Predictive Transport Models Research Articles

Flow rate and kinetics of trace organic contaminants removal in black carbon-amended engineered media filters for improved stormwater runoff treatment

Urban stormwater runoff is considered a key component of future water supply portfolios for water-stressed cities. Beneficial use of runoff, such as capture for recharge of drinking water aquifers, relies on improved stormwater treatment. Many dissolved constituents, including metals and trace organic contaminants (TrOCs) such as hydrophilic pesticides and poly- and perfluoroalkyl substances (PFASs), are of concern due to their toxicity, persistence, prevalence in stormwater runoff, and poor removal in conventional stormwater control measures. This study explores the operational flow rate limitations of black carbon (BC)-amended engineered media filters for removal of a wide suite of dissolved metals and TrOCs and provides validation for a previously developed predictive TrOC transport model. Column experiments were conducted with face velocities of 40 and 60 cm h−1 to assess Douglas Fir-based biochar and regenerated activated carbon (RAC) filter performance in light of media-contaminant removal kinetic limitations. This study found that increasing the face velocity in BC-amended filters to 40 and 60 cm h−1, which are representative of field conditions, decreased the removal of total suspended solids, turbidity, dissolved hydrophilic TrOCs, and PFASs when expressed as volume treated relative to previous studies conducted at 20 cm h−1. Dissolved metals and hydrophobic TrOCs removal were not substantially affected by the increased flow rates. A predictive 1-d intraparticle pore diffusion-limited sorption model with sorption and effective tortuosity parameters determined previously from experiments conducted at 20 cm h−1 was validated for these higher flow rates. This work provides insights to the kinetic limitations of contaminant removal within biochar and RAC filters and implications for stormwater filter design and operation.

Spatial Trends and Distribution Patterns of Toxic Heavy Metal Contamination in an Urbanized Watershed

Rapid urban growth in developing nations exacerbates pressures on water resources through increased pollution loading if management practices cannot adapt efficiently. This study evaluated industrial effluent impacts on river systems in Nigeria contaminated by discharge from beverage, oil and biscuit manufacturing plants. Physicochemical parameters and heavy metal concentrations were monitored at sites upstream and downstream from waste outfalls during wet and dry seasons. Results demonstrated exceedances of national water quality standards for indicators of organic pollution like biochemical oxygen demand and chemical oxygen demand. Notably, highly toxic heavy metals exceeded World Health Organization limits by over 100 times, posing serious public health concerns through various exposure pathways. Seasonal variations reflected changes in pollution inputs. Spatial trends showed metal levels decreasing with distance, though remaining well above safe levels 100m downstream. A predictive transport model was formulated based on field measurements incorporated into the advection-dispersion equation. Key coefficients for the dispersion rate and velocity/dispersion ratio were quantified, allowing simulation of concentration changes under differing scenarios. Model predictions closely aligned with observed metal distribution patterns. Findings highlight the need for upgraded wastewater treatment and emissions controls to mitigate pollution over-burdening natural assimilative capacity. Continuous monitoring programs should track remediation effectiveness. This study provides insights to help authorities balance rapid industrialization, environmental protection and sustainable development goals through evidence-based regulatory strategies ensuring public health.

Multiscale Modeling of the Membrane Electrode Assembly in Polymer Electrolyte Fuel Cells: Interaction of Pore Size and Wettability of Porous Layers

Design of the membrane electrode assembly (MEA) plays a crucial role in polymer electrolyte membrane fuel cells (PEMFCs). However, predictive modeling of mass, charge and heat transport considering primary microstructural information rather than previously measured effective transport properties is challenged by the multiscale pore structure found in an MEA, which spans around 4-5 orders of magnitude (from tens of nanometers in the catalyst layer to tens of micrometers in the gas diffusion layer). It is not clear how to incorporate microstructural information to guide design, while preserving moderate computational cost (see log-scale pore size distribution in an MEA in Figure a). In this work, a 3D two-phase, non-isothermal model is presented, which combines continuum and pore network (PN) formulations in a single CFD framework. Transport at fine pore scales of the catalyst layer and the microporous layer (below 1 um) is modeled by means of a continuum bundle-of-capillary-tubes (BCT) submodel, and transport at coarse pore scales of the gas diffusion layer and manufacturing defects above 1 um (cracks, gaps, etc.) by means of a continuum-based PN submodel. The BCT submodel accounts for microscopic (<20 nm), mesoscopic (20-300 nm) and macroscopic (300 nm-1 um) pore scales considering a subdivision of pore size distributions in the integral volume-averaged formulation. The interplay between pore size distribution and wettability of the various porous layers is analyzed, with a focus on the role of microporous layer wettability and defects, depending on operating relative humidity and temperature (see water distribution in Figure b from an invasion-percolation simulation). Best design practices of porous assemblies are extracted for maximum performance from a large computational campaign, including stochastic effects from different sample realizations. Analysis of degradation phenomena will be the focus of subsequent work to determine optimal microstructures arising from a trade-off between performance and durability. Figure 1

