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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Measuring the β-decay Properties of Neutron-rich Exotic Pm, Sm, Eu, and Gd Isotopes to Constrain the Nucleosynthesis Yields in the Rare-earth Region

Abstract The β -delayed neutron-emission probabilities of 28 exotic neutron-rich isotopes of Pm, Sm, Eu, and Gd were measured for the first time at RIKEN Nishina Center using the Advanced Implantation Detector Array (AIDA) and the BRIKEN neutron detector array. The existing β -decay half-life ( T 1/2 ) database was significantly increased toward more neutron-rich isotopes, and uncertainties for previously measured values were decreased. The new data not only constrain the theoretical predictions of half-lives and β -delayed neutron-emission probabilities, but also allow for probing the mechanisms of formation of the high-mass wing of the rare-earth peak located at A ≈ 160 in the r -process abundance distribution through astrophysical reaction network calculations. An uncertainty quantification of the calculated abundance patterns with the new data shows a reduction of the uncertainty in the rare-earth peak region. The newly introduced variance-based sensitivity analysis method offers valuable insight into the influence of important nuclear physics inputs on the calculated abundance patterns. The analysis has identified the half-lives of 168 Sm and of several gadolinium isotopes as some of the key variables among the current experimental data to understand the remaining abundance uncertainty at A = 167–172.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1977

Airframe noise of component interactions on a large transport model

Airframe noise produced by aircraft components and their interactions on a model of a wide body transport was investigated in an open jet anechoic flow facility. The two-meter span model had leading edge and multigapped trailing edge flaps, undercarriage, and associated stowage compartments. Each of these components were extended and retracted so their individual and interactive contributions to the measured airframe noise could be determined. An analysis of variance was performed which revealed a statistical significance of the component interactions. Various airframe noise reduction techniques were tried to reduce airframe noise. The effects of these techniques on the model's lift and drag were also measured.

Shearin, J. G.↗
DOE OSTI2020

A comparison of cost and quality of three methods for estimating density for wild pig (Sus scrofa)

A critical element in efective wildlife management is monitoring the status of wildlife populations; however, resources to monitor wildlife populations are typically limited. We compared cost efectiveness of three common population estimation methods (i.e. non-invasive DNA sampling, camera sampling, and sampling from trapping) by applying them to wild pigs (Sus scrofa) across three habitats in South Carolina, U.S.A where they are invasive. We used mark-recapture analyses for fecal DNA sampling data, spatially-explicit capture-recapture analyses for camera sampling data, and a removal analysis for removal sampling from trap data. Density estimates were similar across methods. Camera sampling was the least expensive, but had large variances. Fecal DNA sampling was the most expensive, although this technique generally performed well. We examined how reductions in efort by method related to increases in relative bias or imprecision. For removal sampling, the largest cost savings while maintaining unbiased density estimates was from reducing the number of traps. For fecal DNA sampling, a reduction in efort only minimally reduced costs due to the need for increased lab replicates while maintaining high quality estimates. For camera sampling, efort could only be marginally reduced before inducing bias. We provide a decision tree for researchers to help make monitoring decisions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Employing Sensitivity Derivatives to Estimate Uncertainty Propagation in CFD

Two methods that exploit the availability of sensitivity derivatives are successfully employed to predict uncertainty propagation through Computational Fluid Dynamics (CFD) code for an inviscid airfoil problem. An approximate statistical second-moment method and a Sensitivity Derivative Enhanced Monte Carlo (SDEMC) method are successfully demonstrated on a two-dimensional problem. First- and second-order sensitivity derivatives of code output with respect to code input are obtained through an efficient incremental iterative approach. Given uncertainties in statistically independent, random, normally distributed flow parameters (input variables); these sensitivity derivatives enable one to formulate first- and second-order Taylor Series approximations for the mean and variance of CFD output quantities. Additionally, incorporation of the first-order sensitivity derivatives into the data reduction phase of a conventional Monte Carlo (MC) simulation allows for improved accuracy in determining the first moment of the CFD output. Both methods are compared to results generated using a conventional MC method. The methods that exploit the availability of sensitivity derivatives are found to be valid when considering small deviations from input mean values.

