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The integration of increasing shares of intermittent renewable energy necessitates flexibility in both energy generation and consumption. Typically, the operation of flexible energy resources is orchestrated through optimization models. However, the manual creation of these models is a complex and error-prone task, often requiring the expertise of domain specialists. This work introduces a methodology for the automatic generation of optimization models for systems of flexible energy resources to simplify the modeling process and increase the use of energy flexibility. This methodology utilizes a modular, generic model structure designed to depict systems of flexible energy resources. It incorporates algorithms for model parameter derivation from operational data and an information model that represents the system’s structure and dependencies of resources. The efficacy of this methodology is demonstrated in two case studies, highlighting its relevance and ability to significantly streamline the optimization modeling process by minimizing the need for manual intervention.

To identify promising sustainable fuels, e.g., to select novel synthetic fuels with the greatest impact on minimizing global warming, new methods for rapid and economical technical fuel assessment are urgently needed. Here, numerical models that are capable of predicting technical key data quickly and without experimental setup are necessary. One method is the use of chemical kinetic models, which are able to predict the technical key parameters related to combustion behavior. For a rapid technical fuel assessment, these chemical kinetic models need to be validated for new fuel components and for different temperature and pressure ranges. This work presents a new approach to extend the existing semi-detailed chemical kinetic models. For the application of the approach, the semi-detailed reaction mechanism DLR Concise was selected and extended for the low temperature combustion modeling of n-heptane and isooctane. The open-source software reaction mechanism generator (RMG) was used for this extension. Furthermore, an optimization of the merged chemical kinetic model with the linear transformation model (linTM) was conducted in order to improve the reproducibility of ignition delay times. The improvement of the predictive performance of ignition delay times at low temperatures for both species was successfully demonstrated. Therefore, this approach can be used to quickly add new species or reaction pathways to an existing semi-detailed reaction mechanism to enable a model-based technical fuel assessment for the early identification of promising fuels.

Advancements in data acquisition, storage and retrieval are progressing at an extraordinary rate, whereas the same in the field of knowledge extraction from data is yet to be accomplished. The challenges associated with hydrological datasets, including complexity, non-linearity and multicollinearity, motivate the use of machine learning to build hydrological models. Increasing global climate change and urbanization call for better understanding of altered rainfall-runoff processes. There is a requirement that models are intelligible estimates of underlying physics, coupling explanatory and predictive components, maintaining parsimony and accuracy. Genetic Programming, an evolutionary computation technique has been used for short-term prediction and forecast in the field of hydrology. Advancing data science in hydrology can be achieved by tapping the full potential of GP in defining an evolutionary flexible modelling framework that balances prior information, simulation accuracy and strategy for future uncertainty. As a preliminary step, GP is used in conjunction with a conceptual rainfall-runoff model to solve model configuration problem. Two datasets belonging to a tropical catchment of Singapore and a temperate catchment of South Island, New Zealand with contrasting characteristics are analyzed in this study. The results indicate that proposed approach successfully combines the merits of evolutionary algorithm and conceptual knowledge in the generation of optimal model structure and associated parameters to capture runoff dynamics of catchments.

As cities expand, many old towns face the threat of being renovated or demolished. In recent years, the drawbacks of extensive urban renewal have become increasingly apparent, and the focus of urban development is gradually shifting from efficiency to quality. This study aims to combine urban renewal with emerging technologies to address the dilemma between efficiency and quality in urban renewal. The study found that algorithm models based on graph theory, topology, and shortest path principles neglect the influence of internal states and visual features on pedestrian activity, resulting in lower simulation accuracy. Although incorporating internal states and visual features into the core of the algorithm further improved the simulation accuracy, the model operates in a 3D environment with lower efficiency. To address the problems of insufficient simulation accuracy and low efficiency, this study proposes a dynamic pedestrian activity model based on a multi-agent system and incorporating visual features. The model simulates pedestrian daily activity paths using pheromones and virtual sensors as the core, and it was found that using Visibility Graph Analysis could accurately divide pheromones in the environment, thus obtaining more accurate simulation results. Subsequently, based on the optimized pedestrian model’s agent activity rules and dynamic pheromone theory, a model for automatically generating road paving in urban renewal projects was developed, and the generated results were verified for their rationality through design practice. This technology can effectively promote urban renewal and the preservation of historic neighborhoods, providing technical support for achieving sustainable urban development.

