Title: Numerical modeling of scour around porous hydraulic structures: an evaluation of the porosity model
Porous hydraulic structures, such as Large Woody Debris (LWD) and Engineered Log Jams (ELJs), play a very important role in erosion control and habitation conservation in rivers. Previous experimental research has shed some light on the flow and sediment dynamics through and around porous structures. It was found that the scour process is strongly dependent on porosity. Computational models have great value in revealing more details of the processes which are difficult to capture in laboratory experiments. For example, previous computational modeling work has shown that the level of resolution of the complex hydraulic structures in computer models has great effect on the simulated flow dynamics. The less computationally expensive porosity model, instead of resolving all geometric details, can capture the bulk behavior for the flow field, especially in the far field. In the near field where sediment transport is most intensive, the flow result is inaccurate. The way in which this error is translated to the sediment transport results is unknown. This work aims to answer this question. More specifically, the suitability and limitations of using a porosity model in simulating scour around porous hydraulic structures are investigated. To capture the evolution of the sediment bed, an immersed boundary more »
method is implemented. The computational results are compared against flume experiments to evaluate the performance of the porosity model. « less
Huang, Shijie; Huang, Zhenhua(
, Proceedings of the Thirtieth (2020) International Ocean and Polar Engineering Conference)
Wave-induced scour plays a key role in the stability analysis of coastal structures, submarine pipelines or cables. There is a rich literature in current-induced scour, but more research is needed to understand the characteristics of wave-induced scour and the mechanisms that are important to the scour process. Sediment transport and flow-induced scour are three-phase (air-water-sediment) flow problems in nature and multi-phase flow simulation is a useful tools that can provide information difficult to obtain from physical tests. Most existing numerical models developed for simulating local scours are based on one-way coupling, which neglects effects of sediment phase on hydrodynamics of the flow. The present study uses a three-phase (air, water and sediment) flow model, which allows for a two-way coupling, to simulate wave-induced local scour problems. The three-phase flow model captures the air-water interface using a modified VOF method, and uses an improved rheology for the sediment phase for better results. The model is validated and verified using one set of existing experiment results for local scour around a submerged horizontal pipe. The detailed flow fields of both the sediment phase and the water phase around the scour are analyzed to understand the scour process. All three-phase flow simulations flowmore »simulations on XSEDE’s Stampede2 supercomputers. The applicability of the model to other local scour problems is also discussed.« less
Kazemi, Amirkhosro; Castillo, Luciano; Curet, Oscar M.(
, Scientific Reports)
Abstract Mangrove swamps are extremely productive ecosystems providing many ecological services in coastal regions. The hydrodynamic interactions of mangrove roots and water flow have been proposed as a key element to mitigate erosion. Several studies reveal that precise prediction of the morphological evolution of coastal areas, in the face of global warming and the consequent sea-level rise, requires an understanding of interactions between root porosity (the fraction of the volume of void space over the total volume), water flows, and sediment transport. Water flows around the mangrove prop roots create a complex energetic process that mixes up sediments and generates a depositional region posterior to the roots. In this work, we investigated the boundary layer behind permeable arrays of cylinders (patch) that represent the mangrove roots to explore the impact of patch porosity on the onset of sediment transport. The flow measurements were performed in a vertical plane along the water depth downstream of the mangrove root models. A high-resolution Particle Image Velocimetry (PIV) was used in a flume to observe the impact of porosity on the mean flow, velocity derivatives, skin friction coefficient, and production of turbulent kinetic energy for Reynolds number of 2500 (based on patch diameter length-scale).more »Here, we proposed a predictive model for critical velocity for incipient motion that takes into account the mangrove roots porosity and the near-bed turbulence effect. It is found that the patch with the $$\phi =47\%$$ ϕ = 47 % porosity, has the maximum critical velocity over which the sediment transport initiates. We found the optimum porosity has the minimum sediment erosion and creates negative vorticity sources near the bed that increases the critical velocity. This signifies an optimum porosity for the onset of sediment transport consistent with the porosity of mangroves in nature. The phenomenological model is elucidated based on an analysis of the vorticity evolution equation for viscous incompressible flows. For the optimum porous patch, a sink of vorticity was formed which yielded to lower the near-bed turbulence and vorticity. The minimum velocity fluctuations were sufficient to initiate the boundary layer transition, however, the viscous dissipation dominated the turbulence production to obstruct the sediment transport. This work identified the pivotal role of mangrove root porosity in sediment transport in terms of velocity and its derivatives in wall-bounded flows. Our work also provides insight into the sediment transport and erosion processes that govern the evolution of the shapes of shorelines.« less
Hussain, Mir Zaman; Hamilton, Stephen; Robertson, G. Philip; Basso, Bruno(
)
Abstract
Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may
leach legacy P from past cropland management.
