Investigation of heat flux deposition on divertor target on the Large Helical Device with EMC3-EIRENE modelling

The measured divertor heat flux profiles are compared to the EMC3-EIRENE simulations for two different times of an LHD discharge, corresponding to higher and lower edge temperatures. The relation between the three-dimensional magnetic field structure and the heat flux distributions on the divertor has been analysed. The modelled heat flux for the lower plasma temperature case has a better agreement with the experimental result obtained by the Langmuir probes, which shows a qualitative reproduction of the experimental profile shape. However, the heat flux distribution for the high plasma temperature case shows a different behaviour between the simulation results and the experimental measurements. The detailed analysis of the heat flux distribution for the higher temperature case which has a larger discrepancy has been performed, both quantitatively and qualitatively. The radiation of the eroded impurity from divertor target plates has a minor effect on the heat flux distribution. Non-uniform cross-field transport coefficients are used in the simulations and its impact on the heat flux distributions is discussed for the case of the high plasma temperature

Vertical profiles and two-dimensional distributions of carbon line emissions from C2+−C5+ ions in attached and RMP-assisted detached plasmas of large helical device

In Large Helical Device (LHD), the detached plasma is obtained without external impurity gas feed by supplying an m/n = 1/1 resonant magnetic perturbation (RMP) field to a plasma with an outwardly shifted plasma axis position of Rax = 3.90 m where the magnetic resonance exists in the stochastic magnetic field layer outside the last closed flux surface. The plasma detachment is triggered by the appearance of an m/n = 1/1 island when the density, increased using hydrogen gas feed, exceeds a threshold density. The behavior of intrinsically existing impurities, in particular, carbon originating in the graphite divertor plates, is one of the important key issues to clarify the characteristic features of the RMP-assisted plasma detachment although the particle flux still remains on some divertor plates even in the detachment phase of the discharge. For this purpose, vertical profiles and two-dimensional (2-D) distributions of edge carbon emissions of CIII to CVI have been measured at extreme ultraviolet wavelength range, and the results are compared between attached and RMP-assisted detached plasmas. It is found that the CIII and CIV emissions located in the stochastic magnetic field layer are drastically increased near the m/n = 1/1 island O-point and in the vicinity of both inboard and outboard edge separatrix X-points during the RMP-assisted detachment, while those emissions are only enhanced in the vicinity of the outboard edge X-point in attached plasmas without RMP. The result clearly indicates a change in the magnetic field lines connecting to the divertor plates, which is caused by the growth of the m/n = 1/1 edge magnetic island. In contrast, the intensity of CVI emitted radially inside the magnetic island significantly decreases during the detachment, suggesting an enhancement of the edge impurity screening. The measured carbon distribution is analyzed with a three-dimensional edge plasma transport simulation code, EMC3-EIRENE, for the attached plasmas without RMP. It is found that the narrow strip-shaped impurity trace emitted along the edge X-point and its width are sensitive to the cross-field impurity diffusion coefficient, DZ⊥. As a result, the value of DZ⊥ of C3+ ions is evaluated to be 20 times larger than that of the bulk ions in the Rax = 3.90 m configuration, while the reason is unclear at present. The measured 2-D carbon distribution is also discussed and compared to the structure of the m/n = 1/1 magnetic island, which quickly expanded during the appearance of the plasma detachment

Experimental observations and modelling of radiation asymmetries during N2 seeding in LHD

N2 gas has been seeded in the Large Helical Device (LHD) to reduce the divertor heat load through enhanced radiation. Radiation is observed by two imaging bolometers, viewing the same poloidal cross-section from top and bottom ports, at a location which is 36° toroidally removed from the N2 gas puff nozzle located at the bottom of the machine. During N2 seeding, these measurements both confirm that additional radiation from the outboard side is coming exclusively from the top of the cross-section, indicating up/down asymmetry, which is also reproduced by modelling with EMC3-EIRENE using a half torus model. In addition, a toroidally localized, magnetic field direction-dependent radiation enhancement is observed with N2 seeding, but is not reproducible by the model

