Real-time Dynamic Simulation Research Articles

Real-time hydrogen molecular dynamics satisfying the nuclear spin statistics of a quantum rotor

Apparent presence of the nuclear-spin species of a hydrogen molecule, para-hydrogen and ortho-hydrogen, associated with the quantum rotation is a manifestation of the nuclear quantum nature of hydrogen, governing not only molecular structures but also physical and chemical properties of hydrogen molecules. It has been a great challenge to observe and calculate real-time dynamics of such molecularized fermions. Here, we developed the non-empirical quantum molecular dynamics method that enables real-time molecular dynamics simulations of hydrogen molecules satisfying the nuclear spin statistics of the quantum rotor. While reproducing the species-dependent quantum rotational energy, population ratio, specific heat, and H-H bond length and frequency, we found that their translational, orientational and vibrational dynamics becomes accelerated with the higher rotational excitation, concluding that the nuclear quantum rotation stemmed from the nuclear spin statistics can induce various kinds of dynamics and reactions intrinsic to each hydrogen species.

Study on dynamic model and dynamic characteristics of Delingha 50 MW trough solar field

China General Nuclear Power Group (CGNPC) Delingha 50 MW parabolic trough solar thermal power plant is the first commercial trough solar plant in China, and its solar field consists of 190 parallel heat collecting loops. For large-scale trough solar plant, balancing the flow rate of heat absorbing medium in each loop and stabilizing the outlet temperature are the key technologies and difficult problems. Regarding the Delingha 50 MW solar field as the research object, this paper mainly focusing on the dynamic characteristics of flow and heat transfer in the solar field. The hydrodynamic calculation model and the heat transfer dynamic model are established on the real-time dynamic simulation platform STAR-90. With the actual operation data, the built solar field model is validated by comparing the simulation results. On this basis, the disturbance simulations of direct normal irradiance (DNI), heat transfer oil’s mass flow and heat transfer oil’s inlet temperature are carried out. The dynamic response curves of disturbance and the thermal inertia time constant of the loops are obtained. The conclusions lay a theoretical foundation for the formulation of outlet medium’s temperature control strategy in the solar field of large-scale trough solar thermal power generation system.

Researching a Simulation of Real-Time Nonlinear Dynamical Systems for Digital Power Grids in Massive IoT

“Digital real-time simulation” refers to the replication of output waveforms with the required accuracy, which duplicates the behavior of a real power system that is being simulated. A variety of problems relating to the functioning of power systems can be effectively solved using real-time simulators. Fortunately, modern state-of-the-art technological advancements have solved the energy problems. When used as a real-time application of electromagnetic transient-type simulation, the real-time digital simulator takes advantage of the traveling wave properties of cables and transmission lines in conjunction with a parallel processing platform. Power Control (PC), Hardware Under Test (HUT), Time Scale of Events (TSE), and Control Action (CA) are used as independent variables and the Real-Time Dynamic Simulation (RTDS) has been used as dependent variables. The objective of study improves the energy production in Digital Power Grids using Real-time Dynamic Nonlinear System Simulation System. The data was collected by using questionnaires based on 5-Likert Scale. The data has been analyzed using smart PLS 3. The questionnaire were filled from 50 technicians of power grids. The results are that Real-Time Dynamic Simulation improves the energy production in Digital Power Grids.

Development and Performance Verification of Frequency Control Algorithm and Hardware Controller Using Real-Time Cyber Physical System Simulator

Frequency stability is a critical factor in maintaining the quality of the power grid system. A battery energy storage system (BESS) with quick response and flexibility has recently been used as a primary frequency control (PFC) resource, and many studies on its control algorithms have been conducted. The cyber physic system (CPS) simulator, which can perform virtual physical modelling and verification of many hardware systems connected to the network, is an optimal solution for the performance verification of control algorithms and hardware systems. This study introduces a large-scale real-time dynamic simulator that includes the national power system. This simulator comprises a power grid model, an energy management system (EMS) model, a BESS system model, and a communication model. It performs the control algorithm performance evaluation and the hardware controller’s response performance evaluation. The performance of the control algorithm was evaluated by tracking the power system’s characteristic trajectory in the transient state based on the physical response delay time between the output instruction of the frequency regulation controller (FRC), a hardware controller, and the output response of the BESS. Based on this, we examined the response performance evaluation results by linking them to the optimally designed actual FRC. As a result, we present an analysis of the BESS’s characteristic trajectories in the transient state, such as frequency, power system inertia, and power grid constant, and provide FRC response performance evaluation results at a level of 163 ms, by connecting the BESS installed at the actual site with the CPS simulator.

