Exploration of instantaneous frequency for local control assessment in real-time hybrid simulation

Local control parameters such as instantaneous delay and instantaneous amplitude play an essential role in evaluating the performance and maintaining the stability of real-time hybrid simulation(RTHS).However,existing methods have limitations in obtaining this local assessment in either the time domain or frequency domain.In this study,the instantaneous frequency is introduced to determine local control parameters for actuator tracking assessment in a real-time hybrid simulation.Instantaneous properties,including amplitude,delay,frequency and phase,are then calculated based on analytic signals translated from actuator tracking signals through the Hilbert transform.Potential issues are discussed and solutions are proposed for calculation of local control parameters.Numerical simulations are first conducted for sinusoidal and chirp signals with time varying amplitude error and delay to demonstrate the potential of the proposed method.Laboratory tests also are conducted for a predefined random signal as well as the RTHS of a single degree of freedom structure with a self-centering viscous damper to experimentally verify the effectiveness of the proposed use of the instantaneous frequency.Results from the ensuing analysis clearly demonstrate that the instantaneous frequency provides great potential for local control assessment,and the proposed method enables local tracking parameters with good accuracy.

Big Data Application Simulation Platform Design for Onboard Distributed Processing of LEO Mega-Constellation Networks

Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.

Models to Simulate Effective Coverage of Fire Station Based on Real-Time Travel Times

In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be severely affected by traffic conditions,changing the effective coverage of fire stations.However,it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions.This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel.In addition,it proposes a new index of total effective coverage area(TECA)based on the time-weighted average of the effective coverage area(ECA)to evaluate the urban fire services.It also selects China as the case study to validate the feasibility of the models,a fire station(FS-JX)in Changsha.FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points,respectively.A total of 196 simulation scenarios throughout a consecutiveweek are analyzed.Eventually,1,933,815 sets of valid sample data are obtained.The results showed that the TECA of FS-JX is 3.27 km^(2),which is far below the standard requirement of 7.00 km^(2) due to the traffic conditions.The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage.The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.

A multi-sensor-based distributed real-time measurement system for glacier deformation

Glacier disasters occur frequently in alpine regions around the world,but the current conventional geological disaster measurement technology cannot be directly used for glacier disaster measurement.Hence,in this study,a distributed multi-sensor measurement system for glacier deformation was established by integrating piezoelectric sensing,coded sensing,attitude sensing technology and wireless communication technology.The traditional Modbus protocol was optimized to solve the problem of data identification confusion of different acquisition nodes.Through indoor wireless transmission,adaptive performance analysis,error measurement experiment and landslide simulation experiment,the performance of the measurement system was analyzed and evaluated.Using unmanned aerial vehicle technology,the reliability and effectiveness of the measurement system were verified on the site of Galongla glacier in southeastern Tibet,China.The results show that the mean absolute percentage errors were only 1.13%and 2.09%for the displacement and temperature,respectively.The distributed glacier deformation real-time measurement system provides a new means for the assessment of the development process of glacier disasters and disaster prevention and mitigation.

Data-based real-time petrochemical gas diffusion simulation approach on virtual reality

Background Petrochemical products possess a high risk of flammability,explosivity,and toxicity,making petrochemical accidents exceedingly destructive.Therefore,disaster analysis,prediction,and real-time simulations have become important means of controlling and reducing accident hazards.Methods This study proposes a complete real-time simulation solution of gas diffusion with coordinate and concentration data,which was mainly aimed at simulating the types of harmful gas leakage and diffusion accidents in the petrochemical industry.The rendering effect was more continuous and accurate through grid homogenization and trilinear interpolation.This study presents a data processing and rendering parallelization process to enhance simulation efficiency.Gas concentration and fragment transparency were combined to synthesize transparent pixels in a scene.To ensure the approximate accuracy of the rendering effect,improve the efficiency of real-time rendering,and meet the requirement of intuitive perception using concentration data,a weighted blended order-independent transparency(OIT)with enhanced alpha weight is presented,which can provide a more intuitive perception of the hierarchical information of concentration data while preserving depth information.This study compares and analyzes three OIT algorithms-depth peeling,weighted blended OIT,and weighted blended OIT with enhanced alpha weight-in terms of rendering image quality,rendering time,required memory,and hierarchical information.Results Using weighted blended OIT with an enhanced alpha weight technique,the rendering time was shortened by 53.2%compared with that of the depth peeling algorithm,and the texture memory required was significantly smaller than that of the depth peeling algorithm.The rendering results of weighted blended OIT with an enhanced alpha weight were approximately accurate compared with those of the depth peeling algorithm as the ground truth,and there was no popping when surfaces passed through one another.Simultaneously,compared with weighted blended OIT,weighted blended OIT with an enhanced alpha weight achieved an intuitive perception of the hierarchical information of concentration data.

