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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Manganese Oxidation States in Volcanic Soils across Annual Rainfall Gradients

Manganese (Mn) exists as Mn(II), Mn(III), or Mn(IV) in soils, and the Mn oxidation state controls the roles of Mn in numerous environmental processes. However, the variations of Mn oxidation states with climate remain unknown. Here we determined the Mn oxidation states in highly weathered bulk volcanic soils (primary minerals free) across two rainfall gradients covering mean annual precipitation (MAP) of 0.25–5 m in the Hawaiian Islands. With increasing MAP, the soil redox conditions generally shifted from oxic to suboxic and to anoxic despite fluctuating at each site; concurrently, the proportions of Mn(IV) and Mn(II) decreased and increased, respectively. Mn(III) was low at both low and high MAP, but accumulated substantially, up to 80% of total Mn, in soils with prevalent suboxic conditions at intermediate MAP. Mn(III) was likely hosted in Mn(III,IV) and iron(III) oxides or complexed with organic matter, and its distribution among these hosts varied with soil redox potentials and soil pH. Soil redox conditions and rainfall-driven leaching jointly controlled exchangeable Mn(II) in soils, with its concentration peaking at intermediate MAP. The Mn redox chemistry was at disequilibrium, with the oxidation states correlating with long-term average soil redox potentials better than with soil pH. The soil redox conditions likely fluctuated between oxic and anoxic conditions more frequently at intermediate than at low and high MAP, creating biogeochemical hot spots where Mn, Fe, and other redox-sensitive elements may be actively cycled.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Probabilistic Mixture Model-Based Spectral Unmixing

Spectral unmixing attempts to decompose a spectral ensemble into the constituent pure spectral signatures (called endmembers) along with the proportion of each endmember. This is essential for techniques like hyperspectral imaging (HSI) used in environment monitoring, geological exploration, etc. Several spectral unmixing approaches have been proposed, many of which are connected to hyperspectral imaging. However, most extant approaches assume highly diverse collections of mixtures and extremely low-loss spectroscopic measurements. Additionally, current non-Bayesian frameworks do not incorporate the uncertainty inherent in unmixing. We propose a probabilistic inference algorithm that explicitly incorporates noise and uncertainty, enabling us to unmix endmembers in collections of mixtures with limited diversity. We use a Bayesian mixture model to jointly extract endmember spectra and mixing parameters while explicitly modeling observation noise and the resulting inference uncertainties. We obtain approximate distributions over endmember coordinates for each set of observed spectra while remaining robust to inference biases from the lack of pure observations and the presence of non-isotropic Gaussian noise. As a direct impact of our methodology, access to reliable uncertainties on the unmixing solutions would enable robust solutions to noise, as well as informed decision-making for HSI applications and other unmixing problems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1994

Use of photostress to analyze behavior of an aft skirt test specimen

Strains at twenty-one selected points in the critical lower weld region of a aft skirt of a solid rocket booster of the shuttle were measured using photoelastic coatings and stress separator gages. Data were taken at loads of 5, 14, 20, 28, 42, 56, and 70 percent of the design limit load. Results indicate that general yielding occurred in the weld metal and for a short distance outside the fusion boundaries on either side of the weld metal. The fusion boundaries did not yield at the 70 percent load. Slight non-linearity in the load strain curves were observed at several points above the 20 percent load level. Maximum measured strains occurred at points in the forged metal of the holddown post along a line 0.50 inches from the centerline of the weld. Maximum shearing strains within the area covered by the photoelastic coating occurred at points approximately 0.33 inches to the right of the weld centerline near points 6 and 7 and lying along a yellow vertical line extending from just below point 6 to point 11. Photoelastic coatings were shown to be an excellent method to provide the whole field strain distribution in the region of the critical weld and to enhance the overall understanding of the behavior of the welded joint.

Gambrell, S. C., Jr.↗
DOE OSTI2022

Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies for multiple time series, however, is a challenging subject, owing to the intricate interdependencies among the constituent series. We hypothesize that anomalies occur in low density regions of a distribution and explore the use of normalizing flows for unsupervised anomaly detection, because of their superior quality in density estimation. Moreover, we propose a novel flow model by imposing a Bayesian network among constituent series. A Bayesian network is a directed acyclic graph (DAG) that models causal relationships; it factorizes the joint probability of the series into the product of easy-to-evaluate conditional probabilities. We call such a graph-augmented normalizing flow approach GANF and propose joint estimation of the DAG with flow parameters. We conduct extensive experiments on real-world datasets and demonstrate the effectiveness of GANF for density estimation, anomaly detection, and identification of time series distribution drift.

