Breakout Session

Virtual commissioning, industry 4.0, digital twin are today's buzzwords in machine's engineering and factory planning. Development based on 3D simulation is already standard in many areas today. But with Industrial Physics from machineering, new far-reaching simulation possibilities are emerging.
The Digital Twin accompanies machines their lifetime long- daily business, changes or modifications on the machine, service works…. Its goal is to prevent errors, optimize machines and av+A1:I11oid failures.
Use the simulation model as basis of the Digital Twin in the early stages of the development process to safeguard your concepts.

The design of the newest kitchen house appliances systems is strongly influenced by the important objective of environmental impact reduction. Extensive studies are being carried out on the range hood ventilation system with the purpose of improving the efficiency in order to decrease both the energy consumption and pollutant emissions. On the other side the European Commission, by new regulations and by the energy labels on the products, is motivating companies to optimize the main kitchen range hood through the utilization of new technologies. The fluid-dynamic efficiency improvement in the ventilation system is one of the main topics to which numerous efforts are being devoted.
The present work mainly focuses on the implementation of a numerical model for the ventilation system, characterized, in modern range hood, by a blower and a impeller. The numerical simulation, performed by using computational fluid dynamics (CFD) methodology, was used to optimize the blower fluid dynamics efficiency maintaining the same motor system.

The effect of fluids on fibers can be taken into account through DEM-CFD coupled simulations with Ansys Fluent®. Within this coupling, fluid forces acting on each particle and heat exchanged with the fluid due to convection are computed on the DEM side. A drag law correlation, developed for long slender fibers immersed in turbulent flows, is used in order to better predict the correct particle/flow behavior by taking into account the particle shape and its alignment with the flow field. All of these capabilities are implemented on both multi-core HPC and multi-GPU solvers, providing scalability when dealing with a large number of particles that aims both for efficiency and for reduced response time.
Several application examples will be presented in order to demonstrate the capacity of the developed model for the simulation of dynamic fiber systems, ranging from the modeling of hair strands flowing in a vacuum cleaner device to the simulation of hay compaction into storage bales. The numerical results of these examples are compared to experimental/field data and show good agreement, proving this approach as a useful tool for evaluating new designs and operating conditions, reducing the cost involved with new prototypes and experimental testing.

There is no month that passes by that we hear another successful application of machine learning in our daily lives. Virtual personal assistants, recommendation engines, fraud detection are only few of such applications. Every industry is thinking and working on how else it can leverage these new set of tools to improve their business and solve customer problems. Design and engineering is no different. There are many articles on how we can leverage machine learning for the engineering design, simulation and operation community. Of course, these fields come with their own challenges such as data in the form of 3D shapes that are not directly consumable by machine learning methods and tools. In this session, I will be presenting some of the use cases we have leveraged machine learning to address common design challenges. Some of these are very practical and can be leveraged today. Some on the other side are thought provoking ideas that are still under investigation.

Hydac is a leader company in hydraulics components and solutions for complex cooling system. Almost each installation is customized to customer specification, making the production system management tricky. The industrial challenge is to make the key components standard, up to the balance point between process efficiency and customization.
The solution is to develop an innovative CAE-CFD approach to support the standards definition of heat exchangers, ducts and fans system. An advanced mathematical model with dedicated boundary conditions, turbulence and fan models coupled with porous flows was developed by Moxoff and implemented in an OpenFOAM solver. Several thermo-fluid dynamics simulations were performed in a range of design and operating conditions. As a result, the optimal design parameters were identified to support the key component standard definition and, at a management level, to orchestrate the design workflow, the production and manufacturing processes.

WeldGalaxy project develops a highly visible and transparent knowledge based B2B online platform that brings together global buyers and EU sellers (manufacturers/suppliers/distributors/service providers) of welding equipment and consumables, along with accessories and services. Based on EKON's flagship Dynamic Knowledge Management platform, a group of 6 European partners is driving the DKM platform to novel knowledge & marketplace ecosystem that will highly improve both customer experience and visibility in the welding domain. The project integrates innovative services based on state-of-the-art technologies such as AI Chatbot, an advanced dynamic Knowledge-Based Engineering (KBE) tool which utilizes intelligent welding rules for the users, Analytics, Simulations and secure automatic tender processes, based on blockchain technologies. From the data aspect, we are harnessing large data-sets of welding knowledge, facilitating and supporting 3rd party projects through open-innovation competitions. The WeldGalaxy B2B platform will underpin progress in endeavours to enhance the visibility of EU’s welding products, prototypes and services to global users via enhanced digital marketing strategies, provide innovative web-based services to boost EU market share and competitiveness, and enable plug and produce digital manufacturing of the right equipment to specified customers/end-users requirements and regulatory compliance.

Engineering fields with high technological contents involve manufacturing requirements in which the control of the margins of tolerance, as well as the verification of the manufactured components, has economic impacts in the relationships with the customers. The verification of the actual geometry after manufacturing acquires then paramount importance and can be substantially improved by adopting the digital twin approach: the CAE model of the system is adapted onto the actual manufactured shape making the numerical prediction individual manufactured component specific. CAE Up aims at implementing a cloud-based software tool whose core is the comparison of the structural performances between the CAE model relative to the nominal design of a certain product and the digital twin of the real product as built. The digital twin is updated on HPC cloud and the performance prediction recomputed adopting a variation of the CAE model shaped like the actual manufactured part.

