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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Estimation of Instantaneous TOA Albedo at 670 nm over Ice Clouds from POLDER Multidirectional Measurements

An algorithm that determines the 670-nm top-of-atmosphere (TOA) albedo of ice clouds over ocean using Polarization and Directionality of the Earth's Reflectance ( POLDER) multidirectional measurements is developed. A plane-parallel layer of ice cloud with various optical thicknesses and light scattering phase functions is assumed. For simplicity, we use a double Henyey-Greenstein phase function to approximate the volume-averaged phase function of the ice clouds. A multidirectional reflectance best-fit match between theoretical and POLDER reflectances is used to infer effective cloud optical thickness, phase function and TOA albedo. Sensitivity tests show that while the method does not provide accurate independent retrievals of effective cloud optical depth and phase function, TOA albedo retrievals are accurate to within similar to 3% for both a single layer of ice clouds or a multilayer system of ice clouds and water clouds. When the method is applied to POLDER measurements and retrieved albedos are compared with albedos based on empirical angular distribution models (ADMs), zonal albedo differences are generally smaller than similar to 3%. When albedos are compared with those on the POLDER-I ERB and Cloud product, the differences can reach similar to 15% at small solar zenith angles.

Sun, W.↗
NASA NTRS2008

Development of Time-Distance Helioseismology Data Analysis Pipeline for SDO/HMI

The Helioseismic and Magnetic Imager of SDO will provide uninterrupted 4k x 4k-pixel Doppler-shift images of the Sun with approximately 40 sec cadence. These data will have a unique potential for advancing local helioseismic diagnostics of the Sun's interior structure and dynamics. They will help to understand the basic mechanisms of solar activity and develop predictive capabilities for NASA's Living with a Star program. Because of the tremendous amount of data the HMI team is developing a data analysis pipeline, which will provide maps of subsurface flows and sound-speed distributions inferred form the Doppler data by the time-distance technique. We discuss the development plan, methods, and algorithms, and present the status of the pipeline, testing results and examples of the data products.

DuVall, T. L., Jr.↗
NASA NTRS2009

A Simple and Efficient Computational Approach to Chafed Cable Time-Domain Reflectometry Signature Prediction

A method for the prediction of time-domain signatures of chafed coaxial cables is presented. The method is quasi-static in nature, and is thus efficient enough to be included in inference and inversion routines. Unlike previous models proposed, no restriction on the geometry or size of the chafe is required in the present approach. The model is validated and its speed is illustrated via comparison to simulations from a commercial, three-dimensional electromagnetic simulator.

Kowalski, Marc Edward↗
NASA NTRS2021

The Case for a 50+ Year Radioisotope Power System

The Johns Hopkins University Applied Physics Laboratory (JHU/APL) is leading the NASA funded Interstellar Probe study to explore the “Very Local” interstellar medium. To perform this exploration the mission will be required to last at least 50 years in regions of space where solar power is no longer practical. Additionally, several new studies for the National Academies’ Planetary Science and Astrobiology Decadal Survey are planning missions lasting 20-35 years. The Decadal Survey is used to build consensus on priority of national science goals. These proposed missions are inconsistent with the NASA’s current Radioisotope Power Systems (RPS) life requirement of 14 years (flight). Paramount to these proposed long-duration missions are questions about the longevity of such a mission. Evidence exists that space-borne Radioisotope Power Systems can indeed last a long time. LES-9, Voyager I, and Voyager II are over 40 years old, LES-8, Pioneer 10, and Pioneer 11 lasted 28, 30, and 22 years, respectively, and New Horizons is still active 15 years after launch. This paper explores the need for RPS designs that are intended to last much longer than the current requirement of 14 years (17 years after fueling) and explores the historical record for actual vs design lifetimes to show the feasibility of building long lasting RPS. We also exercise a current RTG performance model of the General-Purpose Heat Source RTG using the JPL Lifetime Performance Prediction (LPP) tool to make top-level inferences about power output at end-of-mission, and discusses how reliability engineering and testing methods can be brought to bear to increase confidence in delivering sufficient power at end-of-mission.