Dai, Enyan↗
DOE OSTI2022

Joint Inversion of Body Wave Arrival Times, Surface Wave Dispersion Data and Receiver Functions: Method and Application to South China

In this work, we have developed a new joint inversion method that incorporates body wave arrival times, surface wave dispersion and receiver functions to simultaneously update earthquake locations and constrain three-dimensional P-wave (Vp) and S-wave velocity (Vs) models. Due to complementary sensitivities of the three types of data, the proposed joint inversion algorithm can reduce the intrinsic non-uniqueness of inversions using fewer types of data and better determine smooth velocity variations and velocity discontinuities. Synthetic tests demonstrate the advantages of this new joint inversion algorithm in resolving velocity structures, especially in constraining velocity gradients across the Moho interface. We have applied the proposed joint inversion algorithm to image the lithosphere velocity structure of south China. The inverted Vp and Vs models fit body wave arrival times, surface wave dispersion and receiver functions well. We further analyzed the distribution of Vs gradients across the Moho interface in detail, which helps us better understand tectonics in south China.

58 GEOSCIENCES↗
DOE OSTI2021

THERMAL/STRUCTURAL ANALYSIS OF THE AXION QUANTUM METROLOGY CAVITY AND ITS COMPONENTS

This research was centered around maximizing the capability to cool dielectric material within a containment unit, or Photonic Band Gap (PBG) cavity, designed for detecting axion dark matter and identifying the unit’s thermal properties. There are multiple types of PBG cavities, but the latest version that axion researchers wish to use has been theorized to contain possible issues related to its thermal properties. Thermal conductivity is an issue with the dielectric material because it is made from alumina which is highly insulative. This is important since the research is being done in a cryogenic environment and the thermal noise affects the quantum bit used for detecting the axion to photon conversion process. Therefore, any improvements to this unit should be justified and implemented but are not entirely limited to thermal contact related aspects of PBG cavities. A prospect of using sapphire in place of alumina also exists, but this is a more expensive, less tested, and more elusive material to justify the creation of a full dielectric structure out of sapphire. Thermal aspects of the cavity were analyzed using finite element method (FEM) and an experiment designed to test different thermal joint materials. FEM was used to check the contraction of the cavity during cooling, the contact quality between the dielectric material and cavity wall, theheat flow rate through the assembly, and helps visualize the cavity’s reaction to different design changes. A simple comparison between thermal conductivity curves justified the usage of sapphire over alumina from a thermal property point of view. Thus, the method of experimentation had an additional dielectric material, sapphire, to test with and compare to alumina. The thermal test identified which material is best to use for a thermal joint but also simultaneously found the conductance of the joint and dielectric material as well as an estimate for what the temperature is inside the larger scale cavity since a thermometer cannot be placed inside the PBG when it cools. Initially, the cavity simulation was tested for structural deformation properties and stress distribution since it is made of copper which contracts heavily in comparison to most other materials. The reduction in volume of the cavity was less than anticipated which gave more room for a possible usage of a modified thermal joint for additional contact area and higher conductivity.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Quantum nonlocal modulation cancelation with distributed clocks

We demonstrate nonlocal modulation of entangled photons with truly distributed radio frequency (RF) clocks. Leveraging a custom radio-over-fiber (RFoF) system characterized via classical spectral interference, we validate its effectiveness for quantum networking by multiplexing the RFoF clock with one photon from a frequency-bin-entangled pair and distributing the coexisting quantum-classical signals over fiber. Phase modulation of the two photons reveals nonlocal correlations in excellent agreement with theory: in-phase modulation produces additional sidebands in the joint spectral intensity, while out-of-phase modulation is nonlocally canceled. Our simple, feedback-free design attains subpicosecond synchronization—namely, drift less than ~0.5 ps in a 5.5 km fiber over 30 min (fractionally only ~2×10 -8 of the total fiber delay)—and should facilitate frequency-encoded quantum networking protocols such as high-dimensional quantum key distribution and entanglement swapping, unlocking frequency-bin qubits for practical quantum communications in deployed metropolitan-scale networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Distributed Transient Safety Verification via Robust Control Invariant Sets: A Microgrid Application

