Search NASA⌕ Search

SEARCH · Search NASA

Results for “experimental database”

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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Framework for Analysis and Visualization of Simulation Data (VizAly)

As supercomputing resources increase, cosmological scientists are able to run more detailed and larger simulations generating massive amounts of data. Analyzing these simulations with an available open-source toolkit is important for collaborative Department of Energy scientific discovery across labs, universities, and other partners. Developed software as a part of this collection include: comparing data with other existing simulations, verifying and validating results with observation databases, new halo finder algorithms, and using analytical tools to get insights into the physics of the cosmological universe. The goal of this software project is to provide a set of open-source libraries, tools, and packages for large-scale cosmology that allows scientists to visualize, analyze, and compare large-scale simulation and observational data sets. Developed software will provide a variety of methods for processing, visualization, and analysis of astronomical observation and cosmological simulation data. These tools are intended for deployment on multiple scientific computing platforms, including but not limited to personal computers, cloud computing, experimental sites (telescopes) and high performance supercomputers.

Wendelberger, Joanne↗
NASA NTRS1997

Combustion of Interacting Droplet Arrays in a Microgravity Environment

This research program involves the study of single droplets and linear arrays of droplets in weakly-buoyant and non-bouyant environments. The primary purpose of the single droplet work was to (1) provide a data base from which to compare droplet array results and (2) to correlate the effects of buoyancy on flame shape. Traditionally convective effects in droplet combustion are represented in terms of the Reynolds number, Re, for forced convection and the Grashof number, Gr, for natural convection. Typically, corrections to the burning rate constant for convective effects are written in terms of Re or Gr(exp 1). The Stefan velocity is not included in these correlations, even though from purely physical reasons, one would expect it to be important, especially at higher burning rates. The flame distortion due to convective effects is less documented quantitatively. Kumagai and Isoda do predict flame shape in natural and forced convective flow fields. Their focus, however, was to predict the actual flame dimensions. Law and co-workers used reduced pressure, high oxidizer ambients to obtain spherical flames. This implies that buoyant flows were reduced at the low pressures, as indicated by a very small Grashof number. Ross et al, however, using scaling arguments showed that reducing the pressure does not have a large effect on the magnitude of the buoyant velocity. Struk et al showed elongated flame shapes during simulated (porous sphere) droplet combustion. The elongation of the flames was due to residual gravity levels aboard the reduced gravity aircraft on which the experiments were conducted. These flame shapes, as well as some data from the literature were interpreted based on a dimensionless grouping called the sphericity parameter, Sp. Sp is the ratio of a characteristic computed buoyant velocity to the Stefan velocity at the flame front. One purpose of the droplet arrays work is to extend the database and theories that exist for single droplets into the regime where droplet interactions are important. The eventual goal being to use the results of this work as inputs to models on spray combustion where droplets seldom burn individually; instead the combustion history of a droplet is strongly influenced by the presence of the neighboring droplets. Recently, Annamali and Ryan have summarized he current status of droplet array, cloud and spray combustion. A number of simplified theories led numerical studies of droplet vaporization/combustion where multiple droplet effects are present are now available. These theories all neglect the effect of buoyancy. Experimentally, most studies to date suffer the effects of buoyancy. It is the dominant transport mechanism in the problem. Only the works of Law and co-worker and more recently by Mikami et al were performed in an environment where buoyancy effects were small. Law and co-workers were limited to high oxygen index, low pressure ambient environments since there studies were conducted in normal gravity.

Dietrich, Daniel L.↗
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 OSTI2024

Non-Electricity Based Renewable Fuels: Theory and Computation for Solar Thermochemical Hydrogen

