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Optically pumped gas molecular terahertz (THz) lasers are promising for generating high-power and high-beam-quality coherent THz radiation. However, for pulsed gas THz lasers, the temporal behavior of the output THz pulse has rarely been investigated. In this study, the temporal behavior of a pulsed gas THz pumped by a fundamental-mode TEA CO

An efficient and widely tunable Si-prism-array coupled terahertz-wave parametric oscillator (TPO) with a very narrow linewidth is demonstrated by using a shallow surface cross-pump configuration. The shallow surface cross-pumping form was realized by totally reflect the pump beam at the THz-wave exit surface. Widely THz-wave tuning range of 0.6–3.9 THz is realized with a 1-mm diameter pump, and the high-frequency end even reached 5.5 THz with a 2-mm diameter pump to increase the nonlinear gain length. The measured Stokes linewidth was 0.07 nm, which is compressed by 70% for that of the conventional TPOs. The highest THz-wave output energy of 2.4 μJ was achieved at 2 THz under 15 mJ pumping, corresponding to an energy conversion efficiency of 1.6 × 10−4. The mechanism of the frequency-selection effect in cross-pumped stimulated polariton scatterings was discussed by calculating the single-pass parametric gain for different phase mismatches.

Lint percentage of seed cotton is one of the important bases for pricing in the trading segment. Unfortunately, the conventional methods of lint percentage are manually operated, which relies on the abundant experience of experts, and restrained by personal, physical and environmental factors. Up to date, the calculation of the lint percentage of seed cotton has not fully automated. In this paper, we proposed a non-destructive detection method for automatically obtaining lint percentage of seed cotton based on optical penetration imaging and machine vision, for the first time to our knowledge. The cotton seed image was obtained by the penetration imaging setup with a LED white backlight source. To accurately identify the number of cotton seeds, the image features of the cotton seed was studied and three key features was been found, which are the circumference, area, and greyscale value, respectively. A calculation system based on the three key features was presented to process the images and then automatically calculate the lint percentage of seed cotton. The first step of the system is to segment the original image using adaptive thresholding followed by morphological operations. Afterwards, the number of cotton seed was obtained by the three key features of the cotton seed. Then, the lint percentage was achieved by a professional industry formula. The suggested lint percentage detection methods were verified by the experiments with two seed cotton varieties samples of H219 and ZHM19. The experimental results indicated that the detection average accuracy of the developed system for seed cotton varieties H219 and ZHM19 were 96.33% and 95.40%

The oil level of the power transformer is an important predictor to analyze the working state. The conventional oil level detection method cannot detect the level when the power transformer is working, which is not convenient for the actual application. In this paper, an oil level detection method based on double pressure sensors is proposed to online measure the oil level for power transformer, the basic principle of the proposed oil level detection method is explained and studied, and a prototype is developed to verify the proposed method. The tested results show that the developed prototype can reach high precision for oil level detection of a transformer.

To ensure the energy conversion efficiency of a proton-exchange membrane fuel cell (PEMFC), it is necessary to establish a water-cooled cooling system to keep the inlet temperature of fuel-cell coolant and the temperature difference between the inlet and outlet temperature within the set range. First, a semi-empirical and semi-mechanism model was built in Simulink. Then, a variable-universe fuzzy PID controller was designed to adjust the quantization factor and scaling factor by scaling factor α1, α2 and β to improve the accuracy of the control results. Finally, the cooling system model with controller is simulated. The results show that compared with other control methods, the simulation results of the variable-universe fuzzy PID controller have smaller maximum overshoot, faster response speed and more gentle temperature fluctuation.

The paper proposed and verified a method of object edge detection based on the innovative defect spot working mode of the position-sensitive detector (PSD). With the output characteristics of the PSD in the defect spot mode and the size transformation properties of a focused beam, edge-detection sensitivity could be improved. Calibration experiments with the piezoelectric transducer (PZT) and object edge-detection experiments indicate that the object edge-detection sensitivity and accuracy of our method could reach 1 and 20 nm, respectively. Therefore, this method can be widely used in high-precision alignment, geometric parameters measurement, and other fields.

