Hybrid Simulation Research Articles

Influences of δB Contribution and Parallel Inertial Term of Energetic Particles on MHD-Kinetic Hybrid Simulations: A Case Study of the 1/1 Internal Kink Mode

Abstract The magnetohydrodynamic-kinetic (MHD-kinetic) hybrid model [Park et. al., 1992] has been widely applied in studying energetic particles (EPs) problems in fusion plasmas for past decades. The pressure-coupling scheme or the current-coupling scheme is adopted in this model. However, two noteworthy issues arise in the model application: firstly, the coupled term introduced in the pressure-coupling scheme, (∇⋅P_h )_⊥, is often simplified by ∇⋅P_h, which is equivalent to neglecting the parallel inertial term of EPs; secondly, besides the δf contribution caused by changing in the EP distribution function, the magnetic field perturbation (the δB contribution) generated during development of the instabilities should also be considered, but it is often ignored in existing hybrid simulations. In this paper, we derive the analytical formulations under these two coupling schemes and then numerically study the representative case of the linear stability of the m/n=1/1 internal kink mode (IKM) [Fu et. al., 2006] by using the CLT-K code. It is found that the approximated models can still yield reasonable results when EPs are isotopically distributed. But it fails completely in cases with anisotropic EP distributions. In addition, we further investigate the influence of EP’s orbit width on the stability of IKM and verify the equivalence between pressure-coupling scheme and the current-coupling scheme.

Hybrid simulation with model updating method based on the multi-stepsize constrained cubature Kalman filter

Hybrid simulation with model updating method based on the multi-stepsize constrained cubature Kalman filter

Mastering seismic time series response predictions using an attention-Mamba transformer model for bridge bearings and piers across varied testing conditions

This research introduces an advanced method for predicting seismic responses and hysteresis curves of instrumented bridge piers and bearings under various loading conditions, leaning solely on a single deep learning architecture and the same hyperparameters tuning. Test specimens are subjected to ground accelerations including vertical seismic loads and axial forces. To accurately capture peak values, particularly on the negative side of the hysteresis loop (unloading region), the model employs a stacked deep architecture. A key component to overcome the challenges is the self-attention-Mamba-driven transformer layer, which enhances the model’s ability to capture long-range dependencies in seismic data. This layer works in conjunction with other deep learning techniques to ensure robust and precise predictions. Implemented with Python’s Keras functional API, the model processes inputs like ground accelerations, actuator loads, effective height, moment of inertia, and superstructure mass. The model is evaluated with a dataset of 95 real-time hybrid simulation (RTHS) tests for lead rubber bearings, 29 RTHS tests for bridge piers, and 17 cyclic tests (10 fast and 7 slow). Extensive hyperparameter tuning demonstrates the model’s proficiency to capture hysteresis and residual deformations accurately. Achieving an impressive correlation with experimentally measured values, ranging from 88.1 to 98.9%, and a reasonable dissipated energy error ratio are notable. The deep learning model reduces the need for additional tests, offering time and cost savings, and provides rapid, and accurate insights into bridge behavior. This supports timely and precise bridge design and aids decision-makers during emergencies.

A workflow for the hybrid modelling and simulation of multi-timescale biological systems

With the steady advance of in-silico biological experimentation, model construction and simulation becomes a ubiquitous tool to understand and predict the behaviour of many biological systems. However, biological processes may contain components from different types of reaction networks, resulting in models with different (e.g., slow and fast) timescales. Hybrid simulation is one approach which can be employed to efficiently execute multi-timescale models. In this paper, we present a methodology and workflow utilizing (coloured) hybrid Petri nets to construct smaller and more complicated hybrid models. The presented workflow integrates algorithms and ideas from hybrid simulation of biochemical reaction networks as well as Petri nets. We also construct multi-timescale hybrid models and then show how these models can be efficiently executed using three different advanced hybrid simulation algorithms.

Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations

AbstractThis study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.

Saturation of the compression of two interacting magnetized plasma toroids evidenced in the laboratory.

