最新刊期
卷 55 ， 期 12 ， 2021
- 摘要：To solve the severe electricity consumption problem caused by renewable energy sources, the combined cooling, heating and power system based on compressed air and thermochemical energy storage technology is proposed. During the charging process, the compression heat energy is transformed to chemical energy in the form of syngas fuel through methanol decomposition reaction; during the discharging process, the syngas fuel is burned with the high-pressure air in the combustion chamber to drive the gas turbine for electricity generation. In addition, the steam Rankine cycle, absorption refrigeration cycle and heating sub-system are employed to output cooling and heating energy based on the different energy grades to achieve high-efficiency cascade utilization of waste heat and further improve power generation. The thermodynamic model of the system is established to investigate the performance of the proposed system by developing computer code. The results indicate that higher reaction temperature and lower reaction pressure could enhance the efficiency of methanol decomposition. The electricity production could be increased by increasing the pressure ratio of both air compressor and gas turbine, and decreasing the isentropic efficiency of air compressor and the air fuel ratio. The present work could provide theoretical foundation for further engineering application of the proposed system.关键词：compressed air energy storage;thermochemical reaction;combined cooling, heating and power system;multi-energy complementarity;thermodynamic analysis1129|7|0更新时间：2025-07-09
- 摘要：A scheme of waste heat recovery and utilization of hydraulic system based on organic Rankine cycle is proposed to solve the problems of high energy consumption and serious waste of heat energy during cooling of traditional oil coolers in hydraulic system and to improve the comprehensive energy efficiency of the system. A test platform for waste heat recovery of the hydraulic system is built to study the influences of electric load, oil flow rate, and flow rate of the working fluid on the system operation and energy characteristics. Experimental results show that, compared with the oil cooler of the same specification, the system' maximum thermal efficiency of the proposed scheme increases to 2.56% under the same working conditions. The pressure ratio of the expander and the thermal efficiency of the system increase with the increase of electric load and oil flow rate. With the increase of the flow rate of the working fluid, the superheat of the working fluid at the inlet of the expander decreases significantly, while the heat flow rate in the evaporator and the output power of the expander increase. Under the testing condition, the maximum heat flow rate in the evaporator is 4.18 kW, and the maximum output power of the expander is 356 W. The test results verify the energy saving effect of the waste heat recovery system of the hydraulic system, and reveal the influence laws of operating parameters on the system performance.关键词：hydraulic system;waste heat recovery;organic Rankine cycle(ORC);experimental study;operating characteristic264|6|0更新时间：2025-07-09
- 摘要：To improve heat transfer effect of internal cooling channel in the mid-chord region of gas turbine blade, the optimization of flow and heat transfer performance of ribbed thick-wall channel in a turbine blade with Reynolds number ranging from 10 000 to 60 000, aspect ratio ranging from 0.25 to 4.00 and rib angle ranging from 30° to 90° is investigated based on the existing experimental data. Sobol method is used to analyze the global sensitivity of flow and heat transfer performance of the ribbed thick-wall channel to Reynolds number, aspect ratio and rib angle. A BP(back propagation)neural network is used to perform multi-input and multi-output nonlinear fitting for 60 groups of experimental data, and a multi-input and multi-output neural network model is obtained which can accurately predict average Nusselt number, friction factor and comprehensive thermal coefficient of the ribbed thick-wall channel. Then structural parameters of the ribbed thick-wall channel are optimized by using a genetic algorithm under different working conditions. Results show that the heat transfer performance of the ribbed thick-wall channel is highly sensitive to the change of inlet Reynolds number, but is less sensitive to the change of channel aspect ratio and rib angle. The flow performance is highly sensitive to the change of channel aspect ratio, but is less sensitive to the change of rib angle, and insensitive to the change of inlet Reynolds number. The maximum prediction deviations of the trained BP neural network model for average Nusselt number, friction factor and comprehensive thermal coefficient are 3.9%, 2.8% and 4.6% respectively. The optimal values of aspect ratio and rib angle increase with the increase of Reynolds number. At different Reynolds numbers, the optimal aspect ratio and rib angle based on the optimization of average Nusselt number are from 2.23 to 2.75 and 41.12° to 55.16°, respectively, while the optimal aspect ratio and rib angle based on the optimization of comprehensive thermal performance are from 1.75 to 2.11 and 56.68° to 65.2°, respectively. This research may provide reference for the design of blade cooling structure of heavy duty gas turbine in future.关键词：ribbed channel;cooling performance;neural network;genetic algorithm;prediction and optimization143|8|0更新时间：2025-07-09
