Hydrodynamic memory force or Basset force has been known since the 19th century. Its influence on Brownian motion remains, however, mostly unexplored. Here we investigate its role in nonlinear transport and diffusion within a paradigmatic model of tilted washboard potential. In this model, a giant enhancement of driven diffusion over its potential-free limit [Phys. Rev. Lett. 87, 010602 (2001)PRLTAO0031-900710.1103/PhysRevLett.87.010602] presents a well-established paradoxical phenomenon. In the overdamped limit, it occurs at a critical tilt of vanishing potential barriers. However, for weak damping, it takes place surprisingly at another critical tilt, where the potential barriers are clearly expressed. Recently we showed [Phys. Rev. Lett. 123, 180603 (2019)PRLTAO0031-900710.1103/PhysRevLett.123.180603] that Basset force could make such a diffusion enhancement enormously large. In this paper, we discover that even for moderately strong damping, where the overdamped theory works very well when the memory effects are negligible, substantial hydrodynamic memory unexpectedly makes a strong impact. First, the diffusion boost occurs at nonvanishing potential barriers and can be orders of magnitude larger. Second, transient anomalous diffusion regimes emerge over many time decades and potential periods. Third, particles' mobility can also be dramatically enhanced, and a long transient supertransport regime emerges.
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http://dx.doi.org/10.1103/PhysRevE.102.012139 | DOI Listing |
Bioinspir Biomim
January 2025
Southwest Research Institute for Hydraulic and Water Transport Engineering, Chongqing Jiaotong University, Chongqing, People's Republic of China.
The study of fish swimming behaviours and locomotion mechanisms holds significant scientific and engineering value. With the rapid advancements in artificial intelligence, a new method combining deep reinforcement learning (DRL) with computational fluid dynamics has emerged and been applied to simulate the fish's adaptive swimming behaviour, where the complex fish behaviour is decoupled to focus on the fish's response to the hydrodynamic field, and the simulation is driven by reward-based objectives to model the fish's swimming behaviour. However, the scale of this cross-disciplinary method is directly affected by the efficiency of the DRL model.
View Article and Find Full Text PDFPhys Rev E
October 2024
Department of Mechanical and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland.
The double distribution function approach is an efficient route toward an extension of kinetic solvers to compressible flows. With a number of realizations available, an overview and comparative study in the context of high-speed compressible flows is presented. We discuss the different variants of the energy partition, analyses of hydrodynamic limits, and a numerical study of accuracy and performance with the particles on demand realization.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
November 2024
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802.
The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends.
View Article and Find Full Text PDFPLoS One
November 2024
Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China.
Human breathing is crucial for studying indoor environments and human health. Computational Fluid Dynamics (CFD) is a key tool for simulating human respiration. To enhance the accuracy of CFD simulations and reduce computation time, a new simulation strategy for human respiration is proposed in this paper.
View Article and Find Full Text PDFACS Chem Neurosci
November 2024
Department of Pharmaceutical Sciences, School of Pharmacy, College of Health and Human Sciences, North Dakota State University, Fargo, North Dakota 58108-6050, United States.
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