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The purpose of this paper is to show the effect of randomness of wind speed on the capacity value estimation of wind power. Three methods that incorporate hourly wind speed have been evaluated.

Wind speed is simulated using autoregressive moving average method and is included in the calculation of reliability index as a negative load on an hourly basis. The reliability index is calculated before and after the addition of wind capacity. Increment of load or alteration of conventional capacity will lead to capacity estimation.

Among the aforementioned three methods, the former two exclude the availability rate and give the exact value for wind capacity addition. The third method is based on the availability rate and provides a little higher capacity value, indicating a clear correlation between availability and capacity value.

The methods that exclude the availability rate show consistent results. By including the availability rate, the third method predicts the inverse relation between the availability rate and the capacity value.

In this study, a novel dynamic testing method was established to measure the moisture variation of fabric surface during the process of moisture liberation in simulated windy condition. The paper aims to discuss these issues

In this method, the samples were rotating during the test process so as to simulate the external windy environment. Effects of simulated wind speed, moisture regains and fabric materials on the surface moisture of fabric were investigated.

Experimental results showed that the surface moisture presented a trapezoidal moisture liberation curve, it increased at first, then kept stable for a while, and decreased finally with the increase of time. It took longer time for the fabric to complete the liberation process when the moisture regain of the fabric increased or the simulated wind speed decreased. The fiber materials of the fabric affected the time for the moisture liberation process under a specific windy condition.

This study will benefit the designing and development of clothing such as sportswear.

A dynamic testing method was proposed to characterize the surface humidity of textiles under simulated windy conditions.

Perils of tornado and hail cause large amounts of loss every year. Based on the data provided by Property Claims Services, since 1949, tornado, hail and straight‐line‐wind losses account for more than 40 percent of total natural losses in the USA. Given the high frequency of tornado and damaging hail in the continental USA, quantifying these risks will be an important advancement in pricing them for insurance/reinsurance purposes. In the absence of a realistic physical model, which would look at these perils on a cluster/outbreak basis, it is not possible to underwrite these risks effectively. The purpose of this paper is to focus on the tornado risk.

A tornado wind‐field model is developed based on the model used by Wen and Ang. The model is calibrated to the specifications given in the Fujita intensity scale. To estimate the tornado hazard, a historical database is generated and de‐trended using the information provided by Storm Prediction Center along with the dataset given by Grazulis. This new historical database together with a reinsurance timeframe criterion in mind was used to define outbreaks. These outbreaks are used in a Monte‐Carlo simulation process to generate a large number of outbreaks representing 35,000 years of simulated data. This event‐set is used to estimate spatial frequency contours and loss analyses.

The results focus on the spatial frequency of occurrence of tornadoes in the USA. The losses are tallied using multiple occurrences of tornado and/or hail per outbreak. The distribution of loss, both on per occurrence and on aggregate basis, are discussed.

This paper is believed to be the first one to use a tornado wind‐field model, outbreak model, and vulnerability models, which estimate both spatial distribution of hazard and location‐based distribution of losses. Estimation of losses due to hail is also provided.

Recently, many countries have been pushing for a higher share of renewable energy sources, especially wind, in their generation mix. However, the intermittent and uncertain nature of wind power imposes a limit on the extent it can replace the conventional generation resources. In a high wind penetration scenario, the Battery Energy Storage System (BESS) offers a solution to the grid operation problems. The purpose of this paper is to evaluate the merits of price‐based operation of BESS in a real‐time market with high wind penetration using frequency‐linked pricing.

The authors propose a real‐time market in which real‐time prices are based on the grid frequency. A model for real‐time price‐based operation of a conventional generator and a BESS is presented. Simulations for different wind penetration scenarios are carried out on an isolated area test system. Wind speed sequence is generated using composite wind speed model. A simplified model of wind speed to power conversion is adopted to observe the impact of increase in wind power generation on the grid frequency and the real‐time prices.

The result of simulations show that BESS not only helps in dealing with uncertainty in wind power forecasts, but also reduces the fluctuations in frequency due to wind power's intermittency. Price‐based operation of BESS results in higher operating revenues by discharging it at peak prices and reduces operating costs by charging it at minimum prices.

The study helps in achieving the societal goal of replacing fossil fuel generation by environment friendly generation and reducing green house gas emissions.

The novelty of this paper lies in the use of frequency‐linked pricing in real‐time market and proposing a control algorithm for operating BESS using these price signals.

Determining the variation law of the oxygen concentration in the ullage space of the fuel tank is the key to the design of the inert system. Among various factors affecting the oxygen concentration in the ullage space of the fuel tank, the temperature difference between day and night shows particular importance while relevant analysis and calculation are scarce.

This study establishes a theoretical simulation model of the central wing fuel tank of an aircraft according to the relevant provisions of day-night temperature variation in FAR25 airworthiness regulations, verifies the model with the existing experimental data and discusses the corresponding relationship between the oxygen concentration in the ullage space of the fuel tank and the day-night temperature difference. The influence of day and night temperature difference, fuel type, fuel load rate, initial oxygen concentration, dissolved oxygen evolution and other factors on the oxygen concentration in the ullage space of the fuel tank were analyzed, and the limit of initial oxygen concentration of the fuel tank before the shutdown at night meeting the requirements of the airworthiness provisions was proposed.

