Network Simulation Model Research Articles

Evidential Neural Network Model for Groundwater Salinization Simulation: A First Application in Hydro-Environmental Engineering

Evidential Neural Network Model for Groundwater Salinization Simulation: A First Application in Hydro-Environmental Engineering

Study on the deployment of differentiated well patterns for shale gas in southern Sichuan region

Shale gas blocks are of great geological differences in southern Sichuan, deploying differentiated well patterns accordingly is therefore significant. For two typical shale gas blocks in southern Sichuan, Dingshan normal pressure area and Yongchuan south area, a hydraulic fracturing interlayer law model based on the cohesive zone model and a fracture extension model based on rock damage theory were established. Hydraulic fracture height and length charts under different natural fracture density and operation parameters were drawn. Using these charts, fracture parameters in different areas were determined, and hydraulic and natural fracture models were established. By coupling an embedded discrete fracture model with geological models and fracture models, a complex fracture network simulation model was built to predict gas production. Horizontal well spacing, orientation, and length were optimized under different geological characteristics to maximize production and economic indicators, and a differentiated well pattern deployment strategy was proposed. Results show that, firstly, for Dingshan area, the fracture height range is 40-64 m with an average of 51 m, that of Yongchuan is 42-65 m with an average of 53 m. The fracture length range of Dingshan area is 215-350 m with an average of 300 m, whereas that of Yongchuan is 290-350 m with an average of 330 m. The optimal horizontal well spacing in Dingshan area is 300 m, the optimal well orientation is 60°-90°, and the optimal lateral length is 2000 m; and those of Yongchuan are 300 m, 90°, and 1680 m. Well pattern on-site can be adjusted according to the optimal parameters to achieve higher Estimated Ultimate Recovery (EUR) and Net Present Value (NPV).

Numerical Simulation and Neural Network Model for Hydromagnetic Nanofluid Convection in a Porous Wavy Channel with Thermal Non-Equilibrium Model

Abstract The present study aims to analyze the nonfluid MHD convective heat transfer in a porous wavy channel with a local thermal non-equilibrium (LTNE) model. Such a model finds applications related to enhancement in thermal performance, increasing the heat transfer coefficient in the compact design of heat exchangers for the aerospace and automotive industries and elevation in the efficiency of the solar collector. A sinusoidal porous wavy LTNE channel containing nanofluid and subjected to the induced and applied magnetic fields is considered. A uniform magnetic field is applied orthogonal to the channel and the induced magnetic field effects are considered due to the large magnetic Reynolds number. The momentum, continuity, energy, and nanoparticle volume fraction equations constitute the coupled nonlinear system of differential equations and are solved using the Galerkin finite element method. The reliability of the technique is assessed by comparing the proposed procedure with the results from earlier sources. A detailed analysis is presented to determine the effects of different physical parameters arising in the system on temperature, nanoparticle concentration, and velocity profiles. As an illustration, the findings exhibit that increasing the modified diffusivity ratio increases the values of the nanoparticle volume fraction whereas, reducing the modified diffusivity ratio enhances the temperature distribution. A higher value of thermal Rayleigh number presents a significant involvement of buoyancy forces, potentially resulting in the development of convective currents. A higher Nield number indicates more effective heat transport from the solid surface to the nanofluid. Consequently, there is a minimal thermal difference between the solid surface and the bulk nanofluid. Effective heat transmission enhances the nanofluid ability to absorb heat and generates a more consistent dispersion of temperature inside the fluid. The performance of the designed algorithms of the artificial neural network, namely, the Levenberg – Marquardt algorithm, in the problem under consideration is evaluated and the methodology is found reasonably precise with the matching of order around 6 to 7 decimal places of accuracy.

Machine learning insights into the evolution of flood Resilience: A synthesized framework study

