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?

In the field of Computational Fluid Dynamics, the pursuit of simulation efficiency and accuracy is the name of the game. These two seemingly contradictory goals often appear at opposite ends of the spectrum. But is this always the case? Is it possible to have both efficiency and accuracy without making sacrifices? We believe it is. And we’re not referring to hardware efficiency, a topic extensively covered in our previous articles (see here and here). What we mean is state-of-the-art software development which is in the DNA of the FIFTY2 team. What we want to talk about in detail is one of our most prominent features available only in PreonLab. We named it Continuous Particle Size, or in short, CPS.

We are delighted to announce our new release – PreonLab 6.1. It is loaded with new features and improvements, enabling new engineering possibilities and applications. Empowered by the new GPU capabilities, PreonLab 6.1 takes another leap forward in efficient computing, delivering results even quicker. Read on to find out more about: Enhanced GPU performance: PreonLab’s GPU implementation just got a significant boost. Not only does PreonLab 6.1 support multi-GPU computing now, but it also enables continuous particle size (CPS), dynamic sampling, and adaptive sampling on GPU, making your simulations even more efficient. Airflow and Car Suspension Model (CSM): PreonLab 6.1 also enables airflow import and CSM support for simulations on GPU. These are some of the key application enablers making it possible to simulate various use cases also on GPU. Snow Model on GPU: Snow modeling has been an important part of existing PreonLab capabilities. We are thrilled to announce that our snow model is now also available on the GPU platform. Enhanced Thermodynamics: Convective boundary condition has been added to the list of available boundary conditions.  Convective Boundary Conditions can represent a more physical heat transfer, that predominantly occurs due to convective heat transport at fluid-fluid or fluid-solid interfaces. It is designed to conveniently represent natural or forced convection of heated solid bodies or fluids, in applications like heat exchangers, heat sinks, and even e-motors. 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!

“Why can’t I run PreonLab on my GPU?” We often heard this question and didn’t have a good answer. But behind the scenes, we have been experimenting with GPUs for quite some time. We are excited to announce that we are finally ready. PreonLab 6.0 is here, and it changes everything. This is how: GPU Support using Nvidia CUDA: PreonLab 6.0 delivers the greatest leap in efficiency of any update so far. For a wide range of applications, a single GPU can deliver up to 6x faster performance compared to a state-of-the-art multi-socket CPU system. If you have the chance, please try PreonLab on GPU – you will not want to go back. A New Engine for PreonLab: We believe that the future of PreonLab is multi-platform. CUDA is an amazing platform, but it is not the only one we wish to explore in the future. To this end, we have rewritten our simulation core with platform independence in mind. Expect more on this in upcoming releases. Lateral Adhesion: The new experimental “Lateral Adhesion” option models droplet sticking and runoff behavior more accurately. This is important for applications such as tailgate runoff simulation. Improved User Interface: Various changes to the graphical user interface such as the new welcome screen make using PreonLab more enjoyable than ever. This is just a selection of new features and improvements. Check out the changelog to learn about all the changes.

Ein Traum wird Wirklichkeit. – Die FIFTY2 Technology GmbH und die AVL Deutschland GmbH laden zum ersten PreonDay in die Motorworld nach Böblingen ein. An diesem Tag wird sich alles um die Simulationssoftware PreonLab und die PreonLab Community drehen. Der PreonDay richtet sich gleichermaßen an die User von PreonLab, als auch an die, die es noch werden wollen.  MAKE IT REAL.

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!

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.

Preoneers all over the globe know, that there is no such thing as an ordinary PreonLab release. But this time, we have something very special for you. Not only because the FIFTY2 engineers are numberphiles and version 5.2 is special for us for obvious reasons. But also because PreonLab 5.2 will push things to the limit, starting with Continuous Particle Size giving more degrees of freedom and a significantly improved workflow. But there is even more. Don‘t miss the news and join us for this event!

Reliability is one of the key drivers of our product development at FIFTY2. Users of PreonLab can always be sure, that new versions have been thoroughly tested before we release them. On the other hand, our quality awareness should not slow us down implementing new features. In this article we show how our development process is structured such that we don’t sacrifice one goal for the other. We continuously validate the simulation results and the application behavior. This means, that every new feature is carefully looked at. Our engineering team analyzes the result and validates physical aspects. New development should also not break existing workflows. There are a lot of different aspects of quality assurance, which in combination makes PreonLab an enjoyable user experience. At FIFTY2, we have different stages where different variants of software testing is strictly incorporated in the development process. Like this, we minimize the risk of regressions and side effects and ensure reliable simulation results. It is always our goal to ensure, that the PreonLab version deployed to our users is the best version so far.

The automotive industry is facing one of the most demanding challenges in its history: how to make automated travel safe in all conditions. With advanced and autonomous vehicles entering the market, solving problems linked to illumination and weather conditions such as rain, fog and snow is key to ensuring a safe environment for drivers, passengers and pedestrians. FIFTY2 is proud partner of the international AI-SEE Project (Artificial Intelligence enhancing vehicle vision in low visibility conditions) helping to tackle these challenges with PreonLab.