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Forecasting of Power Grid Investment in China Based on Support Vector Machine Optimized by Differential Evolution Algorithm and Grey Wolf Optimization Algorithm

In recent years, the construction of China’s power grid has experienced rapid development, and its scale has leaped into the first place in the world. Accurate and effective prediction of power grid investment can not only help pool funds and rationally arrange investment in power grid construction, but also reduce capital costs and economic risks, which plays a crucial role in promoting power grid investment planning and construction process. In order to forecast the power grid investment of China accurately, firstly on the basis of analyzing the influencing factors of power grid investment, the influencing factors system for China’s power grid investment forecasting is constructed in this article. The method of grey relational analysis is used for screening the main influencing factors as the prediction model input. Then, a novel power grid investment prediction model based on DE-GWO-SVM (support vector machine optimized by differential evolution and grey wolf optimization) algorithm is proposed. Next, two cases are taken for empirical analysis to prove that the DE-GWO-SVM model has strong generalization capacity and has achieved a good prediction effect for power grid investment forecasting in China. Finally, the DE-GWO-SVM model is adopted to forecast power grid investment in China from 2018 to 2022

Comprehensive Evaluation of the Sustainable Development of Power Grid Enterprises Based on the Model of Fuzzy Group Ideal Point Method and Combination Weighting Method with Improved Group Order Relation Method and Entropy Weight Method

As an important implementing body of the national energy strategy, grid enterprises bear the important responsibility of optimizing the allocation of energy resources and serving the economic and social development, and their levels of sustainable development have a direct impact on the national economy and social life. In this paper, the model of fuzzy group ideal point method and combination weighting method with improved group order relation method and entropy weight method is proposed to evaluate the sustainable development of power grid enterprises. Firstly, on the basis of consulting a large amount of literature, the important criteria of the comprehensive evaluation of the sustainable development of power grid enterprises are preliminarily selected. The opinions of the industry experts are consulted and fed back for many rounds through the Delphi method and the evaluation criteria system for sustainable development of power grid enterprises is determined, then doing the consistent and non dimensional processing of the evaluation criteria. After that, based on the basic order relation method, the weights of each expert judgment matrix are synthesized to construct the compound matter elements. By using matter element analysis, the subjective weights of the criteria are obtained. And entropy weight method is used to determine the objective weights of the preprocessed criteria. Then, combining the subjective and objective information with the combination weighting method based on the subjective and objective weighted attribute value consistency, a more comprehensive, reasonable and accurate combination weight is calculated. Finally, based on the traditional TOPSIS method, the triangular fuzzy numbers are introduced to better realize the scientific processing of the data information which is difficult to quantify, and the queuing indication value of each object and the ranking result are obtained. A numerical example is taken to prove that the model of fuzzy group ideal point method and combination weighting method with improved group order relation method and entropy weight method is feasible and effective for evaluating the sustainable development of power grid enterprises

A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis

As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational Analysis-Modified Particle Swarm Optimization-Least Squares Support Vector Machine (EMD-GRA-MPSO-LSSVM) load forecasting model is proposed. The model uses the de-noising method combining empirical mode decomposition and grey relational analysis to process the original load series and forecasts the processed subsequences by the algorithm of modified particle swarm optimization and least square support vector machine. Then, the final forecasting results can be obtained after reconstructing the forecasting series. This paper takes the Jibei area as an example to produce an empirical analysis for load forecasting. The model input includes the hourly load one week before the forecasting day and the daily maximum temperature, daily minimum temperature, daily average temperature, relative humidity, wind force, date type of the forecasting day. The model output is the hourly load of the forecasting day. The models of BP neural network, SVM (Support vector machine), LSSVM (Least squares support vector machine), PSO-LSSVM (Particle swarm optimization-Least squares support vector machine), MPSO-LSSVM (Modified particle swarm optimization-Least squares support vector machine), EMD-MPSO-LSSVM are selected to compare with the model of EMD-GRA-MPSO-LSSVM using the same sample. The comparison results verify that the short-term load forecasting model of EMD-GRA-MPSO-LSSVM proposed in this paper is superior to other models and has strong generalization ability and robustness. It can achieve good forecasting effect with high forecasting accuracy, providing a new idea and reference for accurate short-term load forecasting