Enhancing urban resilience represented a viable strategy to mitigate flooding induced by intense human activities and climate change. However, existing studies often concentrated on system attributes or isolated resilience characteristics, failing to offer a holistic evaluation of urban flood resilience performance. Thus, it was imperative to develop a comprehensive flood resilience framework that incorporated the resilience evolution process including resistance, economic and function recovery. Consequently, this study endeavored to devise a synthesized framework for evaluating urban flood resilience, subsequently employing a Convolutional Neural Network (CNN) model for simulation. The findings indicated that: (1) Guangzhou’s maximum resistance capacity diminished from 0.52 to 0.50 as rainfall return periods altered, while Dongguan exhibited the lowest resistance, decreasing from 0.42 to 0.40. Regarding functional recovery capacity, Guangzhou ranked highest (0.35) and Foshan lowest (0.19); (2) according to Triangular Fuzzy Number-based AHP (TFN-AHP) analysis, the area classified as highest in resilience decreased from 15.6% to 12.1% of the total, whereas the low resilience area increased from 7.6% to 8.7%; (3) Zhuhai and Zhaoqing were primarily clustered along the resistance axis, in contrast, Dongguan was distinguished by its advancement along the axis of functional recovery.(4) CNN simulations yielded precise outcomes, with the Area Under the Receiver Operating Characteristic Curve (AUC) and predictive accuracy (ACC) values exceeding 0.8,respectively. The insights provided by this research were crucial for entities tasked with flood risk management.

Comparison of acoustic, optical, and heat release rate based flame transfer functions for a lean-burn injector under engine-like conditions

Determining the flame transfer function plays a crucial role during the development phase of lean-burn injectors to predict the overall stability of the combustion system. This study develops an enhanced acoustic post-processing strategy using acoustic network modeling and compressible large-eddy simulation for a lean-burn aero-engine injector in a realistic test rig. Results show that optical flame transfer functions experimentally obtained align with those derived from large-eddy simulation based on heat release rate. However, discrepancies, especially in gain, are observed when using the standard acoustic post-processing method. By employing an acoustic network model, it is shown that comparable flame transfer functions can be achieved through refined post-processing strategies. This study highlights the limitations of conventional acoustic post-processing and underscores the necessity of explicit acoustic modeling in complex setups to accurately obtain the flame response. A generalized formulation applicable to a broader range of setups, extending beyond simple configurations, is presented as an alternative to the conventional acoustic post-processing method.

Integer quantum Hall transition: An S -matrix approach to random networks

In this paper we propose an S-matrix approach to numerical simulations of network models and apply it to random networks that we proposed in a previous work [I. A. Gruzberg, A. Klümper, W. Nuding, and A. Sedrakyan, ]. Random networks are modifications of the Chalker-Coddington (CC) model for the integer quantum Hall transition that more faithfully captures the physics of electrons moving in a strong magnetic field and a smooth disorder potential. This method has considerable advantages compared to the transfer matrix approach and gives the value ν≈2.4 for the critical exponent of the localization length in a random network. This finding confirms our previous result and is surprisingly close to the experimental value νexpt≈2.38 observed at the integer quantum Hall transition but substantially different from the CC value νCC≈2.6. Published by the American Physical Society 2024

CaloShowerGAN, a generative adversarial network model for fast calorimeter shower simulation

CaloShowerGAN, a generative adversarial network model for fast calorimeter shower simulation

Development of a simulation model and adjustment of hyperparameters of a neural network for predicting possible states of a network of small space carriages

The purpose of the research is to develop a simulation model and tune the hyperparameters of a neural network to predict possible states of a network of small spacecraft.The research methods are based on the concepts of the theory of artificial intelligence to control a group of small spacecraft (SSV) – the use of adaptive methods and tools to make decisions, similar to the mechanisms of human thinking. In relation to space communication systems with a heterogeneous structure, artificial intelligence methods and technologies are aimed at predicting the state of communication channels between network nodes and automatically reconfiguring a network of devices based on neural network learning processes. One of the most important functions of network software for the application of cognitive algorithms is to predict the quality of communication between pairs of SSVs.Results. A method has been developed for using the Transformer architecture neural network to predict possible states of the SSV network, which provides aggregation and time synchronization of data on the state of the SSV network, their use for training the neural network, as well as using the neural network to predict the quality of communication. The data format for the training sample has been created, based on the representation of the state of the SSV network, which ensures the generation of the initial state of the network, modeling the proactive mode of its operation, collecting SSV network state markers to generate training data sets in the form of chronological sequences grouped into frames, and allowing to reduce the amount of data transferred between SSVs when creating a training set. A simulation model of the SSV network has been developed, which provides generation of the initial state of the network, modeling of the proactive mode of its operation, and collection of information about the state of the SSV network to generate sets of synthetic training data.Conclusion. The article develops a simulation model of the SSV network for generating synthetic training data and predicting possible states of the SSV network, as well as a method for using a neural network to predict possible states of the SSV network.

