317 research outputs found
Data-driven discovery of dimensionless numbers and scaling laws from experimental measurements
Dimensionless numbers and scaling laws provide elegant insights into the
characteristic properties of physical systems. Classical dimensional analysis
and similitude theory fail to identify a set of unique dimensionless numbers
for a highly-multivariable system with incomplete governing equations. In this
study, we embed the principle of dimensional invariance into a two-level
machine learning scheme to automatically discover dominant and unique
dimensionless numbers and scaling laws from data. The proposed methodology,
called dimensionless learning, can reduce high-dimensional parametric spaces
into descriptions involving just a few physically-interpretable dimensionless
parameters, which significantly simplifies the process design and optimization
of the system. We demonstrate the algorithm by solving several challenging
engineering problems with noisy experimental measurements (not synthetic data)
collected from the literature. The examples include turbulent Rayleigh-Benard
convection, vapor depression dynamics in laser melting of metals, and porosity
formation in 3D printing. We also show that the proposed approach can identify
dimensionally-homogeneous differential equations with minimal parameters by
leveraging sparsity-promoting techniques
SWheg: A Wheel-Leg Transformable Robot With Minimalist Actuator Realization
This article presents the design, implementation, and performance evaluation
of SWheg, a novel modular wheel-leg transformable robot family with minimalist
actuator realization. SWheg takes advantage of both wheeled and legged
locomotion by seamlessly integrating them on a single platform. In contrast to
other designs that use multiple actuators, SWheg uses only one actuator to
drive the transformation of all the wheel-leg modules in sync. This means an
N-legged SWheg robot requires only N+1 actuators, which can significantly
reduce the cost and malfunction rate of the platform. The tendon-driven
wheel-leg transformation mechanism based on a four-bar linkage can perform fast
morphology transitions between wheels and legs. We validated the design
principle with two SWheg robots with four and six wheel-leg modules separately,
namely Quadrupedal SWheg and Hexapod SWheg. The design process, mechatronics
infrastructure, and the gait behavioral development of both platforms were
discussed. The performance of the robot was evaluated in various scenarios,
including driving and turning in wheeled mode, step crossing, irregular terrain
passing, and stair climbing in legged mode. The comparison between these two
platforms was also discussed
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