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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 OSTI2025

Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification

Critical heat flux (CHF) is a key quantity in nuclear system modeling due to its impact on heat transfer, safety margins, and reactor performance. This study develops and validates an uncertainty-aware hybrid modeling approach that combines machine learning with physics-based models to predict CHF in cases of dryout. The Biasi and Bowring empirical correlations were paired with three ML uncertainty quantification (UQ) techniques: deep neural network (DNN) ensembles, Bayesian neural networks (BNNs), and deep Gaussian processes (DGPs). A pure ML model without a base model was evaluated for comparison. Model performance was assessed under plentiful (7,350 points) and limited (9 points) training data scenarios using parity, uncertainty distributions, and calibration curves. Results show that the Biasi hybrid DNN ensemble achieved the best overall performance, with a mean absolute relative error of 1.846%, and well-calibrated uncertainty estimates. The BNN-based hybrids showed slightly higher error (2.14%) but superior uncertainty calibration. DGP models underperformed, with over 6% error and poor uncertainty calibration. All hybrid models outperformed pure machine learning configurations, demonstrating resistance against data scarcity. These findings indicate that hybrid modeling significantly improves predictive accuracy, interpretability, and resilience to data scarcity. The integration of uncertainty awareness provides actionable confidence in CHF predictions, which is vital for safety-critical decisions in nuclear applications. This hybrid approach offers a viable pathway for deploying ML models in reactor analysis tools while preserving domain knowledge and physical consistency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Aided Active Learning (AAL) for Enhanced Critical Heat Flux Prediction

Accurate prediction of critical heat flux (CHF) is crucial for the safe and efficient operation of nuclear reactors. Traditional CHF modeling methods often require extensive experimental data, which are hard to obtain. This study introduces the Aided Active Learning (AAL) framework, which strategically minimizes data requirements without sacrificing model accuracy. Unlike conventional Active Learning (AL), AAL introduces an additional step of randomly selecting a subset from the sample pool before applying the query strategy. To evaluate the performance of AAL, two query strategies—uncertainty-based sampling and error-reduction sampling—were evaluated across the following models: random forest (RF), feedforward neural network (FNN), and variational feedforward neural network (vFNN). The proposed framework demonstrated that AAL effectively reduces the number of training samples needed to achieve comparable predictive accuracy. For the RF model, AL required only 710 samples to achieve an R2 score of 0.98, as compared to the 4,785 samples needed by random sampling. Similarly, the FNN model achieved the same R2 score with just 355 samples when using AL, a significant improvement over the 825 samples required by random sampling. In case of uncertainty-based sampling strategy, vFNN attained an R2 of 0.98 with 3,420 samples, reducing the sample requirement by 47% relative to the 6,440 samples needed for random sampling. Its performance suggests that larger training data are required to fully leverage its uncertainty quantification capabilities.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1972

Combined buoyancy and flow direction effects on saturated boiling critical heat flux in liquid nitrogen

Buoyancy effects on the critical heat flux and general data trends for a liquid nitrogen internal flow system were determined by comparison of upflow and downflow data under identical test conditions. The test section had a 1.28 cm diameter flow passage and a 30.5 cm heated length which was subjected to uniform heat fluxes through resistance heating. Test conditions covered a range of pressures from 3.4 to 10.2 atm, inlet velocities from 0.23 to 3.51 m/sec, with the liquid nitrogen temperature at saturated inlet conditions. Data comparisons showed that the critical heat flux for downflow could be up to 36 percent lower than for upflow. A nonmonotonic relationship between the critical heat flux and velocity was determined for upflow but not for downflow. A limiting inlet velocity of 4.12 m/sec was determined to be the minimum velocity required to completely suppress the influence of buoyancy on the critical heat flux for this saturated inlet flow system. A correlation of this limiting fluid velocity is presented that was developed from previously published subcooled liquid nitrogen data and the saturated data of this investigation.

Papell, S. S.↗
NASA NTRS1993

The effects of buoyancy on the critical heat flux in forced convection

The critical heat flux (CHF) in forced convection over a flat surface at relatively low flow velocities has been found, not unexpectedly, to depend upon the orientation of the buoyancy. The CHF for R-113 was measured at various heating surface orientations for test section Reynolds numbers ranging between 3000 and 6500. In this flow range, the buoyancy force acting on the vapor generally dominates over the flow inertia, yet the inertia would still be substantial were gravity to be reduced. In the experiments of this study, the CHF is determined for heating surface orientations ranging from 0 deg to 360 deg, for flow velocities between 4 cm/s and 35 cm/s, and for subcoolings between 2.8 C and 22.2 C. The results presented here demonstrate the strong influence of buoyancy at low flow velocities, which diminishes as the flow velocity and subcooling are increased.

