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contributor authorSuman, Alessio
contributor authorVulpio, Alessandro
contributor authorCasari, Nicola
contributor authorPinelli, Michele
date accessioned2022-05-08T09:15:46Z
date available2022-05-08T09:15:46Z
date copyright10/20/2021 12:00:00 AM
date issued2021
identifier issn0742-4795
identifier othergtp_144_01_011022.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4284917
description abstractNatural events and human activities are responsible for the generation and transport of large amounts of microsized particles, which could contaminate several engineering devices like solar panels, wind turbines, and aero-engines. In industrial processes, systems as heat exchangers, fans, and dust collectors are continuously affected by nanoparticles' interaction. For several applications, the adhesion of such nanoparticles is detrimental, generating safety and performance issues. Particle-to-particle and particle-to-surface interactions are well known, even if a general explanation of nanoparticle deposit growth is still unknown. In this paper, an interpretation of deposit growth due to nanoparticle deposition can predict particle adhesion, and layer accretion is proposed. A statistical model and a set of coefficients are used to generalize nanoparticle deposits' growth by an S-shaped function. In particular, the nanoparticle deposits grow analogously to a typical autonomous population settlement in a virgin area following statistical rule, which includes the initial growth, the successive stable condition (development), and catastrophic events able to destroy the layer. This approach generalizes nanoparticle adhesion/deposition behavior, overpassing the constraints reported in common deposition models, mainly focused on the mechanical aspect of the nanoparticle impact event. The catastrophic events, such as layer detachment, are modeled with a Poisson's distribution, related to material characteristics and impact conditions. This innovative approach, analogies, and coefficients applied to common engineering applications may be the starting point for improving the prediction capability of nanoparticle deposition.
publisherThe American Society of Mechanical Engineers (ASME)
titleA Stochastic Model for Nanoparticle Deposits Growth
typeJournal Paper
journal volume144
journal issue1
journal titleJournal of Engineering for Gas Turbines and Power
identifier doi10.1115/1.4051988
journal fristpage11022-1
journal lastpage11022-9
page9
treeJournal of Engineering for Gas Turbines and Power:;2021:;volume( 144 ):;issue: 001
contenttypeFulltext


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