John J. Lee | Searcharxiv

arXiv2024

Modeling and simulations of high-density two-phase flows using projection-based Cahn-Hilliard Navier-Stokes equations

Accurately modeling the dynamics of high-density ratio ($\mathcal{O}(10^5)$) two-phase flows is important for many material science and manufacturing applications. This work considers numerical simulations of molten metal oscillations in microgravity to analyze the interplay between surface tension and density ratio, a critical factor for terrestrial manufacturing applications. We present a projection-based computational framework for solving a thermodynamically-consistent Cahn-Hilliard Navier-Stokes equations for two-phase flows with large density ratios. The framework employs a modified version of the pressure-decoupled solver based on the Helmholtz-Hodge decomposition presented in Khanwale et al. [{\it A projection-based, semi-implicit time-stepping approach for the Cahn-Hilliard Navier-Stokes equations on adaptive octree meshes.}, Journal of Computational Physics 475 (2023): 111874]. We validate our numerical method on several canonical problems, including the capillary wave and single bubble rise problems. We also present a comprehensive convergence study to investigate the effect of mesh resolution, time-step, and interfacial thickness on droplet-shape oscillations. We further demonstrate the robustness of our framework by successfully simulating three distinct physical systems with extremely large density ratios ($10^4$-$10^5:1$), achieving results that have not been previously reported in the literature.

physics.flu-dyn↗

arXiv2021

Machine Learning Enhanced Blockchain Consensus with Transaction Prioritization for Smart Cities

In the given technology-driven era, smart cities are the next frontier of technology, aiming at improving the quality of people's lives. Many research works focus on future smart cities with a holistic approach towards smart city development. In this paper, we introduce such future smart cities that leverage blockchain technology in areas like data security, energy and waste management, governance, transport, supply chain, including emergency events, and environmental monitoring. Blockchain, being a decentralized immutable ledger, has the potential to promote the development of smart cities by guaranteeing transparency, data security, interoperability, and privacy. Particularly, using blockchain in emergency events will provide interoperability between many parties involved in the response, will increase timeliness of services, and establish transparency. In that case, if a current fee-based or first-come-first-serve-based processing is used, emergency events may get delayed in being processed due to competition, and thus, threatening people's lives. Thus, there is a need for transaction prioritization based on the priority of information and quick creation of blocks (variable interval block creation mechanism). Also, since the leaders ensure transaction prioritization while generating blocks, leader rotation and proper election procedure become important for the transaction prioritization process to take place honestly and efficiently. In our consensus protocol, we deploy a machine learning (ML) algorithm to achieve efficient leader election and design a novel dynamic block creation algorithm. Also, to ensure honest assessment from the followers on the blocks generated by the leaders, a peer-prediction-based verification mechanism is proposed. Both security analysis and simulation experiments are carried out to demonstrate the robustness and accuracy of our proposed scheme.

cs.CR↗