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In remote sensing problems, broadband scattering investigation becomes increasingly important than ever before. The scattering characteristic changes significantly with the variation of range. Thus, the conventional evaluation method becomes invalid in many situations. Meanwhile, an efficient method for the simulation of electromagnetic wave propagation in electrically large remote sensing problem is also an urgently demanding. In order to alleviate such problems, an alternative Crank-Nicolson Direct-Splitting algorithm is proposed in open regions. To infinite extension of the computational domain, convolutional perfectly match layer with higher order formulation is proposed not only to further enhance the absorption at low-frequency band but also to decrease the reflections during the entire time domain simulation. Efficient method is proposed for scattering characteristics evaluation which ranges from near-field to far-field. Radar remote sensing Problems including ground penetrating radar and early warning radar are introduced to demonstrate the effectiveness of the algorithm. Through the results, it can be concluded that the proposed algorithm shows considerable performance in the simulation of remote sensing problems.

With the popularity of deep learning (DL), more and more studies are focusing on replacing time-consuming numerical simulations with efficient surrogate models to predict the production of multi-stage fractured horizontal wells. Previous studies on constructing surrogate models for the prediction of the production of fractured horizontal wells often relied on directly applying existing deep learning architectures without incorporating physical constraints into the model. When dealing with the large number of variables necessary for characterizing the properties of fractures, the input variables of proxy models are often oversimplified; meanwhile, lots of physical information is lost. Consequently, predictions are sometimes physically inconsistent with the underlying principles of the domain. In this study, by modifying the traditional Seq2Seq (LSTM–LSTM) deep learning architecture, a physics-informed encoder–decoder (PIED) architecture was developed to surrogate the numerical simulation codes for predicting the production of horizontal wells with unequal-length intersecting hydraulic fractures on a 2D plane. The encoder is a LSTM network, and the decoder consists of LSTM and fully connected layers. The attention algorithm is also applied in the Seq2Seq architecture. The PIED model’s encoder is capable of extracting the physical information related to fractures. And the attention module effectively passes on the most relevant physical information related to production to the decoder during the training process. By modifying Seq2Seq architecture, the decoder of the PIED incorporates the intermediate input, which is the constant production time, along with the extracted physical information to predict production values. The PIED model excels in extracting sufficient physical information from high-dimensional inputs while ensuring the integrity of the production time information. By considering the physical constraints, the model predicts production values with improved accuracy and generalization capabilities. In addition, a multi-layer perceptron (MLP) which is broadly used as a proxy model; a regular Seq2Seq model (LSTM–Attention–LSTM); and the PIED were compared via a case study, and their MAE values were shown to be 241.76, 184.07, 168.81, respectively. Therefore, the proposed model has higher accuracy and better generalization ability. In the case study, a comparative experiment was conducted by comparing LSTM–MLP (with an MAE of 221.50) and LSTM–LSTM to demonstrate that using LSTM as the decoder structure is better for predicting production series. Moreover, in the task of predicting production sequences, LSTM outperforms MLP. The Seq2Seq architecture demonstrated excellent performance in this problem, and it achieved a 48.4% reduction in MSE compared to MLP. Meanwhile, the time cost for build datasets was considered, and the proposed model was found to be capable of training in a small dataset (e.g., in the case study, 3 days were used to generate 450 samples for training.); thus, the proposed model has a certain degree of practicality.

For the simulation of gyrotropic plasma structure with fine geometry details along a single direction in open regions, the hybrid implicit-explicit (HIE) algorithm with improved absorbing boundary condition is modified based on the finite-difference time-domain (FDTD) algorithm. More precisely, incorporating the perfectly matched layer (PML) formulation, the higher order concept and the HIE procedure, the proposed implementation shows its significant superiority in terms of efficiency, accuracy and absorption. To simulate the unique anisotropic characteristic inside the gyrotropic plasma regions, the bilinear transform (BT) method is employed during the calculation. The effectiveness can be further demonstrated theoretically and numerically. Through the results, it can be concluded that the proposed scheme exhibits remarkable behaviors in the gyrotropic plasma open region problems. It can not only obtain considerable accuracy compared with the theoretical solution but also receive better absorption and efficiency compared with the published work. In addition, the results also indicate that the proposed scheme can maintain the stability of the algorithm with dissatisfaction of the Courant-Friedrichs-Levy condition between time step and mesh size.

By incorporating higher order formulation with perfectly matched layer (PML) implementation, unconditionally stable computationally efficient locally one-dimensional (LOD) algorithm with improved absorption is proposed for simulating ground penetrating radar and its open region problems in finite-difference time-domain (FDTD) algorithm. To take advantages of these methods, the proposed implementation shows characteristics of maintaining considerable accuracy, enhancing computational efficiency and improving the entire absorption. These above-mentioned advantages are illustrated through the ground penetrating radar related problem in open regions. It can be concluded from the calculation results that the proposal shows advantages of higher order formulation, PML implementation and LOD procedure which are efficient in remarkable accuracy, considerable efficiency and improved absorption with increment of CFLNs. Meanwhile, the simulation results also indicate that the proposal is an unconditionally stable procedure with the dissatisfaction of Courant-Friedrichs-Levy stability condition.