Research on the minimum network loss optimization control strategy of the active distribution network based on the distributed power flow controller

With large-scale access to distributed new energy, the power flow form of the distribution network tends to be complex. The distribution loop network rate increases, resulting in serious circulation of the distribution network, which places significant pressure on the loss of the distribution network. The problem of network loss change caused by distributed new energy grid connection, coupled with the inconsistency between load fluctuation and output power change of distributed new energy, will have a significant impact on the safety, reliability, and economic operation of the distribution network. Therefore, it is urgent to have good economic and technical means of power flow control. This paper proposes a minimum network loss optimization control strategy for active distribution networks based on a distributed power flow controller. First, according to the characteristics of the distribution network load and the new energy power supply along the distribution line, the multiple sub-modules of the distributed power flow controller are arranged in sections in the distribution loop network line. The regulation effect of the distributed power flow controller on the voltage and power flow of the distribution loop network is analyzed. The loop network current equation and power loss equation after the distributed power flow controller are constructed, and the power equation with the minimum active loss of the active distribution loop network is derived. Second, the control strategy for minimum network loss of the active distribution network based on the distributed power flow controller is proposed. Finally, the simulation model of the active distribution loop network is constructed by PSCAD/EMTDC. The loss of the loop network before and after the distributed power flow controller is simulated and analyzed, and the effectiveness of the proposed strategy is verified.

Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling.

The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.

Active Control of Longitudinal Combustion Instability in Bluff-Body Stabilized Premixed Flames with Multiple Neural Network Controller

ABSTRACT The suppression performance of multiple neural network controller on the longitudinal combustion instability is investigated in the Volvo bluff-body stabilized premixed flame. Two controlled plant models, e.g. one-dimensional (1D) reduced order acoustic network model and three-dimensional (3D) large eddy simulation model are adopted to verify this control strategy. In order to build the reduced order Volvo acoustic network model, large eddy simulation is adopted to extract the characteristics of system heat release under wide bandwidth and construct the flame transfer functions. For the 1D acoustic network model, this controller can completely eliminate the pressure oscillation using fuel valve as actuator. For the 3D large eddy simulation model that mimics the real-time oscillating characteristics, clustered prediction method and amplitude limiting method are adopted in multiple neural network control strategy to better account for the nonlinear characteristics of the 3D Volvo flame. This control strategy enables the modulation of the phase relationship between the heat release rate oscillation and pressure oscillation by introducing small perturbations to the inlet equivalence ratio, and attenuates the amplitude of the second harmonic of the unstable mode effectively.

A comprehensive exploration of approximate DNN models with a novel floating-point simulation framework

This paper introduces TorchAxf11https://github.com/knuscent/TorchAxf., a framework for fast simulation of diverse approximate deep neural network (DNN) models, including spiking neural networks (SNNs). The proposed framework utilizes various approximate adders and multipliers, supports industrial standard reduced precision floating-point formats, such as bfloat16, and accommodates user-customized precision representations. Leveraging GPU acceleration on the PyTorch framework, TorchAxf accelerates approximate DNN training and inference. In addition, it allows seamless integration of arbitrary approximate arithmetic algorithms with C/C++ behavioral models to emulate approximate DNN hardware accelerators.We utilize the proposed TorchAxf framework to assess twelve popular DNN models under approximate multiply-and-accumulate (MAC) operations. Through comprehensive experiments, we determine the suitable degree of floating-point arithmetic approximation for these DNN models without significant accuracy loss and offer the optimal reduced precision formats for each DNN model. Additionally, we demonstrate that approximate-aware re-training can rectify errors and enhance pre-trained DNN models under reduced precision formats. Furthermore, TorchAxf, operating on GPU, remarkably reduces simulation time for complex DNN models using approximate arithmetic by up to 131.38× compared to the baseline optimized CPU implementation. Finally, we compare the proposed framework with state-of-the-art frameworks to highlight its superiority.

A transient gas pipeline network simulation model for decoupling the hydraulic-thermal process and the component tracking process

The metering mode is gradually changing from volumetric metering to energy metering under the open-access operation of gas pipelines. The gas component tracking by operating simulation is an effective method to realize the energy metering of gas networks. In this paper, coupling and decoupling transient component tracking models of gas networks are proposed. The pipeline governing equations, compressor equations, valve equations, and node relationship equations are considered in the proposed models. The implicit central difference method and the damped Newtonian method are adopted to solve the models. The coupling model considers the component tracking part and hydraulic-thermal simulation as a whole, while the decoupling model splits the whole process into the gas component tracking part, the parameters calculation part, and the hydraulic-thermal simulation part. In the component tracking part, the spatiotemporal variation of the gas mass fraction and density can be obtained through the input velocity of each node. Then, the parameters in the gas state equation can be solved, the hydraulic-thermal simulation can be performed, and the new velocity of each node can be calculated. The iteration for each time step ends if the deviation of flow velocity in the component tracking process and the hydraulic-thermal process satisfies the requirements. Two cases are studied to verify the effectiveness of the proposed models, and the boundary condition adaptability of the transient simulation models is analyzed. The proposed models can efficiently simulate the spatiotemporal variation of the operating variables and gas components, providing a reference in the operation of natural gas pipeline networks.