From the perspective of improving the thermodynamic properties of epoxy resin, it has become the focus of research to enhance the operational stability of GIS (Gas Insulated Substation) basin insulators for UHV (Ultra-High Voltage) equipment. In this paper, three aminosilane coupling agents with different chain lengths, (3-Aminopropyl)trimethoxysilane (KH550), Aminoethyl)-γ-aminopropyltrimethoxysilane (KH792) and 3-[2-(2-Aminoethylamino)ethylamino]propyl-trimethoxysilane (TAPS), were used to modify nano-SiO2 and doped into epoxy resin, respectively, using a combination of experimental and molecular dynamics simulations. The experimental results showed that the surface-grafted KH792 model of nano-SiO2 exhibited the most significant improvement in thermal properties compared with the undoped nanoparticle model. The storage modulus increased by 276 MPa and the Tg increased by 61 K. The simulation results also showed that the mechanical properties of the nano-SiO2 surface-grafted KH792 model were about 3 times higher than that of the undoped nanoparticle model, the Tg increased by 36.5 K, and the thermal conductivity increased by 24.5%.

With rapid development of rail transport in our country, more and more people choose because of on time, fast and convenient. Safety of the subway is urgent with passenger increasing, and it's very important to inspire hidden danger. The paper proposed The auto-inspection method based on Infrared Laser Imaging and Deep Learning to detect foreign objects between subway doors and the platform screen doors(PSDs). Fast-RCNN Algorithm based on TensorFlow Deep Learning frame was adopted and the image information were fused with classification model, vgg16. The detecting system was built and experiments were made and analyzed. The experimental results showed that this system and method was robust to The illumination variations and focussing. The system is simple and cost-effective and The algorithm is promising for detecting accuracy. The method and technology can be potentially applied for The subway safety detection.

Rolling quality is a key index of the cotton quality, which directly influences the quality of the lint and textiles, however, it is mainly decided through visual classification by skilled personnel. In order to realize the intelligent rapid classification of cotton quality, this paper proposed a decision-level fusion recognition method for the cotton quality grade based on colored-image information. After the preprocessing of images, RGB and HSV features were calculated, respectively. The features are normalization processed and principal component analysis (PCA) is employed to extract the greater contribution features of RGB and HSV images, which are adopted as BP neural network (BPNN) input parameters to identify the quality grade recognition of cotton, respectively, and then output parameters of BPNN are used as independent evidence to construct Basic Probability Assignment (BPA). Finally, D-S Theory is used to obtain the decision fusion and realize the high accuracy the recognition of cotton quality grades. The compared experimental results show that the precision of proposed method is significantly superior to classification using RGB and HSV features respectively. The method provided in this paper can realize the intelligent rapid classification of cotton quality, and proves the feasibility of cotton-graded artificial intelligent classification.

A high power gas terahertz laser based on L-shaped cavity was demonstrated. We, for the first time, investigated the output performances of D2O gas 385 μm THz laser by optimizing the output coupler transmittance of the L-shaped cavity both in theory and experiment. Under the pump energy of 1.6 J, the optimum output coupler transmittance was about 0.78 and the optimum gas pressure was about 500 Pa. Up to 8.4 mJ pulse energy at 385 μm was achieved at output coupler transmittance of 0.8, corresponding to a photon conversion efficiency of 43.7%. Pulse width of 120 ns, and beam quality factor M2 of 1.58 were obtained at the highest output energy. In addition, the experimental results are in agreement with the theoretical simulation results.

Tooth wear is one of the most common faults in gear transmission systems. However, due to its limited influences on gear dynamics, the condition monitoring of gear wear experiences more challenges. To investigate the relationship between tooth wear and gear dynamics, this study develops an eight-degree of freedom dynamic model which takes into account the gear mesh impact induced by tooth wear. In this model, the gear mesh impact force induced by tooth wear was calculated as an extra input excitation for the gear dynamic model. Besides, a comparative study was carried out to explore the real reason that causes the poor dynamic behaviour due to the wear from two points of view: 1) the reduction in gear mesh stiffness; 2) the increase in gear mesh impact force. The simulation results show that the wear causes a slight reduction in gear mesh stiffness, thus leading to the decrease in the amplitude of gear mesh frequency. This is not consistent with the phenomena observed in the lab experiment which shows the increase in the gear vibrations. The second simulation of impact force demonstrates that the wear causes an increase in the impact force which further leads to the increase in the amplitude of gear mesh frequency. This study investigates the cause for the deterioration in gear dynamics induced by gear wear, which can benefit the early condition monitoring and fault diagnosis of the gear tooth wear.

