Real-time Simulation Model Research Articles

Analysis of Predicted Thermal Manikin Physiological Responses when Wearing Compression Gear for American College and Pro-Level Football Athletes

From 2012 to 2019, there were approximately 3455 large-scale National Football League injuries reported. There is widespread commercial acceptance of compression garments as tools for reducing injury, promoting recovery, and improving performance in athletic activity. While studies have been conducted to assess the exercise physiology benefits of compression garments, there remains a gap in the scientific literature regarding the clothing comfort and resultant thermoregulation effects of compression girdles for performance athletes. This study aimed to assess the effects of tighter fitting, multi-layer compression systems when worn by American football athletes. A negative impact on thermoregulation may negate or outweigh any realized local benefit such as increased skin blood flow or reduced muscle oscillatory properties. Therefore, the purpose of this research was to determine the predicted hypothalamus temperature, skin blood flow, skin temperature, sweat rate, temperature sensation, and comfort perceptions of the male human body when wearing a compression girdle. A sweating thermal manikin was utilized to predict the physiological responses of a 50th percentile male when wearing football girdles in both practice and play settings. The results indicate significant differences in skin blood flow, skin temperature, core temperature, sweat rate, and comfort and sensation perceptions when wearing compression girdles compared to boxer briefs in replicated practice and play settings. Findings also demonstrate the use of real-time manikin simulation modeling for predicting physiological response outcomes of wearing compression garments to a realized extent.

Challenges and opportunities for the application of digital twins in hard-to-abate industries: a review

The energy-intensive industry, heavily reliant on heat generation processes, faces the critical challenge of drastically reducing greenhouse gas emissions. This study reviews the potential of digital twin technology in decarbonizing combustion-dependent manufacturing processes. While primarily focusing on scholarly articles, our analysis may not cover all current industrial practices and digital twin implementations. This paper highlights challenges and opportunities in deploying practical digital twins for industrial furnaces within the Industry 4.0 framework. Digital twins, integrating real-time simulation models with predictive capabilities, aim to enhance industrial furnaces’ design, operation, and maintenance by combining high-fidelity simulations with data-driven modelling techniques. The findings indicate that digital twins can facilitate the transition to furnace electrification and adopting zero-carbon fuels, significantly reducing emissions and optimizing overall furnace performance. However, challenges like model reliability and high upfront investments in IT and connectivity infrastructures limit their widespread adoption. Despite these limitations, ongoing technological advancements predict a rapid expansion of digital twin applications in industrial furnaces, making them indispensable for achieving decarbonization goals in energy-intensive industries.

A Coupling Physics Model for Real-Time 4D Simulation of Cardiac Electromechanics

Cardiac simulators can assist in the diagnosis of heart disease and enhance human understanding of this leading cause of mortality. The coupling of multiphysics, such as electrophysiology and active–passive mechanics, in the simulation of the heart poses challenges in utilizing existing methodologies for real-time applications. The low efficiency of physically-based simulation is mostly caused by the need for electrical-stress conduction to use tiny time steps in order to prevent numerical instability. Additionally, the mechanical simulation experiences sluggish convergence when dealing with significant deformation and stiffness, and there are also concerns regarding volume inversion. We provide a coupling physics model that transforms the active–passive dynamics into multiphysics solving constraints, aiming at boosting the real-time efficiency of the cardiac electromechanical simulation. The multiphysics processes are initially divided into two levels: cell-level electrical stimulation and organ-level electrical-stress diffusion/conduction. This separation is achieved by employing operator splitting in combination with the quasi-steady-state method, which simplifies the system equations. Next, utilizing spatial discretization, we employ the matrix-free conjugate gradient approach to solve the electromechanical model, therefore improving the efficiency of the simulation. The experimental results illustrate that our simulation model is capable of replicating intricate cardiac physiological phenomena, including 3D spiral waves and rhythmic contractions. Moreover, our model achieves a significant advancement in real-time computation while maintaining a comparable level of accuracy to current methods. This improvement is advantageous for interactive medical applications.

