The JET 2019-2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major Neutral Beam Injection (NBI) upgrade providing record power in 2019-2020, and tested the technical & procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle physics in the coming D-T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed Shattered Pellet Injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design & operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D-T benefited from the highest D-D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER.
Alpha particles with energies on the order of megaelectronvolts will be the main source of plasma heating in future magnetic confinement fusion reactors. Instead of heating fuel ions, most of the energy of alpha particles is transferred to electrons in the plasma. Furthermore, alpha particles can also excite Alfvénic instabilities, which were previously considered to be detrimental to the performance of the fusion device. Here we report improved thermal ion confinement in the presence of megaelectronvolts ions and strong fast ion-driven Alfvénic instabilities in recent experiments on the Joint European Torus. Detailed transport analysis of these experiments reveals turbulence suppression through a complex multi-scale mechanism that generates large-scale zonal flows. This holds promise for more economical operation of fusion reactors with dominant alpha particle heating and ultimately cheaper fusion electricity.
The forecasting of air pollution is an important and popular topic in environmental engineering. Due to health impacts caused by unacceptable particulate matter (PM) levels, it has become one of the great est concerns in metropolitan cities like Karaj City in Iran. In this study, the concentration of PM 2.5 was predicted by applying a multilayer percepteron (MLP) neural network, a radial basis function (RBF) neural network and a Markov chain model. Two months of hourly data including temperature, NO, NO 2 , NO x , CO, SO 2 and PM 10 were used as inputs to the artifi cial neural networks. From 1,488 data, 1,300 of data was used to train the models and the rest of the data were applied to test the models. The results of using artificial neural networks indicated that the models performed well in predicting PM 2.5 concen trations. The application of a Markov chain described the probable occurrences of unhealthy hours. The MLP neural network with two hidden layers including 19 neurons in the first layer and 16 neurons in the second layer provided the best results. The coeffi cient of determination (R 2 ), Index of Agreement (IA) and Efficiency (E) between the observed and the predicted data using an MLP neural network were 0.92, 0.93 and 0.981, respectively. In the MLP neu ral network, the MBE was 0.0546 which indicates the adequacy of the model. In the RBF neural net work, increasing the number of neurons to 1,488 caused the RMSE to decline from 7.88 to 0.00 and caused R 2 to reach 0.93. In the Markov chain model the absolute error was 0.014 which indicated an ac ceptable accuracy and precision. We concluded the probability of occurrence state duration and transi tion of PM 2.5 pollution is predictable using a Markov chain method.
The complicated non‐linear relationships between water quality and environmental parameters involved in predicting algal blooms necessitate a new approach, using data‐driven modelling. Accordingly, a multilayer perceptron (MLP) and time delay neural network (TDNN) were used to predict the eutrophication status of two monitoring stations in the Amirkabir Reservoir in Iran. Six scenarios for each monitoring station were performed to select a significant, independent input using 12 years of monthly data. The final inputs were temperature, turbidity, phosphate (PO4), nitrate (NO3), nitrite (NO2), ammonium (NH3), dissolved oxygen (DO) and electrical conductivity (EC). Applying an MLP neural network to the upstream monitoring station with 21–38 neurons in the first and second hidden layers, the minimum mean squared errors (MSE) in training, validating and testing were 0.083, 0.81 and 1.95 cells/100 ml, respectively. Further, when the TDNN network was used with the same neuron numbers in the hidden layer for the similar monitoring station, the minimum MSE values for model training, validating and testing were 0.06, 0.72 and 1.76 cells/100 ml, respectively. For the Beylaghan monitoring station, using the MLP neural network with 29–23 neurons in the first and second hidden layer, the minimum MSE values gained in training, validating and testing were 0.181, 0.58 and 0.95 cells/100 ml, respectively. Using the TDNN network with the same neurons in the hidden layers of the MLP neural network for the station, the minimum MSE values for training, validating and testing were 0.152, 0.43 and 0.84 cells/100mL, respectively. Thus, TDNN exhibited a high accuracy and workability, compared to the MLP. Sensitivity analysis of the Amirkabir Reservoir dataset indicated increasing the value of nitrate is the first factor, followed by turbidity and NH3, having the greatest impacts on eutrophication prediction.
