Transport Model Research Articles

Study on the habitat evolution after dam removal in a habitat-alternative tributary of large hydropower station

The establishment of large hydropower stations in the main stream poses a threat to fish habitats. Selecting suitable tributaries as alternative habitats is a practical measure for ecological environment protection during large hydropower station's construction. The small dams constructed on certain tributaries need to be removed in order to restore river connectivity. The removal of dams will activate hydro-sedimentary dynamics and change the original habitat in terms of topography and hydrodynamics. To explore the evolution of fish habitats following the removal of small dams, a dam-removed reach of a habitat-alternative tributary was selected as the research object, and the model of water-sediment transport and riverbed evolution in strongly disturbed dam-removed reaches and the model of fish habitat suitability evaluation were established. The key parameters calibration and model verification were completed by field monitoring results. The simulation results showed dramatic evolution in the reservoir riverbed in the initial stage after dam removal and during the high discharge period. One year after dam removal, there was a noticeable 4.0 m incision in front of the dam, along with a decrease in channel slope at the dam site from about 4.8% to approximately 1.5%. Downstream of the dam, alterations to the riverbed were mainly concentrated near the dam, and sedimentary bodies with a height of around 2.0 m have formed on the left bank following the high discharge period. The fish habitat in most areas of the dam-removed reach was suitable, except for the downstream high-velocity area. To compare the evolution process of fish habitat under two dam removal periods in wet and dry seasons, two dam removal schemes were implemented in March and June. The results showed that the riverbed evolved more gradually in the March scheme, creating a larger and continuous suitable habitat for fish. Therefore, the March scheme was recommended. By revealing the evolutionary pattern of fish habitat after dam removal, this research provides a reliable model for assessing and restoring habitats in dam-removed reaches, and enjoys significant implications for protecting river ecology in hydropower development reaches.

Numerical modeling of the fracturing mechanisms of unconsolidated sand reservoirs under water injection

Produced Water Re-Injection is a standard practice in oil and gas operations. When performed at sufficiently high pressures, it can trigger the fracturing of the reservoir, which should be controlled to stimulate the formation without compromising its safety. While the mechanisms of the hydraulic fracturing of brittle rocks are well-understood, this understanding is more challenging in the case of unconsolidated sand reservoirs. Recent experimental studies indicate that pseudofractures in such formations primarily result from shear banding and flow channelization. There is a need for further interpretation of the experimental findings through numerical models that account for the proper physical phenomena. For that, a coupled finite element model of water percolation, particle transport and strain localization is developed to replicate the experiments of radial injection of water in mixtures of sand and fines by Nguyen et al., 2022: An experimental setup with radial injection cell for investigation of fracturing in unconsolidated sand reservoirs under fluid injection. Journal of Petroleum Science and Engineering 213:11036. To simulate shear banding, the model accounts for strain-softening behavior and introduces material imperfections in a few weak elements. Particle mobilization starts once a critical fluid velocity is reached and permeability is assumed to be a function of particle concentration. The numerical models can replicate the geometry of the observed radial pseudofractures, as well as the measured fracturing pressures. They also qualitatively capture the effects of the initial stress state on the fracturing pressure, the fracture length and on the permeability increases during the fracturing regime as observed in the laboratory tests. Notably, the numerical results gave hints on the role of the coupling between strain localization and particle transport on the formation of the pseudofractures. These findings are used to propose an updated conceptual model for the hydraulic fracturing of sand packs.

