Open-source Simulation Tool Research Articles

A massively parallel and memory-efficient FEM toolbox with a hybrid total FETI solver with accelerator support

In this article, we present the ExaScale PaRallel finite element tearing and interconnecting SOlver (ESPRESO) finite element method (FEM) library, which includes an FEM toolbox with interfaces to professional and open-source simulation tools, and a massively parallel hybrid total finite element tearing and interconnecting (HTFETI) solver which can fully utilize the Oak Ridge Leadership Computing Facility Titan supercomputer and achieve superlinear scaling. This article presents several new techniques for finite element tearing and interconnecting (FETI) solvers designed for efficient utilization of supercomputers with a focus on (i) performance—we present a fivefold reduction of solver runtime for the Laplace equation by redesigning the FETI solver and offloading the key workload to the accelerator. We compare Intel Xeon Phi 7120p and Tesla K80 and P100 accelerators to Intel Xeon E5-2680v3 and Xeon Phi 7210 central processing units; and (ii) memory efficiency—we present two techniques which increase the efficiency of the HTFETI solver 1.8 times and push the limits of the largest possible problem ESPRESO that can solve from 124 to 223 billion unknowns for problems with unstructured meshes. Finally, we show that by dynamically tuning hardware parameters, we can reduce energy consumption by up to 33%.

An application-independent continuum mechanics interface for virtual engineering

Digitalization in industry applies to all product life cycle’s stages. An automation of the early conceptual design phase promises to make use of the maximum benefit, since decisions in the early development phases have the greatest potential for reducing the product’s life cycle costs (Pahl et al. in Engineering design, a systematic approach. Springer, London, 2007). There are several approaches to automate the early development phase: from more individual code or script-based solutions to formalized design methods such as knowledge-based engineering or model-based systems engineering. Running high-fidelity simulations leads to an improved decision basis in the early design phase, where the significant decisions, as the product architecture, are taken. This work shows a generic interface between the Unified Modeling Language or Java and continuum mechanics (CM) engineering tools. The interface introduces an application-independent layer containing all the (product) information to create, run and postprocess CM simulations. The boundary and physical conditions are modeled application independently in their generic representations. This approach offers the following advantages: first, an easy creation of engineering workflows through the automated generation and execution of engineering simulations. Second, the engineer is freed from the need for simulation application-specific knowledge. Finally, development costs are saved as commercial simulation tools, become arbitrarily interchangeable with open-source simulation tools.

Osteotomy Planner: An open-source tool for osteotomy simulation.

There has been a recent emphasis in surgical science on supplementing surgical training outside of the Operating Room (OR). Combining simulation training with the current surgical apprenticeship enhances surgical skills in the OR, without increasing the time spent in the OR practicing. Computer-assisted surgical (CAS) planning consists of performing operative techniques virtually using three-dimensional (3D) computer-based models reconstructed from 3D cross-sectional imaging. The purpose of this paper is to present a CAS system to rehearse, visualize and quantify osteotomies, and demonstrate its usefulness in two different osteotomy surgical procedures, cranial vault reconstruction and femoral osteotomy. We found that the system could sufficiently simulate these two procedures. Our system takes advantage of the high-quality visualizations possible with 3DSlicer, as well as implements new infrastructure to allow for direct 3D interaction (cutting and positioning) with the bone models. We see the proposed osteotomy planner tool evolving towards incorporating different cutting templates to help depict several surgical scenarios, help 'trained' surgeons maintain operating skills, help rehearse a surgical sequence before heading to the OR, or even to help surgical planning for specific patient cases.

Supervised and Characterized Smart Monitoring Network for Sensing Environmental Quantities

Environmental monitoring networks are essential for understanding environmental dynamics of a given region if dedicated sensors and sensing systems are used. They are sensitive to external and internal fluctuations that can produce loss of capabilities of correct detection and retrieval of desired environmental quantities. Recent findings, in terms of smart grids and Internet of Things, have allowed the implementation of testing and characterization of such networks using new computation and simulation platforms that can deliver excellent results. This paper presents a procedure for evaluating the informative content of an actual network and a new approach of implementing a characterization of hydrological and environmental networks of sensors to be “stress-tested.” The proposed characterization is implemented by using Ptolemy II tool. It is an open-source simulation and modeling tool intended for experimenting with system design techniques, particularly those that involve combinations of different types of models. It is user friendly as described in final comments. This paper does not deal with power systems, intended as smart grid, but with smart sensors networking.

