Search NASA⌕ Search

SEARCH · Search NASA

Results for “engine CFD”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Development of an Optimized Gasoline Surrogate Formulation for PACE Experiments and Simulations

New powertrain solutions are needed to address societal challenges stemming in part from a transportation sector which relies heavily on combustion of conventional hydrocarbon fuels like gasoline. One way to advance new solutions is through predictive simulations. Predictive simulations can potentially limit the extent of experimental validations, reduce time spent in engineering design cycles, unlock new strategies for high efficiency combustion with power density, and minimize tailpipe emissions. However, the simulation tools currently available are either too computationally expensive, inadequate in their accuracy, or a combination of the two. A DOE-funded consortium of national laboratories called the Partnership to Advance Combustion Engines (PACE) seeks to address this gap by rapidly delivering new knowledge and tools which enable market-competitive powertrain solutions for light-duty vehicles. Approaches to modeling combustion in powertrain systems typically incorporate computational fluid dynamics (CFD) simulations with chemical kinetic models. These CFD simulations generally use surrogate fuels featuring a limited number of existing components in a reduced kinetic model to limit computational costs. Such surrogate fuels often sacrifice matching several or more combustion and physical properties of the target gasoline fuel. Validation of surrogate fuels is also often only pursued for a small subset of standard metrics such as research or motor octane number (RON, MON). The purpose of this project is to develop an optimal E10 gasoline surrogate fuel suitable for tasks across PACE to facilitate rapid common analysis and progression toward consortium goals. This project received contributions from tasks led by eight PACE principal investigators. The project funding here reflects all tasks under Pitz, whose tasks also contributed to additional PACE projects. Based on the optimal gasoline surrogate fuel composition, a reduced chemical kinetic model will be shared with PACE researchers, industry, and the broader combustion community.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Ensemble Kalman filter for data assimilation coupled with low-resolution computations techniques applied in fluid dynamics

This paper presents an innovative Reduced-order model (ROM) for merging experimental and simulation data using data assimilation (DA) to estimate the "True" state of a fluid dynamics system, leading to more accurate predictions. Our methodology introduces a novel approach by implementing the ensemble Kalman filter (EnKF) within a reduced-dimensional framework, grounded in a robust theoretical foundation and applied to fluid dynamics. To address the substantial computational demands of DA, the proposed ROM employs low-resolution (LR) techniques to drastically reduce computational costs. This innovative approach involves downsampling datasets for DA computations, followed by an advanced reconstruction technique based on low-cost singular value decomposition (lcSVD). The lcSVD method, a key innovation in this paper, has never been applied to DA before and offers a highly efficient way to enhance resolution with minimal computational resources. Our results demonstrate significant reductions in both computation time and RAM usage through these LR techniques without compromising the accuracy of the estimations. For instance, in a turbulent test case, for a data compression rate of 15.9, the LR approach can achieve a speed-up of 13.7 and a RAM compression of 90.9% while maintaining a low relative root mean square error (RRMSE) of 2.6%, compared to 0.8% in the high-resolution (HR) reference. Furthermore, we highlight the effectiveness of the EnKF in estimating and predicting the state of fluid flow systems based on limited observations and given low-fidelity numerical data. This paper highlights the potential of the proposed DA method in fluid dynamics applications, particularly for improving computational efficiency in CFD and related fields. Its ability to balance accuracy with low computational and memory costs makes it especially suitable for large-scale and real-time applications, such as environmental monitoring or engineering design. This method will be incorporated into ModelFLOWs-app.

Data Assimilation↗
DOE OSTI2024

Challenges in Rotor Aerodynamic Modeling for Non-Uniform Inflow Conditions

Within an international collaboration framework, the accuracy of rotor aerodynamic models used for design load calculations of wind turbines is being assessed. Where the use of high-fidelity computation fluid dynamics (CFD) and mid-fidelity free-vortex wake (FVW) models has become commonplace within the wind energy community, these still fail to meet the requirements in terms of execution time and computational cost needed for design load calculations. The fast but engineering fidelity blade-element/momentum (BEM) method can therefore still be considered the industry workhorse for design load simulations. At the same time, upscaling of wind turbine rotors makes inflow non-uniformities (e.g. shear, veer, turbulence) more important. The objective of this work is to assess model accuracy in non-uniform inflow conditions, which violate several BEM assumptions. Thereto a comparison in turbulent inflow has been executed including a wide variety of codes, focusing on the DanAero field measurements, where a 2.3-MW turbine was equipped with, among other sensors, a pressure measurement apparatus. The results indicate that, although average load patterns are in good agreement, this does not hold for the unsteady loads that drive fatigue damage and aero-elastic stability. A simplified comparison round in vertical shear was initiated to investigate the observed differences in a more controlled manner. A consistent offset in load amplitude was observed between CFD and free-vortex codes on the one hand and BEM-type codes on the other hand. To shed more light on the observations, dedicated efforts are ongoing to pinpoint the cause for these differences, in the end leading to guidelines for an improved BEM implementation.

