Real-time Simulation Framework Research Articles

LEOCN: Real-time and complete network simulation framework for LEO constellation networks1

With the development of the space industry, LEO simulation platforms are increasingly important. Prior work mainly focused on the dynamics of constellation topology, routing algorithms against the topological motion, and the behavior of LEO constellation networks, but neglected real-time and completeness properties. In our work, we design a real-time and complete network simulation framework, named LEOCN, that is more meaningful and detailed for researchers than previous work on LEO satellite constellation simulations. LEOCN is supported by Linux System, which makes the framework more realistic and relieves the computational burden. Meanwhile, LEOCN deeply considers topology and routing algorithms, visualization, satellite mobility, communication protocols, and network traffic problems. On LEOCN, we can deploy routing algorithm experiments via SDN. We evaluate the performance of LEOCN in large-scale constellation scenarios, and the results demonstrate the superiority of the real-time and complete network simulation framework.

Real-Time Acoustic Simulation Framework for tFUS: A Feasibility Study Using Navigation System

Transcranial focused ultrasound (tFUS), in which acoustic energy is focused on a small region in the brain through the skull, is a non-invasive therapeutic method with high spatial resolution and depth penetration. Image-guided navigation has been widely utilized to visualize the location of acoustic focus in the cranial cavity. However, this system is often inaccurate because of the significant aberrations caused by the skull. Therefore, acoustic simulations using a numerical solver have been widely adopted to compensate for this inaccuracy. Although the simulation can predict the intracranial acoustic pressure field, real-time application during tFUS treatment is almost impossible due to the high computational cost. In this study, we propose a neural network-based real-time acoustic simulation framework and test its feasibility by implementing a simulation-guided navigation (SGN) system. Real-time acoustic simulation is performed using a 3D conditional generative adversarial network (3D-cGAN) model featuring residual blocks and multiple loss functions. This network was trained by the conventional numerical acoustic simulation program (i.e., k-Wave). The SGN system is then implemented by integrating real-time acoustic simulation with a conventional image-guided navigation system. The proposed system can provide simulation results with a frame rate of 5 Hz (i.e., about 0.2 s), including all processing times. In numerical validation (3D-cGAN vs. k-Wave), the average peak intracranial pressure error was 6.8 ± 5.5%, and the average acoustic focus position error was 5.3 ± 7.7 mm. In experimental validation using a skull phantom (3D-cGAN vs. actual measurement), the average peak intracranial pressure error was 4.5%, and the average acoustic focus position error was 6.6 mm. These results demonstrate that the SGN system can predict the intracranial acoustic field according to transducer placement in real-time.

An IoT and machine learning enhanced framework for real-time digital human modeling and motion simulation

The Digital Human Model (DHM) is a computational technique employed for simulating human motion and behavior, finding applications across sectors such as manufacturing, healthcare, and education. Despite its potential, current DHM simulations grapple with challenges like high computational costs, significant latency, low precision, and limited realism. Addressing these challenges, this paper introduces a novel DHM simulation approach that synergizes the Kinect motion capture system, Internet of Things (IoT) devices, and advanced machine learning techniques. Distinctively, our method captures real-time human motion data through IoT devices and the Kinect system, followed by data preprocessing and feature extraction. In contrast to traditional methodologies, we harness deep learning to establish time-series models, integrating convolutional neural networks and dilated convolutional kernels to amplify processing capabilities and cater to multi-scale data. Furthermore, we employ virtual reality technology to craft a digital human structural model, streamlining motion simulation and optimization. Experimental evaluations underscore that our approach consistently outperforms conventional methods across all metrics, demonstrating robust real-time simulation capabilities. In essence, this paper pioneers an innovative technological paradigm for DHM simulation, heralding new avenues for research and applications in related domains.

