Innovation corner

Wie gewährleisten Sie, dass es in Ihrem Fahrzeug zu keinen Schädigungen durch Wasserkorrosion oder Eindringen von Wasser oder Schnee in ungewünschte Bereiche kommt? Durch aufwendige und späte physische Tests oder simulieren Sie schon? Lernen Sie den Marktführer im Bereich SPH-Simulation PreonLab kennen! PreonLab bietet eine maßgeschneiderte Lösung, um bereits während der Konstruktionsphase Ihre Bauteile zu bewerten und zu optimieren. Dabei können auf einfachste Weise komplexe Bauteile und Bewegungen simuliert und bewertet werden. Im Gegensatz zu klassischen, auf finiten Volumen basierten CFD-Methoden, ist die Diskretisierung des Berechnungsvolumens nicht erforderlich und auch die Aufbereitung von CAD-Daten ist nicht notwendig. Mit minimalem Aufwand und sehr kurzen Rechenzeiten von wenigen Tagen bis hin zu Stunden können Sie die Wasserpfade im Fahrzeug simulieren. PreonLab sichert somit Ihre Entwicklung ab und reduziert die Zeit bis zur Markteinführung erheblich. Michael Reichl, Senior Berechnungsingenieur und langjähriger PreonLab User, zeigt Ihnen in unserem kostenfreien Live-Webinar, reale Beispielanwendungen aus dem Bereich Wassermanagement und Schneeablagerung. Sie erhalten als Teilnehmer die Möglichkeit, mit einer Testlizenz die Usability der GUI und Performance des Solvers selbst zu testen und zu erleben. Melden Sie sich jetzt an und seien Sie mit dabei, wenn es heißt: „Virtual fluids. Real insights.“

Die Absicherung des Fahrzeugs und kritischer Komponenten gegenüber einem Wassereintritt gehört zu entscheidenden Versuchen in der Automobilentwicklung, ohne die keine Fahrzeugfreigabe erfolgt. Aus diesem Grund gehören die Wasserdurchfahrt und Fahrzeugberegnung zu den Standardtests in der Fahrzeugentwicklung. Diese dienen zum einen der Absicherung, dass kein Wasser in kritische Fahrzeugbereiche eindringt, wie z. B. in die Batterie, in elektrische Komponenten oder in die Lufteinlässe. Zum anderen dürfen die durch das Wasser verursachten Kräfte die Karosserie nicht beschädigen oder das Fahrzeug zum Aufschwimmen bringen. Immer mehr OEMs gehen daher den Weg, kostenintensive Prototypentests durch den virtuellen Versuch zu ersetzen, um frühzeitig Schwachstellen am Fahrzeug zu erkennen und zu optimieren. PreonLab unterstützt Sie in dieser frühen Entwicklungsphase, die richtigen Design-Entscheidungen zu treffen. Wasserdurchfahrten oder Fahrzeugbegegnungen lassen sich mit minimalem Aufwand und in kurzen Rechenzeiten von nur wenigen Tagen bis hin zu Stunden simulieren. PreonLab sichert somit Ihre Entwicklung ab und reduziert die Zeit zur Markteinführung erheblich.

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.

When it comes to complex production processes in the automotive industry like e-coating, using reliable CFD simulation tools to supplement expensive prototyping and physical testing has proven to be a very efficient method to optimize the design stage. This includes optimizing the various design parameters like tank dimensions, vehicle trajectory, line speed and geometrical details of the Body in White (BIW). For this purpose, CFD tools providing not only high accuracy, but also lean workflows and short computational times are required.

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.

With our recent release of PreonLab 5.2, which was a very special release for us, we introduced our new feature – Continuous Particle Size (CPS), where particles can have any size in a user defined range. In this article, we shall take a look at the development of adaptive refinement and coarsening in PreonLab and the benefits of CPS along with the challenges and limitations that go with it.