Modular Simulation Environment Research Articles

The future of road freight transport and alternative technologies: A case study for Italy

The truck sector produces one-fourth of the greenhouse gas emissions in Italy. Therefore, it calls for an urgent emission reduction to achieve the national mid-century net-zero pledges. In this work, a methodology is developed, based on a model for estimating the long-term energy transitions, the ModUlar energy system Simulation Environment (MUSE), to project the long-term freight heavy-duty sector evolution in Italy. The methodology applies a bottom-up description of the sector, where technologies are characterised in terms of costs, efficiencies, and emissions. Results show that efficiency improvements and technological cost reduction due to learning can only reduce the sector emissions by a limited amount. In fact, although, lowering costs of alternative powertrains and fuels will have a significant effect on reducing tailpipe emissions, this should occur alongside a ban of internal combustion engines. Additional renewable generation should need to grow between 30 and 64 TW/y to substantially reduce emissions and leading to a zero-tailpipe truck sector by 2050. The replacement of conventional engines by alternative powertrains remains dependent on distribution and fuelling infrastructure expansion, which should go hand in hand with the promotion of electric and hydrogen-fuelled vehicles.

MUSE-RASA captures human dimension in climate-energy-economic models via global geoAI-ML agent datasets

This article provides a combined geospatial artificial intelligence-machine learning, geoAI-ML, agent-based, data-driven, technology-rich, bottom-up approach and datasets for capturing the human dimension in climate-energy-economy models. Seven stages were required to conduct this study and build thirteen datasets to characterise and parametrise geospatial agents in 28 regions, globally. Fundamentally, the methodology starts collecting and handling data, ending with the application of the ModUlar energy system Simulation Environment (MUSE), ResidentiAl Spatially-resolved and temporal-explicit Agents (RASA) model. MUSE-RASA uses AI-ML-based geospatial big data analytics to define eight scenarios to explore long-term transition pathways towards net-zero emission targets by mid-century. The framework and datasets are key for climate-energy-economy models considering consumer behaviour and bounded rationality in more realistic decision-making processes beyond traditional approaches. This approach defines energy economic agents as heterogeneous and diverse entities that evolve in space and time, making decisions under exogenous constraints. This framework is based on the Theory of Bounded Rationality, the Theory of Real Competition, the theoretical foundations of agent-based modelling and the progress on the combination of GIS-ABM.

Modular and reconfigurable simulation environment for evaluating the dynamic behavior of coupled robots performing milling tasks

Modular and reconfigurable simulation environment for evaluating the dynamic behavior of coupled robots performing milling tasks

A Modular Ice Cream Factory Dataset on Anomalies in Sensors to Support Machine Learning Research in Manufacturing Systems

A small deviation in manufacturing systems can cause huge economic losses, and all components and sensors in the system must be continuously monitored to provide an immediate response. The usual industrial practice is rather simplistic based on brute force checking of limited set of parameters often with pessimistic pre-defined bounds. The usage of appropriate machine learning techniques can be very valuable in this context to narrow down the set of parameters to monitor, define more refined bounds, and forecast impending issues. One of the factors hampering progress in this field is the lack of datasets that can realistically mimic the behaviours of manufacturing systems. In this paper, we propose a new dataset called MIDAS (Modular Ice cream factory Dataset on Anomalies in Sensors) to support machine learning research in analog sensor data. MIDAS is created using a modular manufacturing simulation environment that simulates the ice cream-making process. Using MIDAS, we evaluated four different supervised machine learning algorithms (Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron) for two different problems: anomaly detection and anomaly classification. The results showed that multilayer perceptron is the most suitable algorithm with respect to model accuracy and execution time. We have made the data set and the code for the experiments publicly available, to enable interested researchers to enhance the state of the art by conducting further studies.

Modular Fuel Cell System Simulation Environment with Focus on Membrane Water Management

Modular Fuel Cell System Simulation Environment with Focus on Membrane Water Management

MUSE: An open-source agent-based integrated assessment modelling framework

Integrated assessment models (IAMs) are a cornerstone of an effective approach to climate change mitigation. Despite the variety of methodologies for characterising the energy system, land use change, economics, and climate response, the modelling community has an open and urgent request for tools capable of more realistic interpretation of the energy transition, capturing human behaviour, and embodying the principles of transparency, reproducibility, and flexibility of use.This paper presents an open-source modelling framework designed to fill that gap. Named MUSE (ModUlar energy systems Simulation Environment), this new agent-based model supports flexible characterisation of agent decision-making, including individual goals, bounded-rationality, imperfect foresight, and limited knowledge during the decision process. MUSE integrates this agent-based approach in a partial-equilibrium framework and enables a technology-rich description of the energy systems with an unprecedented degree of flexibility for including technological, temporal, and geographical granularity. The structure of MUSE creates the ability to produce climate change mitigation assessments that are more grounded, and more tangible model outputs for conceiving effective approaches to mitigation. MUSE is available open source under a GNU General Public License v3.0 on GitHub at this link https://github.com/SGIModel/MUSE_OS.

