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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Verification as a Foundation for Validation of a Nuclear Fuel Performance Code

Complex multiphysics simulations such as nuclear fuel performance analysis are composed of many submodels used to describe specific phenomena. These phenomena include, as examples, the relationship between stress and strain, heat transfer across a gas gap, and mechanical contact. These submodels work in concert to simulate real-world events, like the behavior of a fuel rod in a reactor. If a simulation tool is able to represent real-world behavior, the tool is said to be validated. While much emphasis is rightly placed on validation, model verification may be undervalued. Verification involves showing that a model performs as intended, that it computes results consistent with its mathematical description. This paper explains the differences between verification and validation and shows how validation should be preceded by verification. Specific verification problems, including several specific to nuclear fuel analysis, are given. Validation results are also presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Light Water Reactor Sustainability Program Status of Adaptive Surrogates within the RAVEN framework

The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification, data mining analysis and optimization studies. RAVEN has demonstrated a good level of maturity in terms of state-of-art and advanced analysis methodologies. Indeed, in the past year the RAVEN code has been released as open-source project and is available to download (raven.inl.gov) free of charge. The main subject of this report is to show the activities that have been recently accomplished with respect the adaptive surrogate modeling: Implementation of the metric system for the assessment of surrogate model validity; Implementation of cross-validation techniques for the global assessment of surrogate models; Implementation of an automatic infrastructure for switching between a high-fidelity model (e.g. physical code) and a surrogate or set of surrogates; Implementation of a scheme for automatic optimization of surrogate model parameters. The aim of this document is to report the status of the development of methods, within the RAVEN framework, to assess the validity of the predictive capabilities of surrogate models. Indeed, after the construction of a surrogate tight to a certain physical model, it is crucial to assess the goodness of its representation, in order to be confident with its prediction. A cross-validation technique has been employed. This report will highlight the implementation details and proof its correct implementation by an application example. The implementation of a way to assess the predictive capabilities of a surrogate allowed the construction of a RAVEN new entity named “HybridModel” that is aimed to switch between an high-fidelity code and its linked surrogate. In addition, leveraging the optimization algorithms that have been implemented in RAVEN last fiscal year, a new scheme has been developed for automatic tuning of the surrogates’ parameters (e.g. penalty factors in support vector machines).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Experimentally validated finite element model of electrocaloric multilayer ceramic structures

A novel finite element model to simulate the electrocaloric response of a multilayer ceramic capacitor (MLCC) under real environment and operational conditions has been developed. The two-dimensional transient conductive heat transfer model presented includes the electrocaloric effect as a source term, as well as accounting for radiative and convective effects. The model has been validated with experimental data obtained from the direct imaging of MLCC transient temperature variation under application of an electric field. The good agreement between simulated and experimental data, suggests that the novel experimental direct measurement methodology and the finite element model could be used to support the design of optimised electrocaloric units and operating conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Photochemical Kinetic Modeling of Degradation of Aqueous Polyvinyl Alcohol in a UV/H{sub 2}O{sub 2} Photoreactor

This study presents a photochemical kinetics model to describe the degradation of water-soluble PVA (Polyvinyl Alcohol) polymer in a UV/H{sub 2}O{sub 2} batch reactor. Under the effect of UV light, the photolysis of hydrogen peroxide into hydroxyl radicals can generate a series of polymer scission reactions. For a better understanding and analysis of the UV/H{sub 2}O{sub 2} process in the cracking of the PVA macromolecules, a chemical reaction mechanism of the degradation process and a relevant photochemical kinetics model are developed to describe the disintegration of the polymer chains. Taking into account the probabilistic fragmentation of the polymer, the statistical moment approach is used to model the molar population balance of live and dead polymer chains. The model predicts the PVA molecular weight reduction, the acidity of the solution, and hydrogen peroxide residual. In addition to previously published data collected in this laboratory, a new set of experiments were conducted using a 500 mg/L PVA aqueous for different hydrogen peroxide/PVA ratios for model validation. Measurements of average molecular weights of the polymer, hydrogen peroxide concentrations and pH of the PVA solution were determinant factors in constructing a reliable photochemical model of the UV/H{sub 2}O{sub 2} process. Experimental data showed a decrease in the PVA molecular weight and a buildup of the solution acidity. The experimental data also served to determine the kinetics rate constants of the PVA photochemical degradation and validate the model whose predictions are in good agreement with data. The model can provide a comprehensive understanding of the impact of the design and operational variables.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A detailed reaction mechanism for oxidative coupling of methane over Mn/Na 2 WO 4 /SiO 2 catalyst for non-isothermal conditions

