Coagulation in a diffusing Gaussian aerosol puff: Comparison of analytical approximations with numerical solutions

Anand, S.; Mayya, Y. S.

doi:10.1016/j.jaerosci.2008.12.004

Cited by 16 publications

(4 citation statements)

References 27 publications

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“…The aerosol system evolves by continuous injection of particles at a constant rate, by coagulation, and by removal/dilution with clean air at a constant rate. It must be pointed out that spatial inhomogeneity (Kasper, 1984) is more of a rule than an exception when particles are released from localized sources, and the complexity of addressing these problems has been discussed recently (Anand & Mayya, 2009). However, if the aerosol is mixed rapidly through mixing elements such as fans or induced ventilation, one may, to a first approximation, treat the aerosol concentration as spatially homogeneous at all times.…”

Section: Formulation Of the Problemmentioning

confidence: 99%

A numerical study of coagulation of nanoparticle aerosols injected continuously into a large, well stirred chamber

Anand

Mayya

et al. 2012

Journal of Aerosol Science

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Section: Formulation Of the Problemmentioning

confidence: 99%

A numerical study of coagulation of nanoparticle aerosols injected continuously into a large, well stirred chamber

Anand

Mayya

et al. 2012

Journal of Aerosol Science

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“…In this study, we use Mathematica NDSolve [49] to numerically solve the nonlinear delayed differential equations of the Koren-Feingold model [50] and to provide a truth to evaluate the performance of ANNs and PINNs when learning the conceptual model. Mathematica is a widely used mathematical computation software and has been applied successfully to aerosol-related research, e.g., [51][52][53][54].…”

Section: The Simple Conceptual Model For the Aerosol-cloud-precipitat...mentioning

confidence: 99%

Predicting and Reconstructing Aerosol–Cloud–Precipitation Interactions with Physics-Informed Neural Networks

Hu,

Kabala

2023

Atmosphere

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Interactions between clouds, aerosol, and precipitation are crucial aspects of weather and climate. The simple Koren–Feingold conceptual model is important for providing deeper insight into the complex aerosol–cloud–precipitation system. Recently, artificial neural networks (ANNs) and physics-informed neural networks (PINNs) have been used to study multiple dynamic systems. However, the Koren–Feingold model for aerosol–cloud–precipitation interactions has not yet been studied with either ANNs or PINNs. It is challenging for pure data-driven models, such as ANNs, to accurately predict and reconstruct time series in a small data regime. The pure data-driven approach results in the ANN becoming a “black box” that limits physical interpretability. We demonstrate how these challenges can be overcome by combining a simple ANN with physical laws into a PINN model (not purely data-driven, good for the small data regime, and interpretable). This paper is the first to use PINNs to learn about the original and modified Koren–Feingold models in a small data regime, including external forcings such as wildfire-induced aerosols or the diurnal cycle of clouds. By adding external forcing, we investigate the effects of environmental phenomena on the aerosol–cloud–precipitation system. In addition to predicting the system’s future, we also use PINN to reconstruct the system’s past: a nontrivial task because of time delay. So far, most research has focused on using PINNs to predict the future of dynamic systems. We demonstrate the PINN’s ability to reconstruct the past with limited data for a dynamic system with nonlinear delayed differential equations, such as the Koren–Feingold model, which remains underexplored in the literature. The main reason that this is possible is that the model is non-diffusive. We also demonstrate for the first time that PINNs have significant advantages over traditional ANNs in predicting the future and reconstructing the past of the original and modified Koren–Feingold models containing external forcings in the small data regime. We also show that the accuracy of the PINN is not sensitive to the value of the regularization factor (λ), a key parameter for the PINN that controls the weight for the physics loss relative to the data loss, for a broad range (from λ=1×103 to λ=1×105).

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“…Moment models (McGraw, 1997) track a few low-order moments of the particle population, but do not explicitly resolve the size distribution. Anand and Mayya (2009) have developed a formalism based on an analytical solution of the coagulationdiffusion equation for estimating the survival fraction of aerosols in dispersing puffs and plumes under the assumption of an initially Gaussian distributed particle number concentration and spatially separable size spectra. The parameterization scheme has been further developed and is termed "Log Normal Method Of Moments -Diffusion Coagulation" (LNMOM-DC) model, enabling the simultaneous treatment of aerosol coagulation and dispersion in an expanding exhaust plume.…”

Section: Review Of Current Aerosol Process Modelsmentioning

confidence: 99%

Description and evaluation of the community aerosol dynamics model MAFOR v2.0

Karl

Pirjola

Grönholm

et al. 2021

Preprint

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Abstract. Numerical models are needed for evaluating aerosol processes in the atmosphere in state-of-the-art chemical transport models, urban-scale dispersion models and climatic models. This article describes a publicly available aerosol dynamics model MAFOR (Multicomponent Aerosol FORmation model; version 2.0); we address the main structure of the model, including the types of operation and the treatments of the aerosol processes. The main advantage of MAFOR v2.0 is the consistent treatment of both the mass- and number-based concentrations of particulate matter. An evaluation of the model is also presented, against a high-resolution observational dataset in a street canyon located in the centre of Helsinki (Finland) during an afternoon traffic rush hour on 13 December 2010. The experimental data included measurements at different locations in the street canyon of ultrafine particles, black carbon, and fine particulate mass PM1. This evaluation has also included an intercomparison with the corresponding predictions of two other prominent aerosol dynamics models, AEROFOR and SALSA. All three models fairly well simulated the decrease of the measured total particle number concentrations with increasing distance from the vehicular emission source. The MAFOR model reproduced the evolution of the observed particle number size distributions more accurately than the other two models. The MAFOR model also predicted the variation of the concentration of PM1 better than the SALSA model. We also analysed the relative importance of various aerosol processes based on the predictions of the three models. As expected, atmospheric dilution dominated over other processes; dry deposition was the second most significant process. Numerical sensitivity tests with the MAFOR model revealed that the uncertainties associated with the properties of the condensing organic vapours affected only the size range of particles smaller than 10 nm in diameter. These uncertainties do not therefore affect significantly the predictions of the whole of the number size distribution and the total number concentration. The MAFOR model version 2 is well documented and versatile to use, providing a range of alternative parametrizations for various aerosol processes. The model includes an efficient numerical integration of particle number and mass concentrations, an operator-splitting of processes, and the use of a fixed sectional method. The model could be used as a module in various atmospheric and climatic models.

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Coagulation in a diffusing Gaussian aerosol puff: Comparison of analytical approximations with numerical solutions

Cited by 16 publications

References 27 publications

A numerical study of coagulation of nanoparticle aerosols injected continuously into a large, well stirred chamber

A numerical study of coagulation of nanoparticle aerosols injected continuously into a large, well stirred chamber

Predicting and Reconstructing Aerosol–Cloud–Precipitation Interactions with Physics-Informed Neural Networks

Description and evaluation of the community aerosol dynamics model MAFOR v2.0

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