SimEON: an open-source elastic optical network simulator for academic and industrial purposes

In comparison with wavelength division multiplexing optical networks, elastic optical networks are much more flexible, presenting an efficient alternative to use the frequency spectrum to establish call requests in a scenario with different transmission bit rates and modulation schemes. Due to the variety of optical devices, node architectures and physical topologies, it is difficult to implement optical network prototypes to analyze operational scenarios in terms of blocking probability of call requests, the impact of capital and operational expenditures and energy consumption, for example. To our knowledge, there is no available open-source simulation tool exclusively for elastic optical networks in the literature. There are a few simulation tools that were used to obtain results for optical networks but most of their source codes are unavailable. The state of the art of these simulators and our developed simulation tool called Simulator for Elastic Optical Networks (SimEON) are presented. SimEON is an open-source software. It is developed under the GNU Lesser General Public License and takes into account principles of software engineering. In this article, we present the characteristics of our simulator, its architecture and considered parameters, along with some examples of the simulations that can be executed on it.

Open‐source python‐OpenDSS interface for hybrid simulation of PV impact studies

This study introduces an open-source Python-OpenDSS simulation tool where the phasor simulation software OpenDSS is interfaced with an electromagnetic transient (EMT) program-type simulation tool implemented in Python environment. The tool is designed to perform impact study of photovoltaic (PV) integration in distribution systems. It is composed of an EMT model of a grid-tied PV system interfaced with a phasor model of the distribution network. The proposed tool provides an add-on that expands OpenDSS capabilities in order to perform various PV impact studies that cannot be performed inherently by OpenDSS since they require detailed modelling. This study describes the methodology and theory behind the interfacing of the EMT model with OpenDSS. The tool is tested with various case studies using the IEEE 37-bus system with multiple interconnected PV systems. The tool is validated by comparing its results with those of a full EMT model of the IEEE 37-bus system simulated using SimPowerSystems. The results indicate that the hybrid tool offers significant advantages over single-rate simulations that use either full phasor or full EMT simulation approach. It provides faster simulation time and less computational intensity, and the accuracy is remarkably close to the full blown, more complex and slower, EMT simulation.

ExRET-Opt: An automated exergy/exergoeconomic simulation framework for building energy retrofit analysis and design optimisation

Energy simulation tools have a major role in the assessment of building energy retrofit (BER) measures. Exergoeconomic analysis and optimisation is a common practice in sectors such as the power generation and chemical processes, aiding engineers to obtain more energy-efficient and cost-effective energy systems designs. ExRET-Opt, a retrofit-oriented modular-based dynamic simulation framework has been developed by embedding a comprehensive exergy/exergoeconomic calculation method into a typical open-source building energy simulation tool (EnergyPlus). The aim of this paper is to show the decomposition of ExRET-Opt by presenting modules, submodules and subroutines used for the framework’s development as well as verify the outputs with existing research data. In addition, the possibility to perform multi-objective optimisation analysis based on genetic-algorithms combined with multi-criteria decision making methods was included within the simulation framework. This addition could potentiate BER design teams to perform quick exergy/exergoeconomic optimisation, in order to find opportunities for thermodynamic improvements along the building’s active and passive energy systems. The enhanced simulation framework is tested using a primary school building as a case study. Results demonstrate that the proposed simulation framework provide users with thermodynamic efficient and cost-effective designs, even under tight thermodynamic and economic constraints, suggesting its use in everyday BER practice.

Deformation Process Numerical Analysis of T-stub Flanges with Pre-loaded Bolts

A numerical analysis and experimental validation of T-stub steel joint with pre-loaded bolts is done. Design codes have a very imprecise analysis methodology that might not take into account the actual behavior of this joint. The calculations are done with state-of-the-art open source software Code_Aster by taking into account large strains, nonlinear plasticity and contact mechanics. Experimental validation of truss T-stub joint's ultimate load and displacements shows good agreement with numerical simulation results. Parametric studies show, that the ultimate load and stiffness is highly affected by the pre-loading level of bolts and thickness of flange. By increasing the thickness of flanges up to 40mm, nearly equal stress distribution in all bolts is obtained. The joint with 70% pre-loaded bolts has around 30% higher ultimate load than the joint with 0% pre-loaded bolts. The design of T-stub flanges can be significantly improved by using state-of-the-art open-source finite element simulation tools.

Content-aware and QoE Optimization of Video Stream Scheduling over LTE Networks using Genetic Algorithm and Random Neural Networks

Long Term Evolution (LTE) networks support Quality of Service (QoS) of multimedia services with fast communication connectivity, high data transfer speed and high level of security. Video streaming over LTE networks is one of the highest proportions of global mobile data traffic and is growing; this has led to the development of several scheduling algorithms aimed at improving the performance of these networks. The performance analysis and evaluation of existing scheduling algorithms are generally limited to QoS parameters. It is not clear how these scheduling algorithms perform in terms of Quality of Experience (QoE) which is the overall acceptability of a service or application, as perceived subjectively by end users. Video content has a major impact on QoE; thus its analysis in scheduling algorithms performance is critical. The aim of this study is to classify video content based on the impact of video content on quality over LTE networks. This classification is then used to develop novel QoE-aware optimization scheduling of video traffic in order to achieve maximum QoE. Our approach focuses on the development of optimization downlink scheduling based on a novel integration between random neural networks (RNN) and genetic algorithms (GA) to learn complex non-linear mapping of QoE and to search for the optimal parameters, respectively. An open source simulation tool for LTE networks (LTE-Sim) has been used to collect unique RNN training database based on existing scheduling algorithms. A comparison between the proposed scheduler and state-of-the-art LTE downlink scheduling algorithms (FLS, EXP-rule, and LOG-rule) has been made under different network conditions. Simulation results showed an increase in performance of about 15% in terms of QoE and throughput while maintaining fairness.