The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation

The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.

A deep reinforcement learning approach to gasoline blending real-time optimization under uncertainty

The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.

Distributed Computation Models for Data Fusion System Simulation

An attempt has been made to develop a distributed software infrastructure model for onboard data fusion system simulation, which is also applied to netted radar systems, onboard distributed detection systems and advanced C3I systems. Two architectures are provided and verified: one is based on pure TCP/IP protocol and C/S model, and implemented with Winsock, the other is based on CORBA (common object request broker architecture). The performance of data fusion simulation system, i.e. reliability, flexibility and scalability, is improved and enhanced by two models. The study of them makes valuable explore on incorporating the distributed computation concepts into radar system simulation techniques.

Phase behavior of gas condensate in porous media using real-time computed tomography scanning

The phase behavior of gas condensate in reservoir formations differs from that in pressure-volume-temperature(PVT)cells because it is influenced by porous media in the reservoir formations.Sandstone was used as a sample to investigate the influence of porous media on the phase behavior of the gas condensate.The pore structure was first analyzed using computed tomography(CT)scanning,digital core technology,and a pore network model.The sandstone core sample was then saturated with gas condensate for the pressure depletion experiment.After each pressure-depletion state was stable,realtime CT scanning was performed on the sample.The scanning results of the sample were reconstructed into three-dimensional grayscale images,and the gas condensate and condensate liquid were segmented based on gray value discrepancy to dynamically characterize the phase behavior of the gas condensate in porous media.Pore network models of the condensate liquid ganglia under different pressures were built to calculate the characteristic parameters,including the average radius,coordination number,and tortuosity,and to analyze the changing mechanism caused by the phase behavior change of the gas condensate.Four types of condensate liquid(clustered,branched,membranous,and droplet ganglia)were then classified by shape factor and Euler number to investigate their morphological changes dynamically and elaborately.The results show that the dew point pressure of the gas condensate in porous media is 12.7 MPa,which is 0.7 MPa higher than 12.0 MPa in PVT cells.The average radius,volume,and coordination number of the condensate liquid ganglia increased when the system pressure was between the dew point pressure(12.7 MPa)and the pressure for the maximum liquid dropout,Pmax(10.0 MPa),and decreased when it was below Pmax.The volume proportion of clustered ganglia was the highest,followed by branched,membranous,and droplet ganglia.This study provides crucial experimental evidence for the phase behavior changing process of gas condensate in porous media during the depletion production of gas condensate reservoirs.

Genetic Analysis of Structural Styles in the Makran Accretionary Wedge–Insight from Physical Simulations