Methods
Experimental details The Biofuel Cropping System Experiment (BCSE) is located at the W.K. Kellogg Biological Station (KBS) (42.3956° N, 85.3749° W; elevation 288 m asl) in southwestern Michigan, USA. This site is a part of the Great Lakes Bioenergy Research Center (www.glbrc.org) and is a Long-term Ecological Research site (www.lter.kbs.msu.edu). Soils are mesic Typic Hapludalfs developed on glacial outwash54 with high sand content (76% in the upper 150 cm) intermixed with silt-rich loess in the upper 50 cm55. The water table lies approximately 12–14 m below the surface. The climate is humid temperate with a mean annual air temperature of 9.1 °C and annual precipitation of 1005 mm, 511 mm of which falls between May and September (1981–2010)56,57. The BCSE was established as a randomized complete block design in 2008 on preexisting farmland. Prior to BCSE establishment, the field was used for grain crop and alfalfa (Medicago sativa L.) production for several decades. Between 2003 and 2007, the field received a total of ~ 300 kg P ha−1 as manure, and the southern half, which contains one of four replicate plots, received an additional 206 kg P ha−1 as inorganic fertilizer. The experimental design consists of five randomized blocks each containing one replicate plot (28 by 40 m) of 10 cropping systems (treatments) (Supplementary Fig. S1; also see Sanford et al.58). Block 5 is not included in the present study. Details on experimental design and site history are provided in Robertson and Hamilton57 and Gelfand et al.59. Leaching of P is analyzed in six of the cropping systems: (i) continuous no-till corn, (ii) switchgrass, (iii) miscanthus, (iv) a mixture of five species of native grasses, (v) a restored native prairie containing 18 plant species (Supplementary Table S1), and (vi) hybrid poplar. Agronomic management Phenological cameras and field observations indicated that the perennial herbaceous crops emerged each year between mid-April and mid-May. Corn was planted each year in early May. Herbaceous crops were harvested at the end of each growing season with the timing depending on weather: between October and November for corn and between November and December for herbaceous perennial crops. Corn stover was harvested shortly after corn grain, leaving approximately 10 cm height of stubble above the ground. The poplar was harvested only once, as the culmination of a 6-year rotation, in the winter of 2013–2014. Leaf emergence and senescence based on daily phenological images indicated the beginning and end of the poplar growing season, respectively, in each year. Application of inorganic fertilizers to the different crops followed a management approach typical for the region (Table 1). Corn was fertilized with 13 kg P ha−1 year−1 as starter fertilizer (N-P-K of 19-17-0) at the time of planting and an additional 33 kg P ha−1 year−1 was added as superphosphate in spring 2015. Corn also received N fertilizer around the time of planting and in mid-June at typical rates for the region (Table 1). No P fertilizer was applied to the perennial grassland or poplar systems (Table 1). All perennial grasses (except restored prairie) were provided 56 kg N ha−1 year−1 of N fertilizer in early summer between 2010 and 2016; an additional 77 kg N ha−1 was applied to miscanthus in 2009. Poplar was fertilized once with 157 kg N ha−1 in 2010 after the canopy had closed. Sampling of subsurface soil water and soil for P determination Subsurface soil water samples were collected beneath the root zone (1.2 m depth) using samplers installed at approximately 20 cm into the unconsolidated sand of 2Bt2 and 2E/Bt horizons (soils at the site are described in Crum and Collins54). Soil water was collected from two kinds of samplers: Prenart samplers constructed of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) in replicate blocks 1 and 2 and Eijkelkamp ceramic samplers (http://www.eijkelkamp.com) in blocks 3 and 4 (Supplementary Fig. S1). The samplers were installed in 2008 at an angle using a hydraulic corer, with the sampling tubes buried underground within the plots and the sampler located about 9 m from the plot edge. There were no consistent differences in TDP concentrations between the two sampler types. Beginning in the 2009 growing season, subsurface soil water was sampled at weekly to biweekly intervals during non-frozen periods (April–November) by applying 50 kPa of vacuum to each sampler for 24 h, during which the extracted water was collected in glass bottles. Samples