- 摘要：In view of the difficulties in identifying the event-related potential(ERP)signals due to their characteristics of strong individual differences, and the overfitting problem of traditional convolutional neural network(CNN)for small samples, based on a fusion model of CNN and support vector machine(SVM), a CNN and SVM combined classifier for ERP signal classification and recognition is proposed. This method takes the filtered original multi-channel ERP signals as input. Firstly, a one-dimensional time convolution kernel is used to convolve the time domain of the signals, then the spatial convolution is performed by using a one-dimensional spatial convolution kernel to learn features from temporal and spatial information of signals. Finally, the training of the CNN model is accomplished by such operations as down-sampling and full connection. After that, the signals are imported into the trained model again to extract the down-sampling layer features of the signals, and SVM is finally used to classify and identify the features. Classification results show that the proposed combined classifier can effectively recognize the P300 signal component under a small number of repeated visual stimuli. The average recognition accuracy of the proposed method is 94.08% after the repetition of more than four times of visual stimuli, and it has an average accuracy improvement of 4.36% compared with the traditional CNN method. Compared with the classical algorithms of stepwise linear discriminant analysis(SWLDA)and Bayesian linear discriminant analysis(BLDA), the average recognition accuracy of the combined classifier is improved by 6.83% and 4.16%, respectively. The combined classifier only needs a small number of repeated experiments to obtain higher target recognition accuracy, and effectively improves the recognition effect of ERP signals.关键词：event-related potential;fusion model;convolutional neural network;support vector machine256|4|0更新时间：2025-07-09
- 摘要：The mapping relationship between the joint angular velocity of the external skeleton and the joint torque generated by the interaction force of the legs is studied to solve the problem of poor human-robot interaction flexibility and discomfort caused by excessive human-robot interaction force in the use of lower limb assisted exoskeleton. A model to simplify the human lower limb swing phase leg into a plane two-bar model with the hip joint as the fulcrum is proposed, and a dynamic model of swing leg of human lower limb is established to obtain the gravity compensation item of human body. A force sensing unit is used to obtain the interaction force between the human body and the exoskeleton, and the leg interaction force between the exoskeleton and the human body is converted into the desired angle of the human hip and knee joint movement at the next moment. A non-linear mapping relationship between the swing phase joint angular velocity of human body and the leg interaction torque is established by means of the S-curve mapping and the admittance control model respectively. Experimental results show that both the expected angle prediction algorithm based on S-curve mapping and the expected angle prediction algorithm based on the admittance control model realize the continuous tracking of human motion intention from the exoskeleton. The expected angle prediction algorithm based on S-curve mapping makes the peak value of the thigh and leg interaction force less than 25 N and the peak value of shank and leg interaction force less than 20 N in the process of the exoskeleton following the human body movement; while the expected angle prediction algorithm based on the admittance control model makes the peak value of thigh-leg interaction force less than 15 N and the peak value of calf-leg interaction force less than 15 N in the process of the exoskeleton following human movement, and the joint angle curve is smooth, which can better identify human movement intention and improve the flexibility of human-robot interaction.关键词：lower limb exoskeleton;human-robot interaction force;human motion intention;admittance control110|5|0更新时间：2025-07-09