17 WIND ENERGY↗
DOE OSTI2021

Demonstration and Validation of Additively Manufactured Intensified Device for Enhanced Carbon Capture

In FY 2018 and 2019, ORNL personnel designed, prototyped, characterized, and tested the enhancement of amine-based carbon capture using additively manufactured intensified devices, i.e., devices combining multiple thermodynamic and kinetic processes into one unit operation. State of the art scrubbing technology often employs inter-stage cooling which requires multiple stages of single-purpose unit operations, leading to larger equipment size, higher capital cost, and sometimes less than optimal operating conditions for each piece of equipment. It was proposed that integrating multiple thermodynamic and kinetic operations into one unit could mitigate or negate these drawbacks. Heat and mass transfer studies demonstrated that an additively manufactured intensified device could significantly improve capture efficiency at bench scale. This project is a continuation of FEAA130 and aims at (1) demonstration and validation of enhanced CO 2 capture with 3D printed intensified devices (i.e., mass exchanger packing with internal cooling channels), for low-aqueous-solvent (LAS) based capture at the ORNL-constructed laboratory-scale (4.5-feet tall) column, namely Column A, with built-in cooling and (2) computational scoping on the conceptual design of a flexible and modular larger column, namely Column B, at ORNL for further demonstration of enhanced capture for aqueous solvent. To execute this multi-faceted project scope, an integrated project team from ORNL’s Manufacturing Science Division is tasked with applying capabilities in computational fluid dynamics (CFD), additive manufacturing, and absorber-scale demonstration/validation experiments.

42 ENGINEERING↗
DOE OSTI2023

Numerical Optimization of Spray-Guided Spark Assistance for Cold Idle Operation in a Heavy-Duty Gasoline Compression Ignition Engine

This article describes the results of a response surface model (RSM)-based numerical optimization campaign for spray-guided spark assistance at cold operations in a heavy-duty gasoline compression ignition (GCI) engine. On the basis of an earlier work on spark-assisted GCI cold combustion, a space-filling design of experiments (DoE) method was first undertaken to investigate a multitude of hardware design variables and engine operating parameters. The main design variables included the number of injector nozzles, fuel split quantities and injection timings, and spark timing. The objective variables were engine combustion efficiency (ŋ c ), maximum pressure rise rate (MPRR), and engine-out nitrogen oxide (NO x ) emissions. A total of 150 design candidates were automatically generated using the Sobol sequence method provided by the commercial software package, CAESES. Then, closed-cycle computational fluid dynamic (CFD) spark-assisted GCI simulations under cold idling operations were performed. The outcomes from the CFD-DoE design campaign were utilized to construct high-fidelity RSMs that allowed for further design optimization of the spark plug- and fuel injector-related design variables, along with fuel injection strategy parameters. A merit function with respect to objective variables was formulated with an appropriate weight assignment on each objective variable. Finally, the best design candidate was identified from the RSM-based optimization process and further validated in the CFD analysis. The best design candidate showed the potential to significantly improve combustion efficiency (ŋ c > 90%) over the baseline at cold idle while satisfying MPRR and NOx emissions constraints (MPRR < 5 bar/CAD and NO x < 4.5 g/kWh).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Design Overview of a High-Pressure Helium Flow Visualization Apparatus for Blanket Cooling Studies