Direct production of ethylene from natural gas or other methane sources has proven to be challenging from catalytic, process design and economic perspectives, with no reports of a commercially viable process to date. Understanding of the underlying non-ideal homogeneous and heterogeneous reactions involved could lead to success, enabling utilization of vast natural gas resources as a replacement for petrochemicals. In this regards, an improved reaction mechanism and computational model, validated experimentally, for oxidative coupling of methane (OCM) over a Mn/Na 2 WO 4 /SiO 2 catalyst is developed, taking into account the non-isothermal behavior exhibited by the gas and surface reactions. The reaction mechanism involves detailed gas-phase and surface elementary steps to describe the OCM chemistry and employing a reduced model for computational efficiency. Our model uses laboratory-scale packed-bed reactor experiments for kinetic mechanism development. The modeling and simulation is particularly focused on a one-dimensional packed-bed reactor. The kinetic model is validated over a wide range of experimental conditions. The model-predicted results and their comparisons with the experimental data are presented wide temperature ranges and methane-to-oxygen feed ratios under adiabatic reactor conditions. The kinetic model is first of its kind in which non-isothermal OCM kinetic behavior is captured using detailed gas-phase and heterogeneous reaction kinetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

IBEX: An open infrastructure software platform to facilitate collaborative work in radiomics

Purpose: Radiomics, which is the high-throughput extraction and analysis of quantitative image features, has been shown to have considerable potential to quantify the tumor phenotype. However, at present, a lack of software infrastructure has impeded the development of radiomics and its applications. Therefore, the authors developed the imaging biomarker explorer (IBEX), an open infrastructure software platform that flexibly supports common radiomics workflow tasks such as multimodality image data import and review, development of feature extraction algorithms, model validation, and consistent data sharing among multiple institutions. Methods: The IBEX software package was developed using the MATLAB and C/C++ programming languages. The software architecture deploys the modern model-view-controller, unit testing, and function handle programming concepts to isolate each quantitative imaging analysis task, to validate if their relevant data and algorithms are fit for use, and to plug in new modules. On one hand, IBEX is self-contained and ready to use: it has implemented common data importers, common image filters, and common feature extraction algorithms. On the other hand, IBEX provides an integrated development environment on top of MATLAB and C/C++, so users are not limited to its built-in functions. In the IBEX developer studio, users can plug in, debug, and test new algorithms, extending IBEX’s functionality. IBEX also supports quality assurance for data and feature algorithms: image data, regions of interest, and feature algorithm-related data can be reviewed, validated, and/or modified. More importantly, two key elements in collaborative workflows, the consistency of data sharing and the reproducibility of calculation result, are embedded in the IBEX workflow: image data, feature algorithms, and model validation including newly developed ones from different users can be easily and consistently shared so that results can be more easily reproduced between institutions. Results: Researchers with a variety of technical skill levels, including radiation oncologists, physicists, and computer scientists, have found the IBEX software to be intuitive, powerful, and easy to use. IBEX can be run at any computer with the windows operating system and 1GB RAM. The authors fully validated the implementation of all importers, preprocessing algorithms, and feature extraction algorithms. Windows version 1.0 beta of stand-alone IBEX and IBEX’s source code can be downloaded. Conclusions: The authors successfully implemented IBEX, an open infrastructure software platform that streamlines common radiomics workflow tasks. Its transparency, flexibility, and portability can greatly accelerate the pace of radiomics research and pave the way toward successful clinical translation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

An Evaluation of Semiempirical Models for Partitioning Photosynthetically Active Radiation Into Diffuse and Direct Beam Components