- 摘要：A backstepping control strategy of command filtering with output constraint and error compensation for two-link flexible joint manipulator(named BLF-CFBC)is proposed to achieve the control goal of improving the tracking response speed and tracking accuracy of the two-link flexible joint manipulator system. Firstly, a logarithmic barrier Lyapunov function is constructed to replace the conventional quadratic Lyapunov function in the backstepping control design, so that the trajectory tracking error is constrained within the preset range and the trajectory tracking accuracy is ensured while the error convergence speed is accelerated. Secondly, the control signal and its derivative are output from the command filter so as to avoid the problem of computational explosion caused by the repeated derivation of control variables in conventional backstepping control method based on Lyapunov theory, thus effectively reducing the calculation dimension. Finally, an error compensation mechanism is introduced to construct system variables by using the errors between the control signal and the output of the filter, so as to reduce the influence of the error of command filter on the tracking accuracy of the manipulator. A two-link flexible joint manipulator system model is built and simulated in Matlab software. Results and a comparison with the command filter backstepping control strategy(CFBC)without considering output constraint and error compensation show that the proposed control strategy increases the trajectory tracking response speed of joint 1 and joint 2 by 20% and 30%, respectively, and the trajectory tracking error reduces by 4% and 3.5%, respectively.关键词：two-link flexible joint manipulator;barrier Lyapunov function;command filtering;filtering error91|4|0更新时间：2025-07-09
- 摘要：Aiming at the complex conditions of variable fault types and degrees of rolling bearings, a new adaptive feature extraction method for 2D texture domain signals is proposed to obtain more abundant fault information. In this newly proposed 2D texture domain construction method, 1D vibration signal is transformed into a 2D texture matrix. It is proved that the constructed 2D vibration signal texture domain has strong fault symptom ability for rolling bearings with different fault types and different fault degrees. To make up the limitation of texture pixel size when extracting features directly from original signal texture, an adaptive texture-domain extraction method based on 2D empirical wavelet transform is introduced. Adopting the 2D empirical wavelet transform, the texture of 2D vibration signal is decomposed adaptively into multiple texture components, and multi-scale texture feature extraction is carried out respectively considering both the macro texture and the texture details, thus the restriction and influence of texture pixel size on extraction of texture domain are eliminated to accurately extract the bearing fault feature of vibration signal. The rolling bearings with different fault degrees and different fault types are identified by support vector machine. Compared with the method without 2D empirical wavelet transform, the proposed method improves the recognition accuracy from 19.8% to 98.1%, which verifies the effectiveness of this proposed method.关键词：2D empirical wavelet transform;rolling bearing;texture domain;fault diagnosis99|5|0更新时间：2025-07-09
- 摘要：It is difficult to guarantee assembly accuracy of steam turbine flow clearance due to repeated assembly and adjustments in the case of lack of prior simulation, prediction and correction. To meet the requirements of digital assembly transformation, a steam turbine flow clearance accuracy prediction and repair planning method is proposed. The primary repair is carried out following the rule-based reasoning to ensure the turbine, except the flow clearance, to meet the main assembly requirements and prevent the parts from being repaired due to assembly failure. The assembly feature network is constructed according to the product assembly process information, and the depth first search algorithm is used to realize the generation of the flow clearance assembly dimension chains, then the accuracy prediction of flow clearance is realized. Considering the coupling characteristics of the dimension chains, the secondary repair is carried out following the case-based reasoning, and via case retrieval the repair range of repair ring is obtained and the optimal repair quantity is achieved with particle swarm optimization algorithm to ensure the flow clearance to meet the design requirements. A turbine assembly unit is used as an example for application verification. The results show that this method can successfully predict the accuracy of flow clearance according to the collected measurement data and can output corresponding repair suggestions, and then improve the first-time assembly success rate and assembly efficiency of steam turbine.关键词：flow clearance;accuracy prediction;repair planning;rule-based reasoning65|4|0更新时间：2025-07-09
- 摘要：Aiming at the problems, such as serious interference, difficulty in automatic identification of wear region and inaccurate measurement of wear quantity in micro drill and micro milling tool wear images, a visual detection method for tool wear based on adaptive region growth is proposed by combining the maximum inter-class variance method with the region growing algorithm, where the wear image is automatically trimmed by Otsu algorithm, and the starting point and initial threshold of region growth are determined according to the change rule of image pixels. The threshold is updated according to the inter-class variance to obtain the binary image of the wear area. Then the tool contour is reconstructed by least square method, and automatic measurement of tool wear is realized. The consistency analysis of wear measurement data is performed to verify the effectiveness of the proposed algorithm. It is indicated that the measurement results from the proposed method coincide well with the manual measurement results. Compared with the existing machine vision detection methods, this method can effectively avoid the influence of the interference area, and accurately extract the wear information of the micro drill and micro milling tool, thereby accurately realizing the detection of the wear state of the micro drill and micro milling tool.关键词：micro drill and micro milling tool;adaptive region growth;automatic measurement;consistency analysis66|4|0更新时间：2025-07-09
