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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Direct numerical simulations of three-component Rayleigh–Taylor mixing and an improved model for multicomponent reacting mixtures

We present direct numerical simulations of a three-layer Rayleigh–Taylor instability (RTI) problem with a configuration based on the experiments of Suchandra & Ranjan ( J. Fluid Mech. , vol. 974, 2023, A35) and Jacobs & Dalziel ( J. Fluid Mech. , vol. 542, 2005, pp. 251–279). The problem consists of a layer of light fluid between two layers of heavy fluid with an Atwood number of 0.3. These simulations are first validated through comparison with available experimental data. The validated simulations are then utilized to analyse statistics in this three-component flow. First, length scales are examined utilizing spectra and two-point spatial correlations of velocity and species concentration fluctuations. Next, joint probability density functions (p.d.f.s) of species concentration are compared against several model p.d.f.s representing generalizations of the bivariate beta distribution. Notably, the joint p.d.f.s do not appear to be accurately described by a Dirichlet distribution, indicating the marginal distributions do not conform to a beta distribution. Finally, similarity of the present configuration to three-component mixing found in inertial confinement fusion (ICF) applications is exploited to develop and validate an improved model for the impact of multicomponent mixing on thermonuclear (TN) reaction rates. A single time instant from the present simulations is chosen for a TN burn calculation under the hypothetical assumption of ICF materials and temperatures. Total TN output from this second calculation is then compared against the prediction of the improved model. The new model is found to accurately predict TN reaction rates in both premixed and non-premixed configurations.

42 ENGINEERING↗
DOE OSTI2023

Data-Driven RANS Turbulence Closures for Forced Convection Flow in Reactor Downcomer Geometry

Recent progress in data-driven turbulence modeling has shown its potential to enhance or replace traditional equation-based Reynolds-averaged Navier-Stokes (RANS) turbulence models. Here, this work utilizes invariant neural network (NN) architectures to model Reynolds stresses and turbulent heat fluxes in forced convection flows (when the models can be decoupled). As the considered flow is statistically one dimensional, the invariant NN architecture for the Reynolds stress model reduces to the linear eddy viscosity model. To develop the data-driven models, direct numerical and RANS simulations in vertical planar channel geometry mimicking a part of the reactor downcomer are performed. Different conditions and fluids relevant to advanced reactors (sodium, lead, unitary-Prandtl-number fluid, and molten salt) constitute the training database. The models enabled accurate predictions of velocity and temperature, and compared to the baseline k–τ turbulence model with the simple gradient diffusion hypothesis, do not require tuning of the turbulent Prandtl number. The data-driven framework is implemented in the open-source graphics processing unit–accelerated spectral element solver nekRS and has shown the potential for future developments and consideration of more complex mixed convection flows.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

VADER: A Tool for Criticality Safety Validation

The purpose of criticality safety is to prevent any inadvertent criticality from occurring during the handling or storage of fissile material. Calculations are frequently used to demonstrate that a sufficient subcritical margin exists. Validation is a key aspect of the evaluation process, establishing the suitability, accuracy, and associated uncertainty of the computational method and data to be used for the intended application. The validation process is performed by comparing the results of critical experiments with the calculated results from models of the experiments using the computational method to be validated. Laboratory critical experiments are controlled systems that achieve a k eff of approximately 1 in order to investigate the parameters at which such a critical condition is achieved. The validation parameters that are traditionally applied to safety analysis calculations are the bias and the bias uncertainty . The bias is the deviation of the average k eff of the validation suite from unity. The bias uncertainty accounts for the statistical uncertainty in the bias based on the standard deviation, sample size, and distribution of k eff values of the validation suite. The values of bias and bias uncertainty ensure that the systems predicted to be subcritical by the computational method will indeed be subcritical. The bias and bias uncertainty are often combined to determine an upper subcritical limit (USL) or computational margin that can then be applied to safety analysis calculations. Many methods have been developed by different organizations to calculate the bias and bias uncertainty for various types of criticality analyses. Each of these methods typically requires that the validity of various underpinning statistical assumptions be confirmed to demonstrate that the method is appropriate for the analysis of a given validation suite. An example of the validation decision making flow is shown in Fig.1. As shown in Fig. 1, the analyst performing the validation fits a trend line to the data and performs a test to determine if the trend was a statistically better representation of the data than if it were treated as an uncorrelated sample. If the trend line is a better representation of the data, then the analyst uses any one of a number of trending techniques to determine the bias and bias uncertainty. If a trend is not an appropriate representation of the data, then the analyst proceeds to perform a normality assessment for the data. If the normal assumption can be shown to be acceptable, then the analyst calculates the bias and bias uncertainty with the parametric technique. If the assumption of normality cannot be justified, then the nonparametric technique is used. Once the decision flow has been followed and the appropriate technique has been selected, the bias and bias uncertainty is typically combined with an administrative margin to determine a USL below which calculated values of k eff for safety analysis models can be considered subcritical. The calculations used in each decision are often performed with spreadsheets or with small programs available at various sites performing criticality analyses. Expertise in understanding and interpreting the results must be maintained to perform these calculations. This can often be an error-prone process. Oak Ridge National Laboratory (ORNL) is currently developing the Validation and Data Evaluation Resource (VADER) to simplify and automate the criticality safety validation process and to provide a software quality assurance pedigree to the calculational methods used. This paper discusses the use of the Fulcrum user interface with VADER, the anticipated initial capabilities of VADER to perform validation analyses, and the output from the code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Mixing and detonation structure in a rotating detonation engine with an axial air inlet

