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The influence of curvature ratio (CR) within helical tubes on secondary flows and subsequent enhancement of heat transfer is well-established. Furthermore, the interaction between the shell fluid and the helical tube is recognized as pivotal in this regard. In this paper, the impact of varying CR and coil geometry on the performance of heat exchangers (HEs) through experimental heat transfer analysis conducted on five distinct coils viz., straight helical (ϴ= 90°), conical (ϴ= 70°,50°,30°), and spiral (ϴ= 0°) configurations have been studied. Moreover, correlations for modified effectiveness are proposed for all HEs. The Reynolds number range chosen for the analysis spans from 3700 to 20000, encompassing laminar and turbulent flow regimes of the coil hot water. The optimal HE is identified based on thermal and hydrodynamic parameters, including hot water temperature difference, effectiveness, modified effectiveness, rate of heat transfer, pressure drops of the coil, shell fluids, and pumping power. Observations reveal that helical cone coil heat exchangers (HCCHEs) demonstrate superior thermal and hydrodynamic characteristics when the fluid flow aligns with increasing CR. Notably, for both laminar and turbulent flows, the highest hot water temperature difference, effectiveness, and rate of heat transfer are observed for ϴ= 30° HCCHE, while the lowest values are attributed to ϴ= 90° HE. Tube side Nusselt numbers, pressure drops, and friction factors show agreement with the predictions of researchers. The analysis reveals that the coil fluid pressure drop is maximal for ϴ =0° HE, whereas the maximum shell fluid pressure drop is encountered for ϴ =90° HE. Furthermore, the highest pumping power per unit heat transfer area for coil and shell fluids are noted for ϴ= 0° HE and ϴ= 90°HE, respectively, while ϴ= 30° HCCHE exhibits comparable performance to the remaining HEs within the specified parameter range, establishing its optimality. [ABSTRACT FROM AUTHOR]

Climate models help us simulate and predict how the Earth's climate is going to change in the future. Wind speed data is critical for developing and validating such models. However, in the real world, often owing to many factors such as station maintenance and sensor failures, a considerable amount of wind data goes missing. The Generative Adversarial Network (GAN) has been used to impute missing wind data, but the handling of unrealistic GAN output has remained largely unstudied. In this paper, we propose a novel hybrid approach that combines both the GAN and dual annealing algorithms to not only impute missing wind speed data but also counter unrealistic GAN outcomes. The hourly mean wind data has been collected from the National Centers for Environmental Information for four Indian stations, viz. Ahmedabad, Indore, Mangaluru and Mumbai. We compared the performance of the proposed approach with those of k-nn, soft imputation, and plain GAN-based approaches on mean, variance, standard deviation, kurtosis, skewness, and R-square. We found that our approach ranks number one based on the R-square value for all the considered stations. Our model consistently produces realistic results, unlike plain GAN. We observed that Mumbai has the lowest percentage of missing data (13.14%) and the highest R-square value (0.9999186451). However, Indore has the highest percentage of missing data (46.6463%) and the lowest R-square value (0.9046885604). [ABSTRACT FROM AUTHOR]

The capacity factor (CF) and power coefficient (C p) are two important wind turbine characteristics. CF describes the power generation capacity during a given period, and C p describes the efficiency of the wind turbine. Both quantities depend on the rated wind speed. Determining the optimal rated wind speed that maximizes a function of CF and C p that is directly related to a wind turbine's output wind power density thus is of utmost importance as it leads to a maximum energy output. This paper proposes a novel Month-based Turbine Performance Index (MTPI) that considers the hourly mean wind speed data month-wise and enables the evaluation of this desired optimum rated turbine speed (V r,opt) for a given site. Here, the 2-parameter Weibull distribution is employed as a single tool to parameterize the wind speed data and determine the wind speed probability density function, wind power density, vertical wind shear, CF , and C p of the wind turbine. The examined stations taken for the analysis are from Trivandrum, Ahmedabad, and Calcutta in India. Our index is especially important in regions with intra annular variability, since it is the first to consider monthly instead of annual data. • Novel Monthly Turbine Performance Index, MTPI, is formulated for monthly wind speed variation. • The MTPI is used to estimate optimum rated wind speed (Vr,opt) for wind turbine design. • To maximize energy output, the Vr, opt optimizes Capacity Factor and Power Coefficient. • In highly varied climate regions, monthly stochastics enhances energy output efficacy. • Turbine generator design in wind farm as per main wind direction for sustainable energy. [ABSTRACT FROM AUTHOR]

Accurate wind resource assessment lies on the precise information provided by a probability distribution function (PDF). Therefore, it is an essential prerequisite to find the most appropriate PDF to model the wind speed data at the planning stage. Earlier, researchers have compared several distributions of 1, 2-parameters such as Rayleigh, Gamma, Exponential, Normal family, Weibull distributions, etc. Among these, 2-paramters Weibull distribution was a widely acceptable distribution for wind speed data modeling. However, its comparison with a multi-parameter (3 and 4 parameters) distribution has rarely been studied. In this paper, the Weibull distribution has been compared with four new distributions, which have rarely been studied for wind speed data modeling previously. They are 2-parameter Nakagami and Rician distribution, 4-parameter Johnson SB distribution, and 5-parameter Generalized Hyperbolic distribution. The sites selected for the case study are Trivandrum, Ahmedabad, Calcutta, Jaipur, New Delhi, and Port Blair of India. The result indicates that the Generalized Hyperbolic and Johnson distributions are ranked 1st and 2nd; Weibull and Nakagami distributions perform equally well and are ranked 3rd and 4th among the five compared distributions for five Indian stations. However, for one station (Ahmedabad), which is less skewed and has low kurtosis, the performance of Weibull distribution is better than those of the other distributions. The achieved results reveal that the skewness and kurtosis are equally important as the mean and standard deviation of wind speed data, which may influence the accuracy of the distribution. Wind behavior is stochastic, and a single distribution cannot be accepted as a universally accepted distribution for all locations of India. [ABSTRACT FROM AUTHOR]