Brusstar, Matthew J.↗
NASA NTRS2023

Universal Two-Phase Convection Heat Transfer Correlations for Cryogenic Pipe Chilldown

This paper presents a set of universal two-phase convection heat transfer correlations for modeling boiling heat transfer during pipe chilldown fit over the widest available range of cryogenic fluids and thermodynamic conditions. The correlations improve upon prior correlations that were developed separately for liquid nitrogen (LN2) and liquid hydrogen (LH2) pipe quenching datasets. The new correlations include equations to calculate the single-phase vapor heat transfer, film boiling heat transfer, transition boiling heat transfer, nucleate boiling heat transfer, single-phase liquid heat transfer, bulk vapor temperature during high quality film boiling, Leidenfrost temperature, critical heat flux, critical heat flux temperature, and the onset of nucleate boiling temperature. The correlations were validated against LH2, LN2, liquid methane, liquid oxygen, and liquid argon pipe quenching datasets. The eight datasets cover the following parameter ranges: pipe lengths of 0.1 to 6.5m; outer pipe diameters of 12.7 to 25.4 mm; pipe wall thicknesses from 0.51 to 1.64 mm; flow directions of upward, downward, and horizontal; gravity levels of 1g and 0g±0.01g. A numerical model assumes homogeneous mixing between the vapor and liquid and implicitly integrates the coupled energy equations for the pipe and fluid, as well as continuity for the fluid. The model was used to estimate the equilibrium quality and fluid mass accumulation along the pipes during chilldown from estimates of the fluid-pipe heat transfer extracted from temperature measurements. The correlations can be implemented in lumped parameter codes such as SINDA/FLUINT and the Generalized Fluid System Simulation Program (GFSSP) to improve the accuracy of chilldown time and chilldown boiloff mass predictions. Such predictions are useful for designing ground or in-space cryogenic liquid transfer systems.

Universal Correlations↗
NASA NTRS1998

Forced Convection Boiling and Critical Heat Flux of Ethanol in Electrically Heated Tube Tests

Electrically heated tube tests were conducted to characterize the critical heat flux (transition from nucleate to film boiling) of subcritical ethanol flowing at conditions relevant to the design of a regeneratively cooled rocket engine thrust chamber. The coolant was SDA-3C alcohol (95% ethyl alcohol, 5% isopropyl alcohol by weight), and tests were conducted over the following ranges of conditions: pressure from 144 to 703 psia, flow velocities from 9.7 to 77 ft/s, coolant subcooling from 33 to 362 F, and critical heat fluxes up to 8.7 BTU/in(exp 2)/sec. For the data taken near 200 psia, critical heat flux was correlated as a function of the product of velocity and fluid subcooling to within +/- 20%. For data taken at higher pressures, an additional pressure term is needed to correlate the critical heat flux. It was also shown that at the higher test pressures and/or flow rates, exceeding the critical heat flux did not result in wall burnout. This result may significantly increase the engine heat flux design envelope for higher pressure conditions.

Meyer, Michael L.↗
DOE OSTI2026

Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal-Hydraulics Code

Critical heat flux (CHF) marks the transition from nucleate to film boiling, where heat transfer to the working fluid can rapidly deteriorate. Accurate CHF prediction is essential for efficiency, safety, and preventing equipment damage, particularly in nuclear reactors. Although widely used, empirical correlations frequently exhibit discrepancies when compared to experimental data, limiting their reliability in diverse operational conditions. Traditional machine learning (ML) approaches have demonstrated potential for CHF prediction but often suffer from limited interpretability, data scarcity, and insufficient knowledge of physical principles. Hybrid model approaches, which combine data-driven ML with base models, mitigate these concerns by incorporating prior knowledge of the domain. This study integrates an externally trained purely data-driven ML model and two hybrid models (using the Biasi and Bowring CHF correlations) within the CTF subchannel code via a custom Fortran framework. Performance was evaluated using two validation cases: a subset of the Nuclear Regulatory Commission (NRC) CHF database and the Bennett dryout experiments. In both cases, the hybrid models demonstrated significantly lower error metrics compared to conventional empirical correlations, with the best models often reducing relative error by about 5 percentage points. The pure ML model achieved comparable accuracy, outperforming the hybrid Biasi model in the NRC test case (3.3% versus 5.5% relative error) but exhibiting slightly higher error against the hybrid Bowring model in the Bennett test case (7.7% versus 6.1%). Trend analysis of error parity indicated that ML-based models reduced the tendency for CHF overprediction, improving overall accuracy. These results demonstrate that ML-based CHF models can be effectively integrated into subchannel codes and could potentially increase performance compared to conventional methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Critical heat flux on zircaloy and accident tolerant fuel cladding under prototypical conditions of pressurized and boiling water reactors