High-fidelity simulations of an experimental rotating detonation engine with an axial air inlet were conducted. The system operated with hydrogen as fuel at globally stoichiometric conditions. Instantaneous data showed that the detonation front is highly corrugated, and is considerably weaker than an ideal Chapman–Jouguet wave. Regions of deflagration are present ahead of the wave, caused by mixing with product gases from the previous cycle, as well as the injector recovery process. It is found that as the post-detonation high pressure flow expands, the injectors recover unsteadily, leading to a transient mixing process ahead of the next cycle. The resulting flow structure not only promotes mixing between product and reactant gases, but also increases likelihood of autoignition. These results show that the detonation process is very sensitive to injector design and the transient behavior during the detonation cycle. Phase-averaged statistics and conditionally averaged data are used to understand the overall reaction structure. As a result, comparisons with available experimental data on this configuration show remarkable good agreement of the predicted reacting flow structure.

42 ENGINEERING↗
DOE OSTI2022

Induced microearthquakes predict permeability creation in the brittle crust

Predicting the evolution of permeability accurately during stimulation at the reservoir scale and at the resolution of individual fractures is essential to characterize the fluid transport and the reactive/heat-transfer characteristics of reservoirs where stress exerts significant control. Here, we develop a hybrid machine learning (ML) model to visualize in situ permeability evolution for an intermediate-scale (~10 m) hydraulic stimulation experiment. This model includes an ML model that was trained using the well history of flow rate and wellhead pressure and MEQ data from the first three stimulation episodes to predict average permeability from the statistical features of the MEQs alone for later episodes. Moreover, a physics-inspired model is integrated to estimate in situ fracture permeability spatially. This method relates fracture permeability to fracture dilation and scales dilation to the equivalent MEQ magnitude, according to laboratory observations. The seismic data are then applied to define incremental changes in permeability in both space and time. Our results confirm the excellent agreement between the ground truth and model-predicted permeability evolution. The resulting permeability map defines and quantifies flow paths in the reservoir with the averaged permeability comparing favorably with the ground truth of permeability.

induced seismicity↗
DOE OSTI2011

Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT)

This report summarizes work carried out by the Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) Team for the period of January 1, 2011 through June 30, 2011. It discusses highlights, overall progress, period goals, and collaborations and lists papers and presentations. To learn more about our project, please visit our UV-CDAT website (URL: http://uv-cdat.org). This report will be forwarded to the program manager for the Department of Energy (DOE) Office of Biological and Environmental Research (BER), national and international collaborators and stakeholders, and to researchers working on a wide range of other climate model, reanalysis, and observation evaluation activities. The UV-CDAT executive committee consists of Dean N. Williams of Lawrence Livermore National Laboratory (LLNL); Dave Bader and Galen Shipman of Oak Ridge National Laboratory (ORNL); Phil Jones and James Ahrens of Los Alamos National Laboratory (LANL), Claudio Silva of Polytechnic Institute of New York University (NYU-Poly); and Berk Geveci of Kitware, Inc. The UV-CDAT team consists of researchers and scientists with diverse domain knowledge whose home institutions also include the National Aeronautics and Space Administration (NASA) and the University of Utah. All work is accomplished under DOE open-source guidelines and in close collaboration with the project's stakeholders, domain researchers, and scientists. Working directly with BER climate science analysis projects, this consortium will develop and deploy data and computational resources useful to a wide variety of stakeholders, including scientists, policymakers, and the general public. Members of this consortium already collaborate with other institutions and universities in researching data discovery, management, visualization, workflow analysis, and provenance. The UV-CDAT team will address the following high-level visualization requirements: (1) Alternative parallel streaming statistics and analysis pipelines - Data parallelism, Task parallelism, Visualization parallelism; (2) Optimized parallel input/output (I/O); (3) Remote interactive execution; (4) Advanced intercomparison visualization; (5) Data provenance processing and capture; and (6) Interfaces for scientists - Workflow data analysis and visualization construction tools, and Visualization interfaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Thermal-fluctuation effects on small-scale statistics in turbulent gas flow