were filtered using different filter types (all 0.45 µm pore size) depending on the volume of leachate collected: 33-mm dia. cellulose acetate membrane filters when volumes were less than 50 mL; and 47-mm dia. Supor 450 polyethersulfone membrane filters for larger volumes. Total dissolved phosphorus (TDP) in water samples was analyzed by persulfate digestion of filtered samples to convert all phosphorus forms to soluble reactive phosphorus, followed by colorimetric analysis by long-pathlength spectrophotometry (UV-1800 Shimadzu, Japan) using the molybdate blue method60, for which the method detection limit was ~ 0.005 mg P L−1. Between 2009 and 2016, soil samples (0–25 cm depth) were collected each autumn from all plots for determination of soil test P (STP) by the Bray-1 method61, using as an extractant a dilute hydrochloric acid and ammonium fluoride solution, as is recommended for neutral to slightly acidic soils. The measured STP concentration in mg P kg−1 was converted to kg P ha−1 based on soil sampling depth and soil bulk density (mean, 1.5 g cm−3). Sampling of water samples from lakes, streams and wells for P determination In addition to chemistry of soil and subsurface soil water in the BCSE, waters from lakes, streams, and residential water supply wells were also sampled during 2009–2016 for TDP analysis using Supor 450 membrane filters and the same analytical method as for soil water. These water bodies are within 15 km of the study site, within a landscape mosaic of row crops, grasslands, deciduous forest, and wetlands, with some residential development (Supplementary Fig. S2, Supplementary Table S2). Details of land use and cover change in the vicinity of KBS are given in Hamilton et al.48, and patterns in nutrient concentrations in local surface waters are further discussed in Hamilton62. Leaching estimates, modeled drainage, and data analysis Leaching was estimated at daily time steps and summarized as total leaching on a crop-year basis, defined from the date of planting or leaf emergence in a given year to the day prior to planting or emergence in the following year. TDP concentrations (mg L−1) of subsurface soil water were linearly interpolated between sampling dates during non-freezing periods (April–November) and over non-sampling periods (December–March) based on the preceding November and subsequent April samples. Daily rates of TDP leaching (kg ha−1) were calculated by multiplying concentration (mg L−1) by drainage rates (m3 ha−1 day−1) modeled by the Systems Approach for Land Use Sustainability (SALUS) model, a crop growth model that is well calibrated for KBS soil and environmental conditions. SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, N fertilizer application, and tillage), and genetics63. The SALUS water balance sub-model simulates surface runoff, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons63. The SALUS model has been used in studies of evapotranspiration48,51,64 and nutrient leaching20,65,66,67 from KBS soils, and its predictions of growing-season evapotranspiration are consistent with independent measurements based on growing-season soil water drawdown53 and evapotranspiration measured by eddy covariance68. Phosphorus leaching was assumed insignificant on days when SALUS predicted no drainage. Volume-weighted mean TDP concentrations in leachate for each crop-year and for the entire 7-year study period were calculated as the total dissolved P leaching flux (kg ha−1) divided by the total drainage (m3 ha−1). One-way ANOVA with time (crop-year) as the fixed factor was conducted to compare total annual drainage rates, P leaching rates, volume-weighted mean TDP concentrations, and maximum aboveground biomass among the cropping systems over all seven crop-years as well as with TDP concentrations from local lakes, streams, and groundwater wells. When a significant (α = 0.05) difference was detected among the groups, we used the Tukey honest significant difference (HSD) post-hoc test to make pairwise comparisons among the groups. In the case of maximum aboveground biomass, we used the Tukey–Kramer method to make pairwise comparisons among the groups because the absence of poplar data after the 2013 harvest resulted in unequal sample sizes. We also used the Tukey–Kramer method to compare the frequency distributions of TDP concentrations in all of the soil leachate samples with concentrations in lakes, streams, and groundwater wells, since each sample category had very different numbers of measurements.