Low-pressure discharge causes air ionization resulting in performance degeneration or failure for the satellite sensors in outer space. Here, a one-step Crank-Nicolson Direct-Splitting (CNDS) algorithm is proposed to evaluate the electrical behavior of satellite sensors under the low-pressure discharge circumstance. To be more specific, the CNDS algorithm is proposed in the Lorentz medium, which can accurately analyze the ionized air and generated plasma. Higher order perfectly matched layer (PML) is modified in the Lorentz medium to efficiently terminate the unbounded lattice. It can be concluded that the proposed algorithm shows entire considerable performance in the low-pressure discharge evaluation. The proposed PML formulation behaviors enhanced absorbing performance compared with the existing algorithm. Through the experiments, it can be observed that the low-pressure discharge phenomenon causes performance variation, which shows a significant influence on the satellite sensors. Meanwhile, results show considerable agreement between the simulation and experiment results which indicates the effectiveness of the algorithm.

A large number of sensors work in the narrow bandpass circumstance. Meanwhile, some of them hold fine details merely along one and two dimensions. In order to efficiently simulate these sensors and devices, the one-step leapfrog hybrid implicit-explicit (HIE) algorithm with the complex envelope (CE) method and absorbing boundary condition is proposed in the narrow bandpass circumstance. To be more precise, absorbing boundary condition is implemented by the higher order convolutional perfectly matched layer (CPML) formulation to further enhance the absorption during the entire simulation. Numerical examples and their experiments are carried out to further illustrate the effectiveness of the proposed algorithm. The results show considerable agreement with the experiment and theory resolution. The relationship between the time step and mesh size can break the Courant–Friedrichs–Levy condition which indicates the physical size/selection mesh size. Such a condition indicates that the proposed algorithm behaviors are considerably accurate due to the rational choice in discretized mesh. It also shows decrement in simulation duration and memory consumption compared with the other algorithms. In addition, absorption performance can be improved by employing the proposed higher order CPML algorithm during the whole simulation.

By incorporating the higher-order concept with the perfectly matched later (PML) scheme, unconditionally stable approximate Crank–Nicolson algorithm is proposed for plasma simulation in open region problems. More precisely, the proposed implementation is based on the CN Direct-Splitting (CNDS) procedure for the finite-difference time-domain (FDTD) unmagnetized plasma simulation. The unmagnetized plasma can be regarded as frequency-dependent media which can be calculated by the piecewise linear recursive convolution (PLRC) method. The proposed implementation shows the advantages of higher-order concept, CNDS procedure, and PLRC method in terms of improved absorbing performance, enhanced computational efficiency, and outstanding calculation accuracy. Numerical examples are introduced to indicate the effectiveness and efficiency. It can be concluded from results that the proposed scheme shows considerable efficiency, accuracy, absorption, and unconditional stability.

The nanocatalysts of Mn−Co/TiO2 and Mn−Fe/TiO2 were synthesized by hydrothermal method and comprehensively compared from nanostructures, catalytic performance, kinetics, and thermodynamics. The physicochemical properties of the nanocatalysts were analyzed by N2 adsorption, transmission electron microscope (TEM), X-ray diffraction (XRD), H2-temperature-programmed reduction (TPR), NH3-temperature-programmed desorption (TPD), and X-ray photoelectron spectroscopy (XPS). Based on the multiple characterizations performed on Mn−Co/TiO2 and Mn−Fe/TiO2 nanocatalysts, it can be confirmed that the catalytic properties were decidedly dependent on the phase compositions of the nanocatalysts. The Mn−Co/TiO2 sample presented superior structure characteristics than Mn−Fe/TiO2, with the increased surface area, the promoted active components distribution, the diminished crystallinity, and the reduced nanoparticle size. Meanwhile, the Mn4+/Mnn+ ratios in the Mn−Co/TiO2 nanocatalyst were higher than Mn−Fe/TiO2, which further confirmed the better oxidation ability and the larger amount of Lewis acid sites and Bronsted acid sites on the sample surface. Compared to Mn−Fe/TiO2 nanocatalyst, Mn−Co/TiO2 nanocatalyst displayed the preferable catalytic property with higher catalytic activity and stronger selectivity in the temperature range of 75⁻250 °C. The results of mechanism and kinetic study showed that both Eley-Rideal mechanism and Langmuir-Hinshelwood mechanism reactions contributed to selective catalytic reduction of NO with NH3 (NH3-SCR) over Mn−Fe/TiO2 and Mn−Co/TiO2 nanocatalysts. In this test condition, the NO conversion rate of Mn−Co/TiO2 nanocatalyst was always higher than that of Mn−Fe/TiO2. Furthermore, comparing the reaction between doping transition metal oxides and NH3, the order of temperature−Gibbs free energy under the same reaction temperature is as follows: Co3O4 < CoO < Fe2O3 < Fe3O4, which was exactly consistent with nanostructure characterization and NH3-SCR performance. Meanwhile, the activity difference of MnOx exhibited in reducibility properties and Ellingham Diagrams manifested the promotion effects of cobalt and iron dopings. Generally, it might offer a theoretical method to select superior doping metal oxides for NO conversion by comprehensive comparing the catalytic performance with the insight from nanostructure, catalytic performance, reaction kinetics, and thermodynamics.

