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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Particle removal from a flat surface using a translating bounded vortex flow

A bounded vortex flow is a hydrodynamic approach for removal of particles from a surface without scattering the particles onto nearby surfaces. The bounded vortex flow field is generated by a nozzle that combines azimuthally tilted jets arranged in a circular pattern and a central suction port. When the nozzle face is directed toward an ‘impingement surface’, the flow develops a wall-normal intake vortex below the suction outlet, which causes high shear stress on the impingement surface. When particles are present on the impingement surface, the high shear stress causes particles to roll along the surface and to be lifted off the surface and transported up the core of the wall-normal vortex into the suction outlet. In typical applications, the nozzle would be translated along the impingement surface to clean particles from the surface. The current paper reports on an experimental study of the effect of nozzle translation on the effectiveness of the bounded vortex flow field for particle mitigation. The effectiveness of particle mitigation was examined as a function of flow rate through the nozzle, particle size, and nozzle translation velocity relative to the impingement surface. As a result, numerical computations are used to relate the flow rate to the maximum shear stress on the impingement surface, which is then used to theoretically predict onset of particle motion.

42 ENGINEERING↗
DOE OSTI2021

3D Printed Nickel–Molybdenum-Based Electrocatalysts for Hydrogen Evolution at Low Overpotentials in a Flow-Through Configuration

Three-dimensional (3D) printed, hierarchically porous nickel molybdenum (NiMo) electrocatalysts were synthesized and evaluated in a flow-through configuration for the hydrogen evolution reaction (HER) in 1.0 M KOH(aq) in a simple electrochemical H-cell. 3D NiMo electrodes possess hierarchically porous structures because of the resol-based aerogel precursor, which generates superporous carbon aerogel as a catalyst support. Relative to a traditional planar electrode configuration, the flow-through configuration allowed efficient removal of the hydrogen bubbles from the catalyst surface, especially at high operating current densities, and significantly decreased the overpotentials required for HER. An analytical model that accounted for the electrokinetics of HER as well as the mass transport with or without the flow-through configuration was developed to quantitatively evaluate voltage losses associated with kinetic overpotentials and ohmic resistance due to bubble formation in the porous electrodes. Furthermore, the chemical composition, electrochemical surface area (ECSA), and roughness factor (RF) were also systematically studied to assess the electrocatalytic performance of the 3D printed, hierarchically porous NiMo electrodes. An ECSA of 25163 cm2 was obtained with the highly porous structures, and an average overpotential of 45 mV at 10 mA cm–2 was achieved over 24 h by using the flow-through configuration. The flow-through configuration evaluated in the simple H-cell achieved high electrochemical accessible surface areas for electrochemical reactions and provided useful information for adaption of the porous electrodes in flow cells.

08 HYDROGEN↗
DOE OSTI2021

Impact of Lateral Flow on Surface Water and Energy Budgets over the Southern Great Plains – A Modeling Study

As we see the horizontal grid spacing decrease, treatment of hydrologic processes in land surface models, such as the lateral flow of surface and subsurface flow, need to be explicitly represented. Unlike previous studies that mainly focused on the mountainous regions, in this study the offline WRF-Hydro model is employed to study the impact of lateral flow on soil moisture and energy fluxes over the relatively flat southern Great Plains (SGP). The vast amount of measurements over the SGP provide an unique opportunity to assess the model behavior. In addition, newly developed land surface properties and input forcing are ingested into the model, in an attempt to reduce uncertainties associated with the initial and boundary forcing and help to identify model deficiencies. Our results show that the more realistic inputs (parameters, soil types, forcing) lead to larger underestimation of latent heat flux and dry bias, indicating the existence of model structural uncertainty (embedded errors) in WRF-Hydro that need to be characterized to inform future model development efforts. Including lateral flow processes partly mitigates the model deficiencies in representing hydrologic processes and alleviates the dry bias. In particular, both surface and subsurface lateral flow increase soil moisture mainly over the lower elevations, except that subsurface flow also affects soil moisture over steeper terrains. Additional simulations are performed to assess the effect of routing resolution on model results. When LSM resolution is high, noticeable differences in soil moisture are produced between different routing resolutions especially over steep terrain. Whereas when LSM resolution is coarse, differences between routing resolutions become negligible, especially over flat terrain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Dynamic Pore Network Model for Imbibition Simulation Considering Corner Film Flow

