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Graphical abstract Highlights • Refined variational mode decomposition with adaptive level number is developed. • Conditional kernel density estimation with optimized bandwidth is proposed. • Based on above techniques, real time decomposition-based hybrid method is offered. • Deterministic and probabilistic predictions are provided by the hybrid method. • Case studies show hybrid method performs well in reliability and stability. Abstract Short-term wind speed prediction is an important task for the wind energy development. However, due to intermittency and uncertainty of wind resources, it is difficult to be achieved only by the deterministic prediction. Hence, there is a motivation to develop a novel method to further consider the uncertainty. The originality is to develop an innovative hybrid wind speed forecasting model based on empirical mode decomposition, variational mode decomposition, sample entropy and conditional kernel density estimation. More specifically, the original data are decomposed in real time by the refined variational mode decomposition, where the number of decomposition levels is adaptively determined by empirical mode decomposition and sample entropy. Then, conditional kernel density estimation with the bandwidth optimized by normal reference criterion is used to obtain the predictive probability density function for each subseries, by which the expectations and variances are derived. Finally, the deterministic prediction is produced by summarizing these expectations and the probabilistic prediction interval is obtained by the covariance and the Gaussian distribution assumption on target wind speed. It should be emphasized that some hypotheses are adopted in the proposed method: the correlations among different subseries can be characterized by the covariance and the target wind speed follows Gaussian distribution. Two case studies based on the measured data are used to evaluate the performance of the proposed method. The results show the improvement by the proposed method is up to 20% compared with empirical mode decomposition-based method. The main conclusion is that the proposed method may provide the better prediction over other models for the wind speed data with nonstationarity. [ABSTRACT FROM AUTHOR]

The short-term wind speed prediction is important for the connection of wind power to the grid. However, it is challenging due to the stochastic and intermittent characteristics of wind speed. In this paper, a novel wind speed prediction method named as EWT-ARIMA-LSSVM-GPR-DE-GWO method is proposed, where the empirical wavelet transform (EWT) is applied to decompose the original wind speed signal into multiple intrinsic mode functions (IMF), and various prediction models including the statistical model of autoregressive integrated moving average (ARIMA), artificial intelligence models of least squares support vector machine (LSSVM) and Gaussian process regression (GPR) are used for predicting the IMF in sequence from low frequency to high frequency based on the superiority of each model. Differential evolution-grey wolf optimizer (DE-GWO) algorithm is used to optimize the hyperparameters of artificial intelligence prediction models of LSSVM and GPR. The proposed short-term wind speed prediction method shows superior performance in both accuracy and stability with validation by the measurement data. Both the EWT signal decomposition and DE-GWO hyperparameter optimization are effective to promote the prediction accuracy for wind speeds with large non-stationary. • A novel short-term wind speed prediction method is proposed. • The effect of signal decompositon on the wind speed prediction is analyzed. • The hyperparameter optimization is used to improve the prediction performance. [ABSTRACT FROM AUTHOR]

For the wind-resistant design of infrastructures such as long-span bridges, transmission lines and wind turbines in the complex mountainous terrain, simulating the associated wind field is an important task. Due to the existence of many rolling mountains, the mountainous terrain model should be truncated at the proper distance from the site of bridge or other structures. The truncation will result in the sudden drop between the top edge of terrain model and the wind tunnel floor or the bottom of computational domain. This “artificial cliff” causes the flow separation and the unrealistic flow pattern. In order to minimize adverse effects caused by the “artificial cliff” and make the flow smooth, a transition section connecting the wind tunnel floor or the bottom of the numerical computational domain and the top terrain edge is necessary. Based on the wind tunnel contraction, a novel transition curve is proposed for modeling mountainous deep-cutting valley terrain in the wind tunnel test or numerical simulation. Firstly, a set of transition curves are derived from the wind tunnel contraction. Then, a novel curve derived from Witoszynski curve is determined by comparing flow characteristics of several transition curves using 2D Reynold-averaged Navier-Stokes (RANS) simulation. Finally, the applicability of the proposed curve in 3D wind field numerical simulation for a deep-cutting valley is verified based on field measurement data. [ABSTRACT FROM AUTHOR]

