News

Call for Papers in the Field of Simulation-driven Design of Maritime Systems

Call for Papers in the Field of Simulation-driven Design of Maritime Systems

Submit your article in the field of “Simulation-driven Design of Maritime Systems”.

On the special occasion of the upcoming 90th birthday of Prof. Dr.-Ing. Dr. h.c. Horst Nowacki, former head of the section Ship Design at the Technical University Berlin, Germany, and former professor at the University of Michigan, USA, a special issue of Ship Technology Research shall be published in the first half of 2023.

Tackling the EEXI and CII Challenge with Hydrodynamic Optimization

Tackling the EEXI and CII Challenge with Hydrodynamic Optimization

On January 1, 2023, the Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII) will come into force, creating new challenges for operators of commercial vessels. Ship owners and managers must prepare for these requirements by assessing and improving their vessels as needed. This is crucial in order to earn the proper certificates, allowing them to continue sailing and trading internationally.

Successful CAESES User Conference 2022

Successful CAESES User Conference 2022

For a long time, the COVID-19 pandemic had restricted global travel and events, forcing us to postpone our CAESES User Conference, originally scheduled for autumn 2021. Finally, in 2022, national and international restrictions were eased, and it was exciting to once again be able to host a face-to-face conference to meet our users, educate further and effectively network – something that had long been missed.

CFD Analysis and Optimization of a Novel Left Ventricular Assist Device (LVAD)

CFD Analysis and Optimization of a Novel Left Ventricular Assist Device (LVAD)

The present case study looks at a Left Ventricular Assist Device (LVAD) based on an axial flow pump. CAESES was used as the central hub in the automation workflow. In addition to CAD generation, it was also used as the process integration and design optimization (PIDO) platform. CAESES was coupled to the Ansys Workbench in which the 3D meshing and CFD simulations took place, utilizing the CFX solver.

Geometry to Parameter Mapping Based on Neural Networks

Geometry to Parameter Mapping Based on Neural Networks

In the process of geometry-based optimization, the problem of having to model a resulting geometry without knowing the exact values of characteristic parameters can arise. In order to open and further edit the optimized geometry inside the CAESES environment, the parameter set belonging to the new shape needs to be determined. The use of neural networks offers a great opportunity to solve this kind of problem since they are a powerful tool for constructing a predictive model based on data.