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Ice crystal icing occurs in jet engine compressors, which can severely degrade jet engine performance. In this paper, two different numerical calculation methods, the Eulerian method and the Lagrangian method, were used to evaluate the dynamics, mass transfer, heat transfer, phase transition and trajectory of ice crystals. Then, we studied the effects of initial diameter, initial sphericity, initial temperature of ice crystal, and relative humidity of airflow on the phase transition and collection characteristics of ice crystal particles. Results indicate that the non-spherical characteristics of ice crystals have a significant impact on their impingement limits and collection characteristics. The collection coefficient of unmelted ice crystals is positively correlated with the initial particle diameter and sphericity, and negatively correlated with the initial particle temperature and the relative humidity of airflow. The melting rate of ice crystal particles on the impact surface increases exponentially with the initial diameter of the particles, linearly increases with the relative humidity of the airflow and initial temperature of the particles, and exponentially decreases with the sphericity of the particles. [ABSTRACT FROM AUTHOR]

The widely used plastics in our daily lives have resulted in ubiquitous microplastics and nanoplastics in wastewater, such as polyethylene terephthalate (PET). As an emerging green process for wastewater treatment and resource recovery, microalgal–bacterial granular sludge (MBGS) aligns with the concept of the circular economy. However, it is unclear whether the tiny PET can affect the MBGS process. Thus, this study investigated the impact of nano–sized PET (nPET) on the MBGS process. The results showed that 10 to 30 mg/L nPET had no obvious impact on pollutant removal as compared with the control group. However, the performance of the MBGS with the addition of 50 mg/L nPET became worse after 15 days. Scanning electron microscopy revealed that the MBGS adsorbed nPET by generating extracellular polymeric substances. Further microbial analyses showed that the algal abundance in prokaryotes slowly declined with increasing concentrations of nPET, while the reduced energy storage and electron transfer in eukaryotes might lead to an inferior performance at 50 mg/L nPET. Overall, the MBGS was demonstrated to exhibit good adaptability to nPET–containing wastewater, which showed the potential to be applied for the treatment of municipal wastewater containing nanoplastics. [ABSTRACT FROM AUTHOR]

In a chiller plant, primary or critical sensors are used to control the system operation while secondary sensors are installed to monitor the performance/health of individual equipment. Current sensor fault detection and diagnosis (SFDD) approaches are not applicable to secondary sensors which usually are not involved in the system control. Consequently, a hybrid multiple sensor fault detection, diagnosis and reconstruction (HMSFDDR) algorithm for chiller plants was developed. Machine learning and pattern recognition were used to predict the primary sensor faults through the comparison of the weekly performance curves. With the primary sensor signals reconstructed, the secondary sensor faults were estimated based on mass and energy balance. By applying the algorithm with various logged plant data and comparison with site checking results, a maximum of 75% effectiveness could be achieved. The merits of the present approach were further justified through off-site sensor testing which reinforced the usefulness of proposed HMSFDDR algorithm. [ABSTRACT FROM AUTHOR]

The building sector is one major primary energy consumer and pollutant emission source. In recent years, the building-related research studies on the potential use of Maisotsenko-cycle in energy systems have been increasing in recent years. The growing interest lies in its expanded applications beyond the air-conditioning systems (the main “energy consumers” in buildings) into the prime movers (the key players in power generation). In order to evaluate its application merits in the practical tri-generation system of the urban districts, an extensive computer simulation platform has been developed. Based on a case study, this paper describes the techniques in the mixed use of numerical tools in performing system optimization studies for distributed power application on a university campus site. The practicality of the methodology is demonstrated through a hypothetical tri-generation system primed with Maisotsenko combustion turbine cycle. The findings are very much interesting.