Kolmogorov's theory of turbulence assumes that the small-scale turbulent structures in the energy cascade are universal and are determined by the energy dissipation rate and the kinematic viscosity alone. However, thermal fluctuations, absent from the continuum description, terminate the energy cascade near the Kolmogorov length scale. Here, we propose a simple superposition model to account for the effects of thermal fluctuations on small-scale turbulence statistics. Furthermore, for compressible Taylor–Green vortex flow, we demonstrate that the superposition model in conjunction with data from direct numerical simulation of the Navier–Stokes equations yields spectra and structure functions that agree with the corresponding quantities computed from the direct simulation Monte Carlo method of molecular gas dynamics, verifying the importance of thermal fluctuations in the dissipation range.

42 ENGINEERING↗
DOE OSTI2022

Crash Risks Evaluation of Urban Expressways: A Case Study in Shanghai

We report that proactive traffic safety management systems can reduce crashes by identifying crash precursors, evaluating real-time crash risks, and implementing suitable interventions. The basic prerequisite for developing such a system is to propose a reliable crash risk evaluation model that takes real-time traffic flow data as input. Previous studies have primarily focused on real-time crash prediction using some statistical or machine-learning methods. However, further quantitative evaluation and classification of crash risks have been ignored. In this study, we conduct a systematic crash risk evaluation workflow, including crash risk prediction, crash risk quantification, and crash risk classification. Specifically, the crash risk prediction using an extended logit model is proposed, from which CAS, CSD, UAS, DAS, DTV are identified to be contributing factors of crash risks. Then a crash risk quantification model based on the parameter evaluation of the extended logit model is developed. The crash risks of urban expressways and their spatial-temporal evolution trends are quantified. Finally, the crash risks are classified into high crash risk level, moderate crash risk level, and low crash risk level by the k-means cluster algorithm. Then the threshold boundaries of different crash risk levels are determined. The research results provide a proactive guidance for traffic safety management of urban expressways.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Statistical analysis of historic hydrocarbon production data from Gulf of Mexico oil and gas fields and application to dynamic capacity assessment in CO 2 storage

Numerical modeling of CO 2 injection and reservoir flow is typically performed to forecast the number of wells, sustainable injection rates, and total storage volume (or mass). A critical determination for CO 2 storage in depleted oil and gas reservoirs is characterization of reservoir compartmentalization which informs boundary conditions in simulating injection scenarios. Constraining boundary conditions during CO 2 injection into geological formations is a key factor for feasible deployment project. Production history data from 616 wells in 100 oil and gas fields from Gulf of Mexico (GOM) offshore basin can be used to evaluate boundary conditions and total production (oil and gas) and therefor constrain the capacity for potential carbon storage. In general, the combination of decline curve analysis and statistical analysis (to specify constraining boundary conditions) allows the determination of the range of reservoir performance if existing inactive production wells in GOM area of study are used reversibly for CO 2 injection. To constrain the mass of CO 2 which can be injected, it is useful to consider estimates of cumulative bulk (hydrocarbon + brine) production (CBP), which can be converted to equivalent CO 2 mass considering reservoir conditions. Summary CBP statistics are presented as a probability of non-exceedance (PNE), providing a forecast of likely injection rates and masses for other located CO 2 storage projects with similar geology and boundary conditions in the future. The 50% PNE for Equivalent CO 2 is a novel quantitative approach to investigate the possible injection capacity in CO 2 storage projects. The PNE sensitivity analysis shows that reservoir age, drive mechanism, reservoir trap, and reservoir porosity are the key controlling parameters for productivity and consequently optimum CO 2 storage capacity. Furthermore another key finding is the negligible correlation between CBP with reservoir transmissivity and porosity, which implies that other factors than just petrophysical parameters should be studied as constraining factors for CO 2 storage statistical analysis.