Phan, Brian↗
NASA NTRS2024

Recipes for Forming a Carbon–Rich Giant Planet

The exploration of carbon-to-oxygen ratios has yielded intriguing insights into the composition of close-in giant exoplanets, giving rise to a distinct classification: carbon-rich planets, characterized by a carbon–to–oxygen ratio ≥ 1 in their atmospheres, as opposed to giant planets exhibiting carbon–to–oxygen ratios close to the protosolar value. In contrast, despite numerous space missions dispatched to the outer solar system and the proximity of Jupiter, Saturn, Uranus, and Neptune, our understanding of the carbon-to-oxygen ratio in these giants remains notably deficient. Determining this ratio is crucial as it serves as a marker linking a planet’s volatile composition directly to its formation region within the disk. This article provides an overview of the current understanding of the carbon-to-oxygen ratio in the four gas giants of our solar system and explores why there is yet no definitive dismissal of the possibility that Jupiter, Saturn, Uranus, or Neptune could be considered carbon-rich planets. Additionally, we delve into the three primary formation scenarios proposed in existing literature to account for a bulk carbon-to-oxygen ratio ≥ 1 in a giant planet. A significant challenge lies in accurately inferring the bulk carbon-to-oxygen ratio of our solar system’s gas giants. Retrieval methods involve integrating in situ measurements from entry probes equipped with mass spectrometers and remote sensing observations conducted at microwave wavelengths by orbiters. However, these methods fall short of fully discerning the deep carbon-to-oxygen abundance in the gas giants due to their limited probing depth, typically within the 10–100 bar range. To complement these direct measurements, indirect determinations rely on understanding the vertical distribution of atmospheric carbon monoxide in conjunction with thermochemical models. These models aid in evaluating the deep oxygen abundance in the gas giants, providing valuable insights into their overall composition.

Protosolar nebula↗
DOE OSTI2018

Wastewater treatment plant online monitoring and control

A method of operating a waste water treatment plant (WWTP) having at least one of an aerobic digester (AD) and a membrane bioreactor (MBR) is described. The method of operating AD is comprised of monitoring and controlling AD in real-time using an online extended Kalman filter (EKF) having a online dynamic model of AD. The EKF uses real-time AD measured data, and online dynamic model of AD to update adapted model parameters and estimate model based inferred variables for AD, which are used for AD control by AD control system having supervisory and low-level control layers. The method of operating MBR is similar to that of AD. The supervisory control ensures the WWTP satisfying the effluent quality requirement while minimize the operation cost. A WWTP having at least one of AD or MBR is disclosed. The method of operating a WWTP can be implemented using a computer.

Kumar, Aditya↗
NASA NTRS1975

The inversion of aureole measurements to derive aerosol size distributions

An iterative method to invert size distributions from simulated scattered radiance measurements at small angles from the sun has been investigated. The inferred size distributions were represented by piecewise linear and cubic spline functions. Various relevant characteristics were investigated and it was found that: (1) the inverted size distribution was insensitive to the number of knots in the piecewise linear spline; (2) within the range of sensitivity, the choice of initial guess had little effect on the inverted size distribution; (3) five per cent random noise in the simulated radiances appreciably deteriorated the result but variations are still tolerable when compared with other methods for determining size distributions; (4) the inverted distribution was insensitive to the index of refraction used in the kernel for particle radii greater than 1 micron; (5) the choice of wavelength between 0.40 and 0.70 microns has a negligible effect on the inverted distribution; (6) a range of tropospheric aerosol size distributions gives acceptable inverted results; and (7) the cubic spline representation can give reasonable inverted distributions, but may become unstable.

Twitty, J. T.↗
DOE OSTI2025

AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks

We propose the artificial intelligence velocimetry-thermometry (AIVT) method to reconstruct a continuous and differentiable representation of the temperature and velocity in turbulent convection from measured three-dimensional (3D) velocity data. AIVT is based on physics-informed Kolmogorov-Arnold networks and trained by optimizing a loss function that minimizes residuals of the velocity data, boundary conditions, and governing equations. We apply AIVT to a set of simultaneously measured 3D temperature and velocity data of Rayleigh-Bénard convection, obtained by combining particle image thermometry and Lagrangian particle tracking. This enables us to directly compare machine learning results to true volumetric, simultaneous temperature and velocity measurements. We demonstrate that AIVT can reconstruct and infer continuous, instantaneous velocity and temperature fields and their gradients from sparse experimental data at a high resolution, providing an additional approach for understanding thermal turbulence.

