In this paper, a mesh-less numerical approach is utilized to solve Euler's equation that is the governing equation of the irrotational flow of ideal fluids. A fractional step method of discritization is applied which consists to split each time step in two steps. This numerical method is based on moving-particle semi-implicit method (MPS) for simulating incompressible inviscid flows with free surfaces. The motion of each particle is calculated through interactions with neighboring particles covered with the kernel function. There are limitations for getting a stable solution by MPS method. In this paper, various kernel functions are considered and applied to improve the stability of MPS method. Based on these studies a kernel function is introduced that improves the stability of MPS method. The numerical results of the model are in good agreement with experimental results. The applicability of this model to simulate hydraulic problems with free surface is shown through the solution of dam break problem. The present method is a very useful utility for solving problems with irregular free surface in hydraulic and coastal engineering when an accurate prediction of free water surface is required.

The cytoskeleton precisely tunes its mechanics by altering interactions between semiflexible actin filaments, rigid microtubules, and crosslinking proteins. We use optical tweezers microrheology and confocal microscopy to characterize how varying crosslinking motifs impact the mesoscale mechanics and mobility of actin-microtubule composites. We show that, upon subtle changes in crosslinking patterns, composites can exhibit two distinct classes of force response – primarily elastic versus more viscous. For example, a composite in which actin and microtubules are crosslinked to each other but not to themselves is markedly more elastic than one in which both filaments are independently crosslinked. Notably, this distinction only emerges at mesoscopic scales in response to nonlinear forcing, whereas varying crosslinking motifs have little impact on the microscale mechanics and mobility. Our unexpected scale-dependent results not only inform the physics underlying key cytoskeleton processes and structures, but, more generally, provide valuable perspective to materials engineering endeavors focused on polymer composites.

Numerical models of heat and moisture diffusion in the soil-vegetation-atmosphere continuum are linked through the moisture flux from the surface to the atmosphere. This mass flux represents a heat exchange as latent heat flux, coupling water, and energy balance equations. In this paper, a new approach for estimating key parameters governing moisture and heat diffusion equation and the closure function which links these equations, is introduced. Parameters of the system are estimated by developing objective functions that link atmospheric forcing, surface states, and unknown parameters. This approach is based on conditional averaging of heat and moisture diffusion equations on land surface temperature and moisture states, respectively. A single objective function is expressed that measures moisture and temperaturedependent errors solely in terms of observed forcings and surface states. This objective function is minimized with respect to the parameters to identify evaporation and drainage models and estimate water and energy balance flux components. The approach is calibration free (surface flux observations are not required), it is not hampered by missing data and does not require continuous records. Uncertainty of parameter estimates is obtained from the inverse of Hessian of the objective function, which is an approximation of the error covariance matrix. Uncertainty analysis and analysis of the covariance approximation, guides the formulation of a well-posed estimation problem. Accuracy of this method is examined through its application over three different field sites. This approach can be applied to diverse climates and land surface conditions with different spatial scales, using remotely sensed measurements.

Surface heat fluxes play a crucial role in the surface energy and water balance. In situ measurements are costly and difficult, and large‐scale flux mapping is hindered by surface heterogeneity. Previous studies have demonstrated that surface heat fluxes can be estimated by assimilating land surface temperature (LST) and soil moisture to determine two key parameters: a neutral bulk heat transfer coefficient (CHN) and an evaporative fraction (EF). Here a methodology is proposed to estimate surface heat fluxes by assimilating Soil Moisture Active Passive (SMAP) soil moisture data and Geostationary Operational Environmental Satellite (GOES) LST data into a dual‐source (DS) model using a hybrid particle assimilation strategy. SMAP soil moisture data are assimilated using a particle filter (PF), and GOES LST data are assimilated using an adaptive particle batch smoother (APBS) to account for the large gap in the spatial and temporal resolution. The methodology is implemented in an area in the U.S. Southern Great Plains. Assessment against in situ observations suggests that soil moisture and LST estimates are in better agreement with observations after assimilation. The RMSD for 30 min (daytime) flux estimates is reduced by 6.3% (8.7%) and 31.6% (37%) for H and LE on average. Comparison against a LST‐only and a soil moisture‐only assimilation case suggests that despite the coarse resolution, assimilating SMAP soil moisture data is not only beneficial but also crucial for successful and robust flux estimation, particularly when the uncertainties in the model estimates are large.