Search

Based on the sample entropy algorithm in nonlinear dynamics, an improved sample entropy method is proposed in the aerodynamic system instability identification for the stall precursor detection based on the nonlinear feature extraction algorithm in an axial compressor. The sample entropy algorithm is an improved algorithm based on the approximate entropy algorithm, which quantifies the regularity and the predictability of data in time series. Combined with the spatial modes representing for the rotating stall in the circumferential direction, the recognition capacity of the sample entropy is displayed well on the detection of stall inception. The indications of rotating waves are extracted by the circumferential analysis from modal wave energy. The significant ascendant in the amplitude of the spatial mode is a pronounced feature well before the imminence of stall. Data processing with the spatial mode effectively avoids the problems of inaccurate identification of a single measuring point only depending on pressure. Due to the different selections of similarity tolerance, two kinds of sample entropy are obtained. The properties of the development process of the identification model show obvious mutation phenomena at the boundary of instability, which reveal the inherent characteristic in aerodynamic system. Then the dynamic difference quotient is computed according to the difference quotient criterion, after the smooth management by discrete wavelet. The rapid increase of difference quotient can be regarded as a significant feature of the system approaching the flow instability. It is proven that based on the principle of sample entropy algorithm, the nonlinear characteristic of rotating stall can be well described. The inception can be suggested by about 12–68 revolutions before the stall arrival. This prediction method presenting is accounted for the nonlinearity of the complex flow in stall, which is in a view of data fusion system of pressure for the spatial mode tracking.

The balance between accuracy and computational complexity is currently a focal point of research in dynamical system modeling. From the perspective of model reduction, this paper addresses the mode selection strategy in Dynamic Mode Decomposition (DMD) by integrating an embedded fractal theory based on fractal dimension (FD). The existing model selection methods lack interpretability and exhibit arbitrariness in choosing mode dimension truncation levels. To address these issues, this paper analyzes the geometric features of modes for the dimensional characteristics of dynamical systems. By calculating the box counting dimension (BCD) of modes and the correlation dimension (CD) and embedding dimension (ED) of the original dynamical system, it achieves guidance on the importance ranking of modes and the truncation order of modes in DMD. To validate the practicality of this method, it is applied to the reduction applications on the reconstruction of the velocity field of cylinder wake flow and the force field of compressor blades. Theoretical results demonstrate that the proposed selection technique can effectively characterize the primary dynamic features of the original dynamical systems. By employing a loss function to measure the accuracy of the reconstruction models, the computed results show that the overall errors of the reconstruction models are below 5%. These results indicate that this method, based on fractal theory, ensures the model’s accuracy and significantly reduces the complexity of subsequent computations, exhibiting strong interpretability and practicality.

Germline or somatic loss-of-function mutations of fumarate hydratase (FH) predispose patients to an aggressive form of renal cell carcinoma (RCC). Since other than tumor resection there is no effective therapy for metastatic FH-deficient RCC, an accurate method for early diagnosis is needed. Although MRI or CT scans are offered, they cannot differentiate FH-deficient tumors from other RCCs. Therefore, finding noninvasive plasma biomarkers suitable for rapid diagnosis, screening, and surveillance would improve clinical outcomes. Taking advantage of the robust metabolic rewiring that occurs in FH-deficient cells, we performed plasma metabolomics analysis and identified 2 tumor-derived metabolites, succinyl-adenosine and succinic-cysteine, as excellent plasma biomarkers for early diagnosis. These 2 molecules reliably reflected the FH mutation status and tumor mass. We further identified the enzymatic cooperativity by which these biomarkers are produced within the tumor microenvironment. Longitudinal monitoring of patients demonstrated that these circulating biomarkers can be used for reporting on treatment efficacy and identifying recurrent or metastatic tumors.

The widespread use of polystyrene has brought great convenience to people's lives, but inappropriate recycling practices can also have a significant negative impact on the environment and public health. In this work, waste polystyrene was refined using maleic acid anhydride to produce flow improvers. The effect of the modified polystyrene perception of viscosity reduction and pour point depression of Henan oil was evaluated. The results show that modified polystyrene reduces the viscosity of Henan oil by 96.5% at most and depresses the pour point by 6.1 °C at most. Fourier transform infrared (FT-IR) and proton nuclear magnetic resonance (1HH NMR) were used to investigate the modified polystyrene. The mechanism of modified polystyrene that improves the fluidity of heavy oil was proposed and analyzed through the study of wax crystal morphology (at 20 °C below the pour point). [ABSTRACT FROM AUTHOR]

