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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Validation of a Two-Phase CFD Model for Predicting Tank Self-Pressurization in the Ground-Based K-Site Experiment

A two-phase CFD model for self-pressurization of a cryogenic storage tank partially filled with liquid hydrogen is presented using the Volume-Of-Fluid approach for representing the phase boundary and the associated interfacial heat, mass and momentum transfer between the liquid and the vapor regions. The CFD model is validated against self-pressurization experiment performed in the K-site flightweight hydrogen storage tank at NASA Glenn Research Center. Laminar and turbulent simulations together with conjugated heat transfer analysis are performed. Effects of turbulence, as well as tank wall conduction are presented and discussed. Predicted tank pressures and fluid temperatures are compared with the experimental data at two different heat loads for validating the CFD model.

phase change↗
NASA NTRS2018

NASA ACC High Energy Dynamic Impact Methodology and Outcomes

For High Energy Dynamic Impact (HEDI) events, testing to evaluate the structural response of primary aircraft structure for design and certification is both expensive and time consuming. This paper discusses current work seeking to assess, develop, and validate appropriate analytical models that accurately predict physical response, damage, and failure modes for large scale composite structures in HEDI events. Four state-of-the-art Progressive Damage Analysis (PDA) methods were employed for this phased project: LS-DYNA MAT162, LS-DYNA MAT261, Smoothed Particle Galerkin (SPG), and EMU Peridynamics. Multiple material systems were considered, namely T700/5208 textile-infusion triaxial braid, T800/AMD-825 textile-infusion triaxial braid, IM7/8552 uni-directional tape, and SPG 196-PW/8552 plain-weave fabric. Extensive ballistic impact testing was performed to support this activity and measured results were compared to predictive models for assessment using panel delamination, panel displacement, force at the load cells, and threshold velocity as measures. Ultimately, the work under this activity provided significant progress in advancing the state-of-the-art in the use of PDA for HEDI events. Each material model had favorable performance comparing to test in some parameters and needed improvement in others. With the lessons learned from this activity, significant progress was made in the ability to predict panel behavior for a more general case beyond the flat panel in a ballistic impact event. Subsequent Phase II of the NASA ACC HEDI effort will continue to build on the coupon testing, flat panel ballistic impact testing, and analysis performed to-date with application of the PDA methods for intended material selections to test articles with greater complexity of configuration, curvature, and scale. It is not the intention of this paper to present a full set of data, but rather to give an overview of the NASA HEDI effort and show a small representative subset of the test and analysis results.

Hunziker, Kenneth J.↗
DOE OSTI2020

Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images

Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). Overall, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R 2 of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R 2 of up to 0.73.

14 SOLAR ENERGY↗
DOE OSTI2016

A Review of Current Machine Learning Methods Used for Cancer Recurrence Modeling and Prediction

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type has become a necessity in cancer research. A major challenge in cancer management is the classification of patients into appropriate risk groups for better treatment and follow-up. Such risk assessment is critically important in order to optimize the patient’s health and the use of medical resources, as well as to avoid cancer recurrence. This paper focuses on the application of machine learning methods for predicting the likelihood of a recurrence of cancer. It is not meant to be an extensive review of the literature on the subject of machine learning techniques for cancer recurrence modeling. Other recent papers have performed such a review, and I will rely heavily on the results and outcomes from these papers. The electronic databases that were used for this review include PubMed, Google, and Google Scholar. Query terms used include “cancer recurrence modeling”, “cancer recurrence and machine learning”, “cancer recurrence modeling and machine learning”, and “machine learning for cancer recurrence and prediction”. The most recent and most applicable papers to the topic of this review have been included in the references. It also includes a list of modeling and classification methods to predict cancer recurrence.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1979

Parametric instabilities in computer system performance prediction

Results found by applying a predictive model to a particular system (University of Maryland Computer Center's Univak 1100/42) are presented. Given the correct model parameters, the system performance from the model closely matches the actual system observed performance. However, the major application of system modeling is the prediction of performance when the system configuration is altered. The prediction results presented are disappointing but useful with the major problem traced to parametric instabilities. The interactions between the user demands, overhead activities, and device characteristics must be considered in developing system models.

