DataSheet7_DriftScalarDyFoam: An OpenFOAM-Based Multistage Solver for Drifting Snow and Its Distribution Around Buildings.PDF

There is a complex coupling relationship between the airflow and snow cover. In a period of hours or even days, the airflow will cause the redistribution of snow, and the redistribution of snow will cause the airflow to change. This study develops a dynamic mesh technology applied in snow drifting simulation through a real-time dynamic mesh update to depict the snow surface evolution process under long-period snow drifting, and a solver application named driftScalarDyFoam based on OpenFOAM is implemented. This solver divides the long-period snow drifting process into several stages, in each of which a snow transport equation is applied to predict the spatial distribution of snow, and finally, the snow surface evolves according to the erosion–deposition model. This method that we have proposed has been validated for several measured cases, including snow distribution on a flat roof and snow distribution around a building.</p

Image4_DriftScalarDyFoam: An OpenFOAM-Based Multistage Solver for Drifting Snow and Its Distribution Around Buildings.EPS

There is a complex coupling relationship between the airflow and snow cover. In a period of hours or even days, the airflow will cause the redistribution of snow, and the redistribution of snow will cause the airflow to change. This study develops a dynamic mesh technology applied in snow drifting simulation through a real-time dynamic mesh update to depict the snow surface evolution process under long-period snow drifting, and a solver application named driftScalarDyFoam based on OpenFOAM is implemented. This solver divides the long-period snow drifting process into several stages, in each of which a snow transport equation is applied to predict the spatial distribution of snow, and finally, the snow surface evolves according to the erosion–deposition model. This method that we have proposed has been validated for several measured cases, including snow distribution on a flat roof and snow distribution around a building.</p

Macroscopic and Spectroscopic Investigations of the Adsorption of Nitroaromatic Compounds on Graphene Oxide, Reduced Graphene Oxide, and Graphene Nanosheets

The surface properties and adsorption mechanisms of graphene materials are important for potential environmental applications. The adsorption of <i>m</i>-dinitrobenzene, nitrobenzene, and <i>p</i>-nitrotoluene onto graphene oxide (GO), reduced graphene oxide (RGO), and graphene (G) nanosheets was investigated using IR spectroscopy to probe the molecular interactions of graphene materials with nitroaromatic compounds (NACs). The hydrophilic GO displayed the weakest adsorption capability. The adsorption of RGO and G was significantly increased due to the recovery of hydrophobic π-conjugation carbon atoms as active sites. RGO nanosheets, which had more defect sites than did GO or G nanosheets, resulted in the highest adsorption of NACs which was 10–50 times greater than the reported adsorption of carbon nanotubes. Superior adsorption was dominated by various interaction modes including π–π electron donor–acceptor interactions between the π-electron-deficient phenyls of the NACs and the π-electron-rich matrix of the graphene nanosheets, and the charge electrostatic and polar interactions between the defect sites of graphene nanosheets and the −NO₂ of the NAC. The charge transfer was initially proved by FTIR that a blue shift of asymmetric −NO₂ stretching was observed with a concomitant red shift of symmetric −NO₂ stretching after m-dinitrobenzene was adsorbed. The multiple interaction mechanisms of the adsorption of NAC molecule onto flat graphene nanosheets favor the adsorption, detection, and transformation of explosives

Direct Observation, Molecular Structure, and Location of Oxidation Debris on Graphene Oxide Nanosheets

The presence of oxidation debris (OD) complicates the structures and properties of graphene oxide (GO) nanosheets, thereby impacting their potential applications. However, the origin of OD is still in dispute. Moreover, characterizing the structure and location of supposed OD on nanosheets of GO produced during the oxidation process is difficult. Herein, the attached state and size of OD on graphene oxide nanosheets were directly observed using HRTEM, the molecular structure of OD was initially proposed based on the spectroscopic characterization and Q-TOF mass spectrometry, and the locations of OD on the GO nanosheets were detected through the adsorption of probe molecules onto as-prepared GO (a-GO) and base-washed GO (bw-GO). The results indicated that OD possesses a highly crystalline structure and can be defined as several nanometre-sized polyaromatic molecules with a considerable number of oxygen-containing functional groups attached on the edges. The dark nanodot seated on a-GO was clearly observed in the HRTEM images, whereas it appeared as a clean nanosheet in the image of bw-GO, indicating that OD is removed by base-washing treatment. Following the base-washing treatment, the contents of carboxyl groups on bw-GO unexpectedly increased and subsequently contributed to the desorption of OD from a-GO due to the electrostatic repulsion being stronger than primary π–π interactions. Compared with a-GO, the adsorption of phenanthrene, as an aromatic probe, onto bw-GO increased by 6-fold via π–π stacking interactions, whereas the increase in the adsorption of m-dinitrobenzene, as a defect probe, was not as remarkable as that of phenanthrene. Reasonably, the OD nanoparticles were primarily located at the sp² structures on the GO nanosheets through π–π interactions rather than attached on defects/edges. The insights regarding the existence, molecular structures and attached sites of OD nanoparticles on GO nanosheets provide a theoretical basis for preparing OD-free GO for optimizing the potential applications of GO nanosheets

