Grid Simulation Research Articles

Mass-transfer Outbursts Reborn: Modeling the Light Curve of the Dwarf Nova EX Draconis

EX Draconis is an eclipsing dwarf nova that shows outbursts with moderate amplitude (≃2 mag) and a recurrence timescale of ≃20–30 days. Dwarf novae outbursts are explained in terms of either a thermal-viscous instability in the disc or an instability in the mass-transfer rate of the donor star (MTIM). We developed simulations of the response of accretion discs to events of enhanced mass transfer, in the context of the MTIM, and applied them to model the light curve and variations in the radius of the EX Dra disc throughout the outburst. We obtain the first modeling of a dwarf nova outburst by using χ 2 to select, from a grid of simulations, the best-fit parameters to the observed EX Dra outbursts. The observed time evolution of the system brightness and the changes in the radius of the outer disc along the outburst cycle are satisfactorily reproduced by a model of the response of an accretion disc with a viscosity parameter α = 4.0 and a quiescent mass-transfer rate Ṁ2(quiescence)=4.0×1016 g s−1 to an event of width Δt = 6.0 × 105 s (∼7 days) where the mass-transfer rate increases to Ṁ2(outburst)=1.5×1018 g s−1.

Green Light for Bidirectional Charging? Unveiling Grid Repercussions and Life Cycle Impacts

Bidirectional charging, such as Vehicle-to-Grid, is increasingly seen as a way to integrate the growing number of battery electric vehicles into the energy system. The electrical storage capacity can be enhanced by using electric vehicles as flexible storage units. However, large-scale applications of Vehicle-to-Grid may require significant expansion of distribution grids. Previous studies lack a comprehensive environmental assessment of related impacts. Contributing to this research gap, this article combines techno-economic grid simulations with scenario-based Life Cycle Assessments. The case study focuses on rural distribution grids in Southern Germany, projecting the repercussions of different charging scenarios by 2040. Besides a Vehicle-to-Grid scenario, a mixed scenario of Vehicle-to-Home, Vehicle-to-Grid, and direct charging is investigated. Results indicate that Vehicle-to-Grid charging increases grid impacts due to higher charging simultaneities and power losses, especially when following spot market prices. Despite these challenges, the secondary use of battery electric vehicles as storage units can offset adverse environmental effects. Bidirectional charging allows for higher use of volatile renewable energies and can accelerate their integration into the power system. When considering these diverse environmental effects, bidirectional charging scenarios show overall lower impacts on climate change per battery electric vehicle compared to direct charging. The insights provided are valuable for researchers, industry, utilities, and policymakers to understand the potential positive and negative impacts of large-scale battery electric vehicle integration. The article highlights the most influential parameters that should be considered before large-scale penetration.

Development of a Virtual Chemistry Reaction Mechanism for H2/CH4 Turbulent Combustion Modelling

Abstract The identification of the combustion model is always guided by the compromise between accuracy and computational cost. While species transport models offer accuracy, their computational cost requirement can become prohibitive, especially when de-aling with higher-order hydrocarbon fuels. To mitigate this, the virtual mechanism definition aims to optimize the predictivity minimizing the amount of information to be transported. Thereby the virtual mechanism leverages fictitious species and a few step reactions whose parameters calibration is performed with a genetic algorithm. This work outlines the procedure for the derivation of a virtual reaction mechanism for the study of lean H2/CH4 fuel mixtures with 60% of H2 content (by vol.). These conditions require an adequate characterization of the virtual species differential diffusion oriented to reconstruct the flame sensitivity toward the aerodynamic stretch. After the mechanism derivation, its predictivity has been validated on a swirl-stabilized perfectly premixed turbulent test case. The artificially thickened flame model has been adopted to allow the flame front discretization on an large eddy simulation (LES) grid and to model the turbulence chemistry interaction. The numerical results show a very good agreement with the experimental optical measurements confirming the effectiveness of this approach for predicting the H2/CH4 blend.