- 摘要：Aiming at the worse fault diagnosis of rolling bearing and poor generalization ability in a strong noise environment and variable working conditions, an improved CNN-based fault diagnosis method for rolling bearing under variable working conditions is proposed. A multi-scale feature extraction module is designed, and convolutional layers of different scales are adopted to extract features from the input data to maximize the extraction of feature information in the fault data. The channel attention mechanism is then introduced to extract the more important and critical components from this module. A convolution module with skip connection lines is designed to prevent the extracted rich features from being lost when the convolutional layer is forwarded. Regarding softmax cross entropy as the loss function, the Adam optimization algorithm is chosen to realize the fault diagnosis for rolling bearing. The proposed method is verified by experiments on the bearing dataset and gearbox dataset from Case Western Reserve University. The results show that in the variable noise experiment on the bearing dataset from Case Western Reserve University, the proposed method achieves an average diagnostic accuracy rate of 96.49%, and the diagnostic accuracy rate is beyond 90% in variable working conditions, which are obviously higher than the competing methods. On the gearbox bearing data set, the diagnostic accuracy rate of the proposed method with better noise resistance and generalization ability reaches 99.54%.关键词：fault diagnosis;rolling bearing;variable working condition;convolutional neural network;attention mechanism314|6|0更新时间：2025-07-09
- 摘要：An ultrasonic overlapping echo separation method based on blind deconvolution is proposed to solve the problem of echo signal overlap in ultrasonic testing technology, which may result in large positioning error of near surface defects. Firstly, the ultrasonic echo signal is processed by inverse Fourier transform and smoothing technique to initialize the impulse response function. Then, convex optimal deconvolution models are established for a reflection sequence function and an impulse response function respectively, and the split Bregman algorithm and the alternating direction multiplier algorithm(ADMM)are alternatively used to solve the both models. Finally, the estimated values of the reflection sequence function and the impulse response function are obtained through judging whether the stop condition is satisfied, so as to realize the separation of ultrasonic overlap echo. Simulation and experimental results show that the proposed method effectively separates the overlapping echoes after 10 alternating iterations under different intensity of noise interference, and has good robustness to noise interference. This method reduces the positioning error of near surface defects to 0.97%, and is suitable for practical ultrasonic testing.关键词：ultrasonic testing;overlapping echo separation;alternating iteration;blind deconvolution96|5|0更新时间：2025-07-09
- 摘要：An algorithm to monitor the control signal of drone using residual neural network is proposed to solve the problems that remote control signals of unmanned aerial vehicles(UAV)are usually susceptible to random noise and narrowband or broadband interference, and it is difficult to extract the frequency hopping period and rate of frequency hopping sequence of a remote control signal. Firstly, a time-spectrogram is obtained via a sliding time window, and a threshold of signal spectrum detection is calculated by a joint adaptive method. Then, pre-processing operations such as binarization and interference elimination are performed on the time-spectrogram to construct the time spectrum to be measured. Turther, a large number of processed spectrograms of different control signals are used as a data set to train and test the deep residual neural network, so as to avoid the problem of difficult extraction of frequency hopping features. Finally, the trained network is used to recognize the current remote control signal and its model in real time. The proposed DRN-UVA algorithm overcomes the adverse effects such as occlusion and UVA size, and is an effective supplement to anti-UVA system based on radar or optics. Experimental results show that the DRN-UAV algorithm shortens the single recognition time to about 1/25 of the traditional reading method. At the same error detection rate, the signal spectrum detection threshold obtained by the DRN-UAV algorithm reduces by 1.4 dBm compared with that of the traditional method and the detection range is effectively increased on different hardware platforms. When the signal-to-noise ratio is higher than 5.5 dB, the detection error rate can reach less that 0.01% under the interferences of single narrowband fixed-frequency signal and WiFi.关键词：drone;remote control signal;frequency hopping sequence;recognition;residual neural network80|4|0更新时间：2025-07-09
