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Abstract In renewable energy power system, it has been the focus of attention to improve the system's flexibility to promote renewable energy utilization and low carbon emission. To improve the adjustment capability of power system integrating renewable energy, a new method that considers joint operation of nuclear power plants and pumped storage stations is proposed. First, to take the operational characteristics of nuclear power plants and pumped storage stations into account, the operational models of the two kinds of power stations are constructed. Second, an assessment model that is to evaluate the benefits of different peak shaving source is constructed. Furthermore, a system of benefits assessment indices is presented. Finally, the case of both nuclear and pumped storage stations participating in peak shaving adjustment is analyzed, verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The Japanese government officially announced the discharge of Fukushima nuclear wastewater into the sea on April 13, 2021, which has caused concern and strong opposition from countries worldwide and international environmental organizations. Through the analysis of relevant data, it is found that the hazards of ship ballast nuclear wastewater are reflected in the following: ship ballast water flows widely, the high discharge of ship ballast water, and the high reserves of Fukushima nuclear wastewater. In this case, the evolutionary game models of the maritime regulator and the carrier, and the society, maritime regulator and the carrier were constructed, respectively. In the evolutionary game model between the maritime regulator and the carrier, the carrier is not very motivated to deal with the ship's ballast water due to the influence of several related factors, especially the exorbitant treatment cost. After introducing the social supervision and reward system, maritime regulators found that controlling their own supervision cost and giving appropriate rewards to freighters with the participation of social supervision can effectively improve the motivation of freighters to deal with the ship's ballast water. In addition, through the comparative analysis of the before-and-after game model, it is found that the high cost required by the carriers to deal with ships' ballast water will directly affect the stable state that the game model tends to be. Therefore, the sustainable growth of the port's ecological environment benefits from pursuing affordable and effective ship's ballast water treatment. [Display omitted] • After the Fukushima nuclear wastewater was discharged, the hazard of ship ballast nuclear wastewater was obvious. • In the evolutionary game model composed of maritime regulators and carriers, the choice of maritime regulators is closely related to the cost of supervision. • With the participation of social supervision, the enthusiasm of the carrier to deal with the ship's ballast water is significantly improved. • Under the social supervision, the processing cost of the carrier is an important factor affecting the choice of the three-party strategy in the game. [ABSTRACT FROM AUTHOR]

• Frequency containment reserve using wind farm and contingency state analysis. • An enhanced model of Frequency-constrained Contingency-State-Analysis is developed. • Both conventional generators and converter-interfaced generators as well as Under-Frequency-Load-Shedding are considered in the new models. • A set of new frequency dynamic indexes are calculated based on a Cross-Entropy-based Monte Carlo simulation. With the increasing penetration of converter-interfaced generators, the frequency containment reserve (FCR) from conventional generators keeps going down, leading to a potential risk of frequency instability under contingencies. Consequently, Converter-interfaced generators are required to provide FCR and participate in the corrective rescheduling. Nevertheless, how to assess the FCR and quantify the adequacy of FCR under contingencies is a big challenge in modern new power system. To address this challenge, a cross-entropy-based frequency-constrained contingency-state-analysis (FC-CSA) model is proposed in this paper. Notably, both frequency control (FC) of units (i.e., conventional synchronous generators and converter-interfaced generators), and under frequency load shedding (UFLS) are incorporated in the primary frequency response. Then a unified system frequency response (SFR) function representing frequency dynamic is derived. This SFR function is extracted and reformulated as a group of mixed-integer linear constraints and participates in the traditional CSA model. Moreover, a set of frequency dynamic indexes, i.e., Expectation of UFLS risk, Expectation of FCR from conventional and converter-interfaced generators, is extended to depict the FCR that the power system requires. These indexes are calculated by the FC-CSA in a cross-entropy-based monte carlo simulation (CE-MCs). Case studies on a modified IEEE 6-bus test system and IEEE 118-bus test system are carried out to demonstrate the effectiveness of the proposed FC-CSA model. [ABSTRACT FROM AUTHOR]

• A conditional PDF model is presented to characterize fatigue life distribution under CAC loading. • A fatigue damage accumulation model is developed to account for the loading sequence effects. • The fatigue damage accumulation rule of material under the multi-level cyclic loading is elaborated. • A general framework is proposed to predict fatigue life under the multi-level cyclic loading. Fatigue life prediction of materials under the cyclic loading is still a particularly challenging task remains to be resolved. This study aims to develop a generic framework for fatigue life prediction under the multi-level cyclic (MLC) loading with consideration of the loading sequence effects. Firstly, a conditional probability density function (PDF) model is presented to quantify the fatigue life distributions of materials under any constant amplitude cyclic (CAC) loading. Subsequently, a fatigue damage accumulation model is proposed from the perspective of cumulative failure probability, which is capable of accounting for the nonlinear characteristics of damage accumulation and the loading sequence effects. Finally, a generic framework for fatigue life prediction of materials under the MLC loading is developed based on the proposed models. The fatigue life data of fiber reinforced composites under the two-level and three-level cyclic loading are utilized to verify the proposed framework. The results show that the predicted fatigue lives agree well with the fatigue test data of fiber reinforced composites. Moreover, the proposed framework can be easily extended to calculate fatigue life of materials under any MLC loading. [ABSTRACT FROM AUTHOR]