Dowdy, L. W.↗
DOE OSTI2022

Search for coherent elastic neutrino-nucleus scattering at a nuclear reactor with CONNIE 2019 data

The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) is taking data at the Angra 2 nuclear reactor with the aim of detecting the coherent elastic scattering of reactor antineutrinos with silicon nuclei using charge-coupled devices (CCDs). In 2019 the experiment operated with a hardware binning applied to the readout stage, leading to lower levels of readout noise and improving the detection threshold down to 50 eV. The results of the analysis of 2019 data are reported here, corresponding to the detector array of 8 CCDs with a fiducial mass of 36.2 g and a total exposure of 2.2 kg-days. The difference between the reactor-on and reactor-off spectra shows no excess at low energies and yields upper limits at 95% confidence level for the neutrino interaction rates. In the lowest-energy range, 50 − 180 eV, the expected limit stands at 34 (39) times the standard model prediction, while the observed limit is 66 (75) times the standard model prediction with Sarkis (Chavarria) quenching factors.[graphic not available: see fulltext]

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Enhanced matter power spectrum from axion kination after Big Bang nucleosynthesis

Despite stringent constraints from Big Bang Nucleosynthesis (BBN) and cosmic microwave background (CMB) observations, it is still possible for well-motivated particle physics models to substantially alter the cosmic expansion history between BBN and recombination. In this work we consider two different axion models that can realize a period of first matter domination, then kination, in this epoch. We perform fits to both primordial element abundances as well as CMB data and determine that up to a decade of late axion domination is allowed by these probes of the early universe. We establish the implications of late axion domination for the matter power spectrum on the scales 1/Mpc ≲ k ≲ 10 3 /Mpc. Our 'log' model predicts a relatively modest bump-like feature together with a small suppression relative to the standard ΛCDM predictions on either side of the enhancement. Our 'two-field' model predicts a larger, plateau-like feature that realizes enhancements to the matter power spectrum of up to two orders of magnitude. These features have interesting implications for structure formation at the forefront of current detection capabilities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Measurement of the $H \rightarrow \gamma \gamma $ and $H \rightarrow ZZ^* \rightarrow 4 \ell $ cross-sections in $pp$ collisions at $\sqrt{s}=13.6$ TeV with the ATLAS detector

The inclusive Higgs boson production crosssection is measured in the di-photon and the Z Z* → 4$\ell$ decay channels using 31.4 and 29.0 fb –1 of pp collision data respectively, collected with the ATLAS detector at a centreof-mass energy of $\sqrt{s}$ = 13.6 TeV. To reduce the model dependence, the measurement in each channel is restricted to a particle-level phase space that closely matches the channel’s detector-level kinematic selection, and it is corrected for detector effects. These measured fiducial cross-sections are σ fid,γγ = $76^{+14}_{–13}$ fb, and $σ_{\text{fid,4}\ell}$ = 2.80 ± 0.74 fb, in agreement with the corresponding Standard Model predictions of 67.6±3.7 fb and 3.67±0.19 fb. Assuming Standard Model acceptances and branching fractions for the two channels, the fiducial measurements are extrapolated to the full phase space yielding total cross-sections of σ (pp → H) = $67^{+12}_{–11}$ pb and 46±12 pb at 13.6 TeV from the di-photon and Z Z* → 4$\ell$ measurements respectively. The two measurements are combined into a total cross-section measurement of σ (pp → H) = 58.2±8.7 pb, to be compared with the Standard Model prediction of σ (pp → H) SM = 59.9 ± 2.6 pb.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Total kinetic energy release in the fast neutron induced fission of actinide nuclei

The total kinetic energy release and fission mass distributions for the fast neutron (En = 3–100 MeV) induced fission of 232 Th, 233 U, 235 U, 237 Np, 239 Pu, 240 Pu, and 242 Pu have been measured using the LANSCE facility. The neutron energies were deduced from time-of- flight measurements. The fission fragments were detected using Si PIN diode detectors, giving us the fragment energies. The actinide targets were made by vapor deposition leading to high-quality targets, that were thin and uniform with reduced impurities. Corrections were made to the data for pulse height defect and the fragment energy loss in the target and its backing. The TKE distributions were Gaussian in shape and their mean value as a function of incoming neutron energy could be fitted with second order polynomials. In the case of 233 U and 235 U, our measurements agree with prior work. Our measurements for 232 Th are unique. Our data agree with Viola scaling. The constant position of the heavy mass peak is interpreted as being due to the influence of the N = 88 and Z = 50 shells. The GEF model predictions agree with the data in general as do the CGMF model predictions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1992