Modern safety-critical energy infrastructures are increasingly operated in a hierarchical and modular control framework which allows for limited data exchange between the modules. In this context, it is important for each module to synthesize and communicate constraints on the values of exchanged information in order to assure system-wide safety. To ensure transient safety in inverter-based microgrids, we develop a set invariance-based distributed safety verification algorithm for each inverter module. Applying Nagumo's invariance condition, we construct a robust polynomial optimization problem to jointly search for safety-admissible set of control set-points and design parameters, under allowable disturbances from neighbors. We use sum-of-squares (SOS) programming to solve the verification problem and we perform numerical simulations using grid-forming inverters to illustrate the algorithm.

Bouvier, Jean-Baptiste H.↗
DOE OSTI2022

Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models

Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r >0.95 for ML and r =0.14~0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r =0.74 for ML and r <0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM 2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

Evaluation of joint probability density function models for turbulent nonpremixed combustion with complex chemistry

Two types of mixing sub-models are evaluated in connection with a joint-scalar probability density function method for turbulent nonpremixed combustion. Model calculations are made and compared to simulation results for homogeneously distributed methane-air reaction zones mixing and reacting in decaying turbulence within a two-dimensional enclosed domain. The comparison is arranged to ensure that both the simulation and model calculations a) make use of exactly the same chemical mechanism, b) do not involve non-unity Lewis number transport of species, and c) are free from radiation loss. The modified Curl mixing sub-model was found to provide superior predictive accuracy over the simple relaxation-to-mean submodel in the case studied. Accuracy to within 10-20% was found for global means of major species and temperature; however, nitric oxide prediction accuracy was lower and highly dependent on the choice of mixing sub-model. Both mixing submodels were found to produce non-physical mixing behavior for mixture fractions removed from the immediate reaction zone. A suggestion for a further modified Curl mixing sub-model is made in connection with earlier work done in the field.

Smith, N. S. A.↗
NASA NTRS1999

Cooperation and Coordination Between Fuzzy Reinforcement Learning Agents in Continuous State Partially Observable Markov Decision Processes

Successful operations of future multi-agent intelligent systems require efficient cooperation schemes between agents sharing learning experiences. We consider a pseudo-realistic world in which one or more opportunities appear and disappear in random locations. Agents use fuzzy reinforcement learning to learn which opportunities are most worthy of pursuing based on their promise rewards, expected lifetimes, path lengths and expected path costs. We show that this world is partially observable because the history of an agent influences the distribution of its future states. We consider a cooperation mechanism in which agents share experience by using and-updating one joint behavior policy. We also implement a coordination mechanism for allocating opportunities to different agents in the same world. Our results demonstrate that K cooperative agents each learning in a separate world over N time steps outperform K independent agents each learning in a separate world over K*N time steps, with this result becoming more pronounced as the degree of partial observability in the environment increases. We also show that cooperation between agents learning in the same world decreases performance with respect to independent agents. Since cooperation reduces diversity between agents, we conclude that diversity is a key parameter in the trade off between maximizing utility from cooperation when diversity is low and maximizing utility from competitive coordination when diversity is high.

Berenji, Hamid R.↗
NASA NTRS2003

Investigation of Overlap Correction Techniques for Application in the Micro-Pulse Lidar Network (MPLNET)