Multivariate sensitivity-adaptive polynomial chaos expansion for high-dimensional surrogate modeling and uncertainty quantification

This work develops a novel basis-adaptive method for constructing anisotropic polynomial chaos expansions of multidimensional (vector-valued, multi-output) model responses. The adaptive basis selection is based on multivariate sensitivity analysis metrics that can be estimated by post-processing the polynomial chaos expansion and results in a common anisotropic polynomial basis for the vector-valued response. This allows the application of the method to problems with up to moderately high-dimensional model inputs (in the order of tens) and up to very high-dimensional model responses (in the order of thousands). The method is applied to different engineering test cases for surrogate modeling and uncertainty quantification, including use cases related to electric machine and power grid modeling and simulation, and is found to produce highly accurate results with comparatively low data and computational demand.

Study on Flexural Performance of Reinforced Concrete Beams Strengthened with FRP Grid–PCM Composite Reinforcement

To study the flexural performance of fiber-reinforced polymer (FRP) grid–polymer cement mortar (PCM)-composite-strengthened RC beams, the finite element numerical simulation of FRP grid–PCM composite RC beams is carried out using ABAQUS to analyze the effects of the amount of FRP grid reinforcement, the type of FRP grid material, and the geometry of FRP grid on the flexural performance of reinforced concrete beams and to establish the flexural capacity calculation formula of FRP grid–PCM-reinforced RC beams in case of debonding failure, based on analysis of the influencing factors. The results show that increasing the reinforcement of the FRP grid and increasing the stiffness of the FRP grid can improve the flexural bearing capacity of RC beams, and the change of FRP grid geometry has little effect on the flexural bearing capacity of RC beams. The established formula for calculating the flexural bearing capacity of FRP grid-reinforced concrete beams can better predict the flexural capacity of reinforced concrete beams under peeling failure.

RedDots: Limits on habitable and undetected planets orbiting nearby stars GJ 832, GJ 674, and Ross 128

Context. The nearby (d < 5 pc) M dwarfs GJ 832, GJ 674, and Ross 128 each host a single exoplanet, with Ross 128 b located within the optimistic habitable zone. Due to their low mass and close proximity, these three systems are prime candidates for further characterization studies. Aims. Using HARPS spectroscopic data obtained by the RedDots campaign, as well as archival data from HARPS and CARMENES, supplemented with ASH2 and T90 photometry, we aim to search for additional planets in the three systems. We also aim to determine limits on possible undetected, habitable planets. We investigate (i) the reliability of the recovered orbital eccentricities and (ii) the reliability of Bayesian evidence as a diagnostic for selecting the best model. Methods. We employed Markov-chain Monte Carlo, nested sampling, and Gaussian process (GP) analyses to fit a total of 20 different models comprising 0–2 Keplerian signals and three different GP kernels for stellar activity. We used the residuals to create grids for injection-recovery simulations to obtain detection limits on potentially undiscovered planets. Results. Our refined orbital elements for GJ 832 b, GJ 674 b, and Ross 128 b confirm (GJ 832, GJ 674) or increase (Ross 128) prior eccentricity determinations. No additional planets were found in any of the systems. The detection limits obtained for all three systems are between 30 and 50 cm s−1 for orbital periods in the range of 1–10 000 days. This corresponds to habitable planet masses of <1.5M⊕for GJ 832 and < 1M⊕ for GJ 674 and Ross 128. Using N-body simulations, we find that undiscovered secondary planets are unlikely (Ross 128) or incapable (GJ 674) of having caused the observed eccentricities of the known planets. We find that the eccentricity of GJ 832b is not significantly different from zero. Conclusions. GJ 832 b, GJ 674 b, and Ross 128 b retain their status as hosting lonely and (for the latter two) eccentric planets (e = 0.04, 0.24, 0.21; respectively). This is unexpected in classical planet formation scenarios, which favor circular orbits and multiplanet configurations, demonstrating that planet formation in these cases is more complicated than traditionally thought. Additionally, the eccentricity of Ross 128 indicates that it spends some of its orbit outside of the optimistic habitable zone. Finally, our results show that Bayesian evidence, when used in conjunction with GP, is not a robust diagnostic for selecting the best model in cases of low- activity stars. In such cases, we advise an inspection of the shapes of the posterior distributions and to ensure that relevant simulations are performed to assess the validity of the perceived best model.

