Abstract:Measurements of air density determined gravimetrically and by using the CIPM-81/91 formula, an equation of state, have a relative deviation of 6.4 × 10 −5 .This difference is consistent with a new determination of the mole fraction of argon x Ar carried out in 2002 by the Korea Research Institute of Standards and Science (KRISS) and with recently published results from the LNE. The CIPM equation is based on the molar mass of dry air, which is dependent on the contents of the atmospheric gases, including the co… Show more
“…Characteristics of a typical cough. Parameters include the temperature of the fluid exhaled T f , its relative humidity RH f and the resulting density ⇢ f computed using the evaporation model of Picard et al (2008). The ambient air density ⇢ a = 1.172 kg m 3 for typical winter conditions (temperature 23 C, relative humidity 19.1 %).…”
Violent respiratory events such as coughs and sneezes play a key role in transferring respiratory diseases between infectious and susceptible individuals. We present the results of a combined experimental and theoretical investigation of the fluid dynamics of such violent expiratory events. Direct observation of sneezing and coughing events reveals that such flows are multiphase turbulent buoyant clouds with suspended droplets of various sizes. Our observations guide the development of an accompanying theoretical model of pathogen-bearing droplets interacting with a turbulent buoyant momentum puff. We develop in turn discrete and continuous models of droplet fallout from the cloud in order to predict the range of pathogens. According to the discrete fallout model droplets remain suspended in the cloud until their settling speed matches that of the decelerating cloud. A continuous fallout model is developed by adapting models of sedimentation from turbulent fluids. The predictions of our theoretical models are tested against data gathered from a series of analogue experiments in which a particle-laden cloud is ejected into a relatively dense ambient. Our study highlights the importance of the multiphase nature of respiratory clouds, specifically the suspension of the smallest drops by circulation within the cloud, in extending the range of respiratory pathogens.
“…Characteristics of a typical cough. Parameters include the temperature of the fluid exhaled T f , its relative humidity RH f and the resulting density ⇢ f computed using the evaporation model of Picard et al (2008). The ambient air density ⇢ a = 1.172 kg m 3 for typical winter conditions (temperature 23 C, relative humidity 19.1 %).…”
Violent respiratory events such as coughs and sneezes play a key role in transferring respiratory diseases between infectious and susceptible individuals. We present the results of a combined experimental and theoretical investigation of the fluid dynamics of such violent expiratory events. Direct observation of sneezing and coughing events reveals that such flows are multiphase turbulent buoyant clouds with suspended droplets of various sizes. Our observations guide the development of an accompanying theoretical model of pathogen-bearing droplets interacting with a turbulent buoyant momentum puff. We develop in turn discrete and continuous models of droplet fallout from the cloud in order to predict the range of pathogens. According to the discrete fallout model droplets remain suspended in the cloud until their settling speed matches that of the decelerating cloud. A continuous fallout model is developed by adapting models of sedimentation from turbulent fluids. The predictions of our theoretical models are tested against data gathered from a series of analogue experiments in which a particle-laden cloud is ejected into a relatively dense ambient. Our study highlights the importance of the multiphase nature of respiratory clouds, specifically the suspension of the smallest drops by circulation within the cloud, in extending the range of respiratory pathogens.
“…as discussed by Picard et al [7] where M a = 28.96546 Â 10 À3 kg mol À1 is the molar mass of dry air, M v = 18.01528 Â 10 À3 kg mol À1 is the molar mass of water, Z is the air compressibility, R = 8.314472 J mol À1 K À1 is the CODATA-2006 recommended value for the universal gas constant, and x v is the mole fraction of water vapour for the corresponding pressure, temperature and relative humidity conditions for the surrounding ambient air. The previous formulae may now be combined and rearranged as a nonlinear function f ⭑ ðp; q ⭑ 1 ; .…”
Section: F (T T Ref ) and F (T T Ref ) =1 + A (T à T Ref )mentioning
Abstract. In the field of pressure metrology the effective area is A e = A 0 (1 + lP) where A 0 is the zero-pressure area and l is the distortion coefficient and the conventional practise is to construct univariate probability density functions (PDFs) for A 0 and l. As a result analytical generalized non-Gaussian bivariate joint PDFs has not featured prominently in pressure metrology. Recently extended lambda distribution based quantile functions have been successfully utilized for summarizing univariate arbitrary PDF distributions of gas pressure balances. Motivated by this development we investigate the feasibility and utility of extending and applying quantile functions to systems which naturally exhibit bivariate PDFs. Our approach is to utilize the GUM Supplement 1 methodology to solve and generate Monte Carlo based multivariate uncertainty data for an oil based pressure balance laboratory standard that is used to generate known high pressures, and which are in turn cross-floated against another pressure balance transfer standard in order to deduce the transfer standard's respective area. We then numerically analyse the uncertainty data by formulating and constructing an approximate bivariate quantile distribution that directly couples A 0 and l in order to compare and contrast its accuracy to an exact GUM Supplement 2 based uncertainty quantification analysis.
“…where p a , t a and h a represents the atmospheric pressure, temperature and relative humidity respectively following the notation of Picard et al [12] where the relative humidity is represented by a number such that 0 ≤ h a ≤ 1. In the above model S is the actual instantaneous area at temperature t and by setting α = α p + α c where α p and α c are the linear thermal expansion coefficents of the piston and cylinder respectively one may account for the thermal expansion/contraction of the piston-cylinder when there is a temperature difference (t− t ref…”
Section: Mathematical Model For Pressure Balancementioning
confidence: 99%
“…If the pressure balance is operated in absolute mode then the atmospheric pressure p a above the piston will typically consist of a working gas such as nitrogen in which case the density ρ a can be calculated from the equation of state, whilst if the pressure balance is operated in gauge mode then ρ a can be calculated in terms of p a , t a and h a as per the CIPM-2007 formula for the density of moist air [12].…”
Section: Mathematical Model For Pressure Balancementioning
confidence: 99%
“…where ε is a term to acount for the underlying uncertainty in the CIPM-2007 equation for the air density factoring in consideration of the mole fraction of argon and other terms and which is ε = (22 × 10 −6 )ρ a according to the literature [12].…”
Section: As a = S/φ(t T Ref ) Along With Its Corresponding Standard mentioning
Abstract. The behaviour of piston-cylinder operated pressure balances is characterized by the distortion coefficient λ and zero-pressure effective area A0 which model the variation of a pressure balance's area in terms of the applied pressure. This paper determines the uncertainties in λ and A0 when utilizing the method of cross-floating with another pressure balance standard whose parameters and associated uncertainties are known. A limitation that is frequently encountered in many attempts of the uncertainty analysis for a pressure balance is that no readily accessible uncertainty quantification framework for the distortion coefficient is present. As a result the uncertainty in a pressure balance's area at elevated applied pressures is typically underestimated in the absence of this uncertainty information. We firstly review the uncertainty formulation for a pressure balance generated pressure involving correlation effects in terms of an implicit multivariate matrix equation approach and then utilizing the resulting solution present the methodology to consistently perform the uncertainty analysis for λ and A0.
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