Framework for Design Validation of Control Algorithms used on Mechanical Hardware-in-the-Loop Nacelle Test Rigs

Full scale multi-MW Wind Energy Converter (WEC) nacelles including the mechanical and electrical components as well as the main and converter control systems, are tested at the Dynamic Nacelle Testing Laboratory (DyNaLab) at Fraunhofer IWES. A Hardware-in-the-Loop environment emulates the missing rotor and accurately applies torque to the nacelle via a motor and the test rig drivetrain. In a current nacelle test campaign, several structural changes have been made to reduce the necessary adaptor tolerances of the test rig interface and to include the nacelle hub. These changes can potentially impact the system’s dynamic response and controller performance. This paper presents the current research with a focus on improving the control algorithms for accurate application of the rotor torque by implementing a simulative validation framework. The objectives of this framework include addressing the impact of modifications to the test rig drivetrain, variations in the nacelle adaptor, and communication delays between real-time simulation models, the test rig, and the WEC controller prior to performing tests on the real system. The methodology shown therefore involves three steps: controller design using a simplified multi-mass torsional model of all components necessary for testing, validation with a high-fidelity virtual test bench model and testing the resulting controller performance during the commissioning phase for the actual test. Overall, this approach is expected to reduce the commissioning duration, enhance the performance of the test rig, and identify the limitations in achieving stable torque control before the application to the real wind turbine nacelle on the test rig.

A real-time simulation model of water quality with the impact of internal pollution for water source reservoir.

The water source reservoirs are the important urban water source in northern China. Although external pollution has been greatly improved, the internal pollutants in reservoirs continue to accumulate with the complex deposition and release processes, resulting in potential risks to water supply safety. To address the aforementioned issue, this paper proposed a simulation model of water quality named ECOlab EU1-WSR to simulate the spatio-temporal changes of water quality under the influence of internal pollution for the water source reservoirs. Based on the analysis of the water quality characteristics and the distribution of benthic vegetation in the reservoir, a three-dimensional hydrodynamic model was established based on MIKE3, the corresponding parameters and the related state variables were set, the ECOlab EU1-WSR model was established by secondly developing the original ECOlab EU1 template, and the real-time dynamic outputs of pollutant content in sediment were added to link the water quality index with sediment nutrition index for better revealing the impact of the internal pollution on the water quality. The performance of the model was evaluated by the case application on the water quality simulation of Daye reservoir and the optimization of the connection project between Daye reservoir and Xueye reservoir in Shandong Province China. The results showed that the model can accurately and simultaneously simulate the pollution in water and sediment by the comparative verification of hydrodynamics, water temperature, and water quality. Moreover, the model can effectively reflect the influence of the accumulation, deposition, and release of internal pollution on water quality by analyzing the correlation between the content of various pollution in water body and those in sediment, such as the total nitrogen and total phosphorus in the water body at the bottom of the water intake, were negatively correlated with the total nitrogen and total phosphorus in the sediments with correlation coefficients of 0.538 and 0.917, respectively. In addition, the optimal water inlet position and water flow rate of the connection project can be optimized and determined by using the model to effectively control water quality. The established model will be a useful tool for the design and management of a reservoir, the interconnection projects, and other water bodies by adaptively recoded.