Assessing dynamics of urban solar PV power generation using grid divisional method

The assessment of solar energy potential and urban density has become a crucial prerequisite for urban sustainable development. However, two significant challenges persist: the high computational cost associated with assessments across large urban areas and the lack of detailed rooftop information for each building. This study aims to integrate solar photovoltaic (PV) systems in urban environments of varying built density in an Indian city and assesses the solar energy potential (SEP) using grid divisional method and simulations. The methodology involved extracting, filtering and developing a 2.5 D model with GIS using the open buildings dataset, followed by simulations of grid-connected PV systems using PV-Sol. The results demonstrated the dynamics in correlation of SEP and grid morphology indicators, with built density and roof area showing a strong positive relation. The financial analysis resulted in average payback period of 8–10 years. The study also highlighted significant CO2 emission reductions, with thermal power plants generating 26,982 tons/kWh compared to just 1,513 tons/kWh for solar power. The findings demonstrate the financial viability and environmental benefits of PV adoption in urban areas, offering crucial insights for sustainable energy planning and policy development.

A fourth-order compact finite volume method on unstructured grids for simulation of two-dimensional incompressible flow

This paper introduces a novel and efficient compact reconstruction procedure for high-order finite volume methods applied to unstructured grids. In this procedure, we establish a set of constitutive relations that ensure the continuity of the reconstruction polynomial and its normal derivatives between adjacent elements at control points. The paper delves into the details of the fourth-order compact reconstruction method specifically designed for two-dimensional triangular grids. This method can be considered an extension of the one-dimensional compact scheme and cubic spline interpolation methods to two-dimensional triangular unstructured grids. In the cubic polynomial reconstruction, a two-dimensional cubic polynomial is reconstructed using the cell averages of elements in the standard stencil and the function values at control points. The determination of function values at control points is achieved by adhering to the constraint of normal derivative continuity. This reconstruction is solved using a relaxation iteration method and has been verified to be solvable for triangular grids. Compared to other fourth-order implicit compact polynomial reconstructions, our method requires solving a smaller number of unknowns, which means less computational cost in reconstruction. By applying this method to solve the Poisson equation, the linear convection equation, and incompressible flow benchmarks, it demonstrates that the proposed method exhibits the expected high-order accuracy and performs well in incompressible flow problems.

Improved ACO algorithm fused with improved Q-Learning algorithm for Bessel curve global path planning of search and rescue robots

Addressing issues with traditional ant colony and reinforcement learning algorithms, such as low search efficiency and the tendency to produce insufficiently smooth paths that easily fall into local optima, this paper designs an improved ant colony optimization algorithm fusion with improved Q-Learning (IAC-IQL) algorithm for Bessel curve global path planning of search and rescue (SAR) robots. First, the heuristic function model in the ant colony algorithm is improved, the elite ant search strategy and the adaptive pheromone volatility factor strategy are introduced, and the initial path is searched in realize the motion environment with the help of the improved ant colony algorithm, and the initialized pheromone matrix is constructed. Second, the improved ant colony algorithm and Q-Learning (QL) algorithm are fused by utilizing the similarity between the pheromone matrix in the improved ant colony algorithm and the Q-matrix in the QL algorithm. A heuristic learning evaluation model is designed to dynamically adjust the learning factor and provide guidance for the search path. Additionally, a dynamic adaptive greedy strategy is introduced to balance the exploration and exploitation of the robot in the environment. Finally, the paths are smoothed using third-order Bessel curves to eliminate the problem of excessive steering angles. Through three sets of comparative simulation experiments conducted in Pycharm platform, the effectiveness, superiority, and practicality of the IAC-IQL algorithm were verified. The experimental results demonstrated that the IAC-IQL algorithm integrates the strong search capability of ant colony algorithm and the self-learning characteristics of QL algorithm. SAR robots equipped with the improved IAC-IQL algorithm exhibit significantly enhanced iterative search efficiency in grid simulation environment and image sampling simulation environment. The global path optimization indicators demonstrate high efficiency, and the paths are smoother.