Analyzing the runtime of a real-time application is particularly difficult with interferences from concurrently running processes. Low-overhead tracing is usually the most reliable tool to understand and check the behavior of such applications. In previous work, the automatic detection of common real-time problems was proposed, using models and constraints over the behavior of the application and operating system. Such a model automates system verification, alleviating the need for a thorough and deep understanding of all the system internals, and reduces drastically the time needed to find root causes for problems. Nevertheless, coming up with a model is not trivial. In this paper, we present a way to automate building a model of the process with constraints, based on user-space and kernel execution traces. Recurrent event sequences are used to build an approximate model of the behavior, and typical timings are used for setting up tentative constraints. The resulting model can then be refined as needed through user intervention. Our algorithms and their scalability have been tested and the experimental results show that our approach allows to build a model to automatically detect common problems in applications, with a relatively modest analysis cost.

The use of Third-Party Logistics (TPL) is a common practice among manufacturing companies seeking to increase profitability. However, the tender process in selecting a TPL service provider can be challenging, requiring significant effort from both the tendering company and the service provider. The latter must meticulously plan processes and calculate pricing positions while running the risk of losing the bid. This risk impedes verifying logistical feasibility and comparing different logistic concepts extensively, such as layouts, which are often work-intensive. With the ongoing progress of research toward automatic simulation model generation for material flow, it is left to answer whether such approaches can improve the planning processes of TPL service providers by using planning data to generate simulation models. Therefore, this work presents a system with an underlying ontology to generate material flow simulations by developing a model transformation methodology. The system’s functions are tested to determine whether they can support the planning process using defined case studies that cover everyday planning decisions. As a result, the system is capable of verifying the performance of planned logistic systems with minimal manual modelling efforts. This encompasses the evaluation of alternative logistical concepts for configuring the planned systems.

This paper proposes a model-driven approach for the security modelling and analysis of blockchain based protocols. The modelling is built upon the definition of a UML profile, which is able to capture transaction-oriented information. The analysis is based on existing formal analysis tools. In particular, the paper considers the Tweetchain protocol, a recent proposal that leverages online social networks, i.e., Twitter, for extending blockchain to domains with small-value transactions, such as IoT. A specialized textual notation is added to the UML profile to capture features of this protocol. Furthermore, a model transformation is defined to generate a Tamarin model, from the UML models, via an intermediate well-known notation, i.e., the Alice &Bob notation. Finally, Tamarin Prover is used to verify the model of the protocol against some security properties. This work extends a previous one, where the Tamarin formal models were generated by hand. A comparison on the analysis results, both under the functional and non-functional aspects, is reported here too.

Discrete-event simulation is a well-accepted method for planning, evaluating, and monitoring processes in production and logistics. To reduce time and effort spent on creating simulation models, automatic simulation model generation is an important area in modeling methodology research. When automatically generating a simulation model from existing data sources, the correct reproduction of dynamic behavior of the modeled system is a common challenge. One example is the representation of dispatching and scheduling strategies of production jobs. When generating a model automatically, the underlying rules for these strategies are typically unknown but yet have to be adequately emulated. In this paper, we summarize our work investigating the suitability of various data mining and supervised machine learning methods for emulating job scheduling decisions based on data obtained from production data acquisition. We report on the performance of the algorithms and give recommendations for their application, including suggestions for their integration in simulation systems.

In this article, we present an automatic model generation process for building QSAR models using Gaussian Processes, a powerful machine learning modeling method. We describe the stages of the process that ensure models are built and validated within a rigorous framework: descriptor calculation, splitting data into training, validation and test sets, descriptor filtering, application of modeling techniques and selection of the best model. We apply this automatic process to data sets of blood–brain barrier penetration and aqueous solubility and compare the resulting automatically generated models with ‘manually’ built models using external test sets. The results demonstrate the effectiveness of the automatic model generation process for two types of data sets commonly encountered in building ADME QSAR models, a small set of in vivo data and a large set of physico-chemical data.

The purpose of this paper is to present an in-depth survey of the rotary single inverted pendulum system from a control engineer's point of view. The scope of the survey includes modeling and open-loop analysis of the system as well as design and verification of balancing and swing up controllers which ensure successful stabilization of the pendulum in the unstable upright equilibrium. All relevant tasks and simulation experiments are conducted using the appropriate function blocks, GUI applications and demonstration schemes from a Simulink block library developed by the authors of the paper. The library is called Inverted Pendula Modeling and Control (IPMaC) and offers comprehensive program support for modeling, simulation and control of classical (linear) and rotary inverted pendulum systems.