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ANSYS DesignXplorer was developed to leverage the power of ANSYS Workbench for parametric analyses. ANSYS Workbench makes it easy to create and manage parameters across a wide range of ANSYS products, has a persistent setup and makes automatic updates. ANSYS DesignXplorer takes advantage of these ANSYS Workbench strengths and enables you to explore, understand and optimize your design so you can apply simulation to drive product development.
You can add ANSYS DesignXplorer’s systems (goal-driven optimisation, parameters correlation, response surface and six sigma analysis) to the ANSYS Workbench schematic with drag-and-drop simplicity. ANSYS DesignXplorer systems can then use any of a variety design of experiment (DOE) algorithms to efficiently and scientifically factor the design space and apply state-of-the-art response surface technology to interpolate the results. Our parametric technology calculates correlation and sensitivity along with a host of other insightful metrics so you can more completely understand your design space. Optimisation algorithms help to determine the best combination of parameters, and six sigma analysis ensures that your design is robust.
ANSYS DesignXplorer Features Include:
Design of Experiments
ANSYS DesignXplorer features a variety of DOE types, from basic Latin hypercube sampling (LHS) to central composite design (CCD) factoring to optimal space filling (OSF) even to adaptive sparse grid or kriging methods. These scientific methods subdivide your design space to efficiently develop a series of simulation experiments for exploring designs. The DOE table of design points can be solved in batch mode on your local machine or remotely distributed for a simultaneous solve.
Our powerful response surface methods include full second-order polynomial, kriging, non-parametric regression and neural network approaches. These serve to interpolate between the data points in multidimensional space. They can be visualised as a 2-D or 3-D description of the relationships between design variables and design performance.
DesignXplorer can use the response surfaces as a reduced-order model. For example, while looking at optimisation trade-offs, the algorithm can search the response surface to rapidly solve many thousands of samples. You can also probe the response surface or add design points at will.
Our adaptive response surface methods, such as Kriging with Auto Refinement or Sparse Grid, will actually refine until sufficient accuracy is achieved. These methods feature a convergence plot.
Once you have explored the design and understand correlations and sensitivities, you may want to optimise the design. DesignXplorer includes several algorithms that help identify the most suitable candidates — taking into account multiple objectives and performance trade-offs.
Six Sigma (Probabilistic) Analysis
Simulation often begins with specified deterministic values for dimensions, loads, boundary conditions and material properties. However, in the real world, these values often vary due to manufacturing tolerances or the range of operating conditions. A Six Sigma analysis runs a series of small variations on these inputs and calculates the expected output variation. This can help you to determine whether or not your design meets robustness requirements.
|Applying Six Sigma to Drive Down Product Defects, ANSYS DesignXplorer software generated a response surface showing sensitivity ofe ach input variable to contact force. Courtesy Advanced Engineering Solutions||Dark blue squares represent data points that meet all design requirements and have minimal temperature.|
|Visualising the impact of fin thickness and base thickness on the maximum temperature of a cell stack. Courtesy of Advanced Engineering Solutions||Response surfaces, enable users to clearly visualize the complex relationship between multiple input and output parameters in parametric analysis studies.|
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