• Non-gaussian wind speed model can accurately describe the wind characteristics. • Vibration acceleration sensor contributes a lot to the real-time monitoring of wind turbine. • Reliability analysis can provide general maintenance strategy. After decades of rapid development, the wind power industry may be entering a period of technological maturity and maintenance. The components of the wind turbines mostly fail in a high-failure-risk period. Research on the reliability components can offer a basis for decisions on maintenance operations. In this paper, a wind turbine blade is subjected to wind load impacts year-round. The Lévy index is used to describe the instantaneous wind law. The data-driven method is used to describe the variation of blade failure related parameters. Based on this, the degradation failure and Lévy index of Wiener process are constructed. Based on the example analysis of blade failure of wind turbine in a wind farm in Daban city, Xinjiang province, a competitive model of failure probability of wind turbine under non-gaussian wind load is established. The statistical predictive model for competitive failures is closer to the actual characteristics of the wind farm. This offers data to help support site selection for individual turbines, and subsequent blade maintenance. [ABSTRACT FROM AUTHOR]

• The inherent correlation between stiffness degradation and strength degradation of FRP is explored. • A probability model of stiffness degradation is proposed based on the same damage state principle. • The proposed model is flexible enough to fit the stiffness degradation data of FRP laminates with good agreement. The stiffness degradation of fiber reinforced polymer (FRP) has significant effects on its fatigue life and reliability. In this study, the inherent correlation between stiffness degradation and strength degradation is explored, and a probability model that describing stiffness degradation of FRP is presented. Firstly, the evolution of fatigue damage in FRP is analyzed, and then it is characterized by stiffness degradation and strength degradation, respectively. Secondly, the randomness of initial stiffness and critical stiffness of FRP is considered, and a probability model of stiffness degradation is proposed based on the same damage state principle. Finally, the test data of FRP laminates are used to verify the proposed model, and the fitting accuracy is compared with several existing models. The results show that the stiffness degradation of FRP presents a fast-slow-fast trend during fatigue failure process, and the proposed model is capable of fitting stiffness degradation data of FRP with good agreement. [ABSTRACT FROM AUTHOR]

• A novel machine learning method is used to predict the multiaxial fatigue life of various materials. • Non-proportionality and phase differences are used to describe the loading paths in non-data form. • Adam algorithm is used to optimize the original model to avoid getting trapped in a local optimum. • Six metallic materials are used to validate the predictive performance of the proposed model. • The generalization and extrapolation capability of different machine learning models are compared. In this study, a neuro-fuzzy-based machine learning method is developed to predict the multiaxial fatigue life of various metallic materials. First, the fuzzy inference system and neural network are combined to identify and capture the nonlinear mapping relationship between multiaxial fatigue damage parameters and fatigue life. Non-proportionality and phase differences are introduced to characterize different loading paths. Next, the Adam algorithm is employed to update the premise parameters of the original model to achieve fast and accurate convergence. Then, subtractive clustering is applied to extract fuzzy rules between input variables and output for more efficient prediction. Moreover, the hyperparameters in the proposed model are automatically optimized by the adaptive opposition slime mould algorithm to obtain the optimal model. The predictive performance of the proposed model is verified by fatigue experimental data for six materials in published literature, which indicates that the proposed model can effectively predict the fatigue life of various materials under different loading paths. Meanwhile, compared with six classical machine learning models, it is found that the proposed model has better predictive performance and extrapolation capability. [ABSTRACT FROM AUTHOR]

• • A unified fatigue damage model is proposed based on the performance degradation of composites. • • A pair of residual strength and stiffness models is presented to characterize the degradation rules. • • Correlation between strength degradation and stiffness degradation of composites is elaborated. • • Fatigue life prediction methods are developed from the viewpoint of performance degradation. The performance degradation of composite materials under fatigue loading is so complex that the present knowledge is far from complete. This study aims to present a pair of residual strength and residual stiffness models based on a unified fatigue damage formula, and predict fatigue life under variable amplitude cyclic loading. Firstly, a unified fatigue damage model for composite materials is proposed, which is capable of characterizing fatigue damage development caused by strength degradation and stiffness degradation, respectively. Subsequently, a pair of residual strength and residual stiffness models is presented to estimate the performance degradation of composite materials. Finally, the corresponding life prediction methods are developed from the viewpoints of strength degradation and stiffness degradation, respectively. A set of experimental data for composite laminates are utilized to validate the proposed models and methods. The results show that the proposed residual strength and stiffness models are flexible enough to fit strength and stiffness degradation data with good agreement. Moreover, the developed life prediction methods are capable of calculating fatigue life of composite laminates with high accuracy. [ABSTRACT FROM AUTHOR]

A modified Partially-Averaged Navier-Stokes (PANS) turbulence model has been proposed and coupled with the actuator line method (ALM) to investigate the wind turbine wakes. The hybrid model has not yet been applied in wind turbine related simulations, and our work would be the first attempt to evaluate its capabilities in wind turbine wake study. Comprehensive studies have been carried out regarding mesh resolution effect and different levels of inflow turbulence intensity using ALM-PANS method. Numerical simulations of two wind turbines in tandem have been carried out and validated against the popular Large-eddy Simulation (LES) results and experimental data. Relative errors of the rotor power and thrust predictions from ALM-PANS computations against the experimental data are within 5%, which is the same level as LES computations. Profiles of normalized mean velocity and Reynolds stress in the wake have been presented to describe the wake propagation, and the simulation results from PANS and LES are in good agreement with measured data. Due to model simplicity and its low mesh resolution requirement, ALM-PANS has excellent potential in wind farm investigations. • A modified SST k − ω partially-averaged Navier-Stokes model has been proposed. • The new ALM-PANS model gives good predictions on wind turbine wakes. • Compared to ALM-LES, ALM-PANS method provides reliable results with lower costs. [ABSTRACT FROM AUTHOR]