Innovation corner

With every PreonLab update, we aim to continuously enhance your simulation experience by increasing efficiency and reducing the memory footprint. While simulation on CPU is still the bread and butter for CFD, it is undeniable that GPUs can provide a significant performance boost towards reducing computation time. Nevertheless, one advantage CPUs generally have over GPUs is a larger memory space. Due to the limited memory of single GPU cards, simulating very large scenes with a lot of particles can be quite challenging. In addition, importing large tensor fields like airflows can also occupy a lot of precious memory space on the graphic card. While PreonLab can cleverly resample such airflows to fit on single GPU hardware, there will always come a point when sacrificing accuracy will be inevitable. One logical solution is making use of state-of-the-art GPU hardware that can accommodate large simulation scenes. Professional GPU cards like Nvidia’s H100 GPUs can already offer memory space up to 80 GB. However, does this mean that GPU simulations are only possible through the acquisition of larger and larger professional GPU cards? And what about scenes which might require even more memory space than the latest hardware available?

Slamming is a term that is often used in a maritime context to describe the sudden impact of the ship hull on a water surface. It leads to pressure spikes on the hull along with rapid and intricate deformations of the fluid surface. Physical testing, especially for large-scale applications in the maritime world, is not only time-consuming and expensive but often even unfeasible. Computational fluid dynamics can complement real-world experiments and hence reduce costs as well as accelerate development. However, simulating the water entry of solid bodies is no easy task. Using traditional grid-based methods requires periodical remeshing due to the moving geometry and discretizing the entire simulation domain. Particle-based simulation methods on the other hand can generate insights without these inconveniences, saving valuable computation time. This article aims to show how PreonLab can be used to simulate the water entry of free-falling rigid bodies.

This test case aims to reproduce the results from the experiment done by Bennion and Gilberto [1] with PreonLab. They devised an experiment that measures the heat transfer of an impinging oil jet under different conditions. Some of those conditions are relevant for Electric Motor (E-Motor) cooling applications. The study is divided into two parts. The goal of the first part is to validate the simulation against both experimental data and existing empirical models on a flat target. For the second part, the flat target is replaced by a textured surface replicating the surface of a copper end-winding inside an E-Motor.

Thermal validation benchmarks are great tools to build confidence in thermal solvers. The rod conduction benchmark is useful to check that conduction phenomena are accurately simulated. In this article, the results from various configurations of the rod conduction benchmark will be presented alongside a thorough comparison with the popular CFD code OpenFOAM.

The Poiseuille flow serves as an important benchmark for the validation of CFD methods as well as for studying fundamental aspects of pressure-induced incompressible internal flows. In this article, capabilities of PreonLab in capturing hydrodynamically fully developed 2-D laminar flows between parallel plates (known as planar Poiseuille flow) are evaluated for different Reynolds numbers.

The lid-driven cavity problem is of high importance in fluid dynamics serving as a benchmark for the validation of CFD methods as well as for studying fundamental aspects of incompressible flows in confined volumes driven by the tangential motion of one or more bounding walls. In this article, capabilities of PreonLab in capturing 2-D flows inside a cavity are evaluated for different Reynolds numbers.

For coarsely-resolved simulations, especially those for applications that require numerous quick design iterations to asses the plausibility of a design, a simulation that both runs quickly and delivers results in the correct ballpark is needed. The coarse resolution allows for fast computation, but such simulations tend to suffer from gradient underprediction at the walls. This happens in particular for flow regimes which may be considered as turbulent, where the steep gradients at the wall mean that a very fine resolution is required to correctly reproduce them. With the effect of wall-bounded phenomena (e.g. wall shear stress, wall heat flux) being a key result for studies that such simulations are used for, it is clear that there is potential for improvement here.

The automotive world is changing. In order to keep up with the challenges faced in the context of digitalization, Audi established the simulation of water in the development process. In the beginning of this century the Audi portfolio had five segments with different models. From year to year, new model lines were added to meet growing customer needs. Currently, the automotive industry is faced with new challenges in fulfilling the criteria for sustainability and climate neutrality, resulting in even more models with various new types of powertrains, such as hybrid or the e-tron.

PreonLab is successfully employed by Renault to validate and improve heat shield designs which guarantee that no flammable liquid gets in contact with hot parts of the engine. Testing on a real prototype is difficult since first the leakage itself has to be reproduced and secondly, hot liquids may be dangerous.

Thanks to the electrification of the powertrain, innovation in the automotive industry is happening faster than ever. Short time to market for electric vehicles (EVs) and hybrid vehicles (HEVs) is one key to be successful for OEMs and tier 1 suppliers.