An Event-Driven Real-Time Simulation for Power Electronics Systems Based on Discrete Hybrid Time-Step Algorithm

Real-time (RT) hardware-in-the-loop (HIL) simulation aims to speed up the validation process for power electronic systems (PES). The complex PESs with high switching frequency constitute some of the most challenging applications in RT-HIL. Conventional RT-HIL relies on adding extra expensive computing hardware to achieve sub-microsecond step-size, reducing errors caused by unavoidable sampling delays. This paper proposes a CPU-based event-driven real-time (EDRT) simulation framework by improving the algorithm rather than using additional hardware. The framework consists of two parts: 1) the synchronous-cycle event detection (SCED) sampling method, which eliminates the delay error by detecting switching events; 2) the discrete hybrid time-step (DHT) numerical algorithm, which combines variable and fixed step-size simulation to improve the calculation efficiency and uses the ideal model to improve the modeling accuracy. The proposed framework is applied to a power electronic transformer (PET) with 24 switches and a 20 kHz switching frequency as a simulated case. Comparing the proposed simulation results with experimental results and other simulation results, the proposed EDRT framework can achieve the same numerical accuracy as the offline simulation but only requires 1/36 of the computation time. Furthermore, the hardware cost to achieve the same computational scale is reduced to 1/20 of the conventional HIL.

CapSense: A Real-Time Capacitive Sensor Simulation Framework for Physical Human-Robot Interaction

This letter presents CapSense, a real-time open-source capacitive sensor simulation framework for robotic applications. CapSense provides raw data of capacitive proximity sensors based on a fast and efficient 3D finite-element method (FEM) implementation. The proposed framework is interfaced to off-the-shelf robot and physics simulation environments to couple dynamic interaction of the environment with an electrostatic solver for capacitance computation in real-time. The FEM method proposed in this letter relies on a static tetrahedral mesh of the sensor surrounding without a-posteriori re-meshing and achieves high update rates by an adaptive update step. CapSense is flexible due to various configuration parameters (i.e. number, size, shape and location of electrodes) and serves as a platform for investigation of capacitive sensors in robotic applications. By using the proposed framework, researchers can simulate capacitive sensors in different scenarios and investigate these sensors and their configuration prior to installation and fabrication of real hardware. The proposed framework opens new research opportunities via sim-to-real transfer of capacitive sensing. The simulation approach is validated by comparing real-world results of different scenarios with simulation results. In order to showcase the benefits of CapSense in physical Human-Robot Interaction (pHRI), the framework is evaluated in a robotic healthcare scenario.

A real-time hybrid simulation framework for floating offshore wind turbines

Offshore wind farms are experiencing rapid growth globally where floating offshore wind turbines (FOWTs) have been attracting increasing research and industry investment. To achieve secure application, it is essential to develop numerical models and conduct laboratory testing to develop an in-depth understanding of the complex structural behavior of FOWTs under combined wind-wave loading. However, model testing of FOWTs is complicated by the Reynolds-Froude scaling incompatibilities. This research proposes a real-time hybrid simulation (RTHS) framework to study the structural performance of FOWTs under wind-wave loading. In the framework, the blades (nacelle) and tower are numerically modeled, and the floating platform is tested in real-time via an actuation system in a laboratory. The numerical and physical substructures communicate at the tower-floater interface. The National Renewable Energy Lab 5 MW spar-type FOWT on a scale of 1:50 is simulated to evaluate the feasibility of the proposed framework. Errors caused by delays and noises are quantified through sensitivity analyses. Results show that the delays in the sensors and actuators influence the performance of the RTHS framework more significantly than the noises. Overall, the response relative errors in the RTHS framework are small and tolerable under delays, noises, and representative wind-wave conditions. The proposed framework and sensitivity analyses presented in this study provide important information for future implementation and further development of the RTHS technology for similar marine structures.

