Controlling the shape of fluid streams is important across scales: from industrial processing to control of biomolecular interactions. Previous approaches to control fluid streams have focused mainly on creating chaotic flows to enhance mixing. Here we develop an approach to apply order using sequences of fluid transformations rather than enhancing chaos. We investigate the inertial flow deformations around a library of single cylindrical pillars within a microfluidic channel and assemble these net fluid transformations to engineer fluid streams. As these transformations provide a deterministic mapping of fluid elements from upstream to downstream of a pillar, we can sequentially arrange pillars to apply the associated nested maps and, therefore, create complex fluid structures without additional numerical simulation. To show the range of capabilities, we present sequences that sculpt the cross-sectional shape of a stream into complex geometries, move and split a fluid stream, perform solution exchange and achieve particle separation. A general strategy to engineer fluid streams into a broad class of defined configurations in which the complexity of the nonlinear equations of fluid motion are abstracted from the user is a first step to programming streams of any desired shape, which would be useful for biological, chemical and materials automation.

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Fiber-based sensors are desirable to provide an immersive experience for users in the human−computer interface. We report a hierarchically porous silver nanowire-bacterial cellulose fiber that can be utilized for sensitive detection of both pressure and proximity of human fingers. The conductive fiber was synthesized via continuous wetspinning at a speed of 20 m/min, with a diameter of 53 μm, the electrical conductivity of 1.3 × 10 4 S/cm, a tensile strength of 198 MPa, and elongation strain of 3.0% at break. The fibers were coaxially coated with a 10 μm thick poly(dimethylsiloxane) dielectric elastomer to form the fiber sensor element which is thinner than a human hair. Two of the sensor fibers were laid diagonally, and the capacitance changes between the conductive cores were measured in response to pressure and proximity. In the touch mode, a fiber-based sensor experienced monotonic capacitance increase in the pressure range from 0 to 460 kPa, and a linear response with a high sensitivity of 5.49 kPa −1 was obtained in the low-pressure regime (<0.5 kPa). In touchless mode, the sensor is highly sensitive to objects at a distance of up to 30 cm. Also, the fiber can be easily stitched into garments as comfortable and fashionable sensors to detect heartbeat and vocal pulses. A fiber sensor array is able to serve as a touchless piano to play music and accurately determine the proximity of an object. A 2 × 2 array was further shown for two-and threedimensional location detection of remote objects.

The ability to control the shape of a flow in a passive microfluidic device enables potential applications in chemical reaction control, particle separation, and complex material fabrication. Recent work has demonstrated the concept of sculpting fluid streams in a microchannel using a set of pillars or other structures that individually deform a flow in a predictable pre-computed manner. These individual pillars are then placed in a defined sequence within the channel to yield the composition of the individual flow deformations -and ultimately complex user-defined flow shapes. In this way, an elegant mathematical operation can yield the final flow shape for a sequence without an experiment or additional numerical simulation. Although these approaches allow for programming complex flow shapes without understanding the detailed fluid mechanics, the design of an arbitrary flow shape of interest remains difficult, requiring significant design iteration. The development of intuitive basic operations (i.e. higher-level functions that consist of combinations of obstacles) that act on the flow field to create a basis for more complex transformations would be useful in systematically achieving a desired flow shape. Here, we show eight transformations that could serve as a partial basis for more complex transformations. We initially used inhouse, freely available custom software (uFlow), which allowed us to arrive at these transformations that include making a fluid stream concave and convex, tilting, stretching, splitting, adding a vertex, shifting, and encapsulating another flow stream. The pillar sequences corresponding to these transformations were subsequently fabricated and optically analyzed using confocal imaging -yielding close agreement with uFlowpredicted shapes. We performed topological analysis on each transformation, characterizing potential sequences leading to these outputs and trends associated with changing diameter and placement of the pillars. We classify operations into four sets of sequence-building concatenations: stacking, recursion, mirroring, and shaping. The developed basis should help in the design of microfluidic systems that have a phenomenal variety of applications, such as optofluidic lensing, enhanced heat transfer, or new polymer fiber design.

The En4DVar method is designed to combine the flow-dependent statistical covariance information of EnKF into the traditional 4DVar method. However, the En4DVar method is still hampered by its strong dependence on the adjoint model of the underlying forecast model and by its complexity, maintenance requirements, and the high cost of computer implementation and simulation. The primary goal of this paper is to propose an alternative approach to overcome the main difficulty of the En4DVar method caused by the use of adjoint models. The proposed approach, the nonlinear least squares En4DVar (NLS-En4DVar) method, begins with rewriting the standard En4DVar formulation into a nonlinear least squares problem, which is followed by solving the resulting NLS problem by a Gauss–Newton iterative method. To reduce the computational and implementation complexity of the proposed NLS-En4DVar method, a few variants of the new method are proposed; these modifications make the model cheaper and easier to use than the full NLS-En4DVar method at the expense of reduced accuracy. Furthermore, an improved iterative method based on the comprehensive analysis on the above NLSi-En4DVar family of methods is also proposed. These proposed NLSi-En4DVar methods provide more flexible choices of the computational capabilities for the broader and more realistic data assimilation problems arising from various applications. The pros and cons of the proposed NLSi-En4DVar family of methods are further examined in the paper and their relationships and performance are also evaluated by several sets of numerical experiments based on the Lorenz-96 model and the Advanced Research WRF (ARW) Model, respectively.

As new observation systems are developed and deployed, new and presumably more precise information is becoming available for weather forecasting and climate monitoring. To take advantage of these new observations, it is desirable to have schemes to accurately retrieve the information before statistical analyses are performed so that statistical computation can be more effectively used where it is needed most. The authors propose a sequential variational approach that possesses advantages of both a standard statistical analysis [such as with a three-dimensional variational data assimilation (3DVAR) or Kalman filter] and a traditional objective analysis (such as the Barnes analysis). The sequential variational analysis is multiscale, inhomogeneous, anisotropic, and temporally consistent, as shown by an idealized test case and observational datasets in this study. The real data cases include applications in two-dimensional and three-dimensional space and time for storm outflow boundary detection (surface application) and hurricane data assimilation (three-dimensional space application). Implemented using a multigrid technique, this sequential variational approach is a very efficient data assimilation method.

Rockfalls and rockslides often occur in mountainous areas, and they may develop into rock avalanches because of fragmentation. A series of laboratory experiments were conducted to study the contributions of rock mass structure to the emplacement of fragmenting rockfalls and rockslides. In these experiments, we considered the process of breakable analog blocks with different structures sliding along an inclined plane, impacting at the kink point with a horizontal plane where deposition occurs. The results show that the initial geometrical subdivision (i.e., the rock mass structure) of the source rock can greatly influence the impact of the fragmentation process and total runout, while the degree of fragmentation controls the travel distance of the center of mass. The occurrence of transversal discontinuities enhances the momentum transfer efficiency from the rear to the front part of the rock mass. A negative correlation between the apparent friction coefficient (linked to the total runout) and equivalent friction coefficient (linked to the center of mass runout) was found, which appears to be induced by fragmentation. We proposed a new fragmentation-spreading model to describe this negative correlation. This simple physical model supports the importance of fragmentation in rock fragment trajectories and the runout of rockfalls and rockslides. Fragmentation is an energy-sinking process that will shorten the runout of the center of mass. Thus, we suggest that impact fragmentation does not fully account for the long runout of large rockfalls and rockslides.

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