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In this study the comparison is presented for the variation in cross-sectional shape along the height of the building model. For this purpose Model B and Model C are having the considerable variation and Model A result can be easily predicted on the basis of the result of Model B and C while Model X is considered for the validation purposes only and it is well established that the results are within the allowable limit. This paper aims to discuss these wind generated effects in the tall building model.

Computational Fluid Dynamics (CFD) in ANSYS: CFX is used to investigate the wind effects on varying cross-sectional shape along the height of the building model.

From pressure contours, it was observed that shape and size of the face is independent of the pressure distribution. It is also observed that pressure distribution for the windward face (A) was less than the magnitude of the leeward face for both models. The leeward face and lateral faces had similar pressure distribution. Also slight changes in pressure distribution were observed at the periphery of the models.

This study has been performed to analyse and compare the wind effect on tall buildings having varying cross sections with variation of different cross sections along the height. Most of the studies done in the field of tall buildings are concentrated to one particular cross-sectional shape while the present study investigates wind effects for combination of two types of cross sections along the height. This analysis is performed for wind incidence angles ranging from 0° to 90° at an interval of 30°. Analysis of wind flow characteristics of two models, Models B and C will be computed using CFD. These two models are the variation of Model A which is a combination of two types of cross section that is square and plus. Square and plus cross-sectional heights for Model B are 48 m and 144 m, respectively. Similarly, square and plus cross-sectional heights for Model C are 144 m and 48 m, respectively. The results are interpreted using pressure contours and streamlines, and comparative graphs of drag and lift forces are presented.

Convective heat transfer features of a turbulent slot jet impingement are comprehensively studied using two different computational approaches, namely, URANS (unsteady Reynolds-averaged Navier–Stokes equations) and SAS (scale-adaptive simulation). Turbulent slot jet impingement heat transfer is used where a considerable heat transfer enhancement is required, and computationally, it is a quite challenging flow configuration.

Customized OpenFOAM 4.1, an open-access computational fluid dynamics (CFD) code, is used for SAS (SST-SAS k-ω) and URANS (standard k-ε and SST k-ω) computations. A low-Re version of the standard k-ε model is used, and other models are formulated for good wall-refined calculations. Three turbulence models are formulated in OpenFOAM 4.1 with second-order accurate discretization schemes.

It is observed that the profiles of the streamwise turbulence are under-predicted at all the streamwise locations by SST k-ω and SST SAS k-ω models, but follow similar trends as in the reported results. The standard k-ε model shows improvements in the predictions of the streamwise turbulence and mean streamwise velocity profiles in the zone of outer wall jet. Computed profiles of Nusselt number by SST k-ω and SST-SAS k-ω models are nearly identical and match well with the reported experimental results. However, the standard k-ε model does not provide a reasonable profile or quantification of the local Nusselt number.

Hybrid turbulence model is suitable for efficient CFD computations for the complex flow problems. This paper deals with a detailed comparison of the SAS model with URANS and LES for the first time in the literature. A thorough assessment of the computations is performed against the results reported using experimental and large eddy simulations techniques followed by a detailed discussion on flow physics. The present results are beneficial for scientists working with hybrid turbulence models and in industries working with high-efficiency cooling/heating system computations.

This paper explored factors that impact insurance choices of demand (farmers) and supply (insurance companies) side, respectively.

Specially designed survey questions allow one to fully observe the demand tendency from farmers and partially observe the supply tendency from insurance companies. Using bi‐vairate probit model, a joint estimation of insurance decisions of both supply and demand sides suggested that factors perform different roles in affecting insurance participation.

Farmer's age and education have positive impacts on insurance demand, but are indifference to insurance providers. Insurance suppliers care about farmers' experience in the fields when providing insurance services, however, on the demand side, farmers' experience occasionally results in overconfidence and hence, impedes farmers' insurance purchasing. Production scales, proxy by sow inventory, are put more weight by farmers than insurance suppliers when making decisions. Production efficiency measures perform as incentives for farmers to purchase insurance. While suppliers prefer customers who use vaccine, farmers tend to treat vaccine as a substitute for insurance to prevent disease risk.

Results from bi‐vairate probit model offer deeper understandings about livestock insurance choices and provide further insights to improve policy design and promote participation.

The study designed a special questionnaire and firstly used bi‐vairate probit model to offer more understandings about demand and supply sides of livestock insurance.

The purpose of this paper is to demonstrate the use of the Meteorological Research Institute (MRI) global 20-km mesh Atmospheric General Circulation Model (AGCM), called MRI-AGCM, to simulate rainfall and mean surface air temperature. Through calibration and validation the MRI-AGCM was adapted for Bangladesh for generating rainfall and temperature scenarios.

The model generated rainfall was calibrated with ground-based observed data in Bangladesh during the period of 1979-2006. The Climate Research Unit (CRU) data are also used for understanding of the model performance. Better performance of MRI-AGCM obtained through validation process increased confidence in utilizing it in the future rainfall projection for Bangladesh.

Rainfall and mean surface air temperature projection for Bangladesh is experimentally obtained for the period of 2075-2099. This work finds that the MRI-AGCM simulated rainfall and temperature are not directly useful in application purpose. However, after validation and calibration, acceptable performance is obtained in estimating annual rainfall and mean surface air temperature in Bangladesh.

Change of rainfall is projected about 0.64 percent in monsoon season (JJAS), 1.90 percent in post-monsoon season (ON) and 13.46 percent in Winter season (DJF) during the period of 2075-2099. Similarly, change of mean surface air temperature is projected about 2.5 degrees Celsius for the same period.

Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework.

A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models.

The results suggest that GSCA with reflective composite indicators (GSCA_R) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA_R performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods.

Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.

When prediction is the primary study aim, researchers should generally revert to GSCA_R, considering its performance for observed and operative prediction together.

This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.

This study aims to validate a model for estimating platoon delay due to pedestrian crossing for use in Kuwait City.

The model was modified slightly for the scenario used in Kuwait, in which the presence of raised crosswalk meant that all incoming traffic would slow down automatically. Using video footage to observe the site, several variables were collected, and a model was used to calculate the delays suffered by the vehicles because of pedestrian crossing. The model was validated using the actual footage and manual observation to measure the delays.

The model showed a good match fit to the observed data, as the average delays differed by 22.5% between the two methods. Following the comparison, a sensitivity analysis was made on three variables: the acceleration rate, deceleration rate, as well as the pedestrian walking time. The analysis has shown that deceleration rate has approximately twice the effect on the model than the acceleration rate has. It has also shown that the pedestrian walking time has a major effect on the model, in an almost one-to-one correlation. A 50% change of the pedestrian walking time is associated with approximately 50% change in the model’s output delay.

A model for estimating platoon delay because of pedestrian crossing was validated for use in Kuwait City. The model was modified slightly for the scenario used in Kuwait, in which the presence of raised crosswalk meant that all incoming traffic would slow down automatically.

The purpose of this study is to ascertain whether nonlinearities could be present in electricity loads observed in subtropical environments, where none or little heating is required, and whether threshold autoregressive (TAR)-type regime switching models could be advantageous in the modeling of those loads.

The actual observed load of a Brazilian regional electricity distributor from January 2013 to August 2012 was modeled using a popularly employed ARMA model for reference, and smooth and non-smooth TAR transition (non-linear) models were used as non-linear regime switching models.

Evidence of nonlinearities were found in the load series, and evidence was also found on the intrinsic resistance of this type of models to structural breaks in the data. Additionally, to reacting well to asymmetries in the data, these models avoid the use of exogenous variables. Altogether, this could prove to be a definite advantage of the use of such model alternatives.

However, even if the present work may have been limited by the observation frequency of the available data, it appears TAR models appear to be a viable alternative to forecasting short-term electricity loads. Nonetheless, additional research is required to achieve a higher accuracy of forecast data.

If such models can be successfully used, it will be a great advantage for electricity generators, as the computational effort involved in the use of such models is not significantly larger than regular linear ones.

To our knowledge, this type of research has not yet been made with subtropical/tropical electricity load data.

Studies a non‐homogeneous Poisson process software reliability model with failure rate based on Zipf’s law. Discusses the rate function, mean value function and the estimation of parameters. The proposed model can be used to analyse the reliability growth. The results of applying the proposed model and Duane model to several actual failure data sets show that the model with failure rate observed from Zipf’s law can fit not only in operating software but also in testing software. The result also indicates that the proposed model has better long‐term predictive capability than the Duane model for failure data sets with power law’s failure rates

The presence of air in the water flow over the hydrofoil is investigated. The examined hydrofoil is ClarkY 11.7% with an angle of attack of 8 deg. The flow simulations are performed with the assumption of different models. The Singhal cavitation model and the models which resolve the non-condensable gas including 2phases and 3phases are implemented in the numerical model. The calculations are performed with the uRANS model with assumption of the constant temperature of the mixture. The two-phase flow is simulated with a mixture model. The dynamics and structures of cavities are compared with literature data and experimental results.

The cavitation regime can be observed in some working conditions of turbomachines. The phase transition, which appears on the blades, is the source of high dynamic forces, noise and also can lead to the intensive erosion of the blade surfaces. The need to control this process and to prevent or reduce the undesirable effects can be fulfilled by the application of non-condensable gases to the liquid.

The results show that the Singhal cavitation model predicts the cavity structure and related characteristics differently with 2phases and 3phases models at low cavitation number where the cavitating flow is highly dynamic. On the other hand, the impact of dissolved air on the cloud structure and dynamic characteristic of cavitating flow is gently observable.

The originality of this paper is the evaluation of different numerical cavitation models for the prediction of dynamic characteristics of cavitating flow in the presence of air.

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a comprehensive survey and comparison of these techniques.

The present article provides an overview of the statistical short‐term price forecasting (STPF) models. The basic theory of these models, their further classification and their suitability to STPF has been discussed. Quantitative evaluation of the performance of these models in the framework of accuracy achieved and computation time taken has been performed. Some important observations of the literature survey and key issues regarding STPF methodologies are analyzed.

It has been observed that price forecasting accuracy of the reported models in day‐ahead markets is better as compared to that in real time markets. From a comparative analysis perspective, there is no hard evidence of out‐performance of one model over all other models on a consistent basis for a very long period. In some of the studies, linear models like dynamic regression and transfer function have shown superior performance as compared to non‐linear models like artificial neural networks (ANNs). On the other hand, recent variations in ANNs by employing wavelet transformation, fuzzy logic and genetic algorithm have shown considerable improvement in forecasting accuracy. However more complex models need further comparative analysis.

This paper is intended to supplement the recent survey papers, in which the researchers have restricted the scope to a bibliographical survey. Whereas, in this work, after providing detailed classification and chronological evolution of the STPF techniques, a comparative summary of various price‐forecasting techniques, across different electricity markets, is presented.