This paper addresses the difference between one time and real time decompositions in the wind speed prediction and results show the real time decomposition-based method may be ineffective in practice. Then the comprehensive analysis about challenges on applying the real time decomposition-based method is conducted, which is less addressed in literature. Such challenges mainly include: (i) the subseries decomposed from the training part are constantly changing with newly obtained data; (ii) the illusive components introduced by the decomposition reduce decomposition effectiveness; (iii) the end effect increases subseries volatility. Furthermore, to reduce these difficulties in prediction, a new hybrid method of correlation-aided discrete wavelet transform (DWT), least squares support vector machine (LSSVM) and generalized autoregressive conditionally heteroscedastic (GARCH) model is proposed. In this method: (i) if the correlation coefficient between each subseries and original data is smaller than the selected threshold, the corresponding subseries will be eliminated as illusive component; (ii) GARCH model is used to characterize the error for the remaining subseries and better capture the volatility in these subseries; (iii) model parameters are adjusted in real time to better reflect the wind speed change. Finally, case studies show that the proposed method has satisfactory performance in both accuracy and stability. [ABSTRACT FROM AUTHOR]

Steel roofing is widely used for non-residential facilities. However, it is vulnerable to high winds. This paper addresses a damage estimation framework that incorporates wind loading correlation and wind directionality effects for steel roofing. In this framework, external pressures were measured from wind tunnel testing. At positions where pressure measurements are not available, a proper orthogonal decomposition (POD) method is introduced to interpolate external wind pressures. Internal pressures due to openings in the building envelope are taken into account by simulation. Then, the internal forces on fasteners distributed on the steel roof are evaluated by the influence-surface-based method, with corresponding peak values estimated by a Gumbel conversion approach. Furthermore, the failure probability of a single cladding element and the damage ratio for the whole roof are determined based on Monte Carlo simulation (MCS), where the correlation among internal forces of fasteners is incorporated by a Nataf transformation. Finally, wind directionality effects are integrated in order to provide a comprehensive damage assessment for the roofing. Although the proposed framework works for existing buildings, it may potentially benefit the performance-based design for new low-rise buildings. [ABSTRACT FROM AUTHOR]

Capturing uranium(vi) from seawater is of paramount interest for the rapid expansion of energy needs. We report on materials covalently functionalized with amine groups to improve gas adsorption and remove metal ions. In this paper, polyacrylonitrile fibers (PANFs) covalently modified with hyperbranched polyethylenimine (HPEI) of different grafting degrees were facilely synthesized, and their performance to capture U(vi) in the presence of competitive ions and simulated seawater evaluated. In addition, the adsorption properties of the as-prepared adsorbents, including thermodynamics, kinetics and the influence of critical factors affecting U(vi) adsorption from aqueous solution were examined. The fibers with the best performance were characterized by X-ray photoelectron spectroscopy, scanning electron microscopy and Fourier transformed infrared spectroscopy. The high density of amine groups on the modified fiber surface improves the adsorption ability by electrostatic attraction and surface interaction during the reaction process. From our findings, PAN–HPEI offers wide-ranging potential in the wide field of the removal of metal ions, and, in particular, the removal of U(vi) from seawater with high throughput. [ABSTRACT FROM AUTHOR]

Simulation of the devastating excitations such as the ground motion and transient extreme winds is an important task in the structural response analysis when it comes to the nonlinearity, system stochasticity, parametric excitations and so on. Although the classic spectral representation method (SRM) is widely used in the nonstationary process simulation, it suffers from lower efficiency due to the unavailability of use of fast Fourier transform (FFT). In this study, the classic SRM is extended to the nonstationary process with the time-varying coherence. Then, the FFT-aided and almost accurate simulation algorithm for the nonstationary process with the time-varying coherence is developed with the help of the proper orthogonal decomposition (POD) which is used to factorize the decomposed evolutionary spectra. Especially, the more efficient simulation for the nonstationary process with time-invariant coherence is also proposed, where the spectral matrix decomposition, use of POD and execution of FFT can be reduced significantly. Two examples including downburst and typhoon winds are employed to evaluate the accuracy and efficiency of the proposed method. Results show that the method has the good performance in terms of the efficiency and accuracy. [ABSTRACT FROM AUTHOR]