Moving Safety Evaluation of High-speed Train on Post-earthquake Bridge Utilizing Real-time Hybrid Simulation

ABSTRACT This study aimed to provide an experimental evaluation method for train’s moving safety on a post-earthquake bridge. First, an improved real-time hybrid simulation (RTHS) framework was constructed, based on utilizing the moving load superposition algorithm to solve the train-track-bridge interaction (TTBI) problem. An RTHS test was then conducted for the TTBI problem. The train model was tested using a shake table and was dynamically linked to the numerical substructure. A post-earthquake seven-span high-speed railway simply supported bridge was studied, in which earthquake-induced damage, such as stiffness reductions, residual displacements of piers, girder gap expansions, and pier settlements were all tested.

실시간 시뮬레이터를 활용한 철도차량 PMSM 제어시스템의 시계열 기반 안정도 추정

This paper deals with numerical analysis method for system stability, which is applied to the mathematical model of permanent magnet synchronous motor for railway vehicle as one of the application of real-time digital simulation. Specifically, this study deals with bifurcation numerical analysis, one of analytic method to study nonlinear dynamics of a system, for system design or its inspection technique of the system model. XPPAUT is used to analyze the electrical motion of this complex system, so it can introduce theoretical approach to design real-time computing environment based on OPAL-RT for electrification system parts of railroad vehicle model. Within the real-time simulation framework, the paper also provides time-series based system stability estimation by performing statistical processes for auto-regressive model construction. The proposed approach can reduplicate verification process by screen studying of the model for improvement during real-time simulation or further hardware-in-the-loop test system.

Effects of force and moment actuation in active two dimensional VIV control of an elastic circular cylinder in power-law fluid flow

The coupled in-line/crosswise (2-DOF) Vortex-Induced Vibration (VIV) control of a flexibly-supported impenetrable circular cylinder immersed in uniform 2D laminar cross-flow of incompressible non-Newtonian power-law fluids is numerically investigated. Two effective active closed-loop control strategies are separately applied and compared in a real-time collaborative simulation framework that interactively connects the Fluent CFD solver with Matlab/Simulink. Numerical simulations reveal the important effects of power-law rheology and controller configuration on the key cylinder response/aerodynamic parameters and flow structure for a wide range of power-law index parameters (0.4≤n≤1.8). In the absence of the control system, the boundary layer thickness, the attached wake size/strength, and the length/thickness of the associated “shear stretching layer” (vortex shedding frequency) are found to noticeably increase (decrease) with increasing the power law index parameter. As the value of power-law index is largely increased, there is a notable shift of the synchronization (lock-in) region into the higher Reynolds number range besides its marked contraction which can passively complement the cylinder VIV control problem in the highly viscous shear-thickening fluid. When the active control system becomes operative, the superior performance and efficiency of the moment-based controller in effective and rapid mitigation of cylinder VIV and weakening of the vortex intensities through a desynchronization type of action with minimum actuator power requirements is demonstrated.

Virtual Radar: Real-Time Millimeter-Wave Radar Sensor Simulation for Perception-Driven Robotics

This article presents ViRa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Available online: https://vira.aau.at/., a real-time open-source millimeter-wave radar simulation framework for perception-driven robotic applications. ViRa provides (i) raw data of radar sensors in real-time, simulation of (ii) multi-antenna configurations for spatial estimation of objects, (iii) wave penetration of non-conductive objects to infer information in occluded situations, (iv) different radar beam patterns, and, (v) configurations of radar sensors as given by real-world radars. By using ViRa, researchers can simulate radar sensors in different robotic scenarios and investigate radars prior to the installation. This allows an acceleration in the development of radar sensors for robotic applications without the need of real hardware. Contrary to simple model abstractions, which only output loose features, ViRa generates raw radar data using computer graphics techniques on graphics processing unit (GPU) level embedded inside a game engine environment. ViRa allows to feed data directly into machine learning frameworks, which enables further improvement in novel research directions, such as safe human-robot interaction or agile drone flights in obstacle-rich environments. The proposed simulation framework is validated with data from different scenarios in robotics such as human tracking for human-robot interaction. The obtained results are compared with a reference simulation framework and show significantly higher correlation when compared to real-world measurement data.

