Hybrid Dynamic Simulation Research Articles

Numerical Simulation Analysis of the Correlation between Hydrodynamic Noise of Hydraulic Turbines and Defects in Runner Blades

To investigate the hydrodynamic noise characterization of hydraulic turbines with runner blade defects, this article establishes the intact machine model and three kinds of models with runner blade defects. Using the Computational Fluid Dynamic (CFD) and Computational Acoustic (CA) hybrid simulation computational methods, the hydrodynamic noise field of the hydraulic turbine is numerically simulated, and the results of the acoustic near field and acoustic far field are shown. 1. The double-row leaf grille and the runner are the primary sound source areas of the hydraulic turbine, and the intensity of sound radiation from these areas is positively correlated with the degree of runner blade defects. 2. As the runner blade defects develop, the sound power level (SWL) increases more significantly in the guide vanes near the nose of the spiral case in the double-row leaf grille. The most pronounced increase in the SWL is observed at the defective craters on the runner blades. 3. The frequency of the defective noise signal is primarily concentrated in the low-frequency band. The dominant frequency amplitude associated with runner blade defects increases and rises after the occurrence of defects. Secondary frequency changes are also observed, and the location of these changes varies at different receiving points.

Parameter Calibration of Wind Farm With Error Tracing Technique and Correlated Parameter Identification

With the increasing penetrations of wind power in power systems, periodical calibration of the large-scale Wind Farm (WF) model is crucial to maintaining high-quality model performance for stability investigations. However, the existing calibration methods mainly focus on correcting synchronous generators, not the more dynamical WF model. Challenges in tracing errors of inaccurate models and identifying the correlated parameters would further restrain the calibrating performance. To address such limitations, a novel parameter calibration approach for the WF model is developed explicitly in this article. With the development of a trajectory sensitivity based dominant parameters selection strategy, the parameters which significantly impact the model responses can be precisely located. Then, for discriminating the ill-conditioned parameters (deviating from their actual values), an improved error tracing method incorporating Hybrid Dynamic Simulation (HDS) and Hilbert-Huang Transform (HHT) is proposed. Lastly, to enhance the model performance, the ill-conditioned parameters are iteratively tuned by a well-designed multistage identification approach, with the consideration of parameter correlations. Additionally, theoretical proof of the error tracing technique is provided to guarantee the proposed method's validity mathematically. With realistic WF and a modified IEEE 39-bus system, the proposed method demonstrated its excellent performance in tracing and tuning inaccurate parameters.

Hybrid simulation of dissipative particle dynamics and computational fluid dynamics for friction drag reduction of polymer coatings

Frictional drag reduction (DR) can lead to substantial fuel savings in marine vehicle propulsion. The application of polymer coatings on vehicle surfaces has been found to assist in reducing drag. However, there has been limited investigation into such non-uniform polymer drag reduction in external flows. This study proposes a novel numerical model that combines a mesoscopic method with computational fluid dynamics (CFD) techniques. The CFD portion of the model uses direct numerical simulation (DNS) and large eddy simulation (LES) methods, which are verified against reference data. Firstly, the mesoscopic dissipative particle dynamics (DPD) method is coupled with DNS and LES methods, and the finitely extensible nonlinear elastic approach is used to validate the results obtained using this DNS/LES-DPD method. The performance of the model is evaluated at three representative Reynolds numbers (180, 395, and 590) to confirm its universal applicability. The model is then used to investigate the influence of the height of the polymer region on non-uniform polymer DR. Notably, the LES method offers computational savings when the Reynolds numbers are high. The model also reveals that the polymer DR efficiency is significant even when the polymer region does not extend across the entire fluid area such as the application of polymer coating. This model represents a foundation for further research on polymer coatings DR and energy conservation of vessels.

Dynamic phasor-based hybrid simulation for multi-inverter grid-connected system

To realize the efficient transient simulation of a grid-connected power generation system based on multiple inverters, this paper proposes a hybrid simulation method integrating the models of electromagnetic transient and dynamic phasors. Based on a demonstration of the concepts and properties of dynamic phasors, the models of single-phase and three-phase inverters described by dynamic phasors are established first. Considering the numerical compatibility problem between dynamic phasors and instantaneous values, an interface scheme between dynamic phasors and instantaneous values is designed, and the efficiency and precision differences of various transformation methods are compared in detail. Finally, by utilizing MATLAB/Simulink, a hybrid simulation platform of a multi-inverter grid-connected system is built, and the efficiency and accuracy of the hybrid simulation are validated via comparison with the full electromagnetic transient simulation.