Several models have been proposed to analyze and predict levels of pollutants in the ambient air.The artificial neural network (ANN) is one of these methods. ANN, which is a branch of artificial intelligence, has spread among researchers both because of its nonlinear mathematical structure and its ability to make accurate predictions. We compared the abilities of two different types of ANN, the multilayer perceptron (MLP) and the radial basis function (RBF), to forecast particulate matter (PM) with diameters of 2.5 microns or less (PM 2.5 ) based on meteorological data from District 20 of the municipality of Tehran (Shahre Ray City). The input data were hourly air temperature, wind speed, and percent humidity, and output was PM 2.5 concentrations. The mean bias error (MBE) of the results were 0.0503 and 0.0032 in the MLP and RBF networks, respectively. The coefficient of determination (R 2 ) and the index of agreement (IA) between the observed data and the predicted data were 0.954 and 0.987, respectively, for the MLP, whereas for the RBF the R 2 was 0.99 and the IA was 0.998. Sensitivity analysis performed for the MLP indicated that percent humidity is the most important factor in the prediction of PM 2.5 . (1995) predicted the ozone (O 3 ) levels at five stations in Mexico City using a neural network and nonlinear regression. Tasadduq, Rehman, and Bubshait (2002) applied an MLP neural network to predict the hourly average temperature in Jedah, Saudi Arabia. The results of their study indicated the suitability of their learning algorithm, which was "backpropagation," or "backward propagation of errors." Zickus, Greig, and Niranjan (2002) used four machine learning methods, including logistic regression, ANN, multivariate regression, and decision trees, to predict daily concentrations of PM 10 in the air in Finland. Zickus et al.'s results indicated that for predicting air pollution levels, the first three methods provided favorable performances compared to the decision tree method. Kukkonen et al. (2003) applied an ANN, PM, along with other air pollutants, pose serious hazards to human health.
This experimental study aimed to evaluate the potential of cold atmospheric plasma jet to deactivate Escherichia coli from drinking water. We studied the effect of the volume of water samples on the performance of plasma jet on deactivation of E. coli of 1, 500, 1,000, 1,500, and 2,000 cubic centimetres. The results of deactivation of E. coli in 500 and 1,000 cc water samples were the same as one cc of a water sample and we observed 8-log reduction of E. coli using 50 W. In 1,500 and 2,000 cc water samples at 8 min using a power of 50 W, 4.5 and 2.9 log reduction of E. coli was achieved and while we used 20 W, 2.5 and 1.8 log reduction of E. coli bacteria was performed. This indicated that the increasing volume of water above 1,500 cc caused the reduction of the efficiency of E. coli removal. Also, increasing power caused to increase E. coli removal efficiency. In addition, we monitored changes in pH values and temperature during experiments. Using 20 W, the temperature was increased (natural temperature of the water was 22 • C) 2 • C after 8 min while applying 50 W, the temperatures were raised 5 • C. pH of the water after 8 min in the 1,000 cc water sample, with an input power of 20 W, decreased from 7.1 to 5.5; while the input power was 50 W, pH changed from 7.1 to 4.3. With an increase in plasma irradiation time, the number of E. coli had a significant decrease per min while using in samples of 1 cc. After 8 min, we observed 4-log reductions of E. coli with the input power of 20 W and 8-log reduction of bacteria with the input power of 50 W. In 1,500 and 2,000 cc of water samples using plasma radiation for 8 min, 2.5 log and 1.8 log reduction of E. coli was achieved, respectively. This means that an increasing volume of water above 1,500 cc needs more power and time to deactivate E. coli from the water.
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