42 ENGINEERING↗
DOE OSTI2023

High-fidelity Experiments of Turbulent Buoyant Jets from Simultaneous Particle Image Velocimetry and Laser Induced Fluorescence

Accurate models of turbulent buoyant flows are essential for the design of nuclear reactors thermal hydraulics and passive safety systems. However, available models fail to fully capture the physics of turbulent mixing when buoyancy becomes predominant with respect to momentum. Therefore, high-fidelity experiments of well-controlled fundamental flows are needed to develop and validate more accurate models. We performed experiments of positive and negative turbulent buoyant jets, both in uniform and stratified environments, with the aim of understanding the thermal hydraulics of turbulent mixing with variable density and providing high-fidelity data for the development and validation of turbulence models. Non-intrusive, simultaneous Particle Image Velocimetry and Laser Induced Fluorescence measurements were carried out to acquire instantaneous velocity and concentration fields on a vertical section parallel to the axis of a jet in the self-similar region. The Refractive Index Matching method was applied to measure high-resolution buoyant jets with up to 8.6% density difference. These data are free of the typical errors that characterize optical measurements of buoyancy driven flows (e.g., natural and mixed convection) where the refractive index of the fluid is inhomogeneous throughout the measurement domain. Turbulent statistics and entrainment of buoyant jets in uniform and stratified environments are presented. These data are compared with non-buoyant jets in uniform environment, as a reference to investigate the effects of buoyancy and stratification on turbulent mixing. The results will be used for the assessment of current turbulence models and as basis for the development of a new one that captures turbulent mixing.

42 ENGINEERING↗
DOE OSTI2018

I/O load balancing for big data HPC applications

High Performance Computing (HPC) big data problems require efficient distributed storage systems. However, at scale, such storage systems often experience load imbalance and resource contention due to two factors: the bursty nature of scientific application I/O; and the complex I/O path that is without centralized arbitration and control. For example, the extant Lustre parallel file system-that supports many HPC centers-comprises numerous components connected via custom network topologies, and serves varying demands of a large number of users and applications. Consequently, some storage servers can be more loaded than others, which creates bottlenecks and reduces overall application I/O performance. Existing solutions typically focus on per application load balancing, and thus are not as effective given their lack of a global view of the system. In this paper, we propose a data-driven approach to load balance the I/O servers at scale, targeted at Lustre deployments. To this end, we design a global mapper on Lustre Metadata Server, which gathers runtime statistics from key storage components on the I/O path, and applies Markov chain modeling and a minimum-cost maximum-flow algorithm to decide where data should be placed. Evaluation using a realistic system simulator and a real setup shows that our approach yields better load balancing, which in turn can improve end-to-end performance.

Paul, Arnab K.↗
DOE OSTI2024

High-Fidelity Velocity and Concentration Measurements of Turbulent Buoyant Jets

Accurate models of turbulent buoyant flows are essential for the design of the cooling circuit of nuclear reactors and passive safety systems. However, available models fail to fully capture the physics of turbulent mixing when buoyancy becomes predominant with respect to momentum. Therefore, high-fidelity experiments of well-controlled fundamental flows are needed to develop and validate more accurate models. We analyze experiments of positive and negative turbulent buoyant jets, both in uniform and stratified environments, with the aim of understanding the thermal hydraulics of turbulent mixing with variable density and providing high-fidelity data for the development and validation of turbulence models. Non-intrusive, simultaneous particle image velocimetry and laser-induced fluorescence measurements were carried out to acquire instantaneous velocity and concentration fields on a vertical section parallel to the axis of a jet in the self-similar region. The refractive index matching method was applied to measure high-resolution buoyant jets with up to 8.6% density difference. These data are free of the typical errors that characterize optical measurements of buoyancy-driven flows (e.g. natural and mixed convection) where the refractive index of the fluid is inhomogeneous throughout the measurement domain. Turbulent statistics and entrainment of buoyant jets in uniform and stratified environments are presented. These data are compared with non-buoyant jets in a uniform environment, as a reference to investigate the effects of buoyancy and stratification on turbulent mixing. The results will be used for the assessment of current turbulence models and as a basis for the development of a new model that captures turbulent mixing.