Approach for Parameter Calibration to Microgrid Model With Problematic Parameter Identification

Periodical validation and calibration to microgrid models are necessary for investigating the behaviors of microgrids and evaluating the stability of connected power grids. However, instead of calibrating the problematic parameters that vary from their actual values, the commonly used methods normally adjust the selected sensitive parameters. The performance of the calibrated models is still in doubt. Therefore, this article proposes a generic approach for microgrid model calibration, which aims to accurately locate the indeed “ill-conditioned” problematic parameters, as well as address the multiple solutions issue. With the application of sensitivity and correlation analysis, the potentially problematic parameters (PPPs), which are sensitive and independent, are selected to ensure the accuracy and uniqueness of parameter calibration results. Then, the problematic parameters are further screened out by comparing the energy distribution of PPPs and model errors in the frequency domain. Also, hybrid dynamic simulation based parameter estimation is employed to adjust the problematic parameters, by which each device in microgrids can be calibrated independently. Finally, the simulation results demonstrate the outstanding performance in parameter calibration of a real microgrid.

Rigid-flexible hybrid modeling and dynamic simulation of three-coordinate heavy-load transfer vehicle

Rigid-flexible hybrid modeling and dynamic simulation of three-coordinate heavy-load transfer vehicle

Implementation of dynamic hybrid simulation for response evaluation of shear building

Dynamic hybrid simulation is a testing method, which is developed in the past few decades, to find the dynamic response of a structure. It involves splitting of the structure into two parts: physical substructure that contains the key region of interest, which is tested in a laboratory, and virtual substructure that contains the remaining part of the structure in the form of a numerical model. This paper presents the implementation of DHS for response evaluation of a shear building. The bottom storey alone was tested physically in the laboratory. Electromagnetic shaker was used to emulate the effect of the remaining structure. DHS is found to be an effective tool for dynamic response evaluation of structures.

Hybrid simulation of evolution dynamics in the neural networks basis

Conceptual solutions for constructing a hybrid simulation (HS) system for interpreting behavior of the marine dynamic objects (MDO) in an evolving medium are discussed. The analysis is carried out in the functional spaces of behavior and control of the modern catastrophe theory (MCT). The theoretical basis for the implementation of the multifunctional model of the interpretation of HS determines the principle of structural and parametric synthesis of neurodynamic models under conditions of uncertainty based on the hierarchical structure of the software package. The analysis of behavior MDO and the choice of a solution in a neurodynamic environment is carried out on the basis of ensemble forecast and a matrix of strategic decisions. The interpretation of MDO behavior in HS systems on the base neural-fuzzy and multi-agent environment (MMS) is carried out as part of the Big Data processing strategy for large volumes of data un the regime of urgent computing (UC).

Robustness Improvement on PMU Based Dynamic Equivalent Modeling of Distributed Small Hydropower Generator Stacks

Due to the geographic features and fruitful water resources, the distributed generation of Small Hydropower Generator Stack (SHGS) are vigorously developed in southwest China. As the more concerns about security and stability of power grids with the large amount of SHGS feed into the grid, the accurate models are critical to analyze those potential impacts. With the development of online monitoring technologies in smart grid, PMU based dynamic equivalent modeling for SHGS drawn many attentions. However, field applications reveal that traditional SHGS equivalent model may not be robustness enough to adapt the different disturbances. To overcome this deficiency, method to improve the robustness of SHGS dynamic equivalent modeling is proposed. In the paper, a coherency identification based multi-machine modeling method is introduced to enhance the accuracy of equivalent model. Sensitivity and correlation analysis based key parameters selection scheme is implemented to select the critical parameters, meanwhile avoiding the multiple solutions problem. Moreover, multi-objective optimization algorithm is designed for key parameters identification of the equivalent model. Additionally, hybrid dynamic simulation has adopted into the equivalent process to improve the computational efficiency. The effectiveness of the proposed method has been validated with an actual case from China Southwest grids.

Approach to Enhance the Robustness on PMU-Based Power System Dynamic Equivalent Modeling

Due to the maturity of wide area measurement technology, phasor measurement unit (PMU)-based dynamic equivalent modeling method (PDM) has been wildly used in large-scale power system modeling. However, traditional “event-based” PDM equivalent model may face serious robustness issues, such as although derived equivalent models show the good ability on reproduction of the system behaviors under recorded events where they derived from, they may not work well for other possible disturbances. In this article, to improve the robustness of equivalent model, a novel method on dynamic equivalent modeling of power system is proposed. Based on the result of coherency identification, generators inside the equivalent system can be aggregated into different coherent subgroups and represented by multimachine equivalent model. Key parameter selection, which can shrink the scale of identified parameters, is designed to avoid multiple solutions of the equivalent model. Moreover, a multiobjective parameter identification algorithm is developed to fully consider the PMU measurements in different system events. In addition, hybrid dynamic simulation is performed so that each equivalent submodel can be identified separately. Finally, the effectiveness of the proposed method is verified in an actual power grids in China.

