Heat transport measurements in turbulent rotating Rayleigh-Benard convection

We present experimental heat transport measurements of turbulent Rayleigh-B\'{e}nard convection with rotation about a vertical axis. The fluid, water with Prandtl number ($\sigma$) about 6, was confined in a cell which had a square cross section of 7.3 cm$\times$7.3 cm and a height of 9.4 cm. Heat transport was measured for Rayleigh numbers $2\times 10^5 <$ Ra $< 5\times 10^8$ and Taylor numbers $0 <$ Ta $< 5\times 10^{9}$. We show the variation of normalized heat transport, the Nusselt number, at fixed dimensional rotation rate $\Omega_D$, at fixed Ra varying Ta, at fixed Ta varying Ra, and at fixed Rossby number Ro. The scaling of heat transport in the range $10^7$ to about $10^9$ is roughly 0.29 with a Ro dependent coefficient or equivalently is also well fit by a combination of power laws of the form $a Ra^{1/5} + b Ra^{1/3}$. The range of Ra is not sufficient to differentiate single power law or combined power law scaling. The overall impact of rotation on heat transport in turbulent convection is assessed.Comment: 16 pages, 12 figure

Transmutation

Thesis (MM) – Indiana University, Music, 202

ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse problems such as optimal control and motion planning. Simulating deformable objects is, however, more challenging compared to rigid body dynamics. The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and therefore they are significantly more computationally expensive to simulate. Computing gradients with respect to physical design or controller parameters is typically even more computationally challenging. In this paper, we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical simulator for deformable objects, ChainQueen, based on the Moving Least Squares Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects including contact and can be seamlessly incorporated into inference, control and co-design systems. We demonstrate that our simulator achieves high precision in both forward simulation and backward gradient computation. We have successfully employed it in a diverse set of control tasks for soft robots, including problems with nearly 3,000 decision variables.Comment: In submission to ICRA 2019. Supplemental Video: https://www.youtube.com/watch?v=4IWD4iGIsB4 Project Page: https://github.com/yuanming-hu/ChainQuee

Maglev Facility for Simulating Variable Gravity

An improved magnetic levitation apparatus ("Maglev Facility") has been built for use in experiments in which there are requirements to impose variable gravity (including zero gravity) in order to assess the effects of gravity or the absence thereof on physical and physiological processes. The apparatus is expected to be especially useful for experiments on the effects of gravity on convection, boiling, and heat transfer in fluids and for experiments on mice to gain understanding of bone loss induced in human astronauts by prolonged exposure to reduced gravity in space flight. The maglev principle employed by the apparatus is well established. Diamagnetic cryogenic fluids such as liquid helium have been magnetically levitated for studying their phase transitions and critical behaviors. Biological entities consist mostly of diamagnetic molecules (e.g., water molecules) and thus can be levitated by use of sufficiently strong magnetic fields having sufficiently strong vertical gradients. The heart of the present maglev apparatus is a vertically oriented superconducting solenoid electromagnet (see figure) that generates a static magnetic field of about 16 T with a vertical gradient sufficient for levitation of water in normal Earth gravity. The electromagnet is enclosed in a Dewar flask having a volume of 100 L that contains liquid helium to maintain superconductivity. The Dewar flask features a 66-mm-diameter warm bore, lying within the bore of the magnet, wherein experiments can be performed at room temperature. The warm bore is accessible from its top and bottom ends. The superconducting electromagnet is run in the persistent mode, in which the supercurrent and the magnetic field can be maintained for weeks with little decay, making this apparatus extremely cost and energy efficient to operate. In addition to water, this apparatus can levitate several common fluids: liquid hydrogen, liquid oxygen, methane, ammonia, sodium, and lithium, all of which are useful, variously, as rocket fuels or as working fluids for heat transfer devices. A drop of water 45 mm in diameter and a small laboratory mouse have been levitated in this apparatus

Task-Oriented Over-the-Air Computation for Multi-Device Edge AI

Departing from the classic paradigm of data-centric designs, the 6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient execution of AI task. Targeting end-to-end system performance, such techniques are sophisticated as they aim to seamlessly integrate sensing (data acquisition), communication (data transmission), and computation (data processing). Aligned with the paradigm shift, a task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device split-inference system. In the considered system, local feature vectors, which are extracted from the real-time noisy sensory data on devices, are aggregated over-the-air by exploiting the waveform superposition in a multiuser channel. Then the aggregated features as received at a server are fed into an inference model with the result used for decision making or control of actuators. To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy. The problem is made tractable by measuring the inference accuracy using a surrogate metric called discriminant gain, which measures the discernibility of two object classes in the application of object/event classification. It is discovered that the conventional AirComp beamforming design for minimizing the mean square error in generic AirComp with respect to the noiseless case may not lead to the optimal classification accuracy. The reason is due to the overlooking of the fact that feature dimensions have different sensitivity towards aggregation errors and are thus of different importance levels for classification. This issue is addressed in this work via a new task-oriented AirComp scheme designed by directly maximizing the derived discriminant gain

Improved Charge Injection and Transport of Light-Emitting Diodes Based on Two-Dimensional Materials

Light-emitting diodes (LEDs) are considered to be the most promising energy-saving technology for future lighting and display. Two-dimensional (2D) materials, a class of materials comprised of monolayer or few layers of atoms (or unit cells), have attracted much attention in recent years, due to their unique physical and chemical properties. Here, we summarize the recent advances on the applications of 2D materials for improving the performance of LEDs, including organic light emitting diodes (OLEDs), quantum dot light emitting diodes (QLEDs) and perovskite light emitting diodes (PeLEDs), using organic films, quantum dots and perovskite films as emission layers (EMLs), respectively. Two dimensional materials, including graphene and its derivatives and transition metal dichalcogenides (TMDs), can be employed as interlayers and dopant in composite functional layers for high-efficiency LEDs, suggesting the extensive application in LEDs. The functions of 2D materials used in LEDs include the improved work function, effective electron blocking, suppressed exciton quenching and reduced surface roughness. The potential application of 2D materials in PeLEDs is also presented and analyzed

Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context, multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. However, existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network and ignore driver privacy. To deal with these problems, this paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow (OPF) to distribute power flow in real time. A mathematical model is developed to describe the RDN load. The EV charging control problem is formulated as a Markov Decision Process (MDP) to find an optimal charging control strategy that balances V2G profits, RDN load, and driver anxiety. To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed. Comprehensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the diversity of the charging control strategy, the power fluctuations on RDN, the convergence efficiency, and the generalization ability

Complex Locomotion Skill Learning via Differentiable Physics

Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers. However, existing work typically only delivers NN controllers with limited capability and generalizability. We present a practical learning framework that outputs unified NN controllers capable of tasks with significantly improved complexity and diversity. To systematically improve training robustness and efficiency, we investigated a suite of improvements over the baseline approach, including periodic activation functions, and tailored loss functions. In addition, we find our adoption of batching and an Adam optimizer effective in training complex locomotion tasks. We evaluate our framework on differentiable mass-spring and material point method (MPM) simulations, with challenging locomotion tasks and multiple robot designs. Experiments show that our learning framework, based on differentiable physics, delivers better results than reinforcement learning and converges much faster. We demonstrate that users can interactively control soft robot locomotion and switch among multiple goals with specified velocity, height, and direction instructions using a unified NN controller trained in our system. Code is available at https://github.com/erizmr/Complex-locomotion-skill-learning-via-differentiable-physics