Investigating the Vibration Mitigation Efficiency of Tuned Sloshing Dampers Using a Two-Fluid CFD Approach

The efficiency of a Tuned Sloshing Damper (TSD) when mitigating wind-induced structural vibrations is investigated. We assessed the performance in terms of peak structural displacements and accelerations, compared to that of the Tuned Mass Damper (TMD). One load scenario considers oncoming gusts due to natural turbulence, whereas the other assumes predominant vortex shedding at a low turbulence intensity. The known optimum tuning rules for TSDs and TMDs were adopted. We combined numerical models for fluids and structures to simulate the dynamic effects caused by wind loading. A two-fluid Computational Fluid Dynamics (CFD) approach was used for the realistic simulation of the TSD. The interaction between the flow, the structural behavior and the added devices was captured. All of these computational methods and respective models represent the necessary components of a modular and flexible simulation environment. The study demonstrates that this workflow is suited to model the inclusion of TSDs and TMDs, as well as to capture the effect of transient wind at full scale. We specifically used it to quantify the efficiency of added dampers. The process highlights challenges in properly tuning a TSD and its reduced efficiency compared to that of a TMD. Such an outcome is attributed to the water mass and potential added damping only being partially activated. The computational framework promises the ability to improve such designs by enabling numerical optimization for better efficiency.

ALPACA - a level-set based sharp-interface multiresolution solver for conservation laws

ALPACA is a simulation environment for simulating hyperbolic and (incompletely) parabolic conservation laws with multiple distinct and immiscible phases. As prominent example, consider the compressible Navier-Stokes equations (NSE). Solutions to these equations give insight and understanding of many important engineering applications. Numerical simulations of nonlinear parabolic systems of equations are very challenging for their complex nonlinear dynamics including the propagations of discontinuities such as shocks and phase interfaces. Accurate predictions require high temporal and spatial resolutions for such multi-scale problems. We utilize low dissipation high-resolution methods to capture the dynamics inside the separate phases. Their interaction is modeled by a sharp-interface level-set method with conservative interface-interaction. This allows to accurately locate the interface position and to easily prescribe arbitrary coupling conditions. We tackle the resulting immense computational loads by using a block-based multiresolution (MR) algorithm and adaptive local time stepping. The level-set treatment is integrated into the MR algorithm with little overhead by employing a smart tagging system and adaptive storage of the fluid data in the MR nodes. We embed these methods in a C++20 object-oriented modular framework using state-of-the-art programming paradigms. Furthermore, our implementation is capable to exploit the multiple levels of parallelism in modern high-performance computing (HPC) systems efficiently. We demonstrate the capabilities of our framework by simulating a variety of compressible multi-phase flow problems. Problem-sizes are of O(1010) effective degree of freedom (DOFs). By the use of MR, we typically achieve memory and compute compressions of >90%. We demonstrate near-optimal parallel performance for scaling runs using O(104) cores, regardless of the employed numerical models. Program summaryProgram Title: ALPACA - Adaptive Level-set Parallel Code AlpacaCPC Library link to program files:https://doi.org/10.17632/5zr3sg83ct.1Developer's repository link:https://gitlab.lrz.de/nanoshock/ALPACALicensing provisions: GPLv3Programming language: C++20Supplementary material: Code: Copy of the git repository, Videos: Air-helium shock bubble interaction (front and back view), Air-R22 shock bubble interaction, Three bubble shock interface interaction.Nature of problem: Numerical simulation of conservation laws such as the compressible Navier-Stokes equation with several interacting gaseous and liquid phases remains challenging even today. These flows often involve singularities such as shocks and interfaces as well as instabilities driven by their interaction. The inherent highly nonlinear dynamics of those systems leads to a broad range of temporal and spatial scales that have to be resolved. There exists a variety of mutually exclusive numerical models that are able to tackle these challenges. Those, however, are computationally expensive and require computational power that is offered only by large-scale state-of-the-art distributed-memory machines.Solution method: We have developed a modular simulation environment for conservation laws allowing exchange of numerical methods without loss of parallel performance. Computational efficiency is enhanced by employing multi-resolution schemes with adaptive local-time stepping. Our block-based implementation of these schemes is parallelized using the Message Passing Interface and exploits vectorization capabilities of the compute hardware. To simulate distinct and immiscible phases inside the computational domain, we utilize a sharp-interface level-set method. The level-set method allows to accurately locate the interface position and to easily prescribe arbitrary coupling conditions. The narrow-band approach reduces the computational load of the level-set method. We extend this method by a smart tagging system that exploits the block-based nature of the algorithm and further reduces the computational load. The simulation framework is written in modern C++20 and provides a Python interface for integration in Machine Learning and Uncertainty Quantification toolchains. We use parallel HDF5 in combination with XDMF to output field quantities. CMake is used as build system. We have completely annotated the source code using doxygen-style comments, allowing automated documentation generation in different formats. The source code is equipped with CI/CD-automated unit tests and an exhaustive integration test suite.Additional comments including restrictions and unusual features: ALPACA relies on open-source third-party libraries for input and testing. These are packaged as git submodules and automatically integrated. ALPACA is tested on Linux and macOS.