Adsorption of Ba and 226Ra on illite: A comparative experimental and modelling study

Illite, smectite and illite/smectite mixed layers are major phases in various argillaceous rock formations foreseen as potential host rocks for the deep geological disposal of high-level radioactive waste and are important sorbents for cationic radionuclides potentially released in the repository. 226Ra is a critical radionuclide in the safety analysis and an important source of radioactivity in technically enhanced naturally occurring radioactive materials. A comprehensive study was carried out on the adsorption of Ba and Ra on purified homoionic Na-illite (Illite du Puy) over a wide range of experimental conditions (pH, concentration, ionic strength) allowing for the development of a quasi-mechanistic Ba/Ra adsorption model for illite. Ba adsorption isotherms obtained at fixed ionic strength and pH exhibit a non-linear behaviour in the Ba equilibrium concentration range between ∼10−7 and ∼10−2 M. The pH dependent adsorption of trace 226Ra and Ba was investigated at different ionic strengths and evidenced a more pronounced adsorption of 226Ra than Ba on illite. Finally, a competition experiment of trace 226Ra in the presence of increasing Ba concentrations showed an unexpected 226Ra adsorption behaviour which was not observed for montmorillonite in earlier studies. This large set of experimental data could be successfully modelled by Ba/226Ra exchange reactions against Na, together with selectivity coefficients, on two different site types, namely planar sites and high affinity sites. The modelling of the pH dependent adsorption of Ba and 226Ra at high ionic strength and high pH required an additional surface complexation reaction. Two additional exchange sites had to be introduced to quantitatively describe the 226Ra uptake on illite in the 226Ra/Ba competition experiment. The nature of these sites, however, remains unclear. The implementation of the 226Ra adsorption model into predictive transport modelling codes is of key importance for the safety analysis of deep geological disposal of radioactive waste.

A systematic investigation of single solute, binary and ternary PFAS transport in water-saturated soil using batch and 1-dimensional column studies: Focus on mixture effects

This study aimed to investigate the transport and release of per- and polyfluoroalkyl substances (PFAS), as single solutes and binary and ternary mixtures, and associated competitive sorption effects in water-saturated soil. Batch sorption isotherm and desorption, and one-dimensional miscible displacement studies were conducted. For the batch study, the mixtures exhibited extensive sorption isotherm nonlinearity at aqueous concentrations exceeding 20 µg/L. At and above this threshold, competitive effects significantly decreased PFAS sorption, mostly affecting perfluorooctanoic acid (PFOA) and perfluorohexane sulfonate (PFHxS). Importantly, mixture effects exacerbated isotherm nonlinearity and may increase the leaching of PFAS in subsurface soil and groundwater. Further, up to 100% desorption occurred for single solutes and mixtures, indicating that the studied PFAS were weakly sorbed. For the column study, at influent concentrations (21 – 27 µg/L, depending on PFAS) near the threshold, PFOA and PFHxS breakthrough curves (BTC) generally exhibited equilibrium (nonlinear) transport, whereas perfluorooctane sulfonate (PFOS) exhibited nonequilibrium transport, with minimal or no mixture effects. Nonequilibrium transport of PFOS was driven by rate-limited sorption, especially as flow interruption tests confirmed the absence of physical nonequilibrium. The sorption distribution coefficients (Kd) from moment and frontal analyses, and 2-site modelling of the BTC, were consistent with the batch-derived Kd, although comparatively smaller. Such discrepancies may limit the applicability of batch-derived Kd values for predictive transport modelling purposes. Overall, understanding mixture impacts may aid effective predictive modelling of PFAS transport and leaching, especially in aqueous film forming foam (AFFF)-source zone areas associated with elevated PFAS concentrations. At low or environmental PFAS concentrations, mixture effects can be expected to be play a minor role in influencing PFAS transport.