A modified three-level laser kinetics model for a pulsed high-power optically pumped gas terahertz laser is introduced and used to model the lasing kinetics process of a gas terahertz laser system. We, for the first time to our knowledge, investigated the time evolution dynamics process of the pump intensity, population distribution among the energy levels, pump and THz signal gain coefficient, and the THz laser intensity within the pulsed D2O gas THz laser. High-power THz pulse with peak power of about 7.4 kW and pulse width of 145 ns at wavelength of 385 μm were obtained in the simulation, using an incident pump pulse with peak power of 2.2 MW and pulse width of 110 ns. THz pulse delay of 40 ns and pulse broadening of 35 ns were quantitatively analyzed. In addition, the experimental results for the pulse profile, pulse width, pulse broadening, pulse energy, and peak power are in agreement with the theoretical simulation results.

Sludge management represents a critical challenge because of complex compositions and poor dewaterability. Fe

To monitor the gradual wear process of gears, the wear behavior at different lifetime phases has to be fully understood in theory. The traditional Archard’s wear model was mainly developed for dry contact. However, for the most gear transmission systems, the gears normally work under the mixed EHL regime, and the two contact gear surfaces are separated by a thin film to avoid the asperity contact. Therefore, the traditional Archard’s wear model is inappropriate to estimate the wear depth of the lubricated contact. Moreover, the numerical simulation of helical gear wear was rarely studied because of the time-varying characteristics. To simulate the wear depth of helical gear in more realistic, the helical gear was regarded as a combination of a number of spur gears with a continuous different angle. By estimating the wear depth of each slice of the spur gear, the wear depth of the helical gear can be achieved. Besides, this paper employs a modified Archard’s wear model which is suitable for the contact in the mixed EHL regime. In this model, the asperity contact pressure was derived from the Hertzian contact theory, and both the friction effect, contact temperature, surface roughness, load sharing, and lubricant characteristics were considered for more realistic operating conditions. The sliding distance at each meshing position was determined by the single point observation method. The numerical results illustrate that the wear depth simulated from the mixed EHL regime is much lower than that under the dry contact condition, which is in good accordance with the lubrication theory. In addition, the wear depth increases with the operating load but decreases with the operating speed.

Purified terephthalic acid (PTA) wastewater with properties of poor biodegradation and high toxicity is produced from refining and synthesis of petrochemical products. In this study, a lab-scale hollow fiber membrane bioreactor (HF-AnMBR) fed with synthetic PTA wastewater was operated over 200 days with stepwise decreased hydraulic retention time (HRT) to investigate the long-term performance, membrane fouling mechanism and microbial community evolution. Results showed that a stable chemical oxygen demand (COD) removal rate of 65.8 ± 4.1% was achieved at organic loading rate of 3.1 ± 0.3 g-COD/ L -reactor/d and HRT 24 h, under which the methane production rate reached 0.33 ± 0.02 L/ L -reactor/d. Further shortening HRT, however, led to the decreased COD removal efficiency and low methane bioconversion. A mild membrane fouling occurred due to the production of colloidal biopolymers and the interaction between increased colloidal substances secreted/cracked by microorganisms and membrane interface. Further 16S rRNA analysis indicated that microbial diversity and richness had changed with the variation of HRT while Methanosaeta, and Methanolinea species were always the dominant methanogens responsible for methane production. The results verify that HF-AnMBR is an alternative technology for PTA wastewater treatment along with energy harvesting, and provide a new avenue toward sustainable petrochemical wastewater management.

Bioeletromethanogenesis, as a cutting-edge option to capture CO2 and produce multi-carbon biofuels, has received extensive attraction. However, how electroactive biofilm (EAB) as the biocatalyst drives CO2 electromethanogenesis is still not well recognized. In this study, a two-chamber bioelectrochemical cell equipped with a hybrid skirt-shaped cathode was constructed and the electrocatalytic performance of EAB and the electron shuttling mechanisms involved in extracellular electron transfer (EET) were systematically studied. The EAB colonizing on biocathode showed an excellent cathodic electrocatalytic activity and the minimum charge transfer resistance. The CH4 production rate of 298.0 ± 46.7 mL/L/d was obtained at the cathodic potential of −1.0 V vs. Ag/AgCl with the highest Coulombic efficiency of 75.8 ± 9.9%. The gel-like extracellular polymeric substances, secreted by EAB, facilitated the adhesion/aggregation of microbes and EAB development. Further analysis suggested that CO2 electromethanogenesis exhibited a positive association with Methanobacterium (54.4%) in EAB. Moreover, metagenome analysis confirmed the presence of direct EET-related genes (i.e., hdrA, ehaA, and ehbC), which accelerated the formation of corresponding functional protein complexes (particularly heterodisulfide reductase A, HdrA) and electron exchange. The mechanism for electron shuttling process in catalyzing CO2 electromethanogenesis was further proposed. This study provides a new insight into direct extracellular electron transfer (DEET) mechanisms of CO2 electromethanogenesis, and is useful for promoting EAB electrocatalytic activities and CO2 emission reduction and reuse.

