Design Library Solution Patterns in SysML for Concept Design and Simulation

AbstractObject-oriented models in the Systems Modeling Language (SysML) are developed in this paper to support the concept development phase within engineering design. Generic libraries in SysML for functions, according to the functional basis, and structural components, are presented in previous work by the authors. This paper extends this work and proposes the use of multi-solution patterns in SysML that combine a new behavior simulation library together with the previous generic libraries describing functions and components. These patterns capture coherent solutions to known problems that can be reused in concept design with the aim to save modeling effort. Since they are based on solution-neutral functions, they also offer multiple potential solutions at once. The new behavior simulation library and solution patterns are demonstrated in this paper using a 3D printer case study with two different kinematic solutions

Shape mode analysis exposes movement patterns in biology: flagella and flatworms as case studies

We illustrate shape mode analysis as a simple, yet powerful technique to concisely describe complex biological shapes and their dynamics. We characterize undulatory bending waves of beating flagella and reconstruct a limit cycle of flagellar oscillations, paying particular attention to the periodicity of angular data. As a second example, we analyze non-convex boundary outlines of gliding flatworms, which allows us to expose stereotypic body postures that can be related to two different locomotion mechanisms. Further, shape mode analysis based on principal component analysis allows to discriminate different flatworm species, despite large motion-associated shape variability. Thus, complex shape dynamics is characterized by a small number of shape scores that change in time. We present this method using descriptive examples, explaining abstract mathematics in a graphic way.Comment: 20 pages, 6 figures, accepted for publication in PLoS On

Active phase and amplitude fluctuations of flagellar beating

The eukaryotic flagellum beats periodically, driven by the oscillatory dynamics of molecular motors, to propel cells and pump fluids. Small, but perceivable fluctuations in the beat of individual flagella have physiological implications for synchronization in collections of flagella as well as for hydrodynamic interactions between flagellated swimmers. Here, we characterize phase and amplitude fluctuations of flagellar bending waves using shape mode analysis and limit cycle reconstruction. We report a quality factor of flagellar oscillations, $Q=38.0\pm 16.7$ (mean$\pm$s.e.). Our analysis shows that flagellar fluctuations are dominantly of active origin. Using a minimal model of collective motor oscillations, we demonstrate how the stochastic dynamics of individual motors can give rise to active small-number fluctuations in motor-cytoskeleton systems.Comment: accepted for publication in Physical Review Letter

Secondary control activation analysed and predicted with explainable AI

The transition to a renewable energy system poses challenges for power grid operation and stability. Secondary control is key in restoring the power system to its reference following a disturbance. Underestimating the necessary control capacity may require emergency measures, such as load shedding. Hence, a solid understanding of the emerging risks and the driving factors of control is needed. In this contribution, we establish an explainable machine learning model for the activation of secondary control power in Germany. Training gradient boosted trees, we obtain an accurate description of control activation. Using SHapely Additive exPlanation (SHAP) values, we investigate the dependency between control activation and external features such as the generation mix, forecasting errors, and electricity market data. Thereby, our analysis reveals drivers that lead to high reserve requirements in the German power system. Our transparent approach, utilizing open data and making machine learning models interpretable, opens new scientific discovery avenues.Comment: 8 pages, 6 figure

Physics-Informed Machine Learning for Power Grid Frequency Modeling

The operation of power systems is affected by diverse technical, economic, and social factors. Social behavior determines load patterns, electricity markets regulate the generation, and weather-dependent renewables introduce power fluctuations. Thus, power system dynamics must be regarded as a nonautonomous system whose parameters vary strongly with time. However, the external driving factors are usually only available on coarse scales and the actual dependencies of the dynamic system parameters are generally unknown. Here, we propose a physics-informed machine learning model that bridges the gap between large-scale drivers and short-term dynamics of the power system. Integrating stochastic differential equations and artificial neural networks, we construct a probabilistic model of the power grid frequency dynamics in continental Europe. Its probabilistic prediction outperforms the daily average profile, which is an important benchmark, on a time horizon of 15 min. Using the integrated model, we identify and explain the parameters of the dynamical system from the data, which reveal their strong time-dependence and their relation to external drivers such as wind power feed-in and fast generation ramps. Finally, we generate synthetic time series from the model, which successfully reproduce central characteristics of the grid frequency such as their heavy-tailed distribution. All in all, our work emphasizes the importance of modeling power system dynamics as a stochastic nonautonomous system with both intrinsic dynamics and external drivers

