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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Local-Scale Simulations of Nucleate Boiling on Micrometer Featured Surfaces: Preprint

A high-fidelity computational fluid dynamics (CFD)-based model for bubble nucleation of the refrigerant HFE7100 on micrometer-featured surfaces is presented in this work. The single-fluid incompressible Navier-Stokes equations, along with energy transport and natural convection effects are solved on a featured surface resolved grid. An a priori cavity detection method is employed to convert raw profilometer data of a surface into well-defined cavities. The cavity information and surface morphology are represented in the CFD model by geometric mesh deformations. Surface morphology is observed to initiate buoyancy-driven convection in the liquid phase, which in turn results in faster nucleation of cavities. Simulations pertaining to a generic rough surface show a trend where smaller size cavities nucleate with higher wall superheat. This local-scale model will serve as a self-consistent connection to larger device scale continuum models where local feature representation is not possible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A ROBUST MECHANISTIC APPROACH TO PREDICTION OF DEPARTURE FROM NUCLEATE BOILING

departure from nucleate boiling (DNB) sets a regulatory limit for licensing pressurized water reactors (PWRs). Under such conditions, the heated surface is permanently blanketed by a vapor film, leading to a sharp deterioration of the heat transfer coefficient at the heater/coolant interface and an abrupt temperature rise. Unfortunately, the path for an accurate, robust prediction of DNB has been elusive due to lack of consensus on its triggering mechanism. This work reviews existing physics-driven modeling tools in the first place. An evolutionary channel-scale mechanistic model that leverages key assumptions in the relatively well-accepted mechanisms of liquid sublayer dryout and near-wall bubble crowding is then proposed. Detailed validation of the proposed model has demonstrated its improved predictive capabilities over previous data/physics-driven models with regard to an extensive DNB-specific CHF test matrix covering a wide range of flow conditions. The unique feature of the proposed model lies in its ability to predict DNB for different heater geometries (including round tube, annulus, and rod bundle), which is essential in deciphering fuel performance metrics from different facilities and reactor types. The proposed model will be further implemented in fuel performance codes to help improve modeling of transient DNB scenarios such as during a reactivity-initiated accident (RIA).

Zhao, Xingang↗
DOE OSTI2016

Experimental investigation on the phenomena around the onset nucleate boiling during the impacting of a droplet on the hot surface

Onset of nucleate boiling of a droplet when impacted onto hot surface was investigated. Three kinds of surfaces, normal stainless steel (NSS), stainless steel with TiO{sub 2} coating (UVN), and stainless steel with TiO{sub 2} coating and radiated by ultraviolet ray were employed to examine the effect of wettability. The droplet size was 2.4 mm diameter, and dropped under different We number. The image is generated by high speed camera with the frame speed of 1000 fps. The boiling conditions are identified as natural convection, nucleate boiling, critical heat flux, transition, and film boiling. In the present report, the discussion will be focused on the beginning of nucleate boiling on the droplet. Nucleate boiling occurs when bubbles are generated. These bubbles are probably caused by nucleation on the impurities within the liquid rather than at nucleation sites on the heated surface because the bubbles appear to be in the bulk of the liquid instead of at the liquid-solid interface. In addition, the smaller the contact angle, the fastest the boiling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Progress Toward Simulating Departure from Nucleate Boiling at High-Pressure Applications with Selected Wall Boiling Closures