Vehicle dynamics response due to the adjustment of K&C parameters with the aid of a dynamic simulator with 9 DOF and a correlated virtual model

Given the increasing use of virtual simulation in automotive vehicle designs, the correlation of virtual models is a fundamental part of the process. The present work seeks to develop a methodology for the development of virtual vehicle dynamics models correlated with kinematics and compliance (K&C) parameters for use in a real-time vehicle dynamics simulator. Aiming this and with the aid of an SPMM 5000e to obtain data from the physical vehicle and from the ADAMS/Car and VI-CarRealTime (VI-CRT) software to the development and simulation and from MATLAB to the correlation, a multibody virtual model correlated in K&C to a hatchback car, with McPherson strut and twist-beam front and rear suspensions, respectively, is developed. At the end of the work, it was observed that the correlation between the virtual model and the test vehicle was found successfully and that, therefore, the virtual simulation allows to find reliable results regarding the vehicle’s suspension and steering system behavior. Subsequently, with the tests carried out with a driver in a VI-Grade DiM 150, a real-time driver-in-the-loop simulator with 9 degrees of freedom (DOF), the model and thus the methodology is validated through the steering ratio analysis: The lateral acceleration analysis in the vehicle’s center of gravity in different maneuvers allowed to observe that the model with higher steering ratio requires greater steering wheel angle values about the model with the lowest steering ratio so that the same maneuver is performed.

Contact-centric deformation learning

We propose a novel method to machine-learn highly detailed, nonlinear contact deformations for real-time dynamic simulation. We depart from previous deformation-learning strategies, and model contact deformations in a contact-centric manner. This strategy shows excellent generalization with respect to the object's configuration space, and it allows for simple and accurate learning. We complement the contact-centric learning strategy with two additional key ingredients: learning a continuous vector field of contact deformations, instead of a discrete approximation; and sparsifying the mapping between the contact configuration and contact deformations. These two ingredients further contribute to the accuracy, efficiency, and generalization of the method. We integrate our learning-based contact deformation model with subspace dynamics, showing real-time dynamic simulations with fine contact deformation detail.

Development of a Real-Time Simulator for a Semi-Autonomous Tele-Robot in an Unknown Narrow Path

This study proposes a teleoperation system for assistive controlling of the movement of a mobile robot in a narrow path. The teleoperation system is created by combining data from real and virtual devices. In CoppeliaSim we applied the Braitenberg algorithm and the open motion planning library module (OMPL). The Braitenberg algorithm is a sensor-based automatic motion designed to aid the robot operator in maneuvering through the narrow path. While the OMPL is used to create a path for the operator to control the robot in narrow paths or intersections within a narrow path, the simulation uses a virtual proximity sensor to fulfill the Braitenberg algorithm requirement. With dynamic simulation a laser rangefinder obtains the environmental data and displays it on the simulation screen. The simulation scene is subjected to the virtual proximity sensor and the Braitenberg algorithm is applied to the simulation scene. Afterward, simulation scripts are written to incorporate the linear and angular velocities into a robot operating system for real-time robot control. The results showed that the system was capable of combining real-time dynamic simulation with the real world. Furthermore, the proposed system could aid the operator in narrow path environments while avoiding collision.

Analysis of Logistics Linkage by Digital Twins Technology and Lightweight Deep Learning.

The present work expects to meet the personalized needs of the continuous development of various products and improve the joint operation of the intraenterprise Production and Distribution (P-D) process. Specifically, this paper studies the enterprise's P-D optimization. Firstly, the P-D linkage operation is analyzed under dynamic interference. Secondly, following a literature review on the difficulties and problems existing in the current P-D logistics linkage, the P-D logistics linkage-oriented decision-making information architecture is established based on Digital Twins. Digital Twins technology is mainly used to accurately map the P-D logistics linkage process's real-time data and dynamic virtual simulation. In addition, the information support foundation is constructed for P-D logistics linkage decision-making and collaborative operation. Thirdly, a Digital Twins-enabled P-D logistics linkage-oriented decision-making mechanism is designed and verified under the dynamic interference in the linkage process. Meanwhile, the lightweight deep learning algorithm is used to optimize the proposed P-D logistics linkage-oriented decision-making model, namely, the Collaborative Optimization (CO) method. Finally, the proposed P-D logistics linkage-oriented decision-making model is applied to a domestic Enterprise H. It is simulated by the Matlab platform using sensitivity analysis. The results show that the production, storage, distribution, punishment, and total costs of linkage operation are 24,943 RMB, 3,393 RMB, 2,167 RMB, 0 RMB, and 30,503 RMB, respectively. The results are 3.7% lower than the nonlinkage operation. The results of sensitivity analysis provide a high reference value for the scientific management of enterprises.

Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact

Real-time dynamic simulation remains a significant challenge for spatiotemporal data of high dimension and resolution. In this study, we establish a transformer neural network (TNN) originally developed for natural language processing and a separate convolutional neural network (CNN) to estimate five-dimensional (5D) spatiotemporal brain–skull relative displacement resulting from impact (isotropic spatial resolution of 4 mm with temporal resolution of 1 ms). Sequential training is applied to train (N = 5184 samples) the two neural networks for estimating the complete 5D displacement across a temporal duration of 60 ms. We find that TNN slightly but consistently outperforms CNN in accuracy for both displacement and the resulting voxel-wise four-dimensional (4D) maximum principal strain (e.g., root mean squared error (RMSE) of ∼1.0% vs. ∼1.6%, with coefficient of determination, R2 >0.99 vs. >0.98, respectively, and normalized RMSE (NRMSE) at peak displacement of 2%–3%, based on an independent testing dataset; N = 314). Their accuracies are similar for a range of real-world impacts drawn from various published sources (dummy, helmet, football, soccer, and car crash; average RMSE/NRMSE of ∼0.3 mm/∼4%–5% and average R2 of ∼0.98 at peak displacement). Accuracy in strain rate is also illustrated in one case (NRMSE of 7.8% and of 0.91). Sequential training is effective for allowing instantaneous estimation of 5D displacement with high accuracy, although TNN poses a heavier computational burden in training. This work enables efficient characterization of the intrinsically dynamic brain strain in impact critical for downstream multiscale axonal injury model simulation. This is also the first application of TNN in biomechanics and biomedical engineering, which offers important insight into how real-time dynamic simulations can be achieved across diverse engineering fields.

Resonant coherent excitation and energy loss of slow channeling helium ions in AlN

By performing real-time time-dependent density-functional theory dynamic simulations, we report a velocity-dependent charge resonance on slow initial bare ions when channeling through the rocksalt crystalline AlN. Such charge resonance is accompanied by tremendously enhanced stopping at narrow velocity windows, which is attributed to the depletion in electronic screening by bound electrons, when the formation of defect state on projectile is depressed dramatically by the periodical field from lattice arrangement. The enhancement of stopping is not found in simulations with initial neutral incident ion, suggesting the effect of such charge resonance process mainly resides in the charge accumulation during the preequilibrium state, while making marginal influence on the excitation of bound state charge. We also investigated the charge resonance with revised lattice parameter, confirming the periodic atomic arrangement of lattice plane along ion trajectory is the main cause of such charge resonance.

Physics-Based Digital Twins Merging With Machines: Cases of Mobile Log Crane and Rotating Machine

Real-world products and physics-based simulations are becoming interconnected. In particular, real-time capable dynamic simulation has made it possible for simulation models to run in parallel and simultaneously with operating machinery. This capability combined with state observer techniques such as Kalman filtering have enabled the synchronization between simulation and the real world. State estimator techniques can be applied to estimate unmeasured quantities, also referred as virtual sensing, or to enhance the quality of measured signals. Although synchronized models could be used in a number of ways, value creation and business model development are currently defining the most practical and beneficial use cases from a business perspective. The research reported here reveals the communication and collaboration methods that lead to economically relevant technology solutions. Two case examples are given that demonstrate the proposed methodology. The work benefited from the broad perspective of researchers from different backgrounds and the joint effort to drive the technology development towards business relevant cases.

Direct Dynamics with Nuclear-Electronic Orbital Density Functional Theory.