First of a kind high spatial and temporal resolution temperature measurements of Pressurized Water Reactor and Boiling Water Reactor simulated fuel pins (high heat flux cosine profile heaters) in annular rod type geometry during nucleate boiling, departure from nucleate boiling and re-wetting has been performed. Distributed fiber optic measurements of the cladding axial temperature with spatial resolutions down to 2.5 mm axially at up to three equally spaced azimuthal locations per rod have demonstrated the similarities and differences of the different accident tolerant fuel cladding materials. These experiments show the performance of accident tolerant cladding with a precise measurement of the location and evolution of dryout followed by the re-wetting phenomena (see supplementary time history of a critical heat flux event). Experimental parameters varied coolant mass fluxes from 1695 to 2712 kg/m 2 s, pressures from 10 to 20 MPa, and inlet subcooling from 10 to 55 °C. It was found that the coatings and different material have little effect on the value of the critical heat flux. Any discrepancies between the local critical heat flux verified among the cladding material are attributed to different departure from nucleate boiling phenomena captured during the experiment with the high-resolution optical fiber temperature data. Substantial differences in the performance of the different cladding material to survive a critical heat flux event were however observed by post-test X-ray and optical inspection. External evident damage and internal cracks to the simulated fuel pin having the bare zircaloy cladding were observed in regions associated with the occurrence of the critical heat flux for longer periods. Such damages were associated with zirconium oxidation, and not noticed on the Cr coated zircaloy or FeCrAl claddings. These results indicate that the use of FeCrAl or a Cr coated zircaloy as cladding in Pressurized Water Reactor and Boiling Water Reactor may increase the survivability of the cladding material for short transients.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Experimental study for critical heat flux in 2x2 rod bundles at high pressure conditions

Critical heat flux (CHF) phenomena has been a topic of study for over fifty years yet remains a serious concern across industries and especially in water-cooled nuclear reactor design. CHF data in the high-pressure range is limited, and the effects of bundle geometry and non-uniform heat flux profile are challenging to quantify. In this study, CHF experiments were performed in upward flowing water in a 2 × 2 rod bundle with a cosine profile heat flux. Data were collected between 16.5 and 18 MPa at two mass flux conditions with three inlet subcooling conditions. Under these conditions, average CHF was shown to decrease with increasing pressure. However, pressure had less of an effect than decreasing the inlet subcooling or the mass flux, both of which reduce the CHF value considerably. Of interest, correlations that have been developed for lower pressure continued to predict CHF occurrences with moderate accuracy outside their ranges of validity. Nearly all predicted values were within 20% of experimental values.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Development of physics-consistent conditional diffusion model to overcome data scarcity in critical heat flux

Deep generative modeling provides a powerful pathway to overcome data scarcity in energy-related applications where experimental data are often limited. By learning the underlying probability distribution of the training dataset, deep generative models, such as the diffusion model, can generate high-fidelity synthetic samples that statistically resemble the training data. Such synthetic data generation can significantly enrich the size and diversity of the available training data, and more importantly, improve the robustness of downstream machine learning models in predictive tasks. The objective of this paper is to investigate the effectiveness of diffusion models for overcoming data scarcity in nuclear energy applications. By leveraging a public dataset on critical heat flux which covers a wide range of commercial nuclear reactor operational conditions, we developed a diffusion model that can generate an arbitrary amount of synthetic samples. Since a vanilla diffusion model can only generate samples randomly, we also developed a conditional diffusion model capable of generating targeted critical heat flux data under user-specified thermal-hydraulic conditions. The performance of the diffusion model was evaluated based on its ability to capture empirical feature distributions and pair-wise correlations, as well as to maintain physical consistency. The results showed that both the diffusion model and conditional diffusion model can successfully generate realistic and physics-consistent critical heat flux data. Furthermore, uncertainty quantification results demonstrate that the conditional diffusion model is highly effective in augmenting critical heat flux data while maintaining acceptable levels of uncertainty.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Predicting critical heat flux using localized sensing at invisible vapor-liquid interfaces