Biofilm-induced effect on the buoyancy of plastic debris: An experimental study

Plastic floating on the ocean surface represents about 1 % of all plastic in the ocean, despite the buoyancy of most plastics. Biofouling can help to sink debris, which could explain this discrepancy. A set of laboratory experiments was conducted to investigate biofilm-induced effects on the buoyancy of different plastic debris. Ten materials of different densities (buoyant/non-buoyant), sizes (micro/meso/macro), and shapes (irregular/spherical/cylindrical/flat), including facemasks and cotton swabs, were evaluated. Biofilm was incubated in these materials from a few weeks to three months to investigate the effect of different growth levels on their buoyancy. Biofilm levels and rising/settling velocities were measured and compared at seven time-points. The results show a hindered buoyancy for solid materials, while hollow and open materials showed the opposite trend in early biofilm colonization stages. A relationship was established between biofilm-growth and equivalent sphere diameter that can be used to improve predictive modeling of plastic-debris transport.

Predicting transport of intra-articularly injected growth factor fusion proteins into human knee joint cartilage

There are no drugs or treatment methods known to prevent the development of post-traumatic osteoarthritis (PTOA), a type of osteoarthritis (OA) that is triggered by traumatic joint injuries and accounts for ∼12% of the nearly 600 million OA cases worldwide. Lack of effective drug delivery techniques remains a major challenge in developing clinically effective treatments, but cationic delivery carriers can help overcome this challenge. Scaling up treatments that are effective in in vitro models to achieve success in preclinical in vivo models and clinical trials is also a challenging problem in the field. Here we use a cationic green fluorescent protein (GFP) as a carrier to deliver Insulin-Like Growth Factor 1 (IGF-1), a drug considered as a potential therapeutic for PTOA. GFP-IGF-1 conjugates were first synthesized as fusion proteins with different polypeptide linkers, and their transport properties were characterized in human cartilage explants. In vitro experimental data were used to develop a predictive mathematical transport model that was validated using an independent in vitro experimental data set. The model was used to predict the transport of these fusion proteins upon intra-articular injection into human knee joints. The predictions included results for the rate and extent of fusion protein penetration into cartilage, and the maximum levels of fusion proteins that would escape into systemic circulation through the joint capsule. Together, our transport measurements and model set the stage for translation of such explant culture studies to in vivo preclinical studies and potentially clinical application. Statement of significanceThe lack of blood supply in cartilage and rapid clearance of drugs injected into human knees presents a major challenge in developing clinically effective treatments for osteoarthritis. Cationic delivery carriers can target negatively charged cartilage and help overcome this problem. Scaling up treatments that are effective in vitro to achieve success in vivo is also challenging. Here, we use a cationic green fluorescent protein (GFP) to deliver Insulin-Like Growth Factor-1 (IGF-1) into cartilage. Experiments measuring transport of GFP-IGF-1 fusion proteins in human cartilage explants were used to develop and validate a mathematical model to predict fusion protein transport upon injection into human knee joints. This work translates such explant culture studies to in vivo preclinical studies and potentially clinical application.

Transport properties of graphene oxide nanofiltration membranes: Electrokinetic modeling and experimental validation

AbstractThere is a need for developing reliable models for water and solute transport in graphene oxide (GO) membranes for advancing their emerging industrial water processing applications. In this direction, we develop predictive transport models for GO and reduced‐GO (rGO) membranes over a wide solute concentration range (0.01–0.5 M) and compositions, based on the extended Nernst–Planck transport equations, Donnan equilibrium condition, and solute adsorption models. Some model parameters are obtained by fitting experimental permeation data for water and unary (single‐component) aqueous solutions. The model is validated by predicting experimental permeation behavior in binary solutions, which display very different characteristics. Sensitivity analysis of salt rejections as a function of membrane design parameters (pore size and membrane charge density) allows us to infer design targets to achieve high salt rejections. Such models will be useful in accelerating structure‐separation property relationships of GO membranes and for separation process design and optimization.

