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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Subsonic high-angle-of-attack aerodynamic characteristics of a cone and cylinder with triangular cross sections and a cone with a square cross section

Experiments were conducted in the 12-Foot Pressure Wind Tunnel at Ames Research Center on three models with noncircular cross sections: a cone having a square cross section with rounded corners and a cone and cylinder with triangular cross sections and rounded vertices. The cones were tested with both sharp and blunt noses. Surface pressures and force and moment measurements were obtained over an angle of attack range from 30 deg to 90 deg and selected oil-flow experiments were conducted to visualize surface flow patterns. Unit Reynolds numbers ranged from 0.8x1,000,000/m to 13.0x1,000,000/m at a Mach number of 0.25, except for a few low-Reynolds-number runs at a Mach number of 0.17. Pressure data, as well as force data and oil-flow photographs, reveal that the three dimensional flow structure at angles of attack up to 75 deg is very complex and is highly dependent on nose bluntness and Reynolds number. For angles of attack from 75 deg to 90 deg the sectional aerodynamic characteristics are similar to those of a two dimensional cylinder with the same cross section.

Clarkson, M. H.↗
DOE OSTI2023

Predicting oxidation damage of ultra high-temperature carbide ceramics in extreme environments using machine learning

Determining the oxidation resistance of UHTC carbides in extreme environments is challenging theoretically and experimentally due to the high dimensional complexity of influencing variables and intricate testing setups. Herein we demonstrate the use of machine learning (ML) models trained with experimental literature data to predict the oxide thickness of UHTC carbides exposed to air based on composition, mean grain size, relative densification, holding time, and temperature. A multi-dimensional database with 76 occurrences is created containing experimental results of Hf, Zr, and Ta carbides plus additives. In this study, the preprocessed database is then used to train ML models to predict their oxidation behavior. The trained model predicts the oxidation damage in the form of an average oxide thickness in UHTC carbides with a Mean Absolute Error (MAE) of ±65.45 μm for samples in the testing set that developed thicknesses up to 1000 μm. The model successfully predicted oxidation damage for a recession rate lower than 60 μm/min. It is noticed that the ensemble method MAE is increased to ±134.34 μm while forecasting the oxidation of samples with a recession rate higher than the threshold. The unprecedented approach is a novel way to predict the damage through the oxidation of carbide compounds before processing for a smarter design with room for improvement.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fast tree-based algorithms for DBSCAN for low-dimensional data on GPUs

DBSCAN is a well-known density-based clustering algorithm to discover arbitrary shape clusters. While conceptually simple in serial, the algorithm is challenging to efficiently parallelize on manycore GPU architectures. Common pitfalls, such as asynchronous range query calls, result in high thread execution divergence in many implementations. In this paper, we propose a new framework for GPU-accelerated DBSCAN, and describe two tree-based algorithms within that framework. Both algorithms fuse the search for neighbors with updating cluster information, but differ in their treatment of dense regions of the data. We show that the time taken to compute clusters is at most twice that of determination of the neighbors. We compare the proposed algorithms with existing CPU and GPU implementations, and demonstrate their competitiveness and performance using a fast traversal structure (bounding volume hierarchy) for low dimensional data. We also show that the memory usage can be reduced by processing object neighbors dynamically without storing them.

Prokopenko, Andrey↗
DOE OSTI2016

Development of Criteria for Flashback Propensity in Jet Flames for High Hydrogen Content and Natural Gas Type Fuels

Due to increasingly stringent air quality requirements stationary power gas turbines have moved to lean-premixed operation, which reduces pollutant emissions but can result in flashback. Curtailing flashback can be difficult with hydrocarbon fuels and becomes even more challenging when hydrogen is used as the fuel. In fact, flashback is a key operability issue associated with low emission combustion of high hydrogen content fuels. Flashback can cause serious damage to the premixer hardware. Hence, design tools to predict flashback propensity are of interest. Such a design tool has been developed based on the data gathered by experimental study to predict boundary layer flashback using non-dimensional parameters. The flashback propensity of a premixed jet flame has been studied experimentally. Boundary layer flashback has been investigated under turbulent flow conditions at elevated pressures and temperatures (i.e. 3 atm to 8 atm and 300 K to 500 K). The data presented in this study are for hydrogen fuel at various Reynolds numbers, which are representative of practical gas turbine premixer conditions and are significantly higher than results currently available in the literature. Three burner heads constructed of different materials (stainless steel, copper, and zirconia ceramic) were used to evaluate the effect of tip temperature, a parameter found previously to be an important factor in triggering flashback. This study characterizes flashback systematically by developing a comprehensive non-dimensional model which takes into account all effective parameters in boundary layer flashback propensity. The model was optimized for new data and captures the behavior of the new results well. Further, comparison of the model with the single existing study of high pressure jet flame flashback also indicates good agreement. The model developed using the high pressure test rig is able to predict flashback tendencies for a commercial gas turbine engine and can thus serve as a design tool for identifying when flashback is likely to occur for a given geometry and condition.