Wetting films can develop in the corners of angular pores under strong wetting conditions. Modeling the dynamics of corner film remains elusive using direct numerical simulations because of the significant scale difference between main meniscus and corner film flow. In this paper, the modified interacting capillary bundle model (ICB), developed in our previous work to describe accurately corner film dynamics in a single square tube, is incorporated into a single-pressure dynamic pore network model (DPNM) to simulate imbibition in strongly wetting porous media with corner film flow. The traditional pore network is decomposed into several layers of interacting subpore networks where the 0th layer of subpore network simulates the main meniscus flow and higher layers the corner film flow. The fluid flow between different layers is captured by interlayer throats. In addition, the snap-off mechanism caused by the thickening of wetting corner film is considered. The accuracy of the developed model is validated for four cases: spontaneous imbibition in a single square tube, wetting fluid redistribution through corner films under a capillary pressure difference, snap off in a narrow throat connecting two large pores, and imbibition dynamics in a real microfluidic porous geometry. The validated model is then used to simulate both spontaneous and controlled imbibition in a pore network with random pore size distribution. Finally, the interaction between corner film and main meniscus flow in porous media is analyzed from a pore-scale perspective.

58 GEOSCIENCES↗
DOE OSTI2023

Impacts of Permeability Heterogeneity and Background Flow on Supercritical CO 2 Dissolution in the Deep Subsurface

Motivated by CO 2 capture and sequestration (CCS) design considerations, we consider the coupled effects of permeability heterogeneity and background flow on the dissolution of a supercritical CO 2 lens into an underlying deep, confined aquifer. We present the results of a large-scale Monte Carlo simulation study examining the interaction of background flow rate and three parameters describing multi-Gaussian log-permeability fields: mean, variance, and correlation length. Hundreds of high-resolution simulations were performed using the PFLOTRAN finite volume software to model CO 2 dissolution in a kilometer-scale aquifer over 1,000 years. Predictive dimensionless scaling relationships relating CO 2 dissolution rate to heterogeneity statistics, Rayleigh (Ra) and Péclet (Pe) numbers were developed for both gravitationally dominated free convection to background flow-dominated forced convection regimes. An empirical criterion, Pe = Ra 3/4 , was discovered for regime transition. All simulations converged quickly to a quasi-steady, approximately linear dissolution rate. However, this rate displayed profound variability between permeability field realizations sharing the same heterogeneity statistics, even under mild permeability heterogeneity. In general, increased heterogeneity was associated with a lower mean and higher variance of dissolution rate, undesirable from a CCS design perspective. The relationship between dissolution rate and background flow was found to be complex and nonlinear. Dimensionless scaling relationships were uncovered for a number of special cases. Results call into question the validity of the Boussinesq approximation in the context of modest-to-high background flow rates and the general applicability of numerical simulations without background flow.

