Open-source Simulation Tool Research Articles

HeatNetSim: An open-source simulation tool for heating and cooling networks suitable for future energy systems

Low temperature district heating and cooling networks are a promising solution to increase the share of renewable energy within the heating and cooling sector. However, the transition to bidirectional networks poses new challenges (representation of fluid-specific properties, coordinate operation with power grid, retrofitting of existing networks) requiring a new generation of simulation tools. In this paper, we present a simulation tool for thermal networks capable of bidirectional flows, zero mass flows and a dynamic temperature calculation. The structure of the tool allows coupling to other sector’s simulation tools enabling multi-domain analysis of relevant interactions with power and gas grids. We showcase the tool with two case studies inspired by the heating and cooling infrastructure of our campus. The first case study analyzes the operation of a unidirectional high temperature district heating network supplied by a power-driven combined heat and power plant. The case study highlights the suitability of district heating networks as a source of flexibility to the power grid by using its thermal inertia. The second case study investigates the transition from independently operated district heating and district cooling networks into a bidirectional low temperature district heating and cooling network highlighting the energy saving potential by thermal balancing effects.

Assessing wind turbine blade durability longevity utilizing national renewable energy laboratory tools

With the escalating significance of renewable energy, there exists a pressing demand for precise software and tools capable of designing and forecasting wind turbine blade service life. OpenFAST, TurbSim, and MLife are pivotal open-source wind turbine simulation tools that integrate aerodynamics, structural dynamics, wind simulation, and control systems. They enable accurate prediction of wind turbine performance. This study aims to leverage these software resources to model turbulent wind conditions using predefined basic parameters and then calculate fatigue and longevity of the wind turbine blade to determine service life. Particularly, MLife was employed for rose loading, enhancing the fidelity of the loading cycle simulation. Collaboration between the National Renewable Energy Laboratory and the research team played a crucial role in establishing a foundational baseline and implementing realistic input parameters for evaluating the ultimate load, determining damage equivalent loads (DEL), and projecting service life. In all simulations, a constant factor of safety (FOS) of 1.5 was consistently applied. Additionally, Finite Element Analysis (FEA) was employed to validate our numerical experiments, ensuring the accuracy of the obtained results by correlating simulation outcomes with real-world scenarios, further strengthening the credibility of the study.

Enhancing Urban Sustainability: Developing an Open-Source AI Framework for Smart Cities

To address the growing need for advanced tools that enable urban policymakers to conduct comprehensive cost-benefit analyses of traffic management changes, the Urbanite H2020 project has developed innovative artificial intelligence methods. Among them is a robust decision support system that assists policymakers in evaluating and selecting optimal urban mobility planning modifications by combining objective and subjective criteria. Utilising open-source microscopic traffic simulation tools, accurate digital models (or “digital twins”) of four pilot cities—Bilbao, Amsterdam, Helsinki, and Messina—were created, each addressing unique mobility challenges. These challenges include reducing private vehicle access in Bilbao’s city center, analysing the impact of increased bicycle traffic and population growth in Amsterdam, constructing a mobility-enhancing tunnel in Helsinki, and improving public transport connectivity in Messina. The research introduces five key innovations: the application of a consistent open-source simulation platform across diverse urban environments, addressing integration and consistency challenges; the pioneering use of Dexi for advanced decision support in smart cities; the implementation of advanced visualisations; and the integration of the machine learning tool, Orange, with a user-friendly GUI interface. These innovations collectively make complex data analysis accessible to non-technical users. By applying multi-label machine learning techniques, the decision-making process is accelerated by three orders of magnitude, significantly enhancing urban planning efficiency. The Urbanite project’s findings offer valuable insights into both anticipated and unexpected outcomes of mobility interventions, presenting a scalable, open-source AI-based framework for urban decision-makers worldwide.

Performance Analysis of Routing Protocols for Urban VANET Based on SUMO and NS2.35

To make the dream of intelligent transportation systems come true, the development of the autonomous cars is considered as a tremendous disruptive innovation in the coming years. Vehicular ad hoc networks (VANETs) are being used as a tool for improving road safety by alarming the drivers about accidents occurred ahead of them or for providing internet access via gateways along the road. Due to highly dynamic nature of nodes in VANETs, designing a routing protocol for VANET is quite challenging compared to other networks environment. Researchers have suggested several routing algorithms for VANETs. Few routing algorithms are based on topology based whereas others have considered position based etc. As no standard scheme is available for choosing a routing protocol in VANETs, this paper gives an insight on how to choose a routing protocol depends on varying condition of traffic such as number of nodes on the roads and maximum speed of nodes. The popular protocols of Topology Based Routings and Position Based Routing have been chosen for analysis on varying traffic environment. All the protocols have been critically tested for different metrics such as packet delivery ratio and average end-to-end delay during the simulation. Simulation is carried out with the help of SUMO traffic simulator and open-source simulation tools NS2.35 network simulator. Simulation result show that Position Based Routing is performing better at low average end-to-end delay than Topology Based Routings, but Position Based Routing has the lowest possible performance in packet delivery ratio than Topology Based Routings.