Putko, Michele M.↗
NASA NTRS2006

Operational Concept for Flight Crews to Participate in Merging and Spacing of Aircraft

The predicted tripling of air traffic within the next 15 years is expected to cause significant aircraft delays and create a major financial burden for the airline industry unless the capacity of the National Airspace System can be increased. One approach to improve throughput and reduce delay is to develop new ground tools, airborne tools, and procedures to reduce the variance of aircraft delivery to the airport, thereby providing an increase in runway throughput capacity and a reduction in arrival aircraft delay. The first phase of the Merging and Spacing Concept employs a ground based tool used by Air Traffic Control that creates an arrival time to the runway threshold based on the aircraft s current position and speed, then makes minor adjustments to that schedule to accommodate runway throughput constraints such as weather and wake vortex separation criteria. The Merging and Spacing Concept also employs arrival routing that begins at an en route metering fix at altitude and continues to the runway threshold with defined lateral, vertical, and velocity criteria. This allows the desired spacing interval between aircraft at the runway to be translated back in time and space to the metering fix. The tool then calculates a specific speed for each aircraft to fly while enroute to the metering fix based on the adjusted land timing for that aircraft. This speed is data-linked to the crew who fly this speed, causing the aircraft to arrive at the metering fix with the assigned spacing interval behind the previous aircraft in the landing sequence. The second phase of the Merging and Spacing Concept increases the timing precision of the aircraft delivery to the runway threshold by having flight crews using an airborne system make minor speed changes during enroute, descent, and arrival phases of flight. These speed changes are based on broadcast aircraft state data to determine the difference between the actual and assigned time interval between the aircraft pair. The airborne software then calculates a speed adjustment to null that difference over the remaining flight trajectory. Follow-on phases still under development will expand the concept to all types of aircraft, arriving from any direction, merging at different fixes and altitudes, and to any airport. This paper describes the implementation phases of the Merging and Spacing Concept, and provides high-level results of research conducted to date.

Baxley, Brian T.↗
NASA NTRS2016

Green Propellant Landing Demonstration at U.S. Range

The Green Propellant Loading Demonstration (GPLD) was conducted December 2015 at Wallops Flight Facility (WFF), leveraging work performed over recent years to bring lower toxicity hydrazine replacement green propellants to flight missions. The objective of this collaboration between NASA Goddard Space Flight Center (GSFC), WFF, the Swedish National Space Board (SNSB), and Ecological Advanced Propulsion Systems (ECAPS) was to successfully accept LMP-103S propellant at a U.S. Range, store the propellant, and perform a simulated flight vehicle propellant loading. NASA GSFC Propulsion (Code 597) managed all aspects of the operation, handling logistics, preparing the procedures, and implementing the demonstration. In addition to the partnership described above, Moog Inc. developed an LMP-103S propellant-compatible titanium rolling diaphragm flight development tank and loaned it to GSFC to act as the GPLD flight vessel. The flight development tank offered the GPLD an additional level of flight-like propellant handling process and procedures. Moog Inc. also provided a compatible latching isolation valve for remote propellant expulsion. The GPLD operation, in concert with Moog Inc. executed a flight development tank expulsion efficiency performance test using LMP-103S propellant. As part of the demonstration work, GSFC and WFF documented Range safety analyses and practices including all elements of shipping, storage, handling, operations, decontamination, and disposal. LMP-103S has not been previously handled at a U.S. Launch Range. Requisite for this activity was an LMP-103S Risk Analysis Report and Ground Safety Plan. GSFC and WFF safety offices jointly developed safety documentation for application into the GPLD operation. The GPLD along with the GSFC Propulsion historical hydrazine loading experiences offer direct comparison between handling green propellant versus safety intensive, highly toxic hydrazine propellant. These described motives initiated the GPLD operation in order to investigate the handling and process safety variances in project resources between LMP-103S and typical in-space propellants. The GPLD risk reduction operation proved successful for many reasons including handling the green propellant at a U.S. Range, loading and pressurizing a flight-like tank, expelling the propellant, measuring the tank expulsion efficiency, and most significantly, GSFC propulsion personnel's new insight into the LMP-103S propellant handling details.