42 ENGINEERING↗
DOE OSTI2021

Resonant amplification of enzymatic chemical oscillations by oscillating flow

Using theory and simulation, we analyzed the resonant amplification of chemical oscillations that occur due to externally imposed oscillatory fluid flows. The chemical reactions are promoted by two enzyme-coated patches located sequentially on the inner surface of a pipe that transports the enclosed chemical solution. In the case of diffusion-limited systems, the period of oscillations in chemical reaction networks is determined by the rate of the chemical transport, which is diffusive in nature and, therefore, can be effectively accelerated by the imposed fluid flows. In this work, we first identify the natural frequencies of the chemical oscillations in the unperturbed reaction–diffusion system and, then, use the frequencies as a forcing input to drive the system to resonance. We demonstrate that flow-induced resonance can be used to amplify the amplitude of the chemical oscillations and to synchronize their frequency to the external forcing. In particular, we show that even 10% perturbations in the flow velocities can double the amplitude of the resulting chemical oscillations. Particularly, effective control can be achieved for the two-step chemical reactions where during the first half-period, the fluid flow accelerates the chemical flux toward the second catalytic patch, while during the second half-period, the flow amplifies the flux to the first patch. The results can provide design rules for regulating the dynamics of coupled reaction–diffusion processes and can facilitate the development of chemical reaction networks that act as chemical clocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A resistive MHD model and simulation on plasma flow evolution in the presence of resonant magnetic perturbation in a tokamak

Nonaxisymmetric magnetic fields such as the intrinsic error field and the externally applied resonant magnetic perturbation (RMP) in a tokamak are known to influence the plasma momentum transport and flow evolution through plasma response, which itself strongly depends on the plasma flow as well. The nonlinear interaction between plasma response and flow has been previously modeled in the conventional error field theory with the “no-slip” condition, which has been recently extended to allow the “free-slip” condition. In this work, we further target this specific process and numerically simulate the nonlinear plasma response and flow evolution in the presence of a single-helicity RMP in a circular-shaped model tokamak configuration, based on the full resistive MHD model in the initial-value code NIMROD. Time evolution of the parallel (to k) flow or “slip frequency” profile and its asymptotic steady state obtained from the NIMROD simulations are compared with both conventional and extended nonlinear response theories. Here, k is the wave vector of the propagating island. Good agreement with the extended theory with free-slip condition has been achieved for the parallel flow profile evolution in response to RMP in all resistive regimes, whereas the difference from the conventional theory with the no-slip condition tends to diminish as the plasma resistivity approaches zero.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

On the statistical theory of self-gravitating collisionless dark matter flow: High order kinematic and dynamic relations

Dark matter, if it exists, accounts for five times as much as ordinary baryonic matter. To better understand the self-gravitating collisionless dark matter flow on different scales, a statistical theory involving kinematic and dynamic relations must be developed for different types of flow, e.g., incompressible, constant divergence, and irrotational flow. This is mathematically challenging because of the intrinsic complexity of dark matter flow and the lack of a self-closed description of flow velocity. Here, this paper extends our previous work on second-order statistics Xu to kinematic relations of any order for any type of flow. Dynamic relations were also developed to relate statistical measures of different orders. The results were validated by N-body simulations. On large scales, we found that (i) third-order velocity correlations can be related to density correlation or pairwise velocity; (ii) the pth-order velocity correlations follow ∝ a (p+2)/2 for odd p and ∝ a p/2 for even p, where a is the scale factor; (iii) the overdensity δ is proportional to density correlation on the same scale, $\langle$δ$\rangle$∝$\langle$δδ'$\rangle$; (iv) velocity dispersion on a given scale r is proportional to the overdensity on the same scale. On small scales, (i) a self-closed velocity evolution is developed by decomposing the velocity into motion in haloes and motion of haloes; (ii) the evolution of vorticity and enstrophy are derived from the evolution of velocity; (iii) dynamic relations are derived to relate second- and third-order correlations; (iv) while the first moment of pairwise velocity follows $\langle$Δu L $\rangle$=-Har (H is the Hubble parameter), the third moment follows $\langle$(Δu L ) 3 $\rangle$ ∝ ε u ar that can be directly compared with simulations and observations, where ε u ≈ 10 -7 m 2 /s 3 is the constant rate for energy cascade; (v) the pth order velocity correlations follow ∝ a (3p-5)/4 for odd p and ∝ a 3p/4 for even p. Finally, the combined kinematic and dynamic relations lead to exponential and one-fourth power-law velocity correlations on large and small scales, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid-scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term prediction of the underlying chaotic dynamics with our framework compared to using only neural network parametrizations for future prediction. Moreover, we demonstrate that these data-driven parametrization models can handle the non-Gaussian statistics of subgrid-scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular nonintrusive way.