Adaptive and low-cost resource synchronization based on data distribution service in high dynamic networks

In disaster-stricken areas monitoring, management, search, and rescue operations, unmanned aerial vehicles (UAVs) play a crucial role in disaster management and emergency communication due to their high mobility. To efficiently coordinate and plan UAVs and their carried sensor and base station resources, synchronization is essential to establish consistency, laying the foundation for high-level demands. In such scenarios, synchronization relies on request–response (RR) or publish–subscribe (PS) forms of information exchange. Existing research in the field typically focuses on higher-level applications and selects either RR or PS synchronization, thereby overlooking the potential advantages that could be gained from combining both methods to meet synchronization requirements. We propose a resource synchronization method based on the Data Distribution Service (DDS) and a linear time complexity subscription mechanism tailored to specific query demands, which considers the pros and cons of the above two information exchange forms and the bottom-layer network topology. Experimental results using open-source simulation tools demonstrate that the proposed method adapts to scene requirements and decreases bandwidth by at least 21.2% and packet rate by at least 3.7% compared to different baseline methods across three topologies, while satisfying delay and query success rate requirements. Furthermore, the method maintains robust performance in the face of dynamic changes in network topology, showcasing its robustness.

IDSimF: An Open-Source Framework for the Simulation of Molecular Ion Dynamics in Mass Spectrometry and Ion Mobility Spectrometry.

The development of mass spectrometric and ion mobility devices heavily depends on a comprehensive understanding of the behavior of ions within such systems. Therefore, numerical modeling of ion paths helps to optimize and verify existing devices, and contributes to the development of innovative ion optical systems and multipole geometries. This Article introduces IDSimF (Ion Dynamics Simulation Framework), an open-source simulation tool tailored to the nonrelativistic dynamics of molecular ions in mass and ion mobility spectrometry applications. Addressing limitations in existing software packages, as for example SIMION, OpenFOAM, and COMSOL, IDSimF offers a specialized platform for simulating ion trajectories in electric fields. IDSimF efficiently accounts for space charge effects and considers various ion-neutral collision models while handling chemical kinetics. The framework is highly modular with reduced user input configuration complexity and aims to support simulation efforts in development and optimization of in mass spectrometers.

Esra (elevator simulation, research & analysis): an open-source software tool for elevator traffic simulation, research, and analysis

ABSTRACT Today, although elevator companies typically have their own elevator simulation and analysis tools, these remain inaccessible to the public due to commercial concerns and rivalry in the sector. Consequently, due to the lack of modular open-source software tools, interest in the design and development of elevator systems in academia is quite limited, contrary to the vast potential of the field. This paper addresses this gap introducing an open-source software tool, the Elevator Simulator for Research and Analysis (ESRA), developed using the MATLAB programming language and an object-oriented approach. ESRA features built-in control methods, and visualization options with the capability to save and reuse traffic data, facilitating in-depth comparative analysis. Moreover, its modular architecture allows a design and development basis for further research in the field. The proposed software aims to enable developers to design, implement, analyse, and compare their control algorithms with other methods.

Web-based simulation and motion planning for human-robot and multi-robot applications

ABSTRACT This paper introduces the human-robot and multi-robot programming and simulation capabilities of a web-based, open-source robot programming and simulation tool called Assembly. The tool supports scenarios with up to three robots and provides support for simulating human arms working closely with a robot. To facilitate the simulation of scenarios involving human-robot and robot-robot collaboration, the paper proposes an improved version of the neural network-based Adaptive Simplex Motion Planner, which is capable of performing motion planning at a rate of over 3500 Hz. The improved toolpath planner uses an obstacle avoidance approach that leverages ray casting and shadow volume teachniques. The new planner is evaluated on the basis of two human-robot and multi-robot simulation scenarios and the results are compared to those of the original algorithm.