Propellant↗
NASA NTRS2016

Green Propellant Loading Demonstration at U.S. Range

The Green Propellant Loading Demonstration (GPLD) was conducted December 2015 at Wallops Flight Facility (WFF), leveraging work performed over recent years to bring lower toxicity hydrazine replacement green propellants to flight missions. The objective of this collaboration between NASA Goddard Space Flight Center (GSFC), WFF, the Swedish National Space Board (SNSB), and Ecological Advanced Propulsion Systems (ECAPS) was to successfully accept LMP-103S propellant at a U.S. Range, store the propellant, and perform a simulated flight vehicle propellant loading. NASA GSFC Propulsion (Code 597) managed all aspects of the operation, handling logistics, preparing the procedures, and implementing the demonstration. In addition to the partnership described above, Moog Inc. developed an LMP-103S propellant-compatible titanium rolling diaphragm flight development tank and loaned it to GSFC to act as the GPLD flight vessel. The flight development tank offered the GPLD an additional level of flight-like propellant handling process and procedures. Moog Inc. also provided a compatible latching isolation valve for remote propellant expulsion. The GPLD operation, in concert with Moog Inc. executed a flight development tank expulsion efficiency performance test using LMP-103S propellant. As part of the demonstration work, GSFC and WFF documented Range safety analyses and practices including all elements of shipping, storage, handling, operations, decontamination, and disposal. LMP-103S has not been previously handled at a U.S. Launch Range. Requisite for this activity was an LMP-103S Risk Analysis Report and Ground Safety Plan. GSFC and WFF safety offices jointly developed safety documentation for application into the GPLD operation. The GPLD along with the GSFC Propulsion historical hydrazine loading experiences offer direct comparison between handling green propellant versus safety intensive, highly toxic hydrazine propellant. These described motives initiated the GPLD operation in order to investigate the handling and process safety variances in project resources between LMP-103S and typical in-space propellants. The GPLD risk reduction operation proved successful for many reasons including handling the green propellant at a U.S. Range, loading and pressurizing a flight-like tank, expelling the propellant, measuring the tank expulsion efficiency, and most significantly, GSFC propulsion personnel's new insight into the LMP-103S propellant handling details.

Green Propellant↗
DOE OSTI2025

Neural Active Manifolds: Nonlinear Dimensionality Reduction for Uncertainty Quantification

We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a one-dimensional neural active manifold (NeurAM) capturing the model output variability, through the aid of a simultaneously learnt surrogate model with inputs on this manifold. Our method only relies on model evaluations and does not require the knowledge of gradients. The proposed dimensionality reduction framework can then be applied to assist outer loop many-query tasks in scientific computing, like sensitivity analysis and multifidelity uncertainty propagation. In particular, we prove, both theoretically under idealized conditions, and numerically in challenging test cases, how NeurAM can be used to obtain multifidelity sampling estimators with reduced variance by sampling the models on the discovered low-dimensional and shared manifold among models. Several numerical examples illustrate the main features of the proposed dimensionality reduction strategy and highlight its advantages with respect to existing approaches in the literature.

Autoencoders↗
NASA NTRS2010

Relationships Among Lower Body Strength, Power, and Performance of Functional Tasks

