Niggemann, Oliver

Molecular dynamics simulations with first-principles methods, such as density functional theory, are a cornerstone in the development of new battery and fuel cell materials. However, due to their high computational demand, their application is mostly limited to small systems and short time horizons. AI-based methods are a promising approach for accelerating first-principles simulations while maintaining high simulation accuracy. A key challenge, however, is the efficient training of such AI-based methods for specific systems of interest. In this article, we provide an overview of the training approach being researched at the Professorship of Computer Science in Mechanical Engineering at Helmut-Schmidt University, Hamburg.

The dtec.bw project CoupleIT! – IT-based sector coupling: Digitally controlled fuel cell and electrolyzer technologies for stationary and mobile applications is an interdisciplinary approach to combine a wide range of competencies from disciplines as varied as electrical power systems, economic and social sciences, computer sciences and networks as well as sustainable development and social acceptance research. As such, this article is composed of individual contributions, constituting the main chapters that showcase general approaches and motivations but also concrete results. This compendium article starts in with a delineation of the motivation behind research in so-called microgrids composed of fuel cell and electrolyzer components and a presentation of the microgrid architecture opted for in this project. Chapter two goes into more detail on the side of electrical engineering and the feasibility of a parallel operation of inverters in microgrids to achieve the ability for an upscaling. Chapter three highlights economic and technological factors for an economically viable and grid-maintaining deployment of a hydrogen-based energy system. In addition, degradation of Li-ion batteries is discussed against the background of their flexible operation in a microgrid and other scenarios. Chapter four grants a glimpse into the field of computer science and the possibility to use artificial intelligence and neural networks for a new way to simulate the behaviour of matter on atomic and molecular scales. This approach holds potential to increase the efficiency of fuel cells by improving the molecular design of fuel cell membranes used within this project. Chapter five elucidates the intricacies of secure communication within one but also between multiple microgrids, an important aspect for achieving a resilient system. Chapter six concludes this compendium by highlighting the human perspective seen from the field of psychological acceptance research nested in the broader context of sustainable development. Among other things, areas of potential barriers to a public acceptance of hydrogen technology are identified and ways to overcome those barriers proposed. This interdisciplinary round trip starts with electrical engineering (chapters one and two), economic and social sciences (chapter three), followed by computer sciences (chapter four) and computer networks (chapter five) whence the baton is passed for one last time to the field of sustainable development and psychological acceptance research (chapter six).

Kubeflow integrates a suite of powerful tools for Machine Learning (ML) software development and deployment, typically showcased independently. In this study, we integrate these tools within an end- to-end workflow, a perspective not extensively explored previously. Our case study on anomaly detection using telemetry data from the International Space Station (ISS) investigates the integration of various tools—Dask, Katib, PyTorch Operator, and KServe—into a single Kubeflow Pipelines (KFP) workflow. This investigation reveals both the strengths and limitations of such integration in a real-world context. The insights gained from our study provide a comprehensive blueprint for practitioners and contribute valuable feedback for the open source community developing Kubeflow.

In many use cases cyber-physical systems are employed to produce products of small batch sizes as efficiently as possible. From an engineering standpoint, a major drawback of this flexibility is that the architecture of the cyber-physical system may change multiple times over its lifetime to accommodate new product variants. To keep a cyber-physical system working normally it has become common to employ fault diagnosis algorithms. These algorithms partly rely on physical first-principles models that need to be updated when the architecture of the system changes which usually has to be done manually. In this article we present a practical approach to obtain such a first-principles model through evaluating piping and instrumentation diagrams (P&IDs) with visual questions answering (VQA) models. We demonstrate that it is possible to leverage VQA models to construct physical equations which are a preliminary stage for the creation of models suitable for fault diagnosis. We evaluate our approach on OpenAIs GPT-4 Vision Preview model using a P&ID we created for a benchmark water tank system. Our results show that VQA models can be used to create physical first-principles models.

This paper examines the integration of Continuous-Time Neural Networks (CTNNs), including Neural ODEs, CDEs, Neural Laplace, and Neural Flows, into engineering practices, particularly in dynamical system modeling. We provide a detailed introduction to CTNNs, highlighting their underutilization in engineering despite similarities with traditional Ordinary Differential Equation (ODE) models. Through a comparative analysis with conventional engineering approaches, using a spring-mass-damper system as an example, we demonstrate both theoretical and practical aspects of CTNNs in engineering contexts. Our work underscores the potential of CTNNs to harmonize with traditional engineering methods, exploring their applications in Cyber- Physical Systems (CPS). Additionally, we review key open-source software tools for implementing CTNNs, aiming to facilitate their broader integration into engineering practices.

Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsulate production functions as services have shown to be promising in order to increase the flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding a production plan based on provided functionalities for a demanded product, especially when there is no direct (i.e., syntactic) match between demanded and provided functions. While there is a variety of approaches to production planning, flexible production poses specific requirements that are not covered by existing research. In this contribution, we first capture these requirements for flexible production environments. Afterwards, an overview of current Artificial Intelligence approaches that can be utilized in order to overcome the aforementioned challenges is given. For this purpose, we focus on planning algorithms, but also consider models of production systems that can act as inputs to these algorithms. Approaches from both symbolic AI planning as well as approaches based on Machine Learning are discussed and eventually compared against the requirements. Based on this comparison, a research agenda is derived.

One possibility to extend the service life of engi-neering structures is to provide adequate maintenance based on Structural Health Monitoring (SHM). Typically, SHM involves a sensor network which is spatially distributed at the surface or within the structure to be monitored. Each sensor measures at least one physical quantity, the data of all sensors then have to be properly evaluated to derive the health state and to predict the remaining service life. Health issues may be detected by machine learning methods by looking for anomalous behaviour in sensor data. Hereby the problem is that malfunctions differ excessively in the representation of the data collected by sensors such that specialisation of methods on anomaly types is required. The current contribution suggests the simulation of sensor malfunction based on established criteria by creating different types of artificial anomalous data indicating different types of issues. Several proposed autoencoder approaches are verified for different anomaly representations, which are artificially introduced in a set of data. The final solutions are different autoencoder specialized on different types of simulated anomaly data, making the conclusions drawn from the measured data more reliable. As a case study, data of a numerical experiment of fibre pull-out are considered.