Open-source Simulation Software Research Articles

MPNG: A MATPOWER-Based Tool for Optimal Power and Natural Gas Flow Analyses

MPNG is an open-source simulation package for solving Optimal Power and Natural Gas Flow (OPNGF) problems. It is coded in <small>Matlab</small> and developed using the widely-used tool for power systems research and education, <small>Matpower</small>. MPNG assembles a nonlinear steady state OPNGF formulation for analyzing the integration of electricity and natural gas networks with order-of-priority classes of consumers and gas nodal price-controlled storage facilities. This paper presents the details of the nonlinear optimization model behind MPNG, as well as several features that were designed into it for the purpose of achieving robustness. These include an approximation of the Weymouth equation for handling bi-directional gas flows in pipelines, a per-unit representation for the natural gas network, and penalized slack variables for power and gas balances. Simulation results using a test case and the Colombian power and natural gas networks are presented to highlight MPNG&#x0027;s capabilities to solve OPNGF problems.

Multi-axial computational model for simulating the seismic behavior of concrete columns including axial collapse

This paper describes a coupled multi-axial material model that can capture the interaction between axial/shear/flexure and simulate the lateral cyclic response of non-retrofitted and retrofitted concrete columns under seismic loads up to complete loss of lateral and axial strength. The new material model is intended for use within two-dimensional lumped-plasticity framework and implemented in the open-source simulation software OpenSees as CMACA2D. The material model was calibrated using a newly compiled database of 719 cyclic and monotonic pushover column tests and only requires the input of column geometric and material properties. It includes several capacity models to capture all the salient lateral and axial strength-degradations including flexure, shear, flexure-shear, splice, up to complete axial collapse. These capacity models for each axial/shear/flexure degree-of-freedom (DOF) depend on the boundary conditions and applied demands in its own and other DOFs and constantly change during the analysis so the governing strength-degradation mode is not known a priori of the analysis. The material model employs an energy dissipation term to define the cyclic behavior response and to adjust the strength damage rate observed in test data. Finally, the performance of the proposed computational tools is compared with results from shake table tests.

A scientific process for a sustainable architectural design: A case study of a rural pavilion in a hot semi-arid climate

The present research study is an essential contribution to sustainable design following a new process of rethinking design in architecture. Yet, through researchers’ investigations (building engineers, material engineers, climate and data engineers, etc), open-source data platforms, building and simulation software, products data sheets, built experience and local-know-how, are deemed necessary tools to master each decision concerning the building architectural design and the building construction systems. The present paper is a demonstration to the cited process. Indeed, the case study is a pavilion building located in a rural area characterized by a (BSh) climate according to Köppen-Geiger classification. The current project is designed to reduce the energy needs by passive measures considering the envelope, and the cooling/heating systems. The studied building allies both natural and conventional materials, taking advantage of each one depending on the uses. The results of the simulation study, using TRNSYS software, show that limestone-based variant (V2) and rammed earth-based (V3) perform better as their annual load is reduced by 57.2% and 56%, respectively, compared to cement-block reference case (V1). The variants (V2) and (V3) perform better than the double red clay bricks (V4), by 12% and 9.6%, respectively. However, according to different selection criteria, rammed-earth-based variant is considered, in this context, the most sustainable one among the four. Indeed, the comparison is based on both Life-Cycle-Assessment and the Embodied Energy analysis, contextualized depending on the cost, the local know-how and the social and cultural heritage.