Influence of spatiotemporal and meteorological variation on Norwegian atmospheric pollen seasonality

Climate change is and will continue to alter plant responses to their environment. This is especially prominent concerning the adaptive tracking in reproductive phenology. For wind pollinated plants, this will substantially influence their pollen seasonality, yet there are gaps in knowledge about how environmental variation influences pollen seasonality. To investigate this, we monitored daily atmospheric pollen concentrations of seven pollen types from ecologically, economically and allergenically important plants (alder, hazel, willow, birch, pine, grass and mugwort) in twelve Norwegian locations spanning the entire country for up to 28 years. Six daily meteorological variables (maximum temperature, precipitation, wind speed, relative humidity, solar radiation and atmospheric pressure) was obtained from the MET Nordic dataset with full data cover. The pollen seasonality was then modelled using four spatial, three temporal and the six meteorological variables in a generalized linear model approach with a negative binomial distribution to investigate how each variable group thematically and individually contribute to variation in pollen seasonality. We found that the full models explained the most variation, ranging from R2 = 20.3 % to 59.5 %. The models were also highly accurate, being able to predict 54.5 % to 99.1 % of daily pollen concentrations to within 20.1 pollen grains/m3. Overall, the temporal variables were able to explain more variation than spatial and meteorological variables for most pollen types. Month, altitude and maximum temperature were the most important single variables for each category. The importance of each variable could be traced back to their individual effects of reproductive phenology, plant metabolism, species distributions and pollen release processes. We further emphasise the importance of source maps and atmospheric regional transport models in further model improvements. By understanding the relevance of environmental variation to pollen seasonality we can make better predictions regarding the consequences of climate change on plant populations.

Reactive direct simulation Monte Carlo modeling of gas reactive transport in three dimensional porous fibrous media across rarefied to continuum regimes

Gas flow in complex porous media can vary across rarefied to continuum regimes depending on the characteristic size of porous media. To study the gas reactive transport process in three dimensional porous fibrous media across rarefied to continuum regimes, a comprehensive reactive direct simulation Monte Carlo model coupling with heat conduction and radiation model as well as reaction process is proposed. The results calculated by the proposed model are verified to fit well with those obtained using analytical model and experimental data. The “inlet” effect promoting the skeleton temperature is found. There are existing competitions between the heat conduction, reaction rate and convection, radiation process. The temperature of porous fibrous media skeleton increases with the catalyst coverage, and decreases with increase of Knudsen number. When the proportion of hydrogen increases, the increase in the temperature of the porous fibrous media skeleton is initially rapid and then gradual. These studies help to deeply understand the chemical reaction mechanism in porous media across rarefied to continuum regimes.

Development of a dynamic traffic microsimulator for on-demand transit operations within an integrated modelling system

ABSTRACT This study develops an intelligent dynamic agent-based microsimulation (iDAMS) module for on-demand transit (ODT) operations within an integrated transport, land-use and energy (iTLE) model. A real-time optimization component within the iDAMS is formulated by the utilization of travelling salesman problem and simulated annealing metaheuristics that perform dynamic passenger-vehicle assignment. It simulates ODT operations for meeting the 24-hour auto-trip demand of Halifax, Canada, to compare the performance of the proposed system with personal cars (PCs). The optimization objectives are to determine the optimal fleet size and seat capacity that satisfies maximum trip requests while minimizing waiting time, travel time and vehicle kilometres travelled (VKT). Simulation results indicate that the ODT system can deliver service similar to PCs in Halifax while decreasing cost and emissions (13% reduction in VKT). The tools developed in this research will provide transit planners ability to conduct ODT scenario simulation and test system performance in real time.

Correlations of light scattering properties in human skin with the person’s age assessed using a non-invasive technique

We analyze the influence of a person’s age on the thicknesses and reduced scattering coefficients of the epidermis and dermis in visible part of the spectrum. Their values were assessed using a non-invasive technique which combines pulsed photothermal radiometry and diffuse reflectance spectroscopy with Monte Carlo modeling of light transport in a four-layer model of skin. The analysis is affected by the strong influences of the melanin content on the reduced scattering coefficient of the epidermis, aepi, and blood content in the case of dermis (ader). Separating their contributions reveals a significant decrease of ader with the person’s age at an average rate of –0.25 mm–1 per decade, while the contribution of blood in the papillary dermis amounts to 1.0 mm–1%–1. Meanwhile, no influence of the person’s age was found on aepi and the thicknesses of the epidermis or dermis.