Framework for Assessing the Sustainability Impacts of Truck Routing Strategies

The impact of freight on the transportation system is accentuated by the fact that trucks consume a greater roadway capacity than other vehicles and therefore cause more significant problems including traffic congestion, traffic delays, crashes, and pavement damage. Evaluating the actual repercussions of truck traffic becomes paramount in locales where roadway expansion is unfeasible. Trucks are vital to the economy, providing essential services to commerce and industry, and yet it is crucial that their operation does not contribute to the deterioration of infrastructural quality or compromise public safety. Currently, we lack methodologies in practice for the real-time management of traffic, specifically for truck routing, to minimize travel times and prevent delays due to non-recurrent congestion, such as traffic incidents. Accordingly, this study aimed to devise a truck routing strategy utilizing a traffic micro-simulation model (VISSIM) and to assess its effects on reducing travel delays. This involved the development of real-time truck re-routing simulation models that take into account non-recurrent congestion and the resulting travel delays and fuel consumption. The VISSIM model was applied to the I-75 corridor in Marion County, Florida, focusing on non-recurrent congestion effects on travel delays and fuel consumption. The initial findings suggest that the implementation of a dynamic truck re-routing system can significantly alleviate traffic congestion, resulting in a marked decrease in travel delays and fuel usage, demonstrating the potential for such strategies to enhance the overall efficiency of the transportation system.

Real-time multibody simulation of vehicle wheel suspensions of different topologies with elastokinematic properties

AbstractDriving simulators are becoming increasingly important in vehicle development, as they allow test drivers to evaluate the driving characteristics of a vehicle at an early stage of the development process. Vehicle dynamics models used for such real-time applications are usually simplified to achieve sufficiently low computation times, especially with respect to the elastokinematic properties of the wheel suspension. This contribution introduces a real-time capable multibody simulation model for wheel suspensions taking into account their elastokinematics. The underlying data model allows the parametrization of different suspension topologies. Linear-implicit integration methods enable real-time capable integration of the underlying numerically stiff equation of motion without major model simplifications. It is shown how the computational effort of linear-implicit integration can be reduced by explicitly taking the suspension topology into account. A versatile process for the analytical online-linearization of the equation of motion of arbitrary suspension topologies is presented. Both a double wishbone and a multilink suspension are simulated with very high accuracy in real-time on a standard PC.

A hybrid twin based on machine learning enhanced reduced order model for real-time simulation of magnetic bearings

The simulation of magnetic bearings involves highly non-linear physics, with high dependency on the input variation. Moreover, such a simulation is time consuming and can’t run, within realistic computation time for control purposes, when using classical computation methods. On the other hand, classical model reduction techniques fail to achieve the required precision within the allowed computation window. To address this complexity, this work proposes a combination of physics-based computing methods, model reduction techniques and machine learning algorithms, to tackle the requirements. The physical model used to represent the magnetic bearing is the classical Cauer Ladder Network method, while the model reduction technique is applied on the error of the physical model’s solution. Later on, in the latent space a machine learning algorithm is used to predict the evolution of the correction in the latent space. The results show an improvement of the solution without scarifying the computation time. The solution is computed in almost real-time (few milliseconds), and compared to the finite element reference solution.

Challenges Faced During Implementation of Digital Twin in Construction Project Monitoring

Digital Twins (DTs) are gaining popularity because they provide precise digital copies of assets, processes, and systems. This is especially true when these DTs are paired with real-time simulation models that make use of modern technologies like machine learning, artificial intelligence, and data analytics. These combinations can provide a comprehensive and dynamic view of the monitored systems. Digital twin (DT) has shown tremendous potential to bring about revolutionary improvements in the field of construction site surveillance. There is, however, a notable paucity of empirical research identifying the constant elements affecting DT adoption in this industry. This research tries to fill that void by identifying the important elements that determine the usage of DT in construction. The study adopts a complete framework with the goal of increasing the use of DT in building site monitoring. The elements influencing the adoption and effectiveness of distributed ledger technology (DT) are divided into three categories: technological, organizational, and economic. Technological factors include the system's appropriateness and the robustness of the data infrastructure. Organizational considerations include the company's openness to innovation and leadership support. Economic aspects include things like return on investment (ROI) and cost-effectiveness. The research technique combines case studies and literature reviews to examine the benefits and drawbacks of DT in construction monitoring. This study's expected output is a comprehensive framework that aids construction businesses in optimizing the use of DT in site monitoring. This would allow for more efficient, data-driven, and forward-thinking processes. The study's ultimate purpose is to provide critical knowledge that will assist the building sector in adopting cutting-edge methods. The industry may better plan for the integration of this sophisticated technology into their operations by knowing the potential of DT and the variables driving its adoption. This, in turn, can lead to more efficiency, lower risks, and improved overall performance

NYUS.2: an automated machine learning prediction model for the large-scale real-time simulation of grapevine freezing tolerance in North America.