03 NATURAL GAS↗
NASA NTRS1992

Internal Shock Interactions in Propulsion/Airframe Integrated Three-Dimensional Sidewall Compression Scramjet Inlets

The advantages and design requirements of propulsion/airframe integration for high Mach number flight have led to extensive study of the three-dimensional sidewall compression scramjet inlet in recent years. Recent research publications have indicated testing over a broad range of Mach number (2 to 18) in a variety of test gases, such as air, helium, and tetrafluoromethane. Multiple experimental techniques have been employed to obtain detailed internal shock interaction data, performance data, and inlet starting limits. Computational fluid dynamics has been effectively used for preliminary parametric studies as well as in parallel with experiments to aid in the explanation of unusual or unexpected flow phenomena. Inlets of this genre afford a relatively simple, generic geometry while producing a highly complex, three-dimensional flow field dominated by shock/shock and shock/boundary layer interactions. While the importance of the viscous effects in high speed inlet interactions is recognized, the present work addresses in a parametric fashion the inviscid effects of leading edge sweep, sidewall compression, and inflow Mach number on the internal shock structure in terms of inlet compression and mass capture. In the process, the source of the of the Mach number invariance with leading edge sweep for a constant sidewall compression class of inlet is identified, and a previously undocumented spillage phenomenon in a constant effective wedge angle class of inlets is discussed.

Holland, Scott D.↗
NASA NTRS1991

Standard test case for a low-speed, turbulent junction vortex flow

The mean flow structure upstream, around within, and in the near wake of a turbulent junction or horseshoe vortex is reported for an incompressible subsonic flow. Measurements of the primitive variables of velocity and pressure are reported on all surfaces bounding the region of the vortex flow and on three transverse and one streamwise plane within the flowfield itself for comparisons between the measured and any calculated flow variables. Detailed surface flow visualizations and some direct force measurements of surface shear stress are also available. The data is a highly detailed, coherent, self-consistent set offered to the computational fluid mechanics community as a standard test case for the evaluation of the capability of numerical solvers intended for predicting the flowfield in such a complex, separated, three-dimensional turbulent flow. This data base is available for copying to user supplied tapes or for transmission via BITNET, as well as in two National Technical Information Service (NTIS) reports.

Pierce, F. J.↗
DOE OSTI2022

Status of the OECD/NEA Watts Bar unit 1 benchmark and calculations of local reactor core power data of the Zero Power physics tests

The paper aims to provide an update on the status of the Organization for Economic Cooperation and Development (OECD) / the Nuclear Energy Agency (NEA) Tennessee Valley Authority (TVA) Watts Bar 1 (WB1) Multi-Physics Multi-Cycle depletion benchmark and the results associated with Exercise 1. The benchmark relies on the set of benchmark progression problems developed by the Department of Energy (DOE) Consortium for the Advanced Simulation of Light Water Reactors (CASL) for the Virtual Environment for Reactor (VERA), which are based on real plant design and operational data. The OECD/NEA TVA WB1 benchmark is designed for validation of both traditional and novel high-fidelity multi-physics codes to analyze Pressurized Water Reactors (PWR) depletion cycles. The activities are conducted under the Expert Group on Reactor Systems Multi-Physics (EGMUP) at NEA/OECD. In this work, we analyze Exercise 1 of the benchmark: stand-alone Three-Dimensional (3D) neutronics at Start-up Zero Power Physics Test (ZPPT) at Hot Zero Power Conditions (HZP) and the resulting power maps. The code used to model the exercise is the continuous energy Monte Carlo code Serpent 2.1.31 along with the nuclear data library ENDF/B-VII.1. Serpent results are compared with the high-fidelity deterministic code MPACT, which is part of VERA. The paper presents three selected results from power calculations required output. The results compared are the normalized axially integrated radial core power maps, the normalized axial averaged core power shapes, and the normalized core hottest and coldest assemblies radial power maps. The Root Mean Square Deviation (RMSD) between Serpent and MPACT is 0.51 % for the axially integrated radial core power map, 1.41 % for the axial averaged core power shape, 2.42 % for the hottest assembly axial power shape, 0.88 % for the coldest assembly axial integrated hottest assembly radial pin power map, and 0.17 % for the axially integrated coldest assembly radial pin power map. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Multi-Gas Sensors for Enhanced Reliability of SOFC Operation

