Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms

Reconstruction of tokamak plasma safety factor profile using deep learning

In tokamak operations, accurate equilibrium reconstruction is essential for reliable real-time control and realistic post-shot instability analysis. The safety factor (q) profile defines the magnetic field line pitch angle, which is the central element in equilibrium reconstruction. The motional Stark effect (MSE) diagnostic has been a standard measurement for the magnetic field line pitch angle in tokamaks that are equipped with neutral beams. However, the MSE data are not always available due to experimental constraints, especially in future devices without neutral beams. Here we develop a deep learning-based surrogate model of the gyrokinetic toroidal code for q profile reconstruction (SGTC-QR) that can reconstruct the q profile with the measurements without MSE to mimic the traditional equilibrium reconstruction with the MSE constraint. The model demonstrates promising performance, and the sub-millisecond inference time is compatible with the real-time plasma control system

HRN: Haze-Relevant Network Using Multi-Object Constraints for Single Image Dehazing

In recent years, some deep learning dehazing methods based on atmospheric scattering model mostly solve the dehazing results by using depth convolution neural networks (CNNs) to estimate the medium transmission map in the model. However, these methods usually ignored the potential correlation between the transmission map and the atmospheric light in the atmospheric scattering model, which can lead to colour distortion and incomplete dehazing in the dehazing results. To address this problem, this paper first presents a novel Haze-Veil model to increase the correlation between the model parameters by constructing an atmospheric veil term. Then, based on the proposed model, a haze-relevant end-to-end network (HRN) is designed to estimate the parameters of this model and directly output the final clear image. In addition, a cost function is designed by defining multi-object constraint cost functions to further establish the connections between the statistical attributes of the hazy image and the out of HRN. Experiments on benchmark images, which include synthesized and real images, show that HRN effectively removes haze and outperforms most of the existing and state-of-the-art dehazing methods

Topological optimization of a variable cross-section cantilever-based piezoelectric wind energy harvester

Wind energy is a typical foreseeable renewable energy source. This study constructs and optimizes a variable cross-section cantilever-based piezoelectric energy harvester for low-speed wind energy harvesting. The Galerkin approach is usually used to discretize the continuum model and then get the ordinary differential equations. However, this method is more suitable for calculating uniformity than the variable cross-sectional beam model. To solve this problem, we proposed an improved piecewise Galerkin approach for discretizing the continuum model with a variable cross section. By modifying the boundary expressions and modal functions between segments, it can improve both computation speed and accuracy. COMSOL simulations demonstrate that natural frequencies calculated via the improved method are more accurate than those of the traditional Galerkin method. The method of multiple scales is applied to determine the output power and critical wind velocity. A distinctive numerical approach is presented for shape optimization by combining the analytical calculation method with the particle swarm optimization (PSO) technique for low-speed wind energy harvesting. Additionally, the logic function is chosen to produce the optimal shape’s fitting expression for engineering applications. With all the improvements, the output power of a variable cross-section beam-based harvester reaches as much as 3.668 times that of a uniform beam model, demonstrating the importance of structural optimization for this type of energy harvesters. Finally, experiments are set up to verify the optimization procedure. Actually, it builds an analytical framework for the adaptive selection of variable-section piezoelectric cantilever wind-induced vibration energy harvesters

The advantages of TCM in the treatment of gynecologic malignancies

Gynecological malignancies are characterized by high morbidity and mortality rates. With the development of society, the status of women continues to improve, yet the social pressure they bear increases daily. The incidence rate of gynecological malignancies in the female population has always remained at a high level, and the age of onset has shown a trend of getting younger. Common gynecological malignancies include cervical cancer, ovarian cancer, and endometrial cancer. Alarmingly, over 70% of patients are diagnosed at an advanced stage. This disease is mainly treated through surgery and radiotherapy, but there is still a relatively high recurrence rate after treatment. In recent years, with the development of traditional Chinese medicine (TCM), the advantages of TCM in the treatment of gynecological malignancies have gradually emerged. The entry of TCM into the treatment of Gynecologic malignancies is a tumor treatment method that has received close attention from the international medical community in recent times. TCM can be used throughout the whole process of tumor treatment. Combining Western medicine at different stages of the tumor, or giving different Chinese medicines alone, can minimize the toxic side effects of Western medicine treatment, alleviate symptoms, prolong survival and improve the quality of survival. Therefore, combining traditional Chinese medicine to provide individualized treatment for patients may become a better approach to cancer treatment. This article reviews the status and important role of TCM in gynecological malignancies in the hope of exploring new treatment modalities to mitigate the impact of gynecological malignancies on women’s health

Study on the extraction method of Glycyrrhiza uralensis Fisch. distribution area based on Gaofen-1 remote sensing imagery: a case study of Dengkou county

