Experimental dataset of U-folded falling chain under various openings with high-speed imaging.

A folded chain hanged by its two ends in U-shape at the same level with an opening, distance, between its two tips is known as U-folded chain, as studied in Markou et al. (2023). When one of the tips is fixed and the other one is released, the free tip of the U-folded falling chain accelerates faster than gravity, due to momentum conservation. This counterintuitive fact has long excited mechanicians around the globe. In the current paper we present a group of datasets (tip's coordinate timeseries), comprising three different subsets of this variable-mass dynamical system. A series of experiments of a U-folded falling chain of length 1.51m with total mass 31 gm, have been recorded with a high-speed imaging (2000fps) (Markou et al., 2023). The distance between the tips of the chain (opening) varies throughout the experimental session series: (i) 0.045m, (ii) 0.087m and (iii) 0.128m. Three timeseries of the tip's coordinates (x,y) have been extracted using an edge detector based method, from the recorded high-speed videos.

A dataset for spectral radiative properties of black poly(methyl methacrylate).

The main objective of this article is to provide the spectroscopic radiative properties of black poly(methyl methacrylate) (PMMA) including transmissivity, absorption coefficient, and complex index of refraction. To perform the required spectroscopy, four ultra-thin samples with thicknesses of 33 ± 1.3 , 50 ± 1.3 , 65 ± 1.0 , and 73 ± 1.5  µm were prepared. Then, by using UV-Vis-NIR and FTIR spectrometers, the spectrum of transmissivity was measured for the wavelength regions of 0.25 to 2.5 and 2.5 to 25 µm, respectively. Applying modified Beer's law, the absorption coefficient of black PMMA was extracted. To obtain the refractive index, first the reflectivity of the 6 mm sample of black PMMA measured by UV-Vis-NIR spectrometer. Then, applying the Kramers-Kronig transform and Fresnel relation, the refractive index of black PMMA was extracted. To investigate the effect of temperature on the absorbance of the material, ATR-FTIR spectroscopy was done for the temperatures below melting point (i.e., 160  ∘ C). Finally, a set of data for effective absorption coefficient as a function of depth from the sample surface and source temperature was proposed to be used in pyrolysis modeling.

Airborne and aerosol pathogen transmission modeling of respiratory events in buildings: An overview of computational fluid dynamics.

Pathogen droplets released from respiratory events are the primary means of dispersion and transmission of the recent pandemic of COVID-19. Computational fluid dynamics (CFD) has been widely employed as a fast, reliable, and inexpensive technique to support decision-making and to envisage mitigatory protocols. Nonetheless, the airborne pathogen droplet CFD modeling encounters limitations due to the oversimplification of involved physics and the intensive computational demand. Moreover, uncertainties in the collected clinical data required to simulate airborne and aerosol transport such as droplets' initial velocities, tempo-spatial profiles, release angle, and size distributions are broadly reported in the literature. There is a noticeable inconsistency around these collected data amongst many reported studies. This study aims to review the capabilities and limitations associated with CFD modeling. Setting the CFD models needs experimental data of respiratory flows such as velocity, particle size, and number distribution. Therefore, this paper briefly reviews the experimental techniques used to measure the characteristics of airborne pathogen droplet transmissions together with their limitations and reported uncertainties. The relevant clinical data related to pathogen transmission needed for postprocessing of CFD data and translating them to safety measures are also reviewed. Eventually, the uncertainty and inconsistency of the existing clinical data available for airborne pathogen CFD analysis are scurtinized to pave a pathway toward future studies ensuing these identified gaps and limitations.

Modelling aerosol transport and virus exposure with numerical simulations in relation to SARS-CoV-2 transmission by inhalation indoors.

We provide research findings on the physics of aerosol and droplet dispersion relevant to the hypothesized aerosol transmission of SARS-CoV-2 during the current pandemic. We utilize physics-based modeling at different levels of complexity, along with previous literature on coronaviruses, to investigate the possibility of airborne transmission. The previous literature, our 0D-3D simulations by various physics-based models, and theoretical calculations, indicate that the typical size range of speech and cough originated droplets ( d ⩽ 20 µ m ) allows lingering in the air for O ( 1 h ) so that they could be inhaled. Consistent with the previous literature, numerical evidence on the rapid drying process of even large droplets, up to sizes O ( 100 µ m ) , into droplet nuclei/aerosols is provided. Based on the literature and the public media sources, we provide evidence that the individuals, who have been tested positive on COVID-19, could have been exposed to aerosols/droplet nuclei by inhaling them in significant numbers e.g. O ( 100 ) . By 3D scale-resolving computational fluid dynamics (CFD) simulations, we give various examples on the transport and dilution of aerosols ( d ⩽ 20 µ m ) over distances O ( 10 m ) in generic environments. We study susceptible and infected individuals in generic public places by Monte-Carlo modelling. The developed model takes into account the locally varying aerosol concentration levels which the susceptible accumulate via inhalation. The introduced concept, 'exposure time' to virus containing aerosols is proposed to complement the traditional 'safety distance' thinking. We show that the exposure time to inhale O ( 100 ) aerosols could range from O ( 1 s ) to O ( 1 min ) or even to O ( 1 h ) depending on the situation. The Monte-Carlo simulations, along with the theory, provide clear quantitative insight to the exposure time in different public indoor environments.