NAFEMS World Congress 2021 “Online”

We look back gladly to a very interesting and successful NAFEMS World Congress 2021, which took place October 25-29 as an online congress. We would like to thank all those participants who attended the presentation by our CEO Dr.-Ing. Volker Gravemeier entitled “Advanced Simulation and Uncertainty Quantification of Multiphysics Problems” as well as the subsequent discussion, which covered various questions showing great interest in our methods.

“Benchmark” Issue: “The Need for Speed” and – inevitable in these times – “COVID-19”

“Speed” was the originally intended theme for issue No. 2 of the magazine “Benchmark”, the international magazine for engineering designers and analysts from NAFEMS, in 2020. NAFEMS is the international association for the engineering modelling, analysis and simulation community, a not-for-profit organization established in 1983. Dr.-Ing. Volker Gravemeier, chief executive officer of AdCo Engineering^GW, is an active member of the NAFEMS Multiphysics Working Group (NAFEMS MWG). However, as David Quinn points out in his editorial, it “turns out that life can change quicker than anything else”. In this blog post, we will have a closer look at two articles of this issue.

Peter Langsten and Marc Halpern identify digital strategies for “Overcoming COVID-19 Obstacles” in their contribution. They conclude that “the COVID-19 pandemic will accelerate many important long-delayed trends such as digitalization, integrated flow of product information, and collaborative applications. The post COVID-19 engineering environment will pose new demands… The new reality elevates the importance of simulation and its environment is critical to step up speed, capacity, availability, performance and reliability of design, engineering, manufacturing processes and systems.” The authors focus on their top 10 initiatives to address engineering and design COVID-19 needs, out of which “governance and supporting technologies” is identified to be the most challenging one in terms of time and cost to implement. Among others, the “increased volatility, economic pressures, changing suppliers, and more demanding customers increase the risk of missed purposefulness in design and simulation activities.” One of the keys for governing “increased volatility in design and simulation activities” is uncertainty quantification, and this is also a core topic of the article to be considered next.

In the contribution entitled “The need for speed to reconcile rationalism and empiricism in a data rich world”, Chris Smith states that “there are an increasing number of situations where vast quantities of data collected from asset-based sensors are not utilized effectively due to the lack of an interpretative framework”, and that “this drives the need for physical frameworks on which to base predictive capabilities.” The introductory part of the article particularly refers to a presentation given by Prof. Karen Willcox, who is with the Oden Institute for Computational Engineering and Sciences at the University of Texas in Austin, on the occasion of SC19: The International Conference for High Performance Computing, Networking, Storage, and Analysis at the Colorado Convention Center in Denver, CO, USA, in November 2019. In her presentation, Prof. Willcox points out that “big decisions need more than big data … they need big models too”. In particular, “big decisions must incorporate the predictive power, interpretability, and domain knowledge of physics-based models”. According to Prof. Willcox, the four important aspects in this context are that

• The dynamics of high-consequence applications are driven by complex multiscale multiphysics applications,
• There are high-dimensional parameters underlying the characterization of scientific and engineering systems,
• Data are sparse, intrusive and expensive to acquire, especially in the most critical regimes, and
• Uncertainty quantification (UQ) in model inference and certified predictions in regimes beyond training data play a critical role.

AdCo Engineering^GW bundles know-how from statistics, machine learning and engineering, that is, our “big models”, to provide innovative algorithms for physics-based UQ of systems with high-dimensional parameters in our software QUEENS. This is supplemented by our comprehensive expertise in the solution of complex multiscale multiphysics applications. Thereby, we enable our clients to harness the full potential that digital product development tools and processes can offer. For more information, please visit, for instance,

• Our webpage on UQ,
• Our recent white paper as an introduction to the why and how of UQ, and
• A review article on multi-fidelity approaches for UQ, which gives our clients a competitive edge over users of “standard” methods for UQ.