The results show that the temperature difference between day and night, fuel load rate, initial oxygen concentration and other factors have different effects on the oxygen concentration in the ullage space of fuel tank. The initial oxygen concentration limit before shutdown shall be 2% below the 12% oxygen concentration stipulated by FAA.

The research results in this paper will be of good reference value to the design of the inert system and the calculation of the flammability exposure evaluation time. This paper aims to be good reference of the design of the inert system and the calculation of the flammability exposure evaluation time.

The research results of this paper can provide practical guidance for the current civil airworthiness certification work.

The purpose of this paper is to predict bibliometric indicators based on ARIMA models and to study the short-term trends of bibliometric indicators.

This paper establishes a non-stationary time series ARIMA (p, d, q) model for forecasting based on the bibliometric index data of 13 journals in the library intelligence category selected from the Chinese Social Sciences Citation Index (CSSCI) as the data source database for the period 1998–2018, and uses ACF and PACF methods for parameter estimation to predict the development trend of the bibliometric index in the next 5 years. The predicted model was also subjected to error analysis.

ARIMA models are feasible for predicting bibliometric indicators. The model predicted the trend of the four bibliometric indicators in the next 5 years, in which the number of publications showed a decreasing trend and the H-value, average citations and citations showed an increasing trend. Error analysis of the model data showed that the average absolute percentage error of the four bibliometric indicators was within 5%, indicating that the model predicted well.

This study has some limitations. 13 Chinese journals were selected in the field of Library and Information Science as the research objects. However, the scope of research based on bibliometric indicators of Chinese journals is relatively small and cannot represent the evolution trend of the entire discipline. Therefore, in the future, the authors will select different fields and different sources for further research.

This study predicts the trend changes of bibliometric indicators in the next 5 years to understand the trend of bibliometric indicators, which is beneficial for further in-depth research. At the same time, it provides a new and effective method for predicting bibliometric indicators.

It is possible to see effective use of Artificial Intelligence-based systems in many fields because it easily outperforms traditional solutions or provides solutions for the problems not previously solved. Prediction applications are a widely used mechanism in research because they allow for forecasting of future states. Logical inference mechanisms in the field of Artificial Intelligence allow for faster and more accurate and powerful computation. Machine Learning, which is a sub-field of Artificial Intelligence, has been used as a tool for creating effective solutions for prediction problems.

In this chapter the authors will focus on employing Machine Learning techniques for predicting data for future states of economic using techniques which include Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Dynamic Boltzmann Machine, Support Vector Machine, Hidden Markov Model, Bayesian Learning on Gaussian process model, Autoregressive Integrated Moving Average, Autoregressive Model (Poggi, Muselli, Notton, Cristofari, & Louche, 2003), and K-Nearest Neighbor Algorithm. Findings revealed positive results in terms of predicting economic data.

Discusses the 27 papers in ISEF 1999 Proceedings on the subject of electromagnetisms. States the groups of papers cover such subjects within the discipline as: induction machines; reluctance motors; PM motors; transformers and reactors; and special problems and applications. Debates all of these in great detail and itemizes each with greater in‐depth discussion of the various technical applications and areas. Concludes that the recommendations made should be adhered to.

The aim of the present study is to investigate the effect of fabric weave structure on air permeability and its relation with the garment ventilation.

For this purpose, five groups of cotton/polyester shirting fabrics with plain, T2/1, T2/2, T3/1 and T3/3 weave structures were studied. In order to evaluate ventilation, the garment samples were prepared in different sizes, so that the thickness of the air gap formed between the garment and the body simulator varies by zero, 1.5, 1.2 and 2.9 cm. The effect of wind and its speed (1, 2 and 3 m/s) on clothing ventilation has also been evaluated.

The results indicated that the rise of wind speed and air gap thickness, due to the increased convective heat transfer, would diminish the air gap temperature of clothing and improves its ventilation. In addition, the fabric weave pattern influences the air ability to pass through the fabric, thus affecting the ventilation capability of the garment.

Garments made of fabrics with higher structural firmness, such as the plain, not only have lower air permeability, but also has weaker ventilation capability.

This paper aims to design an optimum vertical axis wind turbine (VAWT) and assess its techno-economic performance for wind energy harvesting at high-speed railway in Malaysia.

This project adopted AutoCAD and ANSYS modeling tools to design and optimize the blade of the turbine. The site selected has a railway of 30 km with six stops. The vertical turbines are placed 1 m apart from each other considering the optimum tip speed ratio. The power produced and net present value had been analyzed to evaluate its techno-economic viability.

Computational fluid dynamics (CFD) analysis of National Advisory Committee for Aeronautics (NACA) 0020 blade has been carried out. For a turbine with wind speed of 50 m/s and swept area of 8 m², the power generated is 245 kW. For eight trains that operate for 19 h/day with an interval of 30 min in nonpeak hours and 15 min in peak hours, total energy generated is 66 MWh/day. The average cost saved by the train stations is RM 16.7 mil/year with battery charging capacity of 12 h/day.

Wind energy harvesting is not commonly used in Malaysia due to its low wind speed ranging from 1.5 to 4.5 m/s. Conventional wind turbine requires a minimum cut-in wind speed of 11 m/s to overcome the inertia and starts generating power. Hence, this paper proposes an optimum design of VAWT to harvest an unconventional untapped wind sources from railway. The research finding complements the alternate energy harvesting technologies which can serve as reference for countries which experienced similar geographic constraints.