Recently, a Eulerian-based two-fluid computational fluid dynamics (CFD) framework with a wall heat flux partitioning approach has been intensively investigated for departure from nucleate boiling (DNB) simulation under the U.S. Department of Energy–funded Consortium for Advanced Simulation of Light Water Reactors (CASL) program. Understanding of the DNB characteristics over a range of pressurized water reactor–like operating conditions and accurate prediction of boiling crisis in the nuclear power system have been grand challenges because of the large impact of DNB on reactor safety and operational economics. The ultimate goal of this task in the CASL program is to introduce a robust multiphase CFD–based DNB modeling framework that is capable of characterizing an entire boiling history in which the wall boiling mode experiences the following through multiple stages of heat transfer mode: (1) single-phase convective heat transfer, (2) nucleate boiling heat transfer, and (3) identification of the departure of nucleate boiling. To validate the CASL boiling model, we have benchmarked simulated DNB over three different flow channel configurations (pipe flow, 5 × 5 fuel bundle with mixing vane tests, and 5 × 5 fuel bundle without mixing vane tests) against experimental measurements, and the validation result with open literature is reported. The DNB detection criteria in the simulation are checked by monitoring the peak wall temperature, wall dryout factor, and net energy balance. In addition to the DNB performance test, some preliminary sensitivity results on closure model selection are reported to address the prediction capability of local void profile against measurements. The boiling simulation tested in this study exhibits a maximum deviation of 24% from the measured DNB value in a high-pressure (i.e., 138 bars) subcooled pipe flow test. The ranges of operating conditions are as follows: 1650 to 2650 kg/m 2 ·s for mass flux and 8.5 to 96 K for subcooled inlet temperature. The deviation is even reduced to 7% when the subcooled temperature is less than 40 K. Besides accuracy, base practice guidelines for DNB detection criteria are tested by monitoring three simulation variables: (1) maximum wall temperature, (2) wall dryout factor (i.e., K-value), and (3) energy balance. Numerical robustness of DNB simulation is largely achieved in most of the validation test except for a few high subcooled test cases.

42 ENGINEERING↗
DOE OSTI2024

The Onset of Nucleate Boiling Prediction in a Mini Channel with the Eulerian Multiphase Flow

The prediction of boiling incipience is a critical issue for a reactor. Vapor lock causes operating instability and undesirable temperature rise, especially for small channels. This study uses the Eulerian multiphase flow boiling model to predict the onset of nucleate boiling in a millimeter scale rectangular channel. Two wall boiling models, namely the RPI (Rensselaer Polytechnic Institute) and the non-equilibrium sub cooled model are applied. Computational fluid dynamics is employed for this study to investigate the impact of operating conditions like liquid flow rate, operating pressure, heat flux, velocity profile, turbulence model, and inlet liquid temperature. Here, the effect of fundamental boiling parameters on the surface wall temperature and vapor volume fraction is also studied. The parameters under consideration are the bubble departure diameter, bubble departure frequency, nucleate site density, quenching time period, and interface heat transfer coefficients. A series of CFD calculations is conducted by varying the considered variables systematically in a wide range of flow conditions covering laminar, transition, and turbulent flows. The sub-component heat fluxes like the liquid convective, the vapor convective, the evaporative, and the quenching terms are monitored to infer the boiling dynamics in the transition region from the single-phase flow to the nucleate boiling zone. It is found that the CFD approach to detecting the boiling incipience point agrees reasonably with available experimental data. However, its limitations, like inaccuracy for lower flow rates and early transition to boiling flow, are also noticed due to the nature of the considered RPI model.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Nucleate boiling performance evaluation of cavities at mesoscale level

Nucleate boiling heat transfer (NBHT) from enhanced structures is an effective way to dissipate high heat flux. Here, a 3D multi-relaxation-time (MRT) phase-change lattice Boltzmann method in conjunction with conjugated heat transfer treatment is proposed and then applied to the study of cavities behaviours for nucleation on roughened surfaces for an entire ebullition cycle without introducing any artificial disturbance. The bubble departure diameter, departure frequency and total boiling heat transfer rate are also explored. We demonstrate that the cavity shapes show significant influence on the features of NBHT. The total heat transfer rate increases with the cavity mouth and cavity base area while decreases with the increase in cavity bottom wall thickness. The cavity with low wetting can enhance the heat transfer and improve the bubble release frequency.

42 ENGINEERING↗
DOE OSTI2016

Effect of surface oxidation on the onset of nucleate boiling in a materials test reactor coolant channel

The onset of nucleate boiling (ONB) serves as the thermal-hydraulic operating limit for many research and test reactors. However, boiling incipience under forced convection has not been well-characterized in narrow channel geometries or for oxidized surface conditions. This study presents experimental data for the ONB in vertical upflow of deionized (DI) water in a simulated materials test reactor (MTR) coolant channel. The channel gap thickness and aspect ratio were 1.96 mm and 29:1, respectively. Boiling surface conditions were carefully controlled and characterized, with both heavily oxidized and native oxide surfaces tested. Measurements were performed for mass fluxes ranging from 750 to 3000 kg/m 2 s and for subcoolings ranging from 10 to 45°C. ONB was identified using a combination of high-speed visual observation, surface temperature measurements, and channel pressure drop measurements. Surface temperature measurements were found to be most reliable in identifying the ONB. For the nominal (native oxide) surface, results indicate that the correlation of Bergles and Rohsenow, when paired with the appropriate single-phase heat transfer correlation, adequately predicts the ONB heat flux. Furthermore, incipience on the oxidized surface occurred at a higher heat flux and superheat than on the plain surface.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Reference Computational Meshing Strategy for Computational Fluid Dynamics Simulation of Departure from Nucleate Boiling