“Battery cells are a key technology for the energy transition” – that is how Ralph Diermann commences his recent article “The race for the battery of the future” published on Spiegel Online. However, at least two severe problems need to be pointed out in this context. On the one hand – and this applies above all to the German or non-Asian, respectively, perspective -, it turns out to be extremely problematic that battery cells are currently still predominantly produced in Asia. Consequently, any added value or existing delivery dependencies in this context are anchored in that region. On the other hand, the automotive industry, a particularly important industrial branch on the way to the energy transition, has so far been focusing almost exclusively on lithium-ion batteries with liquid electrolytes, although it is foreseeable that the potential of this type of battery is already almost completely exploited in terms of achievable energy density.

Various promising candidates have emerged in the race for the “battery of the future”, which is currently taking place with particularly intensive efforts and investments. To avoid bottlenecks regarding the availability of the important commodity lithium, the suitability of alternative materials, such as sodium, magnesium-sulfur, and aluminum, are researched, with their maturity likely to be in a rather distant future, though. That is why the all-solid-state battery is currently considered the “hottest candidate” in the near future. Arndt Remhof, who is with the Swiss research institute Empa, is quoted as follows: “Solid-state batteries promise a 50 percent higher energy density at the cell level than lithium-ion batteries.” Moreover, he states that “solid-state batteries play an important role in the roadmaps of many car manufacturers” and that “Volkswagen, for example, says it wants to produce such batteries in Salzgitter from the middle of the next decade.”

AdCo Engineering^GW acts at the forefront of this research and development, particularly regarding the aforementioned “hottest candidate”, the all-solid-state battery, providing its customers with advanced simulation technology that is key to a better understanding of this new type of battery. The benefits for our customers are more powerful and safer batteries with shorter development times. In this context, we are currently collaborating very closely with BMW AG.

Issue No. 2 of the magazine “Benchmark”, the international magazine for engineering designers and analysts from NAFEMS, in 2019 focuses on using the cloud for simulations. NAFEMS is the international association for the engineering modelling, analysis and simulation community, a not-for-profit organization established in 1983. Dr.-Ing. Volker Gravemeier, chief executive officer of AdCo Engineering^GW, is an active member of the NAFEMS Multiphysics Working Group (NAFEMS MWG).

In the introductory article to this main topic of the issue, “The costs and benefits of using the cloud for simulations”, commissioning editor Althea de Souza invited Lee Margetts, Andrew Jones, Wolfgang Gentzsch, Rodney March and John Baxter to discuss the issues around the total cost of running simulations in the cloud or in an on-site facility. The bottom line of this discussion is that we still remain to be in the early days of moving to the cloud in terms of engineering simulations. Moreover, while “there is certainly a place for the cloud, it is not the answer for everything everywhere”. As pointed out in the article, there are definitely situations to benefit from the cloud: for instance, “for an organization new to HPC, or even considering occasional large simulations, it makes sense to start off using the cloud, or some external model. You start using the cloud to find out if HPC can help, then, maybe find out that it will eventually be cost-effective to move to in-house facilities, but the cloud has allowed to ‘try then buy’”. In another article, “Head in the clouds, feet on the ground: my experience using Saas”, Althea de Souza describes her experience when using SaaS for simulation and states her personal pros and cons in this context.

NAFEMS Technical Officer Ian Symington reached out to members of the NAFEMS Vendor Network to get their thoughts on the topic of cloud computing and SaaS for simulation. Among others, it is discussed how major code vendors adopt their licensing models to accommodate cloud computing platforms. In this context, Wolfgang Gentzsch, President of UberCloud, mentioned that “larger independent software vendors (ISVs), like ANSYS, Dassault Systèmes, and Siemens, tend to have their own home-made cloud solution which is usually quite proprietary and restrictive. Customers describe it as a vendor lock-in in different forms; … This has a good reason and stems from the early days of cloud computing when ISVs believed … that their own customers would tend to prefer affordable, short-term, on-demand licenses over their usually quite expensive traditional annual/perpetual licenses.” However, there is another, probably even more severe reason preventing a full adoption, as noted by Ian Campbell, CEO of OnScale: “traditional engineering simulation software is also not architected for the highly parallel nature of the cloud. ‘Simple’ things like running a model across several hundred nodes of commodity cloud instances is impossible with legacy codebases because these code bases evolved through acquisition (e.g. buying a mechanical solver and then buying a separate electrical solver and stitching them together under the hood in a very inefficient way), as opposed to organic development with a goal of highly scalable delivery”.