Comprehensive Evaluation of the Sustainable Development of Power Grid Enterprises Based on the Model of Fuzzy Group Ideal Point Method and Combination Weighting Method with Improved Group Order Relation Method and Entropy Weight Method

As an important implementing body of the national energy strategy, grid enterprises bear the important responsibility of optimizing the allocation of energy resources and serving the economic and social development, and their levels of sustainable development have a direct impact on the national economy and social life. In this paper, the model of fuzzy group ideal point method and combination weighting method with improved group order relation method and entropy weight method is proposed to evaluate the sustainable development of power grid enterprises. Firstly, on the basis of consulting a large amount of literature, the important criteria of the comprehensive evaluation of the sustainable development of power grid enterprises are preliminarily selected. The opinions of the industry experts are consulted and fed back for many rounds through the Delphi method and the evaluation criteria system for sustainable development of power grid enterprises is determined, then doing the consistent and non dimensional processing of the evaluation criteria. After that, based on the basic order relation method, the weights of each expert judgment matrix are synthesized to construct the compound matter elements. By using matter element analysis, the subjective weights of the criteria are obtained. And entropy weight method is used to determine the objective weights of the preprocessed criteria. Then, combining the subjective and objective information with the combination weighting method based on the subjective and objective weighted attribute value consistency, a more comprehensive, reasonable and accurate combination weight is calculated. Finally, based on the traditional TOPSIS method, the triangular fuzzy numbers are introduced to better realize the scientific processing of the data information which is difficult to quantify, and the queuing indication value of each object and the ranking result are obtained. A numerical example is taken to prove that the model of fuzzy group ideal point method and combination weighting method with improved group order relation method and entropy weight method is feasible and effective for evaluating the sustainable development of power grid enterprises

Comprehensive Evaluation for Operating Efficiency of Electricity Retail Companies Based on the Improved TOPSIS Method and LSSVM Optimized by Modified Ant Colony Algorithm from the View of Sustainable Development

The electricity market of China is currently in the process of a new institutional reform. Diversified electricity retail entities are gradually being established with the opening of the marketing electricity side. In the face of a complex market environment and fierce competition, the operating efficiency can directly reflect the current market position and development of electricity retail companies. TOPSIS method can make full use of the information of original data, calculate the distance between evaluated objects and the ideal solutions and get the relative proximity, which is generally used in the overall department and comprehensive evaluation of the benefits. Least squares support vector machine (LSSVM), with high convergence precision, helps save the training time of algorithm by solving linear equations and is used to predict the comprehensive evaluation value. Considering the ultimate goal of sustainable development, a comprehensive evaluation model on operating efficiency of electricity retail companies based on the improved TOPSIS method and LSSVM optimized by modified ant colony algorithm is proposed in this paper. Firstly, from the view of sustainable development, an operating efficiency evaluation indicator system is constructed. Secondly, the entropy weight method is applied to empower the indicators objectively. After that, based on the improved TOPSIS method, the reverse problem in the evaluation process is eliminated. According to the relative proximity between the evaluated objects and the absolute ideal solutions, the scores of comprehensive evaluation for operating efficiency can then be ranked. Finally, the LSSVM optimized by modified ant colony algorithm is introduced to realize the simplified expert scoring process and fast calculation in the comprehensive evaluation process, and its improved learning and generalization ability can be used in the comprehensive evaluation of similar projects. The example analysis proves that the comprehensive evaluation model proposed in this paper can provide scientific and effective evaluation results of the operating efficiency of electricity retail companies