New open source simulation tools for 3-D multicellular systems biology

New open source simulation tools for 3-D multicellular systems biology

Lightning Mapping Array flash detection performance with variable receiver thresholds.

This study characterizes Lightning Mapping Array performance for networks that participated in the Deep Convective Clouds and Chemistry field program using new Monte Carlo and curvature matrix model simulations. These open‐source simulation tools are readily adapted to real‐time operations or detailed studies of performance. Each simulation accounted for receiver threshold and location, as well as a reference distribution of source powers and flash sizes based on thunderstorm observations and the mechanics of station triggering. Source and flash detection efficiency were combined with solution bias and variability to predict flash area distortion at long ranges. Location errors and detection efficiency were highly dependent on the station configuration and thresholds, especially at longer ranges, such that performance varied more than expected across different networks and with azimuth within networks. Error characteristics matched prior studies, which led to an increase in flash distortion with range. Predicted flash detection efficiency exceeded 95% within 100 km of all networks.

A Comparative Study of Recent Wireless Sensor Network Simulators

Over recent years, the continuous interest in wireless sensor networks (WSNs) has led to the appearance of new modeling methods and simulation environments for WSN applications. A broad variety of different simulation tools have been designed to explore and validate WSN systems before actual implementation and real-world deployment. These tools address different design aspects and offer various simulation abstractions to represent and model real-world behavior. In this article, we present a comprehensive comparative study of mainstream open-source simulation tools for WSNs. Two benchmark applications are designed to evaluate the frameworks with respect to the simulation runtime performance, network throughput, communication medium modeling, packet reception rate, network latency, and power consumption estimation accuracy. Such metrics are also evaluated against measurements on physical prototypes. Our experiments show that the tools produce equivalent results from a functional point of view and capacity to model communication phenomena, while the ability to model details of the execution platform significantly impacts the runtime simulation performance and the power estimation accuracy. The benchmark applications are also made available in the public domain for further studies.

Condor: a simulation tool for flash X-ray imaging.

Flash X-ray imaging has the potential to determine structures down to molecular resolution without the need for crystallization. The ability to accurately predict the diffraction signal and to identify the optimal experimental configuration within the limits of the instrument is important for successful data collection. This article introduces Condor, an open-source simulation tool to predict X-ray far-field scattering amplitudes of isolated particles for customized experimental designs and samples, which the user defines by an atomic or a refractive index model. The software enables researchers to test whether their envisaged imaging experiment is feasible, and to optimize critical parameters for reaching the best possible result. It also aims to support researchers who intend to create or advance reconstruction algorithms by simulating realistic test data. Condor is designed to be easy to use and can be either installed as a Python package or used from its web interface (http://lmb.icm.uu.se/condor). X-ray free-electron lasers have high running costs and beam time at these facilities is precious. Data quality can be substantially improved by using simulations to guide the experimental design and simplify data analysis.

Strober

This paper presents a sample-based energy simulation methodology that enables fast and accurate estimations of performance and average power for arbitrary RTL designs. Our approach uses an FPGA to simultaneously simulate the performance of an RTL design and to collect samples containing exact RTL state snapshots. Each snapshot is then replayed in gate-level simulation, resulting in a workload-specific average power estimate with confidence intervals. For arbitrary RTL and workloads, our methodology guarantees a minimum of four-orders-of-magnitude speedup over commercial CAD gate-level simulation tools and gives average energy estimates guaranteed to be within 5% of the true average energy with 99% confidence. We believe our open-source sample-based energy simulation tool Strober can not only rapidly provide ground truth for more abstract power models, but can enable productive design-space exploration early in the RTL design process.