From novice to expert: advancing step-by-step simulation guideline for urban logistics with an open-source simulation tool

PurposeThis research analyses challenges faced by users at various levels in planning and designing participatory simulation models of cities. It aims to identify issues that hinder experts from maximising the effectiveness of the SUMO tool. Additionally, evaluating current methods highlights their strengths and weaknesses, facilitating the use of participatory simulation advantages to address these issues. Finally, the presented case studies illustrate the diversity of user groups and emphasise the need for further development of blueprints.Design/methodology/approachIn this research, action research was used to assess and improve a step-by-step guideline. The guideline's conceptual design is based on stakeholder analysis results from those involved in developing urban logistics scenarios and feedback from potential users. A two-round process of application and refinement was conducted to evaluate and enhance the guideline's initial version.FindingsThe guidelines still demand an advanced skill level in simulation modelling, rendering them less effective for the intended audience. However, they have proven beneficial in a simulation course for students, emphasising the importance of developing accurate conceptual models and the need for careful implementation.Originality/valueThis paper introduces a step-by-step guideline designed to tackle challenges in modelling urban logistics scenarios using SUMO simulation software. The guideline's effectiveness was tested and enhanced through experiments involving diverse groups of students, varying in their experience with simulation modelling. This approach demonstrates the guideline's applicability and adaptability across different skill levels.

Prediction of multiple-wake velocity and wind power using a cosine-shaped wake model

It is well accepted the wakes induced by upstream turbines have an adverse impact on the power production of downstream turbines and this ubiquitous phenomenon severely affects wind farm performance. The aim of the study is to apply a new analytical wake model to prediction of multiple-wake velocity and power output inside wind farms and evaluate its performance. Unlike the classical top-hat model (Jensen model) widely embedded in industry-standard software, the newly proposed analytical wake model, which is derived based on the conservation of both mass and momentum, assumes a trigonometric distribution for the velocity deficit in the wakes of a stand-alone wind turbine and adopts a variable wake growth rate relating ambient turbulence as well as rotor-generated turbulence. Two superposition models including the SED (superposition of energy deficits) and the SVD (superposition of velocity deficits) are employed to simulate the wake-interaction flows and the performance of the new model with the superposition methods is evaluated in two cases: (1) wake interactions of two wind turbines; (2) power prediction of the Horns Rev offshore wind farm. The open-source wind-farm simulation tool FLORIS is employed to make a further comparison of different wake models. It is found that the new and the Gaussian models with the SED are in reasonably good agreement with large-eddy simulation (LES) and measurements compared with other wake models, whereas lower velocity as well as power output is predicted when using the SVD counterpart. This research proposes the SED model as an effective selection strategy for evaluating power output in wind farms when using the cosine and Gaussian models.

5G-RCOLAB: A system level simulator for 5G and beyond in rural areas

This paper describes 5G-RCOLAB, an open source simulation tool for performance evaluation of 5G/6G wireless networks in rural areas. The tool is designed with a layered architecture, based on three modules: (i) a link-level simulator of a communication channel for remote areas, with a mapping Link to System (L2S) interface; (ii) a Long-Term Evolution (LTE)-based physical layer adapted for remote areas, together with a Medium Access Control (MAC) layer that includes a novel Collaborative Spectrum Sensing (CSS) technique with security features for Byzantine attacks and spectrum usage efficiency; and (iii) an application oriented network layer that emulates the behavior of typical users in rural areas. The tool has the goal to reduce simulation complexity of large scale 5G rural networks and at the same time, to represent coherently the features of the physical and MAC layers, that include new channel models that cover long distances with the use of TV White Spaces (TVWS) in the Ultra High Frequency (UHF) and Very High Frequency (VHF) bands, as well as novel CSS techniques to improve spectrum efficiency. The 5G-RCOLAB was validated both by field test measurements from a Proof of Concept (PoC) prototype and by the simulation and evaluation of three 5G common core use cases in rural areas. The results show that the 5G-RCOLAB features allow a better evaluation of novel technologies implemented in the physical and MAC layers and provide paths for improvements and deployments of cost effective solutions of 5G in rural or remote areas.

S15-03: PK-Sim as an open source simulation tool for human risk assessment of pharmaceuticals: an industry perspective

S15-03: PK-Sim as an open source simulation tool for human risk assessment of pharmaceuticals: an industry perspective

SatelliteCloudGenerator: Controllable Cloud and Shadow Synthesis for Multi-Spectral Optical Satellite Images

Optical satellite images of Earth frequently contain cloud cover and shadows. This requires processing pipelines to recognize the presence, location, and features of the cloud-affected regions. Models that make predictions about the ground behind the clouds face the challenge of lacking ground truth information, i.e., the exact state of Earth’s surface. Currently, the solution to that is to either (i) create pairs from samples acquired at different times or (ii) simulate cloudy data based on a clear acquisition. This work follows the second approach and proposes an open-source simulation tool capable of generating a diverse and unlimited number of high-quality simulated pair data with controllable parameters to adjust cloud appearance, with no annotation cost. The tool is available as open-source. An indication of the quality and utility of the generated clouds is demonstrated by the models for cloud detection and cloud removal trained exclusively on simulated data, which approach the performance of their equivalents trained on real data.