The Micro-Pulse Lidar NETwork (MPLNET) is comprised of micro-pulse lidars (MPL) stationed around the globe to provide measurements of aerosol and cloud vertical distribution on a continuous basis. MPLNET sites are co-located with sunphotometers in the AErosol Robotic NETwork (AERONET) to provide joint measurements of aerosol optical depth, size, and other inherent optical properties. The IPCC 2001 report discusses . the importance of obtaining routine measurements of aerosol vertical structure, especially for absorbing aerosols. MPLNET provides exactly this sort of measurement, including calculation of aerosol extinction profiles, in a near real-time basis for all sites in the network. In order to obtain aerosol profiles, near range signal returns (0-6 km) must be accurately measured by the MPL. This measurement is complicated by the instrument s overlap range: Le., the minimum distance at which returning signals are completely in the instrument s field-of-view (FOV). Typical MPL overlap distances are large, between 5 - 6 km, due to the narrow FOV of the MPL receiver. A function describing the MPL overlap must be determined and used to correct signals in this range. Currently, overlap functions for MPLNET are determined using horizontal MPL measurements along a path with 10-1 5 km clear line-of-sight and a homogenous atmosphere. These conditions limit the location and ease in which successful overlaps can be obtained. Furthermore, the current MPLNET process of correcting for overlap increases the uncertainty and bias error for the near range signals and the resulting aerosol extinction profiles. To address these issues, an alternative overlap correction method using a small-diameter, wide FOV receiver is being considered for potential use in MPLNET. The wide FOV receiver has a much shorter overlap distance and will be used to calculate the overlap function of the MPL receiver. This approach has a significant benefit in that overlap corrections could be obtained without the need for horizontal measurements. A review of both overlap methods is presented, including a discussion of the impact on reducing the uncertainty and bias error in MPLNET aerosol profiles.

Berkoff, Timothy A.↗
NASA NTRS2005

Solution Concepts for Distributed Decision-Making without Coordination

Consider a single-stage problem in which we have a group N agents who are attempting to minimize the expected cost of their joint actions, without the benefit of communication or a pre-established protocol but with complete knowledge of the expected cost of any joint set of actions for the group. We call this situation a static coordination problem. The central issue in defining an appropriate solution concept for static coordination problems is considering how to deal with the fact that if the agents axe faced with a set of multiple (mixed) strategies that are equally attractive in terms of cost, a failure of coordination may lead to an expected cost value that is worse than that of any of the strategies in the set. In this proposal, we describe the notion of a general coordination problem, describe initial efforts at developing a solution concept for static coordination problems, and then outline a research agenda that centers on activities that will be basis for obtaining a complete understanding of solutions to static coordination problems.

Beling, Peter A.↗
NASA NTRS1995

Coupled Monte Carlo Probability Density Function/ SPRAY/CFD Code Developed for Modeling Gas-Turbine Combustor Flows

The success of any solution methodology for studying gas-turbine combustor flows depends a great deal on how well it can model various complex, rate-controlling processes associated with turbulent transport, mixing, chemical kinetics, evaporation and spreading rates of the spray, convective and radiative heat transfer, and other phenomena. These phenomena often strongly interact with each other at disparate time and length scales. In particular, turbulence plays an important role in determining the rates of mass and heat transfer, chemical reactions, and evaporation in many practical combustion devices. Turbulence manifests its influence in a diffusion flame in several forms depending on how turbulence interacts with various flame scales. These forms range from the so-called wrinkled, or stretched, flamelets regime, to the distributed combustion regime. Conventional turbulence closure models have difficulty in treating highly nonlinear reaction rates. A solution procedure based on the joint composition probability density function (PDF) approach holds the promise of modeling various important combustion phenomena relevant to practical combustion devices such as extinction, blowoff limits, and emissions predictions because it can handle the nonlinear chemical reaction rates without any approximation. In this approach, mean and turbulence gas-phase velocity fields are determined from a standard turbulence model; the joint composition field of species and enthalpy are determined from the solution of a modeled PDF transport equation; and a Lagrangian-based dilute spray model is used for the liquid-phase representation with appropriate consideration of the exchanges of mass, momentum, and energy between the two phases. The PDF transport equation is solved by a Monte Carlo method, and existing state-of-the-art numerical representations are used to solve the mean gasphase velocity and turbulence fields together with the liquid-phase equations. The joint composition PDF approach was extended in our previous work to the study of compressible reacting flows. The application of this method to several supersonic diffusion flames associated with scramjet combustor flow fields provided favorable comparisons with the available experimental data. A further extension of this approach to spray flames, three-dimensional computations, and parallel computing was reported in a recent paper. The recently developed PDF/SPRAY/computational fluid dynamics (CFD) module combines the novelty of the joint composition PDF approach with the ability to run on parallel architectures. This algorithm was implemented on the NASA Lewis Research Center's Cray T3D, a massively parallel computer with an aggregate of 64 processor elements. The calculation procedure was applied to predict the flow properties of both open and confined swirl-stabilized spray flames.

