• Energy Research

Turbulence is a complicated phenomenon because of its chaotic behavior with multiple spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting and reconstructing turbulence more challenging. This study proposes a deep-learning approach to reconstruct three-dimensional (3D) high-resolution turbulent flows from spatially limited data using a 3D enhanced super-resolution generative adversarial networks (3D-ESRGAN). In addition, a novel transfer-learning method based on tricubic interpolation is employed. Turbulent channel flow data at friction Reynolds numbers Reτ = 180 and Reτ = 500 were generated by direct numerical simulation (DNS) and used to estimate the performance of the deep-learning model as well as that of tricubic interpolation-based transfer learning. The results, including instantaneous velocity fields and turbulence statistics, show that the reconstructed high-resolution data agree well with the reference DNS data. The findings also indicate that the proposed 3D-ESRGAN can reconstruct 3D high-resolution turbulent flows even with limited training data.

India is ranked fifth in the world in terms of COVID-19 publications accounting for 6.7% of the total. About 60% of the COVID-19 publications in the year 2020 are from United States, China, UK, Italy, and India. We present a bibliometric analysis of the publication trends and citation structure along with the identification of major research clusters. By performing network analysis of authors, citations, institutions, keywords, and countries, we explore semantic associations by applying visualization techniques. Our study shows lead taken by the United States, China, UK, Italy, India in COVID-19 research may be attributed to the high prevalence of COVID-19 cases in those countries witnessing the first outbreak and also due to having access to COVID-19 data, access to labs for experimental trials, immediate funding, and overall support from the govt. agencies. A large number of publications and citations from India are due to co-authored publications with countries like the United States, UK, China, and Saudi Arabia. Findings show health sciences have the highest number of publications and citations, while physical sciences and social sciences and humanities counts were low. A large proportion of publications fall into the open-access category. With India as the focus, by comparing three major pandemics—SARS, MERS, COVID-19—from a bibliometrics perspective, we observe much broader involvement of authors from multiple countries for COVID-19 studies when compared to SARS and MERS. Finally, by applying bibliometric indicators, we see an increasing number of sustainable development-related studies from the COVID-19 domain, particularly concerning the topic of good health and well-being. This study allows for a deeper understanding of how the scholarly community from a populous country like India pursued research in the midst of a major pandemic which resulted in the closure of scientific institutions for an extended time.

We are delighted to introduce this Special Issue focused on novel machine-learning (ML) methods aimed at predicting, modeling, and controlling a variety of complex fluid flow scenarios [...]

AbstractIn parabolic trough technology,the components used as solar receptor is formed by a steel tube confined inside a glass chamber.The space between both tubes is evacuated during the manufacturing process in current concepts.A new concept of absorber tube, currently under demonstration stage, HITECO, is based on the concept of dynamic vacuum, where the gas evacuation is produced in the solar field during the start up.The gassing and the outgassing processes are the main mechanisms controlling the equilibrium pressure and the gas composition in high and ultra-high vacuum systems. In the solar absorber tubes the mechanisms thatcontrol the condition inside the vacuum chamber are adsorption, absorption, desorption, permeation and leakage. All these phenomena have been analyzed in detail in current paper in order to define the performance evolution of absorber tubes placed in commercial solar fields.

Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

AbstractThe emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.

A new simulation tool aimed at characterizing the thermal behavior of parabolic trough collectors over a wide range of pressures and gas mixtures in the vacuum chamber is presented in this study. The model is able to accurately predict overall efficiency and heat losses obtained in a number of experimental sources based on both on-sun and off-sun testing, with vacuum in the annulus and no hydrogen. Excellent agreement of the simulations with experiments including different gas compositions, especially with high hydrogen concentrations, is achieved through the use of molecular dynamics results for the determination of the accommodation coefficient of the mixture. Additional experiments were carried out to validate the accuracy of the model over a range of pressures between 10−4 and 130 mbar. The accurate modelling of rarefied gas dynamics presented here also leads to an excellent agreement between simulations and experiments over the whole pressure range. An accurate determination of the performance in such extreme conditions is critical for an adequate Operation & Maintenance strategy, as well as the development of effective predictive and preventives plans.