A Power Grid Topological Error Identification Method Based on Knowledge Graphs and Graph Convolutional Networks

Precise and comprehensive model development is essential for predicting power network balance and maintaining power system analysis and optimization. The development of big data technologies and measurement systems has introduced new challenges in power grid modeling, simulation, and fault prediction. In-depth analysis of grid data has become vital for maintaining steady and safe operations. Traditional knowledge graphs can structure data in graph form, but identifying topological errors remains a challenge. Meanwhile, Graph Convolutional Networks (GCNs) can be trained on graph data to detect connections between entities, facilitating the identification of potential topological errors. Therefore, this paper proposes a method for power grid topological error identification that combines knowledge graphs with GCNs. The proposed method first constructs a knowledge graph to organize grid data and introduces a new GCN model for deep training, significantly improving the accuracy and robustness of topological error identification compared to traditional GCNs. This method is tested on the IEEE 30-bus system, the IEEE 118-bus system, and a provincial power grid system. The results demonstrate the method’s effectiveness in identifying topological errors, even in scenarios involving branch disconnections and data loss.

Automatic Differentiation Accelerated Shape Optimization Approaches to Photonic Inverse Design in FDFD/FDTD

AbstractShape optimization approaches to inverse design offer low‐dimensional, physically‐guided parameterizations of structures by representing them as combinations of primitives. However, on fixed grids, computing the gradient of a user objective via the adjoint variables method requires a product of forward/adjoint field solutions and the Jacobian of the simulation material distribution with respect to the structural shape parameters. Shape parameters often perturb global parts of the simulation grid resulting in many non‐zero Jacobian entries. These are often computed by finite‐difference (FD) in practice, and hence can be non‐trivial. In this work, the gradient calculation is accelerated by invoking automatic differentiation (AD) in instantiations of structural material distributions, enabled by the development of extensible differentiable feature‐mappings from parameters to primitives and differentiable effective logic operations (denoted AutoDiffGeo or ADG). ADG can also be used to accelerate FD‐based shape optimization by efficient boundary selection. AD‐enhanced shape optimization is demonstrated using three integrated photonic examples: a blazed grating coupler, a waveguide transition taper, and a polarization‐splitting grating coupler. The accelerations of the gradient calculation by AD relative to FD with boundary selection exceed 10, resulting in total optimization wall time accelerations of – on the same hardware with no compromise to device figure‐of‐merit.

A public grid of radiative transfer simulations for Lyman-alpha and metal lines in idealised galactic outflows

The vast majority of star-forming galaxies are surrounded by large reservoirs of gas ejected from the interstellar medium. Ultraviolet absorption and emission lines represent powerful diagnostics to constrain the cool phase of these outflows, through resonant transitions of hydrogen and metal ions. The interpretation of these observations is often remarkably difficult as it requires detailed modelling of the propagation of the continuum and emission lines in the gas. To this aim, we present a large public grid of $ 20,000$ simulated spectra that includes and five metal transitions associated with and which is accessible online. The spectra have been computed with the Monte Carlo radiative transfer code for $5,760$ idealised spherically symmetric configurations surrounding a central point source emission, and characterised by their column density, Doppler parameter, dust opacity, wind velocity, as well as various density and velocity gradients. Designed to predict and interpret and metal line profiles, our grid exhibits a wide diversity of resonant absorption and emission features, as well as fluorescent lines. We illustrate how it can help better constrain the wind properties by performing a joint modelling of observed and spectra. Using simulations and virial scaling relations, we also show that is expected to be a faithful tracer of the gas at $T 10^4-10^5$ K, even if the medium is highly ionised. While is found to probe the same range of temperatures as other metal lines merely trace cooler phases ($T 10^4$ K). As their gas opacity strongly depends on gas temperature, incident radiation field, metallicity and dust depletion, we caution that optically thin metal lines do not necessarily originate from low column densities and may not accurately probe Lyman continuum leakage.

Improving UASS pesticide application: optimizing and validating drift and deposition simulations.