Source record↗
NASA NTRS2018

JPSS-1 VIIRS Solar Diffuser Witness Sample BRF Calibration Using a Table-Top Goniometer at NASA GSFC

In support of the prelaunch calibration of the Joint Polar Satellite System-1 (JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS), the Bidirectional Reflectance Factor (BRF) and Bidirectional Reflectance Distribution Function (BRDF) of a VIIRS solar diffuser (SD) witness sample were determined using the table-top goniometer (TTG) located in the NASA GSFC Diffuser Calibration Laboratory (DCL). The BRF of the sample was measured for VIIRS bands in the reflected solar wavelength region from 410 nm to 2250 nm. The new TTG was developed to extend the laboratory’s BRF and BRDF measurement capability to wavelengths from 1600 to 2250 nm and specifically for the VIIRS M11 band centered at 2250 nm. We show the new features and capabilities of the new scatterometer and present the BRF and BRDF results for the incident/scatter test configuration of 0°/45° and for a set of angles representing of the VIIRS on-orbit solar diffuser calibration. The BRF and BRDF results of the SD witness were used to assist in finalizing the set of BRF values of J1 VIIRS SD to be used on-orbit. Comparison of the BRF results between the JPSS-1 VIIRS SD witness sample and the flight SD panel was made by varying different sample clocking orientations and by analyzing the ratio of BRF to total hemispherical reflectance in effort to minimize the uncertainty of the extrapolated flight BRF value at 2250 nm. Furthermore, differences between the prelaunch BRF results and those used in the VIIRS on-orbit BRF lookup table were examined to improve the VIIRS BRF calibration for future missions.

Zeng, Jinan↗
NASA NTRS2018

JPSS-1 VIIRS Solar Diffuser Witness Sample BRF Calibration Using a Table-Top Goniometer at NASA GSFC

In support of the prelaunch calibration of the Joint Polar Satellite System-1 (JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS), the Bidirectional Reflectance Factor (BRF) and Bidirectional Reflectance Distribution Function (BRDF) of a VIIRS solar diffuser (SD) witness sample were determined using the table-top goniometer (TTG) located in the NASA GSFC Diffuser Calibration Laboratory (DCL). The BRF of the sample was measured for VIIRS bands in the reflected solar wavelength region from 410 nm to 2250 nm. The new TTG was developed to extend the laboratory’s BRF and BRDF measurement capability to wavelengths from 1600 to 2250 nm and specifically for the VIIRS M11 band centered at 2250 nm. We show the new features and capabilities of the new scatterometer and present the BRF and BRDF results for the incident/scatter test configuration of 0°/45° and for a set of angles representing of the VIIRS on-orbit solar diffuser calibration. The BRF and BRDF results of the SD witness were used to assist in finalizing the set of BRF values of J1 VIIRS SD to be used on-orbit. Comparison of the BRF results between the JPSS-1 VIIRS SD witness sample and the flight SD panel was made by varying different sample clocking orientations and by analyzing the ratio of BRF to total hemispherical reflectance in effort to minimize the uncertainty of the extrapolated flight BRF value at 2250 nm. Furthermore, differences between the prelaunch BRF results and those used in the VIIRS on-orbit BRF lookup table were examined to improve the VIIRS BRF calibration for future missions.

Zeng, Jinan↗
NASA NTRSDate not supplied

Learning Based Edge Computing in Air-to-Air Communication Network

This paper studies learning-based edge computing and communication in a dynamic Air-to-Air Ad-hoc Network (AAAN). Due to spectrum scarcity, we assume the number of Air-to-Air (A2A) communication links is greater than that of the available frequency channels, such that some communication links have to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the aggregate spectrum utilization efficiency under resource and fairness constraints. A distributed deep Q learning-based edge computing and communication algorithm is proposed to find the optimal solution. In particular, we design two different neural network structures and each communication link can converge to the optimal operation by exploiting only the local information from its neighbors, making it scalable to large networks. Finally, experimental results demonstrate the effectiveness of the proposed solution in various AAAN scenarios.

Zhe Wang↗