Interactions between magnetic fields advected by matter play a fundamental role in the Universe at a diverse range of scales. A crucial role these interactions play is in making turbulent fields highly anisotropic, leading to observed ordered fields. These in turn, are important evolutionary factors for all the systems within and around. Despite scant evidence, due to the difficulty in measuring even near-Earth events, the magnetic field compression factor in these interactions, measured at very varied scales, is limited to a few. However, compressing matter in which a magnetic field is embedded, results in compression up to several thousands. Here we show, using laboratory experiments and matching three-dimensional hybrid simulations, that there is indeed a very effective saturation of the compression when two independent parallel-oriented magnetic fields regions encounter one another due to plasma advection. We found that the observed saturation is linked to a build-up of the magnetic pressure, which decelerates and redirects the inflows at their encounter point, thereby stopping further compression. Moreover, the growth of an electric field, induced by the incoming flows and the magnetic field, acts in redirecting the inflows transversely, further hampering field compression.

Exploring the abnormal vibration of SEG Plaza via wind-tunnel substructure hybrid simulation

In May 2021, the SEG Plaza in Shenzhen of China experienced abnormal vibrations, which aroused widespread social concern. The reason of the vibrations is summarised as follow: the wind load causes the vortex-induced vibration of the masts located at the top of the building, which in turn results the resonance vibration between the masts and the building. To reproduce the abnormal vibration, the field measurements were carried out to obtain the modal information of the building and the masts firstly. Wind-tunnel substructure hybrid simulation (WTSHS) was proposed to reproduce the abnormal vibration of SEG Plaza. In WTSHS, the outer skin of the cantilever section of the masts is taken as the experimental substructure and modelled by rigidity compression wind-tunnel test. A multi-degree-of-freedom (MDOF) model of the building incorporating the dynamic characteristics (MDOF model) of the cantilever section of the masts is considered as the numerical substructure. The bottom layer force of experimental substructure was calculated and fed back to the numerical substructure to obtain the acceleration response of the building and masts. Ten test conditions of WTSHS with different wind angle (0°-90°) were conducted to reproduce the acceleration responses at specific location, which are close to the acceleration response obtained from the field measurements. The WTSHS's capability to replicate the wind-induced vibrations in buildings offers a novel experimental approach to investigate the dynamic responses of tall structures in the future.

Adaptive compensation using long short‐term memory networks for improved control performance in real‐time hybrid simulation

AbstractReal‐time hybrid simulation (RTHS) divides structural systems into numerical and experimental substructures, providing a cost‐effective solution for analyzing structural systems, especially those that are large or complex. However, the actuation systems between these substructures inevitably introduce delays, affecting the stability and accuracy of RTHS. To address this issue, this study proposes an adaptive compensation method based on a conditional adaptive time series (CATS) compensator and a long short‐term memory (LSTM) network, termed CATS‐LSTM. The LSTM model predicts actuator responses for parameter estimation and calculates prediction errors, improving control performance and reducing delays. The effectiveness of the proposed CATS‐LSTM method and the accuracy of the LSTM prediction are validated through a series of simulations and experiments. The results indicate that the proposed CATS‐LSTM method outperforms both the CATS and phase lead (PL) methods. Compared to the CATS method, the proposed method reduces the maximum delay, root mean square error, and peak error by 3 ms, 3.66%, and 4.78%, respectively, while achieving reductions of 12 ms, 8.4%, and 10.05%, compared to the PL method. Furthermore, the CATS‐LSTM method is significantly less sensitive to initial parameter estimates, compared to the CATS method, enhancing robustness and mitigating the effects of inaccurate or varying initial parameter estimates.

Real‐time hybrid simulation for investigating the seismic response of a bridge isolated with lead rubber bearings

AbstractThis study investigates the seismic response of lead rubber bearings (LRBs) under constant and time‐varying axial forces using real‐time hybrid simulation (RTHS). Although shake table testing can provide realistic seismic responses, it is often expensive and quite challenging for large‐scale structures. RTHS, however, offers a cost‐effective alternative by experimentally testing only the structural component of interest while analytically modeling the remaining structure. With the use of an advanced real‐time force control method, this study implemented RTHSs for a bridge isolated with two LRBs, where the LRBs are subjected to various constant axial forces due to self‐weight as well as time‐varying axial forces induced by vertical ground motions. The lateral response of LRBs was found to be significantly influenced by the magnitude of constant axial force, highlighting the importance of incorporating the effect of axial force due to self‐weight into numerical simulations. Additionally, it is crucial to satisfy the axial force boundary condition when conducting RTHS or cyclic loading tests to obtain more reliable test results. On the other hand, the time‐varying axial force generated by the vertical vibration of the bridge due to vertical ground motions has a limited impact on the lateral response of LRBs.