Potential last-mile impacts of crowdshipping services: a simulation-based evaluation

Crowdsourced delivery services (crowdshipping) represent a shipping alternative to traditional delivery systems, particularly suitable for e-commerce. Although some benefits in terms of reduced pollution and congestion could be obtained by replacing dedicated freight trips, the impacts of crowdshipping are unclear and depend on several factors such as the transport mode used, the match between supply and demand, length of detours, and possible induced demand. For example, private drivers could modify their existing routes or engage in new trips to pick up and drop off packages; similarly, public transport users could carry along packages on their trips and drop them off at lockers installed around the stations. In this paper, we analyze by means of a simulation-based approach the potential impacts of alternative implementation frameworks. In order to account more realistically for last-mile delivery operations, a hybrid dynamic traffic simulation is adopted such that the macroscopic features of traffic (triggering of congestion, queue spillbacks and interactions with traffic signals) are reproduced in combination with the microscopic features of delivery operations (delivery vehicles are tracked along their routes). The effects on traffic and emissions are investigated for the adoption of crowdshipping by carriers delivering parcels in the city center of Rome, Italy. Results show that not only is the mode employed by crowdshippers crucial for the sustainability of such a measure, but also operational aspects involving the length of detour, parking behavior, and daily traffic variations. Crowdsourced deliveries by car have generally higher negative impacts than corresponding deliveries by public transit. However, limiting the deviations of crowdshippers from the original trips, providing adequate parking options, and incentivizing off-peak deliveries, could significantly reduce crowdshipping externalities.

Beyond Dravet Syndrome: Characterization of a Novel, More Severe SCN1A-Linked Epileptic Encephalopathy.

To define a distinct SCN1A developmental and epileptic encephalopathy with early onset, profound impairment, and movement disorder. A case series of 9 children were identified with a profound developmental and epileptic encephalopathy and SCN1A mutation. We identified 9 children 3 to 12 years of age; 7 were male. Seizure onset was at 6 to 12 weeks with hemiclonic seizures, bilateral tonic-clonic seizures, or spasms. All children had profound developmental impairment and were nonverbal and nonambulatory, and 7 of 9 required a gastrostomy. A hyperkinetic movement disorder occurred in all and was characterized by dystonia and choreoathetosis with prominent oral dyskinesia and onset from 2 to 20 months of age. Eight had a recurrent missense SCN1A mutation, p.Thr226Met. The remaining child had the missense mutation p.Pro1345Ser. The mutation arose de novo in 8 of 9; for the remaining case, the mother was negative and the father was unavailable. Here, we present a phenotype-genotype correlation for SCN1A. We describe a distinct SCN1A phenotype, early infantile SCN1A encephalopathy, which is readily distinguishable from the well-recognized entities of Dravet syndrome and genetic epilepsy with febrile seizures plus. This disorder has an earlier age at onset, profound developmental impairment, and a distinctive hyperkinetic movement disorder, setting it apart from Dravet syndrome. Remarkably, 8 of 9 children had the recurrent missense mutation p.Thr226Met. SCN1A Gain of Function in Early Infantile Encephalopathy Berecki G, Bryson A, Terhag J, et al. Ann Neurol. 2019; 85:514-525. To elucidate the biophysical basis underlying the distinct and severe clinical presentation in patients with the recurrent missense SCN1A variant, p.Thr226Met. Patients with this variant show a well-defined genotype-phenotype correlation and present with developmental and early infantile epileptic encephalopathy that is far more severe than typical SCN1A Dravet syndrome. Whole cell patch clamp and dynamic action potential clamp were used to study T226M Nav 1.1 channels expressed in mammalian cells. Computational modeling was used to explore the neuronal scale mechanisms that account for altered action potential firing. T226M channels exhibited hyperpolarizing shifts of the activation and inactivation curves and enhanced fast inactivation. Dynamic action potential clamp hybrid simulation showed that model neurons containing T226M conductance displayed a left shift in rheobase relative to control. At current stimulation levels that produced repetitive action potential firing in control model neurons, depolarization block and cessation of action potential firing occurred in T226M model neurons. Fully computationally simulated neuron models recapitulated the findings from dynamic action potential clamp and showed that heterozygous T226M models were also more susceptible to depolarization block. From a biophysical perspective, the T226M mutation produces gain of function. Somewhat paradoxically, our data suggest that this gain of function would cause interneurons to more readily develop depolarization block. This "functional dominant negative" interaction would produce a more profound disinhibition than seen with haploinsufficiency that is typical of Dravet syndrome and could readily explain the more severe phenotype of patients with T226M mutation.