As unmanned aerial spraying systems (UASS) usage grows rapidly worldwide, a critical research study was conducted to optimize the simulation of UASS applications, aiming to enhance pesticide delivery efficiency and reduce environmental impact. The study examined several key aspects for accurate simulation of UASS application with lattice Boltzmann method (LBM). Based on these discussions, the most suitable grid size and simulation parameters were selected to create a robust model for optimizing UASS performance in various pest management scenarios, potentially leading to more targeted and sustainable pest control practices. The effect of stability parameter, grid size around the rotor and near ground, and parameters at wake flow were carefully analyzed to improve the precision of pesticide drift predictions and deposition patterns. Optimal grid sizes were identified as 0.2 m generally, 0.025 m near rotors, and a 0.1 + 0.2 m scheme for ground proximity, with finer grids improving accuracy but increasing computation time. Wake resolution and threshold significantly influenced simulation results, while wake distance had minimal impact beyond a certain point. The LBM's accuracy was validated by comparing simulated downwash flow and droplet deposition with field test data. This study optimized UASS simulation parameters, balancing computational efficiency with accuracy. The validated model enhances our ability to design more effective UASS for pest management, potentially leading to more precise and targeted pesticide applications. These advancements contribute to the development of sustainable pest control strategies, aiming to reduce pesticide usage and environmental impact while maintaining crop protection efficacy. © 2024 Society of Chemical Industry.

Innovative configurations for heat sink integrated with piezoelectric fans

Piezoelectric (PE) fans are gradually replacing conventional axial flow fans in the field of heat dissipation for microelectronics due to small size and high efficiency. However, the flow characteristic of PE fans was seldom considered when arranging heat sink fins, which could not utilize the fans’ advantages. Here, the design of fin arrangement based on the transient flow field generated by the vibration of dual PE fans in the channel was calculated and analyzed by CFD simulation and dynamic grid technology. Four zones with different flow field characteristics were found: Zones 1 and 4 which located inlet and outlet of the channel were Parallel flow zone. Zone 2 with vortices corresponds to the area ranges from the waist of blades to middle section of the channel was Multiple vortices flow zone. Zone 3 with high-speed vortices and jet flows corresponds to the area ranges from middle section of the channel to halfway downstream of blades region was Diffuse flow zone. Innovative configurations of heat sinks where fins arranged at the inlet and surrounding blades were designed according to the flow field characteristics: the loss of high-speed airflow to the inlet was prevented effectively and heat dissipation in the downstream heat sinks was dispersed by fins arranging in Parallel flow zone and Multiple vortices flow zone. The development and merging of high-speed vortices were strengthened by the inhomogeneous pin-fins arranging in Diffuse flow zone. The optimal coordination between the velocity field and the temperature gradient field among innovative configurations was found based on the field coordination analysis. Heat transfer performance was enhanced by new designs where the average temperature compared to conventional heat sink with PE fans was reduced by 16.7°. Our work provided a new fins arrangement to achieve uniform temperature distribution and high heat transfer performance for heat sink integrated with PE fans and would benefit the application in thermal management of high power devices integrated with PE fans.

Research of the calculation formula for working clearance leakage flow of the hydrogen circulation pump

The hydrogen circulation pump (HCP), a device for recovering unconsumed hydrogen gas in fuel cell systems to improve efficiency, is an important equipment in fuel cell systems. Efficient calculation of the output flow rate of the HCP is crucial for accelerating the product development process, but there is a lack of an effective calculation formula for the working clearance leakage. In this paper, a series of HCP models with different working clearances are established and calculated using an overlapping grid simulation method, verifying that the traditional Roots blower leakage flow formula is feasible for HCP, although the maximum calculation error reaches 10.71%. Further study the pressure distribution law inside the HCP chamber, and revise the traditional calculation formula accordingly, so that the average calculation error of the series models is reduced from 5.75% to 3.82%, and the maximum error is also reduced to 7.73%. Compared with the prototype test data, though the flow values obtained from the two calculation formulas are slightly higher, the calculation error of the correction formula is relatively smaller. The research results indicate that the correction formula can more accurately predict the flow rate of HCP and has important application value.