Digital Twin for Supply Chain Coordination in Modular Construction

Over the past decades, the construction industry has been attracted to modular construction because of its benefits for reduced project scheduling and costs. However, schedule deviation risks in the logistics process of modular construction can derail its benefits and thus interfere with its widespread application. To address this issue, we aim to develop a digital twin framework for real-time logistics simulation, which can predict potential logistics risks and accurate module arrival time. The digital twin, a virtual replica of the physical module, updates its virtual asset based on building information modeling (BIM) in near real-time using internet of thing (IoT) sensors. Then, the virtual asset is transferred and exploited for logistics simulation in a geographic information system (GIS)-based routing application. We tested this framework in a case project where modules are manufactured at a factory, delivered to the site via a truck, and assembled onsite. The results show that potential logistical risks and accurate module arrival time can be detected via the suggested digital twin framework. This paper’s primary contribution is the development of a framework that mediates IoT, BIM, and GIS for reliable simulation which predicts potential logistics risks and accurate module delivery time. Such reliable risk prediction enables effective supply chain coordination, which can improve project performance and the widespread application of modular construction.

Real-time hybrid simulation framework for the investigation of soil-structure interaction effects on the vibration control performance of shape memory alloys

Shape memory alloys (SMAs) are two-phase polycrystal smart materials with hysteretic energy dissipation capacity. Under a mechanically applied strain, SMAs can transform their phases and recover large deformations in a superelastic fashion. Both the hysteretic damping and the deformation recovery are key features for new type of maintenance-free vibration control devices. Various factors influence the energy dissipation process of SMAs. Particularly rates and amplitudes of the applied deformation pattern are crucial for the damping. The studies have focused so far on the interaction between structures and SMAs. This paper goes one step further and seeks to consider the soil-structure interaction (SSI) in the treatment of the SMA control performance as well. A real-time hybrid simulation based cyber-physical methodology is developed for this purpose. Using finite-element modeling technique, a semi-infinite boundary definition is realized for the soil and the soil-foundation interaction is replicated for SMA-controlled structures. The proposed approach is applied to a shear frame structure equipped with SMA wires. The responses of the structure to historic earthquake records are assessed from a macroscopic perspective of the SMA behavior. The study shows that the material and radiation damping of the soil-foundation interaction have a significant influence on the vibration control performance of SMAs.

Use of Radar Quantitative Precipitation Estimates (QPEs) for Improved Hydrological Model Calibration and Flood Forecasting

AbstractFlood forecasting is essential to minimize the impacts and costs of floods, especially in urbanized watersheds. Radar rainfall estimates are becoming increasingly popular in flood forecasting because they provide the much-needed real-time spatially distributed precipitation information. The current study evaluates the use of radar Quantitative Precipitation Estimates (QPEs) in hydrological model calibration for streamflow simulation and flood mapping in an urban setting. Firstly, S-band and C-band radar QPEs were integrated into event-based hydrological models to improve the calibration of model parameters. Then, rain gauge and radar precipitation estimates’ performances were compared for hydrological modeling in an urban watershed to assess radar QPE's effects on streamflow simulation accuracy. Finally, flood extent maps were produced using coupled hydrological-hydraulic models integrated within the Hydrologic Engineering Center- Real-Time Simulation (HEC-RTS) framework. It is shown that the bias correction of radar QPEs can enhance the hydrological model calibration. The radar-gauge merging obtained a KGE, MPFC, NSE, and VE improvement of about + 0.42, + 0.12, + 0.78, and − 0.23, respectively for S-band and + 0.64, + 0.36, + 1.12, and − 0.34, respectively for C-band radar QPEs. Merged radar QPEs are also helpful in running hydrological models calibrated using gauge data. The HEC-RTS framework can be used to produce flood forecast maps using the bias-corrected radar QPEs. Therefore, radar rainfall estimates could be efficiently used to forecast floods in urbanized areas for effective flood management and mitigation. Canadian flood forecasting systems could be efficiently updated by integrating bias-corrected radar QPEs to simulate streamflow and produce flood inundation maps.