A reduced-order model to estimate first wall particle and heat fluxes for systems codes

Current systems codes (SCs) do not assess the impact of plasma filament parameters and dynamics in the SOL when performing systems-level analyses. Simulation tools based on simplified turbulent transport models could address such need, such as the TOKES code, but these tend to run in timescales that are prohibitive for incorporation in SCs. This work presents a technique to build surrogate models for SCs, obtained with the modes of Reduced-Order Models (ROMs), and is exemplified with results from TOKES. These surrogates were developed with Principal Components Analysis (PCA), multivariate regression against TOKES inputs (filament temperatures, densities, and ejection speeds) and k-fold cross-validation, applied to the results after transformations based on rational powers of the inputs to optimize the prediction capabilities of the surrogates. Selection was performed with a modified Kullback-Leibler Divergence (KLD), and validation, with withheld cases. Main results include validation of the methodology due to the relatively low number of modes needed to represent more than 90% of the data variance, and the data transformation exponents that optimize the regression.

Calibration‐free image analysis method for grain‐size distribution of small natural sand samples

AbstractNatural sand samples collected from bedload or suspension transport can be too small to be sieved. However, related transported mass to sediment size is crucial to calibrate sediment transport models. Image analysis technique can be an easy and cheap alternative. The present study proposes a general law valid for natural sand grains and small gravels that statistically relates elongation to flatness of particles and gives after some assumptions on grain shape and orientation of grains at the sieve mesh passage, the closest distribution of mass to sieving technique from images of grains given by a flatbed scanner. The proposed model is based on the hypothesis of an ellipsoidal grain shape and a preferential grain crossing aligned with the sieve diagonal. Any sand either homogeneous or heterogeneous from various origins (fluvial, marine and quarry) can be analysed directly without any further need for calibration.

The penetration phenomenon of the expiratory airflow from thermal plume of human body in the microenvironment around people

Understanding the influence of the human body's thermal plume on exhaled airflow is crucial for accurately assessing indoor infection risks. This study employs numerical simulations and a jet integral modeling method to investigate the airflow dispersion patterns of a single thermal manikin during sustained expiration. It was found that the thermal plume formed a two-stage evolution in exhaled airflow. Initially, the airflow was weakened by the thermal plume in the horizontal dispersion, and subsequently it was bent due to the entrainment of the surrounding air in the upward dispersion of the exhaled airflow. The thermal plume changes the trajectory and lateral dispersion distance of exhaled airflow. When the exhalation velocity is 1 m/s, the airflow is fully deflected towards the head plume region. When the exhalation velocity is between 1.5 and 2 m/s, the airflow is partially deflected, with only part of the upper airflow deflected towards the head plume region. In addition, it is interesting to note that the plume changes the dispersion trajectory of the exhaled airflow, while still adhering to the distributions of buoyant jet flow. This study suggests that the thermal effect of the human body is a significant factor in characterizing the dispersion of exhalation airflow and aerosols. Considering the thermal plume in indoor infection risk assessment is expected to yield more reliable data for theoretical modeling of respiratory airflow transport.

Large-Eddy vs. Reynolds-Averaged Navier–Stokes Simulations of Flow and Heat Transfer in a U-Duct with Unsteady Flow Separation

Large-eddy simulation (LES) and Reynolds-Averaged Navier–Stokes (RANS) equations were used to study incompressible flow and heat transfer in a U-duct with a high-aspect-ratio trapezoidal cross section. For the LES, the WALE subgrid-scale model was employed, and its inflow boundary condition was provided by a concurrent LES of incompressible fully-developed flow in a straight duct with the same cross section and flow conditions as the U-duct. LES results are presented for turbulent kinetic energy, Reynolds stresses, pressure–strain rate, turbulent diffusion, turbulent transport, and velocity–temperature correlations, with a focus on how they are affected by the U-turn region of the U-duct. The LES results were also used to assess three commonly used RANS models: the realizable k-ε with the two-layer model in the near-wall region, the two-equation shear-stress transport model, and the seven-equation stress-omega Reynolds stress model. Results obtained show steady and unsteady RANS to incorrectly predict the effects of unsteady flow separation. The results obtained also identified the terms in the RANS models that need to be modified and suggested how turbulent diffusion should be modeled when there is unsteady flow separation.

