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Record W2951100893

Chaotic Advection for Enhanced Reagent Mixing

2019· dissertation· en· W2951100893 on OpenAlexaboutno aff
Michelle Seeun Cho

Bibliographic record

VenueUWSpace (University of Waterloo) · 2019
Typedissertation
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsAdvectionMixing (physics)Chaotic mixingChaoticReagentComputer scienceMathematicsPhysicsChemistryThermodynamicsArtificial intelligencePhysical chemistryQuantum mechanics
DOInot available

Abstract

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In situ remediation techniques commonly involve the injection of a reagent into the subsurface to create a zone in which biological and/or chemical reactions lead to mass destruction of contaminants. In these injection-driven remedial systems, the delivery of reagent solutions is a key requirement for success; however, the design of an effective delivery system remains a significant challenge. Subsurface heterogeneities create preferential flow pathways over a range of spatial scales that produce an uneven distribution of the injected fluid. For conventional injection methods that use vertical wells, the injected reagent will follow the path of least resistance from the wellbore into the porous medium, and the distribution will be greater in areas of higher hydraulic conductivity (K) with potential to bypass adjacent regions of lower K. In these lower K zones, molecular diffusion is possibly the primary transport mechanism responsible to bring the injected reagent into contact with the contaminant; however, diffusion is a slow process and contributes to inefficient mixing. \nChaotic advection refers to the generation of small-scale structures from the repeated stretching and folding of fluid elements in a laminar flow regime. The small-scale structures produced by this chaotic stirring create fluid elements that are stretched out into long, thin filaments with a length scale sufficiently small for diffusion to promote efficient mixing. It has been theorized that chaotic advection has the potential to overcome preferential flow paths and enhance mixing. One configuration which has been theoretically and experimentally used to invoke chaotic advection in porous media is termed a rotated potential mixing (RPM) flow. An RPM flow system involves periodically re-oriented dipole flow through the transient switching of pressures at a series of radial wells. If chaotic advection can be invoked and controlled in situ, reagent delivery and treatment effectiveness may be significantly improved. Thus, the primary objective of this research effort was to improve our understanding of chaotic advection and its implications on reagent delivery. \nTo investigate if chaotic advection can be engineered in a natural aquifer system using RPM flow, and to assess the consequent impact on the spatial distribution of a conservative tracer, a series of field-scale experiments were completed. Investigations were performed in an experimental gate at the University of Waterloo Groundwater Research Facility at the Canadian Forces Base in Borden, ON, Canada. Each experiment involved the injection of a pre-determined tracer volume in the center of a circular array of injection/extraction wells, followed by either mixing using an RPM flow protocol to invoke chaotic advection, or by natural processes (advection and diffusion) as the control. Hydraulic data and tracer breakthrough responses were used to investigate the presence of chaotic advection. Various quantitative metrics (e.g., integrated volume under the three-dimensional contours of tracer concentration data, variance of tracer concentrations, spatial concentration gradients, and the first two spatial moments of the tracer concentration distribution) were adopted to assess field-scale evidence of mixing. The results from these various quantitative metrics indicated the presence of chaotic advection which led to improved lateral spreading and enhanced mixing to establish uniform concentrations across the monitoring network. The findings demonstrated that an RPM flow system is a viable and efficient approach to enhance reagent mixing. \nPrior to the implementation of a chaotic advection system, determination of the RPM flow protocol will likely require a numerical model for adequate representation of groundwater flow undergoing periodically re-oriented dipole pumping. It is expected that the K field will control the behavior of the system. To capture K heterogeneities in a target treatment zone, hydraulic head responses from multiple independent dipole pumping tests were used in a three-dimensional steady-state hydraulic tomography (SSHT) analysis. For validation of the estimated K field from SSHT analysis, forward simulations of steady-state and transient groundwater flow were performed. The impact of using this K field on the spatial distribution of a hypothetical reagent in the target treatment zone was then investigated using particle tracking methods. The findings demonstrated that the same well system used to invoke chaotic advection is a viable site characterization tool to delineate the variability of the K field using SSHT analysis. Furthermore, the use of this K field in a particle tracking engine led to more spatially and densely distributed particle trajectories indicative of enhanced reagent mixing than those produced by an effective parameter approach (i.e., a single value of K assigned across the entire spatial domain). These results suggested that using K information applicable to a specific area of interest leads to a more effective design of an RPM flow system that can enhance reagent mixing. \nSimulations were performed in two dimensions to investigate whether a conventional modeling method can be used capture the transport behavior of a conservative reagent in the presence of chaotic advection, and to explore the impact of specific engineering controls associated with an RPM flow system on reagent mixing. The multiple lines of evidence assembled in this study demonstrated that this modeling approach captured the key features of the expected transport behavior reported in other studies of chaotic advection over a range of scales (e.g., theory, laboratory and field). Visual observations from the reagent distribution produced, and the results from the quantitative metrics of mixing behavior highlighted the different responses that are possible by the various combinations of RPM flow parameters explored. The findings demonstrated the importance of combining theoretical considerations with practical limitations when designing an RPM flow system. The flow rate and pumping duration were identified as key parameters of an RPM flow system that have direct consequences on the degree of reagent spreading and mixing. In addition, the use of the same RPM flow protocol in a heterogeneous K field led to significantly greater degree of reagent mixing than in a homogeneous K setting. These findings represent a significant step towards the development of a modeling approach for the design of an effective RPM flow system that can support field implementation of chaotic advection and promote enhanced reagent mixing. \nThe tracer experiments described in this proof-of-concept study are significant since these investigations are the first field-scale efforts to extend on the established theoretical underpinnings and observations from bench-scale experiments of chaotic advection. Multiple lines of evidence assembled in this research effort demonstrate that chaotic advection can be engineered at the field scale using an RPM flow system. These findings also provide comprehensive information about chaotic advection as an approach to enhance reagent mixing in a natural aquifer system. The suite of quantitative metrics and numerical efforts presented in this study provide various tools for the design of an RPM flow system, and the subsequent data interpretation to support field applications of chaotic advection. Collectively, the combination of experimental and computational efforts presented in this study provide comprehensive insights into an effective design and implementation of an RPM flow system to generate chaotic advection for enhanced reagent mixing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.204
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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