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In a sudden load increasing process (SLIP), the hydroelectric generating system (HGS) experiences a severe vibration response due to the sudden change of the hydraulic-mechanical-electric parameters (HMEPs). The instability of HGS limits the ability of sudden load increase, and its flexibility and reliability are reduced. Thus, in this study, a new transient nonlinear coupling model of HGS is proposed, which couples the hydro-turbine governing system (HTGS) and the hydro-turbine generator shafting system (HGSS) with the hydraulic-mechanical-electric coupling force, rotating speed, flow rate, hydro-turbine torque, electromagnetic torque, and guide vane opening. By using numerical simulation, the influences of different HMEPs on the vibration characteristics of HGS in SLIP are analyzed. The result shows that, compared with stable operating conditions, the vibration amplitude of HGS increases sharply in SLIP. The increase of the sudden load increasing amount, blade exit flow angle, mass eccentricity and excitation current, and the decrease in guide bearing stiffness and average air gap between the stator and rotor cause abnormal vibration of different degrees in the HGS. Hydraulic factors have the greatest influence on the nonlinear dynamic behavior of HGS. The maximum vibration amplitude of HGS in SLIP is increased by 70.46%, compared with that under stable operating conditions. This study provides reasonable reference for the analysis of the nonlinear dynamic behavior of HGS in SLIP under the multiple vibration sources.

The measurement of rock resistivity currently mainly adopts the method of reducing saturation, which requires the rock sample to reach complete saturation during measurement. In response to the accuracy issue of measuring rock electrical parameters using desaturation methods for unsaturated or incompletely saturated rock samples, the influence of saturation degree on the measurement results of rock electrical parameters and the root causes of the influence were analyzed, and a feasible correction method was established. The results indicate that the saturation degree of rock samples first affects the measurement of porosity, calculation of water saturation, and initial resistivity measurement of saturated saline rock samples, and further transmits to geological factors, cementation index, and lithology coefficient, while the saturation index is not affected by them. After correcting the porosity, water saturation, initial water saturation, and initial resistivity of the rock sample using saturation degree values, a one-to-one correspondence between water saturation and resistivity data was achieved. The base value for calculating the resistivity increase rate is more in line with reality, and the final rock electrical parameters obtained are more reasonable.

This study reports a comparative characterization of Au/n-Si Schottky diodes/contacts (SDs) with hydrothermally synthesized ZnO–PVP and ZnO/Ag 2 WO 4 –PVP interfacial layers, which outperforms conventional metal-semiconductor Schottky diode structures. This characterization is important because these structures outperform traditional metal-semiconductor Schottky diodes due to the presence of an interfacial layer, allowing barrier height control, surface passivation, and leakage current reduction. Based on the thermionic emission (TE) theory assumed to be the dominant current mechanism across, SDs parameters were obtained. As expected, nonlinear rectifying behavior was observed for all SDs, and the divergence from linearity is caused by factors such as the interfacial layer thickness, the interface-state ( N ss ) density, and the bulk series resistance ( R s ). It is important to note that the rectification ratio ( RR ) of the Au/(ZnO/Ag 2 WO 4 –PVP)/n-Si (MPS2) SD is 48 times more than the RR of the Au/n-Si SD and 11 times greater than the RR of the Au/ ZnO–PVP/n-Si (MPS1) SD. The ideality factor ( n ) and zero-bias barrier height (Φ B0 ) were found to be 7.73 and 0.563 for MS, 6.23 and 0.604 for MPS, 4.83 and 0.684 for MPS2 SD. Nearly an order of magnitude less N ss exists for the MPS2 diode than the MS diode. According to these findings, the ZnO–PVP and ZnO/Ag 2 WO 4 –PVP interfacial layers stop Au and n-Si from reacting or diffusing with one another while also passivating the active dangling bonds at the Si surface. The methods of Cheung and Norde were also used to extract the R s , n , and Φ B . The inconsistency between the parameters obtained from these methods could be attributed to the regions where the methods are used differ. Graphical Abstract Highlights ZnO–PVP and ZnO/Ag 2 WO 4 –PVP nanocomposites were hydrothermally synthesized and coated on n-Si using spin-coating method. The crystalline structure and morphological characteristics of ZnO/Ag 2 WO 4 nanocomposites were studied by XRD and SEM-EDX. The electrical measurements show that ZnO–PVP and ZnO/Ag 2 WO 4 –PVP interfacial layers improve the fabricated diodes’ interface quality and performance parameters. The reverse-biased conduction mechanism of the Au/n-Si (MS), Au/ZnO–PVP/n-Si (MPS1), and Au/(ZnO/Ag 2 WO 4 –PVP)/n-Si (MPS2) SDs was evaluated using Schottky or Poole-Frenkel Emission model.