Hybrid-MPET: An Open-Source Simulation Software for Hybrid Electrode Batteries

As the design of single-component battery electrodes has matured, the battery industry has turned to hybrid electrodes with blends of two or more active materials to enhance battery performance. Leveraging the best properties of each material while mitigating their drawbacks, multi-component hybrid electrodes open a vast new design space that could be most efficiently explored through simulations. In this article, we introduce a mathematical modeling framework and open-source battery simulation software package for Hybrid Multiphase Porous Electrode Theory (Hybrid-MPET), capable of accounting for the parallel reactions, phase transformations and multiscale heterogeneities in hybrid porous electrodes. Hybrid-MPET models can simulate both solid solution and multiphase active materials in hybrid electrodes at intra-particle and inter-particle scales. Its modular design also allows the combination of different active materials at any capacity fraction. To illustrate the novel features of Hybrid-MPET, we present experimentally validated models of silicon-graphite (Si-Gr) anodes used in electric vehicle batteries and carbon monofluoride (CFx) - silver vanadium oxide (SVO) cathodes used in implantable medical device batteries. The results demonstrate the potential of Hybrid-MPET models to accelerate the development of hybrid electrode batteries by providing fast predictions of their performance over a wide range of design parameters and operating protocols.

Calibration method for an open source model to simulate building energy at territorial scale

In a context of massive renovation of residential housing, stakeholders need decision-support tools based on knowledge of the current building stock and an accurate simulation of energy demand. For this purpose, we developed a val- idation/calibration method on a territorial/national scale in order to represent the real consumption of housing. This methodological approach provides (1) more reliable identification of energy-saving measures (changes in technology or behaviour) and (2) improved knowledge of the energy simulation tool and its post-calibration performance for optimisation issues. The main contribution of the calibration method described in this paper is the geographical scale concerned: all French residential housing has been modelled, simulated and calibrated with national data (geometries and attributes) on buildings. Furthermore, some occupants' socio-professional characteristics have been taken into account to reflect their actual energy behaviours. This is different from traditional approaches that focus only on a few buildings or archetypes. This paper also describes the application of this methodology on an Open Source simulation software in order to be easily verifiable and usable. This linear model will also be used to optimise renovation solutions at territorial scale in future work. All data used in this paper are Open Data and thus available to the scientific community. This method enabled more than 18 million buildings to be calibrated while reducing the Normalized Root Mean Square Error, between simulated and real energy annual consumption, from 52% to 24% for gas and from 24% to 15% for electricity. In addition, the user of the method is free to prioritise either the maximum error reduction or the number of calibration coefficients if a simpler model is desired. This paper also discusses the results obtained from this method for future improvement.

Platform for investigating continuum manipulator behavior in orthopedics.

The use of robotic continuum manipulators has been proposed to facilitate less-invasive orthopedic surgical procedures. While tools and strategies have been developed, critical challenges such as system control and intra-operative guidance are under-addressed. Simulation tools can help solve these challenges, but several gaps limit their utility for orthopedic surgical systems, particularly those with continuum manipulators. Herein, a simulation platform which addresses these gaps is presented as a tool to better understand and solve challenges for minimally invasive orthopedic procedures. An open-source surgical simulation software package was developed in which a continuum manipulator can interact with any volume model such as to drill bone volumes segmented from a 3D computed tomography (CT) image. Paired simulated X-ray images of the scene can also be generated. As compared to previous works, tool-anatomy interactions use a physics-based approach which leads to more stable behavior and wider procedure applicability. A new method for representing low-level volumetric drilling behavior is also introduced to capture material variability within bone as well as patient-specific properties from a CT. Similar interaction between a continuum manipulator and phantom bone was also demonstrated between a simulated manipulator and volumetric obstacle models. High-level material- and tool-driven behavior was shown to emerge directly from the improved low-level interactions, rather than by need of manual programming. This platform is a promising tool for developing and investigating control algorithms for tasks such as curved drilling. The generation of simulated X-ray images that correspond to the scene is useful for developing and validating image guidance models. The improvements to volumetric drilling offer users the ability to better tune behavior for specific tools and procedures and enable research to improve surgical simulation model fidelity. This platform will be used to develop and test control algorithms for image-guided curved drilling procedures in the femur.