Three typical Mn-based bimetallic nanocatalysts of Mn−Fe/TiO2, Mn−Co/TiO2, Mn−Ce/TiO2 were synthesized via the hydrothermal method to reveal the synergistic effects of dielectric barrier discharge (DBD) plasma and bimetallic nanocatalysts on NOx catalytic conversion. The plasma-catalyst hybrid catalysis was investigated compared with the catalytic effects of plasma alone and nanocatalyst alone. During the catalytic process of catalyst alone, the catalytic activities of all tested catalysts were lower than 20% at ambient temperature. While in the plasma-catalyst hybrid catalytic process, NOx conversion significantly improved with discharge energy enlarging. The maximum NOx conversion of about 99.5% achieved over Mn−Ce/TiO2 under discharge energy of 15 W·h/m3 at ambient temperature. The reaction temperature had an inhibiting effect on plasma-catalyst hybrid catalysis. Among these three Mn-based bimetallic nanocatalysts, Mn−Ce/TiO2 displayed the optimal catalytic property with higher catalytic activity and superior selectivity in the plasma-catalyst hybrid catalytic process. Furthermore, the physicochemical properties of these three typical Mn-based bimetallic nanocatalysts were analyzed by N2 adsorption, Transmission Electron Microscope (TEM), X-ray diffraction (XRD), H2-temperature-programmed reduction (TPR), NH3-temperature-programmed desorption (TPD), and X-ray photoelectron spectroscopy (XPS). The multiple characterizations demonstrated that the plasma-catalyst hybrid catalytic performance was highly dependent on the phase compositions. Mn−Ce/TiO2 nanocatalyst presented the optimal structure characteristic among all tested samples, with the largest surface area, the minished particle sizes, the reduced crystallinity, and the increased active components distributions. In the meantime, the ratios of Mn4+/(Mn2+ + Mn3+ + Mn4+) in the Mn−Ce/TiO2 sample was the highest, which was beneficial to plasma-catalyst hybrid catalysis. Generally, it was verified that the plasma-catalyst hybrid catalytic process with the Mn-based bimetallic nanocatalysts was an effective approach for high-efficiency catalytic conversion of NOx, especially at ambient temperature.

Two-element tapered slot antenna (TSA) array for terahertz (THz) resonant tunneling diode (RTD) oscillators is proposed in this paper. The proposed TSA array has the advantages of both the high directivity and high gain at the horizontal direction and hence can facilitate the horizontal communication between the RTD oscillators and other integrated circuit chips. A MIM (metal-insulator-metal) stub with a T-shaped slot is used to reduce the mutual coupling between the TSA elements. The validity and feasibility of the proposed TSA array have been simulated and analyzed by the ANSYS/ANSOFT’s High Frequency Structure Simulator (HFSS). Detailed modeling approaches and theoretical analysis of the proposed TSA array have been fully addressed. The simulation results show that the mutual coupling between the TSA elements is reduced below −40 dB. Furthermore, at 500 GHz, the directivity, the gain, and the half power beam width (HPBW) at the E-plane of the proposed TSA array are 12.18 dB, 13.09 dB, and 61°, respectively. The proposed analytical method and achieved performance are very promising for the antenna array integrated with the RTD oscillators at the THz frequency and could pave the way to the design of the THz antenna array for the RTD oscillators.

In this paper, in view of the uncertainty of geometric parameters of braided-shielded cable and structural parameters of the shield in practical problems, polynomial chaos expansions (PCE) is used to explore the uncertainty quantification of a crosstalk calculation model of braided-shielded cable. First, the model based on multi-conductor transmission line theory is expanded by PCE. Second, the truncation degrees of polynomials and sample size of PCE are determined by the leave-one-out method, and the statistical characteristic parameters of crosstalk are obtained by combining coefficients of polynomials. Compared with the calculation results of the Monte Carlo method, the mean value, standard deviation and probability density function obtained by the two methods are basically consistent, but the PCE method has obvious advantages in calculation efficiency. Finally, the influence of input variable parameters in the crosstalk calculation model of braided-shielded cable are calculated by combining the PCE and the Sobol's global sensitivity analysis method, which provides theoretical guidance for the electromagnetic compatibility design of electrical and electronic equipment using braided-shielded cable. [ABSTRACT FROM AUTHOR]