AdCo Engineering^GW will soon launch its new cloud-computing platform “AdCo On Demand” and offer its multiphysics software AMSE as well as its UQ software QUEENS as a service. Evidently, there will not be any lock-in effects whatsoever, and all software codes were organically developed with the goal of high scalability. Thus, you will take full advantage of the highly parallel nature of the cloud. Do not hesitate, schedule your individual test drive with “AdCo On Demand” – and enjoy the ride!

The demand on the predictive capability of modern Computer Aided Engineering (CAE) toolchains constantly increases. This implies that simulation models are not only required to correctly represent the underlying physical system, but also that real-world variability and uncertainty must be taken into account for enabling a shifting of the product development cycle more and more from physical experiments to the computer. Uncertainty quantification (UQ) is key in improving the predictive capabilities of state-of-the-art Computer Aided Engineering (CAE) workflows. Furthermore, it speeds up the product design and development processes, particularly in the early phases of those processes, which not only reduces the time required to develop a product, but also the number of potentially expensive physical experiments. For all our clients as well as all visitors of our website who are interested in UQ and its enormous potential, we would like to provide a white paper as an introduction to the why and how of UQ.

AdCo Engineering^GW bundles know-how from statistics, machine learning and engineering to provide innovative algorithms for the quantification of uncertainties in our software QUEENS. Thereby, we enable our clients to harness the full potential that digital product development tools and processes can offer. Please visit our page on UQ.

The third issue of the magazine “Benchmark”, the international magazine for engineering designers and analysts from NAFEMS, in 2018 is entitled “Artificial Intelligence & Machine Learning”. NAFEMS is the international association for the engineering modelling, analysis and simulation community, a not-for-profit organization established in 1983. Dr.-Ing. Volker Gravemeier and Dr.-Ing. Jonas Biehler of AdCo Engineering^GW are active members of the NAFEMS Uncertainty Quantification Working Group, which topics are tightly related to artificial intelligence and machine learning.

In his article on “The Applicability of Artificial Intelligence in Design and Manufacturing”, Phil Cartwright argues that the ability to harness data generated along the product lifecycle and feed this information back into the design and development process will lead to significant benefits. In the remainder of the article, an example for machine learning techniques in combination with state-of-the-art simulation approaches is provided, to reduce rework, scrap, and repair in a liquid composite moulding process. In this context, the machine learning approach results in improved tuning of the manufacturing process parameters and ultimately reduced manufacturing cost for the production company.

Combining techniques from the machine learning community with state-of-the-art simulation approaches is without a doubt a very promising way to go for improving efficiency and speed, which will eventually result in reduced costs. In our view, this does not only hold true for manufacturing processes, but also for the complete product development cycle in general.   This is why AdCo Engineering^GW develops novel approaches to fuse information from simulation models with experimental data through so-called multi-fidelity approaches. These approaches are powerful tools to identify, e.g., those production parameters with the highest impact on the reliability of a product or a component thereof, respectively, via sensitivity analysis.  Further product or system attributes can be assessed as well, and both production and design parameters may be investigated to substantially improve the results in the end.

CADFEM ANSYS Simulation Conference 2018 in Leipzig

We look back with great pleasure on a very interesting and successful CADFEM ANSYS Simulation Conference 2018, which took place from October 10th to 12th in Leipzig. Among other things, we were present with an exhibition stand where you could meet our experts for simulation and uncertainty quantification. During those three days, we had the pleasure of many interesting and informative conversations, and we would like to thank everyone who visited us at our booth during the conference!

We would also like to thank all those who attended the presentation by our CEO Dr.-Ing. Volker Gravemeier on predictive multi-physics simulation of batteries and uncertainty quantification as well as the subsequent discussion.

A well designed, comprehensive review of the conference can be found here.