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3 results for “Hybrid Dynamic Model”
Hybrid dynamic model for shape memory alloy linear and unimorph actuators
<p>Shape memory alloy morphing actuators are a type of soft actuator with many attractive properties. These actuators exhibit large deformation, small form factor, self-sense ability, and physical reservoir computing potential, while also being inexpensive. These morphing actuators are composed of active shape memory alloy wires and a passive base layer that is used to magnify the overall deflection. Although morphing actuators have great potential, the modeling of shape memory alloy actuators is difficult due to both shape memory alloy characteristics and the nonlinearity of the passive layer. Here, a hybrid dynamical model is proposed that couples the phase kinetics & thermal modeling for the shape memory alloy with a dynamic Cosserat nonlinear beam model. This hybrid model is benchmarked against linear and morphing experimental actuators. The model resulted in a root mean squared error of 1.48 mm and 1.63 mm for the morphing actuator configuration for two different actuators. This model can expand the capability and design of novel morphing actuators for a designed deformation profile for use in soft robotics.</p>
Hybrid dynamic model for shape memory alloy linear and unimorph actuators
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Model data for "A hybrid dynamical-statistical model for advancing subseasonal tropical cyclone prediction over the western North Pacific"
<p>The dataset is the potential predictors of the statistical forecast model based on the 4 methods.</p> <p>The files in the document named "Train" are the potential predictors and TC anomalous counts for C1-C7 and TCall in the training period of 1979-2002 with 480-time points. For example, "./data/Train/M1/prepar/Obs_C1_pre-data.txt" contains 7 potential predictors of OLR, SSTA, specific humidity at 700 hPa, omega at 500 hPa, divergence and vorticity at 850hPa defined with method 1, and TC anomalous for TC of C1 prediction.</p> <p>The files named "Model_Lead*_C*_pre-data_*.txt" in "Frcst" the document are the potential predictors in the forecast period of 2003-2013 at lead times of 10, 15, 20, 25, 30, and 35 days with 220-time points. For example, "./data/Frcst/M1/prepar/Model_Lead10_C1_pre-data_00.txt" contains 7 potential predictors from the output of the FLOR model at lead 10 days initialized at Z00 time defined with method 1. Besides, "./data/Frcst/M1/prepar/Obs_C1_pre-data.txt" contains 7 potential predictors from the observation, which is the result of lead 0 days.</p>
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