The Makran accretionary wedge has the smallest subduction angle among any accretionary prism in the world. The factors controlling the spacing and morphological development of its deep thrust faults, as well as the formation mechanism of shallow normal faults, remain unclear. Meanwhile, the factors affecting the continuity of plane faults must be comprehensively discussed. Clarifying the development characteristics and deformation mechanisms of the Makran accretionary wedge is crucial to effectively guide the exploration of gas hydrate deposits in the area. This study aims to interpret seismic data to identify typical structures in the Makran accretionary wedge, including deep imbricate thrust faults, shallow and small normal faults, wedge-shaped piggyback basins, mud diapirs with fuzzy and disorderly characteristics of reflection, décollements with a northward tilt of 1° – 2°, and large seamounts. Physical simulation-based experiments are performed to comprehensively analyze the results of the plane, section, and slices of the wedge. Results reveal that the distances between and shapes of thrust faults in the deep parts of the Makran accretionary wedge are controlled by the bottom décollement. The uplift of the thrust fault-related folds and the upwelling of the mud diapirs primarily contribute to the formation of small normal faults in the shallow part of the area. The mud diapirs originate from plastic material at the bottom, while those that have developed in the area near the trench are larger. Seamounts and mud diapirs break the continuity of fault plane distribution.

DISTRIBUTED SIMULATION SYSTEM BASED ON UNIVERSAL COMPONENTS

Based on MATRIXx, a universal real-time visual distributed simulation system is developed. The system can receive different input data from network or local terminal. Application models in the simulation modules can automatically get such data to be analyzed and calculated, and then produce real-time simulation control information. Meanwhile, this paper designs relevant simulation components to implement the input and output data, which can guarantee the real-time and universal of the data transmission. Result of the experimental system shows that the real-time performance of the simulation is perfect.

Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

Bridging element-free Galerkin and pluri-Gaussian simulation for geological uncertainty estimation in an ensemble smoother data assimilation framework

The facies distribution of a reservoir is one of the biggest concerns for geologists,geophysicists,reservoir modelers,and reservoir engineers due to its high importance in the setting of any reliable decisionmaking/optimization of field development planning.The approach for parameterizing the facies distribution as a random variable comes naturally through using the probability fields.Since the prior probability fields of facies come either from a seismic inversion or from other sources of geologic information,they are not conditioned to the data observed from the cores extracted from the wells.This paper presents a regularized element-free Galerkin(R-EFG)method for conditioning facies probability fields to facies observation.The conditioned probability fields respect all the conditions of the probability theory(i.e.all the values are between 0 and 1,and the sum of all fields is a uniform field of 1).This property achieves by an optimization procedure under equality and inequality constraints with the gradient projection method.The conditioned probability fields are further used as the input in the adaptive pluri-Gaussian simulation(APS)methodology and coupled with the ensemble smoother with multiple data assimilation(ES-MDA)for estimation and uncertainty quantification of the facies distribution.The history-matching of the facies models shows a good estimation and uncertainty quantification of facies distribution,a good data match and prediction capabilities.

Numerical Analysis on Temperature Distribution in a Single Cell of PEFC Operated at Higher Temperature by1D Heat Transfer Model and 3D Multi-Physics Simulation Model

This study is to understand the impact of operating conditions, especially initial operation temperature (Tini) which is set in a high temperature range, on the temperature profile of the interface between the polymer electrolyte membrane (PEM) and the catalyst layer at the cathode (i.e., the reaction surface) in a single cell of polymer electrolyte fuel cell (PEFC). A 1D multi-plate heat transfer model based on the temperature data of the separator measured using the thermograph in a power generation experiment was developed to evaluate the reaction surface temperature (Treact). In addition, to validate the proposed heat transfer model, Treact obtained from the model was compared with that from the 3D numerical simulation using CFD software COMSOL Multiphysics which solves the continuity equation, Brinkman equation, Maxwell-Stefan equation, Butler-Volmer equation as well as heat transfer equation. As a result, the temperature gap between the results obtained by 1D heat transfer model and those obtained by 3D numerical simulation is below approximately 0.5 K. The simulation results show the change in the molar concentration of O2 and H2O from the inlet to the outlet is more even with the increase in Tini due to the lower performance of O2 reduction reaction. The change in the current density from the inlet to the outlet is more even with the increase in Tini and the value of current density is smaller with the increase in Tini due to the increase in ohmic over-potential and concentration over-potential. It is revealed that the change in Treact from the inlet to the outlet is more even with the increase in Tini irrespective of heat transfer model. This is because the generated heat from the power generation is lower with the increase in Tini due to the lower performance of O2 reduction reaction.

FEM model for real-time guide wire simulation in vasculature

A model suitable for describing the mechanical response of thin elastic objects is proposed to simulate the deformation of guide wires in minimally invasive interventions. The main objective of this simulation is to provide doctors an opportunity to rehearse the surgery and select an optimal operation plan before the real surgery. In this model the guide wire is discretized with the multi-body representation and its elastic energy derivate from elastic theory is a polynomial function of the nodal displacements. The vascular structure is represented by a tetrahedron mesh extended from the triangular mesh of the artery, which can be extracted from the patient＇s CT image data. The model applies the energy decline process of the conjugate gradient method to the deformation simulation of the guide wire. Experimental results show that the polynomial relationship between elastic energy and nodal displacements tremendously simplifies the evaluation of the conjugate gradient method and significantly improves the model＇s efficiency. Compared with models depending on an explicit scheme for evaluation, the new model is not only non-conditionally stable but also more efficient. The model can be applied to the real-time simulation of guide wire in a vascular structure.

On modeling approach for embedded real-time software simulation testing

Modeling technology has been introduced into software testing field. However, how to carry through the testing modeling effectively is still a difficulty. Based on combination of simulation modeling technology and embedded real-time software testing method, the process of simulation testing modeling is studied first. And then, the supporting environment of simulation testing modeling is put forward. Furthermore, an approach of embedded real-time software simulation testing modeling including modeling of cross-linked equipments of system under testing （SUT）, test case, testing scheduling, and testing system service is brought forward. Finally, the formalized description and execution system of testing models are given, with which we can realize real-time, closed loop, mad automated system testing for embedded real-time software.

Simulation of large-scale numerical substructure in real-time dynamic hybrid testing

A solution scheme is proposed in this paper for an existing RTDHT system to simulate large-scale finite element （FE） numerical substructures. The analysis of the FE numerical substructure is split into response analysis and signal generation tasks, and executed in two different target computers in real-time. One target computer implements the response analysis task, wherein a large time-step is used to solve the FE substructure, and another target computer implements the signal generation task, wherein an interpolation program is used to generate control signals in a small time-step to meet the input demand of the controller. By using this strategy, the scale of the FE numerical substructure simulation may be increased significantly. The proposed scheme is initially verified by two FE numerical substructure models with 98 and 1240 degrees of freedom （DOFs）. Thereafter, RTDHTs of a single frame-foundation structure are implemented where the foundation, considered as the numerical substructure, is simulated by the FE model with 1240 DOFs. Good agreements between the results of the RTDHT and those from the FE analysis in ABAQUS are obtained.

A Class of Fast Algorithms in Real-Time Simulation

A class of hybrid algorithms of real-time simulation based on evaluation of non-integerstep right-hand side function are presented in this paper. And some results of the convergence and stability of the algorithms are given. Using the class of algorithms, evaluation for the right-hand side function is needed once in every integration-step. Moreover, comparing with the other methods with the same amount of work, their numerical stability regions are larger and the method errors are smaller, and the numerical experiments show that the algorithms are very effective.

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-of- freedom 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 fraane is represented physically in the laboratory as a cantilevered steel column. For real- time 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 controUed at the interface between substructures.

Construction Simulation and Real-Time Control for High Arch Dam

A method of combining dynamic simulation with real-time control was proposed to fit the randomness and uncertainty in the high arch dam construction process. The mathematical logic model of high arch dam construction process was established. By combining dynamic construction simulation with schedule analysis, the process of construction schedule forecasting and analysis based on dynamic simulation was studied. The process of real-time schedule control was constructed and some measures for dynamic adjustment and control of construction schedule were provided. A system developed with the method is utilized in a being constructed hydroelectric project located at the Yellow River in northwest China, which can make the pouring plan of the dam in the next stage (a month, quarter or year) to guide the practical construction. The application result shows that the system provides an effective technical support for the construction and management of the dam.