The objective of this effort is to establish a strategy and process for generation of suitable computational mesh for computational fluid dynamics simulations of departure from nucleate boiling in a 5 by 5 fuel rod assembly held in place by PWR mixing vane spacer grids. This mesh generation process will support ongoing efforts to develop, demonstrate and validate advanced multi-phase computational fluid dynamics methods that enable more robust identification of dryout conditions and DNB occurrence.Building upon prior efforts and experience, multiple computational meshes were developed using the native mesh generation capabilities of the commercial CFD code STAR-CCM+. These meshes were used to simulate two test cases from the Westinghouse 5 by 5 rod bundle facility. The sensitivity of predicted quantities of interest to the mesh resolution was then established using two evaluation methods, the Grid Convergence Index method and the Least Squares method. This evaluation suggests that the Least Squares method can reliably establish the uncertainty associated with local parameters such as vector velocity components at a point in the domain or surface averaged quantities such as outlet velocity magnitude. However, neither method is suitable for characterization of uncertainty in global extrema such as peak fuel surface temperature, primarily because such parameters are not necessarily associated with a fixed point in space. This shortcoming is significant because the current generation algorithm for identification of DNB event conditions relies on identification of such global extrema. Ongoing efforts to identify DNB based on local surface conditions will address this challenge.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Mechanistic Modeling of Departure from Nucleate Boiling under Transient Scenarios

The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) is one of the major limiting factors in the design and operation of pressurized water reactors (PWRs). Various predictive tools have been proposed for steady-state conditions. Empirical correlations and look-up tables yield relatively good agreement with specific experimental datasets and are widely used in subchannel codes for PWR transient simulations. However, experimental studies have revealed that during fast transients, the CHF values can become significantly higher than those in steady-state or slow transient scenarios, causing this modeling approach to result in overly conservative DNB prediction. This paper presents a mechanistic transient CHF model. Based on prior work, two DNB triggering mechanisms prevail in this model—the hydrodynamic thinning process and the thermal thinning process—both of which rely on the generally admitted liquid sublayer dryout theory. These mechanisms evaluate the depletion of the liquid sublayer underneath vapor slugs flowing over the channel. This model is further validated against three sets of power transient experiments at different operating conditions. While it clearly outperforms steady-state approaches and generally agrees closely with measurements, it still remarkably under-estimates CHF for very fast transients at low pressure. Future investigations will address this limitation.

Zhao, X. G.↗
DOE OSTI2018

Predicting departure from nucleate boiling with advanced data- and physics-driven approaches

The subcooled and low-quality saturated flow boiling critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) is one of the major limiting factors in the design and operation of pressurized water reactors (PWRs). Due to lack of agreement on its physical mechanisms, development of an accurate DNB-type CHF model has been elusive. The various steady-state (SS) predictive tools that have been proposed can be grouped into two categories: (1) data-driven methods—namely best-fit empirical correlations and look-up tables that result in relatively good agreement with specific experimental datasets but often fail to extend beyond their ranges of validity, and (2) the physics-based mechanistic models that rely on reasonable yet limited understanding of the underlying physics and apply constitutive relations to close the conservation equations. On one hand, with recent advances in computational capabilities and optimization techniques, machine learning (ML)-based methods constitute an alternative and a more advanced, data-driven approach. With enough data, they can be particularly useful in engineering fields where the physical phenomena are complex and challenging to model. On the other hand, a state-of-the-art physics-driven approach would not only provide more accurate prediction of SS CHF, but it would also help better understand and model transient applications. The data-driven regression models use deep feed-forward neural networks (NNs) and random forests to cross-validate with 1,865 CHF test cases, covering a wide range of flow conditions and channel geometries. The best-estimate ML-based predictors compare favorably with the widely used look-up table for annulus and plate, and sensitivity analysis has confirmed their effectiveness. The key advantage of ML-based methods is their online extensibility of applicability domain. The proposed physics-driven approach combines key assumptions and parameters in the relatively well-accepted theories of liquid sublayer dryout and near-wall bubble crowding. A more realistic understanding of local mechanisms has been modeled. The new model has been optimized and validated against 1,439 tube data, and it shows considerably improved performance when compared to a recent mechanistic model, demonstrating unbiased close agreement with measurements over a wide range of operating conditions. Future work will extend the improved physics-driven model to non-tube geometry applications and will couple the model with ML via a hybrid approach. The mechanistic SS work will also serve to improve understanding and to model transient CHF scenarios.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Experimental investigation on nucleate boiling heat transfer of low GWP refrigerants over metal-foam enhanced tube bundles