Direct dynamics simulations of chemical reactions typically require the selection of a method for generating the potential energy surfaces and a method for the dynamical propagation of the nuclei on these surfaces. The nuclear-electronic orbital (NEO) framework avoids this Born-Oppenheimer separation by treating specified nuclei on the same level as the electrons with wave function methods or density functional theory (DFT). The NEO approach is particularly applicable to proton, hydride, and proton-coupled electron transfer reactions, where the transferring proton(s) and all electrons are treated quantum mechanically. In this manner, the zero-point energy, density delocalization, and anharmonicity of the transferring protons are inherently and efficiently included in the energies, optimized geometries, and dynamics.This Account describes how various NEO methods can be used for direct dynamics simulations on electron-proton vibronic surfaces. The strengths and limitations of these approaches are discussed, and illustrative examples are presented. The NEO-DFT method can be used to simulate chemical reactions on the ground state vibronic surface, as illustrated by the application to hydride transfer in C4H9+. The NEO multistate DFT (NEO-MSDFT) method is useful for simulating ground state reactions in which the proton density becomes bilobal during the dynamics, a characteristic of hydrogen tunneling, as illustrated by proton transfer in malonaldehyde. The NEO time-dependent DFT (NEO-TDDFT) method produces excited electronic, vibrational, and vibronic surfaces. The application of linear-response NEO-TDDFT to H2 and H3+, as well as the partially and fully deuterated counterparts, shows that this approach produces accurate fundamental vibrational excitation energies when all nuclei and all electrons are treated quantum mechanically. Moreover, when only specified nuclei are treated quantum mechanically, this approach can be used to optimize geometries on excited state vibronic surfaces, as illustrated by photoinduced single and double proton transfer systems, and to conduct adiabatic dynamics on these surfaces. The real-time NEO-TDDFT method provides an alternative approach for simulating nonequilibrium nuclear-electronic dynamics of such systems. These various NEO methods can be combined with nonadiabatic dynamics methods such as Ehrenfest and surface hopping dynamics to include the nonadiabatic effects between the quantum and classical subsystems. The real-time NEO-TDDFT Ehrenfest dynamics simulation of excited state intramolecular proton transfer in o-hydroxybenzaldehyde illustrates the power of this type of combined approach. The field of multicomponent quantum chemistry is in the early stages, and the methods discussed herein provide the foundation for a wide range of promising future directions to be explored. An appealing future direction is the expansion of the real-time NEO-TDDFT method to describe the dynamics of all nuclei and electrons on the same level. Direct dynamics simulations using NEO wave function methods such as equation-of-motion coupled cluster or multiconfigurational approaches are also attractive but computationally expensive options. The further development of NEO direct dynamics methods will enable the simulation of the nuclear-electronic dynamics for a vast array of chemical and biological processes that extend beyond the Born-Oppenheimer approximation.

A surrogate model for real-time dynamic simulation of dielectric elastomer actuators via long short-term memory networks

A surrogate model for dynamical behavior of dielectric elastomer actuators was proposed, the reduced-order framework was based on long short-term memory (LSTM) networks, and the combination of proper orthogonal decomposition with the Galerkin projection. To capture the latent dynamics of actuators, both the displacement and velocity fields were employed in this framework with the enforced electric potential signals, and two stacked architectures of LSTM networks were proposed to predict the dynamical evolution. The performance of the proposed surrogate model was demonstrated through two bimorph actuator concepts. The results show that the proposed model is a promising, reliable, and computationally efficient approach for the real-time simulation.

Stable solvers for real-time Complex Langevin

This study explores the potential of modern implicit solvers for stochastic partial differential equations in the simulation of real-time complex Langevin dynamics. Not only do these methods offer asymptotic stability, rendering the issue of runaway solution moot, but they also allow us to simulate at comparatively large Langevin time steps, leading to lower computational cost. We compare different ways of regularizing the underlying path integral and estimate the errors introduced due to the finite Langevin time steps. Based on that insight, we implement benchmark (non-)thermal simulations of the quantum anharmonic oscillator on the canonical Schwinger-Keldysh contour of short real-time extent.

Modelling of off-road wheeled vehicles for real-time dynamic simulation

Simulation of wheel-ground and vehicle-ground interactions is very important in many applications. Achieving accuracy and efficiency is challenging for both soft and hard terrains. This is not only because of the simulation and numerical challenges, but also due to the questionable nature of the existing terrain models. For example, the most widely used terramechanics model is not a representative constitutive relation for a full range of dynamic conditions and applications, but rather a parametrization of steady state conditions. In general, the selection and development of the proper constitutive model and the parametrization of the ground properties are very challenging. Here, we present a unified framework for general wheel-ground interaction which can be used with different terramechanics models. The framework is based on a complementarity formulation and also uses the concept of kinematic constitutive relations, beside the other known concepts for modelling and parametrizing the soil properties. The framework makes it possible to consider the appropriate modelling of the terrain for a broad range of dynamic behaviours and simulation conditions. We will illustrate the material with several examples for off-road conditions.