Cooling of the fusion blanket first wall remains a significant challenge given the adverse conditions of heat and particle flux encountered near the plasma. Helium emerges as an attractive cooling candidate because of its chemical and neutronic inertness and separability from hydrogenic species (e.g. tritium). Because of the low thermal mass of helium, optimization of these coolant channels is warranted to provide high heat transfer performance at low pumping costs. Increasingly, computational fluid dynamics (CFD) simulations are employed to model and optimize these flow channels, and accompanying experimental data are needed to validate the predictions of these models. To provide the aforementioned experimental data, a high-pressure helium flow visualization upgrade has been designed for the Helium Flow Loop Experiment facility. This apparatus was built to American Society of Mechanical Engineers boiler and pressure vessel standards to withstand operating pressure of 4 MPa and mated to high-pressure glass windows. Seedless flow visualization is performed via high-speed background oriented schlieren (BOS), with image correlation used for time-resolved two-dimensional velocimetry at frequencies in excess of 60 kHz. Rectangular flow channel test articles are additively manufactured via laser powder bed fusion and installed into this visualization apparatus, with one-sided heating supplied by resistive heaters. In conclusion, the chosen test geometries were informed by prior CFD simulations, and the helium flow structures observed via BOS (detachment, recirculation, etc.) will be used for the validation of these accompanying models, in support of the design and optimization of blanket cooling channel configurations.

Helium flow↗
DOE OSTI2024

Sensitivity Analyses of Natural Convection in the AP1000 Passive Containment Cooling System Following LBLOCA Using CFD

The AP1000 power plant has implemented multiple layers of passive safety systems to enhance reactor safety and ensure system integrity during postulated scenarios, such as loss of coolant accident (LOCA) and main steam line break (MSLB). Among these, the passive containment cooling system (PCCS) is a safety-related system designed to prevent the containment from exceeding the design limits of both pressure and temperature following a postulated design basis accident, by transferring heat from the steel containment vessel to the atmosphere. During LOCA, natural air convection plays a vital role in removing heat from the steel containment vessel to the atmosphere. This is integrated with the condensate film from the Passive Containment Cooling Water Storage Tank (PCCWST). Air natural convection is also the only heat removal mechanism after the dry-out of the PCCWST and during the loss of shutdown decay heat removal (LOSDHR) event. Therefore, it is essential to investigate the thermal hydraulics behavior of the containment under transient conditions to ensure its safety and integrity. This paper presents a simplified CFD/ANSYS FLUENT model developed to analyze the impact of different parameters, such as air relative humidity, air temperature, and steel containment temperature, on the natural convection capability to remove the decay heat following LOCA. The model aims to examine the thermal behavior of the containment and provides a comprehensive understanding of the system's natural convection capability during postulated accidents. The results obtained from the model can be used to improve the safety and integrity of the containment system and provide valuable insights for future research in the field.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network

The study of solute transport in porous media is of interest in many chemical engineering systems. Some example applications include packed bed catalytic reactors, filtration devices, and batteries. The pore scale modeling of these systems is time consuming and may require large computing resources, for this reason computational fluid dynamics (CFD) simulations are not practical if a large number of simulations is required, like in multiscale modeling, where a model at a large scale calls for pore scale simulations. It has been shown that neural networks can be trained with a dataset of flow simulations and then predict fields orders of magnitude faster, and with less computational resources, in new domains. However, it is crucial to provide the neural network with an effective description of the domain and the undergoing operating conditions to be able to train models that generalize accurately in unseen samples. Therefore, research is needed to employ neural networks in new complex systems. The appropriate training of a network for predicting coupled flow and solute transport processes is an outstanding problem due to the complex interplay between geometry and operating conditions. In this work, we train a multi scale convolutional neural network (MSNet) with a diverse dataset of simulations of transport and chemical reaction in porous media to predict the local concentration fields in images of porous media. Our dataset contains a wide diversity of sphere pack arrangements under different operating conditions (Péclet and Reynolds numbers). Further, we train a robust model by employing different input descriptors that represent the medium and the different operating conditions of each system. Our trained model is able to provide nearly instantaneous predictions, compared to around twenty hours of the CFD workflow, with less than 3.5% error on new geometries and transport conditions. Thus the model could be easily integrated in a multiscale workflow where fast response is needed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Efficient GPU Implementation of Automatic Differentiation for Computational Fluid Dynamics