The diagnosis of blade crack faults is critical to ensuring the safety of turbomachinery. Blade tip timing (BTT) is a non-contact vibration displacement measurement technique, which has been extensively studied for blade vibration condition monitoring recently. The fault diagnosis methods based on deep learning can be summarized as studying the internal logical relationship of data, automatically mining features, and intelligently identifying faults. This research proposes a crack fault diagnostic method based on BTT measurement data and convolutional neural networks (CNNs) for the crack fault detection of blades. There are two main aspects: the numerical analysis of the rotating blade crack fault diagnosis and the experimental research in rotating blade crack fault diagnosis. The results show that the method outperforms many other traditional machine learning models in both numerical models and tests for diagnosing the depth and location of blade cracks. The findings of this study contribute to the real-time online crack fault diagnosis of blades. [ABSTRACT FROM AUTHOR]

Bearing fault diagnosis is an important research field for rotating machinery health monitoring. Recently, many intelligent fault diagnosis methods driven by big data, such as transfer learning, have been studied. However, there are two shortcomings for the prior transfer learning method in industry application. First, it is necessary to design a complex loss function to enhance the similarity between the two domains further. Second, previous studies required big data both in source and target task, without considering the lack of sufficient training samples. Inspired by relevant research work, this article proposes a local joint distribution discrepancy to increase similar features. A sub-domain adaptive transfer learning is designed to detect bearing faults based on the residual network. Two kinds of transfer experiments are designed to verify the method effectiveness. After that, the impact of small training samples and noise on the results is explored. The proposed method reaches high accuracy. [ABSTRACT FROM AUTHOR]

The S reservoir in the X Oilfield in Iraq has great development potential due to its rich geological reserves. However, the low permeability and strong heterogeneity of the reservoir lead to great differences in reservoir stimulation performance. In this study, an integrated reservoir model and differential stimulation mode are put forward to solve the above problems. First, the feasibility of fracturing is evaluated by laboratory experiments. Second, an integrated reservoir model is established, which mainly includes a rock mechanics model, fracturing simulation model, and numerical simulation model, and correct the integrated model by fracturing operation curves and production dynamic curves. Third, three types of stimulation areas are classified according to the combination of sweet spot types, and three different stimulation modes are proposed. In conclusion, a small-scale stimulation mode should be applied in the Type I area to maximize economic benefits. In the Type II area, the medium-scale stimulation mode should be performed to ensure certain productivity while achieving certain economic benefits. In the Type III area, the large-scale stimulation mode should be employed to obtain certain productivity while economic benefits must be above a limit. The differential stimulation model proposed in this paper has made a great reference for the efficient development of low-permeability carbonate rocks. [ABSTRACT FROM AUTHOR]

The improvements in signal-to-noise ratio (SNR) are indispensable to obtain the desired output signal of partial discharge (PD) under square wave pulsewidth modulation (PWM) voltage. In this article, the effect of complex PWM voltage parameters (rise time, fall time, frequency, duty cycle) on the SNR of PD tests is investigated. The results revealed that the sensor's high-frequency gain should be increased to improve the SNR under faster switching. PD impulses are more likely to be submerged in the residual interference under high-frequency voltage. The SNR can be improved by only detecting the PD at the rising front because the PD signal overlaps with commutation noise at the falling front of short duty cycle voltage, such as 0.03% duty cycle. The above results can reference the PWM parameters and improve the SNR in PD tests for rotating machines controlled by power electronics. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) detection plays a vital role in on-line condition monitoring of electrical apparatus in the power systems. However, the noise of PD measurements significantly degrades the performance of detection algorithms. In this paper, we focus on developing an adaptive filtering technique for the PD denoising problem. Heretofore, there are just a few literature reviews addressing the PD denoising based on such a method. The proposed recursive continuous S-shaped (RCSS) algorithm integrates the advantages of recursive strategy and continuous S-shaped function into adaptive noise cancellation (ANC) system, yielding enhanced filtering performance. The proposed algorithm can tackle PD noises in both Gaussian and impulsive scenarios, which is easy to implement in practical applications. The convergence behavior is also analyzed. Extensive simulation and experimental results confirm that the proposed algorithm can address polluted PD pulses, even in excessively noisy conditions, resulting in smaller mean square error (MSE) as compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

