To specifically construct such CFD-friendly ‘engineering models’, a new breed of CAD called upfront CAD systems has come into existence. What is ideally needed for a CFD-based optimisation cycle, is a CAD system that defines the fluid-wetted geometries with a few significant parameters, deliberately dropping off characteristics of lesser significance. Loaded with all the intricate details of the product, these parametric models are good for production or manufacturing purposes, but not for CFD simulation. One subtle thing which needs to be appreciated about parametric modeling for CFD is that not all parametric models build by traditional CAD systems are ‘CFD ready’. More importantly, this approach is most amenable for conducting multi-objective and multi-disciplinary optimisations without any conceptual barriers. The reason being, in this approach, designers can intuitively understand and appreciate each parameter and also it is easy to co-relate how a change in a parameter’s value impacts the design objectives. Various shape optimisation techniques have been in usage in the design industry for many decades and by far, parameter-based optimisation is the most popular among all. Here, every feature, every contour is defined by a set of parameters and at any point in time their values can be modified to bring in a corresponding change in shape. The parameter-based approach is based on the philosophy that, any product/geometry in all its complexity and details can easily be described by a bunch of parameters. When we talk about shape-optimisation, there are various methods and approaches, and we can broadly classify them as parameter-based and parameter-free techniques. In addition, they tremendously expand the knowledge base for making high-quality decisions. The repercussion of all these positives is that it not only helps to create products of superior performance but also expand the range of design variants to choose from. In the early phase, they aid to explore newer ideas and possibilities and in advanced stages of product design, their flexible and rapid responsive abilities help to make last-minute design changes. Shape optimisation can play a crucial role, both in the early and advanced phases of a design cycle. Lastly, optimisation saves cost by avoiding expensive last-minute modifications.
Further, they aid in cutting down the time involved to transfer a product from ‘design table to users table’.
They help in getting a better understanding of the design space and also provide guidance to the design team in creating products with superior performance at lower risks. In a product-design environment, they are pretty handy. Shape optimisation deals with finding the right shape or contour for an engineering component/product within certain constraints, by modifying a set of predetermined boundaries. Figure 2: Parametric variation of turbine blades. In this article, we restrict ourselves to presenting an overview of one small segment in optimisation, namely, shape-optimisation. The field is vast and will rightfully need a text-book to cover the length and breadth of the field. From economics and finance to molecular modeling, to bio-medical, geophysics. The application of optimisation covers the length and breadth of science and engineering. In technical jargon, it means minimizing or maximizing one or multiple objectives within known constraints. The magnitude, tools used, timescales, human effort/planning involved, level of complexity, etc, may differ, but nonetheless, at the core, it is just the same – optimising to find a good, possibly the best solution under the given circumstances. Odd as it may sound, this is no different from designing an aircraft, or designing a fuel-efficient car, or designing a house with proper sunlight and ventilation.
ANSYS 15 FLUENT MORPHER TUTORIAL ON SHAPE OPTIMIZATION SOFTWARE
We may not use fancy gadgets or software to do so, but still, we go about doing it in our mind or on a piece of paper. Whether it is deciding on which television set to buy, or planning for a trip, or hosting a small birthday party, we use optimisation. Knowingly or unknowingly we use it in our day-to-day decision-making process. Figure 1: Parametric variation of impeller blades for CFD-based optimisation.