Fast Aerodynamic Prediction of Airfoil with Trailing Edge Flap Based on Multi-Task Deep Learning

Conventional methods for solving Navier–Stokes (NS) equations to analyze flow fields and aerodynamic forces of airfoils with trailing edge flaps (TEFs) are known for their significant time cost. This study presents a Multi-Task Swin Transformer (MT-Swin-T) deep learning framework tailored for swift prediction of velocity fields and aerodynamic coefficients of TEF-equipped airfoils. The proposed model combines a Swin Transformer (Swin-T) for flow field prediction with a multi-layer perceptron (MLP) dedicated to lift coefficient prediction. Both networks undergo gradient updates through the shared encoder component of the Swin Transformer. Such a trained network model for computational fluid dynamics simulations is both effective and robust, significantly improving the efficiency of complex aerodynamic shape design optimization and flow control. The study further investigates the impact of integrating multi-task learning loss functions, skip connections, and the network’s structural design on prediction accuracy. Additionally, the effectiveness of deep learning in improving the aerodynamic simulation efficiency of airfoils with TEF is examined. Results demonstrate that the multi-task deep learning approach provides accurate predictions for TEF airfoil flow fields and lift coefficients. The strategic combination of these tasks during network training, along with the optimal selection of loss functions, significantly enhances prediction accuracy compared with the single-task network. In a specific case study, the MT-Swin-T model demonstrated a prediction time that was 1/7214 of the time necessitated by CFD simulation.

Scenario analysis on CO2 emission reductions in hinterland transport of Japan through intermodal logistics network simulation

Because transport in the port hinterlands heavily depends on roads with negative externalities, much social interest has been in modal shifts (MSs) to rail and shipping with lower environmental impact. Currently, leading industry sectors to ambitiously embrace technological innovations to decarbonize—consequently, in line with these changes, the significance of MSs should change; however, no previous studies have comprehensively presented the overall CO2 emissions and transport mode shares for future regional cargo transport. This paper presents a scenario analysis of CO2 emission reductions in the Japanese domestic hinterland of international maritime container transport based on future scenarios of technological innovation and change in shipper behavior. The simulation results using the global logistics intermodal network simulation model showed that up to 87% CO2 reduction is possible, and the primary driver for this is technological innovation. Moreover, the significance of MSs fluctuates depending on the advancement of technological innovation, suggesting that MSs hold a degree of import as a decarbonization measure during the transitional phase toward carbon neutrality (CN) as technological innovation advances. The results of this study, which fluctuated considerably by scenario and timing, have policy implications with respect to the path to be followed toward CN, including whether to promote MSs.

Research on Intelligent Identification and Analysis Algorithm of Tunnel Engineering Geological Information

When tunneling, detecting abrupt changes in geological circumstances can be difficult. In recent years, the proliferation of tunneling characteristics has been strongly related to the surrounding geology. These parameters offer significant potential for using data-driven artificial intelligence (AI) approaches to infer patterns from information without reference to known consequences. This research introduces the Simulated Fire Hawk Optimizer-based Deep Action Selection Network (SFHO-DASN) model, which uses a Simulated Fire Hawk Optimizer to anticipate the geological conditions needed for tunneling. The optimized hybrid neural network technique can anticipate geological conditions effectively, as demonstrated by a case study of the constructed model. It is especially significant for rock types, water intrusion, karst caverns, and surface subsidence, for which the predicted accuracy is greater than 95%. These findings imply that the geological circumstances behind the tunnel face could be reliably and correctly predicted by a DASN that has received the proper training. This procedure's most significant advantage is its ability to adjust every scored parameter's weighting in response to variations in geological circumstances. The accuracy performance of the proposed neural network outperforms the conventional neural network, as indicated by the area under the curve (AUC) and performance analysis. A proposed model for geology prediction can attain predictive accuracy with a small number of tunneling parameters, according to an analysis of the feature relevance of each tunneling parameter. The suggested approach ought to be more practical for proposals about tunnel support architecture in the East Asian geological region and for future tunnel building.

