A digital twin (DT) is a virtual replica of a physical system that is designed to monitor and diagnose the system’s performance. It is created by using data from sensors and other sources to build a virtual model of the physical system. DT technologies can be especially useful for systems with spatially distributed dynamics which are described by partial differential equations. The accurate yet efficient description of these systems is a key expertise for the development of high-performing and reliable systems. The proposed Christian Doppler Laboratory for Digital Twins for Distributed Parameter Systems aims to develop core methodologies for modeling, design, parametrization, optimization and monitoring/diagnosis of such systems. The two industrial partners of this Christian Doppler Laboratory (CDL) are AVL List GmbH and Semperit Technische Produkte GmbH. Viewed from the perspective of their application fields, the CDL focuses on digital twin methodologies for automotive fuel cells and batteries, electrolyzers as well as elastomer extrusion processes.
Incipient damage in a fuel cell or battery, which eventually reduces life expectancy of the whole system, is always triggered locally. Similarly, the product quality in an extrusion process critically relies on predefined temperature and pressure distributions across the extrusion device. To accurately describe such systems and processes in a mathematically feasible way, the CDL for Digital Twins for Distributed Parameter Systems sets out to develop real-time capable distributed parameter models of minimal complexity. The spatially distributed dynamics are captured using a network of sensors to record data on the system’s behavior at multiple locations. This data is then used to parametrize and update a virtual model of the system. In this context, the CDL will investigate in-situ sensing and possible soft-sensors to optimize process monitoring.
Another main goal of the CDL is the ability of DT to learn and adapt over time. As data is collected and analyzed, the DT can improve its accuracy and provide more valuable insights into system performance. This allows for continuous optimization and improvement, leading to greater efficiency and cost savings over time.
A third major research challenge of the CDL will be posed by setting up methods to effectively monitor the system performance. This includes e.g. to properly identify and observe stressors for the various components. Based on these methods the development of predictors for life expectancy or product quality be tackled. One major research direction will be use of distributed state observers for diagnosis in digital twins. They can provide several benefits in improving the accuracy of the digital twin by allowing for more precise modeling of the physical system, provide a means for early detection of faults or anomalies in the physical system, thus allowing for proactive maintenance. Also, they can facilitate real-time optimization of the system by providing feedback for control actions.
The expert knowledge of the industry partners together with the high level of basic research at the research partners constitute the ingredients for a successful research program yielding innovative and sustainable results. The existing and long-standing cooperation between partners also guarantee an effective and implementation of the research plan. Ultimately, the Digital Twin methodologies developed in the proposed CDL will deliver cutting-edge technology to the industry partners and provide a platform for long-term developments in the respective companies. The positive impact on environmentally friendly technologies is a further virtue of the proposed program.