GE Research, in partnership with SUNY Polytechnic Institute (SUNY Poly), designed, built, and tested gas sensors for in situ monitoring of H 2 and CO anode tail gases produced with on-site steam reforming in solid oxide fuel cell (SOFC) systems. The knowledge of the H 2 /CO ratio of these anode tail gases should allow accurate determination and control of the efficiency of the reforming process in the SOFC system and should deliver a lower operating cost for SOFC customers. The project objectives were to achieve multi-gas monitoring capability with a single multivariable sensor, and to sustain this performance in the presence of gaseous interferences and a potential poison for the sensor. The duration of the project was 24 months with the project structure that included three technical tasks such as (1) development of design rules of photonic nanostructures for H 2 and CO gas detection, (2) laboratory validation of photonic nanostructures for selective H 2 and CO gas detection, and (3) validation of photonic nanostructures for initial stability and poison-resistance against H 2 S. To build multivariable sensors for selective H 2 and CO gas detection in the presence of interferences, we expanded our earlier knowledge of multi-gas sensors into new fabrication and functionalization methodologies as well as into new methodologies for the spectral data analysis of multi-gas responses. We have advanced our design rules of the three-dimensional (3D) photonic nanostructures that allowed detection of H 2 and CO at high temperatures as individual gases and as their mixtures and rejection of interferences such as CO 2 , H 2 O, CH 4 , and other hydrocarbons for SOFC applications. Our advanced design rules should be attractive for building the new generation of cost-effective industrial sensors. Stability and poison resistance of our 3D photonic nanostructures was tested in the laboratory conditions. While initially we utilized conventional machine learning data analysis tools, we have found that they were unable to correct for the sensor drift. Thus, we have implemented new methods of machine learning for the analysis of our spectral data. These learnings pave the way to move the future studies into advanced testing of effects of interferences, aging and field tests. In future, our work will continue to advance our sensing designs to operate in conditions with known and unknown interferences by implementing nanostructures with enhanced spectral diversity of responses to gaseous species of interest and interferences. Our systematic reduction of technical risks in this completed project and in future studies will ensure transition of this sensing technology to commercialization.

03 NATURAL GAS↗
DOE OSTI2026

Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials

This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain‐specific constraints. We introduce a two‐dimensional readiness model that combines canonical preprocessing patterns with a five‐level operational readiness scale, both tailored to high‐performance computing (HPC) environments. This construct helps outline key challenges in transforming large‐scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross‐domain support for scalable and reproducible AI for science. Finally, we evaluate this maturity matrix in the context of case studies including ClimaX (climate), AFLOW (materials), OpenFold (proteomics), and DIII‐D fusion disruption‐prediction workflows, from which we distill lessons learned and provide recommendations to guide practitioners in developing robust AI‐readiness pipelines. Finally, we discuss remaining cross‐cutting challenges that persist across scientific domains.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

High-lift aerodynamics: Trends, trades, and options

The trend toward the utilization of higher maximum lift coefficient with increased aircraft size and cruise velocities is discussed. The impact of this trend on the need for tradeoffs between cruise performance and takeoff, climb, and landing performance is examined. Theoretical methods for the analysis of the two-dimensional characteristics of flap systems are described and compared with experimental data. Four powered-lift concepts are described to outline some of the options currently being developed. Two jet-flap theories are described which provide analytical methods for estimation of the three-dimensional aerodynamic high-lift performance characteristics of powered lift systems.

Margason, R. J.↗
NASA NTRS1978

Wake vortex measurements of bodies at high angle of attack

Three-dimensional laser velocimeter measurements have been made of the wake vortices of a slender tangent-ogive body which had nose and body fineness ratios of 3.5 and 12, respectively. Data were obtained for an angle of attack to seminose angle ratio of 2.3 at a free-stream Mach number of 0.6 and unit Reynolds number of 2 million/ft. Details of the mean flow field are presented and features of the turbulent and unsteady nature of the vortex flow field are discussed. Problems associated with obtaining meaningful vortex measurements in high-speed flows are addressed.

Owen, F. K.↗
DOE OSTI2020

DPM: A deep learning PDE augmentation method with application to large-eddy simulation

A framework is introduced that leverages known physics to reduce overfitting in machine learning for scientific applications. The partial differential equation (PDE) that expresses the physics is augmented with a neural network that uses available data to learn a description of the corresponding unknown or unrepresented physics. Training within this combined system corrects for missing, unknown, or erroneously represented physics, including discretization errors associated with the PDE's numerical solution. For optimization of the network within the PDE, an adjoint PDE is solved to provide high-dimensional gradients, and a stochastic adjoint method (SAM) further accelerates training. Additionally, the approach is demonstrated for large-eddy simulation (LES) of turbulence. High-fidelity direct numerical simulations (DNS) of decaying isotropic turbulence provide the training data used to learn sub-filter-scale closures for the filtered Navier–Stokes equations. Out-of-sample comparisons show that the deep learning PDE method outperforms widely-used models, even for filter sizes so large that they become qualitatively incorrect. It also significantly outperforms the same neural network when a priori trained based on simple data mismatch, not accounting for the full PDE. Measures of discretization errors, which are well-known to be consequential in LES, point to the importance of the unified training formulation's design, which without modification corrects for them. For comparable accuracy, simulation runtime is significantly reduced. A relaxation of the typical discrete enforcement of the divergence-free constraint in the solver is also successful, instead allowing the DPM to approximately enforce incompressibility physics. Since the training loss function is not restricted to correspond directly to the closure to be learned, training can incorporate diverse data, including experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2016

