40 research outputs found

    Synchrotron x-ray imaging visualization study of capillary-induced flow and critical heat flux on surfaces with engineered micropillars

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    Over the last several decades, phenomena related to critical heat flux (CHF) on structured surfaces have received a large amount of attention from the research community. The purpose of such research has been to enhance the safety and efficiency of a variety of thermal systems. A number of theories have been put forward to explain the key CHF enhancement mechanisms on structured surfaces. However, these theories have not been confirmed experimentally because of limitations in the available visualization techniques and the complexity of the phenomena. To overcome these limitations and elucidate the CHF enhancement mechanism on the structured surfaces, we introduce synchrotron x-ray imaging with high spatial (similar to 2 mu m) and temporal (similar to 20,000 Hz) resolutions. This technique has enabled us to confirm that capillary-induced flow is the key CHF enhancement mechanism on structured surfaces.11Ysciescopu

    BALLU2: A Safe and Affordable Buoyancy Assisted Biped.

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    This work presents the first full disclosure of BALLU, Buoyancy Assisted Lightweight Legged Unit, and describes the advantages and challenges of its concept, the hardware design of a new implementation (BALLU2), a motion analysis, and a data-driven walking controller. BALLU is a robot that never falls down due to the buoyancy provided by a set of helium balloons attached to the lightweight body, which solves many issues that hinder current robots from operating close to humans. The advantages gained also lead to the platforms distinct difficulties caused by severe nonlinearities and external forces such as buoyancy and drag. The paper describes the nonconventional characteristics of BALLU as a legged robot and then gives an analysis of its unique behavior. Based on the analysis, a data-driven approach is proposed to achieve non-teleoperated walking: a statistical process using Spearman Correlation Coefficient is proposed to form low-dimensional state vectors from the simulation data, and an artificial neural network-based controller is trained on the same data. The controller is tested both on simulation and on real-world hardware. Its performance is assessed by observing the robots limit cycles and trajectories in the Cartesian coordinate. The controller generates periodic walking sequences in simulation as well as on the real-world robot even without additional transfer learning. It is also shown that the controller can deal with unseen conditions during the training phase. The resulting behavior not only shows the robustness of the controller but also implies that the proposed statistical process effectively extracts a state vector that is low-dimensional yet contains the essential information of the high-dimensional dynamics of BALLUs walking
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