Learned 1-D passive scalar advection to accelerate chemical transport modeling: a case study with GEOS-FP horizontal wind fields

Abstract We developed and applied a machine-learned discretization for one-dimensional (1-D) horizontal passive scalar advection, which is an operator component common to all chemical transport models (CTMs). Our learned advection scheme resembles a second-order accuracy, three-stencil numerical solver, but differs from a traditional solver in that coefficients for each equation term are output by a neural network rather than being theoretically-derived constants. We subsampled higher-resolution simulation results—resulting in up to 16× larger grid size and 64× larger timestep—and trained our neural network-based scheme to match the subsampled integration data. In this way, we created an operator that is low-resolution (in time or space) but can reproduce the behavior of a high-resolution traditional solver. Our model shows high fidelity in reproducing its training dataset (a single 10-day 1-D simulation) and is similarly accurate in simulations with unseen initial conditions, wind fields, and grid spacing. In many cases, our learned solver is more accurate than a low-resolution version of the reference solver, but the low-resolution reference solver achieves greater computational speedup (500× acceleration) over the high-resolution simulation than the learned solver is able to (18× acceleration). Surprisingly, our learned 1-D scheme—when combined with a splitting technique—can be used to predict 2-D advection, and is in some cases more stable and accurate than the low-resolution reference solver in 2-D. Overall, our results suggest that learned advection operators may offer a higher-accuracy method for accelerating CTM simulations as compared to simply running a traditional integrator at low resolution.

Macroplastic Fate and Transport Modeling: Freshwaters Act as Main Reservoirs

Macroplastic Fate and Transport Modeling: Freshwaters Act as Main Reservoirs

Stochastic model for migration and breakage of detrital and authigenic fines

Mobilisation of attached particles during flow in rocks occurs in geo-energy processes. Particle mobilisation, their migration through rocks and pore plugging yield significant decline in permeability and well injectivity and productivity. While much is currently known about the underlying mechanisms governing the detachment of detrital particles against attracting electrostatic forces, a critical gap exists in the theoretical understanding of detachment by breakage of widely spread authigenic particles, which naturally grow on rock grains during geological times. Previous works derived micro-scale mechanical equilibrium equations for both detrital and authigenic particles, and the upscaling procedure from particle to pore and core scales for detrital fines. In this paper, for the first time we derive a stochastic model for migration and breakage of authigenic fines and authigenic–detrital mixtures. This allows for core-scale transport modelling based on the particle-scale torque balance. We introduce a novel framework for predictive stochastic detachment modelling by particle–rock bond breakage that integrates the beam theory of elastic particle deformation, strength failure criteria and viscous flow around the attached particle. The analytical expressions for stress maxima and stress diagrams for a single particle allow determining the critical failure stresses, breakage points of the beam and breakage flow velocity. The mathematical model describing lab coreflood includes the maximum retention function for both authigenic and detrital fines. The matching laboratory coreflood data under increasing velocity at micro- and core-scales achieved high matching of the experimental data by the model. High matching validates the upscaling and downscaling procedures derived.

A methane monitoring station siting method based on WRF-STILT and genetic algorithm

Reducing methane emissions in the oil and gas industry is a top priority for the current international community in addressing climate change. Methane emissions from the energy sector exhibit strong temporal variability and ground monitoring networks can provide time-continuous measurements of methane concentrations, enabling the rapid detection of sudden methane leaks in the oil and gas industry. Therefore, identifying specific locations within oil fields to establish a cost-effective and reliable methane monitoring ground network is an urgent and significant task. In response to this challenge, this study proposes a technical workflow that, utilizing emission inventories, atmospheric transport models, and intelligent computing techniques, automatically determines the optimal locations for monitoring stations based on the input quantity of monitoring sites. This methodology can automatically and quantitatively assess the observational effectiveness of the monitoring network. The effectiveness of the proposed technical workflow is demonstrated using the Shengli Oilfield, the second-largest oil and gas extraction base in China, as a case study. We found that the Genetic Algorithm can help find the optimum locations effectively. Besides, the overall observation effectiveness grew from 1.7 to 5.6 when the number of site increased from 1 to 9. However, the growth decreased with the increasing site number. Such a technology can assist the oil and gas industry in better monitoring methane emissions resulting from oil and gas extraction.