SODA: A Real-time Simulation Framework for Object Detection and Analysis in Smart Manufacturing

For modern manufacturing firms, automation has already become a norm but constantly needs to be improved as firms still face strong demand to increase their productivity. This can be achieved by reducing dependability on manpower, reaching lean and even unmanned production and this is where some of the standards of Industry 4.0 come in useful, not to mention: Machine Vision, Image Recognition or Machine Learning. In our paper, we present SODA – our approach to build a flexible ML and AI enabled framework for object detection, analysis, and simulation. The framework is designed to support a development process of solutions requiring real-time analysis of images of different types of moving objects on a conveyor belt. In our work we discuss architectural challenges of the developed framework as well as the basic components of the system. We do also provide information on how to use the framework and present a sample implementation of an actual system employing some of the machine learning methods.

Uncertainty-aware state estimation for electrochemical model-based fast charging control of lithium-ion batteries

Fast charging capability is considered a critical factor for the widespread adoption of electric vehicles. High charging currents can, however, severely affect battery health due to the danger of metallic lithium deposition on the anode and consequent degradation reactions. The charging speed should therefore be limited with respect to battery temperature, state of charge, and cell design, governing the onset point of lithium plating. Electrochemical models are a suitable tool providing continuous estimates of the anode potential as the main lithium plating indicator while covering a wide operational range. In this article, we present a novel charging control scheme based on a real-time capable simulation framework with adjustable model resolution. A profound investigation of error sources and modeling uncertainties motivates online state corrections towards a lower bound of the anode potential, which are realized by selective adjustments of the lithium distribution within electrode particles based on the full cell voltage error. Simulations of controlled and conventional CC charging profiles indicate the importance of continuously adapting the charging power for different operating conditions. Further, simulated state and parameter distortions as they might result from initialization and parameterization errors show that our estimation strategy can mitigate the risk of unsafe control operations.

A Speed Control Method at Successive Signalized Intersections Under Connected Vehicles Environment

A growing body of speed control methods for signalized intersections have been developed using V2I and V2V technology. However, limited efforts are made to reduce the vehicle fuel consumption and emissions during the driving process at successive signalized intersections. To fill the gaps, this paper proposed a novel speed control method for the successive signalized intersections under connected vehicles environment. Using the upcoming traffic signal phasing, timing information and the vehicle queues at the signalized intersection, vehicle speed was optimized to reduce the vehicle fuel consumption and emissions. To validate its effectiveness, a real-time simulation framework embodied the characteristics of the connected vehicles environment is conducted using the multi-agent technology. In different traffic flows, the fuel consumption, CO2 emissions and travel time based on the speed control method are compared with that of without speed control. The effectiveness of the proposed speed control method in different signal timing plan was also tested. Simulation results show that the proposed speed control method could reduce more than 18% fuel consumption and CO2 emissions and 9% travel time in the medium density of traffic flow, which is the best control effect.

Integrating Climate Forecasts with the Soil and Water Assessment Tool (SWAT) for High-Resolution Hydrologic Simulations and Forecasts in the Southeastern U.S.

This study provides high-resolution modeling of daily water budget components at Hydrologic Unit Code (HUC)-12 resolution for 50 watersheds of the South Atlantic Gulf (SAG) region in the southeastern U.S. (SEUS) by implementing the Soil and Water Assessment Tool (SWAT) model in the form of a near real-time, semi-automated framework. A near real-time hydrologic simulation framework is implemented with a lead time of nine months (March–December 2017) by integrating the calibrated SWAT model with National Centers for Environmental Prediction coupled forecast system model version 2 (CFSv2) weather data to forecast daily water balance components. The modeling exercise is conducted as a precursor for various future hydrologic studies (retrospective or forecasting) for the region by providing a calibrated hydrological dataset at high spatial (HUC-12) and temporal (1-day) resolution. The models are calibrated (January 2003–December 2010) and validated (January 2011–December 2013) for each watershed using the observed streamflow data from 50 United States Geological Survey (USGS) gauging stations. The water balance analysis for the region shows that the implemented models satisfactorily represent the hydrology of the region across different sub-regions (Appalachian highlands, plains, and coastal wetlands) and seasons. While CFSv2-driven SWAT models are able to provide reasonable performance in near real-time and can be used for decision making in the region, caution is advised for using model outputs as the streamflow forecasts display significant deviation from observed streamflow for all watersheds for lead times greater than a month.