Photosynthesis is more efficient under diffuse than direct beam photosynthetically active radiation (PAR) per unit PAR, but diffuse PAR is infrequently measured at research sites. We examine four commonly used semiempirical models (Erbs et al., 1982, https://doi.org/10.1016/0038-092X(82)90302-4; Gu et al., 1999, https://doi.org/10.1029/1999JD901068; Roderick, 1999, https://doi.org/10.1016/S0168- 1923(99)00028-3; Weiss & Norman, 1985, https://doi.org/10.1016/0168-1923(85)90020-6) that partition PAR into diffuse and direct beam components based on the negative relationship between atmospheric transparency and scattering of PAR. Radiation observations at 58 sites (140 site years) from the La Thuille FLUXNET data set were used for model validation and coefficient testing. All four models did a reasonable job of predicting the diffuse fraction of PAR (Φ) at the 30 min timescale, with site median r2 values ranging between 0.85 and 0.87, model efficiency coefficients (MECs) between 0.62 and 0.69, and regression slopes within 10% of unity. Model residuals were not strongly correlated with astronomical or standard meteorological variables. We conclude that the Roderick (1999, https://doi.org/10.1016/S0168- 1923(99)00028-3) and Gu et al. (1999, https://doi.org/10.1029/1999JD901068) models performed better overall than the two older models. Using the basic form of these models, the data set was used to find both individual site and universal model coefficients that optimized predictive accuracy. A new universal form of the model is presented in section 5 that increased site median MEC to 0.73. Site-specific model coefficients increased median MEC further to 0.78, indicating usefulness of local/regional training of coefficients to capture the local distributions of aerosols and cloud types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Hydrate failure in ITZ governs concrete strength: A micro-to-macro validated engineering mechanics model

Ever since the early days of Féret (1892) and Abrams (1919), concrete research has targeted at relating concrete composition to uniaxial compressive strength. While these activities were mainly characterized by empirical fitting functions, we here take a more fundamental approach based on continuum micromechanics. The loading applied at the concrete level, is first concentrated (''downscaled'') to maximum stresses related to cement paste volumes which are directly adjacent to the aggregates, i.e. to the interfacial transition zones (ITZ). These maximum stresses are further ''downscaled'' to the micron-sized hydrates, in terms of higher-order stress averages. The latter enter a Drucker-Prager failure criterion with material constants derived from nanoindentation tests. The model is successfully validated across the hydrate-to-concrete scales. Strength magnitude is governed by ITZ stress concentrations, and the water-to-cement ratio is its dominant mixture design parameter.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An improved detailed chemical kinetic model for C3-C4 linear and iso-alcohols and their blends with gasoline at engine-relevant conditions

Propanol and butanol isomers have received significant research attention as promising fuel additives or neat biofuels. Robust chemical kinetic models are needed that can provide accurate and efficient predictions of combustion performance across a wide range of engine relevant conditions. Herein, this study seeks to improve the understanding of ignition and combustion behavior of pure C3-C4 linear and iso-alcohols, and their blends with gasoline at engine-relevant conditions. In this work, a kinetic model with improved thermochemistry and reaction kinetics was developed based on recent theoretical calculations of H-atom abstraction and peroxy radical reaction rates. Kinetic model validations are reported, and the current model reproduces the ignition delay times of the C3 and C4 alcohols well. Variations in reactivity over a wide range of temperatures and other operating conditions are also well predicted by the current model. Recent ignition delay time measurements from a rapid compression machine of neat iso-propanol and iso-butanol [Cheng et al., Proc. Combust Inst. (2020)] and blends with a research grade gasoline [Goldsborough et al., Proc. Combust Inst. (2020)] at elevated pressure (20–40 bar) and intermediate temperatures (780–950 K) were used to demonstrate the accuracy of the current kinetic model at conditions relevant to boosted spark-ignition engines. The effects of alcohol blending with gasoline on the autoignition behavior are discussed. The current model captures the suppression of reactivity in the low-temperature and negative-temperature-coefficient (NTC) region when either isopropanol and isobutanol are added to a research grade gasoline. Sensitivity and reaction flux analysis were performed to provide insights into the relevant fuel chemistry of the C3-C4 alcohols.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization

X-ray absorption spectroscopy (XAS) is a commonly employed technique for characterizing functional materials. In particular, X-ray absorption near edge spectra (XANES) encode local coordination and electronic information, and machine learning approaches to extract this information are of significant interest. To date, most ML approaches for XANES have primarily focused on using the raw spectral intensities as input, overlooking the potential benefits of incorporating spectral transformations and dimensionality reduction techniques into ML predictions. Here, in this work, we focused on systematically comparing the impact of different featurization methods on the performance of ML models for XAS analysis. We evaluated the classification and regression capabilities of these models on computed data sets and validated their performance on previously unseen experimental data sets. Our analysis revealed an intriguing discovery: the cumulative distribution function feature achieves both high prediction accuracy and exceptional transferability. This remarkably robust performance can be attributed to its tolerance to horizontal shifts in the spectra, which is crucial when validating models using experimental data. While this work exclusively focuses on XANES analysis, we anticipate that the methodology presented here will hold promise as a versatile asset to the broader spectroscopy community.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Modeling and experimental validation of dynamical effects in Bragg coherent x-ray diffractive imaging of finite crystals

Bragg coherent diffractive imaging (BCDI) is a noninvasive microscopy technique that can visualize the shape and internal lattice deviations of crystals with nanoscale spatial resolution and picometer deformation sensitivity. Its strain imaging capability relies on Fourier transform–based iterative phase retrieval algorithms, which are mostly developed under the kinematical approximation. Such approximation prohibits the application of BCDI on larger crystals, which are commonly seen in most emerging functional materials. Understanding the dynamical effect in BCDI, as well as developing a validated method for modeling BCDI at the dynamical diffraction limit, is crucial for applying BCDI to hierarchical systems that contain micron-sized crystals and grains. Thus we report a comparative study on the impact of dynamical diffraction effects by comparing the reconstruction results from two measurements of the same crystal. Forward simulation is implemented to show subtle changes of interference fringes in the diffraction pattern due to the dynamical diffraction, and is compared directly with the experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Six Machine-Learning Methods for Predicting Hospital-Stay Duration for Patients with Sepsis: A Comparative Study

Sepsis is a life-threatening medical condition that, if not treated promptly, can result in tissue damage, organ failure, and death. According to the Centers for Disease Control, about 270,000 individuals die of sepsis in the US each year. Further, sepsis expenditures accounted for 13% of total US hospital costs in 2013, totaling more than $24 billion. Our project objectives were to determine if Machine Learning algorithms could reliably predict hospital stay duration for patients with sepsis. The data set we used has been de-identified and is freely available through the BupaR package. The data includes 1050 cases, 15214 events, and 16 types of actions related to sepsis patient care. First, we used process mining to determine how long each patient was in the hospital. Using BupaR’s functions, we created several process model graphs. These process models depict the movement of patients at a hospital and provide duration data for each patent case. Second, we identified outlier data and created two dataset versions: one with and one without outliers. We then applied the following analysis methods: Linear Regression, Random Forest, K-Nearest Neighbors, Neural Networks, XGBoost, and lightGBM. We compared the model validations for the six machine learning models using the same data-splitting method. We found that the XGBoost model had the best prediction accuracy of 73.9 percent for cases with outliers, and 79 percent for cases without outliers. We also found that the lightGBM model had the lowest mean absolute error between prediction and actual duration in days with 3.66 days for the case with outliers, and 2.4 days for the case without outliers. These two models outperformed the other four models. This work will be enhanced in the future by exploring new prediction algorithms and comparing them with the results of this study.