Dominated by photovoltaics and wind, current renewable energy sources generate mostly electricity, but 80% of the global final energy consumption occurs in form of fuels. Therefore, direct solar fuel generation would be a major breakthrough for the energy transition. Solar thermochemical hydrogen (STCH) is one of the very few potential routes towards scalable renewable fuels, but currently suffers from lack of an oxide working material that could optimally perform energy conversion within the thermodynamic boundary conditions. Theory and computation can contribute in two distinct ways, through materials search and discovery, but also by providing detailed mechanistic models for specific systems so to advance our understanding of possible design strategies. To enable high-throughput materials screening, we developed a defect graph neural network (dGNN) machine learning approach,[1] which accelerates the prediction of defect formation energies by replacing the tedious density functional theory (DFT) supercell calculations for all possible defect sites. This approach enables high-throughput database screening of oxides, which was integrated with thermodynamic modeling to extract the reduction entropies as additional selection criterion for STCH. Once potential candidate materials are identified, detailed models can guide materials design by predicting performance characteristics. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox active defects start to interact with each other, thereby impeding the formation of additional defects. Introducing a model for the free energy of defect interaction, parametrized on the basis of DFT data, we simulated the complete STCH redox cycle for (Sr,Ce)MnO3 alloys, achieving near-quantitative agreement with experimental data.[2] The analysis of these simulations reveals how defect interactions diminish the reduction entropy and H2 yield, suggesting to include these interactions in design considerations. Finally, we revisit the popular van't Hoff method for analyzing reduction enthalpies and entropies. This method is not ideal, as it involves a temperature-dependent convolution of gas-phase and solid-state entropies, causing uncertainties in the same order of magnitude as the physical quantities of interest. To avoid this problem, we suggest a simple alternative approach which can be applied to experimental and simulated data alike.

first-principles calculations↗
DOE OSTI2024

Nuclear Quadrupole Resonance for Substance Detection

This review paper provides a comprehensive overview of recent advances in nuclear quadrupole resonance (NQR) spectroscopy for substance detection, highlighting its principles, methodologies, and applications. The paper elucidates the fundamental physics underlying NQR spectroscopy, emphasizing the interaction between nuclear quadrupole moments and electric field gradients. It explores the various experimental techniques and instrumentation developments that have enabled the sensitive detection and precise characterization of substances containing quadrupolar nuclei. A significant portion of the survey is dedicated to discussing the diverse applications of NQR spectroscopy, including the detection of explosives, drug pharmaceuticals, and material authentication. Furthermore, the survey examines the challenges and limitations associated with NQR spectroscopy, including issues related to signal-to-noise ratio (SNR), temperature dependency, and substance restrictions. Strategies to overcome these challenges are discussed, offering insights into the future directions of NQR spectroscopy research that includes artificial intelligence (AI), internet of things (IoT) integration, incorporating a cloud database for NQR parameter storage, and multi-modal analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2000

X-38 Experimental Controls Laws

X-38 Experimental Control Laws X-38 is a NASA JSC/DFRC experimental flight test program developing a series of prototypes for an International Space Station (ISS) Crew Return Vehicle, often called an ISS "lifeboat." X- 38 Vehicle 132 Free Flight 3, currently scheduled for the end of this month, will be the first flight test of a modem FCS architecture called Multi-Application Control-Honeywell (MACH), originally developed by the Honeywell Technology Center. MACH wraps classical P&I outer attitude loops around a modem dynamic inversion attitude rate loop. The dynamic inversion process requires that the flight computer have an onboard aircraft model of expected vehicle dynamics based upon the aerodynamic database. Dynamic inversion is computationally intensive, so some timing modifications were made to implement MACH on the slower flight computers of the subsonic test vehicles. In addition to linear stability margin analyses and high fidelity 6-DOF simulation, hardware-in-the-loop testing is used to verify the implementation of MACH and its robustness to aerodynamic and environmental uncertainties and disturbances.

Munday, Steve↗
NASA NTRS2016

Inviscid and Viscous CFD Analysis of Booster Separation for the Space Launch System Vehicle

This paper presents details of Computational Fluid Dynamic (CFD) simulations of the Space Launch System during solid-rocket booster separation using the Cart3D inviscid and Overflow viscous CFD codes. The discussion addresses the use of multiple data sources of computational aerodynamics, experimental aerodynamics, and trajectory simulations for this critical phase of flight. Comparisons are shown between Cart3D simulations and a wind tunnel test performed at NASA Langley Research Center's Unitary Plan Wind Tunnel, and further comparisons are shown between Cart3D and viscous Overflow solutions for the flight vehicle. The Space Launch System (SLS) is a new exploration-class launch vehicle currently in development that includes two Solid Rocket Boosters (SRBs) modified from Space Shuttle hardware. These SRBs must separate from the SLS core during a phase of flight where aerodynamic loads are nontrivial. The main challenges for creating a separation aerodynamic database are the large number of independent variables (including orientation of the core, relative position and orientation of the boosters, and rocket thrust levels) and the complex flow caused by exhaust plumes of the booster separation motors (BSMs), which are small rockets designed to push the boosters away from the core by firing partially in the direction opposite to the motion of the vehicle.