Development and calibration of a bio-geo-reactive transport model for UHS

The increased share of renewable energy sources will lead to large fluctuations in energy availability and increases energy storage’s significance. Large-scale hydrogen storage in the subsurface may become a vital element of a future sustainable energy system because stored hydrogen becomes an energy carrier available on demand. Large hydrogen amounts can be stored in porous formations such as former gas fields or gas storages, while caverns can contribute with high deliverability. However, the storage of hydrogen induces unique processes in fluid-fluid and rock-fluid interactions (for example, bio- and geochemical reactions), which may affect the efficiency of the storage. In the present study, a mathematical model describing the two-phase multicomponent flow in porous media, including bio- and geochemical reactions, is developed to predict these hydrogen-related processes. The proposed model extends an existing model in the open source simulator DuMux describing the bio-reactive transport process considering methanation and sulfate-reduction by geochemical reactions. Significant attention is placed on the reduction from pyrite-to-pyrrhotite coming with the generation of harmful hydrogen sulfide. This reaction is calibrated by developing a kinetic model in DuMux that mimics the observations of reactor experiments from literature. The developed and calibrated model is afterwards used for simulation runs on field scale to assess the impact on Underground Hydrogen Storage (UHS) operations. The developed kinetic model describes the reduction from pyrite-to-pyrrhotite in agreement with the observations in the literature, whereby particular focus was placed on the hydrogen sulfide production rate. The consecutive implementation of the transport model in DuMux on field scale, including the bio- and geochemical reactions, shows the potential permanent hydrogen losses caused by reactions and temporary ones induced by gas-gas mixing with the initial and cushion gas.

Screening of Metal Reduction Potential for Thermochemical Hydrogen Storage

The screening of all non-radioactive metals without lanthanides for thermochemical hydrogen storage was performed based on physical chemistry calculations. The thermodynamic data were collected from the NIST (National Institute of Standards and Technology) public data repository, which was followed by calculations regarding the change in enthalpy, entropy, Gibbs free energy and equilibrium reaction temperature. The results were critically evaluated based on thermodynamic parameters, viable metals were identified, and their hydrogen storage densities and energy–enthalpy ratios were evaluated. The elements viable for controlled thermochemical hydrogen storage via the reversible reduction and oxidation of metal oxides and metals are manganese (Mn), iron (Fe), molybdenum (Mo) and tungsten (W). Manganese has the largest theoretical potential for hydrogen storage with reversible reduction and oxidation of metal oxides and metals. The second candidate is iron, while the other two (Mo and W) have much lower potential. More research efforts should be dedicated to experimental testing of the identified metals (Mn, Fe, Mo and W) and their different oxides for thermochemical hydrogen storage capabilities both on laboratory and pilot scales. Ferromanganese alloy(s) might also prove itself as an efficient and affordable thermochemical hydrogen storage material. Our theoretical investigation expanded the knowledge on thermochemical hydrogen storage and is accompanied with a brief literature review revealing the lack of experimental studies, especially on oxidation of metals with water vapor occurring during the hydrogen release phase of the cycle. Consequently, accurate modelling of transport, kinetics and other phenomena during hydrogen storage and release is scarce.