The objective of this work is to develop an enhanced tube bundle to improve the nucleate boiling heat transfer for the application of flooded evaporators. The enhanced tube bundle was made by deploying metal foam around the outer surface of aluminum tubes, which provide higher heat transfer area and nucleation site density. The pool boiling experiments for horizontal tube bundles were conducted in a pressure vessel at difference heat fluxes. The heat transfer performance of bare aluminum tube bundle was compared against the metal-foam enhanced tube bundle using low GWP refrigerant, R-1234yf. The experimental results showed that the heat transfer coefficient of the metal-foam enhanced tube bundle was at least two times higher than that of the bare tube bundle, and the enhancement ratio depended on the heat flux condition.

Yang, Cheng-Min↗
DOE OSTI2026

Margin to onset of nucleate boiling and flow instability studies for preliminary MITR design-demonstration element thermal-hydraulics

This study covers the onset of flow instability (OFI) preliminary results obtained from leveraging correlations, in addition to the preliminary thermal hydraulics results such as pressure, flow velocity, temperature, and oxide layer over the design demonstration experiment (DDE) for the Massachusetts Institute of Technology Reactor (MITR). Current computational fluid dynamics (CFD) models in fluid structure interaction (FSI) have added the capability of assessing margins to onset of nucleate boiling (ONB). This study initiates the capability to model the margin to OFI and ONB presented for the MITR. Such study is supportive of the United States High Performance Research Reactor (USHPRR) program. Previous studies provided preliminary thermal-hydraulic and mechanical analyses of the hydrodynamic effects in the MITR DDE under conservative approximations for plate power distribution. In conclusion, this study focuses on providing insights into the OFI future research direction optimizing the transport of thermal energy, mass-flow rates, flow-channel geometries, and boundary conditions.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Margin to Onset of Nucleate Boiling studies for MITR and NBSR Design Demonstration Experiments

The United State High Performance Research Reactor (USHPRR) program aims to eliminate more than 200kg of High Enriched Uranium (HEU) from commerce annually by converting five U.S. high-performance research reactors and one associated critical assembly to Low Enriched Uranium (LEU) fuel using a high-density alloy of uranium-10 wt% molybdenum (U-10Mo). The Massachusetts Institute of Technology Reactor (MITR) and the National Bureau of Standards Reactor (NBSR) are two of five research reactors selected for this program. Previous studies have addressed the thermal-hydraulics performance of the fuel Design Demonstration Elements (DDE) for these reactors under conservative assumptions in the Belgian Reactor (BR)-2. This report extends these previous studies by analyzing the margins to Onset of Nucleate Boiling (ONB) for these two DDEs under the conservative conditions used in the previous thermal-hydraulics analyses

42 ENGINEERING↗
DOE OSTI2021

Improved departure from nucleate boiling prediction in rod bundles using a physics-informed machine learning-aided framework

