Poly(butylene succinate) (PBS) has good impact strength and high elongation at break. It is used to toughen biodegradable poly(lactic acid) (PLA) materials because it can considerably improve the toughness of PLA without changing the biodegradability of the materials. Therefore, this approach has become a hotspot in the field of biodegradable materials. A review of the physical and chemical modification methods that are applied to improve the performance of PLA/PBS blends based on recent studies is presented in this article. The improvement effect of PLA/PBS blends and the addition of some common fillers on the physical properties and crystallization properties of blends in the physical modification method are summarized briefly. The compatibilizing effects of nanofillers and compatibilizing agents necessary to improve the compatibility and toughness of PLA/PBS blends are described in detail. The chemical modification method involving the addition of reactive polymers and low-molecular-weight compounds to form cross-linked/branched structures at the phase interface during in situ reactions was introduced clearly. The addition of reactive compatibilizing components is an effective strategy to improve the compatibility between PLA and PBS components and further improve the mechanical properties and processing properties of the materials. It has high research value and wide application prospects in the modification of PLA. In addition, the degradation performance of PLA/PBS blends and the methods to improve the degradation performance were briefly summarized, and the development direction of PLA/PBS blends biodegradation performance research was prospected.
At present, the main purpose of gas turbine fault prediction is to predict the performance decline trend of the whole system, but the quantitative and thorough performance health index (PHI) research of every major component is lacking. Regarding the issue above, a long-short term memory and gas path analysis (GPA) based gas turbine fault diagnosis and prognosis method is proposed, which realizes the coupling of fault diagnosis and prognosis process. The measurable gas path parameters (GPPs) and the health parameters (HP) of every main component of the goal engine are obtained through the adaptive modeling strategy and the gas path diagnosis method based on the thermodynamic model. The predictive model of the Long-Short Term Memory (LSTM) network combines the measurable GPPs and the diagnostic HPs to predict the HPs of each major component in the future. Simulation experiments show that the proposed method can effectively diagnose and predict detailed, quantified, and accurate PHIs of the main components. Among them, the maximum root mean square error (RMSE) of the diagnosed component HPs do not exceed 0.193%. The RMSE of the best prediction model is 0.232%, 0.029%, 0.069%, and 0.043% in the HP prediction results of each component, respectively.
In this study, an interfacial slip model including the limiting shear stress is proposed and applied to the thermal elastohydrodynamic lubrication (EHL) analysis of a helical gear pair. The main difference between the proposed model and the classical EHL model is that, the term of entrainment velocity in Reynolds equation is modified. The influences of interfacial slip, thermal effect, initial limiting shear stress and operating conditions on the tribological properties are evaluated. Due to the interfacial slip, the pressure distribution moves towards the inlet region, and the fluctuation distributions of entrainment velocity and film thickness are similar to the trigonometric function. The influence of thermal effect on interfacial slip cannot be ignored, especially in the case of high speed and heavy load. As the input torque and input rotational speed increase, the interfacial slip gradually extends to the whole meshing process.
As the core power for the aviation industry, shipbuilding industry, and power station industry, it is essential to ensure that the gas turbines operate safely, reliably, greenly and efficiently. Learn from the advantages and disadvantages of the thermodynamic model based and data-driven artificial intelligence based gas-path diagnosis methods, a newfangled gas turbine gas-path diagnosis approach on the basis of knowledge data-driven artificial intelligence is proposed. That is a hybrid method of deep learning and gas path analysis. First, gas turbine thermodynamic model of the object to be diagnosed is constructed by adaptation modeling strategy. And the engine thermodynamic model is taken as the basal model to simulate various gas path faults. Secondly, a large number of knowledge data corresponding to component health parameters and gas turbine boundary condition parameters & gas-path measurable parameters are simulated by setting different component health parameter values and different boundary conditions based on this basal model. And next, define the vector composed of the boundary condition parameters & the gas path measurable parameters in the knowledge database as the input vector, and the component health parameter vector as the output vector, and a deep learning model for regression modeling of this knowledge database is designed. At last, along with the gas turbine engine runs, the trained model outputs component health parameters in real time after trained deep learning model is deployed to the corresponding gas turbine power plant. The simulation experiment results show that, accurate and quantified health parameters of each gas path component can be obtained by the proposed method in this paper, and the overall root mean square error does not exceed 0.033%, and the maximum relative error does not exceed 0.36%, which illustrates the proposed method has great application potential. INDEX TERMSGas turbine; thermodynamic model; component health parameter; gas path analysis; deep learning; SYMBOLS AND ABBREVIATIONS GPA Gas path analysis EOH Equivalent operating hour GRNN Generalized regression neural network BP Back propagation neural network RBF Radial basis function neural network DNN Deep learning model ANN Artificial neural network FC Fully connected layer SF Component health parameters u Boundary condition parameters Z Gas-path measurable parameters
Every country, including China, is deeply concerned and interested in the topic of agricultural machinery automation. The world’s population is growing at an astronomical rate, and as a result, the need of food is also growing at an astronomical rate. Farmers are forced to apply more toxic pesticides since traditional methods are not up to the task of meeting the rising demand. This has a major impact on agricultural practices, and in the long run, the land becomes barren and unproductive. Intelligent technologies such as Internet of Things, wireless communication, and machine learning can help with crop disease and pesticide storage management, as well as water management and irrigation. In this paper, we design and analyze an intelligent system that automatically predicts the agricultural land features for irrigation purpose. Initially, the dataset is collected and preprocessed using normalization. The features are extracted using principal component analysis (PCA). For automatic prediction by the equipment, we propose heterogeneous fuzzy-based artificial neural network (HF-ANN) with genetic quantum spider monkey optimization (GQ-SMO) algorithm. Analyses and comparisons are made between the proposed approach and current methodologies. The findings indicate the effectiveness of the proposed system.
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