Toward simulating superstring/M-theory on a quantum computer

We present a novel framework for simulating matrix models on a quantum computer. Supersymmetric matrix models have natural applications to superstring/M-theory and gravitational physics, in an appropriate limit of parameters. Furthermore, for certain states in the Berenstein-Maldacena-Nastase (BMN) matrix model, several supersymmetric quantum field theories dual to superstring/M-theory can be realized on a quantum device. Our prescription consists of four steps: regularization of the Hilbert space, adiabatic state preparation, simulation of real-time dynamics, and measurements. Regularization is performed for the BMN matrix model with the introduction of energy cut-off via the truncation in the Fock space. We use the Wan-Kim algorithm for fast digital adiabatic state preparation to prepare the low-energy eigenstates of this model as well as thermofield double state. Then, we provide an explicit construction for simulating real-time dynamics utilizing techniques of block-encoding, qubitization, and quantum signal processing. Lastly, we present a set of measurements and experiments that can be carried out on a quantum computer to further our understanding of superstring/M-theory beyond analytic results.

Towards real-time fire data synthesis using numerical simulations

Synthesising data from fire scenarios using fire simulations requires iterative running of these simulations. For real-time synthesising, faster-than-real-time simulations are thus necessary. In this article, different model types are assessed according to their complexity to determine the trade-off between the accuracy of the output and the required computing time. A threshold grid size for real-time computational fluid dynamic simulations is identified, and the implications of simplifying existing field fire models by turning off sub-models are assessed. In addition, a temperature correction for two zone models based on the conservation of energy of the hot layer is introduced, to account for spatial variations of temperature in the near field of the fire. The main conclusions are that real-time fire simulations with spatial resolution are possible and that it is not necessary to solve all fine-scale physics to reproduce temperature measurements accurately. There remains, however, a gap in performance between computational fluid dynamic models and zone models that must be explored to achieve faster-than-real-time fire simulations.

The improvement of real-time dynamic phasor simulation to transient process of LCC-HVDC transmission system

The improvement of real-time dynamic phasor simulation to transient process of LCC-HVDC transmission system

A Framework for Customizable Multi-User Teleoperated Control

Traditional teleoperation (leader/follower) systems primarily focus on one operator controlling one remote robot, but as robots become ubiquitous, there is an increasing need for multiple operators, including autonomous agents, to collaboratively control multiple robots. However, existing teleoperation frameworks do not inherently support the variety of possible collaborations, such as multiple operators, each with an input device (leader), controlling a robot and camera or different degrees of freedom of a single robot (follower). The same concept applies to teleoperating robots in a simulation environment through physical input devices. In this letter, we extend our novel simulation framework that is capable of incorporating multiple input devices asynchronously with a real-time dynamic simulation to incorporate a customizable shared control. For this purpose, we have identified and implemented a sufficient set of coordinate frames to encapsulate the pairing of multiple leaders, followers and cameras in a shared asynchronous manner with force feedback. We demonstrate the utility of this framework in accelerating user training, ease of learning, and enhanced task completion times through shared control by a supervisor.

Examination of Semi-Analytical Solution Methods in the Coarse Operator of Parareal Algorithm for Power System Simulation

With continuing advances in high-performance parallel computing platforms, parallel algorithms have become powerful tools for development of faster than real-time power system dynamic simulations. In particular, it has been demonstrated in recent years that parallel-in-time (Parareal) algorithms have the potential to achieve such an ambitious goal. The selection of a fast and reasonably accurate coarse operator of the Parareal algorithm is crucial for its effective utilization and performance. This paper examines semi-analytical solution (SAS) methods as the coarse operators of the Parareal algorithm and explores performance of the SAS methods to the standard numerical time integration methods. Two promising time-power series-based SAS methods were considered; Adomian decomposition method and Homotopy analysis method with a windowing approach for improving the convergence. Numerical performance case studies on 10-generator 39-bus system and 327-generator 2383-bus system were performed for these coarse operators over different disturbances, evaluating the number of Parareal iterations, computational time, and stability of convergence. All the coarse operators tested with different scenarios have converged to the same corresponding true solution (if they are convergent) and the SAS methods provide comparable computational speed, while having more stable convergence to the true solution in many cases.