Multi-disciplinary Design Exploration & Optimization HyperStudy is a multi-disciplinary design exploration, study, and optimization software for engineers and designers. Using design-of-experiments, metamodeling, and optimization methods, HyperStudy creates intelligent design variants, manages runs, and collects data. Users are guided to understand data trends, perform trade-off studies, and optimize design performance and reliability. HyperStudy’s intuitive user interface combined with its seamless integration to HyperWorks makes design exploration technology accessible to non-experts. Product Highlights • State-of-the-art design exploration, metamodeling, and optimization methods • Data mining tools that are easy to understand and interpret • Direct interface to the most popular CAE solvers • Fully integrated with HyperWorks, seamless shape optimization via HyperMorph® Learn more: altairhyperworks.com/hyperstudy Benefits HyperStudy provides engineers and designers a user-friendly environment with state-of-the-art design exploration methods and data mining tools to: • Efficiently understand the relationships between design parameters and design requirements • Easily sort, analyze, and explore large design data sets • Perform quick trade-offs between conflicting designs requirements • Quickly calibrate simulation models to correlate with test data • Increase product life and robustness • Reduce design development cycles • Increase the return on their CAE solver investments Capabilities Design of Experiments (DOE) DOE helps engineers to clearly understand the relationships between design variables and overall system performance. DOE methods in HyperStudy include: • Box-Behnken • Central composite design • D-Optimal • Direct input of external run-matrix • Fractional factorial • Full factorial • Hammersley • Modified Extensible Lattice Sequences (MELS) • Latin HyperCube • Plackett-Burman • Taguchi • User defined Fit Approach Fit approach is used to create meta-models to replace computationally intensive simulations. They are also used to smooth noisy functions to enable optimization algorithms

Fit models can be used DOE, optimization, and stochastic studies. HyperStudy’s fit module allows use of different methods for different responses. Available fit methods are: • Least squares regression • Moving least squares • Radial basis function • HyperKriging Optimization HyperStudy offers multidisciplinary optimization as well as reliability and robustness optimization. Through multidisciplinary design optimization, engineers can improve the overall design performance. If variations in design and operating environments are critical to design quality, reliability and robustness optimization...