Steinbeis Research Center Virtual Testing
Im Rohr 39
74523 Schwäbisch Hall
Germany
74523 Schwäbisch Hall
Germany
- Phone: +49 7907 943902
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Management:
- Prof. Dr.-Ing. Uwe Janoske
Testing procedures for components and products are typically based on experimental methods. In particular, studying long-term behavior is both time-consuming and costly. Predictions based on digital prototypes in early stages of product development are not widely used in industrial applications. We support you in developing numerical models and computational tools that enable efficient product development and reliably reduce unnecessary development iterations. We utilize a variety of numerical calculation methods, such as CFD, as well as innovative machine learning methods, such as physics-informed neural networks.
Prof. Dr.-Ing. Uwe Janoske
Services
- Modeling and development of virtual testing procedures based on digital prototypes using numerical calculation tools (computational fluid dynamics (CFD), structural simulation, thermoelectric and magnetohydrodynamic simulations, coupled multi-physics methods)
- Application of various methods for describing complex multiphase flows, including CFD models, Smoothed Particle Hydrodynamics (SPH), Discrete Element Methods (DEM), and multi-body simulation models
- Application of machine learning-based methods for physical modeling and process acceleration with and without training data using Physics-Informed Neural Networks (PINNs)
- Numerical simulations for virtual prototypes aimed at solving different problems
- Support with the design and development of experimental validation tests
- Consulting and support with modeling and physical fundamentals
Key Areas
- Modeling of virtual test procedures
- Development of appropriate model reductions for simulating complex test procedures
- Development of new, innovative computational approaches for simulating long-term tests within reasonable computational time frames
- Development of new calculation tools and graphical user interfaces (GUI)
- Creation of comprehensive solutions for virtual testing, ranging from geometry to automated calculation, analysis, and reporting
- Numerical solution of models based on different methods
- Numerical flow simulation using a wide variety of methods tailored to the specific task, e.g., Computational Fluid Dynamics (CFD), Smoothed Particle Hydrodynamics (SPH)
- Application of innovative machine learning approaches, particularly physics-based modeling without the use of training data, i.e., Physics-Informed Neural Networks (PINNs)
- Consulting and support with experimental validation of models
Project Examples
- Temporal deposition and fouling behavior in exhaust systems
- Determination of flow and temperature behavior in agitation systems using physics-informed neural networks for rapid determination of flow fields for control tasks
- Investigation of flow processes and deposition behavior in various components in the wastewater/sewage/biogas sector
- Investigation of the mixing and degassing behavior of plastic melts
- Flow optimization of various systems and machines
- Investigation of fluid flow and solid particle motion in handling devices in the filling and packaging machinery sector using coupled CFD and multi-body simulation models
- Numerical investigation of the cleaning behavior of soiled components in the household and commercial sectors – multiphase modeling and development of deposition and cleaning models
- Virtual simulation of filtration tests – loading behavior of various filtration devices
- Virtual modeling of climate chambers for corrosion testing in the early stages of product development (see Seifritz et al., Berg- und Hüttenmännische Monatshefte 163, 2018)
Media
Steinbeis Transfer Magazine
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A holistic strategy for succeeding as a business (Transfer 2/2024)
Digitale Technologien unterstützen die Produktentwicklung im Spannungsfeld von Kosten, Qualität, Nachhaltigkeitsgesetzgebung und Time-to-Market