Predicting critical heat flux (CHF) in two-phase electronics cooling systems remains a significant challenge due to the sudden onset of boiling crisis and the difficulty in directly visualizing vapor-liquid interfaces. Existing sensing methods rely on lagging temperature measurements, optically accessible systems, or spatially averaged signals that cannot pinpoint CHF initiation at localized high-heat-flux regions. Here, we report a planar capacitive sensing approach that enables real-time, localized detection of vapor-liquid interface dynamics for CHF prediction in boiling heat transfer. The capacitive sensor exploits the dielectric constant difference between liquid and vapor phases to capture bubble nucleation, growth, and departure dynamics with a temporal resolution down to 2 ms. The capacitive sensing reveals distinct signals across boiling regimes: from high-frequency fluctuations during strong nucleate boiling to low-frequency fluctuations with increased amplitudes when approaching CHF. The multi-sensor array experiments demonstrate real-time localized sensing, where each sensor responds exclusively to boiling in its immediate vicinity without crosstalk from neighboring regions. This non-intrusive sensing approach provides predictive rather than lagging sensing signals of CHF occurrence, offering predictive diagnosis of two-phase liquid cooling for the thermal management of high-power-density electronics.

CHF↗
NASA NTRS2002

Investigation of Body Force Effects on Flow Boiling Critical Heat Flux

The bubble coalescence and interfacial instabilities that are important to modeling critical heat flux (CHF) in reduced-gravity systems can be sensitive to even minute body forces. Understanding these complex phenomena is vital to the design and safe implementation of two-phase thermal management loops proposed for space and planetary-based thermal systems. While reduced gravity conditions cannot be accurately simulated in 1g ground-based experiments, such experiments can help isolate the effects of the various forces (body force, surface tension force and inertia) which influence flow boiling CHF. In this project, the effects of the component of body force perpendicular to a heated wall were examined by conducting 1g flow boiling experiments at different orientations. FC-72 liquid was boiled along one wall of a transparent rectangular flow channel that permitted photographic study of the vapor-liquid interface at conditions approaching CHF. High-speed video imaging was employed to capture dominant CHF mechanisms. Six different CHF regimes were identified: Wavy Vapor Layer, Pool Boiling, Stratification, Vapor Counterflow, Vapor Stagnation, and Separated Concurrent Vapor Flow. CHF showed great sensitivity to orientation for flow velocities below 0.2 m/s, where very small CHF values where measured, especially with downflow and downward-facing heated wall orientations. High flow velocities dampened the effects of orientation considerably. Figure I shows representative images for the different CHF regimes. The Wavy Vapor Layer regime was dominant for all high velocities and most orientations, while all other regimes were encountered at low velocities, in the downflow and/or downward-facing heated wall orientations. The Interfacial Lift-off model was modified to predict the effects of orientation on CHF for the dominant Wavy Vapor Layer regime. The photographic study captured a fairly continuous wavy vapor layer travelling along the heated wall while permitting liquid contact only in wetting fronts, located in the troughs of the interfacial waves. CHF commenced when wetting fronts near the outlet were lifted off the wall. The Interfacial Lift-off model is shown to be an effective tool for predicting the effects of body force on CHF at high velocities.