Viscous↗
DOE OSTI2023

Digital Twin for Optimizing Real-time Economy of the Integrated Energy Systems

Economic and safe operation of integrated energy systems (IES) requires real-time optimization (RTO) of the control and actions conducted on each system component. In this regard, digital twins (DTs), which consist of a physical system, a virtual system, and the data communication that occurs between the two, are essential for effective RTO. Through the data warehouse, the virtual system is constantly updated with real-time data from the physical system, and functions as the model in the optimization framework. The reduced-order model of the dynamic process model in the virtual system is used in the optimization framework. The optimization results are then returned, via the data warehouse, as control actions to the physical system. This work demonstrates the software capabilities of DT assets for an IES in the context of preparing a DT for an experimental system comprised of Idaho National Laboratory (INL)’s Thermal Energy Delivery System and battery system. For the virtual demonstration, the DTs encompass (1) a physical system, including the Modelica models of the Thermal Energy Delivery System and the battery system; (2) virtual optimization via the Optimization of Real-Time Capacity Allocation (ORCA) platform; and (3) the open-source data warehouse software DeepLynx. This work assesses the performance of ORCA, which utilizes a reduced-order model built using the Risk Analysis Virtual Environment (RAVEN) and trained on the Modelica models and real-time data pipeline through the graph database hosted in DeepLynx. The proposed optimization workflow will be an RTO model based on DTs and the data they generate.

25 ENERGY STORAGE↗
DOE OSTI2026

nf-core/proteinfamilies: a scalable pipeline for the generation of protein families

The growth of metagenomics-derived amino acid sequence data has transformed our understanding of protein function, microbial diversity, and evolutionary relationships. However, the vast majority of these proteins remain functionally uncharacterized. Grouping the millions of such uncharacterized sequences with the few experimentally characterized ones allows the transfer of annotations, while the inspection of conserved residues with multiple sequence alignments can provide clues to function, even in the absence of existing functional information. To address the challenges associated with this data surge and the need to group sequences, we present a scalable, open-source, parametrizable Nextflow pipeline (nf-core/proteinfamilies) that generates nascent protein families or assigns new proteins to existing families. The computational benchmarks demonstrated that resource usage scales approximately linearly with input size, and the biological benchmarks showed that the generated protein families closely resemble manually curated families in widely used databases.

Nextflow↗
DOE OSTI2016

Measurement of polarization observables in vector meson photoproduction using a transversely-polarized frozen-spin target and polarized photons at CLAS, Jefferson Lab

The study of baryon resonances provides a deeper understanding of the strong interaction because the dynamics and relevant degrees of freedom hidden within them are re ected by the properties of the excited states of baryons. Higher-lying excited states at and above 1.7 GeV/c 2 are generally predicted to have strong couplings to final states involving a heavier meson, e. g. one of the vector mesons, ρ, ω φ, as compared to a lighter pseudoscalar meson, e. g. π and η. Decays to the ππΝ final states via πΔ also become more important through the population of intermediate resonances. We observe that nature invests in mass rather than momentum. The excited states of the nucleon are usually found as broadly overlapping resonances which may decay into a multitude of final states involving mesons and baryons. Polarization observables make it possible to isolate single resonance contributions from other interference terms. The CLAS g9 (FROST) experiment, as part of the N* spectroscopy program at Jefferson Laboratory, accumulated photoproduction data using circularly- & linearly-polarized photons incident on a transversely-polarized butanol target (g9b experiment) in the photon energy range 0:3-2:4 GeV & 0:7-2:1 GeV, respectively. In this work, the analysis of reactions and polarization observables which involve two charged pions, either in the fully exclusive reaction γρ -> ρπ+π- or in the semi-exclusive reaction with a missing neutral pion, γρ -> ρπ + π - (π 0 ) will be presented. For the reaction ρπ + π - , eight polarization observables (I s , I c , P x , P y , $P^s_{x;y}$, $P^c_{x; y}$) have been extracted. The high statistics data rendered it possible to extract these observables in three dimensions. All of them are first-time measurements. The fairly good agreement of Is, Ic obtained from this analysis with the experimental results from a previous CLAS experiment provides support for the first-time measurements. For the reaction γρ -> ρω -> ρπ + π(π 0 , five polarization observables (T, Σ, F, H, P) have been extracted, four of which are first-time measurements at all energies. This analysis thus represents a comprehensive program on vector-meson photoproduction: The ω is observed and studied directly from the data and the polarization observables for the (broad) ω can be extracted from the double-pion reaction in a partial-wave analysis. The 13 polarization observables extracted in this analysis substantially augment the world database of polarization observables for these reactions and are expected to play a crucial role in identifying the contributing baryon resonances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Development of QSAR models to predict blood-brain barrier permeability