Cost optimization of low-salt-rejection reverse osmosis

Low-salt-rejection reverse osmosis (LSRRO) is an emerging membrane-based desalination technology for concentrating brines with potentially lower energy consumption and cost than thermally driven processes. We assess the technoeconomic performance of LSRRO by using an optimization model that minimizes the levelized cost of water (LCOW). We develop detailed models for the RO module and other equipment using WaterTAP (Water treatment Technoeconomic Assessment Platform), an open-source simulation and optimization software for water treatment. We improve upon previous LSRRO cost optimization studies by optimizing the salt permeability for each stage, which has significant implications for technoeconomic performance. We present the cost-optimal design, operation, and performance for three cases: 35 g/L TDS feed at 70 % recovery, 70 g/L at 55 %, and 125 g/L at 35 %. The cost-optimal LCOW and specific energy consumption (SEC) for these cases are 0.70, 1.89, and 7.41 $/m3 and 3.9, 8.4, and 30.4 kWh/m3, respectively. For each case, we use sensitivity analyses to provide guidance on key factors that impact economic viability. We also present the cost-optimal LCOW and SEC for feed concentrations between 5 and 220 g/L TDS and recoveries between 30 and 90 %. Lastly, we compare LSRRO to other high-salinity desalination technologies and find that it may be cost-competitive below feed concentrations of 125 g/L.

Modelling autonomous vehicle interactions with bicycles in traffic simulation

With the undeniable advance of automated vehicles and their gradual integration in day-today urban traffic, many new technologies have been developed that offer great potential for this emerging field of research. However, testing automated vehicle technologies in real road traffic with vulnerable road users (VRUs) is still a complicated and time consuming procedure. The virtual development and evaluation of automated vehicles using simulation tools offers a good opportunity to test new functions efficiently. However, existing models prove to be insufficient in modeling the interaction between autonomous vehicles and vulnerable road users such as bicyclists and pedestrians. In this paper, an automated vehicle model for microscopic traffic simulation tools is developed with the open-source traffic simulation software Simulation of Urban Mobility (SUMO) and evaluated in common traffic interaction scenarios with bicyclists. A controller model is proposed using different path-finding algorithms from the field of robotic and automated vehicle research, which covers all important control levels of a self-driving vehicle. Finally, its performance is compared to existing car-following and lane-changing models. Results showcase that the autonomous vehicle model achieves either comparable results or has a much steadier and more realistic driving behavior when compared to existing driving models while interacting with bicyclists. The whole source code developed over the course of this work is freely accessible at: https://github.com/FlixFix/Kimarite.

Deep Learning based Wireless Channel Prediction: 5G Scenario

In the area of wireless communication, channel estimation is a challenging problem due to the need for real-time implementation as well as system dependence on the estimation accuracy. This work presents a Long-Short Term Memory (LSTM) based deep learning (DL) approach for the prediction of channel response in real-time and real-world non-stationary channels. The model uses the pre-defined history of channel impulse response (CIR) data along with two other features viz. transmitter-receiver update distance and root-mean-square delay spread values which are also changing in time with the channel impulse response. The objective is to obtain an approximate estimate of CIRs using prediction through the DL model as compared to conventional methods. For training the model, a sample dataset is generated through the open-source channel simulation software NYUSIM which realizes samples of CIRs for measurement-based channel models based on various multipath channel parameters. From the model test results, it is found that the proposed DL approach provides a viable lightweight solution for channel prediction in wireless communication.

Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model.

This paper proposes to use deep reinforcement learning to teach a physics-based human musculoskeletal model to ascend stairs and ramps. The deep reinforcement learning architecture employs the proximal policy optimization algorithm combined with imitation learning and is trained with experimental data of a public dataset. The human model is developed in the open-source simulation software OpenSim, together with two objects (i.e., the stairs and ramp) and the elastic foundation contact dynamics. The model can learn to ascend stairs and ramps with muscle forces comparable to healthy subjects and with a forward dynamics comparable to the experimental training data, achieving an average correlation of 0.82 during stair ascent and of 0.58 during ramp ascent across both the knee and ankle joints.

