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.

Join us at the CADFEM ANSYS Simulation Conference 2018 in Leipzig

We are glad to announce that we will take part in the CADFEM ANSYS Simulation Conference 2018 in Leipzig in October. We will present our latest work on predictive multi-physics simulation of batteries and uncertainty quantification both at our booth and in our conference talk. It is aimed at predicting the behavior of battery cells under operational as well as extreme conditions and at gaining a more detailed understanding of the underlying physical and chemical processes. Simulations based on rigorous theoretical modeling and mathematically consistent numerical techniques in combination with validation and quantification of uncertainties can indeed provide predictive results, if applied correctly. We will show examples of coupled multi-physics and electrochemical systems such as all-solid-state batteries to demonstrate the capabilities of today’s simulation technology. Moreover, the important issue of parameter uncertainty will be addressed, and the presentation of methods for efficient uncertainty quantification will conclude the talk.

The talk will be at 10 a.m. on October 11th. More details can be found in the conference program, which is available here.

You are cordially invited to stop by our booth at the CADFEM ANSYS Simulation Conference 2018 in Leipzig to meet our experts in engineering simulation and uncertainty quantification.

The first issue of the magazine “Benchmark”, the international magazine for engineering designers and analysts from NAFEMS, in 2018 is entitled “Bringing it all Together: Multiphysics, Multiscale & Co-Simulation”. 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 his technical editorial, Prof. Henrik Nordborg, chairman of the NAFEMS MWG, addresses some of the trends driving the use of multiphysics. Among others, the driving forces are, on the one hand, the “constantly increasing demand for accuracy in simulations, making it impossible to continue to rely on over-simplified models”, and on the other hand, the fact that “computers are getting faster and cheaper, giving us the necessary computing power to handle complex models”. However, Prof. Nordborg emphasizes that the knowledge for successfully doing multiphysics simulations is particularly demanding: “Actually, the main limiting factor today might be the training of simulation engineers. Although modern simulation tools are easy to use, they are no replacement for knowledge. One cannot design a product or a process based on fluid flows without a basic understanding of fluid mechanics. Likewise, it is not possible to do mechanical, thermal, or electromagnetic design without knowledge of these fields. A major problem with multiphysics simulations is that most of us leave university with only one degree, or if more than one degree, the subsequent ones are usually in a similar area. Successful application of multiphysics simulations therefore depends on either having extremely erudite people or on well-functioning teams of simulation specialists.” Multiphysics is one of our specialist fields at AdCo Engineering^GW, and from our long-term experience in this field, we may add to this that not even adequate knowledge in all of the involved physical fields is sufficient, but particularly the coupling strategies between the fields and thus the knowledge thereof is key to accurate, robust, stable and reliable multiphysics simulations. AdCo Engineering^GW offers you the unified knowledge of the individual fields as well as the coupling of the fields for many industrially relevant multiphysics problems.

Following the technical editorial, a number of articles shed light on various multiphysics applications. Among others, Hubertus Tummescheit provides “an overview of co-simulation with a focus on best practices” entitled “Co-Simulation – Art or Science?”. This article includes a discussion of the drawbacks of co-simulation: “in co-simulation, the smooth approximations of variables at model interfaces are communicated in discrete time intervals. This coupling introduces discontinuities and a delay in the duration of the communication interval. These two flaws introduced by the fundamental setup of co-simulation are the root cause of all difficulties in using it. Even advanced co-simulation techniques with sophisticated master algorithms can reduce, but not eliminate the introduction of these two fundamental flaws.” In a subsequent and more detailed analysis, several issues are addressed, among other things, the stability of co-simulation: “The stability properties … of the coupled model are always worse than the stability properties of the system if it were modeled as a monolithic model. Co-simulation requires either (a) a delay of one time step in the propagation of results from the output of one model to the input of the next model or (b) some extrapolation from past results, to estimate the input to a model from past outputs upstream of that model. Coupling through co-simulation always deteriorates stability, as well known from the theory of feedback control with delays.”. These observations go well together with our own experience, based on which we preferably use and may offer you superior monolithic approaches for most multiphysics applications.