There is a large degree of variability among crewmembers with respect to decrements in muscle strength and power following long duration spaceflight, ranging from 0 to approx.30% reductions. The purpose of this study was to investigate the influence of varying decrements in lower body muscle strength and power (relative to body weight) on the performance of 2 occupationally relevant tasks (ladder climb and supine egress & walk). Seventeen participants with leg strength similar to US crewmembers performed a leg press power test, an isokinetic knee extension strength test and they were asked to complete the 2 functional tasks as quickly as possible. On additional test days the participants were asked to repeat the functional tasks under 3 conditions where a different external load was applied each time using a weighted suit in order to experimentally manipulate participants strength/body weight and power/body weight ratios. The weight in the suit ranged from 20-120% of body weight and was distributed in proportion to limb segment weights to minimize changes in center of gravity. The ladder task consisted of climbing 40 rungs on a ladder treadmill as fast as possible. The supine egress & walk task consisted of rising from a supine position and walking through an obstacle course. Results show a relatively linear relationship between strength/body weight and task time and power/body weight with task time such that the fastest performance times are associated with higher strength and power with about half the variance in task time is accounted for by a single variable (either strength or power). For the average person, a 20% reduction in power/body weight (from 18 to 14.4 W/kg) induces an increase (slowing) of about 10 seconds in the ladder climb task from 14 to 24 seconds (approx.70%) and a slowing of the supine egress & walk task from 14 to 21 seconds (approx.50%). Similar relationships were observed with strength/body weight and task performance. For the average person, a 20% reduction in strength/body weight (from 2.1 to 1.7 Nm/kg) resulted in a slowing of the ladder climb from 10.5 to 24 seconds (approx.128%) and a slowing of the supine egress & walk from 11 to 20 seconds (approx.82%). These data suggest that the single variable of either low body muscle strength or power, relative to body weight is predictive of about 50% of the variance in task performance time, and that considerable slowing in task performance is associated with relatively typical decrements in muscle performance seen with long duration spaceflight. The observation of a relatively linear relationship between strength/power and task time suggests that across the full spectrum of initial crew strengths and typical decrements in strength previously observed, that task performance would be expected to be slowed following long duration spaceflight. These data will be confirmed in actual spaceflight with subsequent studies.

Ploutz-Snyder, Lori↗
DOE OSTI2021

Experimental Studies on Wood Pellets Combustion in a Fixed Bed Combustor Using Taguchi Method

The combustion of wood pellets in a fixed bed combustor of a 20 kW capacity domestic pellet boiler was tested according to several factors including Power, Excess Air (EA), Primary/Secondary air Split Ratio (SR) and Grate Area (GA). The Taguchi method was applied to program the experimental design. Several parameters were measured, including gas emissions (CO), fuel bed temperature (measured at 4 different heights), and efficiency. The experimental results show that the lower CO emission and the higher efficiency were obtained at medium thermal loads and the highest temperature on the fuel bed was obtained at about ¼ of its height (15 mm). The results obtained from the analysis of variance (ANOVA) show that the SR and the Power are the most important factors contributing to the CO reduction and also increase the fuel bed temperature.

Castro, Carlos↗
DOE OSTI2021

Machine Learning Training for HOM reduction and Emittance Preservation in a TESLA-type Cryomodule at FAST

Low emittance electron beams are of high importance at facilities like the LCLS-II at SLAC. Emittance dilution effects due to off-axis beam transport for a TESLA-type cryomodule (CM) have been shown at the Fermilab Accelerator Science and Technology facility. The results showed the correlation between the electron beam-induced cavity high-order modes (HOMs) and submacropulse centroid slewing and oscillation downstream of the CM. Mitigation of emittance dilution can be achieved by reducing the HOM signals and the variances in the submacropulse beam positions downstream of the CM. Here we present a Machine Learning based optimization and model construction for HOM signal level reduction using Neural Networks and Gaussian Processes. To gather training data we performed experiments using single bunch and 50 bunch electron beams with charges up to 125 pC/b. We measured HOM signals of all cavities and beam position with a set of BPMs downstream of the CM. The beam trajectory was changed using V/H125 corrector set located upstream of the CM. The results presented here will inform the LCLS-II injector commissioning and will serve as a prototype for HOM reduction and emittance preservation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Exploration with Scalable Gaussian Process Reinforcement Learning

Exploration is a challenging problem in reinforcement learning (RL), especially in environments with sparse rewards. Quantifying and utilizing the parametric uncertainty has been shown to be paramount for successful exploration [Osband et al., 2018]. Bayesian, or approximately Bayesian, methods present a principled means of estimating the parametric uncertainty in RL problems. Gaussian processes, nonparametric Bayesian models, are often impractical due to poor scalability and computational bottlenecks. We introduce a scalable Gaussian process RL (GPRL) method which directly induces sparsity in the covariance matrix to facilitate faster computation. This is a departure from previous GPRL methods which instead rely on data reduction and subsampling. We compare various covariance-based exploration techniques (Thompson sampling, upper confidence bound, and probabilistic maximum variance) which leverage our scalable GP framework in sparse reward environments. Finally, we show favorable comparison against the bootstrapped deep Q-Network.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2023