Accurate and real-time monitoring of grapevine freezing tolerance is crucial for the sustainability of the grape industry in cool climate viticultural regions. However, on-site data are limited due to the complexity of measurement. Current prediction models underperform under diverse climate conditions, which limits the large-scale deployment of these methods. We combined grapevine freezing tolerance data from multiple regions in North America and generated a predictive model based on hourly temperature-derived features and cultivar features using AutoGluon, an automated machine learning engine. Feature importance was quantified by AutoGluon and SHAP (SHapley Additive exPlanations) value. The final model was evaluated and compared with previous models for its performance under different climate conditions. The final model achieved an overall 1.36°C root-mean-square error during model testing and outperformed two previous models using three test cultivars at all testing regions. Two feature importance quantification methods identified five shared essential features. Detailed analysis of the features indicates that the model has adequately extracted some biological mechanisms during training. The final model, named NYUS.2, was deployed along with two previous models as an R shiny-based application in the 2022-23 dormancy season, enabling large-scale and real-time simulation of grapevine freezing tolerance in North America for the first time.

Development of a Real-Time Tractor Model for Gear Shift Performance Verification

Verification of the system is essential during the development of a tractor; however, there are cost and time limitations when verification is performed on an actual tractor. To solve this problem, we developed a tractor model for real-time simulation to verify the gear shift performance of the tractor and evaluate the control algorithm. This study examined and modeled a dual-clutch transmission (DCT)-type 105 kW class tractor. The proportional control valve, synchronizer, and clutch were modeled to accurately implement the shift behavior, and the developed individual model was verified based on actual individual product test data. The 45 s driving simulation was conducted to confirm whether real-time simulation of the entire developed tractor model was possible and whether it simulated the behavior of the target tractor well. The driving simulation results confirmed that the driving speed of the tractor model matched the engine speed, transmission gear ratio, and tractor specifications, and the gear shift performance of the tractor model according to the number of gears was confirmed. The simulated model thus satisfies the characteristics of the target tractor and can be used to verify the gear shift performance, indicating that the model can verify the performance of the control algorithm in real time.

Unified Real-Time Simulation Method for DC/DC Conversion Systems Consisting of Cascaded Dual-Port Submodules

The DC/DC conversion systems are constructed from multiple cascaded dual-port submodules in series and/or parallel at both the input port and output port. The complexity of the series-parallel structures brings great challenges to real-time simulation. This paper proposes a unified real-time simulation method of series-parallel DC/DC conversion systems, which unifies the input-series-output-series (ISOS), input-series-output- parallel (ISOP), input-parallel-output-parallel (IPOP) and input-parallel-output-series (IPOS) structures. First, based on the nodal analysis method, the dual-port equivalent model of each submodule in the series-parallel DC/DC conversion system is obtained. Second, by cascading each submodule, the unified form of the system dual-port equivalent model of the series-parallel DC/DC conversion system is constructed. For the series connection, the port is equivalent to Thevenin's equivalent circuit. For the parallel connection, the port is equivalent to Norton's equivalent circuit. The real-time simulation model of a specific series-parallel DC/DC conversion system can be obtained by combining the input/output port equivalent circuits. Besides, the high-frequency waveforms of the internal circuit can also be obtained through parallel calculation steps. The real-time simulation of the proposed unified model is carried out with a 250ns time-step on Xilinx K-7 Series FPGA, which verifies the accuracy and simulation efficiency of the unified model.

