Residence Time Distributions for Turbulent, Critical, and Laminar Pipe Flow

Hart, James; Guymer, I.; Sonnenwald, Fred; Stovin, Virginia

doi:10.1061/(asce)hy.1943-7900.0001146

Cited by 24 publications

(16 citation statements)

References 21 publications

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“…As such, it is important to create an approach that considers both the complex network topology and the contamination signals. The challenge with WDNs is that the underlying Navier-Stokes dynamics with dynamic Reynolds numbers is high dimensional and highly non-linear [24]. As such, an analysis of the optimal sampling points as a function of both the network topology and the dynamic equations is non-trivial.…”

Section: State Of the Artmentioning

confidence: 99%

Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics

Pagani

Wei

Silva

et al. 2021

ACM Trans. Internet Technol.

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Infrastructure monitoring is critical for safe operations and sustainability. Like many networked systems, water distribution networks (WDNs) exhibit both graph topological structure and complex embedded flow dynamics. The resulting networked cascade dynamics are difficult to predict without extensive sensor data. However, ubiquitous sensor monitoring in underground situations is expensive, and a key challenge is to infer the contaminant dynamics from partial sparse monitoring data. Existing approaches use multi-objective optimization to find the minimum set of essential monitoring points but lack performance guarantees and a theoretical framework. Here, we first develop a novel Graph Fourier Transform (GFT) operator to compress networked contamination dynamics to identify the essential principal data collection points with inference performance guarantees. As such, the GFT approach provides the theoretical sampling bound. We then achieve under-sampling performance by building auto-encoder (AE) neural networks (NN) to generalize the GFT sampling process and under-sample further from the initial sampling set, allowing a very small set of data points to largely reconstruct the contamination dynamics over real and artificial WDNs. Various sources of the contamination are tested, and we obtain high accuracy reconstruction using around 5%–10% of the network nodes for known contaminant sources, and 50%–75% for unknown source cases, which although larger than that of the schemes for contaminant detection and source identifications, is smaller than the current sampling schemes for contaminant data recovery. This general approach of compression and under-sampled recovery via NN can be applied to a wide range of networked infrastructures to enable efficient data sampling for digital twins.

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Section: State Of the Artmentioning

confidence: 99%

Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics

Pagani

Wei

Silva

et al. 2021

ACM Trans. Internet Technol.

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“…Such an approach enables us to discuss the hydraulic structure and efficiency of UV-reactors. Additionally, these simplifications create a profitable "margin of safety" (e.g., UV-wall reflection enhances the reactor efficiency [9]).…”

Section: Absorption Of Uv Radiation By a Fluidmentioning

confidence: 99%

“…Therefore, for further consideration, it was assumed that it is reasonable to treat disinfection devices as pipe reactors with three different zones inside (see Figure 1): Taking into account the huge amount of information, collected in this way, we make use of rather compact methods of the process description. Likely the most popular and useful approach is determination of the residence time distribution (RTD) [9,10]. Such specification of the flow pays respect to the internal mixing of the moving fluid [11,12] and to the configuration of the reactor including the form of the inlet and outlet [13].…”

Section: Computational Schemes For Determination Of the Velocity Fielmentioning

confidence: 99%

Analysis of the Radiation Dose in UV-Disinfection Flow Reactors

Artichowicz

Łuczkiewicz

Sawicki

2020

Water

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UV-disinfection reactors may appear in a variety of forms, but it is useful for all types to identify the inflow and outflow zones (those in which the fluid approaches or, respectively, recedes from the radiation source) and the direct radiation zone (in which the flow occurs along the UV lamp). Due to the spatial variability of the velocity field and the radiation intensity, the radiation doses received in different zones of a reactor differ. In this work, theoretical considerations regarding functions describing the variability of UV radiation doses in flow reactors are presented. The most basic parameter of a UV-disinfection reactor is the minimal time required by the fluid to pass through the reactor. Such time depends on the maximum fluid velocity in the reactor. Based on the theoretical analysis of this phenomenon, the doses in different zones of the reactor have been compared for laminar and turbulent flows. The result of the analysis states that UV-disinfection reactors should be designed in such a way to deliver the required amount of radiation to the point at which the fluid velocity is the highest. The other main conclusion resulting from this comparison yields that the most effective in terms of disinfection is the direct radiation zone, whereas the influence of the inflow and outflow zones is negligible.

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“…Taylor's pioneer works [2,3] presented the dispersion process in pipes, however under the conditions of the pressurized flow in the pipes (no free surface flow). Dispersion in pressurised pipes (water supply systems) for a wider spectrum of flow regimes was a subject of the work of Hart [4], Abokifa [5] and Romero-Gomez [6]. A large set of experiments was evaluated by Rieckermann et al [7], where 60 different datasets from tracer experiments in different urban systems were analysed for dispersion in conditions of low flows during dry periods.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, it represents the Gaussian response function (also used as the term Gaussian approximation function) to the initial impulse, i.e., instantaneous (pulse) input of substance into the stream. Hart [4] considers this function as a good estimation (approximation) of mixing characteristics for turbulent and critical flows.…”

mentioning

confidence: 99%

Impact of Sediment Layer on Longitudinal Dispersion in Sewer Systems

Sokáč

Velísková

2021

Water

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Experiments focused on pollution transport and dispersion phenomena in conditions of low flow (low water depth and velocities) in sewers with bed sediment and deposits are presented. Such conditions occur very often in sewer pipes during dry weather flows. Experiments were performed in laboratory conditions. To simulate real hydraulic conditions in sewer pipes, sand of fraction 0.6–1.2 mm was placed on the bottom of the pipe. In total, we performed 23 experiments with 4 different thicknesses of sand sediment layers. The first scenario is without sediment, the second is with sediment filling 3.4% of the pipe diameter (sediment layer thickness = 8.5 mm), the third scenario represents sediment filling 10% of the pipe diameter (sediment layer thickness = 25 mm) and sediment fills 14% of the pipe diameter (sediment layer thickness = 35 mm) in the last scenario. For each thickness of the sediment layer, a set of tracer experiments with different flow rates was performed. The discharge ranges were from (0.14–2.5)·10−3 m3·s−1, corresponding to the range of Reynolds number 500–18,000. Results show that in the hydraulic conditions of a circular sewer pipe with the occurrence of sediment and deposits, the value of the longitudinal dispersion coefficient Dx decreases almost linearly with decrease of the flow rate (also with Reynolds number) to a certain limit (inflexion point), which is individual for each particular sediment thickness. Below this limit the value of the dispersion coefficient starts to rise again, together with increasing asymmetricity of the concentration distribution in time, caused by transient (dead) storage zones.

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Residence Time Distributions for Turbulent, Critical, and Laminar Pipe Flow

Cited by 24 publications

References 21 publications

Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics

Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics

Analysis of the Radiation Dose in UV-Disinfection Flow Reactors

Impact of Sediment Layer on Longitudinal Dispersion in Sewer Systems

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