Weakly nonlinear models for turbulent mixing in a plane mixing layer

New closure models for turbulent free shear flows are presented in this paper. They are based on a weakly nonlinear theory with a description of the dominant large-scale structures as instability waves. Two models are presented that describe the evolution of the free shear flows in terms of the time-averaged mean flow and the dominant large-scale turbulent structure. The local characteristics of the large-scale motions are described using linear theory. Their amplitude is determined from an energy integral analysis. The models have been applied to the study of an incompressible mixing layer. For both models, predictions of the mean flow developed are made. In the second model, predictions of the time-dependent motion of the large-scale structures in the mixing layer are made. The predictions show good agreement with experimental observations.

Liou, William W.↗
NASA NTRS1993

Textures and clusters

We discuss the properties of galaxy clusters expected in a texture-seeded, CDM-dominated, Omega = 1 universe. Assuming that the textures are spherical, we use the spherical collapse model to compute the cluster velocity dispersion (or temperature) distribution function. For objects of mass 10 exp 11 to 10 exp 15 solar masses, we find v varies as M super gamma with gamma of about 0.25. An unbiased (b = 1) texture model predicts too many high-velocity dispersion clusters. A biased texture model appears to be compatible with cluster properties inferred from optical and X-ray observations. In the texture model, the cluster velocity distribution functio does not evolve rapidly; thus, the model predicts the existence of rich clusters at moderate redshift (about 1-2).

Bartlett, James G.↗
NASA NTRS2011

Spatial Distribution of Volcanic Hotspots and Paterae on Io: Implications for Tidal Heating Models and Magmatic Pathways

Io, the innermost of Jupiter's Galilean satellites, is the most volcanically active body in the Solar. System. Io's global mean heat flow is approximately 2 W/square m, which is approximately 20 times larger than on Earth. High surface temperatures concentrate within "hotspots" and, to date, 172 Ionian hotspots have been identified by spacecraft and Earth-based telescopes. The Laplace resonance between Io, Europa, and Ganymede maintains these satellites in noncircular orbits and causes displacement of their tidal bulges as the overhead position of Jupiter changes for each moon. Gravitational interactions between Jupiter and Io dominate the orbital evolution of the Laplacian system and generate enormous heat within to as tidal energy is dissipated. If this energy were transferred out of Io at the same rate as it is generated, then the associated surface heat flux would be 2.24 +/- 0.45 W/square m. This estimate is in good agreement with observed global heat flow, but to better constrain tidal dissipation mechanisms and infer how thermal energy is transferred to Io's surface, it is critical to closely examine the spatial distribution of volcanic features. End-member tidal dissipation models either consider that heating occurs completely in the mantle, or completely in the asthenosphere. Mixed models typically favor one-third mantle and two-thirds asthenosphere heating. Recent models also consider the effects of mantle-asthenosphere boundary permeability and asthenospheric instabilities. Deep-mantle heating models predict maximum surface heat flux near the poles, whereas asthenosphere heating models predict maxima near the equator-particularly in the Sub-Jovian and Anti-Jovian hemispheres, with smaller maxima occurring at orbit tangent longitudes. Previous studies have examined the global distribution of Ionian hotspots and patera (i.e., irregular or complex craters with scalloped edges that are generally interpreted to be volcanic calderas), but in this study, we combine a new geospatial analysis technique with an improved hotspot and paterae database .

Hamilton, C. W.↗
NASA NTRS2000

A Strip-Yield Model for Predicting the Growth of Part-Through Cracks Under Cyclic Loading

Flaws exist in aircraft structures due to manufacturing operations and material defects. Under variable amplitude cyclic loading, these flaws grow as part-through cracks reducing the residual strength of structural components. To meet damage tolerant design requirements, accurate flaw growth predictions are needed which account for continual changes in crack shape as well as crack growth retardation and acceleration. Predicting the growth of part-through cracks under cyclic loading using an innovative and computationally efficient model is the focus of the research summarized in this report. In this research effort, a slice synthesis methodology was developed and used to construct a modified strip-yield model for the part-through semi-elliptical surface flaw, enabling prediction of plasticity-induced closure along the crack front and subsequent fatigue crack growth under constant amplitude and variable amplitude loading. While modeling the plasticity-induced closure in a part-through flaw may be performed using three dimensional elastic-plastic finite element analysis, this type of effort is impractical from an engineering perspective. A modified strip-yield model similar to that used in FASTRAN for part-through flaws is a much needed engineering design tool, particularly when computational resources are limited.