Feasibility of an endovascular training and research environment with exchangeable patient specific 3D printed vascular anatomy: Simulator with exchangeable patient-specific 3D-printed vascular anatomy for endovascular training and research

Endovascular interventions have become standard procedures for the therapy of abdominal aortic aneurysms. Therefore, endovascular surgeons need special skills which have to be learned and trained. Additionally, authentic simulators are needed for further development of new endovascular devices and procedures. The aim of this project was to develop an authentic and modular endovascular simulation environment with patient-specific vascular anatomy for training and research purposes. We first designed a prototype with exchangeable 3D-printed patient-specific vascular anatomy. Then, the feasibility of the prototype was validated by a simulation of an EVAR procedure in a clinical setting. We developed an authentic endovascular simulator with an exchangeable patient-specific vascular anatomy and performed an EVAR procedure under realistic conditions. The evaluation of the accuracy of the vascular models showed little deviation when compared with the original CT data. Endovascular simulators based on patient-specific 3D-printed vascular models can realistically mimic endovascular procedures and have the potential to be used for further development of new devices and grafts as well as for training purposes. Furthermore, in our opinion they can reduce the use of animals during developmental processes.

Long-term development of the industrial sector – Case study about electrification, fuel switching, and CCS in the USA

In the urgent quest for solutions to mitigate climate change, the industry is one of the most challenging sectors to decarbonize. In this work, a novel simulation framework is presented to model the investment decisions in industry, the Industrial Sector Module (ISM) of the ModUlar energy system Simulation Environment (MUSE). This work uses the ISM to quantify effects of three combined measures for CO2 emission reduction in industry, i.e. fuel switching, electrification, and adoption of Carbon Capture and Storage (CCS) and to simulate plausible scenarios (base scenario and climate ambitious scenario) for curbing emissions in the iron and steel sector in the USA between 2010 and 2050. Results show that when the climate ambitious scenario is applied, the cumulative emissions into the atmosphere (2,158 Mt CO2) are reduced by 40% in comparison to the base scenario (3,608 Mt CO2). This decarbonization gap between both scenarios intensifies over time; in the year 2050, the CO2 intensity in the climate ambitious scenario is 81% lower in comparison to the base scenario. The study shows that major contributions to industry decarbonization can come from the further uptake of secondary steel production. Results show also that a carbon tax drives the decarbonization process but is not sufficient on its own. In addition, the uptake of innovative low-carbon breakthrough technologies is necessary. It is concluded that industrial electrification is counterproductive for climate change mitigation, if electricity is not provided by low-carbon sources. Overall, fuel switching, industrial electrification, and CCS adoption as single measures have a limited decarbonization impact, compared to an integrated approach that implements all the measures together providing a much more attractive solution for CO2 mitigation.

Clustered spatially and temporally resolved global heat and cooling energy demand in the residential sector

Climatic conditions, population density, geography, and settlement structure all have a strong influence on the heating and cooling demand of a country, and thus on resulting energy use and greenhouse gas emissions. In particular, the choice of heating or cooling system is influenced by available energy distribution infrastructure, where the cost of such infrastructure is strongly related to the spatial density of the demand. As such, a better estimation of the spatial and temporal distribution of demand is desirable to enhance the accuracy of technology assessment. This paper presents a Geographical Information System methodology combining the hourly NASA MERRA-2 global temperature dataset with spatially resolved population data and national energy balances to determine global high-resolution heat and cooling energy density maps. A set of energy density bands is then produced for each country using K-means clustering. Finally, demand profiles representing diurnal and seasonal variations in each band are derived to capture the temporal variability. The resulting dataset for 165 countries, published alongside this article, is designed to be integrated into a new integrated assessment model called MUSE (ModUlar energy systems Simulation Environment) but can be used in any national heat or cooling technology analysis. These demand profiles are key inputs for energy planning as they describe demand density and its fluctuations via a consistent method for every country where data is available.

