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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Latest developments in the MOOSE fluid properties module

The fluid properties module in MOOSE serves a variety of fluid simulation applications based on MOOSE, including the MOOSE Navier Stokes module~\cite{moose_ns}, Pronghorn~\cite{pgh}, SAM~\cite{sam}, the MOOSE thermal hydraulics module, RELAP-7~\cite{relap7} and subchannel~\cite{subchannel}. It is used for coarse mesh multi-dimensional thermal-hydraulics~\cite{pgh}, 1D systems analysis~\cite{sam,relap7} in nuclear reactor analysis, and porous flow simulations~\cite{porous} for underground gas storage and water seepage. The use of consistent fluid properties across fluid flow applications facilitates coupled simulations~\cite{anl_sam_pgh}. The module offers a consistent set of interfaces to implement to create a new fluid property. There are numerous fluid properties of interest in the entirety of all fields of fluid flow simulations, and this is exacerbated by the use of different variable sets depending on the compressibility of the fluid. For single-phase fluids, the following variable sets may be used to compute fluid properties: (pressure, temperature) and (specific volume, specific internal energy). Some properties may also be computed using the (pressure, density) or the (specific volume, specific enthalpy) variable sets. In order to reduce the challenge of adding a new fluid property, properties may be implemented partially.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

The MOOSE fluid properties module

The fluid properties module in MOOSE~\cite{lindsay2022moose} serves a variety of fluid simulation applications based on MOOSE, including the MOOSE Navier Stokes module~\cite{moose_ns}, Pronghorn~\cite{pgh}, the MOOSE thermal hydraulics module, SAM~\cite{sam}, RELAP-7~\cite{relap7}, Sockeye~\cite{sockeye}, Pronghorn-subchannel~\cite{subchannel} and the MOOSE porous flow module~\cite{porous}. These applications are used to solve coarse mesh multi-dimensional thermal-hydraulics~\cite{pgh}, 1D systems analysis~\cite{sam,relap7} in nuclear reactor analysis, heat pipe modeling~\cite{sockeye} and porous flow simulations~\cite{porous} for underground gas storage and water seepage. The use of consistent fluid properties across fluid flow applications facilitates coupled flow simulations~\cite{anl_sam_pgh,osti_1889653}. Each application has historically driven the implementation of several fluid properties, which were later extended to be compatible with other applications. The unique diversity of applications of the module, due to its presence in MOOSE, has driven its expansion to new fluids, such as advanced nuclear reactor coolants and, more recently, arbitrary functions or tables-based property definitions, as detailed in section~\ref{content}, as well as numerous thermophysical properties and variable sets, as detailed in subsection~\ref{sec:prop}. The need for different discretizations of flow equations based on the compressibility of the fluid has motivated support for both primitive (pressure- and temperature-based) and conservative (internal energy- and specific volume-based) flow variables in the module. Thermodynamic relations are used to convert between these two formulations, as needed. The module serves a dual purpose of providing fluid properties for direct use in flow simulations and facilitating the implementation of user-specific fluid properties. Contributions of new properties for existing fluids or new fluids are strongly encouraged.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

The MOOSE fluid properties module

The Fluid Properties module within the Multiphysics Object-Oriented Simulation Environment (MOOSE) is used to compute fluid properties for numerous applications, ranging from nuclear reactor thermal hydraulics to geothermal energy. Those applications drove the development of the module to enable numerous different fluid equations of states, property lookups with primitive and conserved flow variable to cater to pressure and density-driven solvers, and an object-oriented design facilitating expansion and maintenance. Each fluid property is implemented in its own class but inherits capabilities such as automatic differentiation, automated out-of-bounds handling or variable conversion capabilities. Here, this paper presents the module, its design, its user and developer interface, its content in terms of fluids and properties, and several of its applications showing its major role in the MOOSE simulation ecosystem.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Tabulated Fluid Properties Research Report