- 摘要：In order to improve the comprehensive electrical and thermal properties of traditional electrical epoxy composites, surface graft modification of nano-Al2O3 is applied to achieve better dispersion and interface strength in epoxy matrix, so as to improve its influence on the electrical and thermal properties of epoxy nano-composites. Bisphenol-A epoxy resin is taken as the matrix material, and the nano-Al2O3 particles coated with silane coupling agent and long-chain polystyrene(PS)are grafted and modified. The epoxy nano-composites with different mass fractions of surface-modified nano-Al2O3 are prepared by mechanical dispersion method. The effects of different contents of PS-grafted nano-Al2O3 on thermal stability, glass transition temperature, breakdown field strength and dielectric properties of the epoxy composites are studied. The results show that the grafted and modified nano-Al2O3 particles disperse evenly in the epoxy matrix. With the increase of doping amount, both the breakdown strength and electric constant of the grafted and modified nano-Al2O3 epoxy composite increase. Compared with neat epoxy, the grafted and modified nano-Al2O3/epoxy composite with 3% mass fraction shows the highest dielectric breakdown strength and the strength increases by about 5.67%. The dielectric constant of the nano-Al2O3/epoxy composite grafted with 5% styrene increases by 8.59%. It is concluded that grafting styrene to the surface of nano-Al2O3 can greatly improve the compatibility between the nano-particles and the polymer matrix, enhance the dispersibility and further improve the breakdown strength, which may provide a basis for the development and promotion of epoxy nanocomposites in the electrical field.关键词：graft modification;nanocomposites;dielectric constant;thermal properties;breakdown strength147|5|0更新时间：2025-07-09
- 摘要：To reduce the thickness of underwater sound absorbing metamaterials and facilitate their practical application of underwater sound absorption, a design idea of introducing mass concentration effect into underwater thin-plate acoustic metamaterials is proposed. Generally describing, for the classical underwater thin-plate metamaterial, the unit cell structure is composed of a thin steel plate fixed around and an oscillator fixed on the top of the thin steel plate. When a plane wave is vertically incident on the surface of the unit cell at a certain frequency, there is a significant sound absorption peak caused by oscillator resonance. However, in the case of keeping the total mass unchanged, if the original thick oscillator is accurately divided into two adjacent thin oscillators, similar sound absorption properties can be obtained, additional sound absorption peaks appear at the same time with multiple modes, and the thickness of the metamaterial decreases sharply. This phenomenon is called mass concentration effect. To reveal the physical mechanism of sound absorption and optimize sound absorption performance, the effects of important structural parameters on sound absorption performance are discussed by impedance matching mechanism and finite element simulation. Then a multi-cell structure is designed, which is endowed with good broadband sound absorption effect within the range of 200-1 000 Hz.关键词：underwater sound absorption;mass concentration effect;thin-plate metamaterials;low frequency broadband133|7|0更新时间：2025-07-09
- 摘要：In order to obtain reliable basic viscosity data of the new environment mixed refrigeration working medium and to provide technical support for industrial process design, system optimization and equipment research and development, the simplified perturbed-chain statistical association fluid theory is used to establish the function relationship between the reduced residual entropy and the dimensionless reduced viscosity of 11 refrigerants, including hydrofluorolefins, hydrofluorocarbons, alkanes and CO2. The vapor-liquid equilibrium properties and the possible highly non-ideal viscosity properties affected by molecular structure and electrostatic distribution are systematically studied. The parameters of pure compounds are extended to multivariate complex system by combining the predictive mixing rule. The results show that the maximum absolute mean deviation between the predicted viscosities from the model and the experimental data is less than 5.1% for the multivariate complex system, except for some systems containing CO2 in the near critical point. It is verified that the proposed model is capable of predicting the viscosity of complex refrigerant mixtures far away from the critical region. However, the model still needs to be further improved for the complex system in the near critical zone.关键词：viscosity model;residual entropy;refrigerant mixture113|4|0更新时间：2025-07-09
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