Many scientific and engineering applications require repeated calculations of derivatives of output functions with respect to input parameters. Automatic Differentiation (AD) is a method that automates derivative calculations and can significantly speed up code development. In Computational Fluid Dynamics (CFD), derivatives of flux functions with respect to state variables (Jacobian) are needed for efficient solutions of the nonlinear governing equations. AD of flux functions on graphics processing units (GPUs) is challenging as flux computations involve many intermediate variables that create high register pressure and require significant memory traffic because of the need to store the derivatives. This paper presents a forward-mode AD method based on multivariate dual numbers that addresses these challenges and simultaneously reduces the floating-point operation count. The dimension of the multivariate dual numbers is optimized for performance. The flux computations are restructured to minimize the number of temporary variables and reduce register pressure. For effective utilization of memory bandwidth, shared memory is used to store the local flux Jacobian. This AD implementation is compared with several other Jacobian implementations on an NVIDIA V100 GPU (V100). For three-dimensional perfect-gas compressible-flow equations implemented in a practical CFD code, the AD implementation of a flux Jacobian based on multivariate dual numbers of dimension 5 outperforms all other GPU AD implementations on V100. Its performance is comparable with the optimized hand-differentiated version. Finally, the implementation achieves 75% of the peak floating-point throughput and 61 % of the peak global device memory bandwidth usage.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Advanced Light-Duty Spark Ignition Engine Research: Co-Optimization of Fuels and Engines and Partnership to Advance Combustion Engines (FY2020 Annual Progress Report)

This report covers recent progress on research tasks that support both the Co-Optimization of Fuels and Engines (Co-Optima) initiative and the Partnership to Advance Combustion Engines (PACE) consortium. The Co-Optima tasks further the science-base needed by industry stakeholders to co-evolve the next generation of highly efficient direct injection spark ignition (DISI) engines and new gasoline-type fuels. The research emphasis is on fuel effects on multimode spark ignition (SI) engine operation, which uses traditional non-dilute stoichiometric operation for peak load and power but reverts to lean operation at lower loads to provide higher fuel economy. This work focuses on determining desirable fuel specifications in terms of well-established metrics like research octane number (RON) and motor octane number, but it also involves the assessment of new fuel metrics, including fuel sooting propensity and phi-sensitivity. The PACE task supports the development of predictive computational fluid dynamics (CFD) modeling, which promises to unlock new strategies for high-efficiency combustion while minimizing tailpipe emissions. Here, the primary fuel is a regular E10 gasoline (i.e., a regular gasoline blend containing 10% ethanol), and focus is on fuel-spray dynamics and soot emissions. Soot-formation pathways are studied to determine how the pathways change with injection strategies and the thermal state of the engine (i.e., cold-starting vs. fully warmed-up operation). This PACE task also contributed to the development of an optimal E10 gasoline surrogate fuel, as reported in detail elsewhere

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

BlueCRAB Domain Overlapping Coupling: Theory & Verification

Coupling low- and high-fidelity codes is a useful way to model complex engineering systems. Thanks to the high-fidelity code, complex phenomena can be resolved in areas of the system where this is required, yet the efficiency of the lower-fidelity code is still retained in modeling the rest of the system. This document details the theory and implementation of coupling two different thermal-hydraulic codes: the system thermal-hydraulics (STH) code System Analysis Module (SAM) and the coarse-mesh computational fluid dynamics (CFD) code Pronghorn. Both applications are included in the Comprehensive Reactor Analysis Bundle (BlueCRAB) code suite and are based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. The domain overlapping (DO) coupling approach was adopted, as it offers proven advantages over more conventional domain decomposition methods. In the DO coupling, SAM provides Pronghorn with boundary conditions that depend on the system-level simulation of the entire plant. In return, the overlapping coupled SAM components–termed “surrogate components”–receive friction factors and source terms computed online based on the Pronghorn simulation. The framework is designed to be generic and enable coupling regardless of geometry and the number of inlet/outlet boundaries in the DO coupled domain. The developed method leads to consistent pressure drops, enthalpies, and scalar concentrations when comparing the coupled SAM and Pronghorn simulations. This document presents the DO coupling approach, along with two verification and two demonstration cases. The proposed problems explore different physical aspects relevant to nuclear reactor analysis, including buoyancy-driven flows, complex flow patterns, and multiple inlets and outlets. Periodically, new versions of this “BlueCRAB Domain Overlapping Coupling: Theory & Verification” report will be issued to reflect future developments and verification tests.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Data-driven selection of stiff chemistry ODE solver in operator-splitting schemes