Analyzing the role of consumer behavior in coping with intermittent supply in water distribution systems

Abstract A substantial number of water distribution systems (WDS) worldwide are operated as intermittent water supply (IWS) systems, delivering water to consumers in irregular and unreliable manners. The IWS consumers commonly adapt to flexible consumption behaviors characterized by storing the limited water available during shorter supply periods in intermediate storage facilities for subsequent usage during more extended nonsupply periods. Nevertheless, the impacts of such consumer behavior on the performance of IWS systems have not been adequately addressed. Toward this direction, this article presents a novel open-source Python-based simulation tool (EPyT-IWS) for WDS, virtually acting like an IWS modeling extension of EPANET 2.2. The applicability of EPyT-IWS was demonstrated by conducting hydraulic simulations of a typical WDS with representative IWS attributes. Different IWS operation cases were considered by varying the amount and consistency of the water availability to the consumers. EPyT-IWS outputs showed that domestic storage of water within underground tanks and subsequent pumping into overhead tanks allows consumers to cope with the intermittent water availability and suitably meet their demands. Besides the interval, the clock time of the water supply was predicted to influence IWS consumers’ ability to meet water demands.

Infrastructure-specific evaluation of building downtime due to earthquake-induced utility disruption

When it comes to estimate the time for functional recovery of a building subjected to earthquake-induced damage, the evaluation of downtime due to utility disruption may be critical, especially in cases when the building is only slightly damaged and the networks have instead experienced heavy damage. However, such a downtime portion has been traditionally disregarded, or quantified in an approximate way entailing the plain use of utility disruption curves, also called restoration curves. This work attempts to overcome the current limitations by proposing a methodology for the infrastructure-specific evaluation of the utility-related downtime. Any set of restoration curves available from the literature can be employed within a comprehensive framework in which the functionality of utilities is assessed over time in capacitive terms at the local level, allowing to properly estimate the serviceability level for a building of interest. Monte Carlo simulation is adopted for a probabilistic evaluation of downtime. The methodology, herein implemented as an extension of an open-source simulation tool, was illustrated with reference to a realistic infrastructure comprising three utilities and one building. Sensitivity of results to the restoration model adopted was explored. The downtimes for complete and partial restoration of the three utilities were derived from functionality curves expressed in terms of flow-based performance metrics. Using expected downtimes given an event, in place of return period-based ones, is conservative and recommended. Following the presented methodology will allow for an accurate assessment of the total building downtime within comprehensive frameworks for integrated seismic/energy retrofitting of existing buildings.

DIdM-EIoTD: Distributed Identity Management for Edge Internet of Things (IoT) Devices.

The Internet of Things (IoT) paradigm aims to enhance human society and living standards with the vast deployment of smart and autonomous devices, which requires seamless collaboration. The number of connected devices increases daily, introducing identity management requirements for edge IoT devices. Due to IoT devices' heterogeneity and resource-constrained configuration, traditional identity management systems are not feasible. As a result, identity management for IoT devices is still an open issue. Distributed Ledger Technology (DLT) and blockchain-based security solutions are becoming popular in different application domains. This paper presents a novel DLT-based distributed identity management architecture for edge IoT devices. The model can be adapted with any IoT solution for secure and trustworthy communication between devices. We have comprehensively reviewed popular consensus mechanisms used in DLT implementations and their connection to IoT research, specifically identity management for Edge IoT devices. Our proposed location-based identity management model is generic, distributed, and decentralized. The proposed model is verified using the Scyther formal verification tool for security performance measurement. SPIN model checker is employed for different state verification of our proposed model. The open-source simulation tool FobSim is used for fog and edge/user layer DTL deployment performance analysis. The results and discussion section represents how our proposed decentralized identity management solution should enhance user data privacy and secure and trustworthy communication in IoT.

Innovative modeling and simulation of membrane-based dehumidification and energy recovery equipment

Membrane-based dehumidification is a promising solution for building applications because of its low cost and limited energy consumption. Developing an efficient and cost-effective open-source code simulation tool is important for optimizing and evaluating such devices in HVAC applications. This paper describes a physics-based model, which accounts for the fundamental heat and mass transfer between a humid-air vapor stream on the feed side and a flowing stream on the permeate side of a membrane. The developed model comprises two mass transfer submodels—a microstructure model and a performance map model—and adopts a segment-by-segment method for discretizing heat and mass transfer governing equations for flow streams on the feed and permeate sides of a membrane. The model can simulate dehumidifiers and energy recovery ventilators with parallel-flow, cross-flow, and counter-flow configurations, and the predictions compare reasonably well with the measurements. The model was used to evaluate the effect of membrane microstructure parameters and membrane surface deflection factors, as well as to investigate the performance of combined dehumidification and energy recovery exchangers. The model and C++ open-source codes are expected to become a fundamental tool in analyzing future membrane-based dehumidification systems.