Development and Delivery of an Electric Circuits Course Featuring Competency Based Assessment for First Year Engineering

Most common-core first year engineering programs in Canada include an introduction to electric circuits and electromagnetic physics. The launch of the RE-ENGINEERED first year program at the University of Saskatchewan has provided an opportunity to try something different in this arena. The RE-ENGINEERED program includes a “spine” of electric circuit analysis and the related physics that runs through both semesters of the first year.&#x0D; The modular and highly integrated structure of the RE-ENGINEERED program has allowed for accelerated courses that take advantage of timely learning in other courses. In the fall term, students are introduced to direct-current, resistive circuit analysis in a six-week, fifteen-contact-hour module. In the winter term, they experience an accelerated physics course which covers the electricity, magnetism, capacitance, and inductance concepts often taught in tandem with basic circuit analysis. The students then finish the winter term with an intensive course on alternating-current circuit analysis.&#x0D; The fall term course fully adopts the competency based assessment system of the RE-ENGINEERED program, and uses in-house-developed quizzes and tutorials on the most basic concepts and calculations to scaffold students to solving more complex circuit analysis problems. The course forgoes a hands-on lab component and focuses on circuit simulation using an open-source simulation package. Concurrent math and MATLAB courses introduce required linear algebra concepts just in time for use in the circuit analysis problems.&#x0D; This paper describes the development and delivery of the fall term course, including how the learning outcomes were synthesized and then used as the basis for the development of all other aspects of the course to ensure constructive alignment. Instructor and student impressions of the first delivery of the course are presented along how lessons learned will be applied to modify the course for future offerings.

Analysis of fracture interference – Coupling of flow and geomechanical computations with discrete fracture modeling using MRST

A new discrete fracture model (DFM) is implemented in the MATLAB Reservoir Simulation Toolbox (MRST) – an open-source reservoir simulation software – to couple flow and geomechanical computations. Unstructured meshing is used to discretize the gridding domain. The matrix-to-matrix, fracture-to-fracture, and fracture-to-matrix fluxes are computed explicitly in the computational domain following the control volume formulation. The flow calculations and the geomechanical computations are solved sequentially: the governing equations for poroelasticity are incorporated, and the fixed stress splitting coupling methodology is employed. The results are compared with an uncoupled model, where the geomechanical effects are ignored.Several case studies demonstrate the utility of the proposed coupling strategy for analyzing production performance and stress evolution in hydraulically fracturing formations. This model considers the effects of poroelasticity and stress or strain variation in the fractured domains, and factors influencing the behaviour of fracture interference are examined. Finally, an analysis of the computational costs is also presented.This simulation approach can more accurately capture the dynamic behaviour of stress evolution and fracture interference in hydraulically fractured wells. This flexible framework can incorporate more functional modules, including fracture propagation, in future works. The results illustrate the significance of incorporating geomechanical effects in the flow simulation process. Insights pertinent to the operational strategies are also highlighted based on the simulation results.

PATLAB: A graphical computational software package for photoacoustic computed tomography research

Photoacoustic tomography (PAT) provides high resolution optical images of tissue at depths of up to several centimetres. This modality has been of interest to researchers for at least 30 years and is still the subject of intensive research. However, PAT researchers lack access to a comprehensive open-source graphical simulation and reconstruction software package. In this article, we introduce PATLAB, an open-source MATLAB-based graphical software package that can perform both PAT simulation and image reconstruction. PATLAB is simple to use yet is capable of complex PAT data processing tasks and offers advanced users a framework to build and test new methods.