Most computational fluid dynamics simulations of practical combustion applications employ operator-splitting schemes, where chemistry and transport are separated and integrated with distinct numerical methods. The changes in composition due to chemistry are evaluated by solving ordinary differential equations (ODE) in each cell of the computational domain, which typically dominates the computational cost when detailed chemistry is considered. In this work, a data-driven approach for the selection of chemistry ODE solvers in operator-splitting schemes is presented. Neural networks are used to predict the ODE solvers CPU times and errors for a given thermochemical state. This allows the selection of an optimal ODE solver on a cell-by-cell, timestep-by-timestep basis. The models are trained using a wide set of thermochemical states generated through partially-stirred reactors and flames simulations. The methodology is validated by quantifying the prediction errors, the classification accuracy, and the computational speedup. The model predicts the optimal ODE solver for 70 to 95% of the validation cases and decreases the computional cost by a factor of 3 or more. The generalizability of the methodology to different chemical mechanisms and different fuels is assessed and it is shown that the model’s performance is only slightly degraded and its applicability is significantly enhanced if the inputs to the neural networks are restricted to a small set of thermochemical state variables present in most chemical mechanisms. In conclusion, the models are used in an homogeneous reactor case and a multi-dimensional CFD simulation of a diesel spray at high pressure where a speedup of more than 3 is achieved.

42 ENGINEERING↗
DOE OSTI2020

A multi-dimensional dataset for two-phase instability in low pressure natural circulation based on direct transient local measurement

Thermal hydraulic instability can challenge the control and the safety of nuclear reactors. This experimental research captures and investigates the instability in a low-pressure (1–10 atmospheres) natural circulation loop with an adiabatic chimney installed above the heated section. Direct transient local measurement of two-phase flow is performed at multiple axial locations along the flow channel with thermocouples and conductivity probes traversed by customized high-precision mechanisms. Based on the periodicity of oscillations, a post-processing method is proposed to align the data captured non-simultaneously to reconstruct the instability behavior in one representative period. The averaging operation over multiple oscillation cycles is performed to reduce the random errors. Cyclic phenomena are clearly demonstrated by the post-processed data, and rapid void fraction change induced by the flashing is observed to be significant in the driving mechanism for low pressure oscillations. Parametric effects of inlet subcooling, heat flux, inlet flow restriction and system pressure are studied within the experiment range, and qualitative results are explained and compared with existing literature. The ratio between the oscillation period and the travelling time of the fluid particle through the test section is compared with the theoretical value for the density wave oscillation. The new dataset contains directly measured transient multi-dimensional two-phase data along the flow channel for natural circulation under density wave oscillations. The generated dataset is valuable for the validation of system analysis codes and CFD codes in the simulation of natural circulation phenomena and dynamic two-phase flow.

42 ENGINEERING↗
DOE OSTI2020

Smart Proxy Modeling Application of Artificial Intelligence & Machine Learning in Computational Fluid Dynamics

Smart proxy technology leverages the art of artificial intelligence and machine learning in order to build accurate and very fast proxy models for highly complex numerical simulation models. In this project, smart proxy technology is used to replicate the results of a highly complex single phase CFD simulation with reasonable degree of accuracy while reducing the computational cost associated with such CFD simulations. The CFD model under study simulates the combustion of natural gas under various conditions such as varying natural gas composition and flow rate, inlet air flow rate and temperature, and outlet pressure in a High-Pressure Combustion Facility (B6 Combustor) with more than 4 million simulation cells. Only eight CFD simulation runs were used to create a smart proxy model that replicates the detail distribution of Pressure, Temperature, Nitrogen, Oxygen and Carbon-dioxide concentration in the CFD simulation model in seconds with less than 10% error.

03 NATURAL GAS↗
DOE OSTI2026

Development of a coupled experimental–computational approach for engineering optimization of spout-fluidized bed particle coating systems