Mesh morphing is a shape modification technique for numerical models that proven to be effective and reliable: new shape configurations are obtained by just updating nodal positions and volume meshes maintain a good quality even with substantial changes. In the standard workflow in which mesh morphing is used to investigate performances variations, shape modifications setup is directly controlled by the analyst which decides the location and the intensity of surface sculpting. In this work the optimization of a complex shaped industrial part is automatically obtained by a bionic approach, the Biological Growth Method (BGM), which consists in adding or removing material according to surface stress levels. The framework in which the presented workflow is developed is ANSYS® Workbench™ in combination with mesh morphing software RBF Morph™ ACT extension, an advanced mesh morphing App that comes with a complete BGM implementation.

Additive manufacturing has emerged into a new area of 3D (bio)printing or 3D biomanufacturing for the purpose of efficient, personalized medical devices and implants. Latest results indicate that even for biomaterials without living cells different 3D technologies result in different properties of the constructs which lead also to different host response reactions, not anticipated for conventionally made materials.
For a successful digital transformation, there are needs to integrate materials data, processing optimization and feedback from the clinical field to ensure that the 3D biomanufactured device will be safe, efficient, affordable and functional.
In this work we present an outlook for modern 3D biomanufacturing methods, their digital implementation as well as future potential directions.

In the development of prostheses as well as other technical devices which interact with human bodies personalization is more and more important and required. Besides this also medical treatments and diagnoses gather positive impacts by using personalized data. At the University of Applied Sciences in Frankfurt, Germany (FRA-UAS) a research laboratory was established in which the whole process chain for the development of personalized devices shall be presented:
Starting with the load data collection using gait analyses and the mechanical-mathematical modelling of materials behavior of tissue, computer simulation methods are used to develop and improve artificial structures such as prostheses, which interact with the human body. After designing and optimizing the structures they can be machined or 3D-printed. Moreover care is taken of the durability, the material strength or the biocompatibility by carrying out physical tests. In order to solve the tasks which arise out of such a complex problem, 11 researchers are working together in the mentioned research lab, each one with a unique expertise in a specific field.
The topics which are covered by this research lab are shown in two examples:
Example 1 deals with the development of an artificial partial knee joint, example 2 with the diagnosis method and risk assessment of abdominal aortic aneurysms. According to statistics artificial knee joints have to be replaced after about 15 years after the first implantation. Each replacement and revision makes it more difficult to achieve a proper fixation in the bone, resulting in a maximum of about 2-3 revisions. Considering young people with a defect in the cartilage of the knee joint it is important to solve this problem with a minimum of replacement. This leads to the idea of a replacement of only the cartilage part and not of the bony structures themselves as a first solution for the patient. Thus the patient wins one more revision of an artificial joint.
In order to realize that, the process chain described above can be established. The individual biological geometry of the patient is collected by extracting the 3D-geometry of CT- or MRT-images and transferred to CAD-data. Using these CAD-data of the knee joint, an inlay is designed and manufactured for the replacement of the cartilage. During this design-process numerical simulations including biological material models are carried out using those individual data for optimizing the stiffness of the implant. Loads are taken from the gait-analysis which was carried out before. After manufacturing the implant either by machining or 3D-printing it is also tested for material strength, durability and biocompatibility. The here described artificial knee joint is an example for the development of personalized technical products like prostheses as well as other products which interact with the human body, such as helmets or mattresses for instance.
Besides the development of these technical devices which interact with human bodies a second example of the work in the research lab is shown: The development and improvement of treatment methods and diagnoses procedures. The example given here is the improvement of the classification of the rupture risk of aortic aneurysms. Abdominal Aortic Aneurysms (AAA) are highly dangerous due to the fact that after a rupture the mortality rate is at about 50-80%. The prevalence at an age over 65 years is up to 9% of the male and 2 % of the female population. Up to now the criteria for a surgery is simply the diameter of the aneurysm and the annual growth rate. None of the two criteria takes into account the stresses or strains within the biological material which might be the cause for rupture. Therefore the risk of a non-required surgery with all side effects is quite high. Even worse is a required but omitted surgery due to the fact of a too small diameter, which might lead to the patient’s death.
The problem in this field is also the personalization of the biological material data. The inter-patient variability of those data is not negligible. Though it is often assumed that tissue of young people is more flexible than that of older people, this is not always the case. So material models are highly patient specific and should be evaluated in vivo. The approach which is described in this example uses 4D-ultrasound data: time resolved spatial ultrasound. Using these data the individual strains can be computed and by measuring the systolic and diastolic blood pressure the information about the loading is also available. Due to the fact that the ultrasound measurement covers a certain amount of data points the individual material law can be identified assuming a nonlinear anisotropic elasticity. The strains and stresses at the AAA can be compared to limit values. An even better method for assessing the rupture risk might be the evaluation of the discontinuity of strains. The paper describes and illustrates the work at the research lab with two examples. The applicability of Personalized Biomedical Engineering methods of course is much more than that.