To improve the reliability of power systems, online cable condition monitoring must be implemented to replace traditional regular power outage maintenance. Existing online monitoring methods have certain limitations. In this article, a novel method based on low-frequency signal injection is proposed to monitor power cable insulation conditions online. For this method, a low-frequency signal is injected into the power system via the potential transformer (PT) open delta configuration. The cable conductor voltage and leakage current are detected. The interpolating windowed fast Fourier transform (FFT) algorithm is applied to calculate the dielectric loss angle. Then, the cable tangent delta ($\tan \delta $) can be deduced, and the cable condition can be assessed. A distribution network system is built based on the power system computer-aided design (PSCAD) software. The frequency and amplitude of the injected signal are simulated and discussed. An online monitoring platform is established in the laboratory. Thermal-aged cable samples and water tree-aged cable samples are prepared and measured. The experimental results confirm that the proposed method can effectively assess power cable insulation. [ABSTRACT FROM AUTHOR]

This paper presents a novel oscillation wave test system (OWTS) to detect the partial discharge (PD) of mounting defects in power cable insulation. In the proposed system, a charging capacitor is added to the circuit topology based on the traditional OWTS. The charging capacitor is charged to the desired voltage by a high-voltage DC power source. After the charging process, the test cable sample is charged by the capacitor. The resonant circuit is formed by the charging capacitor, the test cable, and the resonant reactor to create an oscillating decay voltage. With this novel method, no space charge is injected into the cable insulation. The effect of the charging capacitance on the charging efficiency is calculated with the Multisim software. This paper describes the working principle and the assembly of a laboratory prototype for the PD detection of a 500 m cable circuit. The test results show the feasibility of the novel OWTS method. [ABSTRACT FROM AUTHOR]

The onsite partial discharge (PD) detection of cable insulation is of great importance for power system maintenance. To preserve more basic features of denoised PD signal, in this paper, a PD denoising by a new class of affine projection algorithm (APA) is proposed based on the high-order error power (HOEP) criterion. We propose a new affine projection least mean absolute third algorithm (APLMATA), which exploits the HOEP criterion ($p=3$) and can achieve reliable performance under Gaussian interference. We further develop a modified APLMATA (MAPLMATA) to reduce the misadjustment with moderate computational burden. Analytical models are derived for MAPLMATA for Gaussian signals. To enhance the stability of the algorithm in generalized Gaussian noise scenarios, an affine project least mean $p$ th power algorithm (APLMPA) that includes the $p$ th power dependence on the error is presented. In particular, we apply the proposed APA-based algorithms to suppress the noise from PD signal in power cables. Simulation and experimental results demonstrate the effectiveness of the proposed algorithms for system identification and PD denoising. [ABSTRACT FROM AUTHOR]

This study investigates the microstructural changes of water trees in different stages of propagation. It is proposed that microstructural changes within a tree influence its macroscopic propagation. Five groups of XLPE cable samples with pinhole defects were subjected to an accelerated water tree aging experiment for different aging times. The morphologies and sizes of water trees in different aging periods were observed by optical and scanning electron microscopes. The microscopic observations show that the propagation rates of water trees can change during different aging stages. In the initial stage, the propagation rate of water trees can reach 0.46 μm/hour. However, in the stagnation stage, the propagation rate decreases considerably to as low as 0.01 μm/hour. In the late stage, water trees with irregular shapes can form, and the propagation rate of the longest water tree branches can rise to 0.33 μm/hour. In addition, the shape of the microvoids within the water trees can change. In the later period of the stagnation stage, microvoids with irregular shapes (not round shape) can form by the connection between microvoids with different sizes. The simulation results of the electric field indicate that the microstructural change within the water trees is the fundamental reason for the changes of the propagation rates. In different propagation stages, the changes in the density, the sizes, and the shape of the microvoids can lead to the changes of water distribution and electric field distribution in water tree regions, which further results in different propagation rates. [ABSTRACT FROM AUTHOR]