Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106382
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Mechanical Engineering | - |
dc.creator | Zhou, K | - |
dc.creator | Jiang, X | - |
dc.creator | Chan, TL | - |
dc.date.accessioned | 2024-05-09T00:53:07Z | - |
dc.date.available | 2024-05-09T00:53:07Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/106382 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Inc. | en_US |
dc.rights | © 2019 Elsevier Inc. All rights reserved. | en_US |
dc.rights | © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.rights | The following publication Zhou, K., Jiang, X., & Chan, T. L. (2020). Error analysis in stochastic solutions of population balance equations. Applied Mathematical Modelling, 80, 531-552 is available at https://doi.org/10.1016/j.apm.2019.11.045. | en_US |
dc.subject | Aerosol dynamics | en_US |
dc.subject | Population balance equations | en_US |
dc.subject | Smoluchowski equation | en_US |
dc.subject | Stochastic methods | en_US |
dc.subject | Stochastic variance | en_US |
dc.subject | Weighted flow algorithm | en_US |
dc.title | Error analysis in stochastic solutions of population balance equations | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 531 | - |
dc.identifier.epage | 552 | - |
dc.identifier.volume | 80 | - |
dc.identifier.doi | 10.1016/j.apm.2019.11.045 | - |
dcterms.abstract | Stochastic simulation of population balance equations (PBEs) is robust and flexible; however, it exhibits intrinsic stochastic errors which decreases at a very slow rate when increasing the computational resolution. Generally, these stochastic methods can be classified into two groups: (i) the classical Gillespie method and (ii) weighted flow algorithm. An analytical relationship is derived for the first time to connect the variances in these two groups. It also provides a detailed analysis of the resampling process, which has not been given appropriate attention previously. It is found that resampling has a profound effect on the numerical precision. Moreover, by comparing the time evolutions between systematic errors (i.e., errors in the mean value) and stochastic errors (i.e., variances), it is found that the former grows considerably faster than the latter; thus, systematic errors eventually dominate. The present findings facilitate the choice of the most suitable stochastic method for a specific PBE a priori in order to balance numerical precision and efficiency. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Applied mathematical modelling, Apr. 2020, v. 80, p. 531-552 | - |
dcterms.isPartOf | Applied mathematical modelling | - |
dcterms.issued | 2020-04 | - |
dc.identifier.scopus | 2-s2.0-85076608894 | - |
dc.identifier.eissn | 0307-904X | - |
dc.description.validate | 202405 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | ME-0286 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | The Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20486998 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Jiang_Error_Analysis_Stochastic.pdf | Pre-Published version | 1.43 MB | Adobe PDF | View/Open |
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