• A hierarchical nanostructure consisting of Co-FeS2 and NiFe-LDH was prepared. • The Co-FeS2 nanowires as trunks ensure a rapid transfer of electrons. • Electronic interaction between sulfide and NiFe-LDH boosts the catalytic activity. • This catalyst showed excellent catalytic performance towards overall water splitting. In this study, a hierarchical cactus-like nanostructure with cobalt doped iron disulfide (Co-FeS 2) needle nanowires as trunks and nickel-iron layered double hydroxide (NiFe-LDH) nanosheets as branches was constructed as a self-supporting electrode for overall water splitting in alkaline electrolytes. A low electron density NiFe-LDH derived from the strong electronic interaction between sulfides and NiFe-LDH is remarkably effective in facilitating the adsorption of H 2 O and OH−, while the presence of Co atoms in FeS 2 facilitates the hydrogen adsorption. Such a synergetic effect improved the HER performance in alkaline electrolytes significantly. In terms of the OER process, the Fe/Co oxyhydroxides produced from in situ phase transition from Co-FeS 2 in conjunction with NiFe-LDH endow the electrode with excellent OER activity. Furthermore, the Co-FeS 2 needle nanowires with metal-like conductivity allow for rapid electron transfer during electrochemical processes, and the hierarchical structure exposes substantial active sites and facilitates electrolyte penetration. In alkaline media, the electrode demonstrated outstanding HER and OER catalytic activity, requiring small overpotentials of 131 mV and 220 mV to reach current densities of 10 mA cm−2 and 20 mA cm−2, respectively. Moreover, it only needed 1.58 V to drive 10 mA cm−2 when utilized simultaneously as both cathode and anode for overall water splitting. A hierarchical cactus-like nanostructure with Co-FeS 2 nanowires as trunks and NiFe-LDH nanosheets as branches for overall water splitting electrocatalysis. [Display omitted] [ABSTRACT FROM AUTHOR]

Removal of surfactant templates from freshly prepared mesoporous metal-loaded silica materials by calcination often induces structure shrinkage, loss of functional groups and metal oxide sintering. This work reported the use of ozone treatment for postsynthesis of Fe-containing SBA-15 with the surfactant almost completely removed and the other properties perfectly maintained. The ozone treatment could handle large sample quantities and treatments at 40–80 °C for 1–3 h could effectively remove the triblock copolymer P123. The hydroxyl (OH) groups of SBA-15, the high dispersion state of Fe at low loadings and the intrinsic nature of β-FeOOH at high loadings kept intact. The arsenic (As(III)) adsorption performances of the as-made, calcined and ozone-treated samples with high Fe loadings were compared. Both the adsorption rate and capacity were boosted by the ozone-treated samples. Their adsorption kinetics followed the pseudo-second-order kinetic model and their adsorption isotherms were Langmuir-type. Complete removal of As(III) and high adsorption capacity (up to 30.4 mg/g) could be reached on the ozone-treated samples. The effects of common co-existing anions on adsorption were clarified. The ozone-treated samples could be regenerated with high cyclic performance. The adsorption mechanism was associated with both electrostatic interaction and surface complexation. This study would provide guideline for surfactant removal and broaden the applicability of mesoporous metal-containing silica wherein OH groups, highly dispersed metal species or active metal hydroxides are necessities. [Display omitted] • Postsynthesis of ordered mesoporous Fe-containing silica materials via ozone treatment. • Effective removal of surfactant P123 under mild conditions. • Little structure change and perfect preservation of the OH groups and Fe species. • Greatly enhanced As(III) adsorption performance for the ozone-treated samples compared with the as-made and calcined ones. • Electrostatic interaction and surface complexation mechanism for the As(III) adsorption. [ABSTRACT FROM AUTHOR]

Accurately forecasting wind speed is a critical mission for the exploitation and utilization of wind power. To improve the prediction accuracy, the nonlinearity and nonstationarity embedded in wind speed time series should be reduced. Because the subseries has less nonlinearity and nonstationarity after decomposition, the decomposition-based forecasting methods are widely adopted to provide the higher predictive accuracy. However, latest studies showed the real time decomposition-based forecasting methods could be worse than the single forecasting models. The aim of this study is to improve the performance of the real time decomposition-based forecasting method after the factors attributed to its unsatisfactory performance are uncovered. In this paper, the feature selection and error correction are adopted in the real time decomposition-based forecasting method to enhance the prediction accuracy. In the proposed method, the raw wind speed time series is decomposed into a number of different subseries by ensemble empirical mode decomposition; then two feature selection methods including kernel density estimation-based Kullback-Leibler divergence and energy measure are used to reduce the disturbance of illusive components; further the least squares support vector machine is adopted to establish the one-step ahead forecasting models for the remaining subseries; finally, the hybrid of least squares support vector machine and generalized auto-regressive conditionally heteroscedastic model is introduced to correct resulting error component if its inherent correlation and heteroscedasticity cannot be neglected. Based on two sets of measured data, the results of this study show that: (1) the real time decomposition-based method may be ineffective in practice; (2) both the feature selection and error correction can improve forecasting performance in comparison with the real time decomposition-based method; (3) compared with other involved methods, the proposed hybrid method has the satisfactory performance in both accuracy and stability. [ABSTRACT FROM AUTHOR]