Glycyrrhiza uralensis Fisch., a perennial medicinal plant with a robust root system, plays a significant role in mitigating land desertification when cultivated extensively. This study investigates Dengkou County, a semi-arid region, as the research area. First, the reflectance differences of feature types, and the importance of bands were evaluated by using the random forest (RF) algorithm. Second, after constructing the G. uralensis vegetation index (GUVI), the recognition accuracy of G. uralensis was compared between the RF classification model constructed based on the January-December GUVI and common vegetation indices feature set and the support vector machine (SVM) classification model constructed on the GUVI feature set. Finally, the spectral characteristics of G. uralensis and other feature types under the 2022 GUVI feature set were analyzed, and the historical distribution of G. uralensis was identified and mapped. The results demonstrated that the blue and near-infrared bands are particularly significant for distinguishing G. uralensis. Incorporating year-round (January-December) data significantly improved identification accuracy, achieving a producer’s accuracy of 97.26%, an overall accuracy of 93.00%, a Kappa coefficient of 91.38%, and a user’s accuracy of 97.32%. Spectral analysis revealed distinct differences with G. uralensis of different years and other feature types. From 2014 to 2022, the distribution of G. uralensis expanded from the northeast of Dengkou County to the central and southwestern regions, transitioning from small, scattered patches to larger, concentrated areas. This study highlights the effectiveness of GUVI and RF classification models in identifying G. uralensis, demonstrating superior performance compared to models using alternative feature sets or algorithms. However, the generalizability of the RF model based on the GUVI feature set may be limited due to the influence of natural and anthropogenic factors on G. uralensis. Therefore, regional adjustments and optimization of model parameters may be necessary. This research provides a valuable reference for employing remote sensing technology to accurately map the current and historical distribution of G. uralensis in regions with similar environmental conditions

Numerical study on complex conductivity characteristics of hydrate-bearing porous media

The complex conductivity method is frequently used in hydro-/petro-/environmental geophysics, and considered to be a promising tool for characterizing and quantifying the properties of subsurface rocks, sediments and soils. We report a study on the complex conductivity characteristics of porous media containing gas hydrates through numerical modelling. The effects of the hydrate saturation, pore-water salinity and micro-distribution mode were studied, and hydrate-saturation evaluation correlations based on complex conductivity parameters were developed. A pore-scale numerical approach for developing the finite-element based models for hydrate-bearing porous media is proposed and a two-dimensional (2D) model is built to compute the complex conductivity responses of porous media under various conditions. We demonstrate that the simple 2D model can capture the dominant characteristics of the complex conductivity of hydrate-bearing porous media within the frequency range related to the induced polarization. The in-phase conductivity, quadrature conductivity and effective dielectric constant can be correlated with the saturation based on a power law in the log-log space, by which the hydrate-saturation evaluation models can be derived. A constant saturation exponent of the power-law correlation between the hydrate saturation and quadrature conductivity can be obtained when the pore-water conductivity exceeds 1.0 S/m. This is highly desirable in the hydrate-saturation models due to the variations of the pore-water conductivity in the processes of hydrate formation and dissociation. Within the framework of the complex conductivity analysis, the micro-distribution modes of hydrates in porous media can be categorized into two types. These are the fluid-suspending mode and grain-attaching mode. The in-phase conductivity exhibits significant variations under the same saturation and salinity but different micro-distribution modes, which can be attributed to the change in the tortuosity of the electrical conduction paths in the void space of porous media

Experimental and numerical perspective on the fire performance of MXene/Chitosan/Phytic acid coated flexible polyurethane foam

Recent discoveries of two-dimensional transitional metal based materials have emerged as an excellent candidate for fabricating nanostructured flame-retardants. Herein, we report an eco-friendly flame-retardant for flexible polyurethane foam (PUF), which is synthesised by hybridising MXene (Ti3C 2) with biomass materials including phytic acid (PA), casein, pectin, and chitosan (CH). Results show that coating PUFs with 3 layers of CH/PA/Ti3C 2 via layer-by-layer approach reduces the peak heat release and total smoke release by 51.1% and 84.8%, respectively. These exceptional improvements exceed those achieved by a CH/Ti3C 2 coating. To further understand the fundamental flame and smoke reduction phenomena, a pyrolysis model with surface regression was developed to simulate the flame propagation and char layer. A genetic algorithm was utilised to determine optimum parameters describing the thermal degradation rate. The superior flame-retardancy of CH/PA/Ti3C 2 was originated from the shielding and charring effects of the hybrid MXene with biomass materials containing aromatic rings, phenolic and phosphorous compounds

The effect of poly-β-hydroxyalkanoates degradation rate on nitrous oxide production in a denitrifying phosphorus removal system

Poly-beta-hydroxyalkanoates (PHAs) and free nitrous acid (FNA) have been revealed as significant factors causing nitrous oxide (N2O) production in denitrifying phosphorus removal systems. In this study, the effect of PHA degradation rate on N2O production was studied at low FNA levels. N2O production always maintained at approximately 40% of the amount of nitrite reduced independent of the PHA degradation rate. The electrons distributed to nitrite reduction were 1.6 times that to N2O reduction. This indicated that electron competition between these two steps was not affected by the PHA degradation rate. Continuous feed of nitrate was proposed, and demonstrated to reduce N2O accumulation by 75%. While being kept low, a possible compounding effect of a low-level FNA could not be ruled out. The sludge used likely contained both polyphosphate- and glycogen-accumulating organisms, and the results could not be simply attributed to either group of organisms. (C) 2014 Elsevier Ltd. All rights reserved