Randomized Cholesky Factorization With Threshold-Based Multisampling for Power Grid Simulation

Randomized Cholesky Factorization With Threshold-Based Multisampling for Power Grid Simulation

High proportion of new energy power system frequency and voltage support and regulation technology based on doubly fed induction generator

Abstract With the increasing proportion of new energy in power systems, issues regarding system voltage, inertia, and primary frequency regulation have become increasingly severe. In response to control and grid safety issues, a high proportion of new energy power system frequency and voltage support and regulation technology based on a doubly fed induction generator (DFIG) is proposed. The voltage-magnetic link equation of DFIG is theoretically derived, and the control principles of active and reactive power of DFIG are proposed. The support and regulation characteristics of improving the voltage of new energy substations are studied. Verification is conducted through large-scale grid simulation and on-site measurements.

Low voltage distribution intelligent control system based on distributed photovoltaic grid connection

Abstract This article mainly studied the use of distributed photovoltaic power generation technology to achieve intelligent control of low-voltage distribution networks. With the rapid development of new energy, distributed photovoltaic power generation technology has become an important means to alleviate the current energy crisis and environmental problems. This article first elaborated on the theory and research status of distributed photovoltaic power generation systems. Based on this, artificial intelligence technology was applied to distributed photovoltaic power generation systems for real-time monitoring to enhance their stable and reliable performance. When the simulated grid voltage was set to 220V, the system output voltage was also 220V, showing a deviation of 0.0%, proving that the system has excellent regulation accuracy at this voltage level. This article is an innovative research that integrates distributed photovoltaic power generation with intelligent control, opening up a practical and feasible technical path for the large-scale utilization of clean energy. Therefore, this study has significant academic value and practical significance.

Geochemical anomaly separation based on geology, geostatistics, compositional data and local singularity analyses: A case study from the kuh panj copper deposit, Iran

This study combines geochemical anomaly separation with geostatistical approaches and compositional data analysis. To have a reasonable model for abnormal areas, suggesting the optimal drilling coordinates, one needs to consider some effective geological controllers on the mineralization such as lithology, alteration, and tectonic processes. The lithogeochemical samples at 608 locations were processed from the copper porphyry mineralization area of Kuh Panj, Iran, to illustrate the proposed approach. The heterogeneity of rock type domains was detected using contact analysis. Based on this, plurigaussian simulation made it possible to model the uneconomic dykes of the area. Copper-molybdenum grades and filler as geochemical components were transformed into multivariate normal data using isometric logarithmic ratio-flow anamorphosis. Hierarchical rock type-grade simulation reproduced the grade behavior at hard rock type boundaries. Selection of a dense and regular simulation grid minimized the smoothing effect caused by interpolation of the singularity index values. Finally, unsupervised machine learning identified the anomaly zone by clustering the results of the singularity analysis. Validation using drilling data and the geological map shows that the anomaly separation, considering the geological conditions and the compositional nature of the geochemical data, can provide a reliable model of the geochemical anomalies in the Kuh Panj deposit.

DC fault current clearance coordinated control strategy for DC grid with hybrid MMC

For DC grid based on the hybrid modular multilevel converter (MMC), the traditional DC fault current clearance scheme takes a long time and greatly affects the active power transmission. A novel DC fault current clearance coordinated control strategy is proposed, which can quickly interrupt the fault current and reduce the impact of DC fault on the DC grid and AC system. For fault-line connected MMC (FLMMC), a negative DC voltage control is employed, which can improve the current attenuation speed and ensure reliable fault isolation. For non-fault-line connected MMC (NFLMMC), the active current-limiting control (ACLC) based on the virtual reactor is adopted, which can reduce the current flow to the fault location and further shorten fault isolation time. To reduce the impact on the AC system during the DC fault, a short-time active power support control is designed. Finally, a four-terminal DC grid simulation model is built based on the RT-LAB OP5600 real-time digital simulation platform. The simulation results show that the proposed coordinated control strategy for the DC grid can quickly clear DC fault current, shorten DC fault isolation time, and strengthen active power support capability.