Chen, Lingtao↗
DOE OSTI2021

TRANSIENT THERMAL MODELING OF THE HIGH BURNUP DEMONSTRATION RESEARCH PROJECT CASK USING STAR-CCM+ AND COBRA-SFS

The Department of Energy in collaboration with the Electric Power Research Institute is in the process of conducting the High Burnup Demonstration Project. Where the objective is to characterize the performance of high-burnup fuel in long term storage. As part of this demonstration, a TN-32B dry storage cask was instrumented and loaded with spent nuclear fuel at North Anna Nuclear Generating Station in November 2017. The project cask provides a unique opportunity to gain information on spent fuel and cask performance from an in-service operational system. The cask was instrumented with thermocouples inside of the fuel assemblies, then loaded and dried using normal procedures. After the drying process a thermal soak period where the cask was left indoors was used to obtain steady temperatures for model comparison along with surface temperatures. This paper details thermal modeling validation work that was done to model both the steady state and transient cases. Two modeling tools were used to predict temperatures in the cask. The general purpose CFD and heat transfer code STAR-CCM+ was used with both a detailed pin-by-pin model was used along with a more efficient k-effective simplification. The other code is COBRA-SFS, a purpose-built detailed thermal modeling tool developed and maintained at PNNL. Results from all the modeling tools were compared blind to the test data. Each model utilized general design information and compared reasonably well to the blind steady state data using convection and surface temperature boundary conditions. Post-test adjustments were later made to better reflect the “as built” conditions of the cask. Based on lessons learned from the steady state analysis the vacuum drying process was modeled in both codes. Utilizing the measured loading conditions the transient models were able to compare very well with the measured data. Overall the project showed an ability to model spent fuel storage conditions very well and future work is planned to generalize the methodology used for vacuum drying.

Thermal Analysis, Spent Fuel, Dry Storage↗
DOE OSTI2014

Advancing Analytical Capabilities to Study Nuclear Materials and Its Implications for Innovative Nuclear Materials Research

We propose the acquisition of the latest precession electron nanodiffraction (PED) DigiSTAR system from Nanomegas to enable and improve our capabilities to gain simultaneous insight about material structure and chemistry at the nanoscale in materials of interest to DOE-NE. This acquisition is directed towards data collection at low length scales to support model validation and DOE-NE’s modeling and simulation efforts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Status Report and Research Plan for Cables Harvested from Crystal River Unit 3 Nuclear Generating Plant

Harvested cables from operating or decommissioned nuclear power plants present an important opportunity to validate models, understanding material aging behavior, and validate characterization techniques. Crystal River Unit 3 Nuclear Generating Plant is a pressurized water reactor that was licensed to operate from 1976 to 2013. Cable segments were harvested and made available to the Light Water Reactor Sustainability research program through the Electric Power Research Institute. Information on the locations and circuits within the reactor from whence the cable segments came, cable construction, sourcing and installation information, and photographs of the cable locations prior to harvesting were provided. The cable variations provided represent six of the ten most common cable insulations in the nuclear industry and experienced service usage for periods from 15 to 42 years. Subsequently, these cables constitute a valuable asset for research to understand aging behavior and measurement of nuclear cables. Received cables harvested from Crystal River Unit 3 Nuclear Generating Plant consist of low voltage, insulated conductor surrounded by jackets in lengths from 24 to 100 feet each. Cable materials will primarily be used to investigate aging under simultaneous thermal and gamma radiation exposure. Each cable insulation and jacket material will be characterized in its as-received condition, including determination of the temperatures associated with endothermic transitions in the material using differential scanning calorimetry and dynamic mechanical analysis. Temperatures for additional thermal exposure aging will be selected following the thermal analysis to avoid transitions in accelerated laboratory aging that do not occur in field conditions. Aging temperatures above thermal transitions may also be targeted to investigate the potential for artifacts in lifetime prediction from rapid accelerated aging. Total gamma doses and dose rates targeted for each material will be determined based on filling gaps in prior work, known limits of material classes and resource constraints. Experimental plans will be developed in the context of existing data for the insulation and jacket materials available in published Department of Energy and Electric Power Research Institute reports toward addressing identified knowledge gaps.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Research and Development in Fire Science

FM Global Technologies LLC and the Fire Science and Technology Department at Sandia National Laboratories have undertaken a Cooperative Research and Development Agreement (CRADA) to perform joint research and development in fire science. The objective of this work is to provide significant technical advances for the prevention and mitigation of high consequence fires. These advances will be accomplished through the acquisition of new, high-quality data for discovery of controlling mechanisms and model validation, formulation of new theories to describe fire behavior, and the development and validation of analytical and numerical fire models to predict fire hazard and mitigation system performance. These advances are expected to provide measurable improvements in fire safety by decreasing fire-related losses of life and property.