The Multiphysics Object-Oriented Simulation Environment (MOOSE) enables a wide range of advanced nuclear reactor simulations.[6] Under the guidance of MOOSE’s Thermal Hydraulics Team,I worked to expand the capabilities of Tabulated Fluid Properties (TFP) in the fluid properties module. The fluid properties module allows the user to determine a variety of fluid properties by interpolating points between tabulated data. I implemented the ability to use bilinear interpolation instead of bicubic interpolation for interpolating tabulated data. I also changed the method of variable set inversions to use a 2-dimensional Newton’s Method utility that I created. Variable set inversions are often done from (v,e) to (p,T), where v is specific volume, e is specific internal energy, p is pressure and T is temperature. New routines have also been added into TFP such that it can be used with more applications, such as the Navier Stokes and Thermal Hydraulics modules in MOOSE for Pronghorn[5] and RELAP-7[1] respectively. This work was spurred by interest from NASA in testing a Nuclear Thermal Propulsion (NTP) engine system. NTP engines have drastically different fluid properties throughout the engine and Tabulated Fluid Properties provides the flexibility needed to properly simulate and test these engines.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Consistent Fluid Property Evaluation in RELAP5-3D

Previous investigations have developed and tested a methodology to improve the consistency between used to determine the fluid property derivatives used in the numerical solution scheme and the solution scheme itself. Within a bounding rectangle of the fluid property table for a fluid state point, linear interpolants for both fluid property values and their derivatives are calculated. A weighted average between these interpolants the analytical approximations of the fluid properties and derivatives is formed with a weighting factor between 0.0 and 1.0. This average has shown potential to reduce mass error relating to the fluid properties in RELAP5-3D. This linear interpolation and weighted averaging methodology has been implemented in RELAP5-3D with two additional options that adjust the weighting factor in the average according to the location within the bounding rectangle. The first calculates the weighting factor based only on the position between the left and right internal energy endpoints. The second also factors the position between the upper and lower pressure grid points into the calculation of the weighting factor. The new coding passes verification testing and expanded testing of the various options. Graphs are presented from three kinds of test cases, a two volume basic model, Edward’s O’Brien blowdown, and typical PWR. Some trends of the effect of crossing fluid property table pressure and internal energy grid line are identified and discussed for the simple and Edwards test cases. Preliminary results show that it the consistent fluid methodology can reduce mass error in simple cases, but in more complex input models, there is no clear best.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Fluid Properties for MOOSE

The Multiphysics Object-Oriented Simulation Environment (MOOSE) enables a wide range of advanced nuclear reactor simulations. Under the guidance of MOOSE's Finite Volume Team, we worked on the fluid properties module. Significant contributions include enabling Tabulated Fluid Properties (TFP) for systems thermal hydraulics analysis, Temperature and Pressure functionalized Fluid Properties, and Lead & Lead-Bismuth properties. Along with improving the capabilities of the fluid properties module, we also improved the documentation to allow for future users and developers to understand how the module works.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

ECO2N V. 2.0: A New TOUGH2 Fluid Property Module for Mixtures of Water, NaCl, and CO 2

ECO2N V2.0 is a fluid property module for the TOUGH2 simulator (Version 2.1) that was designed for applications to geologic sequestration of CO 2 in saline aquifers and enhanced geothermal reservoirs. ECO2N V2.0 is an enhanced version of the previous ECO2N V1.0 module (Pruess, 2005). It expands the temperature range up to about 300°C whereas V1.0 can only be used for temperatures below about 110°C. V2.0 includes a comprehensive description of the thermodynamics and thermophysical properties of H 2 O - NaCl -CO 2 mixtures, that reproduces fluid properties largely within experimental error for the temperature, pressure and salinity conditions of interest (10 °C < T < 300 °C; P < 600 bar; salinity up to halite saturation). This includes density, viscosity, and specific enthalpy of fluid phases as functions of temperature, pressure, and composition, as well as partitioning of mass components H 2 O, NaCl and CO 2 among the different phases. In particular, V2.0 accounts for the effects of water on the thermophysical properties of the CO 2 -rich phase, which was ignored in V1.0, using a model consistent with the solubility models developed by Spycher and Pruess (2005, 2010). In terms of solubility models, V2.0 uses the same model for partitioning of mass components among the different phases (Spycher and Pruess, 2005) as V1.0 for the low temperature range (<99°C) but uses a new model (Spycher and Pruess, 2010) for the high temperature range (>109°C). In the transition range (99-109°C), a smooth interpolation is applied to estimate the partitioning as a function of the temperature. Flow processes can be modeled isothermally or non-isothermally, and phase conditions represented may include a single (aqueous or CO 2 -rich) phase, as well as two-phase mixtures. Fluid phases may appear or disappear in the course of a simulation, and solid salt may precipitate or dissolve. This report gives technical specifications of ECO2N V2.0 and includes instructions for preparing input data

