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For Ti alloys used in oil drilling pipe, the fatigue crack propagation (FCP) resistance is one of the key properties for petroleum industrial applications. In this work, the fatigue crack propagation behaviors of bimodal, equiaxed, lamellar, and Widmanstatten microstructures have been systematically investigated and characterized by SEM and EBSD. The results show that the Widmanstatten microstructure containing α colonies has higher yield strength (877 MPa), moderate elongation (9.3%), and better FCP resistance compared to other specimens. During the process of crack propagation, the large α colonies can effectively deflect the cracks and induce secondary cracks, which significantly impedes the main crack propagation. Additionally, 101¯2<101¯1¯> tensile twinning can occasionally occur within the large α colony along the crack, improving the plasticity of the crack tip and resulting in a decrease in fatigue crack propagation rate (FCPR). Highlights: Fatigue crack propagation behaviors of different microstructures were investigated.The Widmanstatten exhibits good tensile properties and crack propagation resistance.The 101¯2<101¯1¯> tensile twinning was found inside the large α colony along crack. [ABSTRACT FROM AUTHOR]

Temperature compensation is the main measure to solve the problem that the detection accuracy of non-dispersive infrared CO2 gas sensor is affected by temperature. As the measurement accuracy of the non-dispersive infrared CO2 gas sensor is easily affected by the ambient temperature, this article analyzes the reasons why the sensor is affected by temperature, and proposes a temperature compensation method that integrates the Whale Algorithm (WOA) and BP neural network. The whale algorithm is used to optimize the weights and thresholds of the BP neural network to build a temperature compensation model for the non-dispersive infrared CO2 gas sensor and compare the superiority with the traditional BP neural network model and particle swarm optimization (PSO) BP neural network model. The experimental results show that the temperature compensation model error of WOA-BP algorithm is lower than 30 ppm, and the average absolute error percentage is 3.86%, which is far better than BP neural network and PSO-BP neural network, and effectively reduces the influence of temperature on the accuracy of the sensor. [ABSTRACT FROM AUTHOR]

The temporal variation of wind power is primarily influenced by wind speed, exhibiting high levels of randomness and fluctuation. The accuracy of short-term wind power forecasts is greatly affected by the quality of Numerical Weather Prediction (NWP) data. However, the prediction error of NWP is common, and posing challenges to the precision of wind power prediction. To address this issue, the paper proposes a NWP wind speed error correction model based on Residual Network-Gated Recurrent Unit (ResNet-GRU). The model corrects the forecasted wind speeds at different heights to provide reliable data foundation for subsequent predictions. Furthermore, in order to overcome the difficulty of selecting network parameters for the combined prediction model, we integrate the Kepler Optimization Algorithm (KOA) intelligent algorithm to achieve optimal parameter selection for the model. We propose a Convolutional Neural Network-Long and Short-Term Memory Network (CNN-LSTM) based on Attention Mechanism for short-term wind power prediction. Finally, the proposed methods are validated using data from a wind farm in northwest China, demonstrating their effectiveness in improving prediction accuracy and their practical value in engineering applications. [ABSTRACT FROM AUTHOR]

Wind energy has become an essential part of the energy power source of current power systems since it is eco-friendly and sustainable. To optimize the operations of wind farms with the constraint of satisfying the power demand, it is critical to provide accurate predictions of wind power generated in the future. Although deep learning models have greatly improved prediction accuracy, the overfitting issue limits the application of deep learning models trained under one condition to another. A huge number of data are required to train a new deep learning model for another environment, which is sometimes not practical in some urgent situations with only very little training data on wind power. In this paper, we propose a novel learning method, named metareservoir computing (MRC), to address the above issue, combining reservoir computing and meta-learning. The reservoir computing method improves the computational efficiency of training a deep neural network for time series data. On the other hand, meta-learning is used to improve the initial point and other hyperparameters of reservoir computing. The proposed MRC method is validated using an experimental dataset of wind power compared with the traditional training method. The results show that the MRC method can obtain an accurate predictive model of wind power with only a few shots of data. [ABSTRACT FROM AUTHOR]

The wind turbine power curve model is critical to a wind turbine's power prediction and performance analysis. However, abnormal data in the training set decrease the prediction accuracy of trained models. This paper proposes a sample average approach-based method to construct an interval model of a wind turbine, which increases robustness against abnormal data and further improves the model accuracy. We compare our proposed methods with the traditional neural network-based and Bayesian neural network-based models in experimental data-based validations. Our model shows better performance in both accuracy and computational time. [ABSTRACT FROM AUTHOR]