Zhang, Hui↗
NASA NTRS1996

Study of Critical Heat Flux and Two-Phase Pressure Drop Under Reduced Gravity

The design of the two-phase flow systems which are anticipated to be utilized in future spacecraft thermal management systems requires a knowledge of two-phase flow and heat transfer phenomena in reduced gravities. This program was funded by NASA headquarters in response to NRA-91-OSSA-17 and was managed by Lewis Research Center. The main objective of this program was to design and construct a two-phase test loop, and perform a series of normal gravity and aircraft trajectory experiments to study the effect of gravity on the Critical Heat Flux (CHF) and onset of instability. The test loop was packaged on two aircraft racks and was also instrumented to generate data for two-phase pressure drop. The normal gravity tests were performed with vertical up and downflow configurations to bound the effect of gravity on the test parameters. One set of aircraft trajectory tests was performed aboard the NASA DC-9 aircraft. These tests were mainly intended to evaluate the test loop and its operational performance under actual reduced gravity conditions, and to produce preliminary data for the test parameters. The test results were used to demonstrate the applicability of the normal gravity models for prediction of the two-phase friction pressure drop. It was shown that the two-phase friction multipliers for vertical upflow and reduced gravity conditions can be successfully predicted by the appropriate normal gravity models. Limited critical heat flux data showed that the measured CHF under reduced gravities are of the same order of magnitude as the test results with vertical upflow configuration. A simplified correlation was only successful in predicting the measured CHF for low flow rates. Instability tests with vertical upflow showed that flow becomes unstable and critical heat flux occurs at smaller powers when a parallel flow path exists. However, downflow tests and a single reduced gravity instability experiment indicated that the system actually became more stable with a parallel single-phase flow path. Several design modifications have been identified which will improve the system performance for generating reduced gravity data. The modified test loop can provide two-phase flow data for a range of operating conditions and can serve as a test bed for component evaluation.

Abdollahian, Davood↗
DOE OSTI2023

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Accurately predicting post-critical heat flux (CHF) heat transfer is an important but challenging task in water-cooled reactor design and safety analysis. Although numerous heat transfer correlations have been developed to predict post-CHF heat transfer, these correlations are only applicable to relatively narrow ranges of flow conditions due to the complex physical nature of the post-CHF heat transfer regimes. In this paper, a large quantity of experimental data is collected and summarized from the literature for steady-state subcooled and low-quality film boiling regimes with water as the working fluid in vertical tubular test sections. In addition, a low-quality water film boiling (LWFB) database is consolidated with a total of 22,813 experimental data points, which cover a wide flow range of the system pressure from 0.1 to 9.0 MPa, mass flux from 25 to 2750 kg/m 2 s, and inlet subcooling from 1 to 70 °C. Two machine learning (ML) models, based on random forest (RF) and gradient boosted decision tree (GBDT), are trained and validated to predict wall temperatures in post-CHF flow regimes. The trained ML models demonstrate significantly improved accuracies compared to conventional empirical correlations. To further evaluate the performance of these two ML models from a statistical perspective, three criteria are investigated and three metrics are calculated to quantitatively assess the accuracy of these two ML models. For the full LWFB database, the root-mean-square errors between the measured and predicted wall temperatures by the GBDT and RF models are 5.7% and 6.2%, respectively, confirming the accuracy of the two ML models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2021

Experimental Results and Interfacial Lift-off Model Predictions of Critical Heat Flux for Flow Boiling with Subcooled Inlet Conditions – In Preparation for Experiments Onboard the International Space Station

This study investigates critical heat flux (CHF) for subcooled flow boiling of n-Perfluorohexane based on results of pre-launch Earth-gravity Mission Sequence Tests (MSTs) of the Flow Boiling and Condensation Experiment (FBCE), which was launched to the International Space Station (ISS) in August 2021. CHF measurements were made in a rectangular channel having a 2.5 mm by 5 mm cross-section and a 114.6-mm long heated segment. Both single-sided and double-sided heating were tested in vertical upflow in Earth gravity for a variety of inlet conditions. The inlet subcooling was varied in the range of 0.4 – 32.0°C and encompassed both near-saturated and highly subcooled conditions. Experimental trends and high-speed video records were investigated to better understand the mechanism of CHF. Overall trends show CHF increases as flow rate and/or inlet subcooling are increased. Flow features from the events around CHF justify the applicability of the Interfacial Lift-off Model and the determination of limiting criteria for its application. The present experimental data are combined with prior databases for various flow orientations with respect to Earth gravity and microgravity data collected on parabolic flights. Predictions are made using the Interfacial Lift-off Model for this consolidated subcooled-inlet FBCE-CHF database. A heat utility ratio was included in the model to capture the effects of subcooling and corresponding thermodynamic non-equilibrium. An overall mean absolute error of 19.04% indicates good predictive capability of the model for both heating configurations, different gravity environments, and a wide range of inlet subcooling.

flow boiling↗