Physics-inspired machine learning for power grid frequency modelling

The operation of power systems is affected by diverse technical, economic and social factors. Social behaviour determines load patterns, electricity markets regulate the generation and weather-dependent renewables introduce power fluctuations. Thus, power system dynamics must be regarded as a non-autonomous system whose parameters vary strongly with time. However, the external driving factors are usually only available on coarse scales and the actual dependencies of the dynamic system parameters are generally unknown. Here, we propose a physics-inspired machine learning model that bridges the gap between large-scale drivers and short-term dynamics of the power system. Integrating stochastic differential equations and artificial neural networks, we construct a probabilistic model of the power grid frequency dynamics in Continental Europe. Its probabilistic prediction outperforms the daily average profile, which is an important benchmark. Using the integrated model, we identify and explain the parameters of the dynamical system from the data, which reveals their strong time-dependence and their relation to external drivers such as wind power feed-in and fast generation ramps. Finally, we generate synthetic time series from the model, which successfully reproduce central characteristics of the grid frequency such as their heavy-tailed distribution. All in all, our work emphasises the importance of modelling power system dynamics as a stochastic non-autonomous system with both intrinsic dynamics and external drivers.Comment: 21 pages, 5 figure

Contributing to the cultural ecosystem services and human wellbeing debate: A case study application on indicators and linkages

Inadequacies in the indication of cultural ecosystem services (CES) are a hindrance in assessing their comprehensive impacts on human wellbeing. Similarly, uncertainties about the quantity and quality of CES, in real time and space, have hampered the ability of resource managers to precisely take responsive management actions. The aim of the study is to demonstrate, how CES indicators can be identified and qualified in order to link CES to human wellbeing, and to integrate them into the 'ecosystem services cascade' and the Driver-Pressure-State-Impact-Response (DPSIR) models. A case study methodology is applied at the Nairobi-Kiambu (Kenya) peri-urban area. Primary data on CES was collected in the case study through survey, field observations and matrix tables. Secondary data originates from literature analysis. Results show that the participatory identification of CES and human wellbeing indicators could improve their transparency and comprehensibility. The environmental policy formulation and implementation processes have been demonstrated. The tripartite framework of CES-human wellbeing-DPSIR has demonstrated more linkages and feedbacks than initially indicated in the cascade model. For policy formulation and implementation, appropriate communication of results is mandatory. This is illustrated by a terminology that enables the transfer of scientific messages to stakeholders, especially for the local people. The conclusion indicates the importance of consistency in qualifying CES and human wellbeing indicators even at this time of urgency to bridge the gaps existing in CES and human wellbeing research.Catholic Academic Exchange Service (KAAD

Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean

Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this success, the inner workings of DNNs are often not transparent, making them difficult to interpret or understand. This lack of interpretability has led to increased research on inherently interpretable neural networks in recent years. Models such as Neural Additive Models (NAMs) achieve visual interpretability through the combination of classical statistical methods with DNNs. However, these approaches only concentrate on mean response predictions, leaving out other properties of the response distribution of the underlying data. We propose Neural Additive Models for Location Scale and Shape (NAMLSS), a modelling framework that combines the predictive power of classical deep learning models with the inherent advantages of distributional regression while maintaining the interpretability of additive models. The code is available at the following link: https://github.com/AnFreTh/NAMpyComment: Accepted at the 27th International Conference on Artificial Intelligence and Statistics (AISTATS) 202

Regulatory Changes in Power Systems Explored with Explainable Artificial Intelligence

A stable supply of electrical energy is essential for the functioning of our society. Therefore, the electrical power grid's operation and energy and balancing markets are subject to strict regulations. As the external technical, economic, or social influences on the power grid change, these regulations must also be constantly adapted. However, whether these regulatory changes lead to the intended results is not easy to assess. Could eXplainable Artificial Intelligence (XAI) models distinguish regulatory settings and support the understanding of the effects of these changes? In this article, we explore two examples of regulatory changes in the German energy markets for bulk electricity and for reserve power. We explore the splitting of the German-Austrian bidding zone and changes in the pricing schemes of the German balancing energy market. We find that boosted tree models and feedforward neural networks before and after a regulatory change differ in their respective parametrizations. Using Shapley additive explanations, we reveal model differences, e.g. in terms of feature importances, and identify key features of these distinct models. With this study, we demonstrate how XAI can be applied to investigate system changes in power systems.Comment: 7 pages, 3 figure