Optimizing components that must fit into tight spaces can be a daunting task, even for the most experienced designer. Consider the HVAC system of a car, which supplies air to the vehicle’s cabin. Today, air conditioning is deemed standard equipment even in entry-level automobiles, so manufacturers must build it in. Its critical components – manifold ductwork — are located under the hood amid the well-planned jumble of engine, radiator, battery, transmission, and auxiliary structures. Not much room in there … and that’s just one of the complications. Continue reading
I am always impressed by the capabilities of computational fluid dynamics and how companies and their engineers strive to optimize their design to create high-reliability, high-performances products. This is why I was extremely excited when I heard about a new optimization technique known as the adjoint method.
Of course, several different optimization techniques are already commonly used. The easiest technique is to optimize a product by analyzing a large number of configurations and selecting the one that delivers the best performance. This can be done using experiments or simulations. I will only discuss the simulation concept in the rest of this post; after all, we already know that simulation tools delivers great ROI and provide an excellent way to optimize design. The problem is that employing thousands of simulations to find the best design is much too time consuming. But, performing simulation for only a fraction of those designs and using practices like gradient methods, evolutionary algorithms and reduced-order models will identify the best design using a reduced set of simulation results.