Other
Individual spreadsheets in “data table_leaching_dissolved organic carbon and nitrogen.xls” 1. annual precip_drainage 2. biomass_corn, perennial grasses 3. biomass_poplar 4. annual N leaching _vol-wtd conc 5. Summary_N leached 6. annual DOC leachin_vol-wtd conc 7. growing season length 8. correlation_nh4 VS no3 9. correlations_don VS no3_doc VS don Each spreadsheet is described below along with an explanation of variates. Note that ‘nan’ indicate data are missing or not available. First row indicates header; second row indicates units 1. Spreadsheet: annual precip_drainage Description: Precipitation measured from nearby Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Weather station, over 2009-2016 study period. Data shown in Figure 1; original data source for precipitation (https://lter.kbs.msu.edu/datatables/7). Drainage estimated from SALUS crop model. Note that drainage is percolation out of the root zone (0-125 cm). Annual precipitation and drainage values shown here are calculated for growing and non-growing crop periods. Variate Description year year of the observation crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” precip_G precipitation during growing period (milliMeter) precip_NG precipitation during non-growing period (milliMeter) drainage_G drainage during growing period (milliMeter) drainage_NG drainage during non-growing period (milliMeter) 2. Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Variate Description year year of the observation date day of the observation (mm/dd/yyyy) crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate each crop has four replicated plots, R1, R2, R3 and R4 station stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction Fraction of biomass biomass_plot biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate Description year year of the observation method methods of poplar biomass sampling date day of the observation (mm/dd/yyyy) replicate each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground poplar diameter (milliMeter) at the ground diameter_at_15cm poplar diameter (milliMeter) at 15 cm height biomass_tree biomass per plot (Grams_Per_Tree) biomass_ha biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year year of the observation replicate each crop has four replicated plots, R1, R2, R3 and R4 no3 leached annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc. Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc. Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year year of the observation replicate each crop has four replicated plots, R1, R2, R3 and R4 doc leached annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc. volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar). Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year year of the observation growing season length growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” date date of the observation (mm/dd/yyyy) replicate each crop has four replicated plots, R1, R2, R3 and R4 nh4 conc nh4 concentration (milliGrams_N_Per_Liter) no3 conc no3 concentration (milliGrams_N_Per_Liter) 9. Spreadsheet: correlations_don VS no3_doc VS don Description: Correlations of don and nitrate concentrations (milliGrams_N_Per_Liter); and doc (milliGrams_Per_Liter) and don concentrations (milliGrams_N_Per_Liter) in the leachate samples of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data of correlation of don and nitrate concentrations shown in Figure S4 a and doc and don concentrations shown in Figure S4 b. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year year of the observation don don concentration (milliGrams_N_Per_Liter) no3 no3 concentration (milliGrams_N_Per_Liter) doc doc concentration (milliGrams_Per_Liter) More>>
The present study provides fundamental knowledge on an issue in fluid dynamics that is not well understood: flow separation and its association with heat and contaminant transport. In the separated region, a swirling motion increases the fluid drag force on the object. Very often, this is undesirable because it can seriously reduce the performance of engineered devices such as aircraft and turbines. Furthermore, Computational Fluid Dynamics (CFD) has gained ground due to its relatively low cost, high accuracy, and versatility. The principal aim of this study is to numerically elucidate the details behind momentum and passive scalar transport phenomena during turbulent boundary layer separation resulting from a wall-curvature-driven pressure gradient. With Open- FOAM CFD software, the numerical discretization of Reynolds-Averaged Navier-Stokes and passive scalar transport equations will be described in two-dimensional domains via the assessment of two popular turbulence models (i.e., the Spalart-Allmaras and the K-w SST model). The computational domain reproduces a wind tunnel geometry from previously performed experiments by Baskaran et al. (JFM, vol. 182 and 232 “A turbulent flow over a curved hill.” Part 1 and Part 2). Only the velocity and pressure distribution were measured there, which will be used for validation purposes in the presentmore »study. A second aim in the present work is the scientific visualization of turbulent events and coherent structures via the ParaView toolkit and Unity game engine. Thus, fully immersive visualization approaches will be used via virtual reality (VR) and augmented reality (AR) technologies. A Virtual Wind Tunnel (VWT), developed for the VR approach, emulates the presence in a wind tunnel laboratory and has already employed fluid flow visualization from an existing numerical database with high temporal/spatial resolution, i.e., Direct Numeric Simulation (DNS). In terms of AR, a FlowVisXR app for smartphones and HoloLens has been developed for portability. It allows the user to see virtual 3D objects (i.e., turbulent coherent structures) invoked into the physical world using the device as the lens.« less