Tellurium: An extensible python-based modeling environment for systems and synthetic biology

Here we present Tellurium, a Python-based environment for model building, simulation, and analysis that facilitates reproducibility of models in systems and synthetic biology. Tellurium is a modular, cross-platform, and open-source simulation environment composed of multiple libraries, plugins, and specialized modules and methods. Tellurium is a self-contained modeling platform which comes with a fully configured Python distribution. Two interfaces are provided, one based on the Spyder IDE which has an accessible user interface akin to MATLAB and a second based on the Jupyter Notebook, which is a format that contains live code, equations, visualizations, and narrative text. Tellurium uses libRoadRunner as the default SBML simulation engine which supports deterministic simulations, stochastic simulations, and steady-state analyses. Tellurium also includes Antimony, a human-readable model definition language which can be converted to and from SBML. Other standard Python scientific libraries such as NumPy, SciPy, and matplotlib are included by default. Additionally, we include several user-friendly plugins and advanced modules for a wide-variety of applications, ranging from complex algorithms for bifurcation analysis to multidimensional parameter scanning. By combining multiple libraries, plugins, and modules into a single package, Tellurium provides a unified but extensible solution for biological modeling and analysis for both novices and experts. Availability: tellurium.analogmachine.org.

Numerical modeling of thermal aging in steady state rolling tires

In this contribution, thermal material degradation in steady state rolling tires is numerically studied. A phenomenological time-dependent thermal damage approach from the field of continuum damage mechanics (CDM) is implemented into a modular simulation environment (Behnke & Kaliske, 2015), which allows the thermo-mechanically coupled analysis of axisymmetric tires in steady state motion via the finite element method within an Arbitrary Lagrangian Eulerian framework. Within the numerical model, rubber compounds of the tire are represented by their temperature-dependent viscoelastic properties at finite deformations. Energy losses as heat source terms, computed on a physical basis from the dissipative material properties (non-equilibrium stresses), are used to predict the heat build-up in the tire under severe service conditions. Model parameters for the time-dependent thermal damage approach are identified from previously published experimental investigations on thermally aged rubber compounds in order to link the temperature increase and the evolution of thermal damage (thermal aging). The thermal damage approach qualitatively captures the irreversible changes of the rubber compounds’ mechanical properties, which, in turn, lead to an alteration of the entire structural response of the thermally damaged tire.

Multi-agent large-scale parallel crowd simulation

This paper presents design, implementation and performance results of a new modular, parallel, agent-based and large scale crowd simulation environment. A parallel application, implemented with C and MPI, was implemented and run in this parallel environment for simulation and visualization of an evacuation scenario at Gdansk University of Technology, Poland and further in the area of districts of Gdansk. The application uses a parallel MPI I/O run on two different clusters and a two or three node Parallel File System (PFS) to store a current state in a file. In order to make this implementation efficient, we used our previously developed and tuned Byte-addressable Non-volatile RAM Distributed Cache - a solution that allows to access small data chunks from spread locations within a file efficiently. We have presented application execution times versus the number of agents (up to 100000), versus the number of simulation iterations (up to 25000), versus map size (up to 6km2) and versus the number of processes (up to more than 650) showing high speed-ups.

Modular simulation of reefer container dynamics

The amount of food transported long distances in reefer containers is constantly increasing and so is the cost per mile because of rising fuel prices. One way to reduce the cost is to minimize the energy consumed by reefer containers through a better controller but in order to achieve this a fast and flexible simulation model is needed for controller development. The simulation model may also be used for developing fault diagnosis methods for the reefer container and thereby further lowering costs by reducing the amount of functioning spare parts that is replaced and by providing early warning for faults enabling preventive maintenance. In this paper the feasibility of using different simulation methods is assessed with the goal of identifying a fast but accurate method that works well in a multi-rate environment. A modular multi-rate simulation environment for a dynamical system consisting of components with different dynamical speeds is presented with an improvement of previous results. The simulation speed is improved by 350% with no reduction in the accuracy of the solution, by substituting the Matlabode15s solver with an explicit first-order solver with a step-size calculation algorithm that ensures numerical stability and that the error is bounded using a minimum of calculations. The reefer container model is simulated using both ode15s and the proposed method both in multi-rate and monolithic configurations. The results are analyzed and compared with respect to speed and accuracy.

