• Bulletin of the Korean Chemical Society
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• v.32 no.12
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• pp.4199-4204
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• 2011

Using models simulating the environment of two distinct adsorption sites of $H_2$ in metal-organic framework-5 (MOF-5), binding energies of $H_2$ to MOF-5 were evaluated at the MP2 and CCSD(T) level. For organic linker section modeled as dilithium 1,4-benzenedicarboxylate ($C_6H_4(COO)_2Li_2$), the MP2 and CCSD(T) basis set limit binding energies are estimated to be 5.1 and 4.4 kJ/mol, respectively. For metal oxide cluster section modeled as $Zn_4O(CO_2H)_6$, while the MP2 basis set limit binding energy estimate amounts to 5.4 kJ/mol, CCSD(T) correction to the MP2 results is shown to be insignificant with basis sets of small size. Substitution of benzene ring with pyrazine ring in the model for the organic linker section in MOF-5 is shown to decrease the $H_2$ binding energy noticeably at both the MP2 and CCSD(T) level, in contrast to the previous study based on DFT calculation results which manifested substantial increase of $H_2$ binding energies upon substitution of benzene ring with pyrazine ring in the similar model.

• Advances in nano research
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• v.12 no.1
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• pp.25-35
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• 2022

Within the framework of the density functional theory combined with the method of non-equilibrium Green's functions (DFT + NEGF), the features of electron transport in fullerene nanojunctions, which are «core-shell» nanoobjects made of a combination of fullerenes of different diameters C₂₀, C₈₀, C₁₈₀, placed between gold electrodes (in a nanogap), are studied. Their transmission spectra, the density of state, current-voltage characteristics and differential conductivity are determined. It was shown that in the energy range of -0.45-0.45 eV in the transmission spectrum of the "Au-C₁₈₀-Au" nanojunction appears a HOMO-LUMO gap with a width of 0.9 eV; when small-sized fullerenes C₂₀, C₈₀ are intercalation into the cavity C₁₈₀ the gap disappears, and a series of resonant structures are observed on their spectra. It has been established that distinct Coulomb steps appear on the current-voltage characteristics of the "Au-C₁₈₀-Au" nanojunction, but on the current-voltage characteristics "Au-C₈₀@C₁₈₀-Au", "Au-(C₂₀@C₈₀)@C₁₈₀-Au" these step structures are blurred due to a decrease in Coulomb energy. An increase in the number of Coulomb features on the dI/dV spectra of core-shell fullerene nanojunctions was revealed in comparison with nanojunctions based on fullerene C₆₀, which makes it possible to create high-speed single-electron devices on their basis. Models of single-electron transistors (SET) based on fullerene nanojunctions "Au-C₁₈₀-Au", "Au-C₈₀@C₁₈₀-Au" and "Au-(C₂₀@C₈₀)@C₁₈₀-Au" are considered. Their charge stability diagrams are analyzed and it is shown that SET based on C₈₀@C₁₈₀-, (C₂₀@C₈₀)@C₁₈₀- nanojunctions is output from the Coulomb blockade mode with the lowest drain-to-source voltage.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1489-1500
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• 2018

Partial shading analysis of large-scale photovoltaic (PV) arrays has recently become a theoretically and numerically challenging issue, and it is necessary for PV system designers. The main contributions of this study are the following: 1) A PSIM-based macro-model was employed because it is remarkably fast, has high precision, and has no convergence issues. 2) Three types of equivalent macro-models were developed for the transformation of a small PV sub-array with uniform irradiance to a new macro-model. 3) On the basis of the proposed new macro-model, a tearing method was established, which can divide a large-scale PV array into several small sub-arrays to significantly improve the efficiency improvement of a simulation. 4) Three platforms, namely, PSIM, PSpice, and MATLAB, were applied to evaluate the proposed tearing method. The proposed models and methods were validated, and the value of this research was highlighted using an actual large-scale PV array with 2420 PV modules. Numerical simulation demonstrated that the tearing method can remarkably improve the simulation efficiency by approximately thousands of times, and the method obtained a precision of nearly 6.5%. It can provide a useful tool to design the optimal configuration of a PV array with a given shading pattern as much as possible.

• Structural Engineering and Mechanics
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• v.36 no.5
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• pp.599-624
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• 2010

Offshore wind turbines are relatively complex structural and mechanical systems located in a highly demanding environment. In the present paper the fundamental aspects and the major issues related to the design of these special structures are outlined. Particularly, a systemic approach is proposed for a global design of such structures, in order to handle coherently their different parts: the decomposition of these structural systems, the required performance and the acting loads are all considered under this philosophy. According to this strategy, a proper numerical modeling requires the adoption of a suitable technique in order to organize the qualitative and quantitative assessments in various sub-problems, which can be solved by means of sub-models at different levels of detail, for both structural behavior and loads simulation. Specifically, numerical models are developed to assess the safety performances under aerodynamic and hydrodynamic actions. In order to face the problems of the actual design of a wind farm in the Mediterranean Sea, in this paper, three schemes of turbines support structures have been considered and compared: the mono pile, the tripod and the jacket support structure typologies.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1708-1717
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• 2023

