Flow variability and stochastic dispersion in street networks

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dc.contributor.advisor-co1Coeceal, Omduth
dc.contributor.advisor-co1IDhttps://orcid.org/0000-0003-0705-6755
dc.contributor.advisor-co2Auerswald, Torsten
dc.contributor.advisor-co2IDhttps://orcid.org/0009-0007-5827-5487
dc.contributor.advisor-co3Reis Júnior, Neyval Costa
dc.contributor.advisor-co3IDhttps://orcid.org/0000-0002-6159-4063
dc.contributor.advisor-co3Latteshttp://lattes.cnpq.br/4944106074149720
dc.contributor.advisor1Goulart, Elisa Valentim
dc.contributor.advisor1IDhttps://orcid.org/0000-0002-0051-0778
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/0014236670973457
dc.contributor.authorKlippel, Karine
dc.contributor.authorIDhttps://orcid.org/0000-0003-4524-2006
dc.contributor.authorLatteshttp://lattes.cnpq.br/8819752005217100
dc.contributor.referee1Furieri Bruno
dc.contributor.referee1IDhttps://orcid.org/0000-0002-9736-0250
dc.contributor.referee1Latteshttp://lattes.cnpq.br/6585455298349085
dc.contributor.referee2 Santos, Jane Meri
dc.contributor.referee2IDhttps://orcid.org/0000-0003-3933-2849
dc.contributor.referee2Latteshttp://lattes.cnpq.br/0120226021957540
dc.contributor.referee3Soulhac, Lionel
dc.contributor.referee3IDhttps://orcid.org/0000-0003-0358-3486
dc.contributor.referee3Latteshttp://lattes.cnpq.br/4944106074149720
dc.contributor.referee4Carpentieri, Matteo
dc.contributor.referee4IDhttps://orcid.org/0000-0002-8968-9339
dc.date.accessioned2026-01-28T14:19:53Z
dc.date.available2026-01-28T14:19:53Z
dc.date.issued2025-02-25
dc.description.abstractThis study investigates flow variability and stochasticity across different scales and their impact on passive scalar dispersion in a street network. Direct numerical simulations (DNS) data from the DIPLOS project were used for a regular array of rectangular buildings with inflow at a 45° angle to the streets. Puff and continuous sources were positioned near the ground in three locations: short street (S1), intersection (S2), and long street (S3). Simulations were compared to wind tunnel (WT) measurements for flow and scalar dispersion from source S2. The research addresses: (i) street-network scale variability, focusing on sources of flow variability, their effects on scalar releases, and uncertainties from experimental setups; (ii) intersection-scale variability, analysing flow switching and its influence on continuous releases; and (iii) concentration fluctuations, investigating spatial dependence and statistical modelling. Key findings include: (i) flow variability was observed at multiple scales, from small-scale intra-street variations linked to local flow topology, to inter-street differences, street-network-scale variability, and larger-scale variations associated with above-canopy structures. DNS and WT comparisons agreed for flow statistics and mean concentration profiles in continuous releases but differed significantly for puff releases, attributed to flow variability, setup discrepancies, and experimental uncertainties. An implication of these results is that for singular events like accidental releases, characterizing uncertainties is more meaningful than computing ensemble averages. (ii) At intersections, bistable horizontal wind direction switching was observed near the ground (z = 0.125h and z= 0.25h), with peaks at approximately -10° and 110°, offset from the expected alignment with the short (0°) and long (90°) streets. The average switching timescale was 10.5T, where T is the eddy turnover time, with waiting times of 5T and 3T per state. Strong wind direction correlation in diagonally neighbouring intersections was observed with a time lag below 1T. Wind direction switching significantly influenced scalar concentrations from continuous sources, with nearfield anticorrelation in short streets and weaker far-field correlations, highlighting the role of street-network structures and mixing processes. (iii) Clustering analysis of concentration fluctuations identified three regions within the plume with distinct distributions: (1) plume edge, with exponential-like distributions, high intermittency, and extreme skewness and kurtosis; (2) transition region, with asymmetric distributions and reduced intermittency; and (3) plume centre, exhibiting Gaussian-like distributions with negligible intermittency and near-zero skewness. Gamma, Beta, Lognormal, and Weibull distributions were evaluated for modelling these fluctuations. Gamma was the most consistent, capturing distribution shapes and performing well for the 50th and 98th percentiles of the inverse cumulative density function. While Gamma excelled in variance predictions in high-fluctuation regions (cluster 1) and Beta performed better for skewness and kurtosis in low-fluctuation areas (cluster 3), all models struggled with higherorder moments in cluster 1, highlighting challenges in modelling fat-tailed distributions.
dc.description.sponsorshipFundação de Amparo à Pesquisa e Inovação do Espírito Santo (FAPES)
dc.formatText
dc.identifier.urihttp://repositorio.ufes.br/handle/10/20832
dc.languageen
dc.language.isoen
dc.publisherUniversidade Federal do Espírito Santo
dc.publisher.countryBR
dc.publisher.courseDoutorado em Engenharia Ambiental
dc.publisher.departmentCentro Tecnológico
dc.publisher.initialsUFES
dc.publisher.programPrograma de Pós-Graduação em Engenharia Ambiental
dc.rightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/
dc.subjectAr - Poluição
dc.subjectTurbulência atmosférica
dc.subjectFluidodinâmica computacional
dc.subjectProcesso estocástico
dc.subjectUrban areasen
dc.subjectTurbulenceen
dc.subjectScalar dispersionen
dc.subjectStochastic processes
dc.subject.cnpqEngenharia Sanitária
dc.titleFlow variability and stochastic dispersion in street networks
dc.typedoctoralThesis
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