laser induced fluorescence↗
DOE OSTI2014

Statistical study of reconnection exhausts in the solar wind

Magnetic reconnection is a fundamental process that changes magnetic field configuration and converts a magnetic energy to flow energy and plasma heating. This paper presents a survey of the plasma and magnetic field parameters inside 418 reconnection exhausts identified in the WIND data from 1995-2012. The statistical analysis is oriented on the re-distribution of the magnetic energy released due to reconnection between a plasma acceleration and its heating. The results show that both the portion of the energy deposited into heat as well as the energy spent on the acceleration of the exhaust plasma rise with the magnetic shear angle in accord with the increase of the magnetic flux available for reconnection. The decrease of the normalized exhaust speed with the increasing magnetic shear suggests a decreasing efficiency of the acceleration and/or the increasing efficiency of heating in high-shear events. However, we have found that the already suggested relation between the exhaust speed and temperature enhancement would be rather considered as an upper limit of the plasma heating during reconnection regardless of the shear angle.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Large-deviation joint statistics of the finite-time Lyapunov spectrum in isotropic turbulence

One of the hallmarks of turbulent flows is the chaotic behavior of fluid particle paths with exponentially growing separation among them while their distance does not exceed the viscous range. The maximal (positive) Lyapunov exponent represents the average strength of the exponential growth rate, while fluctuations in the rate of growth are characterized by the finite-time Lyapunov exponents (FTLEs). In the last decade or so, the notion of Lagrangian coherent structures (which are often computed using FTLEs) has gained attention as a tool for visualizing coherent trajectory patterns in a flow and distinguishing regions of the flow with different mixing properties. A quantitative statistical characterization of FTLEs can be accomplished using the statistical theory of large deviations, based on the so-called Cramér function. To obtain the Cramér function from data, we use both the method based on measuring moments and measuring histograms and introduce a finite-size correction to the histogram-based method. We generalize the existing univariate formalism to the joint distributions of the two FTLEs needed to fully specify the Lyapunov spectrum in 3D flows. The joint Cramér function of turbulence is measured from two direct numerical simulation datasets of isotropic turbulence. Results are compared with joint statistics of FTLEs computed using only the symmetric part of the velocity gradient tensor, as well as with joint statistics of instantaneous strain-rate eigenvalues. When using only the strain contribution of the velocity gradient, the maximal FTLE nearly doubles in magnitude, highlighting the role of rotation in de-correlating the fluid deformations along particle paths. We also extend the large-deviation theory to study the statistics of the ratio of FTLEs. The most likely ratio of the FTLEs λ{sub 1} : λ{sub 2} : λ{sub 3} is shown to be about 4:1:−5, compared to about 8:3:−11 when using only the strain-rate tensor for calculating fluid volume deformations. The results serve to characterize the fundamental statistical and geometric structure of turbulence at small scales including cumulative, time integrated effects. These are important for deformable particles such as droplets and polymers advected by turbulence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Data-driven Mori–Zwanzig modeling of Lagrangian particle dynamics in turbulent flows

The dynamics of Lagrangian particles in turbulence play a crucial role in mixing, transport, and dispersion in complex flows. Their trajectories exhibit highly nontrivial statistical behavior, motivating the development of surrogate models that can reproduce these trajectories without incurring the high computational cost of direct numerical simulations of the full Eulerian field. This task is particularly challenging because reduced-order models typically lack access to the full set of interactions with the underlying turbulent field. Novel data-driven machine learning techniques can be powerful in capturing and reproducing complex statistics of the reduced-order/surrogate dynamics. In this work, we show how one can learn a surrogate dynamical system that is able to evolve a turbulent Lagrangian trajectory in a way that is point-wise accurate for short-time predictions (with respect to Kolmogorov time) and stable and statistically accurate at long times. This approach is based on the Mori–Zwanzig formalism, which prescribes a mathematical decomposition of the full dynamical system into resolved dynamics that depend on the current state and the past history of a reduced set of observables, and the unresolved orthogonal dynamics due to unresolved degrees of freedom of the initial state. We show how by training this reduced order model on a point-wise error metric on short time-prediction, we are able to correctly learn the dynamics of Lagrangian turbulence, such that also the long-time statistical behavior is stably recovered at test time. This opens up a range of applications, for example, for the control of active Lagrangian agents in turbulence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Physics-informed machine learning of the Lagrangian dynamics of velocity gradient tensor

