Innovation corner

“Time is money”. In the world of simulation this means being able to perform reliable simulations, faster. Throughout its development, PreonLab has already introduced game-changing approaches, like its unique implicit IISPH formulation (learn more about it here) and advanced adaptive refinement and coarsening with Continuous Particle Size (read about it here) for efficient simulation on CPU hardware. Over the past decade, the development of more advanced GPUs has gathered up great speed and it is likely that this trend will continue in the future. Behind the scenes, we too have been experimenting with GPUs for some time now, to fully leverage their potential for PreonLab. Now, PreonLab’s code has been ported to run on GPUs making use of Nvidia’s proprietary CUDA API to push the speed-barrier for particle-based simulation even further.

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.

Every Preoneer knows that with PreonLab we always strive to provide the most reliable, usable and efficient CFD software. But what if you could get your results even faster? PreonLab 6.0 will continue to push the boundaries of what is possible in the world of particle-based CFD. With the introduction of PreonLab being able to simulate on GPU hardware, there will be a performance boost that will provide you with results faster than ever before, following the principle: Make it Real… fast! And there is even more. Don‘t miss the latest updates and join us for this event, as we are already very excited to show you all that is new with PreonLab 6.0!

Water wading simulations can complement physical tests in an efficient and reliable way to detect the failure modes which can occur due to the car’s motion through a significant amount of water, early in the design stage. As part of a rapid virtual prototyping strategy, multiple virtual prototypes are analyzed to reach the optimal designs for physical prototype testing. It is imperative to have a simulation software which offers quick setup times and short simulation times to meet this demand. PreonLab offers lean workflows along with its mesh-free simulation approach to keep simulation setup times extremely low, compared to conventional mesh-based simulation approaches. It also offers advanced adaptive refinement features to give fast and reliable results as well as powerful post-processing tools to analyze these results. This article aims to show you just how low simulation setup times can be – from importing your geometries, describing vehicle motion, setting up the fluid domain all the way up to hitting the simulate button.

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.

We are excited to announce the release of PreonLab 5.3. It improves upon features from previous releases and adds some useful new features. As always, our focus is on improving reliability, performance, and usability. Here are some of the highlights: Pathlines: This release adds more options to accurately select and track fluid in regions of interest. This includes tracking particles passing by geometries in a specified time interval. Airflow import: PreonLab 5.3 can better handle volumetric data saved to the Ensight Gold format and seamlessly integrates the airflow data into the simulation domain. Thermodynamics: PreonLab 5.3 introduces a new heat capacity modifier, which enables thermal simulations with solids to reach equilibrium faster. This is handy for thermal applications where the results from the steady state are the focus of the analysis. Usability & Workflow: Users can now track their action history for a single session and jump between two states with just a click of a button in the action log, which is a dedicated tab in the PreonLab GUI. This release also improves the plot dialog performance making it possible to consider an even larger amount of data for analysis than ever before. This is just a selection of new features and improvements. Check out the changelog to learn about all the changes.  Make sure to follow us on LinkedIn so that you don’t miss new videos, case studies and updates!

When it comes to complex and costly automotive manufacturing processes, using reliable simulation tools to supplement expensive prototyping and physical testing has proven to be a very efficient method of optimizing the design stage. Therefore, the automotive industry is constantly looking for appropriate solutions for it. In the case of e-coating, this includes optimizing the various design parameters like tank dimensions, vehicle trajectory, line speed, and geometrical details of the vehicle body also known as Body in White (BIW). CFD tools, such as PreonLab, that provide not only high accuracy but also lean workflows and short computational times, are highly beneficial for this purpose. The ultimate goal is to gain reliable insights into how design parameters affect the process and to optimize these parameters. PreonLab provides powerful in-built post-processing tools for this purpose, including a wide range of options such as wetting, force, volume, and pathlines sensors. In this article, we will look at the e-coating process and show the capabilities of PreonLab in simulating and optimizing the fluid-dynamic aspects of this process.

In this article, we look at an application from the maritime industry for a breaking dam flow and the wave impact on an obstacle placed in the flow. Initially, a series of uniform resolution simulations were performed with PreonLab 5.1 to determine the particle size necessary for accurate simulation results. Subsequently, simulations have also been performed with PreonLab 5.2 to make use of the new Continuous Particle Size (CPS) feature and analyze the benefits this feature provides towards reducing computational effort without compromising on the accuracy of the results. The simulation results obtained in PreonLab are compared qualitatively and quantitatively with experiment results published in the paper by Kleefsman et.al [1]. The experiments were performed at the Maritime Research Institute Netherlands (MARIN). All the experimental data generated is also available to download from the ERCOFTAC database [2].

The latest PreonLab release for version 5.2 introduced some exciting new features such as Continuous Particle Size (CPS), sensor planes for the solid solver, and heat field. In this article, we will look at the benefits of these features for thermodynamic applications with the example of electric motor cooling. There is indeed a large range of applications across different industries which make use of electric motors for power generation. The focus of this article will be on electric motors in the context of electric vehicles.

We at FIFTY2 configure and maintain a few dozen physical and virtualized servers for our developers and application engineers, so they have a solid, basic infrastructure to develop, test and simulate on. Getting everyone to remember one single password for everything is easy, but certainly not a best practice in operational security. Also, writing down every password in a shared spreadsheet still feels kind of wrong, but leads into the right direction. There are plenty of available password managers to choose from, be it online as a service, offline, shared with other people, or just integrated into the browser you are using right now to read this text. Chosing one of them is no big deal, but what if it comes to automatically accessing those machines that we set up, with passwords that are stored somewhere in a password manager? And how can we gain access to a server when physically standing in front of it, in case a disaster hits the fan? How can different people stay on top of all passwords configured, without reusing a password, ever? And, once we overcome those challenges, what other handy things can we do with such a system? None of those challenges are new or extremely hard to solve problems, but getting them set up initially and making them work smoothly can have some bumps down the road. In this article, we show how we are using password-store to manage various credentials for multiple systems, how we set it up to couple it with the Ansible automation platform. In case you are interested in trying this out yourself, there should be enough code snippets to get you up and running in no time.