Multiple Scattering Theory for Modeling Sonic Booms in Atmospheric Turbulence

Sonic booms are often modeled using Burgers equations accounting for dominant propagation effects. Scattering effects of turbulence, however, have not been incorporated into such equations, although these effects are ubiquitous in measured sonic booms. This paper formulates the mean scattering effects, including backscattering, using multiple scattering theory and ensemble averaging. It obtains an acousto-turbulence interaction energy representing the interaction of an acoustic wave field with a turbulence field. The interaction energy gives rise to a scattering wavenumber, a complex-valued correction to the free-space wavenumber. The scattering wavenumber leads to a dispersion and attenuation of the mean waveform due to backscattering, and can be obtained from a derived exact solution. An existing Burgers equation is extended to include the scattering effects of turbulence as an additional linear term. Numerical simulations of an N-wave and a low boom show that the mean scattering effects lead to shock thickening in the mean waveforms as well as reduce the peak amplitudes and total energy contents. The energy reduction is less severe in the low boom than in the N-wave and is dependent on the variance, correlation length, and thickness of the turbulence field.

sonic booms↗
DOE OSTI2026

DP-TwoLevel: two-stage gradient subspace learning for differentially private federated learning

Federated learning (FL) enables collaborative model training across distributed data sources without sharing raw data, but faces fundamental challenges in communication efficiency and privacy. Differentially private (DP) training mitigates information leakage but introduces noise that degrades model performance, especially in high-dimensional settings. We propose DP-TwoLevel, a hierarchical gradient projection method that improves utility under fixed DP constraints by exploiting low-dimensional structure in model updates. Our approach learns a two-level PCA-based representation of gradients and applies DP noise in a reduced-dimensional subspace, thereby lowering the effective noise magnitude while preserving dominant signal components. We evaluate the method across three datasets (MNIST, Fashion-MNIST, CIFAR-10) and three privacy regimes (ϵ∈0.5, 1.0, 2.0). Across nine experimental settings, DP-TwoLevel consistently outperforms DP-FedAvg, achieving an average accuracy improvement of 9.44%, with larger gains observed in lower ϵ(higher-noise) regimes (up to +22.31%). We further analyze scalability across models ranging from 100K to 1.49M parameters and identify a variance-based success criterion: performance remains strong when the projection preserves more than 75% of gradient variance, degrades in a marginal regime (65–75%), and fails below this threshold. Our results demonstrate that structure-aware dimensionality reduction can significantly improve the privacy–utility tradeoff in FL without modifying formal privacy guarantees. We also provide empirical evidence of scaling limitations for global projections and motivate per-layer extensions for larger models.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗
NASA NTRS1986

A data base and analysis program for shuttle main engine dynamic pressure measurements

A dynamic pressure data base management system is described for measurements obtained from space shuttle main engine (SSME) hot firing tests. The data were provided in terms of engine power level and rms pressure time histories, and power spectra of the dynamic pressure measurements at selected times during each test. Test measurements and engine locations are defined along with a discussion of data acquisition and reduction procedures. A description of the data base management analysis system is provided and subroutines developed for obtaining selected measurement means, variances, ranges and other statistics of interest are discussed. A summary of pressure spectra obtained at SSME rated power level is provided for reference. Application of the singular value decomposition technique to spectrum interpolation is discussed and isoplots of interpolated spectra are presented to indicate measurement trends with engine power level. Program listings of the data base management and spectrum interpolation software are given. Appendices are included to document all data base measurements.