The design of spout-fluidized bed (SFB) coating systems for nuclear particle fuels typically relies on trial-and-error processes, comprising iterative and time-consuming coating deposition experiments and post-deposition characterization. At an engineering scale, this approach to guided SFB system design is inefficient, highlighting the need for streamlined experimental methodologies which can correlate fluidization conditions to downstream coating outcomes. In this study, we combine time-resolved particle image velocimetry (PIV) with CFD–DEM simulations to benchmark hydrodynamic behavior in a 3D spout-fluidized bed. By exploiting easily accessible optical measurements of particle motion at the bed wall and within the spouting region, we obtain quantitative velocity fields that can be directly compared with model predictions of the occluded bed region, without resorting to complex imaging and characterization techniques such as X-ray or magnetic resonance tomography. Experimental benchmarking reveals strong agreement between CFD–DEM and PIV in the spout and annulus regions, while discrepancies near the wall highlight areas for future model development. Here, the proposed integrated experimental–numerical framework will enable a direct connection between measured variables and numerically predicted fluidization performance of dense, surrogate nuclear particle fuel feedstock such that experimental SFB component design can be rapidly evaluated, informing design decisions for nozzle geometry and operating conditions. Future work will extend this framework by correlating quantified fluidization metrics across nozzle geometries and operating conditions with the resulting coating morphology, microstructure, and uniformity. Establishing these correlations will enable predictive links between hydrodynamic performance and coating quality, providing a rational, scalable basis for optimizing SFB design prior to coating deposition.

CFD/DEM↗
DOE OSTI2026

OC7 phase I: CFD investigation of viscous forces on rectangular members of semisubmersibles

In Phase I of the Offshore Code Comparison Collaboration 7 (OC7) project, we aim to address key questions related to the modeling of viscous hydrodynamic loads on floating platforms. Here, this paper presents results from a collaborative effort within Phase I of the OC7 project. We investigate viscous forces acting on the rectangular members of semisubmersible platforms using high-fidelity computational fluid dynamics (CFD) simulations. The parametric CFD analyses include two-and-a-half-dimensional (2.5D) simulations of pontoon sections of two different semisubmersible platform designs (the VolturnUS-S and the INO WINDMOOR) and full three-dimensional (3D) structure simulations of the VolturnUS-S platform. We extract both the added mass and drag coefficients that are typically used in engineering-level hydrodynamic models from the 2.5D forced oscillation simulations with various Keulegan-Carpenter numbers and compare them to the coefficients from the 3D forced oscillation simulations. We observe consistent variation of the coefficients along the pontoon legs due to 3D platform and free-surface effects. With the breadth of the simulations and component-level detailed analyses, this work establishes a useful database and several empirical formulas for selecting hydrodynamic coefficients when modeling semisubmersible platforms in engineering tools.

13 HYDRO ENERGY↗
DOE OSTI2020

Involute Working Group – Validation of CFD Turbulence Models for Steady-State Safety Analysis

There are three research reactors in the world having fuel plates curved as circle-involute (a spiral generated around a circle): The Oak Ridge National Laboratory (ORNL) High Flux Isotope Reactor (HFIR) located in Tennessee, U.S.A. [ORNL, 2018]; The Laue-Langevin Institute (ILL) High Flux Reactor (RHF) located in Grenoble, France [ILL, 2018]; and, The Technical University of Munich (TUM) Research Neutron Source Heinz Maier-Leibnitz (FRM II) located in Garching, Germany [TUM, 2018]. All three reactors are currently using Highly Enriched Uranium (HEU, 235U/U ≥ 20 wt. %) as fuel and all three are actively engaged in activities to convert to Low Enriched Uranium (LEU, 235U/U < 20 wt. %) fuel. More information on these reactors can be found in Section two. For various reasons, these reactors have expressed interest in using Computational Fluid Dynamics (CFD) tools to perform their Steady-State Thermal-Hydraulic (SSTH) safety calculations. Using CFD tools for SSTH analysis represents generally a significant departure from traditional methods and requires thorough verification and validation (V&V) to be accepted by regulators. This is why Argonne National Laboratory (ANL) and the involute reactors formed an informal group to help each other in this endeavor. This so-called Involute Working Group (IWG) aims at supporting the qualification of CFD tools for SSTH safety calculations. Activities include benchmarking, code-to-code comparison, Verification and Validation (V&V) as well as technical support. More information on the IWG and the reasons why involute-plate reactors are interested in CFD can be found in Section three. The present study aims at testing three turbulence models commonly implemented in commercial software (namely K-Epsilon, K-Omega and Reynolds-Stress) and assessing their relevance for involute-plate reactor application. An experiment performed by Gambill and Bundy in 1961 was performed to measure heat transfer coefficients in conditions prototypic of the HFIR reactor [Gambill, 1961]. More details on this experiment are provided in Section four. Based on the information available, CFD models of this experiment have been created and the experimental results have been compared to the ones obtained with these CFD models. CFD models and the results of this comparison are provided in Section five.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