Earthquake-altered flooding hazard induced by damage to storm water systems

Major earthquakes can cause extensive transformations to the land underlying cities, leading to decreased capacity in natural and built drainage systems and, as a consequence, to Increased Flooding Hazard (IFH). This phenomenon causes some areas, which previously were not exposed to flooding, to have the potential to flood, and already flood-prone areas to likely experience increased flood depth during the next rainfall events. This scenario occurred in Christchurch city, New Zealand, after the 2010–2011 Canterbury Earthquake Sequence (CES). The IFH was observed in many urban areas during a series of rainfall events occurred in the years after the CES. This paper proposes a method for analysing and assessing to what extent the earthquake-induced damage to storm water pipelines and the consequent impacts on the connectivity and capacity levels of the pipeline storm water network could contribute to the IFH. A probabilistic analysis, through a Monte Carlo simulation, is suggested for the proposed method so that the uncertainty affecting several key parameters can be accounted for. The proposed probabilistic method for IFH was implemented as an additional module within a recently developed open-source simulation tool, OOFIMS. Results from the added OOFIMS module are presented in terms of maps and cumulative distribution functions of increased flood height and flooded area, impact metrics that can be useful for emergency managers and infrastructure owners. The effectiveness of the proposed method to assess earthquake-altered flooding hazard and the relative OOFIMS-added module are tested using Christchurch as a case-study.

Optimization of the Battery Schedule for Residential Microgrid Applications

Abstract: Optimization of a battery schedule in a microgrid is presented in this paper. The open source simulation tool GridLab-D is used for simulation of microgrid elements. Matlab is used as an environment for running optimization solvers. The tool GridMat is used as an interface between Matlab and GridLab-D. Two microgrid scheduling optimization tasks are solved. The obtained results are analyzed and some conclusions are drawn. The approach presented is promising and it can be applied successfully to microgrids not only in rural but also in urban regions.

QoE-aware Optimization of Video Stream Downlink Scheduling Over LTE Networks Using RNNs and Genetic Algorithm

Long Term Evolution (LTE) is the initial version of fourth-generation (4G) networks which provides ubiquitous broadband access. LTE supports multimedia Quality of Service (QoS) traffic with high data transfer speed, fast communication connectivity, and high security. Multimedia traffic over LTE networks is one of the highest percentages of mobile traffic and it has been growing rapidly in recent years. Our approach focuses on the development of Quality of Experience (QoE) aware optimization downlink scheduling video traffic flow. QoE is the overall acceptability of a service or application, as perceived subjectively by end users. In this work we aim to maximise QoE of video traffic streaming over LTE networks. This work introduces a novel integration framework between genetic algorithm (GA) and random neural networks (RNN) applied to QoE-aware optimization of video stream downlink scheduling. The proposed framework has been applied and evaluated using an open source simulation tool for LTE networks (LTE-Sim). A comparison between our framework and state-of-the-art LTE downlink scheduling algorithms (FLS, EXP-rule, and LOG-rule) has been done under different network conditions. Simulation results have shown that our scheduler can achieve better performance in terms of QoE (∼10% increase), throughput and fairness.

REDBACK: an open-source simulation tool for rock mechanics with dissipative feedbacks, with applications to diagenetic reactions in fluid saturated rocks.

REDBACK: an open-source simulation tool for rock mechanics with dissipative feedbacks, with applications to diagenetic reactions in fluid saturated rocks.

Update on single-screw expander geometry model integrated into an open-source simulation tool

In this paper, a mechanistic steady-state model of a single-screw expander is described with emphasis on the geometric description. Insights into the calculation of the main parameters and the definition of the groove profile are provided. Additionally, the adopted chamber model is discussed. The model has been implemented by means of the open-source software PDSim (Positive Displacement SIMulation), written in the Python language, and the solution algorithm is described. The single-screw expander model is validated with a set of steady-state measurement points collected from a 11 kWe organic Rankine cycle test-rig with SES36 and R245fa as working fluid. The overall performance and behavior of the expander are also further analyzed.

Adaptive Group-based Signal Control by Reinforcement Learning

Group-based signal control is one of the most prevalent control schemes in the European countries. The major advantage of group-based control is its capability in providing flexible phase structures. The current group-based control systems are usually implemented with rather simple timing logics, e.g. vehicle actuated logic. However, such a timing logic is not sufficient to respond to the traffic environment whose inputs, i.e. traffic demands, dynamically change over time. Therefore, the primary objective of this paper is to formulate the existing group-based signal controller as a multi-agent system. The proposed signal control system is capable of making intelligent timing decisions by utilizing machine learning techniques. In this regard, reinforcement learning is a potential solution because of its self-learning properties in a dynamic environment. This paper, thus, proposes an adaptive signal control system, enabled by a reinforcement learning algorithm, in the context of group-based phasing technique. Two different learning algorithms, Q-learning and SARSA, have been investigated and tested on a four-legged intersection. The experiments are carried out by means of an open-source traffic simulation tool, SUMO. Performances on traffic mobility of the adaptive group- based signal control systems are compared against those of a well-established group-based fixed time control system. In the testbed experiments, simulation results reveal that the learning-based adaptive signal controller outperforms group-based fixed time signal controller with regards to the improvements in traffic mobility efficiency. In addition, SARSA learning is a more suitable implementation for the proposed adaptive group-based signal control system compared to the Q-learning approach.