Daniewicz, S. R.↗
NASA NTRSDate not supplied

Validation of a Two-Phase CFD Model for Predicting Tank Self-Pressurization in the Ground-Based K-Site Experiment

A two-phase CFD model for self-pressurization of a cryogenic storage tank partially filled with liquid hydrogen is presented using the Volume-Of-Fluid approach for modeling two-phase flow, as well as interfacial heat, mass and momentum transfer between the liquid and the vapor regions. The CFD model is validated against self-pressurization experiment performed using the K-site flightweight hydrogen storage tank at NASA Glenn Research Center1. Laminar and turbulent simulations are performed together with conjugated heat transfer analysis. Effects of turbulence, the value of accommodation coefficient used for predicting phase change rates, as well as tank wall geometry are presented and discussed. Predicted tank pressures, fluid and wall temperatures are compared with the experimental data at 49% fill level and two different heat loads for validating this CFD model.

Computational Fluid Dynamics↗
DOE OSTI2021

Reliable modeling and prediction of precipitation & radiation for mountainous hydrology

This White Paper focuses on data-driven atmospheric process model emulation and atmospheric process surrogate model development. It proposes leveraging recent AI advances in these approaches to fill in unavoidable observational gaps and enable high-fidelity modeling/predictability of the atmosphere and land-surface interactions in mountainous watersheds. This approach will support studies and predictability of water cycle extremes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Validation of a Two-Phase CFD Model for Predicting Tank Self-Pressurization in the Ground-Based K-Site Experiment

A two-phase CFD model for self-pressurization of a cryogenic storage tank partially filled with liquid hydrogen is presented using the Volume-Of-Fluid approach for modeling two-phase flow, as well as interfacial heat, mass and momentum transfer between the liquid and vapor regions. The CFD model is validated against self-pressurization experiment performed using the K-site flightweight hydrogen storage tank at NASA Glenn Research Center 1 . Laminar and turbulent simulations are performed together with conjugate heat transfer analysis. Effects of turbulence, the value of accommodation coefficient used for predicting phase change rates, as well as tank wall geometry are presented and discussed. Predicted tank pressures, fluid and wall temperatures are compared with the experimental data at 49% fill level and two different heat loads for validating this CFD model.

Computational Fluid Dynamics↗
DOE OSTI2024

Exclusive η Electro-Production Beam Spin Asymmetry Measurements using CLAS12 at Jefferson Lab

The exploration of nucleon structure and electromagnetic transitions from ground-state to excited-state is a cornerstone of nuclear physics research. Meson electro-production experiments have opened new avenues for investigating these phenomena, particularly in the 12 GeV era at Jefferson Lab with the CLAS12 spectrometer. The ?N final states, accessible only through isospin resonances I = 1/2, provide a unique tool for studying nucleon excitations. By simplifying the analysis and enabling a cleaner extraction of resonance properties compared to the extensively studied ?N final states, ? electroproduction offers a complementary approach to unraveling the structure of excited nucleons. This work presents the first-ever measurement of the beam spin asymmetry (BSA) in exclusive ? electroproduction, covering a previously unexplored kinematic region with 1.6 ? W ? 2.2 GeV. The BSA is extracted from the CLAS12 data using a comprehensive analysis framework that carefully considers the statistical limitations of the data set. The results are compared to predictions from theoretical models, such as the Jülich-Bonn-Washington (JBW) and MAID, as well as compared to previously published cross-section and spin observable results from CLAS and SLAC. Notably, the extracted BSA exhibits discrepancies with the model predictions, highlighting the potential for refining theoretical descriptions of nucleon resonances and their electromagnetic couplings through the incorporation of these new data. The high precision data obtained in this previously unmeasured kinematic region now serve as valuable input for theorists to refine their models.

Illari, Isabella↗