Coffin, T.↗
DOE OSTI2024

Uncertainty quantification of graph convolution neural network models of evolving processes

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural networks have proved to be adept at modeling processes with spatial–temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error bounds over the regimes of interest. Hence there is a need to find uncertainty quantification methods that are suitable for neural networks. In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial–temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant. Specifically we apply these methods to graph convolutional neural network models of evolving systems modeled with recurrent neural network and neural ordinary differential equations architectures. We show that Stein variational inference is a viable alternative to Monte Carlo methods with some clear advantages for complex neural network models. For our exemplars, Stein variational interference gave similar pushed forward uncertainty profiles through time compared to Hamiltonian Monte Carlo, albeit with generally more generous variance. As a result, projected Stein variational gradient descent also produced similar uncertainty profiles to the non-projected counterpart, but large reductions in the active weight space were confounded by the stability of the neural network predictions and the convoluted likelihood landscape.

36 MATERIALS SCIENCE↗
NASA NTRS2005

Global Precipitation Variations and Long-term Changes Derived from the GPCP Monthly Product

Global and large regional rainfall variations and possible long-term changes are examined using the 25-year (1979-2004) monthly dataset from the Global Precipitation Climatology Project (GPCP). The emphasis is to discriminate among the variations due to ENSO, volcanic events and possible long-term changes. Although the global change of precipitation in the data set is near zero, the data set does indicate an upward trend (0.13 mm/day/25yr) and a downward trend (-0.06 mm/day/25yr) over tropical oceans and lands (25S-25N), respectively. This corresponds to a 4% increase (ocean) and 2% decrease (land) during this time period. Techniques are applied to attempt to eliminate variations due to ENSO and major volcanic eruptions. The impact of the two major volcanic eruptions over the past 25 years is estimated to be about a 5% reduction in tropical rainfall. The modified data set (with ENSO and volcano effect removed) retains the same approximate change slopes, but with reduced variance leading to significance tests with results in the 90-95% range. Inter-comparisons between the GPCP, SSWI (1988-2004), and TRMM (1998-2004) rainfall products are made to increase or decrease confidence in the changes seen in the GPCP analysis.

Adler, Robert F.↗
NASA NTRS2014

NASA Tech Briefs, February 2014

Topics include: JWST Integrated Simulation and Test (JIST) Core; Software for Non-Contact Measurement of an Individual's Heart Rate Using a Common Camera; Rapid Infrared Pixel Grating Response Testbed; Temperature Measurement and Stabilization in a Birefringent Whispering Gallery Resonator; JWST IV and V Simulation and Test (JIST) Solid State Recorder (SSR) Simulator; Development of a Precision Thermal Doubler for Deep Space; Improving Friction Stir Welds Using Laser Peening; Methodology of Evaluating Margins of Safety in Critical Brazed Joints; Interactive Inventory Monitoring; Sensor for Spatial Detection of Single-Event Effects in Semiconductor-Based Electronics; Reworked CCGA-624 Interconnect Package Reliability for Extreme Thermal Environments; Current-Controlled Output Driver for Directly Coupled Loads; Bulk Metallic Glasses and Matrix Composites as Spacecraft Shielding; Touch Temperature Coating for Electrical Equipment on Spacecraft; Li-Ion Electrolytes Containing Flame-Retardant Additives; Autonomous Robotic Manipulation (ARM); CARVE Log; Platform Perspective Toolkit; Convex Hull-Based Plume and Anomaly Detection; Pre-Filtration of GOSAT Data Using Only Level 1 Data and an Intelligent Filter to Remove Low Clouds; Affordability Comparison Tool - ACT; "Ascent - Commemorating Shuttle" for iPad; Cassini Mission App; Light-Weight Workflow Engine: A Server for Executing Generic Workflows; Model for System Engineering of the CheMin Instrument; Timeline Central Concepts; Parallel Particle Filter Toolkit; Particle Filter Simulation and Analysis Enabling Non-Traditional Navigation; Quasi-Terminator Orbits for Mapping Small Primitive Bodies; The Subgrid-Scale Scalar Variance Under Supercritical Pressure Conditions; Sliding Gait for ATHLETE Mobility; and Automated Generation of Adaptive Filter Using a Genetic Algorithm and Cyclic Rule Reduction.

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