DataSheet13_DriftScalarDyFoam: An OpenFOAM-Based Multistage Solver for Drifting Snow and Its Distribution Around Buildings.PDF

There is a complex coupling relationship between the airflow and snow cover. In a period of hours or even days, the airflow will cause the redistribution of snow, and the redistribution of snow will cause the airflow to change. This study develops a dynamic mesh technology applied in snow drifting simulation through a real-time dynamic mesh update to depict the snow surface evolution process under long-period snow drifting, and a solver application named driftScalarDyFoam based on OpenFOAM is implemented. This solver divides the long-period snow drifting process into several stages, in each of which a snow transport equation is applied to predict the spatial distribution of snow, and finally, the snow surface evolves according to the erosion–deposition model. This method that we have proposed has been validated for several measured cases, including snow distribution on a flat roof and snow distribution around a building.</p

Image6_DriftScalarDyFoam: An OpenFOAM-Based Multistage Solver for Drifting Snow and Its Distribution Around Buildings.EPS

There is a complex coupling relationship between the airflow and snow cover. In a period of hours or even days, the airflow will cause the redistribution of snow, and the redistribution of snow will cause the airflow to change. This study develops a dynamic mesh technology applied in snow drifting simulation through a real-time dynamic mesh update to depict the snow surface evolution process under long-period snow drifting, and a solver application named driftScalarDyFoam based on OpenFOAM is implemented. This solver divides the long-period snow drifting process into several stages, in each of which a snow transport equation is applied to predict the spatial distribution of snow, and finally, the snow surface evolves according to the erosion–deposition model. This method that we have proposed has been validated for several measured cases, including snow distribution on a flat roof and snow distribution around a building.</p

Image12_DriftScalarDyFoam: An OpenFOAM-Based Multistage Solver for Drifting Snow and Its Distribution Around Buildings.EPS

There is a complex coupling relationship between the airflow and snow cover. In a period of hours or even days, the airflow will cause the redistribution of snow, and the redistribution of snow will cause the airflow to change. This study develops a dynamic mesh technology applied in snow drifting simulation through a real-time dynamic mesh update to depict the snow surface evolution process under long-period snow drifting, and a solver application named driftScalarDyFoam based on OpenFOAM is implemented. This solver divides the long-period snow drifting process into several stages, in each of which a snow transport equation is applied to predict the spatial distribution of snow, and finally, the snow surface evolves according to the erosion–deposition model. This method that we have proposed has been validated for several measured cases, including snow distribution on a flat roof and snow distribution around a building.</p

Image3_DriftScalarDyFoam: An OpenFOAM-Based Multistage Solver for Drifting Snow and Its Distribution Around Buildings.EPS

There is a complex coupling relationship between the airflow and snow cover. In a period of hours or even days, the airflow will cause the redistribution of snow, and the redistribution of snow will cause the airflow to change. This study develops a dynamic mesh technology applied in snow drifting simulation through a real-time dynamic mesh update to depict the snow surface evolution process under long-period snow drifting, and a solver application named driftScalarDyFoam based on OpenFOAM is implemented. This solver divides the long-period snow drifting process into several stages, in each of which a snow transport equation is applied to predict the spatial distribution of snow, and finally, the snow surface evolves according to the erosion–deposition model. This method that we have proposed has been validated for several measured cases, including snow distribution on a flat roof and snow distribution around a building.</p

Image10_DriftScalarDyFoam: An OpenFOAM-Based Multistage Solver for Drifting Snow and Its Distribution Around Buildings.EPS

There is a complex coupling relationship between the airflow and snow cover. In a period of hours or even days, the airflow will cause the redistribution of snow, and the redistribution of snow will cause the airflow to change. This study develops a dynamic mesh technology applied in snow drifting simulation through a real-time dynamic mesh update to depict the snow surface evolution process under long-period snow drifting, and a solver application named driftScalarDyFoam based on OpenFOAM is implemented. This solver divides the long-period snow drifting process into several stages, in each of which a snow transport equation is applied to predict the spatial distribution of snow, and finally, the snow surface evolves according to the erosion–deposition model. This method that we have proposed has been validated for several measured cases, including snow distribution on a flat roof and snow distribution around a building.</p

DataSheet9_DriftScalarDyFoam: An OpenFOAM-Based Multistage Solver for Drifting Snow and Its Distribution Around Buildings.PDF

There is a complex coupling relationship between the airflow and snow cover. In a period of hours or even days, the airflow will cause the redistribution of snow, and the redistribution of snow will cause the airflow to change. This study develops a dynamic mesh technology applied in snow drifting simulation through a real-time dynamic mesh update to depict the snow surface evolution process under long-period snow drifting, and a solver application named driftScalarDyFoam based on OpenFOAM is implemented. This solver divides the long-period snow drifting process into several stages, in each of which a snow transport equation is applied to predict the spatial distribution of snow, and finally, the snow surface evolves according to the erosion–deposition model. This method that we have proposed has been validated for several measured cases, including snow distribution on a flat roof and snow distribution around a building.</p