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A demihull of a houseboat has been optimized by studying the influence of bow shape modifications on the vessel’s total resistance. A parametric model was developed and subjected to a RANSE-based multi-objective optimization.

PediaFlow is an implantable heart pump, intended to provide hemodynamics support for infants and young children with heart failure. Cornell University researcher Mansur Zhussupbekov used CAESES to improve the pressure recovery while maintaining biocompatibility.

The research and development project VIT-VI focuses on artificial intelligence (AI) methods and their use in the context of virtual, sustainable aero engine development. The focus is on building and strengthening AI competencies and increasing the use of artificial intelligence methods to enhance productivity in data- and simulation-driven design.

Achieving optimal efficiency and performance is crucial for the design of propellers. A combination of AI and CFD helped win a recent propeller design competition hosted by Daniel Riley, a maker and popular YouTube creator (RCTestflight). Using CAESES and AirShaper we generated two high-performance propellers that demonstrated remarkable efficiency.

The America’s Cup is not only a fierce sporting, but also an engineering competition. Years of development culminate in two months of intense match racing. Designing the hull of an AC75 presents a complex engineering challenge, with numerous factors to consider.

Traditional propeller designs are no longer sufficient to meet energy efficiency requirements, prompting the need for unconventional designs, such as tip-rake propellers. An optimization framework for tip-rake propellers was developed to efficiently generate suitable designs from given input parameters.

Centrifugal pumps are commonly used in industrial and residential applications due to their relatively simple design, manufacturing, and ease-of-maintenance. A centrifugal water pump was designed making full use the capabilities of CAESES in the scope of an internship project.

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.

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.

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.