42 ENGINEERING↗
DOE OSTI2022

Characterization of Electrochemical Behavior for Aqueous Organic Redox Flow Batteries

Use of aqueous redox flow batteries with organic redox-active materials holds great promise for large-scale and sustainable energy storage. The development of low-cost, highly efficient aqueous redox flow batteries lies in a comprehensive understanding of the electrochemical behaviors of redox-active compounds. In this work, an alkaline redox battery with organic dihydroxyphenazine sulfonate (DHPS) anolyte and ferro-/ferricyanide (Fe(CN) 6 ) catholyte is investigated as a typical example of aqueous redox flow batteries using organic redox-active materials. The electrochemical kinetics of DHPS and Fe(CN) 6 are separately characterized using the symmetrical cell design. The resistance components are calculated directly from the experimental measurement. The key kinetic parameters are extracted and compared for DHPS and Fe(CN) 6 electrolytes. The extracted parameters are validated with symmetrical and full flow cell simulations at different operating conditions. Key parameters and internal loss are also compared with all-vanadium redox flow batteries, representing current state of the art. In addition, our extracted key parameters from a symmetrical flow cell are compared with the measured key parameters by cyclic voltammetry, a widely deployed electroanalytical technique. The cell performance prediction of DHPS anolyte on a 780 cm 2 interdigitated cell is made and found the power density is peaked at 475 mW cm -2 at our measurement condition.

25 ENERGY STORAGE↗
DOE OSTI2023

Computational Analysis of the Impact of Boundary Conditions on a Particle-Laden Flow: A Case Study in a Pressurized Oxy-Coal Combustor

Designing an effective burner is vital for the development of coal combustion technologies. Because of high pressure, the volumetric fraction of the coal particles in the injected fuel in a pressurized oxy-combustion (POC) burner approaches or even exceeds the limitations allowed by the commercial computational fluid dynamics codes (e.g., Ansys Fluent). Consequently, for such high particle volumetric fractions, the interplay between the particles, the fluid flow, and the burner wall needs to be re-evaluated. The present computational work is a first step in a systematic analysis of the roles of various characteristics involved in the POC process, such as the method of particle release, its location, and the particle size. Specifically, pulverized coal is burned under an elevated pressure of 15 bar in an O 2 /CO 2 environment. A 100 kW, a POC combustor, is modeled with Ansys Fluent using the Reynolds-averaged Navier−Stokes approach. It is revealed that for this pilot-scale, pressurized burner, the gas phase flow velocity in the near-wall region exhibits anomalies. With the major focus on POC, this work aims to eliminate/reduce the impact of high particle loading on the gas-phase flow. To scrutinize the role of particle loading in the near-wall region and eliminate the impact of this velocity on POC downstream, the particle−gas interplay in the boundary layer is investigated by means of the computational simulations incorporating the coupling between the turbulent flow and the particles. It is found that the tuning of the particle release location makes the gas-phase flow velocity in the presence of particles consistent with the pure gas flow velocity profile. The particles size is also found to have a significant impact on the particle trajectory.

