In our 2nd part of a series on the Adjoint solver, learn how recent developments have focused solely on improving the usefulness of the Adjoint solver for real-world customer problems - allowing for definition of multiple objectives; expanding to include more physics; and allowing more user control over geometry modifications and constraints.
When dealing with a significant number of variables in our simulations, design engineers often find it challenging to work out which variables are the most important, and how to best tune these variables to improve performance. Learn how ANSYS optiSLang now offers a compelling proposition for answering these questions while giving engineers even more tools for exploring the possible performance envelope within key design parameters.
Engineers are continually under pressure to improve the performance of their products and often look to gain an edge using optimisation techniques - trying to reduce drag, increase lift (or downforce), or reduce pressure drop. Rather than relying on intuition to make geometry changes that are often constrained (using a parametric CAD approach), you can now use the new Adjoint solver to compute localised sensitivity data (related to your objectives) and optimize your design semi-automatically.
LEAP and ANSYS congratulates Emirates Team New Zealand for their remarkable victory in the 2017 America’s Cup. More info on some of the unheralded innovations and ingenuity behind this engineering and sporting triumph.
Caravan owners are well aware that the speed they drive at and the shape of their caravan can greatly affect fuel consumption.
In partnership with Caravan World magazine, we've taken a closer look into the performance of caravans with the aid of CFD, including some less obvious factors that can help shave off the drag on your caravan and improve fuel consumption.
Learn about Augmented Reality (AR) technology that was showcased at 2017 National Manufacturing Week / Austech: including the ability to augment pressure contours on the front and rear wings of the MMS racecar, as well as visualise the animated flow streamlines being released forward of the front wing. Students from MMS are using these AR CFD results to collaborate more effectively in a multi-disciplinary team environment where not everyone is a CFD expert and hopefully achieve their ultimate goals of maximising downforce on the front/rear wings and optimising the aerodynamic performance of their racecar.