Reduced models describing the Lagrangian dynamics of the velocity gradient tensor (VGT) in homogeneous isotropic turbulence (HIT) are developed under the physics-informed machine learning (PIML) framework. We consider the VGT at both Kolmogorov scale and coarse-grained scale within the inertial range of HIT. Building reduced models requires resolving the pressure Hessian and subfilter contributions, which is accomplished by constructing them using the integrity bases and invariants of the VGT. The developed models can be expressed using the extended tensor basis neural network (TBNN) introduced by Ling et al. [J. Fluid Mech. 807, 155 (2016)]. Physical constraints, such as Galilean invariance, rotational invariance, and incompressibility condition, are thus embedded in the models explicitly. Our PIML models are trained on the Lagrangian data from a high-Reynolds number direct numerical simulation (DNS). To validate the results, we perform a comprehensive out-of-sample test. We observe that the PIML model provides an improved representation for the magnitude and orientation of the small-scale pressure Hessian contributions. Statistics of the flow, as indicated by the joint PDF of second and third invariants of the VGT, show good agreement with the “ground-truth” DNS data. A number of other important features describing the structure of HIT are reproduced by the model successfully. We have also identified challenges in modeling inertial range dynamics, which indicates that a richer modeling strategy is required. This helps us identify important directions for future research, in particular towards including inertial range geometry into the TBNN.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Freight Analysis Framework Version 5 (FAF5) Base Year 2017 Data Development Technical Report

The Freight Analysis Framework (FAF) integrates data from a variety of sources to create a comprehensive national picture of freight movements among states and major metropolitan areas by all modes of transportation. The latest of this data series is FAF5, which is the fifth generation FAF and is benchmarked on Commodity Flow Survey (CFS) 2017. Except for FAF1 that provided estimates for truck, rail, and water tonnage for calendar year 1998, later generations of FAF (FAF2 through FAF5) were built based on their benchmark year CFS data, for 2002, 2007, 2012, and 2017 respectively. The FAF is produced under a partnership between Bureau of Transportation Statistics (BTS) and Federal Highway Administration (FHWA). As a major data product of the FAF program, the FAF regional database provides a national picture of freight flows to, from, and within the United States (among regions and states), by commodity and mode for the base year, as well as for forecasts up to 30 years into the future in a 5-year interval. Additional FAF data products also include FAF network flows database, where truck movements are routed onto the national highway network, estimates of annual projections, and synchronized historical data series. This report is a technical document prepared to describe the data sources and methodologies applied in the process of building the FAF5 base-year 2017 regional database, released as FAF5.0 in February 2021. This report offers a description of the diverse data sources and modeling methods used in constructing the base year FAF5 regional database. The FAF5 base-year database is used as the base for development of forecasts and for assignment of truck flows on highway network. Similarly, the FAF5 base-year database will be used as the base to generate FAF5 annual estimates. In addition to this report, users are encouraged to refer to the FAF5 User’s Guide, which provides basic information of the data, including definitions of the data attributes, information on how to access the data and tool, as well as detailed data dictionary and code tables.

42 ENGINEERING↗
DOE OSTI2015

Isotherms for Water Adsorption on Molecular Sieve 3A: Influence of Cation Composition

This study is part of our continuing efforts to address engineering issues related to the removal of tritiated water from off-gases produced in used nuclear fuel reprocessing facilities. In the current study, adsorption equilibrium of water on molecular sieve 3A beads was investigated. Adsorption isotherms for water on the UOP molecular sieve 3A were measured by a continuous-flow adsorption system at 298, 313, 333, and 353 K. Experimental data collected were analyzed by the Generalized Statistical Thermodynamic Adsorption (GSTA) isotherm model. The K + /Na + molar ratio of this particular type of molecular sieve 3A was ~4:6. Our results showed that the GSTA isotherm model worked very well to describe the equilibrium behavior of water adsorption on molecular sieve 3A. The optimum number of parameters for the current experimental data was determined to be a set of four equilibrium parameters. This result suggests that the adsorbent crystals contain four energetically distinct adsorption sites. In addition, it was found that water adsorption on molecular sieve 3A follows a three-stage adsorption process. This three-stage adsorption process confirmed different water adsorption sites in molecular sieve crystals. In addition, the second adsorption stage is significantly affected by the K + /Na + molar ratio. In this stage, the equilibrium adsorption capacity at a given water vapor pressure increases as the K + /Na + molar ratio increases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