FUME 2.0 – Flexible Universal processor for Modeling Emissions

Abstract. This paper introduces FUME 2.0, an open-source emission processor for air quality modeling, and documents the software structure, capabilities, and sample usage. FUME provides a customizable framework for emission preparation tailored to user needs. It is designed to work with heterogeneous emission inventory data, unify them into a common structure, and generate model-ready emissions for various chemical transport models (CTMs). Key features include flexibility in input data formats, support for spatial and temporal disaggregation, chemical speciation, and integration of external models like MEGAN. FUME employs a modular Python interface and PostgreSQL/PostGIS backend for efficient data handling. The workflow comprises data import, geographical transformation, chemical and temporal disaggregation, and output generation steps. Outputs for mesoscale CTMs CMAQ, CAMx, and WRF-Chem and the large-eddy-simulation model PALM are implemented along with a generic NetCDF format. Benchmark runs are discussed on a typical configuration with cascading domains, with import and preprocessing times scaling near-linearly with grid size. FUME facilitates air quality modeling from continental to regional and urban scales by enabling effective processing of diverse inventory datasets.

Structural and practical identifiability of contrast transport models for DCE-MRI.

Contrast transport models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

Atmospheric Chemistry Surrogate Modeling With Sparse Identification of Nonlinear Dynamics

AbstractModeling atmospheric chemistry is computationally expensive and limits the widespread use of chemical transport models. This computational cost arises from solving high‐dimensional systems of stiff differential equations. Previous work has demonstrated the promise of machine learning (ML) to accelerate air quality model simulations but has suffered from numerical instability during long‐term simulations. This may be because previous ML‐based efforts have relied on explicit Euler time integration—which is known to be unstable for stiff systems—and have used neural networks which are prone to overfitting. We hypothesize that parsimonious models combined with modern numerical integration techniques can overcome this limitation. Using a small‐scale mechanism to explore the potential of these methods, we have created a machine‐learned surrogate by (a) reducing dimensionality using singular value decomposition to create an interpretably‐compressed low‐dimensional latent space and (b) using Sparse Identification of Nonlinear Dynamics (SINDy) to create a differential‐equation‐based representation of the underlying dynamics in the compressed latent space with reduced stiffness. The root mean square error of ML model prediction for ozone concentration over 9 days is 37.8% of the root mean square concentration across all simulations in our testing data set. The surrogate model is 10× faster with 12× fewer integration timesteps compared to reference model and is numerically stable in all tested simulations. Overall, we find that SINDy can be used to create fast, stable, and accurate surrogates of a simple photochemical mechanism. In future work, we will explore the application of this method to more detailed mechanisms and their use in large‐scale simulations.

Diffusiophoretic Transport of Charged Colloids in Ionic Surfactant Gradients Entirely below versus Entirely above the Critical Micelle Concentration.

When placed in an ionic surfactant gradient, charged colloids will undergo diffusiophoresis at a velocity, uDP = MDP∇ ln S, where MDP is the diffusiophoretic mobility and S is the surfactant concentration. The diffusiophoretic mobility depends in part on the charges and diffusivities of the surfactants and their counterions. Since micellization decreases surfactant diffusivity and alters charge distributions in a surfactant solution, MDP of charged colloids in ionic surfactant gradients may differ significantly when surfactant concentrations are above or below the critical micelle concentration (CMC). The role of micelles in driving diffusiophoresis is unclear, and a previously published model that accounts for micellization suggests the possibility of a change in the sign of MDP above the CMC [Warren, P. B.; . Soft Matter 2019, 15, 278-288]. In the current study, microfluidic channels were used to measure the transport of negatively charged polystyrene colloids in sodium dodecyl sulfate (SDS) surfactant gradients established at SDS concentrations that are either fully above or fully below the CMC. Interpretation of diffusiophoresis was aided by measurements of the colloid electrophoretic mobility as a function of SDS concentration. A numerical transport model incorporating the prior diffusiophoretic mobility model for ionic surfactant gradients was implemented to elucidate signatures of positive and negative diffusiophoretic mobilities and compare with experiments. The theoretically predicted sign of the diffusiophoretic mobility below the CMC was determined to be particularly sensitive to uncertainty in colloid and surfactant properties, while above the CMC, the mobility was consistently predicted to be positive in the SDS concentration range considered in the experiments conducted here. In contrast, experiments only showed signatures of a negative diffusiophoretic mobility for these negatively charged colloids with no change of sign. Colloid diffusiophoretic transport measured in micellar solutions was more extensive than that below the CMC with the same ∇ ln S.