This study presents a gene expression programming (GEP) method for determining soil thermal conductivity (k). A large and reliable database containing 444 thermal conductivity measurements was collected and employed to develop the GEP model. Four governing parameters (i.e., porosity, saturation degree, quartz content and temperature) were selected as inputs based on a Pearson correlation analysis. Through the determination of the optimal parameter setting of GEP, a predictive equation was first proposed. Then, the proposed GEP model was evaluated and compared with several previous models. The results indicate the proposed GEP model has higher prediction precision than the other models. In addition, a monotonicity analysis was performed to validate the rationality of the proposed GEP model. According to the monotonicity analysis results, the soil thermal conductivity increases with the increase of saturation degree, quartz content and temperature, and it decreases with the increasing of the porosity. Finally, a parametric analysis was conducted to investigate the significance of the inputs. The results show that the effect rank of these four input variables on soil thermal conductivity is saturation degree > porosity > temperature > quartz content, which can provide a reference for similar projects.

In gear transmission systems, the gear tooth surface will get inevitable worn due to various abnormal working conditions. The wear on gear tooth surface will cause deformation of the gear tooth profile. Furthermore, the wear will cause changes in gear tooth mesh stiffness, then deteriorate the gear dynamics. In the past decades, a number of studies had been carried out to investigate the process of gear tooth wear and its monitoring techniques. However, the interactions between the gear dynamics and gear tooth wear are still not got fully investigated. Therefore, this study proposed an eight-degree of freedom dynamic model to study the dynamic behaviours of the gear system with gear wear. When modelling the dynamic behaviours, a modified time-varying mesh stiffness (TVMS) model was derived by employing the potential energy method. The numerical results show that with the increase of the wear depth, the amplitudes at 1 × fm1 and 2 × fm2 increase gradually which can reflect the progressive wear process. Furthermore, an experimental study was conducted based on a run-to-fatigue test and the gear vibration responses were studied using the well-known de-noising method Time synchronous averaging (TSA). The experimental validation has a good consistency with the numerical analysis.

The friction arising between gear tooth pairs is a main factor influencing the operational efficiency and fatigue life of gear transmissions. In this paper, a numerical analysis method is proposed for predicting friction characteristics including friction coefficient, normal forces and friction forces of involute spur gear under elastohydrodynamic lubrication (EHL) condition and different wear phases. The gear surface wear model based on widely used Archard’s formula is employed to predict the change of tooth profiles. The results show that friction forces of meshing points in double tooth-meshing zone near dedendum firstly decrease and then increase with the increase of wear depth. Friction forces of meshing points in double tooth-meshing zone near addendum firstly increase and then decrease with the increase of wear depth. Friction forces of meshing points in single tooth-meshing zone decrease slightly. This study provides a theoretical basis for the optimal design of tribological behaviour of gear for reducing the occurrence of gear failures and increasing the gear service life. Moreover, it also helps to understand the dynamic behaviour of gears under different operation phases for developing accurate condition monitoring methods.

As the contact point and contact path seriously affect the spiral bevel gear mesh characterize, the tooth modification method was applied to manufacture, and tooth contact markers can verify the contact area. However, when the contact area wear happen, the actual contact area may be changed which is not the expectation. And hence, the spiral bevel gear transmission is more sensitive to wear compare with parallel shaft gear transmission. The vibration response would reflect the wear severity. Nonetheless, the relative motion of spiral bevel gear contacting areas, which cause tooth surfaces wear, is rather complex on account of time-vary proportion of rolling and sliding. Moreover, the wear is not uniform, if there were some defects on the surface, the wear would intensify the defect. Modulation signal bispectrum (MSB) has been proven to be an effective monitoring approach which is sensitive to gear tooth defect. Spiral bevel gear tooth modification, which can change the contact area, is very important to the dynamic performance. To assess the improvement of the modification to tooth wear, a run-to-failure experiment of modification spiral bevel gear was carried out under accelerated wear conditions, and MSB method is applied to analysis the gearbox housing vibration signal to indicate the wear progress. The experimental results demonstrated the accuracy and reliability of the MSB-SE monitoring approach proposed.