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

ECO2N V2.0: A TOUGH2 fluid property module for modeling CO 2 -H 2 O-NACL systems to elevated temperatures of up to 300°C

Here, we have improved ECO2N, the TOUGH2 fluid property module of the CO 2 -H 2 O-NaCl system. The major enhancements include: (i) the upper temperature limit is increased from 110 to about 300°C; (ii) the thermophysical properties of the CO 2 -rich phase are more accurately calculated as a non-ideal mixture of CO 2 and H 2 O; (iii) the approach to calculate the specific enthalpy of dissolved CO 2 has been improved to make the code more robust in modeling phase transitions under non-isothermal conditions; and (iv) more sophisticated models for effective heat conductivity of formations saturated with supercritical CO 2 have been provided. The new module includes a comprehensive description of the thermodynamic and thermophysical properties of H 2 O–NaCl–CO 2 mixtures, that reproduces fluid properties largely within experimental error for the temperature, pressure and salinity conditions 10°C < T < 300°C, P < 600 bar, and salinity up to halite saturation. This includes density, viscosity, and specific enthalpy of fluid phases as functions of temperature, pressure, and composition, as well as partitioning of mass components H 2 O, NaCl and CO 2 among the different phases. ECO2N with the TOUGH2 reservoir simulator can be applied to a wide range of problems in geologic sequestration of CO 2 in saline aquifers, and in enhanced geothermal reservoirs. Finally, ECO2N can describe both sub- and supercritical states of CO 2 , but applications that involve subcritical conditions are limited to systems in which there is no change of phase between liquid and gaseous CO 2 , and in which no mixtures of liquid and gaseous CO 2 occur.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

System and method for measuring fluid properties using magnetic field techniques via magnetic tracer

A method and apparatus for determining a property of a fluid in a vessel. The method uses magnetic tracer particles and an externally applied magnetic field which orients the particles. When the fluid moves, it changes the orientation of the tracer particles, thus changing the magnetic fields. These changes are detected by external magnetic field sensors. By using mathematical models, the property of the fluid in the vessel is determined from the detected magnetic field. In this manner, fluid vorticity, velocity, strain and stress may be estimated.

42 ENGINEERING↗
DOE OSTI2018

Effective Elastic and Neutron Capture Cross Section Calculations Corresponding to Simulated Fluid Properties from CO2 Push-Pull Simulations

The submission contains a .xls files consisting of 10 excel sheets, which contain combined list of pressure, saturation, salinity, temperature profiles from the simulation of CO2 push-pull using Brady reservoir model and the corresponding effective compressional and shear velocity, bulk density, and fluid and time-lapse neutron capture cross section profiles of rock at times 0 day (baseline) through 14 days. First 9 sheets (each named after the corresponding CO2 push-pull simulation time) contains simulated pressure, saturation, temperature, salinity profiles and the corresponding effective elastic and neutron capture cross section profiles of rock matrix at the time of CO2 injection. Each sheet contains two sets of effective compressional velocity profiles of the rock, one based on Gassmann and the other based on Patchy saturation model. Effective neutron capture cross section calculations are done using a proprietary neutron cross-section simulator (SNUPAR) whereas for the thermodynamic properties of CO2 and bulk density of rock matrix filled with fluid, a standalone fluid substitution tool by Schlumberger is used. Last sheet in the file contains the bulk modulus of solid rock, which is inverted from the rock properties (porosity, sound speed etc) based on Gassmann model. Bulk modulus of solid rock in turn is used in the fluid substitution.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Influence of turbulence and thermophysical fluid properties on cavitation erosion predictions in channel flow geometries