Digital Image Correlation Techniques Applied to Large Scale Rocket Engine Testing

Rocket engine hot-fire ground testing is necessary to understand component performance, reliability and engine system interactions during development. The J-2X upper stage engine completed a series of developmental hot-fire tests that derived performance of the engine and components, validated analytical models and provided the necessary data to identify where design changes, process improvements and technology development were needed. The J-2X development engines were heavily instrumented to provide the data necessary to support these activities which enabled the team to investigate any anomalies experienced during the test program. This paper describes the development of an optical digital image correlation technique to augment the data provided by traditional strain gauges which are prone to debonding at elevated temperatures and limited to localized measurements. The feasibility of this optical measurement system was demonstrated during full scale hot-fire testing of J-2X, during which a digital image correlation system, incorporating a pair of high speed cameras to measure three-dimensional, real-time displacements and strains was installed and operated under the extreme environments present on the test stand. The camera and facility setup, pre-test calibrations, data collection, hot-fire test data collection and post-test analysis and results are presented in this paper.

Gradl, Paul R.↗
DOE OSTI2020

Materials Data on WO3 by Materials Project

WO3 is High-temperature superconductor-like structured and crystallizes in the tetragonal P4/ncc space group. The structure is three-dimensional. W6+ is bonded to six O2- atoms to form corner-sharing WO6 octahedra. The corner-sharing octahedra tilt angles range from 0–12°. There are a spread of W–O bond distances ranging from 1.84–2.06 Å. There are two inequivalent O2- sites. In the first O2- site, O2- is bonded in a linear geometry to two equivalent W6+ atoms. In the second O2- site, O2- is bonded in a linear geometry to two equivalent W6+ atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on WO3 by Materials Project

WO3 is High-temperature superconductor-like structured and crystallizes in the tetragonal P4/nmm space group. The structure is three-dimensional. W6+ is bonded to six O2- atoms to form corner-sharing WO6 octahedra. The corner-sharing octahedral tilt angles are 0°. There are a spread of W–O bond distances ranging from 1.84–2.07 Å. There are two inequivalent O2- sites. In the first O2- site, O2- is bonded in a linear geometry to two equivalent W6+ atoms. In the second O2- site, O2- is bonded in a linear geometry to two equivalent W6+ atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on HgCl2 by Materials Project

HgCl2 is High Pressure (4-7GPa) Tellurium structured and crystallizes in the orthorhombic Pnma space group. The structure is zero-dimensional and consists of four mercuric chloride molecules. Hg2+ is bonded in a distorted linear geometry to two Cl1- atoms. Both Hg–Cl bond lengths are 2.32 Å. There are two inequivalent Cl1- sites. In the first Cl1- site, Cl1- is bonded in a distorted single-bond geometry to one Hg2+ atom. In the second Cl1- site, Cl1- is bonded in a distorted single-bond geometry to one Hg2+ atom.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Materials Data on Sb3Au by Materials Project

AuSb3 is High-temperature superconductor structured and crystallizes in the cubic Im-3m space group. The structure is three-dimensional. Au3+ is bonded to six equivalent Sb1- atoms to form AuSb6 octahedra that share corners with six equivalent AuSb6 octahedra and edges with twelve equivalent SbSb4Au2 octahedra. The corner-sharing octahedral tilt angles are 0°. All Au–Sb bond lengths are 2.98 Å. Sb1- is bonded to two equivalent Au3+ and four equivalent Sb1- atoms to form distorted SbSb4Au2 octahedra that share corners with six equivalent SbSb4Au2 octahedra, edges with four equivalent AuSb6 octahedra, and edges with eight equivalent SbSb4Au2 octahedra. The corner-sharing octahedral tilt angles are 0°. All Sb–Sb bond lengths are 2.98 Å.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on IF3 by Materials Project

IF3 is High Pressure (4-7GPa) Tellurium structured and crystallizes in the orthorhombic Pnma space group. The structure is zero-dimensional and consists of four IF3 clusters. I is bonded in a T-shaped geometry to three F atoms. There is one shorter (1.91 Å) and two longer (2.02 Å) I–F bond length. There are two inequivalent F sites. In the first F site, F is bonded in a single-bond geometry to one I atom. In the second F site, F is bonded in a single-bond geometry to one I atom.

36 MATERIALS SCIENCE↗