Impact of Limited Degree of Freedom Drag Coefficients on a Floating Offshore Wind Turbine Simulation

The worldwide effort to design and commission floating offshore wind turbines (FOWT) is motivating the need for reliable numerical models that adequately represent their physical behavior under realistic sea states. However, properly representing the hydrodynamic quadratic damping for FOWT remains uncertain, because of its dependency on the choice of drag coefficients (dimensionless or not). It is hypothesized that the limited degree of freedom (DoF) drag coefficient formulation that uses only translational drag coefficients causes mischaracterization of the rotational DoF drag, leading to underestimation of FOWT global loads, such as tower base fore-aft shear. To address these hydrodynamic modeling uncertainties, different quadratic drag models implemented in the open-source mid-fidelity simulation tool, OpenFAST, were investigated and compared with the experimental data from the Offshore Code Comparison Collaboration, Continued, with Correlation (OC5) project. The tower base fore-aft shear and up-wave mooring line tension were compared under an irregular wave loading condition to demonstrate the effects of the different damping models. Two types of hydrodynamic quadratic drag formulations were considered: (1) member-based dimensionless drag coefficients applied only at the translational DoF (namely limited-DoF drag model) and (2) quadratic drag matrix model (in dimensional form). Based on the results, the former consistently underestimated the 95th percentile peak loads and spectral responses when compared to the OC5 experimental data. In contrast, the drag matrix models reduced errors in estimates of the tower base shear peak load by 7–10% compared to the limited-DoF drag model. The underestimation in the tower base fore-aft shear was thus inferred be related to mischaracterization of the rotational pitch drag and the heave motion/drag by the limited-DoF model.

Using Simulation Optimization to Improve the Performance of an Automated Manufacturing Line

As manufacturing capital equipment is expensive, it is necessary that the equipment once in operation is reliable and delivers to the business plan targets. Simulation along with an optimization system is an invaluable tool to confirm that an automated manufacturing line can produce to the required business objectives before and after it goes into operation. Simulation in manufacturing is often applied in situations where conducting experiments on a real system is very difficult often because of cost or the time to carry out the experiment is too long. Optimization is the organized search for such designs and operating modes to find the best available solution from a set of feasible solutions. It determines the set of actions or elements that must be implemented to achieve an optimized manufacturing line. As a result of being able to concurrently simulate and optimize equipment processes, the understanding of how the actual production system will perform under varying conditions is achieved. Implementing the actual changes to equipment to improve reliability can be both time consuming and expensive. Simulation in conjunction with optimization can be used to verify these improvements before the equipment is modified. This study has adopted an open-source simulation tool (JaamSim) to develop a digital model of an automated tray loader manufacturing system in the Johnson & Johnson Vision Care (JJVC) manufacturing facility. This paper demonstrates how this digital model was integrated with SimWrapper optimization and how both tools can be used for the optimization and development of an automated manufacturing line in the medical devices industry.

AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy

AtomAI is an open-source software package bridging instrument-specific Python libraries, deep learning, and simulation tools into a single ecosystem. AtomAI allows direct applications of the deep convolutional neural networks for atomic and mesoscopic image segmentation converting image and spectroscopy data into class-based local descriptors for downstream tasks such as statistical and graph analysis. For atomically-resolved imaging data, the output is types and positions of atomic species, with an option for subsequent refinement. AtomAI further allows the implementation of a broad range of image and spectrum analysis functions, including invariant variational autoencoders (VAEs). The latter consists of VAEs with rotational and (optionally) translational invariance for unsupervised and class-conditioned disentanglement of categorical and continuous data representations. In addition, AtomAI provides utilities for mapping structure-property relationships via im2spec and spec2im type of encoder-decoder models. Finally, AtomAI allows seamless connection to the first principles modeling with a Python interface, including molecular dynamics and density functional theory calculations on the inferred atomic position. While the majority of applications to date were based on atomically resolved electron microscopy, the flexibility of AtomAI allows straightforward extension towards the analysis of mesoscopic imaging data once the labels and feature identification workflows are established/available. The source code and example notebooks are available at https://github.com/pycroscopy/atomai.