In oilfield extraction activities, traditional downhole condition monitoring is typically conducted using dynamometer cards to capture the dynamic changes in the load and displacement of the sucker rod. However, this method has severe limitations in terms of real-time performance and maintenance costs, making it difficult to meet the demands of modern extraction. To overcome these shortcomings, this paper proposes a novel fault detection method based on the analysis of motor power parameters. Through the dynamic mathematical modeling of the pumping unit system, we transform the indicator diagram of beam-pumping units into electric power diagrams and conduct an in-depth analysis of the characteristics of electric power diagrams under five typical operating conditions, revealing the impact of different working conditions on electric power. Compared to traditional methods, we introduce fourteen new features of the electrical parameters, encompassing multidimensional analyses in the time domain, frequency domain, and time-frequency domain, significantly enhancing the richness and accuracy of feature extraction. Additionally, we propose a new effectiveness evaluation method for the FCM clustering algorithm, integrating fuzzy membership degrees and the geometric structure of the dataset, overcoming the limitations of traditional clustering algorithms in terms of accuracy and the determination of the number of clusters. Through simulations and experiments on 10 UCI datasets, the proposed effectiveness function accurately evaluates the clustering results and determines the optimal number of clusters, significantly improving the performance of the clustering algorithm. Experimental results show that the fault diagnosis accuracy of our method reaches 98.4%, significantly outperforming traditional SVM and ELM methods. This high-precision diagnostic result validates the effectiveness of the method, enabling the efficient real-time monitoring of the working status of beam-pumping unit wells. In summary, the proposed method has significant advantages in real-time performance, diagnostic accuracy, and cost-effectiveness, solving the bottleneck problems of traditional methods and enhancing fault diagnosis capabilities in oilfield extraction processes.

In this paper, we first establish a model of ultra-large-scale integrated circuits and study the model architecture from basic circuit units to complex circuit units. Then, the circuit optimization problem is mathematically analyzed, and the unconstrained and constrained parametric optimization problems with electrical parameters are investigated. Reinforcement learning is introduced to a reasonably one-to-one correspondence between the parametric optimization problem and the environment in reinforcement learning, which transforms the ordinary optimization problem into a task of reinforcement learning and realizes the optimization of electrical parameters in integrated circuit design. Finally, the effect of optimizing the electrical parameters of the method in this paper is evaluated. In the case of 200 DPPM, 300 DPPM, and 400 DPPM, the number of censored test parameters of this paper’s method is distributed in the range of (10,15), while the number of censored test parameters of the other methods are in the interval of (2,10), and this paper’s method outperforms the other methods. This study has an important reference value to improve the efficiency, reliability, and performance of integrated circuit design, and can provide a reference for the design of integrated circuits.

This work aims to design and predict the performance of a novel heterojunction perovskite solar cell (PSC) based on CsGeI 3 /CsSn(I 1− x Br x ) 3 using machine learning (ML). Electrical parameters including open-circuit voltage ( V OC ), short-circuit current ( J SC ), fill factor (FF), and power conversion efficiency (PCE) are generated using SCAPS-1D due to its high accuracy in matching experimental results, and are used to build two ML models based on polynomial regression (PR) and XGBoost (XGB) by considering the thickness H 1 (CsSn(I 1− x Br x ) 3 ) and H 2 (CsGeI 3 ) and the bromine composition ( x ) as parameters. The polynomial degree and maximum depth are considered as parameters for PR and XGB. Performance metrics including training and testing scores, correlation ( r ), and mean squared error (MSE) are calculated to investigate the accuracy, while the cross-validation (CV) scores are used to determine the stability and the overfitting. The results reveal the best performance for R 2 , r , MSE , and CV for PR of 0.9969, 0.9915, 0.0245, and 0.9676, respectively, while those for XGB are 0.9990, 0.9991, 0.0005, and 0.9864, respectively. Moreover, the best polynomial degree and maximum depth are 9 and 5, respectively. We also reveal that the dependence of the optoelectronic parameters in terms of bromine composition in CsSn(I 1− x Br x ) 3 creates a powerful internal electrostatic field between the two absorbers for low bromine composition, which favors the extraction of the carriers out of the device. Considering both stability and performance, this prototype can achieve PCE of 29.62%, 29.54%, and 29.61% for actual, PR, and XGB, respectively, when H 1 , x , and H 2 are 1 μm, 0.6, and 0.2 μm, respectively, compared to 21.91% and 17.61% for existing CsSn(I 1− x Br x ) 3 and CsGeI 3 homojunction.