Understanding the core confinement in DIII-D super-H experiments by transport modeling

The DIII-D super-H (SH) scenario, which is characterized by a significantly higher pedestal pressure compared to standard high confinement mode (H mode) plasmas, typically exhibits two phases in its temporal dynamics. The early hot ion (SH–HI) phase has higher core ion temperatures and normalized confinement factor (H 98(y,2) ∼ 2) than the later ‘standard’ SH phase, which has similar pedestal pressure characteristics to the SH–HI phase but a lower confinement factor (H 98(y,2) ∼ 1.2) as well as lower pedestal T i/T e ratio. However, beyond the pedestal differences, it is also observed that in the core plasma T i is more peaked and has a significantly larger normalized gradient scale length a/L Ti in the SH–HI phase than in the SH phase. This paper identifies the physics responsible for the different core profiles via gyrokinetic and gyrofluid modeling. It is found that the ion temperature gradient (ITG) mode dominates the core transport for both phases. Absent flow shear effects, the ITG critical gradient (a/L Ti,crit) is shown to be far smaller in the SH–HI phase than the SH phase. The lower a/L Ti,crit in the SH–HI phase is shown to be mainly induced by the hollow carbon (impurity) density profile, which is strongly destabilizing relative to the nearly flat carbon density profile in the SH phase. Differences in the T i/T e ratio between these phases are found to have a minor impact. However, the significantly stronger flow shearing in the SH–HI phase relative to the SH phase enables the achievement of higher core a/L Ti values and is therefore mainly responsible for the higher core T i values observed in the early SH–HI phase. Predictive transport modeling shows that the confinement in the lower-rotation SH phase could be elevated significantly if a peaked impurity density profile can be achieved, and potential applications to the performance improvement of future reactors are discussed.

Impact of Porosity and Boundary Scattering on Thermal Transport in Diameter-Modulated Nanowires

We study the thermal conductivity of diameter-modulated Si nanowires to understand the impact of different nanoscale transport mechanisms as a function of nanowire morphology. Our investigation couples transient suspended microbridge measurements of diameter-modulated Si nanowires synthesized via vapor-liquid-solid growth and dopant-selective etching with predictive Boltzmann transport modeling. We show that the presence of a low thermal conductivity phase (i.e., porosity) dominates the reduction in effective thermal conductivity and is supplemented by increased phonon-boundary scattering. The relative contributions of both mechanisms depend on the details of the nanoscale morphology. Our findings provide valuable insights into the factors that govern thermal conduction in complex nanoscale materials.

Predictive Modeling of Transport and Elastic Moduli of Porous Extreme Fast Charging Li-Ion Electrodes

Extreme-fast-charging of Li-ion batteries, where the state of charge can be increased by 80% in less than 15 minutes, is increasingly important for electric vehicles. This work demonstrates how simulation can illuminate material- and fabrication-dependent ionic and electronic transport as well as mechanical properties of advanced, fast-charging Li-ion electrodes.Developing high-power Li-ion cells without compromising energy density is a challenge because of volumetric trade-offs between electronic and ionic transport and active material energy storage. Electrode materials and manufacturing steps both affect resulting microstructure and subsequently change transport properties.A multi-phase smoothed particle model was developed within LAMMPS to simulate the drying and calendering manufacturing processes for different electrodes. Generated structures were quantitatively assessed in terms of MacMullin number, electronic conductivity, and Young’s modulus to estimate the contribution of different materials and processing factors. Active material size, shape, stiffness, and orientation were varied.Model results suggest that the size of active particles needs to be taken into careful consideration, as the interaction of active material and binder can affect electrode shrinkage and porosity, ultimately affecting the transport and mechanical properties of the electrode (Figure 1).Spherical and disk shapes (representing typical cathode and anode active material shapes) show unexpected transport properties. These results suggest that the difference between a cathode and an anode is most likely due to the active particle intrinsic conductivity and stiffness.The stiffer the particles are, the more they can handle inter-particle stresses without deformation. On the other hand, distinctive particle orientations (a particle property observed in non-spherical anode active material) are decreased after calendering when the particles seem to align more horizontally, regardless of their original alignment.Careful selection of materials and attention to fabrication process parameters can produce Li-ion electrode microstructures more conducive to extreme-fast-charging.Figure 1. Simulated microstructures after drying (left structure in each pair) and calendering (right structure in each pair) with active particle sizes of: (a) 6 µm, (b) 2-15 µm, and (c) 15 µm. Figure 1

Sorption Experiments with HTO, 36-Cl, 125-I and 14-C Labeled Formate on Aged Cement Matrices Retrieved from Long-term In-situ Rock Laboratory Experiments

The long-term performance of a cement-based barrier system, envisaged worldwide in many concepts for deep geological storage of radioactive waste, depends on its material properties and how they evolve with time. Chemical interactions with the service environment may lead to mineralogical alterations and related physico-chemical changes at material interfaces, influencing radionuclide migration. Predictive (reactive) transport modelling therefore requires information on transport properties (e.g. sorption properties, porosity) of aged cement matrices.