On April 20 – 21, the TechNet Alliance Spring Meeting 2018 was held in Berlin, Germany. On this occasion, Prof. Wolfgang A. Wall, shareholder and co-founder of AdCo Engineering^GW GmbH, gave a presentation entitled “Predictive simulation of complex coupled problems – and the rising importance of uncertainty quantification and machine learning”. After an introduction into computational methods for multi-field problems, uncertainty quantification (UQ) and machine learning, innovative predictive physics-based simulations models for three highly interesting fields of application were presented. The fields which are attracting particular attention are 3-D printing in the form of selective laser melting (SLM), battery simulation, and biomedical applications.

The technique of 3-D printing in the form of SLM, where pre-defined contours in successive layers of powder are selectively melted by using a laser beam, offers several benefits, such as a near net-shape production rate and a Novel advanced simulation technology can provide crucial support for the design process of, for instance, complex load bearing components. The second presented field of application is predictive battery simulation, particularly with an eye to electric and hybrid cars. As already outlined in a previous blog post, AdCo Engineering^GW acts at the forefront of this research and development, providing its customers with advanced simulation technology that is key to a better understanding of new types of batteries such as solid-state batteries. In this context, we are currently collaborating very closely with BMW AG.

Finally, as the third field, predictive computational methods for challenging biomedical applications such as abdominal aortic aneurysms (AAA) and the human lung were addressed. All three fields of application have in common that uncertainties have to be taken into account when doing predictive simulations. With our UQ methods, confidence intervals for simulation results as well as worst-case estimates can be provided. Furthermore, global sensitivity analyses are enabled. For all these goals, among others, machine learning such as Gaussian processes can be utilized. Furthermore, deep neural networks can be exploited as surrogate models for UQ (“deep UQ”), where data-efficient deep neural networks are generated with built-in symmetries respecting the individual physics in the form of physics-based deep learning.

More information on TechNet Alliance:

The TechNet Alliance, founded in 1998, is a global network of computer-aided engineering (CAE) companies and, as such, constitutes one of the longest standing CAE Alliances in the world. In addition to members offering solutions for the CAE market (Principal Members), the network also includes companies supporting business activities (Business Support Members). CAE Experts from customers (Corporate Members) as well as worldwide acknowledged professors and retired CAE managers (Honorary Members). Today, the TechNet Alliance consists of over 70 members in Europe, Africa, Brazil, Asian countries such as China, India, Japan, and South Korea, United Arab States, and the United States of America. Since 2000, the TechNet Alliance meets twice a year to share experience and knowledge, with the Spring Meeting 2018 in Berlin being the most recent one.

The performance of the battery is one of the crucial aspects for the future progress of electro mobility. Currently, lithium-ion batteries with a liquid electrolyte solution represent the most widely used battery concept. As outlined in the recent article “Wer baut den Akku der Zukunft (Who builds the battery of the future)?”, authored by Jürgen Pander and published on Spiegel Online, gains regarding the energy content for this type of battery are still considered possible to a limited extent. Nonetheless, intensive research on future variants which have substantially more potential in terms of energy content, such as lithium-oxygen and lithium-sulfur batteries, is already underway. More and more, a particularly promising bearer of hope is emerging, though.

As stated in the aforementioned article, “the so-called solid state battery is considered as a ‘miracle battery’ of the future.” Martin Winter, professor of materials science, energy and electrochemistry at Universität Münster states that “if competitive solid-state batteries can be produced on an industrial scale and used in electric cars, for example, this would mean a dramatic improvement compared to the current state of the art” – but: “All those approaches are still within a stage of research and development.”

AdCo Engineering^GW acts at the forefront of this research and development, 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.