A field study of wind over a simulated block building

A full-scale field study of the wind over a simulated two-dimensional building is reported. The study develops an experiment to investigate the structure and magnitude of the wind fields. A description of the experimental arrangement, the type and expected accuracy of the data, and the range of the data are given. The data are expected to provide a fundamental understanding of mean wind and turbulence structure of the wind field around the bluff body. Preliminary analysis of the data demonstrates the reliability and completeness of the data in this regard

The effect of skill level on darts throwers’ use of different mental skills

Background: In recent years sports psychologists, coaches and athletes have paid a greater focus of attention to mental wellbeing and psychological skills. The purpose of this study was to investigate which psychological skills are important to two levels of skills among Darts players, namely; elite and beginner. Method: The sample consisted of 24 elite and 24 beginner Darts throwers. In order to gain insight into Darts throwing, beginner Darts players attended a national-championship-simulated competition. Both elite and beginner players also completed the Ottawa Mental Skill Questionnaire. Results: Independent t-test results showed that there was a significant difference just in basic psychiatric skills between the beginner and elite Darts throwers (p0.05). Conclusion: Results revealed differences between elite and beginner Darts players in foundation mental skills and commitment and mental practice subscales. Furthermore, results showed that for the commitment skill, elite and beginner Darts throwers were at the highest and lowest level respectively

Low-Complexity Robust Beamforming Design for IRS-Aided MISO Systems with Imperfect Channels

In this paper, large-scale intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) system is considered in the presence of channel uncertainty. To maximize the average sum rate of the system by jointly optimizing the active beamforming at the BS and the passive phase shifts at the IRS, while satisfying the power constraints, a novel robust beamforming design is proposed by using the penalty dual decomposition (PDD) algorithm. By applying the upper bound maximization/minimization (BSUM) method, in each iteration of the algorithm, the optimal solution for each variable can be obtained with closed-form expression. Simulation results show that the proposed scheme achieves high performance with very low computational complexity

Robust Beamforming Design for an IRS-Aided NOMA Communication System With CSI Uncertainty

Intelligent reflecting surface (IRS) is a promising technology that provides high throughput in future communication systems and is compatible with various communication techniques, such as non-orthogonal multiple-access (NOMA). This paper studies the downlink transmission of IRS-assisted NOMA communication, considering the practical case of imperfect channel state information (CSI). Aiming to maximize the system sum rate, a robust IRS-aided NOMA design is proposed to jointly find the optimal beamforming vector for the access point and the passive reflection matrix for the IRS. This robust design is realised using the penalty dual decomposition (PDD) scheme, and it is shown that the results have a close performance to their upper bound obtained from the corresponding perfect CSI scenario. The presented method is compatible with both continuous and discrete phase shift elements of the IRS. Our findings show that the proposed algorithms, for both continuous and discrete IRS, have low computational complexity compared to other schemes in the literature. Furthermore, we conduct a performance comparison between the IRS-aided NOMA and the IRS-aided orthogonal multiple access (OMA). This comparison shows that robust beamforming techniques are crucial for the system to reap the advantages of IRS-aided NOMA communication in the presence of CSI uncertainty

Using a developed PM in order to optimize the production productivity in a cement industry

Cement factories are highly energy and cost intensive industries. Producing the cement requires a lot of energy to transform the raw material into final product. One major area to improve the production productivity is preventive maintenance (PM). It helps to protect assets, increase the useful life ofequipment, improve system reliability, decrease cost of replacement and finally improve system energy consumption. In this paper, the theory of microeconomics firm was used to find a model of optimal production productivity in cement industry. To show the effect of preventive maintenance system in the model, energy consumption of equipment is considered as a function of failure rate of equipment and then added to the set of constraints.Using this model energy consumption is reduced up to 15% and total annual cost is reduced up to 12.7%

Using TurbSim stochastic simulator to improve accuracy of computational modelling of wind in the built environment

Small wind turbines are often sited in more complex environments than in open terrain. These sites include locations near buildings, trees and other obstacles, and in such situations, the wind is normally highly three-dimensional, turbulent, unstable and weak. There is a need to understand the turbulent flow conditions for a small wind turbine in the built environment. This knowledge is crucial for input into the design process of a small wind turbine to accurately predict blade fatigue loads and lifetime and to ensure that it operates safely with a performance that is optimized for the environment. Computational fluid dynamics is a useful method to provide predictions of local wind flow patterns and to investigate turbulent flow conditions at small wind turbine sites, in a manner that requires less time and investment than actual measurements. This article presents the results of combining a computational fluid dynamics package (ANSYS CFX software) with a stochastic simulator (TurbSim) as an approach to investigate the turbulent flow conditions on the rooftop of a building where small wind turbines are sited. The findings of this article suggest that the combination of a computational fluid dynamics package with the TurbSim stochastic simulator is a promising tool to assess turbulent flow conditions for small wind turbines on the roof of buildings. In particular, in the prevailing wind direction, the results show a significant gain in accuracy in using TurbSim to generate wind speed and turbulence kinetic energy profiles for the inlet of the computational fluid dynamics domain rather than using a logarithmic wind-speed profile and a pre-set value of turbulence intensity in the computational fluid dynamics code. The results also show that small wind turbine installers should erect turbines in the middle of the roof of the building and avoid the edges of the roof as well as areas on the roof close to the windward and leeward walls of the building in the prevailing wind direction

A Trellis-based Passive Beamforming Design for an Intelligent Reflecting Surface-Aided MISO System

In this paper, the downlink transmission of an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) system is investigated where the IRS elements are selected from a predefined discrete set of phase shifts. We minimize the mean square error (MSE) of the received symbols in the system via optimizing the phase shifts at the IRS jointly with beamforming vectors at the base station (BS) and equalizers at the user terminals. In order to find the optimal IRS phase shifts, a trellis-based structure is used that smartly selects the discrete phases. Moreover, for the sake of comparison, a semi-definite programming (SDP)-based discrete phase optimization is also presented. The BS beamformer and the optimal equalizers are determined via closed-form solutions. Numerical results demonstrate that the trellis-based scheme has better performance compared to other discrete IRS phase shift designs, such as SDP and quantized majorization-minimization technique, while maintaining a very low computational complexity

AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning

In this paper, the problem of pilot contamination in a multi-cell massive multiple input multiple output (M-MIMO) system is addressed using deep reinforcement learning (DRL). To this end, a pilot assignment strategy is designed that adapts to the channel variations while maintaining a tolerable pilot contamination effect. Using the angle of arrival (AoA) information of the users, a cost function, portraying the reward, is presented, defining the pilot contamination effects in the system. Numerical results illustrate that the DRL-based scheme is able to track the changes in the environment, learn the near-optimal pilot assignment, and achieve a close performance to that of the optimum pilot assignment performed by exhaustive search, while maintaining a low computational complexity