Childress, Amy↗
DOE OSTI2020

SAM Plug-in Development (Phase I Final Report)

The DOE Office of Nuclear Energy (NE) has created an extensive set of advanced modeling and simulation tools for nuclear engineering analysis. The advanced capabilities of these newer analysis codes require more in-depth training, skills, and knowledge in order to effectively utilize them for the design, analysis, and licensing of advanced nuclear systems and experiments. A high learning curve for inexperienced users may deter organizations from incorporating these tools into their internal processes. This project involved development of a plug-in to the Symbolic Nuclear Analysis Package (SNAP) for the System Analysis Module (SAM) tool. SAM is an advanced system analysis tool for reactor transient analyses being developed at Argonne National Laboratory under the U.S. DOE Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. SAM utilizes an object-oriented application framework (MOOSE), and its underlying meshing and finite-element library (libMesh) and linear and non-linear solvers (PETSc), to leverage modern advanced software environments and numerical methods. SNAP provides a highly flexible framework for creating, modifying and documenting input for engineering analysis codes such as SAM as well as extensive functionality for submitting, monitoring, and interacting with the codes through an intuitive graphical user interface (GUI). The common user interface provided by SNAP minimizes the learning curve for engineers starting with a new analysis code and provides an intuitive framework for transitioning between different analysis codes. SNAP provides a powerful but intuitive interface to facilitate access to advanced modeling and simulation tools for inexperienced users. Unlike many “form based” GUI’s, SNAP maps each engineering code’s component input to an internal database which manages all component input parameters along with component interconnections. This level of abstraction permits SNAP to support several advanced capabilities such as renodalization, model validation and consistency checks, embedded documentation, model notebook generation, data ownership and reviewer tracking, and variable assignment for inputs to name a few. SNAP includes a built-in Python interpreter and is interfaced to several commercial and open source packages including CPython, MATLAB/OCTAVE, Microsoft Office, Open Office, and SANDIA’s DAKOTA package which provides Uncertainty Quantification analysis through the SNAP plug-ins. Phase I of this project involved development a fully functional basic SAM plug-in to SNAP. This plug-in provides the ability to import existing models, graphically construct, edit and submit models using SNAP’s extensive functionality.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Modulating factors of hydrologic exchanges in a large-scale river reach: Insights from three-dimensional computational fluid dynamics simulations

Hyporheic exchange is a critical mechanism shaping hydrological and biogeochemical processes along a river corridor. Recent studies on quantifying the hyporheic exchange were mostly limited to local scales due to field inaccessibility, computational demand, and complexity of geomorphology and subsurface geology. Surface flow conditions and subsurface physical properties are well known factors on modulating the hyporheic exchange, but quantitative understanding of their impacts on the strength and direction of hyporheic exchanges at reach scales is absent. In this study, a high resolution computational fluid dynamics (CFD) model that couples surface and subsurface flow and transport is employed to simulate hyporheic exchanges in a 7-km long reach along the main-stem of the Columbia River. Assuming that the hyporheic exchange does not affect surface water flow conditions due to its negligible magnitude compared to the volume and velocity of river water, we developed a one-way coupled surface and subsurface water flow model using the commercial CFD software STAR-CCM+. The model integrates the Reynolds-averaged Navier-Stokes (RANS) equation solver with a realizable k-e two-layer turbulence model, a two-layer all y^+ wall treatment, and the volume of fluid (VOF) method, and is used to simulate hyporheic exchanges by tracking the free water-air interface as well as flow in the river and the subsurface porous media. The model is validated against measurements from acoustic Doppler current profiler (ADCP) in the stream water and hyporheic fluxes derived from a set of temperature profilers installed across the riverbed. The validated model is then employed to systematically investigate how hyporheic exchanges are influenced by surface water fluid dynamics strongly regulated by upstream dam operations, as well as subsurface structures (e.g. thickness of riverbed and subsurface formation layers) and hydrogeological properties (e.g. permeability). The results suggest that the thickness of riverbed alluvium layer is the dominant factor for reach-scale hyporheic exchanges, followed by the alluvium permeability, the depth of the underlying impermeable layer, and the assumption of hydrostatic pressure.

Bao, Jie↗