Prediction of fishbone linear instability in tokamaks with machine learning methods

Abstract A machine learning based surrogate model for fishbone linear instability in tokamaks is constructed. Hybrid simulations with the kinetic-magnetohydrodynamic (MHD) code M3D-K is used to generate the database of fishbone linear instability, through scanning the four key parameters which are thought to determine the fishbone physics. The four key parameters include (1) central total beta of both thermal plasma and fast ions, (2) the fast ion pressure fraction, (3) central value of safety factor q and (4) the radius of q = 1 surface. Four machine learning methods including linear regression, support vector machines (SVM) with linear kernel, SVM with nonlinear kernel and multi-layer perceptron are used to predict the fishbone instability, growth rate and real frequency, mode structure respectively. Among the four methods, SVM with nonlinear kernel performs very well to predict the linear instability with accuracy ≈ 95 % , growth rate and real frequency with R 2 ≈ 98 % , mode structure with R 2 ≈ 98 % .

Accident cause analysis for lifting prefabricated components of assembled buildings based on hybrid simulation technology from human factor perspective

PurposeThis paper aims to explore the characteristics of lifting accidents and the significance of influencing factors and explain the causes from the perspective of human factors, thereby achieving a more accurate understanding of and prevention of lifting accidents.Design/methodology/approachA mixed simulation model for prefabricated component lifting is established by combining discrete event simulation (DES) with the system dynamics (SD) method. In addition, essential parameters and relationships within the system dynamics model are determined through survey questionnaires. Finally, the human factors analysis and classification system (HFACS) is used to analyze the cause of the accident.FindingsThe results show that workers falling from height and workers struck by objects are the most frequent types of lifting accidents. In 2072 experiments, these two types of accidents occurred three and five times, respectively. Besides, the links of “crane movement,” “component binding,” “component placement” and “component unhooking” are particularly prone to lifting accidents. In addition, the completeness of emergency plans, failure to observe the status of the tower crane and lack of safety education and training have emerged as primary influencing factors contributing to the occurrence of lifting accidents.Originality/valueThe findings of the study can serve as a reference basis for practitioners, enabling them to preemptively identify possible risk accidents and adopt corresponding measures to prevent them, ensuring the safety and property of practitioners. Additionally, targeted suggestions and innovative ideas are provided to enhance the safety guarantee of the lifting industry and promote its healthy and stable development through a more concrete theoretical foundation and practical guidance.

Abstraction-based segmental simulation of reaction networks using adaptive memoization

Background Stochastic models are commonly employed in the system and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. Many important models feature complex dynamics, involving a state-space explosion, stiffness, and multimodality, that complicate the quantitative analysis needed to understand their stochastic behavior. Direct numerical analysis of such models is typically not feasible and generating many simulation runs that adequately approximate the model’s dynamics may take a prohibitively long time.Results We propose a new memoization technique that leverages a population-based abstraction and combines previously generated parts of simulations, called segments, to generate new simulations more efficiently while preserving the original system’s dynamics and its diversity. Our algorithm adapts online to identify the most important abstract states and thus utilizes the available memory efficiently.Conclusion We demonstrate that in combination with a novel fully automatic and adaptive hybrid simulation scheme, we can speed up the generation of trajectories significantly and correctly predict the transient behavior of complex stochastic systems.

How does turbulence intensity affect the fatigue life of composite airfoils based on existing CFD and experimental data?

This research investigates the effect of airflow turbulence intensity on the fatigue life of composite airfoil structures in aerospace engineering. Using advanced Computational Fluid Dynamics (CFD) simulations, this study provides a comprehensive analysis of how varying levels of turbulence intensity—ranging from mild (5%) to severe (30%)—impact the mechanical degradation and fatigue performance of carbon-fiber-reinforced polymers (CFRPs) used in airfoil designs. The research quantifies the relationship between turbulence levels and changes in stress distribution, highlighting critical points of fatigue crack initiation and subsequent propagation. The study includes detailed comparisons of CFD results to experimental data to validate the predictive models and discusses how localized stress fluctuations under high-intensity turbulence accelerate the onset of fatigue failure. We present significant findings that demonstrate a non-linear relationship between turbulence intensity and the reduction in fatigue life, suggesting optimal design modifications for enhanced durability. Furthermore, this paper explores current challenges in merging CFD data with real-world fatigue testing and proposes advanced techniques such as adaptive mesh refinement and hybrid simulation models to improve predictive accuracy and computational efficiency in future research.

