Version Fidelity Fine Design3D -Multidisciplinary Design Optimization Software

Discover a Unique Application for Multidisciplinary, Multi-Objective Design Optimization

Multidisciplinary Design Optimization
A truly robust optimization process can account for off-design conditions, mechanical reliability, noise, and even nonphysical aspects, such as manufacturing costs. Fidelity Fine Design3D provides that full range of capabilities via its flexible, powerful evaluators and solvers.

Parametrization
Smart parameterization is key to the success of optimization. Fidelity Fine Design3D includes a parameterization environment dedicated to turbomachinery that can easily handle turbomachinery blading systems, with design models based on blade sections, camber, thickness, and stacking. It includes capabilities such as self-organizing maps to ensure the explored design space is relevant to the design goal for all applications.

Data Mining and Analysis
Data-mining algorithms are used to get a deeper understanding of the design space and to provide more insights during the optimization process, thus accelerating the optimization process. Based on the surrogate model, analysis of variance provides information on which design variables have the most influence over the outcome.

Uncertainty Quantification
Unlike their digital representations, real-world designs and operating conditions are subject to uncertainty and variations. From the intrinsic variability of manufacturing processes to the natural variation in operating conditions, smart optimization needs to be aware of uncertainty.

Fidelity Fine Design 3D can quantify uncertainties and estimate their impact on system performance. The user can identify variables with the most significant sensitivity, calculate the impact of less stringent manufacturing tolerances, and ensure the optimized design is robust enough for the real world.

DoE
The way the design space is explored has an enormous impact on the convergence of the optimization and on the search for the optimum value. Fidelity Fine Design3D relies on advanced space-filling techniques that reduce the number of samples needed for a design of experiment (DoE). Auto-adaptive DoE techniques are available for the efficient handling of high-dimensional design spaces, as well as highly constrained optimization problems. In addition, a task manager enables populating a cluster with CFD runs on a cluster to generate multiple samples in parallel.

Surrogate Modeling
Surrogate modeling is a form of supervised machine learning that accelerates the exploration of the design space by approximating the solution between the DoE points of the sampled design space. Several algorithms are available, including both artificial neural networks and Kriging. The surrogate model is used to apply an iterative optimization algorithm and find the global optimum.

This approach produces benefits at different stages of the development process, from the early concept definition to the final design phase, and shortens the total development cycle of complex products by reducing computational costs related to high-fidelity simulations.