Ha, Quang-Thinh; Roshandelpoor, Athar; Vakili, Pirooz; Goldfarb, Jillian; Ryan, Emily(
, ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY)
By virtue of their extensive potential in energy conversion and storage, catalysis, photocatalysis, adsorption, separation and life science applications, significant interest has been devoted to the design and synthesis of hierarchical porous materials. The main factors which determines the performance of hierarchical porous materials for an application include structure (pore size, porosity, tortuosity), materials (scaffold, dopants) and operating conditions. Traditionally, these hierarchical porous materials are synthesised and fabricated through a manual trial and error procedure, which is an expensive and time-consuming approach. However, there have been significant advances in mathematical, computational and engineering tools toward solving and optimising multiscale descriptions of physical phenomena. This motivates a computational-aided framework to tailor the fabrication of hierarchical porous materials to be optimised in performance for their specific application. In this work, a reactive-transport system in porous media is modelled using computational fluid dynamics. While microscale descriptions are too computationally expensive and macroscale models fail to accurately describe a physical phenomena in specific parts of computational domains, hybrid - or multiscale - algorithms, are used. Using the information provided by the numerical simulation, multiscale model-based design of experiments are developed to optimise the material’s performance on their particular usage. It is proposed that hierarchicalmore »multiscale modeling offers a systematic framework for identification of the important scales and parameters where one should focus experimental efforts on.« less
Song, Yalan, Ismail, Hassan, and Liu, Xiaofeng. Numerical modeling of scour around porous hydraulic structures: an evaluation of the porosity model. Retrieved from https://par.nsf.gov/biblio/10300701. Proceedings of the 10th International Conference on Scour and Erosion (ICSE-10) .
Song, Yalan, Ismail, Hassan, & Liu, Xiaofeng. Numerical modeling of scour around porous hydraulic structures: an evaluation of the porosity model. Proceedings of the 10th International Conference on Scour and Erosion (ICSE-10), (). Retrieved from https://par.nsf.gov/biblio/10300701.
Song, Yalan, Ismail, Hassan, and Liu, Xiaofeng.
"Numerical modeling of scour around porous hydraulic structures: an evaluation of the porosity model". Proceedings of the 10th International Conference on Scour and Erosion (ICSE-10) (). Country unknown/Code not available. https://par.nsf.gov/biblio/10300701.
@article{osti_10300701,
place = {Country unknown/Code not available},
title = {Numerical modeling of scour around porous hydraulic structures: an evaluation of the porosity model},
url = {https://par.nsf.gov/biblio/10300701},
abstractNote = {Porous hydraulic structures, such as Large Woody Debris (LWD) and Engineered Log Jams (ELJs), play a very important role in erosion control and habitation conservation in rivers. Previous experimental research has shed some light on the flow and sediment dynamics through and around porous structures. It was found that the scour process is strongly dependent on porosity. Computational models have great value in revealing more details of the processes which are difficult to capture in laboratory experiments. For example, previous computational modeling work has shown that the level of resolution of the complex hydraulic structures in computer models has great effect on the simulated flow dynamics. The less computationally expensive porosity model, instead of resolving all geometric details, can capture the bulk behavior for the flow field, especially in the far field. In the near field where sediment transport is most intensive, the flow result is inaccurate. The way in which this error is translated to the sediment transport results is unknown. This work aims to answer this question. More specifically, the suitability and limitations of using a porosity model in simulating scour around porous hydraulic structures are investigated. To capture the evolution of the sediment bed, an immersed boundary method is implemented. The computational results are compared against flume experiments to evaluate the performance of the porosity model.},
journal = {Proceedings of the 10th International Conference on Scour and Erosion (ICSE-10)},
author = {Song, Yalan and Ismail, Hassan and Liu, Xiaofeng},
}