SCN1A gain of function in early infantile encephalopathy.

To elucidate the biophysical basis underlying the distinct and severe clinical presentation in patients with the recurrent missense SCN1A variant, p.Thr226Met. Patients with this variant show a well-defined genotype-phenotype correlation and present with developmental and early infantile epileptic encephalopathy that is far more severe than typical SCN1A Dravet syndrome. Whole cell patch clamp and dynamic action potential clamp were used to study T226M Nav 1.1 channels expressed in mammalian cells. Computational modeling was used to explore the neuronal scale mechanisms that account for altered action potential firing. T226M channels exhibited hyperpolarizing shifts of the activation and inactivation curves and enhanced fast inactivation. Dynamic action potential clamp hybrid simulation showed that model neurons containing T226M conductance displayed a left shift in rheobase relative to control. At current stimulation levels that produced repetitive action potential firing in control model neurons, depolarization block and cessation of action potential firing occurred in T226M model neurons. Fully computationally simulated neuron models recapitulated the findings from dynamic action potential clamp and showed that heterozygous T226M models were also more susceptible to depolarization block. From a biophysical perspective, the T226M mutation produces gain of function. Somewhat paradoxically, our data suggest that this gain of function would cause interneurons to more readily develop depolarization block. This "functional dominant negative" interaction would produce a more profound disinhibition than seen with haploinsufficiency that is typical of Dravet syndrome and could readily explain the more severe phenotype of patients with T226M mutation. Ann Neurol 2019;85:514-525.

A Robust approach for the identification of synchronous machine parameters and dynamic states based on PMU data

In this paper an approach for on-line identification of parameters and dynamic states of synchronous generator, is proposed. Synchrophasors data and Hybrid Dynamic Simulation provide the trajectories deviations caused by parameters errors. Simulated variables are combined with measurements to obtain Hybrid Trajectory Sensibility Functions (HTSF), which are used in the identification process based on Nonlinear Least Squares. This approach simplifies the calculation of HTSF. Constraints on the parameters range are enforced and the resulting constrained optimization problems is solved by a Primal-Dual Interior Points method. This solution is discussed in the paper. The method is applied to synthetic data and to a large generator of the Itaipu power plant.

Improvements of Piezo-Actuated Stick–Slip Micro-Drives: Modeling and Driving Waveform

Modeling and waveform optimization are important research topics for piezo-actuated stick–slip micro-drives. In this paper, the dynamics of piezo-actuated stick–slip micro-drives (PASSMDs) are theoretically investigated. We introduce an extended model taking the dynamics of the piezo actuators into account. The model combines the whole macroscopic movement of the drive’s runner and actuators and the microscopic behavior of the frictional contacts in a hybrid dynamic simulation. The macroscopic movements are described via Newtonian mechanics, while the microscopic behavior is computed using the method of dimensionality reduction. Two important characteristics of the drive, the critical actuation amplitude and the force generation, are systematically analyzed. The numerical simulation results show a fine agreement with experimental data of the previously published work. The critical actuation amplitude is found to depend on the behavior of the guiding contacts, the dynamics of the actuators and their interaction. Furthermore, a novel driving waveform, which allows us to increase the operational velocity for the drive, is proposed. The waveform is derived by exploiting micro-vibration and considering the dynamic contact status. Simulation results show that the average velocity of the drive is heightened by about 15 % . The performance of the drive is therefore improved.

Analytical and Computational Studies of Correlations of Hydrodynamic Fluctuations in Shear Flow

We study correlations of hydrodynamic fluctuations in shear flow analytically and also by dissipative particle dynamics~(DPD) simulations. The hydrodynamic equations are linearized around the macroscopic velocity field and then solved by a perturbation method in Fourier-transformed space. The autocorrelation functions~(ACFs) from the analytical method are compared with results obtained from DPD simulations under the same shear-flow conditions. Upto a moderate shear rate, various ACFs from the two approaches agree with each other well. At large shear rates, discrepancies between the two methods are observed, hence revealing strong additional coupling between different fluctuating variables, which is not considered in the analytical approach. In addition, the results at low and moderate shear rates can serve as benchmarks for developing multiscale algorithms for coupling of heterogeneous solvers, such as a hybrid simulation of molecular dynamics and fluctuating hydrodynamics solver, where thermal fluctuations are indispensable.