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington’s Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80–83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70–72% in negative predictivity, and 78–86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2006

Mechanical Properties of T650-35/AFR-PE-4 at Elevated Temperatures for Lightweight Aeroshell Designs

Considerable efforts have been underway to develop multidisciplinary technologies for aeroshell structures that will significantly increase the allowable working temperature for the aeroshell components, and enable the system to operate at higher temperatures while sustaining performance and durability. As part of these efforts, high temperature polymer matrix composites and fabrication technologies are being developed for the primary load bearing structure (heat shield) of the spacecraft. New high-temperature resins and composite material manufacturing techniques are available that have the potential to significantly improve current aeroshell design. In order to qualify a polymer matrix composite (PMC) material as a candidate aeroshell structural material, its performance must be evaluated under realistic environments. Thus, verification testing of lightweight PMC's at aeroshell entry temperatures is needed to ensure that they will perform successfully in high-temperature environments. Towards this end, a test program was developed to characterize the mechanical properties of two candidate material systems, T650-35/AFR-PE-4 and T650-35/RP46. The two candidate high-temperature polyimide resins, AFR-PE-4 and RP46, were developed at the Air Force Research Laboratory and NASA Langley Research Center, respectively. This paper presents experimental methods, strength, and stiffness data of the T650-35/AFR-PE-4 material as a function of elevated temperatures. The properties determined during the research test program herein, included tensile strength, tensile stiffness, Poisson s ratio, compressive strength, compressive stiffness, shear modulus, and shear strength. Unidirectional laminates, a cross-ply laminate and two eight-harness satin (8HS)-weave laminates (4-ply and 10-ply) were tested according to ASTM standard methods at room and elevated temperatures (23, 316, and 343 C). All of the relevant test methods and data reduction schemes are outlined along with mechanical data. These data contribute to a database of material properties for high-temperature polyimide composites that will be used to identify the material characteristics of potential candidate materials for aeroshell structure applications.

Whitley, Karen S.↗
NASA NTRS2009

When Failure Means Success: Accepting Risk in Aerospace Development

Over the last three decades, NASA has been diligent in qualifying systems for human space flight. As the Agency transitions from operating the Space Shuttle, its employees must learn to accept higher risk levels to generate the data needed to certify its next human space flight system. The Marshall Center s Engineering workforce is developing the Ares I crew launch vehicle and designing the Ares V cargo launch vehicle for safety, reliability, and cost-effective operations. This presentation will provide a risk retrospective, using first-hand examples from the Delta Clipper-Experimental Advanced (DC-XA) and the X-33 single-stage-to-orbit flight demonstrators, while looking ahead to the upcoming Ares I-X uncrewed test flight. The DC-XA was successfully flown twice in 26 hours, setting a new turnaround-time record. Later, one of its 3 landing gears did not deploy, it tipped over, and was destroyed. During structural testing, the X-33 s advanced composite tanks were unable to withstand the forces to which it was subjected and the project was later cancelled. These are examples of successful failures, as the data generated are captured in databases used by vehicle designers today. More recently, the Ares I-X flight readiness review process was streamlined in keeping with the mission's objectives, since human lives are not at stake, which reflects the beginning of a cultural change. Failures are acceptable during testing, as they provide the lessons that actually lead to mission success. These and other examples will stimulate the discussion of when to accept risk in aerospace projects.

Dumbacher, Daniel L.↗
NASA NTRS2012

Highlights from a Mach 4 Experimental Demonstration of Inlet Mode Transition for Turbine-Based Combined Cycle Hypersonic Propulsion