Research on Diesel Engine Speed Control Based on Improved Salp Algorithm

To better regulate the speed of diesel engines and optimize the speed overshoot and fast response, a speed control method combining the improved salp swarm algorithm (ISSA) with the proportional integral and differential (PID) controller was proposed. A real-time simulation model for a high-pressure common rail diesel engine was established. Addressing the challenges of the salp swarm algorithm (SSA), such as uneven population distribution and its tendency to become trapped in local optima, logistic-tent chaotic mapping, adaptive parameters was introduced, as well as adaptive dynamic inertia weights, elite strategy, and a dynamic inverse strategy. These enhancements bolstered the algorithm’s precision and efficiency in both global and local searches. Using the enhanced SSA, the parameters of the PID controller for the diesel engine model was optimized. The results indicated that the ISSA offers superior parameter identification precision, strengthening speed control stability. During sudden changes in speed and load, the overshoot decreased by an average of more than 30.3% and more than 8.6%, respectively. Moreover, the settling time decreased by an average of more than 0.76 s and 1.52 s, respectively, significantly enhancing the quality of diesel engine speed control.

General, open-source vertex modeling in biological applications using Tissue Forge

Vertex models are a widespread approach for describing the biophysics and behaviors of multicellular systems, especially of epithelial tissues. Vertex models describe a wide variety of developmental scenarios and behaviors like cell rearrangement and tissue folding. Often, these models are implemented as single-use or closed-source software, which inhibits reproducibility and decreases accessibility for researchers with limited proficiency in software development and numerical methods. We developed a physics-based vertex model methodology in Tissue Forge, an open-source, particle-based modeling and simulation environment. Our methodology describes the properties and processes of vertex model objects on the basis of vertices, which allows integration of vertex modeling with the particle-based formalism of Tissue Forge, enabling an environment for developing mixed-method models of multicellular systems. Our methodology in Tissue Forge inherits all features provided by Tissue Forge, delivering open-source, extensible vertex modeling with interactive simulation, real-time simulation visualization and model sharing in the C, C++ and Python programming languages and a Jupyter Notebook. Demonstrations show a vertex model of cell sorting and a mixed-method model of cell migration combining vertex- and particle-based models. Our methodology provides accessible vertex modeling for a broad range of scientific disciplines, and we welcome community-developed contributions to our open-source software implementation.

무인비행체 물체 낙하 지점 결정을 위한 실시간 시뮬레이션 모델 분석

무인비행체 물체 낙하 지점 결정을 위한 실시간 시뮬레이션 모델 분석

Assessing the impact of climate change on fluvial flood losses in urban areas: a case study of Pamplona (Spain)

ABSTRACT Climate change is expected to increase the frequency and magnitude of flooding. Furthermore, in recent years, there has been an increase in city extent and population densities. This study considers the metropolitan area of Pamplona (Spain) as a case study. The delta changes in precipitation quantiles in the Arga River catchment, obtained from 12 climate models, have been transformed into peak flow delta changes using the Real-time Interactive Basin Simulator (RIBS) model. Also, three percentiles of flood quantile delta changes are selected. Water depths and flood extents expected in future flood events have been obtained using the two-dimensional Iber hydrodynamic model. The Safer_DAMAGE algorithm has been used to assess flood losses in urban areas at the building scale. The results show that flood losses are expected to be smaller for low return periods and greater for high return periods.

Electromagnetic Transient Modeling Method of Photovoltaic Power Station Based on FPGA

The rapid development of renewable energy needs the effective support of simulation technology. Aiming at the shortage of both modeling scale and simulation accuracy of the current large-scale photovoltaic power station, an electromagnetic transient modeling and real-time simulation method suitable for large-scale photovoltaic power stations are proposed. The microsecond accurate real-time simulation of 24 photovoltaic power units is implemented on a single FPGA. Through an equivalent modeling method, the electromagnetic transient model of a photovoltaic power unit including a PV array, DC boost circuit, grid-connected inverter, filter, and grid-side transformer is established. Then, based on the FPGA parallel computing and pipeline time division multiplexing technology, the updating framework of the station collecting network and the power unit is constructed, and the real-time simulation model of the station including 24 photovoltaic power units in 4 sub-stations is built. Finally, the microsecond real-time simulation of the photovoltaic power station is realized through RTRES, a self-developed real-time simulator in China’s Southern Power Grid. The comparison with the detailed PSCAD model shows that the simulation error is within ± 2%, which verifies the correctness of the proposed method and model.