CFD↗
DOE OSTI2022

Designing a Particle Flow Control Apparatus

Flow control within a particle-based Concentrated Solar Power (CSP) system is essential in determining the heat transfer coefficient, and therefore, the power generation capability of these systems. There are three areas where particle flow control is significant: the receivers, storage tanks, and particle-sCO2 heat exchangers. The focus of this work is on designing a new mechanism to control the flow in the particle-sCO2 heat exchangers due to the simplicity and potential cost savings when compared to the other areas of interest. The goal is for this new design to have quicker response times in terms of particle flowrate than a slide gate or flow control valve, which are designs currently used. The design resembles that of a chuck mechanism within a drill where a rotation of the sleeve elicits movement of the jaws both vertically and horizontally to close the outlet area of the nozzle. Additionally, this design will utilize the current actuator that is already used within these heat exchangers to reduce the complexity of implementation. The jaws are designed to be closed at an angle of 76˚ which is just slightly steeper than the hopper leading to the mechanism. Furthermore, this design can be tuned to limit particle bridging and other particle flow phenomena that result in blockages. The prototypes were 3D printed out of polylactic acid (PLA) and scaled up to 100%, 200%, and 400% to be able to observe the velocity profiles of the mechanism more clearly. Experiments are performed with this prototype to compare the inlet and outlet mass flow rates at different configurations of the jaws. The particles used in these experiments are 0.3mm HSP 40/70 that are commonly used in particle-based CSP systems.

14 SOLAR ENERGY↗
DOE OSTI2023

Design and Development of a Particle Flow Control Mechanism for Particle-Based CSP System Applications

Within particle-based Concentrated Solar Power Systems (CSP), particles are the heat transfer medium, and therefore, the heat transfer effectiveness of these systems is reliant on their ability to effectively control particle mass flow rate. The vast majority of approaches for particle flow control in high temperature CSP is based on a slide-gate approach where a gate is linearly actuated across a tear-drop shaped opening. The slide-gate mechanism has a sensitivity of 0.354 g/s-mm, which is defined as the mass flow rate over the position of the gate. In the present work, we have designed and tested a new particle flow control mechanism with the potential for faster response times and different sensitivities than a slide gate approach enabling more control of particle flow. The new device enables flow control through moving jaws diagonally to change the outlet area. The new devices sensitivity value follows a quadratic curved as compared to the slide-gate’s linear slope over the same jaw movement. A larger sensitivity value is directly related to a quicker response time when utilizing the same actuator.

14 SOLAR ENERGY↗
DOE OSTI2024

Implementation and Operation of Power Flow Control Solutions for Transmission Systems

Power-flow controls (PFCs) technologies provide a suite of alternatives to more-efficiently direct the flow of power on the grid, improving flexibility and enabling the grid to be more responsive and resilient. PFCs can be used in many cases to mitigate overloads and consequently defer the installation of grid-expansion projects. They can also be suitable options in situations where new transmission lines cannot be built because of environmental constraints, land use, or other types of restrictions. Other uses include mitigation of loop or unintended flows, forcing of contractual or scheduled flows, reduction of congestion, and mitigation solutions during large maintenance or construction projects. While many studies have demonstrated the potential economic benefits of PFC for different types of applications, the implementation implications of using multiple PFCs for system operation and reliability have not been analyzed in detail. The lack of information and practical experience on these aspects hinders adoption of this technology. One use case would be that multiple PFCs are required to coordinate congestion management. In such cases, multiple PFCs are deployed in a meshed power system to control power flows across a wide area. The challenges are associated with PFC interaction—namely, the self and mutual power-flow sensitivity of each PFC, which must be accounted for in the calculation of the respective operating setpoints. This report analyzes implementation and operation that must be considered in evaluating transmission solutions using PFCs. The report first analyzes attributes with which PFC-based solutions should comply, based on the objective and characteristics of each particular use case. It then describes different approaches for coordinated control of multiple PFC devices for different applications, including options to avoid adverse interactions and reduce risks due to inappropriate coordination. Control and operation are illustrated through case studies performed on both generic and actual power system models. This report provides insights for transmission planners and operation engineers about the implementation of PFC-based transmission solutions; consequently, it will help them make informed decisions about the adoption of the technology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Accuracy optimized neural networks do not effectively model optic flow tuning in brain area MSTd

