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An immersed boundary‐finite element with soft‐body dynamics has been implemented to study steady flow over a finite patch of submerged flexible aquatic vegetation. The flow structure interaction model can resolve the flow interactions with flexible vegetation, and hence the reconfiguration of vegetation blades to ambient flow. Flow dynamics strongly depend on two dimensionless parameters, namely vegetation density and Cauchy number (defined as the ratio of the fluid drag force to the elastic force). Five different flow patterns have been identified based on vegetation density and Cauchy number, including the limited reach, swaying, “monami” A, “monami” B with slow moving interfacial wave, and prone. The “monami” B pattern occurred at high vegetation density and is different from “monami” A, in which the passage of Kelvin‐Helmholtz billows strongly affects the vegetation interface. With soft‐body dynamics, blade‐to‐blade interactions can also be resolved. At high vegetation density, the hydrodynamic interactions play an important role in blade‐to‐blade interactions, where adjacent vegetation blades interact via the interstitial fluid pressure. At low vegetation density, direct contacts among vegetation blades play important roles in preventing unphysical penetration of vegetation blades.

 

Thep‐dispersion problem is a spatial optimization problem that aims to maximize the minimum separation distance among all assigned nodes. This problem is characterized by an innate spatial structure based on distance attributes. This research proposes a novel approach, named thedistance‐based spatially informed property(D‐SIP) method to reduce the problem size of thep‐dispersion instances, facilitating a more efficient solution while maintaining optimality in nearly all cases. The D‐SIP is derived from investigating the underlying spatial characteristics from the behaviors of thep‐dispersion problem in determining the optimal location of nodes. To define the D‐SIP, this research applies Ripley'sK‐function to the different types of point patterns, given that the optimal solutions of thep‐dispersion problem are strongly associated with the spatial proximity among points discovered by Ripley'sK‐function. The results demonstrate that the D‐SIP identifies collective dominances of optimal solutions, leading to buildingthe spatially informed p‐dispersion model. The simulation‐based experiments show that the proposed method significantly diminishes the size of problems, improves computational performance, and secures optimal solutions for 99.9% of instances (999 out of 1,000 instances) under diverse conditions.

 

Aramid nanofibers (ANFs) are a strong and heat‐resistant nanomaterial that can be isolated from commercial para‐aramid fibers, which allow a bottom‐up self‐assembly to form ordered macroscale structures like ANF films. However, the anisotropic nature of high aspect ratio ANFs is not fully exploited when fabricating ANF films for the optimal mechanical properties. In this research, direct ink writing (DIW) is applied to produce ANF‐assembled films with arbitrary shapes, and the shear‐induced alignment of ANFs can follow the printing path direction. Therefore, controlled alignment of ANFs following the computer‐programmed printing pattern is achieved by DIW, which provides a path for the application of topology and nanofiber alignment optimization in nanofiber‐assembled films. In addition, the resulting DIW ANF films exhibit outstanding Young's modulus of 8.39 GPa, tensile strength of 198 MPa, and tensile toughness of 19.4 MJ m⁻³in the alignment direction, together with a wide working temperature range up to 440 °C without losing 50% of its room temperature storage modulus. Moreover, the demonstrated self‐joining ability, rollability, and lamination processability of the DIW ANF films expand their potential applications toward high‐temperature ultrathin tubes, substrates for flexible printed circuit boards, and three‐dimensional all‐ANF lightweight structural parts in extreme environments.

 

This study introduces a new chemistry option in the Weather Research and Forecasting model data assimilation (WRFDA) system, coupled with the WRF‐Chem model (Version 4.4.1), to incorporate aqueous chemistry (AQCHEM) in the assimilation of ground‐level chemical measurements. The new DA capability includes the integration of aqueous‐phase aerosols from the Regional Atmospheric Chemistry Mechanism (RACM) gas chemistry, the Modal Aerosol Dynamics Model for Europe (MADE) aerosol chemistry, and the Volatility Basis Set (VBS) for secondary organic aerosol production. The RACM‐MADE‐VBS‐AQCHEM scheme facilitates aerosol‐cloud‐precipitation interactions by activating aerosol particles in cloud water during the model simulation. With the goal of enhancing air quality forecasting in cloudy conditions, this new implementation is demonstrated in the weakly coupled three‐dimensional variational data assimilation (3D‐Var) system through regional air quality cycling over East Asia. Surface particulate matter (PM) concentrations and four gas species (SO₂, NO₂, O₃, and CO) are assimilated every 6 hr for the month of March 2019. The results show that including aqueous‐phase aerosols in both the analysis and forecast can represent aerosol wet removal processes associated with cloud development and rainfall production. During a pollution event with high cloud cover, simulations without aerosols defined in cloud water exhibit significantly higher values for liquid water path, and surface PM₁₀(PM_2.5) concentrations are overestimated by a factor of 10 (3) when wet scavenging processes dominate. On the contrary, AQCHEM proves to be helpful in simulating the wet deposition of aerosols, accurately predicting the evolution of surface PM concentrations without such overestimation.

 

Motor vehicle accidents are one of the most prevalent causes of traumatic injury in patients needing transport to a trauma center. Arrival at a trauma center within an hour of the accident increases a patient’s chances of survival and recovery. However, not all vehicle accidents in Tennessee are accessible to a trauma center within an hour by ground transportation. This study uses the anti-covering location problem (ACLP) to assess the current placement of trauma centers and explore optimal placements based on the population distribution and spatial pattern of motor vehicle accidents in 2015 through 2019 in Tennessee. The ACLP models seek to offer a method of exploring feasible scenarios for locating trauma centers that intend to provide accessibility to patients in underserved areas who suffer trauma as a result of vehicle accidents. The proposed ACLP approach also seeks to adjust the locations of trauma centers to reduce areas with excessive service coverage while improving coverage for less accessible areas of demand. In this study, three models are prescribed for finding optimal locations for trauma centers: (a) TraCt: ACLP model with a geometric approach and weighted models of population, fatalities, and spatial fatality clusters of vehicle accidents; (b) TraCt-ESC: an extended ACLP model mitigating excessive service supply among trauma center candidates, while expanding services to less served areas for more beneficiaries using fewer facilities; and (c) TraCt-ESCr: another extended ACLP model exploring the optimal location of additional trauma centers.