Process Simulation of Steam Stripping of Bleached Palm Oil Deodorization for Removing Free Fatty Acids using DWSIM

The presence of free fatty acids (FFAs) and odoriferous components in the bleached palm oil (BPO) need to be removed to meet the specific standard quality and make the oil palatable. Because FFAs and odoriferous components is more volatile than the main oil components, they vaporize readily thus being removed from the oil product. Deodorization is the key process in industry to remove FFAs by vaporizing them using steam stripping agent such as steam under vacuum. In this work, a simulation study was adopted for the analysis of deodorization process implemented in an open-source chemical process simulator software, DWSIM. The deodorization section was modelled using absorber column unit operation in the software. The process conditions and BPO compositions fed into the deodorizer were taken from literature. The simulation showed much decrease in the FFAs content below the maximum value as standardized by the authorized association. The deodorization of BPO was analyzed by studying the column profiles such as the individual TGs, FFAs and water interstage flows in vapour and liquid phases, and the total component interstage flow for vapour and liquid phases’ profiles. From the simulation study under defined process conditions, the percentage removal of FFAs from the BPO was >99.99%.

R2ogs5: Calibration of Numerical Groundwater Flow Models with Bayesian Optimization in R.

Large-scale and high-resolution groundwater models are currently becoming increasingly important in order to clarify the extent to which climate trends and extreme weather affect the groundwater balance regionally. As a result, the parameterization of groundwater models is becoming more detailed and more complex, making conventional calibration methods too time-consuming. Moderating the computational demand to find optimal solutions for the resulting potentially multi-modal objective function requires intelligent and efficient global optimization methods. Moreover, the increasing use of modern scripting languages R and Python to craft environmental analysis workflows calls to integrate groundwater flow simulators in such. Here we introduce and exemplify r2ogs5, a tool that integrates version 5 of the open-source simulation software OpenGeoSys into the programming language R. r2ogs5 allows for calibration of numerical groundwater flow models with a sequential model-based optimization approach that combines Bayesian optimization (BO) with surrogate modeling. Here, we describe the structure and function of r2ogs5 as well as the implemented calibration method. We then demonstrate the calibration method by calibrating 4 and 12 parameters of two simple groundwater flow models. The results indicate that this method needs fewer runs of the groundwater flow model than conventional gradient search and Latin hypercube sampling in case of the 12 parameter model. We believe that our method offers the potential to calibrate computationally expensive groundwater flow models. r2ogs5 supports groundwater flow modelers to access the statistical analysis and visualization capabilities of the R language and is a valuable tool for geoscientists already using R.

Entanglement generation in a quantum network with finite quantum memory lifetime

We simulate entanglement sharing between two end-nodes of a linear chain quantum network using SeQUeNCe, an open-source simulation package for quantum networks. Our focus is on the rate of entanglement generation between the end-nodes with many repeaters with a finite quantum memory lifetime. Numerical and analytical simulations show limits of connection performance for a given number of repeaters involved, memory lifetimes, the distance between the end-nodes, and an entanglement management protocol. Our findings demonstrate that the performance of quantum connection depends highly on the entanglement management protocol, which schedules entanglement generation and swapping, resulting in the final end-to-end entanglement.

Discrete adjoint methodology for general multiphysics problems

This article presents a methodology whereby adjoint solutions for partitioned multiphysics problems can be computed efficiently, in a way that is completely independent of the underlying physical sub-problems, the associated numerical solution methods, and the number and type of couplings between them. By applying the reverse mode of algorithmic differentiation to each discipline, and by using a specialized recording strategy, diagonal and cross terms can be evaluated individually, thereby allowing different solution methods for the generic coupled problem (for example block-Jacobi or block-Gauss-Seidel). Based on an implementation in the open-source multiphysics simulation and design software SU2, we demonstrate how the same algorithm can be applied for shape sensitivity analysis on a heat exchanger (conjugate heat transfer), a deforming wing (fluid–structure interaction), and a cooled turbine blade where both effects are simultaneously taken into account.