Accuracy-optimized convolutional neural networks (CNNs) have emerged as highly effective models at predicting neural responses in brain areas along the primate ventral stream, but it is largely unknown whether they effectively model neurons in the complementary primate dorsal stream. We explored how well CNNs model the optic flow tuning properties of neurons in dorsal area MSTd and we compared our results with the Non-Negative Matrix Factorization (NNMF) model, which successfully models many tuning properties of MSTd neurons. To better understand the role of computational properties in the NNMF model that give rise to optic flow tuning that resembles that of MSTd neurons, we created additional CNN model variants that implement key NNMF constraints – non-negative weights and sparse coding of optic flow. While the CNNs and NNMF models both accurately estimate the observer's self-motion from purely translational or rotational optic flow, NNMF and the CNNs with nonnegative weights yield substantially less accurate estimates than the other CNNs when tested on more complex optic flow that combines observer translation and rotation. Despite its poor accuracy, NNMF gives rise to tuning properties that align more closely with those observed in primate MSTd than any of the accuracy-optimized CNNs. This work offers a step toward a deeper understanding of the computational properties and constraints that describe the optic flow tuning of primate area MSTd.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A Flow Rate Dependent 1D Model for Thermally Stratified Hot-Water Energy Storage

Stratified tank models are used to simulate thermal storage in applications such as residential or commercial hot-water storage tanks, chilled-water storage tanks, and solar thermal systems. The energy efficiency of these applications relates to the system components and the level of stratification maintained during various flow events in the tank. One-dimensional (1D) models are used in building energy simulations because of the short computation time but often do not include flow-rate dependent mixing. The accuracy of 1D models for plug flow, plug flow with axial conduction, and two convection eddy-diffusivity models were compared with experimental data sets for discharging a 50-gal residential tank and recharging the tank with hot water from an external hot-water source. A minimum and maximum relationship for the eddy diffusivity factor were found at Re <2100 and >10,000 for recirculation of hot water to the top of the tank and vertical tubes inletting cold water at the bottom. The root mean square error decreased from >4 °C to near 2 °C when considering flow-based mixing models during heating, while the exponential decay of the eddy diffusion results in a root mean square error reduction of 1 °C for cone-shaped diffusers that begin to relaminarize flow at the inlet.

1D flow model↗
DOE OSTI2025

PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions

The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to build models autonomously while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce PowerModel-AI, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model’s suboptimal performance is based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using PowerModels.jl, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Modeling Monthly Nitrate Concentration in a Karst Spring with and without Discrete Conduit Flow

Understanding the coupled continuum pipe-flow framework for modeling contaminant transport in karst systems is critical for protecting water resources therein. This study simulated point and non-point source nitrate concentration in a karst spring and investigated the results generated from the flow and transport model with and without discrete conduit flow. CFPv2 and CMT3D models were integrated to address the changes in nitrate concentration at a monthly scale in a karst spring, and the results were compared with that from an equivalent porous media (EPM) model with high-hydraulic conductivity (K) zones set in the main karstified area to represent conduits. The results show that the CFPv2+CMT3D model is able to describe well the recession of nitrate concentration in spring discharge, and the relatively larger deviation (slower nitrate recession) from the observed trend for the EPM model is probably a result of the limitation of utilizing high hydraulic conductivity cells to represent conduit. Moreover, simulated hydraulic heads in poorly karstified areas from the two models both show slight differences from the observations (the head RMSE values of calibration/validation for CFPv2 and MODFLOW models are 0.16 m/0.25 m and 0.26 m/0.17 m, respectively), indicating the inclusion of conduits may not affect the simulation considerably, and the lower the proportion of karstic area, the slight effects brought from the inclusion of conduits in the model. For highly karstified areas, the CFPv2+CMT3D model could provide more accurate results (head RMSE of calibration/validation for CFPv2 and MODFLOW are 0.22 m/0.06 and 0.52 m/0.47 m, respectively), showing the coupled continuum pipe-flow framework may be more appropriate for applying to highly and maturely karstified areas where the variations in the behavior of flow and contaminant transport are more affected by turbulent flow regime.

54 ENVIRONMENTAL SCIENCES↗