An alternative modelling approach to predict emissions of N2O and NO from forest soils

Emissions of N2O from forest soils in Europe are an important source of global greenhouse gas emissions. However, influencing the emission rates by forest management is difficult because the relations and feedbacks between forest and soils are complex. Process-based models covering both vegetation and soil biogeochemical processes are frequently used to analyse emission patterns. Particularly, the simulation of soil C and N turnover processes driving N2O production, consumption and emission from forest soils requires highly specific input data which renders their regional application difficult since at this scale, soil conditions are often not well understood. Therefore, a soil C and N model (DecoNit) has been developed which describes biogeochemical processes with a simplified structure compared to existing carbon/nitrogen models that nevertheless follows the basic physical and chemical laws involved and which allows to simulate N trace gas emissions. The DecoNit model was previously calibrated using an extensive dataset on decomposition rates of incubated plant materials, microbial dynamics and nitrification. The DecoNit model has now been embedded in a modular simulation environment (MoBiLE) where it is combined with soil water balance and forest process sub-modules. Here, we present the evaluation of MoBiLE-DecoNit with emission data of N2O and NO from forest soils of 15 European sites and compare simulation results with a previous study in which a more complex model (PnET-N-DNDC) was used. Evaluation criteria were as follows: (1) precision of modelled annual average emission rates; (2) coherence of modelled and measured annual average and daily emissions; (3) a dynamic representation of emission rates that correspond with the observed variance of fluxes. The results show that MoBiLE-DecoNit captures average annual emission rates more precisely than the more complex model PnET-N-DNDC. Also the structural underestimation of N trace gas fluxes from forest soils was resolved. Moreover, we present evidence that the new modelling approach is also somewhat more adequate for describing inter-daily emission dynamics. The combined MoBiLE-DecoNit is therefore thought to be a promising approach to simulate forest development and greenhouse gas balances on site and regional scales.

Integrated simulation through the source-code coupling of component models from a modular simulation environment into a comprehensive building performance simulation tool

No single building performance simulation program contains sufficient capabilities and flexibility to fully respond to the full complexity of modern building design and analysis. Consequently, considerable efforts and advances have been made to facilitate the integrated use of multiple simulation tools to provide more extensive modelling capabilities. The research reported in this article has made a contribution towards the goal of integrated simulation by focusing on the internal coupling of component models from a modular simulation environment into a comprehensive building performance simulation tool. A flexible and extensible facility has been designed and developed to enable the use of HVAC component models (TYPEs) from the TRNSYS simulation program within the ESP-r simulation platform. With this, the source code for any number of TRNSYS TYPEs can be compiled with the ESP-r source code to produce an integrated simulation tool that possesses greater capabilities than either simulation program alone.

NeuroCAD – the modular simulation environment for effective biologically plausible neuromodeling

NeuroCAD – the modular simulation environment for effective biologically plausible neuromodeling

Modular Testing and Simulation Environment for Analysis and Optimization of Fuel Cell Systems

Improvement of fuel cell systems with respect to reliability and efficiency is the main issue in preparing them for their commercialization. Stationary, automotive or portable systems are still at the development stage and a lot of tasks have to be solved as to increase the component and system efficiencies by facilitating the system construction or eliminating parasitic components. To accelerate the development and to determine the systems efficiencies as well as the inaccessible system values, effective standardized tests have to be established and linked with a flexible modeling and simulation environment. A modular model-aided system analysis and development environment is presented which has been evaluated and validated at the IWE. The tool has been applied to a complex prototype stationary PEMFC system, a commercial methanol based PEMFC auxiliary power unit and a fictive stationary SOFC system proving the tool flexibility, modularity and accuracy for a wide range of applications.

Hierarchical Modeling and Simulation Environment for Intelligent Transportation Systems

This article presents a hierarchical and modular traffic simulation environment for intelligent transportation systems (ITS). A conventional traffic simulation model is classified as microscopic or macroscopic; however, abstraction-level, vehicle-level, cell-level, road-level, and street-level traffic models can be coherently integrated and developed. Such an abstraction process is important for flexible traffic analysis since it reduces the complexity of a model, retaining its validity relative to the modeling objectives and experimental condition. To do this, the authors have proposed the four-layered approach: (1) system entity structure/model base, (2) model abstraction, (3) traffic modeling, and (4) ITS simulation systems layer. A proposed methodology has been successfully applied for building the advanced traveler information system and the advanced traffic management system. The abstraction method of a road network in traffic modeling and simulation will be widely used because it is possible to express and analyze various requirements of the traffic analyst hierarchically and structurally.