A variable twist blade for horizontal axis wind turbines: Modeling and analysis

A method for analyzing the performance of a wind turbine blade design with an adaptive variable twist is presented. The possibility of implementing morphing features is becoming increasingly possible with innovative materials and manufacturing techniques. This motivates a blade concept consisting of multiple shell sections mounted on a rigid spar and covered by non-structural skin. The design allows the blade shells to adapt the twist angle distribution (TAD). The study is conducted using data acquired from the National Renewable Energy Laboratory (NREL) 20 kW wind turbine. A design problem is formulated to find the TAD that maximizes the aerodynamic efficiency for a discrete set of points that represent the Region 2 wind speed. The TAD is found for each point using a heuristic search algorithm. This algorithm searches through performance data that is acquired using the AeroDyn open source simulation software. Results from the model are validated with computational fluid dynamics (CFD) simulations using the Reynolds-averaged Navier-Stokes equations with the k-ω shear stress transport turbulence model. Through this first part of the analysis, it becomes possible to understand the required amount of blade deformation by the TAD function. Given this understanding, a stress analysis can be performed on the blade structure. The scenario considers the combined effect of twisting with aerodynamic loading. The blade load is determined by combining CFD with finite element analysis to facilitate the one-way fluid-structural interaction. The outcome of this work demonstrates how the proposed method can be used to determine the gain in efficiency, required range of motion, and structural performance of the blade shells for a given wind turbine blade design.

Study of the Effect of Vertical Airfoil Endplates on Diffusers in Vehicle Aerodynamics

Diffusers and the floor ahead of them create the majority of the downforce a vehicle creates. Outside motorsports, the diffuser is relatively unused, although its interaction with the ground is a consistent field of study owing to the aerodynamic benefits. The diffuser flow behavior is governed by three fluid-mechanical mechanisms: ground interaction, underbody upsweep, and diffuser upsweep. In addition, four different flow regimes appear when varying ride height, the vortices of which have great importance on downforce generation. The present study focuses on the diffuser’s fluid-dynamic characteristics undertaken within an academic framework with the objective of finding and understanding a high level of performance in these elements. Once the functioning of diffusers has been analyzed and understood, a new configuration is proposed: rear vertical airfoil endplates. The aim of the paper is to study the effect in performance of vertical airfoil endplates on diffusers in vehicle aerodynamics in a simplified geometry. The candidate to this geometry is the inversed Ahmed body, a geometry that is used as a model that simulates the flow behavior of car diffusers. Three different diffuser configurations are performed, namely 0° diffuser, 25° diffuser, and in the third case vertically installed rear vertical airfoil endplates are added to the 25° diffuser Ahmed body to change the flow field. These analyses are carried out by using open-source CFD simulation software OpenFOAM. An inlet velocity of 20 m/s is considered, as this is a typical velocity when cornering in motorsport. It is concluded that the 25° diffuser configuration generated more downforce than the 0° diffuser, which makes sense as the aim of adding a diffuser is to increase the amount of downforce produced. In addition, and as a result of the newly proposed configuration, the 25° diffuser Ahmed body with the vertical airfoil endplates emerges in a substantial increase of downforce thanks to the low-pressure zone generated at the back of the body.

Prediction of Methanol Production in a Carbon Dioxide Hydrogenation Plant Using Neural Networks

The objective of this research was to design a neural network (ANN) to predict the methanol flux at the outlet of a carbon dioxide dehydrogenation plant. For the development of the ANN, a database was generated, in the open-source simulation software “DWSIM”, from the validation of a process described in the literature. The sample consists of 133 data pairs with four inputs: reactor pressure and temperature, mass flow of carbon dioxide and hydrogen, and one output: flow of methanol. The ANN was designed using 12 neurons in the hidden layer and it was trained with the Levenberg–Marquardt algorithm. In the training, validation and testing phase, a global mean square (RMSE) value of 0.0085 and a global regression coefficient R of 0.9442 were obtained. The network was validated through an analysis of variance (ANOVA), where the p-value for all cases was greater than 0.05, which indicates that there are no significant differences between the observations and those predicted by the ANN. Therefore, the designed ANN can be used to predict the methanol flow at the exit of a dehydrogenation plant and later for the optimization of the system.