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Title: Influence of the Stiffness and Saturated Conditions of Sand on the Numerical Simulation of Free Fall Penetrometers
Impact penetration into soils is one of the most challenging phenomena to model using numerical techniques due to the very rapid large-deformations and water-soil-structure interaction problems involved in the process. In this work, portable free fall penetration testing (FFP) in dry and saturated sands is modeled using the material point method (MPM). MPM is a powerful tool for large-deformation applications in history-dependent materials. A parametric analysis is performed to understand the influence of the soil stiffness and the water excess pore pressures produced during the impact. The effect of the sand stiffness is studied by modifying its Young’s modulus, and the effect of the water is considered by comparing a fully dry model with a fully coupled hydro-mechanical model. The results indicate that the stiffness of the sand strongly controls the appearance of a general bearing capacity failure, which produces deceleration responses with more than one peak, dissimilar to physical tests. In the case of fully saturated sand, the penetration depth is lower than for dry sand with the same properties and the kinematical response of the FFP is consistent with experiments. The results are promising and encourage further development of the simulations.  more » « less
Award ID(s):
1937984
NSF-PAR ID:
10159754
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proc. Geo-Congress 2020
Page Range / eLocation ID:
9 to 18
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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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) 
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  2. Abstract

    Soil water wettability or water repellency is a phenomenon that can affect infiltration and, ultimately, runoff. Thus, there is a need to develop a model that can quantitatively capture the influence of water repellency on infiltration in a physically meaningful way and within the framework of existing infiltration theory. The analytical model developed in this study relates soil sorptivity (an infiltration parameter) with contact angle (a direct measure of water repellency) for variably saturated media. The model was validated with laboratory experiments using a silica sand of known properties treated to produce controlled degrees of water repellency. The measured contact angle and sorptivity values closely matched the model‐predicted values. Further, the relationship between the frequently used water drop penetration time test (used to assess water repellency) and sorptivity was illustrated. Finally, the direct impact of water repellency on saturated hydraulic conductivity was investigated due to its role in infiltration equations and to shed light on inconsistent field observations. It was found that water repellency had minimal effect on the saturated hydraulic conductivity of structureless sand. A quantitative model for infiltration incorporating the effect of water repellency is particularly important for post‐fire hydrologic modeling of burned areas exhibiting water repellent soils.

     
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  3. The dynamic properties of a clean sand under different degrees of saturation is investigated using a modified custom built Direct Simple Shear (DSS) apparatus at the University of New Hampshire. The specific characteristics of the DSS are presented and the testing procedures are discussed. The device utilizes the axis translation and tensiometric techniques to control the matric suction in the soil specimen. The investigation on F75 Ottawa Sand shows a decrease in shear modulus and an increase in damping by increasing the shear strain over the tested range of strains for various degrees of saturation; dry, saturated, and partially saturated. The modulus reduction in the applied range of medium shear strains regardless of the degree of saturation demonstrates the capability of the DSS in consistently capturing the changes of dynamic properties. Experimental results indicate that the matric suction can have a substantial effect on the stiffness of the soil. However, the extent of this effect may depend on the induced strain level the effective stress in unsaturated soil. In addition, partially saturated specimens resulted in lower dynamic compression. 
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  4. Seismic response of unsaturated soil layers may differ from that of saturated or dry soil deposits. A set of centrifuge experiments was conducted to study the influence of partial saturation on seismic response of sand layers under scaled Northridge earthquake motion. Steady state infiltration was implemented to control and provide uniform degrees of saturation profiles in depth. The amplification of peak ground acceleration at the soil surface was inversely proportional to the degree of saturation, especially in low period range. The cumulative intensity amplification of the motion was also higher in unsaturated soils with higher suctions. The lateral deformation and surface settlement of partially saturated sand with higher stiffness were generally lower than that in dry soil. Although neglecting the effect of partial saturation in sand layers might be conservative with respect to seismic deformations, it may result in underestimating the surface design spectra. 
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  5. Abstract

    Plant species vary in how they regulate moisture and this has implications for their flammability during wildfires. We explored how fuel moisture is shaped by variation within five hydraulic traits: saturated moisture content, cell wall rigidity, cell solute potential, symplastic water fraction and tissue capacitance.

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    We also measured predawn water potential, an indication of plant access to soil water that is influenced by root architecture. These data were used to model how root traits influence fuel moisture and ignition time.

    Most variation among species in fuel moisture under fire weather conditions arose from differences in saturated moisture content (3.4‐ to 3.6‐fold variation). Twig capacitance was also an important driver of fuel moisture under these weather conditions (1.9‐ to 2.2‐fold variation in moisture content). A suite of other leaf and root traits influencing how much shoots dry out as they approach wilting point each contributed 1.0‐ to 1.6‐fold variation in projected fuel moisture during fire weather. Fuel moisture variation in turn drove variation in flammability by modifying predicted ignition time.

    Two main life‐history types in fire‐prone habitats are obligate seeders and resprouters. There were no significant differences between these species groups in estimated fuel moisture during fire weather, nor in any measured hydraulic traits.

    Live fuel moisture is an important determinant of wildfire activity. Our data show that variation in tissue saturated moisture content among co‐occurring species represents an important ecological store of variation in flammability in the study communities.

    A freePlain Language Summarycan be found within the Supporting Information of this article.

     
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