A hybrid metaheuristic and simulation approach towards green project scheduling

AbstractThis research tackles the environmental concern of greenhouse gas emissions in the execution of projects, with a focus on multi-site projects where the transportation of resources is a major source of emissions. Despite growing consciousness among consumers and stakeholders about sustainability, the domain of project scheduling has often overlooked the environmental impact. This paper seeks to bridge this oversight by exploring how to reduce greenhouse gas emissions during both project activities and resource transportation. A novel approach is proposed, combining a simulation model with an improved non-dominated sorted genetic algorithm. The simulation model incorporates the stochastic nature of emission rates and costs. This method is further refined with innovative techniques such as magnet-based crossover and mode reassignment. The former is a genetic algorithm operation inspired by magnetic attraction, which allows for a more diverse and effective exploration of solutions by aligning similar ’genes’ from parent solutions. The latter is a strategy for reallocating resources during project execution to optimize efficiency and reduce emissions. The efficacy of the proposed method is validated through testing on 2810 scenarios from established benchmark libraries, 100 additional scenarios adhering to the conventional multi-site problems, and a case study. The Best-Worst Method (BWM) is applied for identifying the best solution. The findings indicate substantial enhancements compared to traditional methods with a 12.7% decrease in project duration, 11.4% in costs, and a remarkable 13.6% reduction in greenhouse gas emissions.

Hybrid Simulation Model of Lifecycle Simulation and Replacement Simulation Considering Carbon Lock-In by Coal-Fired Power Plants

To meet the temperature goal set by the Paris Agreement, cumulative CO2 emissions must be kept below 300 GtCO2. Road transportation accounted for approximately 18% of the CO2 emissions from fuel combustion in 2017. Electric vehicles (EVs) have been rapidly adopted by environmentally conscious consumers in many countries to reduce CO2 emissions. EVs have lower emission intensities than do internal combustion engine vehicles (ICEVs) in most parts of the world, except where the penetration of renewable energy is low in the energy production mix. In such places, the CO2 emissions of EVs are larger than those of ICEVs. Despite the obvious need to increase renewable energy sources to reduce CO2 emissions, Japan and many other countries around the world have yet to shift away from fossil fuels. This is due in part to carbon lock-in, which refers to prior decisions related to technologies and infrastructure that constrain the implementation of better paths toward low-carbon technologies. Coal-fired power plants are the most problematic in terms of carbon lock-in because of their high carbon intensities and long physical lives. In addition, because carbon lock-in by coal-fired power plants has a significant impact on the embodied CO2 intensity of grid power, it impacts society through products that use electric power. In this study, we propose a hybrid simulation model of lifecycle simulation and replacement simulation, considering carbon lock-in by coal-fired power plants. In the replacement simulation, we simulated the replacement of end-of-life coal- and oil-fired power plants with renewable energy power plants using a probability called the lock-in rate and estimated the changes in the embodied CO2 intensity of grid power in Japan. In the lifecycle simulation, we evaluated cumulative CO2 emissions from entire product lifecycles of ICEVs and EVs based on three different EV diffusion scenarios. The results showed that the lock-in rate of coal-fired power plants strongly affects the decarbonization effect due to the market diffusion of EVs.

Diverse Motions and Responses in Crowd Simulation

ABSTRACTA challenge in crowd simulation is to generate diverse pedestrian motions in virtual environments. Nowadays, there is a greater emphasis on the diversity and authenticity of pedestrian movements in crowd simulation, while most traditional models primarily focus on collision avoidance and motion continuity. Recent studies have enhanced realism through data‐driven approaches that exploit the movement patterns of pedestrians from real data for trajectory prediction. However, they have not taken into account the body‐part motions of pedestrians. Differing from these approaches, we innovatively utilize learning‐based character motion and physics animation to enhance the diversity of pedestrian motions in crowd simulation. The proposed method can provide a promising avenue for more diverse crowds and is realized by a novel framework that deeply integrates motion synthesis and physics animation with crowd simulation. The framework consists of three main components: the learning‐based motion generator, which is responsible for generating diverse character motions; the hybrid simulation, which ensures the physical realism of pedestrian motions; and the velocity‐based interface, which assists in integrating navigation algorithms with the motion generator. Experiments have been conducted to verify the effectiveness of the proposed method in different aspects. The visual results demonstrate the feasibility of our approach.

