SPH simulations of turbulence in fixed and rotating boxes in two dimensions with no-slip boundaries

In this paper we study decaying turbulence in fixed and rotating boxes in two dimen- sions using the particle method SPH. The boundaries are specified by boundary force particles, and the turbulence is initiated by a set of gaussian vortices. In the case of fixed boxes we recover the results of Clercx and his colleagues obtained using both a high accuracy spectral method and experiments. Our results for fixed boxes are also in close agreement with those of Monaghan1 and Robinson and Monaghan2 obtained using SPH. A feature of decaying turbulence in no-slip, square, fixed boundaries is that the angular momentum of the fluid varies with time because of the reaction on the fluid of the viscous stresses on the boundary. We find that when the box is allowed to rotate freely, so that the total angular momentum of box and fluid is constant, the change in the angular momentum of the fluid is a factor ~ 500 smaller than is the case for the fixed box, and the final vorticity distribution is different. We also simulate the behaviour of the turbulence when the box is forced to rotate with small and large Rossby number, and the turbulence is initiated by gaussian vortices as before. If the rotation of the box is maintained after the turbulence is initiated we find that in the rotating frame the decay of kinetic energy, enstrophy and the vortex structure is insensitive to the angular velocity of the box. On the other hand, If the box is allowed to rotate freely after the turbulence is initiated, the evolved vortex structure is completely different

Machine Learning in Lithium-Ion Battery:Applications, Challenges, and Future Trends

Machine Learning has garnered significant attention in lithium-ion battery research for its potential to revolutionize various aspects of the field. This paper explores the practical applications, challenges, and emerging trends of employing Machine Learning in lithium-ion battery research. Delves into specific Machine Learning techniques and their relevance, offering insights into their transformative potential. The applications of Machine Learning in lithium-ion-battery design, manufacturing, service, and end-of-life are discussed. The challenges including data availability, data preprocessing and cleaning challenges, limited sample size, computational complexity, model generalization, black-box nature of Machine Learning models, scalability of the algorithms for large datasets, data bias, and interdisciplinary nature and their mitigations are also discussed. Accordingly, by discussing the future trends, it provides valuable insights for researchers in this field. For example, a future trend is to address the challenge of small datasets by techniques such as Transfer Learning and N-shot Learning. This paper not only contributes to our understanding of Machine Learning applications but also empowers professionals in this field to harness its capabilities effectively.</p

Alloys innovation through machine learning:a statistical literature review

This review systematically analyzes over 200 publications to explore the growing role of data-driven methods and their potential benefits in accelerating alloy development. The review presents a comprehensive overview of different aspects of alloy innovation by machine learning and other computational approaches used in recent years. These methods harness the power of advanced simulation techniques and data analytics to expedite materials’ discovery, predict properties, and optimize performance. Through analysis, significant trends and disparities within the data discerned, while highlighting previously overlooked research gaps, thus underscoring areas that require further exploration. Machine Learning techniques are widely applied across various alloys, with a pronounced emphasis on steel and High Entropy Alloys. Notably, researchers primarily investigate the physical, mechanical, and catalytic properties of materials. In terms of methodology, while 68% of the examined papers rely on a single machine learning model, the remainder employ a range of 2 to 12 models, with Neural Network being the most prevalent choice. However, a notable concern arises as 53% of these papers do not share their dataset, and a staggering 81% do not provide access to their code. Paramount importance of adopting a systematic approach when scrutinizing machine learning methodologies is underscored. Analysis shows lack of consistency and diversity in the methods employed by researchers in the field of alloy development, highlighting the potential for improvement through standardization. The critical analysis of the literature not only reveals prevailing trends and patterns but also shines a light on the inherent limitations within the traditional trial-and-error paradigm