Transport modeling in the development of a complex transport scheme on the example of the city of Kyiv (Ukraine)

The object of research is a complex transport scheme using the example of the city of Kyiv (Ukraine). The comprehensive transport scheme of the city of Kyiv provides for a variant design of the city’s transport system. In transport planning and design, for a qualitative assessment of planning decisions, predictive transport models of cities are most often used, which describe the macroscopic parameters of the traffic flow. The proposed options were tested on a transport model. The main task of this model is the modeling of transport correspondence, where the volume of traffic by public transport is about 80-90 % of the total volume of traffic. For this, a model was developed, with the help of which the level of loading of the main network, the volume of transport work, and the total number of passengers carried by public transport were analyzed. When choosing the optimal option, let’s took into account such parameters as the average speed of the traffic flow, general transport delays, and the total number of passengers transported by public transport. As well as the average distance of travel, the average time for the implementation of transport correspondence on public transport, the cost of unproductive loss of time by residents of the city, and so on. Based on the results of transport modeling, the optimal option for the development of the city’s transport system was selected, which provides for the completion of the construction of existing road transport infrastructure facilities, the construction and development of the backbone network, systems outside the street rail transport, and optimization of the public transport route network. The complex transport scheme of the city of Kyiv, which was created using modern technologies in the field of transport modeling, allows to analyze and determine the effectiveness of the implementation of various measures for the development of the city’s transport infrastructure. Transport modeling has shown the feasibility of providing initial data for the feasibility study of investment projects, the arrangement of road transport infrastructure facilities, the improvement of traffic management schemes, the arrangement of new public transport routes.

Predictive Modeling of 30-Day Emergency Hospital Transport of German Patients Using a Personal Emergency Response: Retrospective Study and Comparison with the United States.

BackgroundPredictive analytics based on data from remote monitoring of elderly via a personal emergency response system (PERS) in the United States can identify subscribers at high risk for emergency hospital transport. These risk predictions can subsequently be used to proactively target interventions and prevent avoidable, costly health care use. It is, however, unknown if PERS-based risk prediction with targeted interventions could also be applied in the German health care setting.ObjectiveThe objectives were to develop and validate a predictive model of 30-day emergency hospital transport based on data from a German PERS provider and compare the model with our previously published predictive model developed on data from a US PERS provider.MethodsRetrospective data of 5805 subscribers to a German PERS service were used to develop and validate an extreme gradient boosting predictive model of 30-day hospital transport, including predictors derived from subscriber demographics, self-reported medical conditions, and a 2-year history of case data. Models were trained on 80% (4644/5805) of the data, and performance was evaluated on an independent test set of 20% (1161/5805). Results were compared with our previously published prediction model developed on a data set of PERS users in the United States.ResultsGerman PERS subscribers were on average aged 83.6 years, with 64.0% (743/1161) females, with 65.4% (759/1161) reported 3 or more chronic conditions. A total of 1.4% (350/24,847) of subscribers had one or more emergency transports in 30 days in the test set, which was significantly lower compared with the US data set (2455/109,966, 2.2%). Performance of the predictive model of emergency hospital transport, as evaluated by area under the receiver operator characteristic curve (AUC), was 0.749 (95% CI 0.721-0.777), which was similar to the US prediction model (AUC=0.778 [95% CI 0.769-0.788]). The top 1% (12/1161) of predicted high-risk patients were 10.7 times more likely to experience an emergency hospital transport in 30 days than the overall German PERS population. This lift was comparable to a model lift of 11.9 obtained by the US predictive model.ConclusionsDespite differences in emergency care use, PERS-based collected subscriber data can be used to predict use outcomes in different international settings. These predictive analytic tools can be used by health care organizations to extend population health management into the home by identifying and delivering timelier targeted interventions to high-risk patients. This could lead to overall improved patient experience, higher quality of care, and more efficient resource use.

Effect of Feed Water pH on the Partitioning of Alkali Metal Salts from Aqueous Phase into the Polyamide Active Layers of Reverse Osmosis Membranes.