A hybrid machine learning and simulation framework for modeling and understanding disinformation-induced disruptions in public transit systems

Transportation infrastructure networks are prone to disruptions, most of which are beyond control. However, the spread of disinformation can worsen downtime in these systems by indirectly causing disruptions, such as station closures or rerouting of services based on false reports. The relationship between disinformation and the service disruptions is very important with reference to enhancing the resilience of transportation systems. This paper contributes to the field by applying artificial intelligence techniques to analyze how disinformation impacts service disruptions, particularly focusing on the Port Authority Trans-Hudson (PATH) system in New Jersey and New York, providing insights for improving operational responsiveness. The disruption operational impacts of disinformation are analyzed using several data sources, including schedules, ridership reports, and real-time alerts. A machine learning-based K-means algorithm framework is applied to cluster disruption alerts from social media. Disruption scenarios dominated by disinformation are identified using advanced natural language processing (NLP) methods, specifically BERTopic and Latent Dirichlet Allocation (LDA) topic modeling techniques. A Monte Carlo simulation is applied to quantify the effects of this dominant disinformation-induced disruption scenario on the commuter time and costs. This study reveals that disinformation significantly influences infrastructure reliability and points out the necessity for effective strategies to combat its impacts. The findings reveal the importance of transportation disruptions to the functioning of the transportation system and emphasize the need for robust measures to reduce the adverse effects, hence making the system to be more resilient and secure in the public’s perception.

Venusian ion escape under extreme conditions: A dynamic pressure and temperature simulation study

Context. We investigated the response of the Venusian atmospheric ion escape under the effect of interplanetary coronal mass ejections (ICMEs) using the Latmos Hybrid Simulation (LatHyS). Aims. In particular, we focused on the influence of extreme ICME dynamic pressures and temperatures, with the temperature being a parameter that has not been extensively studied in the past. Methods. Simulations were performed for two different dynamic pressures and three different temperatures. For the case of the dynamic pressure simulations, a density and a velocity enhancement event were studied separately. The H+ and O+ ion escape was then examined and compared for different escape channels. Results. In both dynamic pressure enhancement cases, we find that there is no clear dependence of the O+ ion escape on the dynamic pressure, which is consistent with observations. On the other hand, the temperature of the incoming solar wind positively influences the H+ and O+ ion escape. This is attributed in part to the enhanced gyroradius of the particles, which allows them to penetrate deeper into the planet’s atmosphere.

Hybrid Simulations of the Martian Magnetotail Twist

Three-dimensional global hybrid simulations are performed to explore how the interplanetary magnetic field (IMF), the Martian crustal fields, and planetary pickup ions affect the twisting of the Martian magnetotail. The results agree with previous studies that the crustal magnetic fields cause the Martian magnetotail to twist counterclockwise or clockwise depending on the sign of the IMF Y-component in the Mars solar orbital coordinates. However, the twist is more pronounced when the crustal fields are on the nightside, contradicting the early explanation that the crustal fields affect the twist through dayside magnetic reconnection between the crustal fields and the draped IMF. Additionally, planetary pickup ions also contribute to the twist because their mass loading slows down the plasma flow and leads to the bending of the magnetic field lines in the magnetotail. It is demonstrated that the twist inside Mars’ shadow in the near magnetotail region (at X = −1.5 R M, where R M is Mars’ radius) is mainly attributable to the crustal fields, while the influence of planetary pickup ions starts to dominate outside Mars’ shadow and in regions further away from Mars.

Development and effect of hybrid simulation program for nursing students: focusing on a case of pediatric cardiac catheterization in Korea: quasi-experimental study.

Hybrid simulation has emerged to increase the practicality of simulation training by combining simulators and standardized patient (SP) that implement realistic clinical environments at a high level. This study aimed to develop a hybrid simulation program focused on case of pediatric cardiac catheterization and to evaluate its effectiveness. The hybrid simulation program was developed according to the Analyze, Design, Develop, Implement, and Evaluate (ADDIE) model. And deep learning-based analysis program was used to analyze non-verbal communication with SP and applied it for debriefing sessions. To verify the effect of the program, a quasi-experimental study using a random assignment design was conducted. In total, 48 nursing students (n=24 in the experimental group; n=24 in the control group) participated in the study. Knowledge (F=3.53, p=.038), confidence in clinical performance (F=9.73, p<.001), and communication self-efficacy (F=5.20, p=.007) showed a significant difference in both groups and interaction between time points, and the communication ability of the experimental group increased significantly (t=3.32, p=.003). Hybrid simulation program developed in this study has been proven effective, it can be implemented in child nursing education. Future research should focus on developing and incorporating various hybrid simulation programs using SP into the nursing curriculum and evaluating their effectiveness.