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Diurnal warm layers (DWLs) form near the surface of the ocean on days with strong solar radiation, weak to moderate winds, and small surface-wave effects. Here, we use idealized second-moment turbulence modeling, validated with large-eddy simulations (LES), to study the properties, dynamics, and energetics of DWLs across the entire physically relevant parameter space. Both types of models include representations of Langmuir turbulence (LT). We find that LT only slightly modifies DWL thicknesses and other bulk parameters under equilibrium wave conditions, but leads to a strong reduction in surface temperature and velocity with possible implications for air–sea coupling. Comparing tropical and the less frequently studied high-latitude DWLs, we find that LT has a strong impact on the energy budget and that rotation at high latitudes strongly modifies the DWL energetics, suppressing net energy turnover and entrainment. We identify the key nondimensional parameters for DWL evolution and find that the scaling relations of Price et al. provide a reliable representation of the DWL bulk properties across a wide parameter space, including high-latitude DWLs. We present different sets of revised model coefficients that include the deepening of the DWL due to LT and other aspects of our more advanced turbulence model to describe DWL properties at midday and during the DWL temperature peak in the afternoon, which we find to occur around 1500–1630 local time for a broad range of parameters.

 

Soft robots have shown great potential to enable safe interactions with unknown environments due to their inherent compliance and variable stiffness. However, without knowledge of potential contacts, a soft robot could exhibit rigid behaviors in a goal-reaching task and collide into obstacles. In this paper, we introduce a Sliding Mode Augmented by Reactive Transitioning (SMART) controller to detect the contact events, adjust the robot’s desired trajectory, and reject estimated disturbances in a goal reaching task. We employ a sliding mode controller to track the desired trajectory with a nonlinear disturbance observer (NDOB) to estimate the lumped disturbance, and a switching algorithm to adjust the desired robot trajectories. The proposed controller is validated on a pneumatic-driven fabric soft robot whose dynamics is described by a new extended rigid-arm model to fit the actuator design. A stability analysis of the proposed controller is also presented. Experimental results show that, despite modeling uncertainties, the robot can detect obstacles, adjust the reference trajectories to maintain compliance, and recover to track the original desired path once the obstacle is removed. Without force sensors, the proposed model-based controller can adjust the robot’s stiffness based on the estimated disturbance to achieve goal reaching and compliant interaction with unknown obstacles. 

Abstract Factors thought to influence deep cycle turbulence in the equatorial Pacific are examined statistically for their predictive capacity using a 13-year moored record that includes microstructure measurements of the turbulent kinetic energy dissipation rate. Wind stress and mean current shear are found to be most predictive of the dissipation rate. Those variables, together with the solar buoyancy flux and the diurnal mixed layer thickness, are combined to make a pair of useful parameterizations. The uncertainty in these predictions is typically 50% greater than the uncertainty in present-day in situ measurements. To illustrate the use of these parameterizations, the record of deep cycle turbulence, measured directly since 2005, is extended back to 1990 based on historical mooring data. The extended record is used to refine our understanding of the seasonal variation of deep cycle turbulence.