As the exploration and drilling of oil, natural gas and geothermal wells are expanding continuously, research into high-efficiency rock drilling technology is imperative. High-voltage electro pulse boring (EPB) has the advantages of high rock breaking efficiency and good wall quality, and is a new and efficient potential method of rock breaking. The design of electrode drill bits and the selection of drilling process parameters are the main obstacles restricting the commercialization of EPB. Accordingly, it is necessary to determine the influences on high-voltage EPB. In this study, based on the equivalent circuit of high-voltage electro pulse breakdown, a mathematical model of high-voltage electro pulse discharge in rock is established. Meanwhile, a numerical simulation model of high-voltage EPB of hard granite is established based on a coaxial cylindrical electrode structure, which is often used for electrode drill bits. The simulation analysis software Comsol Multiphysics (Comsol Multiphysics®5.3a, COMSOL Co., Ltd., Stockholm, Sweden) is used to study the influences of granite composition, electrode spacing and electrode shape on the high-voltage EPB process. In addition, the influences of electrical parameters on high-voltage EPB are calculated according to a model of high-voltage electro pulse discharge in rock. Finally, it is demonstrated that high-voltage EPB is influenced by granite composition, electrical parameters, electrode spacing, and electrode shape, and the relationships between these factors are obtained. This study is of guiding significance for improving rock breaking efficiency, reducing energy loss, designing electrode drill bits and selecting drilling process parameters.

The thin layers of ZnO and ZnO: Al (Al doped zinc oxide—AZO) were deposited by the atomic deposition layer (ALD) method on silicon and glass substrates. The structures were deposited using diethylzinc (DEZ) and deionized water as zinc and oxygen precursors. A precursor of trimethylaluminum (TMA) was used to introduce the aluminum dopant. The present study of ALD-deposited ZnO and AZO films was motivated by their applications in photovoltaics. We attempted to expose several properties of such films. Structural, optical (including ellipsometric measurements) and electrical investigations were performed. We discussed the relations between samples doped with different Al fractions and their properties.

The aim of this study was to assess whether electrical parameters (capacitance and conductivity) of fresh engine oils—tested over a wide range of measurement voltage frequencies—can be used for oil quality assessment and its identification, based on physicochemical properties. The study encompassed 41 commercial engine oils with different quality ratings (American Petroleum Institute (API) and European Automobile Manufacturers’ Association (ACEA)). As part of the study, the oils were tested for their total base number (TBN) and total acid number (TAN), as well as their electrical parameters, including impedance magnitude, phase shift angle, conductance, susceptance, capacitance and quality factor. Next, the results for all of the samples were examined for correlations between the mean electrical parameters and the test voltage frequency. A statistical analysis (k-means and agglomerative hierarchical clustering) was applied to group oils with similar readings, drawing on the values for all electrical parameters to produce group oils with the highest similarity to each other into clusters. The results show that the electrical-based diagnostics of fresh engine oils can serve as a highly selective method for identifying oil quality, offering much higher resolution than assessments based on the TBN or the TAN. This is further supported by the cluster analysis, with five clusters generated for electrical parameters of the oils, compared to only three generated for TAN- and TBN-based measurements. Out of all the tested electrical parameters, capacitance, impedance magnitude and quality factor were found to be the most promising for diagnostic purposes. The value of electrical parameters of fresh engine oils is mostly dependent on the test voltage frequency (with the exception of capacitance). The correlations identified in the course of the study can be used to select for those frequency ranges that offer the highest diagnostic utility.

In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 °C and 1000 W/m2. The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 °C and 1000 W/m2 from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting metaheuristic optimization algorithms and analytical methods. The presented results confirm the validity and reliability of the Firefly algorithm in extracting the optimal parameters of the photovoltaic solar cell.