The partitioning of solutes into the polyamide active layers of reverse osmosis (RO) membranes is a key membrane property determining solute permeation. Quantification of partition coefficients and their dependence on feedwater pH would contribute to the development of predictive transport models of contaminant transport through RO membranes; however, neither solute partitioning nor the effect of feed solution pH on partitioning has been thoroughly characterized in the literature. Accordingly, we characterized the partitioning of all chloride salts of alkali metals (CsCl, RbCl, KCl, NaCl, and LiCl) from the aqueous phase into the polyamide active layers of five polyamide RO membranes, including one prepared in-house and four commercial membranes. We evaluated the effect of pH on the partitioning of alkali metal salts and whether the effect of pH on salt partitioning and rejection is consistent with Donnan theory predictions. Results showed that for all membranes, the partition coefficients of all salts were less than one and did not differ substantially among RO membranes. Results also indicated that for all membranes tested, Donnan theory provided an appropriate theoretical framework to estimate the effect of pH on salt partitioning (evaluated for all chloride salts of alkali metals) and salt rejection (evaluated for NaCl). Thus, we conclude that changes in salt rejection resulting from feed solution pH are primarily driven by changes in salt partitioning with comparatively small changes in salt diffusion coefficients.

A field evidence model: how to predict transport in heterogeneous aquifers at low investigation level

Abstract. Aquifer heterogeneity in combination with data scarcity is a major challenge for reliable solute transport prediction. Velocity fluctuations cause non-regular plume shapes with potentially long-tailing and/or fast-travelling mass fractions. High monitoring cost and a shortage of simple concepts have limited the incorporation of heterogeneity into many field transport models up to now. We present an easily applicable hierarchical conceptualization strategy for hydraulic conductivity to integrate aquifer heterogeneity into quantitative flow and transport modelling. The modular approach combines large-scale deterministic structures with random substructures. Depending on the modelling aim, the required structural complexity can be adapted. The same holds for the amount of monitoring data. The conductivity model is constructed step-wise following field evidence from observations, seeking a balance between model complexity and available field data. The starting point is a structure of deterministic blocks, derived from head profiles and pumping tests. Then, subscale heterogeneity in the form of random binary inclusions is introduced to each block. Structural parameters can be determined, for example, from flowmeter measurements or hydraulic profiling. As proof of concept, we implemented a predictive transport model for the heterogeneous MADE site. The proposed hierarchical aquifer structure reproduces the plume development of the MADE-1 transport experiment without calibration. Thus, classical advection–dispersion equation (ADE) models are able to describe highly skewed tracer plumes by incorporating deterministic contrasts and effects of connectivity in a stochastic way without using uni-modal heterogeneity models with high variances. The reliance of the conceptual model on few observations makes it appealing for a goal-oriented site-specific transport analysis of less well investigated heterogeneous sites.

Entropy and Intermittency of River Bed Elevation Fluctuations

River beds evolve as a result of a complex interaction between strongly nonlinear processes such as near‐bed turbulence, particle‐particle interaction, and particle‐bed interaction. This interaction contributes to the initiation and evolution of extremely variable river bed elevation patterns, commonly known as bedforms that span across a range of spatiotemporal scales. In this paper, we employ a refined definition of entropy, that is, the multiscale entropy (MSE), to characterize the observed variability in the fluctuations of bed elevation time series under variable discharges obtained from a field‐scale laboratory flume. The MSE accounts for the sequence of data points in a series and quantifies the complexity and lack of information in a system. We show that the MSE of bed elevation fluctuations is higher for higher discharges. When compared with surrogates, which are the linearized series of a signal, the MSE provides across‐scale information about the underlying nonlinearity and linear correlation in a signal. The MSE difference between the original and surrogate series is due to the inherent nonlinearity that is higher for higher discharge at the smaller scales and peaks at intermediate scales. These results indicate the presence of a heterogeneous arrangement of extreme fluctuations that enhances the underlying complexity in bed elevation, rendering them less predictable at higher discharges. We further investigate the role of asymmetry of the bed elevation increments in observed complexity. Our results of asymmetry together with entropy suggest that characteristics of both small‐ and large‐scale features should be included for the accurate predictive modeling of sediment transport.

Approaching the Practical Conductivity Limits of Aerosol Jet Printed Silver.

