In complex systems like aircraft engines and oil refinery machines, pipeline inspection is an essential task for ensuring safety. Here, we proposed a type of smart material–driven pipeline inspection robot (weight, 2.2 grams; length, 47 millimeters; diameter, <10 millimeters) that could fit into pipes with sub-centimeter diameters and different curvatures. We adopted high–power density, long-life dielectric elastomer actuators as artificial muscles and smart composite microstructure–based, high-efficiency anchoring units as transmissions. Fast assembling of components using magnets with an adjustable number of units was used to fit varying pipeline geometries. We analyzed the dynamic characteristics of the robots by considering soft material’s unique properties like viscoelasticity and dynamic vibrations and tuned the activation voltage’s frequency and phase accordingly. Powered by tethered cables from outside the pipe, our peristaltic pipeline robot achieved rapid motions horizontally and vertically (horizontal: 1.19 body lengths per second, vertical: 1.08 body lengths per second) in a subcentimeter-sized pipe (diameter, 9.8 millimeters). Besides, it was capable of moving in pipes with varying geometries (diameter-changing pipe, L-shaped pipe, S-shaped pipe, or spiral-shaped pipe), filled media (air or oil), and materials (glass, metal, or carbon fiber). To demonstrate its capability for pipeline inspection, we installed a miniature camera on its front and controlled the robot manually from outside. The robot successfully finished an inspection task at different speeds.

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The inertia of wind turbines causes a reduction in their output power due to their inability to operate at the turbine maximum co-efficient of performance point under dynamic wind conditions. In this paper, this dynamic power reduction is studied analytically and using simulations, assuming that a steady-state optimal torque control strategy is used.The concepts of the natural and actual turbine time-constant are introduced, and typical values for these parameters are examined. It is shown that for the typical turbine co-efficient of performance curve used, the average turbine speed can be assumed to be determined by the average wind speed. With this assumption, analytical expressions for the power reduction with infinite and then finite turbine inertia are determined for sine-wave wind speed variations. The results are then generalized for arbitrary wind speed profiles.A numerical wind turbine system simulation model is used to validate the analytical results for step and sine-wave wind speed variations. Finally, it is used with real wind speed data to compare with the analytical predictions.The diagrams of a simplified wind turbine system and its model are shown in Figure 1(a),(b), which consists of the turbine, generator, power electronics and the system controller, which implements the MPPT. In the figures, v is the wind speed, J

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