A bi-level distributed approach for optimizing time-dependent congestion pricing in large networks: A simulation-based case study in the Greater Toronto Area

Congestion pricing is one of the most widely contemplated methods to manage traffic congestion by charging fees for the use of roads, more where and when it is congested, and less where and when it is not. This study presents a bi-level distributed approach for optimal time-dependent congestion pricing in large networks. The bi-level procedure involves a theoretical model of dynamic congestion pricing and a distributed optimization algorithm. The bi-level toll optimization module is integrated into a testbed of hybrid departure time choice and dynamic traffic assignment simulation models for the Greater Toronto Area (GTA). The integrated system provides a unified (location- and time-specific) congestion pricing system that determines optimal tolling and evaluates its impact on road traffic congestion and travellers’ behavioural choices, including departure time and route choices. For the system’s large-scale nature and the consequent computational challenges, the optimization algorithm is executed concurrently on a parallel computing cluster. The system is applied to a simulation-based case study of tolling major highways in the GTA while capturing the network-wide regional effects of tolling. The travel demand and drivers’ attributes are extracted from regional household travel survey data that reflect travellers’ heterogeneity. The main results indicate that: (1) optimal variable pricing reflects congestion patterns and induces departure time re-scheduling and rerouting patterns, resulting in improved average travel times and schedule delays, (2) optimal tolls intended to manage traffic demand are significantly lower than those intended to maximize toll revenues, (3) tolled routes have different sensitivities to identical toll changes, (4) the start times of longer trips are more sensitive (elastic) to variable distance-based tolling policies compared to shorter trips, (5) toll payers benefit from tolling even before toll revenues are spent, and (6) the optimal tolling policies determined offer a win–win solution in which travel times are improved while also raising funds to invest in sustainable transportation infrastructure.

A Numerical Approach for Hybrid Simulation of Power System Dynamics Considering Extreme Icing Events

The global climate change leads to more extreme meteorological conditions such as icing weather, which have caused great losses to power systems. Comprehensive simulation tools are required to enhance the capability of power system risk assessment under extreme weather conditions. A hybrid numerical simulation scheme integrating icing weather events with power system dynamics is proposed to extend power system numerical simulation. A technique is developed to efficiently simulate the interaction of slow dynamics of weather events and fast dynamics of power systems. An extended package for PSS/E enabling hybrid simulation of icing event and power system disturbance is developed, based on which a hybrid simulation platform is established. Numerical studies show that the functionality of power system simulation is greatly extended by taking into account the icing weather events.

Dynamic hybrid modeling and simulation of grinding–flotation circuits for the development of control strategies

Process simulation is a very important tool for the design, development, analysis and optimization of technical processes in the mineral industry. The ability to simulate process behavior without the cost of test runs can prevent the loss of man-hours and production, in addition to providing a platform for the development of control tools and strategies. The usefulness of a simulator ultimately relies on how accurately the underlying mathematical model represents real behavior. In mineral processing, due to varied complexities such as strong non-linearities, variable coupling, time varying parameters, etc.; the development of accurate process models becomes an increasingly difficult task.This paper describes the modeling and simulation of the main components of a concentrator plant, the grinding and flotation circuits. A hybrid dynamic model was favored to better represent the different modes of operation and non-linearities exhibited by the plant. Industrial plant data was used to calibrate the models used in the simulator. Simulation tests show that the performance of the simulator is qualitatively similar to that of a real plant, and it can be effectively used as a tool for the design and simulation of control solutions.

Using High Performance Algorithms for the Hybrid Simulation of Disease Dynamics on CPU and GPU

Abstract In the current work the authors present several approaches to the high performance simulation of human diseases propagation using hybrid two-component imitational models. The models under study were created by coupling compartmental and discrete-event submodels. The former is responsible for the simulation of the demographic processes in a population while the latter deals with a disease progression for a certain individual. The number and type of components used in a model may vary depending on the research aims and data availability. The introduced high performance approaches are based on batch random number generation, distribution of simulation runs and the calculations on graphical processor units. The emphasis was made on the possibility to use the approaches for various model types without considerable code refactoring for every particular model. The speedup gained was measured on simulation programs written in C + + and MATLAB for the models of HIV and tuberculosis spread and the models of tumor screening for the prevention of colorectal cancer. The benefits and drawbacks of the described approaches along with the future directions of their development are discussed.