The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis is a regulatory limit for the licensing of pressurized water reactors (PWRs) worldwide. Despite the abundance of predictive tools available to the reactor thermal-hydraulics community, the path for an accurate CHF model remains elusive. This work approaches the prediction of DNB through a physics-informed machine learning-aided framework (PIMLAF) with the objective of achieving superior predictive capabilities for a rod bundle. In view of the limitations in existing macro-scale physics-driven tools, an improved mechanistic model is first proposed, leveraging key concepts in the liquid sublayer dryout and bubble crowding mechanisms. Furthermore, the proposed mechanistic model is able to predict DNB in different heater geometries for a broad range of flow conditions without the need for recalibration. This model is then incorporated as the physics-informed component of the hybrid framework PIMLAF, which takes advantage of established understanding in the field (i.e., domain knowledge [DK]) and uses machine learning (ML) to capture undiscovered information from the mismatch between the actual and DK-predicted output. Two bundle-related case studies using the PWR subchannel and bundle tests (PSBT) database are carried out to illustrate the PIMLAF’s improved performance over traditional approaches for both interpolation and extrapolation purposes. In light of the PIMLAF’s promising potential to reduce prediction error, reactor vendors are encouraged to leverage their in-house experimental efforts and apply the hybrid framework to potentially achieve margin reductions in the minimum DNB ratio (MDNBR) for the designs of interest.

42 ENGINEERING↗
DOE OSTI2022

Machine Learning-based Prediction of Departure from Nucleate Boiling Power for the PSBT Benchmark

Machine Learning (ML) has seen an exponential growth in its applications due to its advanced data driven prediction capabilities. The study presents a data-driven approach as a preliminary attempt to predict the power at which departure from nucleate boiling (DNB) occurs in pressurized water reactors (PWRs) by constructing an advanced ML algorithm that takes outlet pressure, inlet temperature and inlet mass flux as the input features. DNB is a critical heat flux (CHF) phenomenon seen in PWRs. The experimental data from the PWR subchannel and bundle tests (PSBT) benchmark is first used to train an artificial neural network (ANN) to predict the DNB power, which produces a root mean square error (RMSE) of 6.89 kW/m when tested on a blind subset of the PSBT data. Since the PSBT dataset is relatively small to train an accurate ANN, a data augmentation methodology based on generative adversarial networks (GANs) is used to expand the training dataset. By assuming that the real data follows a certain distribution, GANs try to learn that underlying distribution to generate similar synthetic data to augment the database and to improve the predictive capabilities of the ANN. The data generated from GANs are validated using 1-nearest neighbor and kernel maximum mean discrepancy. To further ensure data from GAN is similar to PSBT, the data is tested and filtered out using the sub-channel thermal-hydraulic code CTF. The results indicate that with the addition of 120 data points from GAN the RMSE reduces to 4.84 kW/m showing promising results for future developments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Best estimate plus uncertainty analysis of departure from nucleate boiling limiting case with CASL core simulator VERA-CS in response to PWR main steam line break event

VERA-CS (Virtual Environment for Reactor Applications, Core Simulator) is a coupled neutron transport and thermal-hydraulics subchannel code under development by the Consortium for Advanced Simulation of Light Water Reactors (CASL). VERA-CS was used to simulate a typical pressurized water reactor (PWR) full core response with 17x17 fuel assemblies for a main steam line break (MSLB) accident scenario with the most reactive rod cluster control assembly stuck out of the core. The accident scenario was initiated at the hot zero power (HZP) at the end of the first fuel cycle with return to power state points that were determined by a system analysis code and the most limiting state point was chosen for core analysis. The best estimate plus uncertainty (BEPU) analysis method was applied using Wilks’ nonparametric statistical approach. In this way, 59 full core simulations were performed to provide the minimum departure from nucleate boiling ratio (MDNBR) at the 95/95 (95% probability with 95% confidence level) tolerance limit. The results show that this typical PWR core remains within MDNBR safety limits for the MSLB accident.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Measurement of the onset of nucleate boiling in liquid xenon

We report the first precision measurement of the superheat temperature required for bubble nucleation in liquid xenon of Δ T wall,ONB = (16.9±0.5) K and Δ T wall,ONB = (19.2 +0.4 -1.1 ) K at pressures of P = (0.98±0.02) bar and P = (1.32 +0.05 -0.01 ) bar, respectively. Both are measured at a subcooled bulk fluid temperature of ~ 162 K. A dedicated liquid xenon setup is used to measure the temperature at which bubble nucleation first appears on the flat surface of a resistor. The associated average heat flux at the surface of the resistor is determined using fluid dynamics simulations. Future experiments can use these experimental results to determine the likelihood of boiling in liquid xenon and to design effective thermal sinks that prevent bubble nucleation.

47 OTHER INSTRUMENTATION↗