Real-time manufacturing modeling and simulation framework using augmented reality and stochastic network analysis

While the development of augmented reality (AR) technologies has made it possible to assign real-time features to many systems and applications, these trends are rare in manufacturing modeling and simulation. This research study proposes a real-time manufacturing layout modeler and material flow simulator. The manufacturing devices of interest are positioned using AR labels, and the generated layout is converted into a stochastic Petri net model, where the validity of material flow and other criteria are checked. In order to overcome the limitations of the Petri net model and enhance analytical functionalities, stochastic network analyses are embedded into the framework. The layout model with greater uncertainty is analyzed, and manufacturing performance indicators such as cycle time, throughput, and work-in process are estimated. The proposed framework is not simply an integration of AR techniques and manufacturing simulations, but provides an efficient AR labeling architecture for large-scale manufacturing environments, and is suitable for a fast, real-time rendering. In order to verify the effectiveness of the proposed framework, real-time modeling and simulation examples were used as case studies. The results showed that the proposed system contributes to more accurate layout design and simulation analysis by using the embedded AR techniques and queuing network methods.

Hardware‐in‐the‐loop real‐time platform for more electric aircraft

Real-time test platform based on hardware-in-the-loop (HIL) simulation technology, troubleshooting for real-time problems such as interrupt latency and interface communication before physical testing, can effectively improve the traditional development process, shorten the research and development cycle, and reduce the cost and risk of test. Based on the technologies of virtual instrument, mathematical modelling, and HIL simulation, a real-time simulation framework combining the discrete-time converter system and the continuous-time motor system is proposed. Combined MATLAB/Simulink, dSPACE, and real-time hardware, an HIL real-time test platform is designed for the prototype and proper testing of the electrical control unit of more electric aircraft. The results and design principles of a three-stage brushless synchronous starter/generator system emulation using HIL simulation are presented.

Model-based framework for multi-axial real-time hybrid simulation testing

Real-time hybrid simulation is an efficient and cost-effective dynamic testing technique for performance evaluation of structural systems subjected to earthquake loading with rate-dependent behavior. A loading assembly with multiple actuators is required to impose realistic boundary conditions on physical specimens. However, such a testing system is expected to exhibit significant dynamic coupling of the actuators and suffer from time lags that are associated with the dynamics of the servo-hydraulic system, as well as control-structure interaction (CSI). One approach to reducing experimental errors considers a multi-input, multi-output (MIMO) controller design, yielding accurate reference tracking and noise rejection. In this paper, a framework for multi-axial real-time hybrid simulation (maRTHS) testing is presented. The methodology employs a real-time feedback-feedforward controller for multiple actuators commanded in Cartesian coordinates. Kinematic transformations between actuator space and Cartesian space are derived for all six-degrees-offreedom of the moving platform. Then, a frequency domain identification technique is used to develop an accurate MIMO transfer function of the system. Further, a Cartesian-domain model-based feedforward-feedback controller is implemented for time lag compensation and to increase the robustness of the reference tracking for given model uncertainty. The framework is implemented using the 1/5th-scale Load and Boundary Condition Box (LBCB) located at the University of Illinois at Urbana- Champaign. To demonstrate the efficacy of the proposed methodology, a single-story frame subjected to earthquake loading is tested. One of the columns in the frame is represented physically in the laboratory as a cantilevered steel column. For realtime execution, the numerical substructure, kinematic transformations, and controllers are implemented on a digital signal processor. Results show excellent performance of the maRTHS framework when six-degrees-of-freedom are controlled at the interface between substructures.