NASA is focused on technologies for combined cycle, air-breathing propulsion systems to enable reusable launch systems for access to space. Turbine Based Combined Cycle (TBCC) propulsion systems offer specific impulse (Isp) improvements over rocket-based propulsion systems in the subsonic takeoff and return mission segments along with improved safety. Among the most critical TBCC enabling technologies are: 1) mode transition from the low speed propulsion system to the high speed propulsion system, 2) high Mach turbine engine development and 3) innovative turbine based combined cycle integration. To address these challenges, NASA initiated an experimental mode transition task including analytical methods to assess the state-of-the-art of propulsion system performance and design codes. One effort has been the Combined-Cycle Engine Large Scale Inlet Mode Transition Experiment (CCE-LIMX) which is a fully integrated TBCC propulsion system with flowpath sizing consistent with previous NASA and DoD proposed Hypersonic experimental flight test plans. This experiment was tested in the NASA GRC 10 by 10-Foot Supersonic Wind Tunnel (SWT) Facility. The goal of this activity is to address key hypersonic combined-cycle engine issues including: (1) dual integrated inlet operability and performance issues-unstart constraints, distortion constraints, bleed requirements, and controls, (2) mode-transition sequence elements caused by switching between the turbine and the ramjet/scramjet flowpaths (imposed variable geometry requirements), and (3) turbine engine transients (and associated time scales) during transition. Testing of the initial inlet and dynamic characterization phases were completed and smooth mode transition was demonstrated. A database focused on a Mach 4 transition speed with limited off-design elements was developed and will serve to guide future TBCC system studies and to validate higher level analyses.

Foster, Lancert E.↗
NASA NTRSDate not supplied

The noise dose range of the X-59 estimated from propagation simulations

NASA’s X-59 aircraft will soon be flown in a series of low-boom community noise surveys across the USA. Community test experimental designs will include noise dose schedules, which require an estimate of the noise dose range that X-59 can produce. To determine the dose range, propagation simulations using NASA’s PCBoom code were completed using CFD-generated X-59 C612A near-field pressure data for an on-design condition (Mach 1.4 at 53,200 feet) and two off-design conditions (Mach 1.4 at 46,000 ft and Mach 1.3 at 43,000 ft). Near-field data at four cardinal aircraft headings were propagated through one year of realistic atmospheric profiles from the Climate Forecast System Version 2 database at several USA locations. Statistics of the mean and range of the Perceived Level (PL) of the booms in the inner 40 km of each carpet as well as the carpet width were computed. Results indicate the typical inner carpet mean PL is approximately 70-87 dB depending on the aircraft condition. The typical PL range across the inner carpet is 6-8 dB (on-design) and 5-13 dB (off-designs). The carpet widths ranged from 32-55 km. Understanding these quantities for various flight and atmospheric conditions is important for planning X-59 community noise surveys.

X-59↗
DOE OSTI2024

Critical Assessment of Electronic Structure Descriptors for Predicting Perovskite Catalytic Properties

The discovery and design of materials which can efficiently catalyze the oxygen reduction and evolution reactions at reduced temperatures is important for facilitating the widespread adoption of fuel cell and electrolyzer technologies. Numerous studies have produced correlations between catalytic properties, such as oxygen surface exchange or electrode area specific resistance (ASR), and properties of the catalyst material. However, correlations have historically been limited in scope (e.g., using only a few materials or at a single temperature) and it has been difficult to provide detailed assessments of their robustness. Here, in this study, we assess the ability of the O p-band center electronic structure descriptor, obtained from density functional theory (DFT) calculations, to correlate with oxygen surface exchange rates, diffusivities, and area specific resistances for a large database of perovskite oxide catalytic properties. By data mining the literature, we obtain 747 catalytic property value data points spanning 299 unique perovskite compositions from 313 studies. We assess linear correlations of each property with the O p-band center and find generally modest correlations that are qualitatively useful (prediction mean absolute errors of about 0.5 log units are typical), where the correlations are improved at higher temperatures (e.g., 800 °C vs. 500 °C) and significantly improve when considering fits to the subset of materials which have multiple independent measurements. These findings suggest that the spread of property data is significantly influenced by experimental uncertainty, and subsequent measurements of additional materials will likely improve the O p-band center correlations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2014

Intermolecular symmetry-adapted perturbation theory study of large organic complexes

Binding energies for the complexes of the S12L database by Grimme [Chem. Eur. J. 18, 9955 (2012)] were calculated using intermolecular symmetry-adapted perturbation theory combined with a density-functional theory description of the interacting molecules. The individual interaction energy decompositions revealed no particular change in the stabilisation pattern as compared to smaller dimer systems at equilibrium structures. This demonstrates that, to some extent, the qualitative description of the interaction of small dimer systems may be extrapolated to larger systems, a method that is widely used in force-fields in which the total interaction energy is decomposed into atom-atom contributions. A comparison of the binding energies with accurate experimental reference values from Grimme, the latter including thermodynamic corrections from semiempirical calculations, has shown a fairly good agreement to within the error range of the reference binding energies.

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