Real time validation of a control system for microgrids with distributed generation and storage resources

Faced with a scenario in which the increase in renewable energy generated near consumer centers can cause problems for the operation of the electrical network, the present work proposes a real-time simulation model for automation and control systems of electrical distribution networks with microgrids, distributed generation, and storage resources. The proposed model consists of a hardware-in-the-loop control with the aid of a simulation tool in conjunction with a Real-Time Digital Simulator (RTDS) and considers the dynamic behavior of switched elements and inverters. A communication platform using TCP/IP protocol between RTDS (power system) and MatLab/Simulink (optimization algorithms) allows the operation of the network in grid-connected and islanded mode, guaranteeing the computational time for experimental implementation. For the first mode, an algorithm is proposed to solve an optimal dispatch energy storage system problem. Second mode, an algorithm is proposed to solve a load shedding problem. The objective is to operate the microgrids optimally and evaluate the performance of a storage system based on real data from the state of Paraná, in Brazil. Results show that the optimization algorithms are experimentally applicable, obtaining reasonable computational time to find optimal solutions and an assertive decision-making to meet the objectives. Thus, the proposed framework is a potential tool to validate algorithms for active management of microgrids in real-time simulation.

Nonlinear system identification of a UAV model with distributed aerodynamics and flexible structure

This paper presents the nonlinear system identification of a slightly flexible 25 kg fixed-wing UAV in the time-domain using a computationally efficient distributed aerodynamics model and a linear structural dynamics representation. The equations of motion are formed by making use of the free vibration modes of the structure and the mean axes formulation. The structural modes and mode shapes are determined from ground vibration tests. The distributed aerodynamics, accounting for elastic deformations, are modeled using a quasi-steady stability and control derivative approach and by applying strip theory. Initial distributions for the derivatives are obtained from vortex-lattice-method calculations. For matching the model response to the measured response, parameters for scaling the initial derivative distributions are introduced. The flexible model is subsequently identified based on flight test data using the output error method in the time-domain and maximum-likelihood estimation. A good overall identification result is achieved with a close match of the fast aircraft dynamics. Finally, an evaluation is given on the suitability of the identified model for real-time simulation, loads’ estimation, and active load control law design.

An Integrated Framework for Real-Time Intelligent Traffic Management of Smart Highways

The new generation smart highways (NGSH) have emerged as irresistible trends to enhance the efficiency and safety of transportation systems. An integral component of the NGSH is the automation of the intelligent traffic management system (ITMS). This study investigates an integrated framework for the ITMS that incorporates the fine-grained microscopic simulation and deep learning technologies based on real-time traffic data. The framework commences by performing dynamic corrections based on the license plate, vehicle speed, location, and other information provided by the real-time bayonet data in order to simulate the realistic traffic flow along the highway. A deep learning model based on long short-term memory (LSTM) is then applied to predict the short-term traffic volume on major highway segments. Based on prediction results, a collaborative management method is constructed that combines variable speed limits and ramp metering. The case study on the Shanghai–Hangzhou–Ningbo Highway in China suggests the real-time simulation model can control the average error of the traffic volume on the main segments by 4.58%. The LSTM-based model can accurately predict the short-term traffic volume with a relative error of 85% below 15% in both offline and online modes. Consequently, the proposed collaborative framework improves the average speed and traffic volume of controlled sections by 3.62% and 4.35%, respectively, demonstrating its effectiveness in improving the operation and management of the smart highways.