Depleted gas reservoirs are important places for the rebuilding of gas-storage reservoirs. In order to demonstrate the feasibility of constructing and operating such underground gas storage, a low-permeability gas-storage seepage model considering fracture development was developed and established. The model was solved using semi-analytical methods, and the pressure–response characteristics during natural gas injection were analyzed. The impact of gas injection volume on formation pressure has been clarified, and the calculation method for ultimate injection pressure has been determined. Additionally, through numerical simulation methods, the migration law of acidic gas during gas injection, the variation law of produced acidic gas concentration, and the main control factors affecting the concentration of the produced acidic gas were studied. Furthermore, measures to reduce the concentration of the acidic gas produced were proposed. Finally, injection and production plans were designed for typical depleted acidic gas reservoirs, simulating the operation of gas storage for 12 cycles. The results indicate that the quality of natural gas produced in the third cycle can meet the Class II standard for commercial natural gas. Through this study, the feasibility of constructing gas-storage facilities for acidic depleted gas reservoirs has been demonstrated, and injection and production strategies for this type of gas reservoir have been proposed. [ABSTRACT FROM AUTHOR]

The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control.

In order to effectively identify the signs of instability in the aerodynamic system of an axial compressor, a wavelet singular spectral entropy algorithm incorporated within the wavelet transform, singular value decomposition and information entropy is proposed to describe the distribution complexity of the spatial modalities in the flow field. This kind of identification design can accurately distinguish the boundary between the stable and unstable states of the internal flow field from the view of a dynamic system. On the basis of the information entropy algorithm, the wavelet singular spectral entropy algorithm is designed to integrate with the advantages of wavelet transform analysis on the time-frequency localization and singular value decomposition for signal processing and data mining together. So that the quantitative analysis of the definition of rebuilding a system image can be achieved by the solution of wavelet singular spectral entropy. This method can automatically extract the transient information of the space mode in the time-frequency domain. It effectively avoids the shortcoming that the feature extraction on spatial information cannot be accomplished from multiple angles with the single information entropy algorithm. In the data processing of instability signals under different speeds, the wavelet singular spectral entropy algorithm shows a greater advantage in the early warning for compressor stall. The result shows that the value of the wavelet singular spectral shows an obvious mutation when the aerodynamic system approaches the instability boundary. According to the threshold set, the identification hybrid algorithm can detect the stall precursor about 23~96 r in advance. Compared to the single information entropy algorithm, the hybrid wavelet singular spectral entropy algorithm is able to shift to an earlier precursor identification by about 11~82 r. This established hybrid identification algorithm accounts for the nonlinearity of the aerodynamic system, providing a new perspective for the nonlinear system instability identification.

Based on the COMSOL software, body forces substituted into the Reynolds-averaged Navier–Stokes (RANS) equations as the source terms instead of the actual blade rows were improved to better predict the compressor performance. Improvements in parallel body force modeling were implemented, central to which were the local flow quantities. This ensured accurate and reliable off-design performance prediction. The parallel force magnitude mainly depended on the meridional entropy gradient extracted from three-dimensional (3D) steady single-passage RANS solutions. The COMSOL software could easily and accurately translate the pitchwise-averaged entropy into the grid points of the body force domain. A NASA Rotor 37 was used to quantify the improved body force model to represent the compressor. Compared with the previous model, the improved body force model was more efficient for the numerical calculations, and it agreed well with the experimental data and computational fluid dynamics (CFD) results. The results indicate that the improved body force model could quickly and efficiently capture the flow field through a turbomachinery blade row.

Focusing on the identification of dynamic system stability, a hybrid neural network model is proposed in this research for the rotating stall phenomenon in an axial compressor. Based on the data fusion of the amplitude of the spatial mode, the nonlinear property is well characterized in the feature extraction of the rotating stall. This method of data processing can effectively avoid the inaccurate recognition of single or multiple measuring sensors only depending on pressure. With the analysis on the spatial mode, a chaotic characteristic was shown in the development of the amplitude with the first-order spatial mode. With the prerequisite of revealing the essence of this dynamic system, a hybrid radial basis function (RBF) neural network was adopted to represent the properties of the system by artificial intelligence learning. Combining the advantages of the methods of K-means and Gradient Descent (GD), the Chaos–K-means–GD–RBF fusion model was established based on the phase space reconstruction of the chaotic sequence. Compared with the two methods mentioned above, the calculation accuracy was significantly improved in the hybrid neural network model. By taking the strategy of global sample entropy and difference quotient criterion identification, a warning of inception can be suggested in advance of 12.3 revolutions (296 ms) with a multi-step prediction before the stall arrival.