Research on the countermeasures of the single-phase-to-earth faults in flexibly grounded distribution networks

In a neutral, flexible, grounded distribution network, the phenomenon that a single-phase ground fault with high impedance connected in parallel with a low resistance can result in a relatively lower zero-sequence current flowing through the faulty feeder. This zero-sequence current can make it a challenge to use zero-sequence protection schemes to accurately identify and effectively protect the faulty feeder. In terms of the aforementioned challenge, based on the zero-sequence equivalent circuit of a single-phase-to-earth fault, this paper conducts an investigation on the phase difference between the zero-sequence current and the non-fault phase line voltage at power frequency in flexible grounding distribution network with different grounded conditions, both before and after the introduction of a parallel low-resistor connection. Subsequently, a criterion for selecting the faulty feeder is developed based on the observed phase difference characteristics following the implementation of the parallel low-resistor connection. Furthermore, a disposal strategy for single-phase-to-earth faults is formulated, which takes fault resistance estimation into account. To validate the proposed method, a simulation model of a flexible grounding distribution network is implemented with PSCAD/EMTDC. The effectiveness and reliability of this method in multiple scenes, including the initial fault conditions, sampling frequency, and distributed generation (DG), are proved through simulation results.

Simulation of Full HIV Cluster Networks in a Nationally Representative Model Indicates Intervention Opportunities.

Clusters of rapid HIV transmission in the United States are increasingly recognized through analysis of HIV molecular sequence data reported to the National HIV Surveillance System. Understanding the full extent of cluster networks is important to assess intervention opportunities. However, full cluster networks include undiagnosed and other infections that cannot be systematically observed in real life. We replicated HIV molecular cluster networks during 2015-2017 in the United States using a stochastic dynamic network simulation model of sexual transmission of HIV. Clusters were defined at the 0.5% genetic distance threshold. Ongoing priority clusters had growth of ≥3 diagnoses/year in multiple years; new priority clusters first had ≥3 diagnoses/year in 2017. We assessed the full extent, composition, and transmission rates of new and ongoing priority clusters. Full clusters were 3-9 times larger than detected clusters, with median detected cluster sizes in new and ongoing priority clusters of 4 (range 3-9) and 11 (range 3-33), respectively, corresponding to full cluster sizes with a median of 14 (3-74) and 94 (7-318), respectively. A median of 36.3% (range 11.1%-72.6%) of infections in the full new priority clusters were undiagnosed. HIV transmission rates in these clusters were >4 times the overall rate observed in the entire simulation. Priority clusters reflect networks with rapid HIV transmission. The substantially larger full extent of these clusters, high proportion of undiagnosed infections, and high transmission rates indicate opportunities for public health intervention and impact.

Selected static characteristics of a parallel active power filter with feedback from the supply voltage

The article presents selected static characteristics of a parallel active filter with voltage control in the supply line (VPAPF – Voltage-controlled Active Power Filter) as a function of parameters of the supply network. The tests were done on the basis of a simulation model of the supply network and an appropriate compensator. The test results showed that VPAPFs are most suitable for operation in weak networks, maintaining an almost constant level of voltage distortion, regardless of the value of the network impedance. In addition, the influence of the parameter G corresponding to the conductance value suppressing higher harmonics of the network voltage on the operation of the active power filter was determined.

Subsynchronous oscillation suppression strategy of wind turbine’s phase-locked loop based on optimal damping ratio

The dynamic characteristics of the phase-locked loop (PLL) of the wind turbine are the key to the synchronous operation of these renewable energy sources with the power grid. In this paper, the coupled dynamic model of PLL of the permanent magnet synchronous generator (PMSG)-base wind turbine and power grid is established firstly. The complex torque method is used to calculate the synchronous and damping torque coefficients in the PLL oscillation mode. Secondly, the influence of the PLL parameter, voltage at the point of common coupling (PCC), wind turbine’s output power and electrical distance on the oscillation damping behavior of the PLL is analyzed. Further, a novel control strategy based on optimal damping ratio is proposed to suppress the subsynchronous oscillation of the system caused by wind turbine’s PLL in weak interconnected grids. Finally, a simulation model of regional power network with high penetration of wind power generation is built to demonstrate the effectiveness of the proposed control scheme in suppressing the system’s subsynchronous oscillation caused by the wind turbine’s PLL.