Previous efforts to directly write conductive metals have been narrowly focused on nanoparticle ink suspensions that require aggressive sintering (>200 °C) and result in low-density, small-grained agglomerates with electrical conductivities <25% of bulk metal. Here, we demonstrate aerosol jet printing of a reactive ink solution and characterize high-density (93%) printed silver traces having near-bulk conductivity and grain sizes greater than the electron mean free path, while only requiring a low-temperature (80 °C) treatment. We have developed a predictive electronic transport model which correlates the microstructure to the measured conductivity and identifies a strategy to approach the practical conductivity limit for printed metals. Our analysis of how grain boundaries and tortuosity contribute to electrical resistivity provides insight into the basic materials science that governs how an ink formulator or process developer might approach improving the conductivity. Transmission line measurements validate that electrical properties are preserved up to 20 GHz, which demonstrates the utility of this technique for printed RF components. This work reveals a new method of producing robust printed electronics that retain the advantages of rapid prototyping and three-dimensional fabrication while achieving the performance necessary for success within the aerospace and communications industries.

Effective flow and transport properties of heterogeneous unsaturated soils

The heterogeneity of field scale soils poses a challenge to predictive large scale flow and transport modeling. The theory of effective macroscale parameters holds good and is applicable in dealing with such problems. But the validity of the analytic stochastic solutions obtained for randomly heterogeneous soils is debatable, as the test cases under which they are validated are of limited scope due to linearization and perturbation approximations. In this study, samples of heterogeneous soils are generated using sets of spatially correlated random field parameters that are either geometrically isotropic, or else, geometrically anisotropic with either horizontal or vertical stratification (perfect or imperfect). Several combinations of ratios of correlation length and capillary dispersion lengths are considered. Numerical simulations of unsaturated flow are performed on each randomly heterogeneous soil sample. The principal components K^ii(Ψ) of the macroscale effective unsaturated conductivity are then obtained as a function of the mean suction Ψ of the sample. They are compared to stochastic spectral perturbation theory, and to a probabilistic semi-empirical Power Average Model (PAM). They are also compared with arithmetic, geometric and harmonic mean conductivity-suction curves. The numerically upscaled principal conductivity curves match quite well the PAM, better than the classical means (Arithmetic, Geometric, Harmonic), and also somewhat better than the curves obtained from stochastic spectral perturbation theory. It is observed that the upscaled principal components Kii(ψ), obtained numerically and with the PAM along directions “i” orthogonal/parallel to perfect stratification coincide with the harmonic/arithmetic mean curves at low suctions (i.e., near saturation), but deviate from it and come closer to the geometric mean at higher suctions. The PAM appears suitable for generation of approximate upscaled conductivity curves, e.g., for obtaining the mesh-scale or block-scale conductivity curves in large scale simulation codes. Transient solute transport simulations are then performed on the detailed random velocity fields obtained from the steady state simulations of unsaturated flow in the randomly heterogeneous soil samples. Snapshots of solute concentration C(x,z,t) are taken at different times. The temporal evolution of spatial moments of concentration is analyzed in order to characterize the macroscale advection and dispersion of the unsaturated concentration plume, and in particular, its macro-dispersion coefficient (D) and dispersivity length scale (A). For the synthetic soil samples considered in this study, the macro-dispersive spreading of the solute is stronger for flow parallel to vertical stratification, compared to flow perpendicular to horizontal stratification, and also, compared to flow in statistically isotropic non-stratified soil.

Effects of natural zeolites on field-scale geologic noble gas transport

Improving predictive models for noble gas transport through natural materials at the field-scale is an essential component of improving US nuclear monitoring capabilities. Several field-scale experiments with a gas transport component have been conducted at the Nevada National Security Site (Non-Proliferation Experiment, Underground Nuclear Explosion Signatures Experiment). However, the models associated with these experiments have not treated zeolite minerals as gas adsorbing phases. This is significant as zeolites are a common alteration mineral with a high abundance at these field sites and are shown here to significantly fractionate noble gases during field-scale transport. This fractionation and associated retardation can complicate gas transport predictions by reducing the signal-to-noise ratio to the detector (e.g. mass spectrometers or radiation detectors) enough to mask the signal or make the data difficult to interpret. Omitting adsorption-related retardation data of noble gases in predictive gas transport models therefore results in systematic errors in model predictions where zeolites are present.Herein is presented noble gas adsorption data collected on zeolitized and non-zeolitized tuff. Experimental results were obtained using a unique piezometric adsorption system designed and built for this study. Data collected were then related to pure-phase mineral analyses conducted on clinoptilolite, mordenite, and quartz. These results quantify the adsorption capacity of materials present in field-scale systems, enabling the modeling of low-permeability rocks as significant sorption reservoirs vital to bulk transport predictions.