Cavitation and cavitation-induced erosion have been observed in fuel injectors in regions of high acceleration and low pressure. Although these phenomena can have a large influence on the performance and lifetime of injector hardware, questions still remain on how these physics should be accurately and efficiently represented within a computational fluid dynamics model. While several studies have focused on the validation of cavitation predictions within canonical and realistic injector geometries, it is not well documented what influence the numerical and physical parameters selected to represent turbulence and phase change will have on the predictions for cavitation erosion propensity and severity. In this work, a range of numerical and physical parameters are evaluated within the mixture modeling approach in CONVERGE to understand their influence on predictions of cavitation, condensation and erosion. Particular attention is paid to grid resolution, turbulence model and near-wall treatment, fuel surrogate properties, and non-condensable gas content. Assessment of cavitation predictions are conducted through comparison of measured and predicted mass flow rates and cavitation probability distributions for flow through a channel with a sharp inlet. Predictions for hydrodynamic impact loading and cavitation erosion are compared with the experimentally measured incubation period and critical site for erosion. Based on these findings, recommendations are provided for modeling turbulent cavitating flows, using the single fluid mixture modeling approach, to improve predictions for cavitation-induced erosion. In particular, to capture the fluid dynamic phenomena characterizing cavitation cloud formation, development and shedding, a Large Eddy simulation with grid resolution as fine as 2.50 μm is recommended. The assumed concentration of non-condensable gas content is observed to have a strong influence on the predicted cavitation erosion severity, which motivates the need for dissolved gas concentration measurements for future cavitation erosion experimental studies. Using the best practices established in this work, good agreement is achieved between the measured and predicted cavitation parameters, as well as the critical site for cavitation-induced erosion.

Magnotti, Gina Maureen↗
DOE OSTI2016

Fouling resistance prediction using artificial neural network nonlinear auto-regressive with exogenous input model based on operating conditions and fluid properties correlations

Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO{sub 2} emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Implementation of High Temperature and Pressure fluid property interpolation tables

This document describes the use of module property_interpolate by John Doherty (Doherty,2006). The document “Fast Lookup of CO 2 Properties” describes the numerical details associated with lookup table. Rajeh Pawar modified property_interpolate for easier implementation in the FEHM code. George Zyvoloski modified the package to produce tables for water and air at very high temperatures and pressures. He also modified the auxiliary codes(drivers) to use newer datasets from the National Institute of Standards and Technology. We note here that within the files associated with FEHM there three property_interpolate modules. They are interpolate_2a.f90 (water), interpolate_2b.f90 (air) , and interpolate_2c.f90(CO 2 ). The interpolate_2c module is different than the earlier module produced by Rajeh Pawar in that it includes high temperature and pressure data. While FEHM has a number of fluid physics modules, this document will describe only the software and algorithms associated with water, water vapor, and heat (WH).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Combustion Kinetics Model Development & Fluid Property Experimental Investigation For Improved Design Of Supercritical CO 2 Power Cycle Components

Supercritical carbon dioxide (sCO 2 ) cycles are being investigated for the future of power generation and will contribute to a carbon-neutral future to combat the effects of climate change. These direct-fired closed cycles will produce power without adding significant pollutants to the atmosphere. For such cycles to be efficient, they will need to operate at significantly higher pressures (e.g., 300 atm for Allam Cycle) than existing systems (typically less than 40 atm). There is limited knowledge on combustion at these high pressures and with a high dilution of carbon dioxide. Also, various experimental and computational investigations and model developments have been performed by the University of Central Florida (UCF), Embry-Riddle Aeronautical University (ERAU), and Stanford University (S.U.) to improve the current knowledge base and support the design and development of sCO 2 combustors. This project's technical aspects include chemical kinetics development, fundamental sCO 2 combustion, combustion model and sub-model development, supercritical fluid injection characterization, and heat transfer characterization.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Sodium Fluid Properties

This software is used to compute numerous properties for the liquid and vapor phases of sodium.

Andrs, David↗
DOE OSTI2023

Potassium Fluid Properties

This software is used to compute numerous properties for the liquid and vapor phases of potassium.

Andrs, David↗