The grade analysis of lead-zinc ore is the basis for the optimal development and utilization of deposits. In this study, a method combining Prompt Gamma Neutron Activation Analysis (PGNAA) technology and machine learning is proposed for lead-zinc mine borehole logging, which can identify lead-zinc ores of different grades and gangue in the formation, providing real-time grade information qualitatively and semi-quantitatively. Firstly, Monte Carlo simulation is used to obtain a gamma-ray spectrum data set for training and testing machine learning classification algorithms. These spectra are broadened, normalized and separated into inelastic scattering and capture spectra, and then used to fit different classifier models. When the comprehensive grade boundary of high- and low-grade ores is set to 5%, the evaluation metrics calculated by the 5-fold cross-validation show that the SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naive Bayes) and RF (Random Forest) models can effectively distinguish lead-zinc ore from gangue. At the same time, the GNB model has achieved the optimal accuracy of 91.45% when identifying high- and low-grade ores, and the F₁ score for both types of ores is greater than 0.9.

• Korean Chemical Engineering Research
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• v.61 no.2
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• pp.328-339
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• 2023

Methanol steam reforming (MSR) is a promising method for hydrogen supplying as a critical step in hydrogen fuel cell commercialization in mobile applications. Modelling and understanding of the reactor behavior is an attractive research field to develop an efficient reformer. Three-layer feed-forward artificial neural network (ANN) and Box-Behnken design (BBD) were used to modelling of MSR process using the Cu-SiO₂ aerogel catalyst. Furthermore, impacts of the basic operational variables and their mutual interactions were studied. The results showed that the most affecting parameters were the reaction temperature (56%) and its quadratic term (20.5%). In addition, it was also found that the interaction between temperature and Steam/Methanol ratio is important on the MSR performance. These models precisely predict MSR performance and have great agreement with experimental results. However, on the basis of statistical criteria the ANN technique showed the greater modelling ability as compared with statistical BBD approach.

• Asian Journal of Atmospheric Environment
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• v.10 no.2
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• pp.67-79
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• 2016

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 greatest 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 artificial 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 $PM_{2.5}$ concentrations. 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 coefficient 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 neural network, the MBE was 0.0546 which indicates the adequacy of the model. In the RBF neural network, 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 acceptable accuracy and precision. We concluded the probability of occurrence state duration and transition of $PM_{2.5}$ pollution is predictable using a Markov chain method.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.6
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• pp.817-826
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• 2022

This study was conducted to determinean optimal soaking solution for inhibiting the texture softening of shrimp Litopenaeus vannamei pressured at high temperature (S-P/HT) in a retort, and also to optimize concentrations of 0.5% calcium chloride (CC) and 5.0% potato starch (PS) for preparation of a mixed solution (MS) and soaking time (ST) in the MS. On the basis of the results of expressible drip (4.6%), water holding capacity (95.1%), hardness (18.4 N/cm²) and sensory texture (7.2 score), the MS was found to be the optimal soaking solution for inhibition of texture softening under S-P/HT conditions, The concentrations of CC (X₁, %), PS (X₂, %), and ST (X₃, min) were selected as independent variables, and hardness (Y₁), springiness (Y₂) and sensory texture (Y₃) were selected as dependent variables. The optimal conditions of X₁, X₂, and X₃ were 0.51%, 6.34%, and 364 min, respectively. Under the optimal conditions, the experimental values of Y₁, Y₂ and Y₃ were 18.3±0.8 N/cm², 4.4±0.3 mm and 7.7±0.2, respectively, which did not diffr significantly from the predicted values (P>0.05). In conclusion, the optimized models of X₁, X₂, and X₃ for the preparation of S-P/HT using CC-PS were suitably fitted.

• Journal of Bio-Environment Control
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• v.23 no.3
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• pp.181-191
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• 2014

Modern commercial greenhouse requires the use of advanced climate control system to improve crop production and to reduce energy consumption. As an alternative to classical sensor-based control method, this paper introduces a model-based control method that consists of two models: the predictive model and the evaluation model. As a first step, this paper presents straightforward models to predict the effect of natural ventilation in a greenhouse according to meteorological factors, such as outdoor air temperature, soil temperature, solar radiation and mean wind speed, and structural factor, opening rate of roof ventilators. A multiple regression analysis was conducted to develop the predictive models on the basis of data obtained by computational fluid dynamics (CFD) simulations. The output of the models are air temperature drops due to ventilation at 9 sub-volumes in the greenhouse and individual volumetric ventilation rate through 6 roof ventilators, and showed a good agreement with the CFD-computed results. The resulting predictive models have an advantage of ensuring quick and reasonable predictions and thereby can be used as a part of a real-time model-based control system for a naturally ventilated greenhouse to predict the implications of alternative control operation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.151-162
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• 1994

A new neural network structure called Self-organized Distributed Networks (SODN) is proposed for developing the neural network-based multidimensional system models. The learning with the proposed networks is fast and precise. Such properties are caused from the local learning mechanism. The structure of the networks is combination of dual networks such as self-organized networks and multilayered local networks. Each local networks learns only data in a sub-region. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the proposed networks. The simulation results of the proposed networks show better performance than the standard multilayer neural networks and the Radial Basis function(RBF) networks.