Dynamics

The distribution of oil and gas phases around ball–raceway local regions is an important basis and foundation for determining whether a bearing is sufficiently lubricated. To obtain the oil phase distribution law in the inner raceway–ball contact local region (IBCR) and outer raceway–ball contact local region (OBCR) of the ball bearing with under-race lubrication, the numerical simulation method is used. The effects of bearing rotation speed, oil flow rate, oil viscosity, and oil density on these two regions are studied. The results indicate that the oil phase exhibited significant periodic changes in both time and space. Compared with that in the IBCR, the oil phase distribution in the OBCR is more uniform. Increasing the bearing rotation speed and reducing the oil flow rate made the IBCR and OBCR more uniform. Changing the oil viscosity only alters the distribution pattern of the OBCR. The oil density may not affect the fluid flow state or the oil phase distribution in the bearing.

The Tea Leaf Paradox (TLP) describes unsteady fluid motions which help entrain and deposit suspended particles at the center of rotation. Various applications depend on the TLP for particle separations—spanning orders of magnitude in length scales—making it an important problem in fluid mechanics. Despite papers describing the phenomenon, the efficacy of particle separation using the TLP remains unclear as to the relative importance of, for example, hydrostatics, particle-fluid density ratio, wall friction, liquid bath aspect ratio and the rotation speed. The dynamics involved are notably complex and require a careful tuning of each variable. In this study, we have investigated the role of the limit of the aggregation dynamics in rotational flows within 3D-printed vessels of various sizes in tandem with particle imaging to probe the dissipation effects on the particle motions. We have found that the liquid bath aspect ratio limits how much aggregation may occur for a particle-fluid density ratio greater than unity (e.g., ρp/ρf>1), where ρp is the density of the particle and ρf is the ambient fluid density.

Tropical cyclones (TCs) can be characterized as a 3D annular structure of elevated potential vorticity (PV). However, strong mature TCs often develop a secondary eyewall, leading to a 3D annular vortex with a double-eyewall structure. Using 2D linear stability analysis, it is shown that three types of barotropic instability (BI) are present for annular vortices with a double-eyewall structure: Type-1 BI across the secondary eyewall, Type-2 BI across the moat of the vortex, and Type-3 BI across the primary eyewall. The overall stability of these vortices (and the type of BI that develops) depends principally upon five vortex parameters: the thickness of the primary eyewall, the thickness of the secondary eyewall, the moat width, the vorticity ratio between the eye and the primary eyewall, and the vorticity ratio between the primary and secondary eyewall. The adiabatic evolution of 3D annular vortices with a double-eyewall structure is examined using a primitive equation model in normalized isobaric coordinates. It is shown that Type-2 BI is the most common type of BI for 3D annular vortices whose vortex parameters mimic TCs with a double-eyewall structure. During the onset of Type-2 BI, eddy kinetic energy budget analysis indicates that barotropic energy conversion from the mean azimuthal flow is the dominant energy source of the eddies, which produces a radial velocity field with a quadrupole structure. Absolute angular momentum budget analysis indicates that Type-2 BI generates azimuthally averaged radial outflow across the moat, and the eddies transport absolute angular momentum radially outward towards the secondary eyewall. The combination of these processes leads to the dissipation of the primary eyewall and the maintenance of the secondary eyewall for the vortex.

In this paper, we consider the flow of a nanofluid in an enclosed lid-driven cavity using a single-phase model. Two cases are considered: one in which the top and bottom walls are kept at adiabatic conditions, and a second case in which the left- and right-side walls are kept in adiabatic conditions. The impact of different viscosity models on the mixed convection heat transfer is examined, and numerical methods are used to obtain solutions for the Navier–Stokes equations for various parameter ranges. Using our robust methods, we are able to obtain novel solutions for large Reynolds numbers and very small Richardson numbers. Using water as the base fluid and aluminium oxide nanoparticles, our results suggest that heat transfer enhancement occurs with increasing particle concentration and decreasing Richardson numbers. There are also significant differences depending on the viscosity model used in terms of the impact of reducing corner recirculation regions in the cavity.

Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of the total power available onboard, thereby limiting the vehicle’s range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives on the development of onboard neuromorphic computers that mimic the operation of a biological brain using the nonlinear–dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs are a semi-classical technology, their technical simplicity and low cost compared to quantum computers make them ideally suited for applications in autonomous AI systems. Providing a perspective on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing.