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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 487 records · Page 27

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NASA NTRS2013

Elemental GCR Observations during the 2009-2010 Solar Minimum Period

Using observations from the Cosmic Ray Isotope Spectrometer (CRIS) onboard the Advanced Composition Explorer (ACE), we present new measurements of the galactic cosmic ray (GCR) elemental composition and energy spectra for the species B through Ni in the energy range approx. 50-550 MeV/nucleon during the record setting 2009-2010 solar minimum period. These data are compared with our observations from the 1997-1998 solar minimum period, when solar modulation in the heliosphere was somewhat higher. For these species, we find that the intensities during the 2009-2010 solar minimum were approx. 20% higher than those in the previous solar minimum, and in fact were the highest GCR intensities recorded during the space age. Relative abundances for these species during the two solar minimum periods differed by small but statistically significant amounts, which are attributed to the combination of spectral shape differences between primary and secondary GCRs in the interstellar medium and differences between the levels of solar modulation in the two solar minima. We also present the secondary-to-primary ratios B/C and (Sc+Ti+V)/Fe for both solar minimum periods, and demonstrate that these ratios are reasonably well fit by a simple "leaky-box" galactic transport model that is combined with a spherically symmetric solar modulation model.

Solar Modulation↗

NASA NTRS2016

Applications of Displacement Transfer Functions to Deformed Shape Predictions of the G-III Swept-Wing Structure

In support of the Adaptive Compliant Trailing Edge [ACTE] project at the NASA Armstrong Flight Research Center, displacement transfer functions were applied to the swept wing of a Gulfstream G-III airplane (Gulfstream Aerospace Corporation, Savannah, Georgia) to obtain deformed shape predictions. Four strainsensing lines (two on the lower surface, two on the upper surface) were used to calculate the deformed shape of the G III wing under bending and torsion. There being an insufficient number of surface strain sensors, the existing G III wing box finite element model was used to generate simulated surface strains for input to the displacement transfer functions. The resulting predicted deflections have good correlation with the finite-element generated deflections as well as the measured deflections from the ground load calibration test. The convergence study showed that the displacement prediction error at the G III wing tip can be reduced by increasing the number of strain stations (for each strain-sensing line) down to a minimum error of l.6 percent at 17 strain stations; using more than 17 strain stations yielded no benefit because the error slightly increased to 1.9% when 32 strain stations were used.

displacement theory↗

NASA NTRSDate not supplied

Analysis of Probe Surveys Conducted in the 10-MW TP3 Arc-Jet Facility 15-Inch Nozzle Flow

Computational simulations and analysis of flow characterization tests in a high enthalpy arc-jet facility at NASA Ames Research Center are reported. These tests were conducted in the 10-MW TP3 facility 15-inch conical nozzle, and pitot pressure and heat flux probes are used to survey test flow downstream of the nozzle exit. Two 1.59-cm (0.625 in) diameter hemisphere probes were used: one with a stagnation-point pressure port, the other with a Gardon heat flux gage. Calibration data were also obtained using 10.16-cm iso-q and 20.32-cm flat-faced slug calorimeters. Experimental surveys of arc-jet test flow with pitot pressure and heat flux probes were obtained at eight arc-heater conditions, covering wide ranges in arc current (316–1756 A) and mass flow rate (32–500 g/s, without any cold-gas injection at the arc-heater plenum) and providing assessment of the flow uniformity in the facility. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzle and test box, including the models tested. These simulations take into account non-uniform total enthalpy and mass flux profiles at the nozzle inlet as well as the expansion waves emanating from the nozzle exit and their effects on the model flowfields. Comparisons of computations with the experimental measurements are presented, showing reasonably good agreement. The probe survey data and accompanying analysis show that the test flow in the TP3 15-inch nozzle is highly non-uniform at most conditions, and the extent of flow non-uniformity depends on arc current and mass flow rate.

Arc-jets↗

NASA NTRSDate not supplied

Analysis of Probe Surveys Conducted in the 10-MW TP3 Arc-Jet Facility 15-Inch Nozzle Flow

Computational simulations and analysis of flow characterization tests in a high enthalpy arc-jet facility at NASA Ames Research Center are reported. These tests were conducted in the 10-MW TP3 facility 15-inch conical nozzle, and pitot pressure and heat flux probes are used to survey test flow downstream of the nozzle exit. Two 1.59-cm (0.625 in) diameter hemisphere probes were used: one with a stagnation-point pressure port, the other with a Gardon heat flux gage. Calibration data were also obtained using 20.32-cm flat-faced slug calorimeters. Experimental surveys of arc-jet test flow with pitot pressure and heat flux probes were obtained at eight arc-heater conditions, covering wide ranges in arc current (316–1756 A) and mass flow rate (32–500 g/s, without any cold-gas injection at the arc-heater plenum) and providing assessment of the flow uniformity in the facility. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzle and test box, including the models tested. These simulations take into account non-uniform total enthalpy and mass flux profiles at the nozzle inlet as well as the expansion waves emanating from the nozzle exit and their effects on the model flowfields. Comparisons of computations with the experimental measurements are presented, showing reasonably good agreement. The probe survey data and accompanying analysis show that the test flow in the TP3 15-inch nozzle is highly non-uniform at most conditions, and the extent of flow non-uniformity depends on arc current and mass flow rate.

Arc-jets↗

NASA NTRS2023

Object Detection and Pose Estimation Public Competition Sample Data

This data was created with open-source tooling for the purpose of running a public competition. This dataset is a sample of what can be generated for the commissioned competition runner to be able to set up their testing infrastructure. It contains images of publicly available spacecraft models in a Blender scene composed of a light source, a background image, and the spacecraft model with bounding box labels.

James Berck↗

NASA NTRSDate not supplied

On Characterization of Flow Disturbances in Arc-Jet Testing

This paper reports computational simulations and analysis of flow characterization tests in high enthalpy arc-jet facilities at NASA Ames Research Center. Flow disturbances and their characterization are presented through case studies, and their implications for testing are discussed. The case studies include free-jet test configurations from three different arc heaters and nozzles: the 60-MW IHF 30-inch conical nozzle, the 10-MW TP3 15-inch conical nozzle, and the 20-MW AHF 12-inch conical nozzle. For all cases, test articles are placed in the jet exiting the conical nozzle, and the existence of a flow disturbance is confirmed through flow survey data using pitot pressure and heat flux probes and accompanying analysis. The paper focuses on cases where the effects of the disturbances on test article surface quantities are important. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzles and test box, including the models tested. Comparisons of computations with the experimental measurements are presented. The computations that reproduce the probe sweep data approximately are essential to interpret the arc-jet test data accurately, while providing insights into several observed calorimeter anomalies caused by flow disturbances.

Arc-jet testing↗

NASA NTRSDate not supplied

On Characterization of Flow Disturbances in Arc-Jet Testing

Arc-jet testing↗

DOE OSTI2023

Reactor Network Analysis with Various Reaction Mechanisms to Investigate Hydrogen vs. Methane Fuel at Varying Flame Temperatures with Experimental Data

Emissions data were evaluated for a set of test hardware that adapts Collins Aerospace’s aeroengine liquid fueled injector technology to a ground-based turbine. The hardware was specifically developed to operate on 100% hydrogen; however it has the ability to operate on both pure methane and hydrogen and mixtures in between. 16 injector configurations and seven factors (air split, fuel and air swirl, pressure drop, preheat temperature, fuel composition, and flame temperature) were investigated based on a Box Behnken statistical model. Of the 16, configuration 3 was selected for further discussion being in the middle of the statistical design space. A chemical reactor network was developed based on the experimental data obtained and was assessed with three different mechanisms: GRI Mech 3.0, UCSD, and Galway. All factors were held constant except the fuel composition to directly compare chemical pathways for methane and hydrogen. This study was conducted across different flame temperatures of 1500K, 1675K, and 1850K for both hydrogen and methane. Additional lower flame temperatures of 1100K and 1300K were evaluated for hydrogen. Moreover, results for fully premixed and non-premixed results were compared to obtain information on the implications of mixedness on emissions. These results were compared with the Leonard and Stegmaier plot for methane and a similar plot was constructed for hydrogen.

08 HYDROGEN↗

DOE OSTI2017

Evolving Waves and Turbulence in the Outer Corona and Inner Heliosphere: The Accelerating Expanding Box

Alfvénic fluctuations in the solar wind display many properties reflecting an ongoing nonlinear cascade, e.g., a well-defined spectrum in frequency, together with some characteristics more commonly associated with the linear propagation of waves from the Sun, such as the variation of fluctuation amplitude with distance, dominated by solar wind expansion effects. Therefore, both nonlinearities and expansion must be included simultaneously in any successful model of solar wind turbulence evolution. Because of the disparate spatial scales involved, direct numerical simulations of turbulence in the solar wind represent an arduous task, especially if one wants to go beyond the incompressible approximation. Indeed, most simulations neglect solar wind expansion effects entirely. Here we develop a numerical model to simulate turbulent fluctuations from the outer corona to 1 au and beyond, including the sub-Alfvénic corona. The accelerating expanding box (AEB) extends the validity of previous expanding box models by taking into account both the acceleration of the solar wind and the inhomogeneity of background density and magnetic field. Our method incorporates a background accelerating wind within a magnetic field that naturally follows the Parker spiral evolution using a two-scale analysis in which the macroscopic spatial effect coupling fluctuations with background gradients becomes a time-dependent coupling term in a homogeneous box. In this paper we describe the AEB model in detail and discuss its main properties, illustrating its validity by studying Alfvén wave propagation across the Alfvén critical point.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1985

Joint design for improved fatigue life of diffusion-bonded box-stiffened panels

Simple photoelastic models were used to identify a cross-section geometry that would eliminate the severe stress concentrations at the bond line between box stiffeners diffusion bonded to a panel skin. Experimental fatigue-test data from titanium test specimens quantified the allowable stress in terms of cycle life for various joint geometries. It is shown that the effect of stress concentration is reduced and an acceptable fatigue life is achieved.

Davis, R. C.↗

DOE OSTI2025

Measuring the Aerosol Collection Efficiency and Detector Face Deposition of the Bladewerx KatanaGBM™ (Glove Box Monitor) Continuous Air Monitor

To assist Bladewerx LLC (the Requestor) in testing their new CAM (continuous air monitor) sampler model Bladewerx™ KatanaGBM™ (Glove Box Monitor), the Laboratory (LANL, i.e. Los Alamos National Laboratory) measured the aerosol particle collection efficiency and detector face deposition for several experimental test conditions. Bladewerx LLC provided a prototype KatanaGBM with a set of requested tests. According to these parameters, LANL designed and performed a series of experiments to (A.) Measure the aerosol particle collection efficiency and detector face deposition of the KatanaGBM at three air flow rates of 5, 42, and 70 ALPM (ambient liters per minute), (B.) Measure the collection efficiency and detector face deposition using two sizes of oil droplet particles: 3±1 and 10±1 µm (micron) AED (aerodynamic equivalent diameter), and (C.) Test the KatanaGBM for aerosol collection efficiency and detector face deposition with the wind tunnel’s air flow at three different angles 0°, 45° and 90° (compared to the KatanaGBM’s filter face).

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2022

A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems

Constructing surrogate models for uncertainty quantification (UQ) on complex partial differential equations (PDEs) having inherently high-dimensional O(10 n ), n ≥ 2, stochastic inputs (e.g., forcing terms, boundary conditions, initial conditions) poses tremendous challenges. The “curse of dimensionality” can be addressed with suitable unsupervised learning techniques used as a pre-processing tool to encode inputs onto lower-dimensional subspaces while retaining its structural information and meaningful properties. In this work, we review and investigate thirteen dimension reduction methods including linear and nonlinear, spectral, blind source separation, convex and non-convex methods and utilize the resulting embeddings to construct a mapping to quantities of interest via polynomial chaos expansions (PCE). Here, we refer to the general proposed approach as manifold PCE (m-PCE), where manifold corresponds to the latent space resulting from any of the studied dimension reduction methods. To investigate the capabilities and limitations of these methods we conduct numerical tests for three physics-based systems (treated as black-boxes) having high-dimensional stochastic inputs of varying complexity modeled as both Gaussian and non-Gaussian random fields to investigate the effect of the intrinsic dimensionality of input data. We demonstrate both the advantages and limitations of the unsupervised learning methods and we conclude that a suitable m-PCE model provides a cost-effective approach compared to alternative algorithms proposed in the literature, including recently proposed expensive deep neural network-based surrogates and can be readily applied for high-dimensional UQ in stochastic PDEs.

42 ENGINEERING↗

DOE OSTI2023

Performance analysis and comparison of data-driven models for predicting indoor temperature in multi-zone commercial buildings

Building thermal models, which characterize the properties of a building’s envelope and thermal mass, are essential for accurate indoor temperature and cooling/heating demand prediction. Because of their flexibility and ease of use, data-driven models are increasingly used. Here, this study compared and analyzed the performance of gray-box (resistance-capacitance) and black-box (recurrent neural network) models for predicting indoor air temperature in a real multi-zone commercial building. The developed resistance-capacitance model served as a benchmark model for which full sets of temporal data and building information were used as inputs. The recurrent neural network models were trained and tested assuming various available types and amounts of temporal data and known building physical information to investigate the effects of data and information availability. Feature importance analysis was conducted to select the key variables for different prediction targets under different scenarios. This research provides guidance in selecting an appropriate building thermal response modeling method based on the measured data availability, building physical information, and application.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations

To realize the full potential of machine learning in diverse real- world domains, it is necessary for model predictions to be readily interpretable and actionable for the human in the loop. Analysts, who are the users but not the developers of machine learning models, often do not trust a model because of the lack of transparency in associating predictions with the underlying data space. To address this problem, we propose Rivelo, a visual analytic interface that enables analysts to understand the causes behind predictions of binary classifiers by interactively exploring a set of instance-level explanations. These explanations are model-agnostic, treating a model as a black box, and they help analysts in interactively probing the high-dimensional binary data space for detecting features relevant to predictions. We demonstrate the utility of the interface with a case study analyzing a random forest model on the sentiment of Yelp reviews about doctors.

Tamagnini, Paolo↗

DOE OSTI2019

Conditioning multi-model ensembles for disease forecasting

In this study we investigate how an ensemble of disease models can be conditioned to observational data, in a bid to improve its predictive skill. We use the ensemble of influenza forecasting models gathered by the US Centers for Disease Control and Prevention (CDC) as the exemplar. This ensemble is used every year to forecast the annual influenza outbreak in the United States. The models constituting this ensemble draw on very different modeling assumptions and approximations and are a diverse collection of methods to approximate epidemiological dynamics. Currently, each models' predictions are accorded the same importance, or weight, when compiling the ensemble's forecast. We consider this equally-weighted ensemble as the baseline case which has to be improved upon. In this study, we explore whether an ensemble forecast can be improved by "conditioning" the ensemble to whatever observational data is available from the ongoing outbreak. "Conditioning" can imply according the ensemble's members different weights which evolve over time, or simply perform the forecast using the top k (equally-weighted) models. In the latter case, the composition of the "top-k-see of models evolves over time. This is called "model averaging" in statistics. We explore four methods to perform model-averaging, three of which are new. We find that the CDC ensemble responds best to the "top-k-models" approach to model-averaging. All the new MA methods perform better than the baseline equally-weighted ensemble. The four model-averaging methods treat the models as black-boxes and simply use their forecasts as inputs i.e., one does not need access to the models at all, but rather only their forecasts. The model-averaging approaches reviewed in this report thus form a general framework for model-averaging any model ensemble.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2014

Simple Statistical Model to Quantify Maximum Expected EMC in Spacecraft and Avionics Boxes

This study shows cumulative distribution function (CDF) comparisons of composite a fairing electromagnetic field data obtained by computational electromagnetic 3D full wave modeling and laboratory testing. Test and model data correlation is shown. In addition, this presentation shows application of the power balance and extention of this method to predict the variance and maximum exptected mean of the E-field data. This is valuable for large scale evaluations of transmission inside cavities.

Models↗

DOE OSTI2023

Probabilistic Evaluation of Geomechanical Risks in CO2 Storage: An Exploration of Caprock Integrity Metrics Using a Multilaminate Model

The probabilistic uncertainty assessment of geomechanical risk—specifically, caprock failure—attributable to CO2 injection, as presented in a simplified hypothetical geological model, was the focus of this study. Our approach amalgamates the implementation of a multilaminate model, the creation of a response surface model in conjunction with the Box–Behnken sampling design, the execution of associated numerical modeling experiments, and the utilization of Monte Carlo simulations. Probability distributions to encapsulate the inherent variability (elastic and mechanical properties of the caprock and reservoir) and uncertainty in prediction estimates (vertical displacement, total strain, and F value) were employed. Our findings reveal that the Young modulus of the caprock is a key factor controlling equivalent total strain but is insufficient as a stand-alone indicator of caprock integrity. It is confirmed that the caprock can accommodate significant deformation without failure, if it possesses a low Young’s modulus and high mechanical strength properties, such as the friction angle and uniaxial compressive strength. Similarly, vertical displacement was found to be an unreliable indicator for caprock integrity, as caprock failure can occur across a broad spectrum of vertical displacements, particularly when both the Young modulus and mechanical strength properties have wide ranges. This study introduces the F value as the most dependable indicator for caprock failure, although it is a theoretical attribute (the shortest distance between the Mohr circle and the nearest failure envelope used to measure the sensitivity to failure) and not physically measurable in the field. Deviatoric stress levels were found to vary based on stress regimes, with the maximum levels observed under extensive and compressive stress regimes. In conjunction with the use of the response surface method, this study demonstrates the efficacy of the multilaminate framework and the Mohr–Coulomb constitutive model in providing a simplified, yet effective, probabilistic model of the mechanical behavior of caprock failure, reducing mathematical and computational complexities.

Energy & Fuels↗

DOE OSTI2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Batatia, Ilyes↗