Science & Technology - Other Topics↗
DOE OSTI2022

EdgeAI: Machine learning via direct attached accelerator for streaming data processing at high shot rate x-ray free-electron lasers

We present a case for low batch-size inference with the potential for adaptive training of a lean encoder model. We do so in the context of a paradigmatic example of machine learning as applied in data acquisition at high data velocity scientific user facilities such as the Linac Coherent Light Source-II x-ray Free-Electron Laser. We discuss how a low-latency inference model operating at the data acquisition edge can capitalize on the naturally stochastic nature of such sources. We simulate the method of attosecond angular streaking to produce representative results whereby simulated input data reproduce high-resolution ground truth probability distributions. By minimizing the mean-squared error between the decoded output of the latent representation and the ground truth distributions, we ensure that the encoding layers and resulting latent representation maintains full fidelity for any downstream task, be it classification or regression. We present throughput results for data-parallel inference of various batch sizes, some with throughput exceeding 100 k images per second. We also show in situ training below 10 s per epoch for the full encoder–decoder model as would be relevant for streaming and adaptive real-time data production at our nation’s scientific light sources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Measurement of U 238 ( n , n ' γ ) cross section data and their impact on reaction models

A better knowledge of (n,xn) reaction cross sections is important for both reaction modeling and energy applications. This article focuses on inelastic scattering of neutrons off 238 U for which improvements are needed to better constrain evaluations and solve inconsistencies in nuclear power reactor calculations. A new precise measurement of (n,xnγ) reaction cross sections on 238 U has been performed at the GELINA (Geel Electron LINear Accelerator) neutron facility operated by EC-JRC-Geel (Belgium) with the GRAPhEME (GeRmanium array for Actinides PrEcise MEasurements) setup. The prompt γ-ray spectroscopy method coupled to time-of-flight measurements is used to extract (n,xnγ) cross section values which can be further combined to infer the total neutron inelastic scattering cross section. Cross section data for 18 γ transitions (five never measured before) are presented and compared to the data in the literature. Here, emphasis is especially given to the uncertainty determination to produce partial cross section data as accurate as possible. Due to intrinsic limitations of the experimental method, the use of additional nuclear structure information coupled with theoretical modeling is required to determine the total (n, n') cross section over the whole neutron energy range. We have investigated modeling aspects of the 238 U(n,n'γ) cross sections related to the description of compound nucleus and preequilibirum mechanisms as well as the discrete part of nuclear structure. Through comparison between experimental and calculated (n,n'γ) cross sections, we pinpoint inaccuracies in the description of specific reaction mechanisms and challenge recently implemented models. This helps improving the whole modeling of the (n, n') reaction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Combinatorial Labeling Method for Improving Peptide Fragmentation in Mass Spectrometry

Annotation of peptide sequence from tandem mass spectra constitutes the central step of mass spectrometry-based proteomics. Peptide mass spectra are obtained upon gas-phase fragmentation. Identification of the protein from a set of experimental peptide spectral matches is usually referred as protein inference. Occurrence and intensity of these fragment ions in the MS/MS spectra are dependent on many factors such as amino acid composition, peptide basicity, activation mode, protease, etc. Particularly, chemical derivatizations of peptides were known to alter their fragmentation. In this study, the influence of acetylation, guanidinylation, and their combination on peptide fragmentation was assessed initially on a lipase (LipA) from Bacillus subtilis followed by a bovine six protein mix digest. The dual modification resulted in improved fragment ion occurrence and intensity changes, and this resulted in the equivalent representation of b- and y-type fragment ions in an ion trap MS/MS spectrum. The improved representation has allowed us to accurately annotate the peptide sequences de novo. Dual labeling has significantly reduced the false positive protein identifications in standard bovine six peptide digest. Our study suggests that the combinatorial labeling of peptides is a useful method to validate protein identifications for high confidence protein inference. .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

Reformulating Non-Monotonic Theories for Inference and Updating

We aim to help build programs that do large-scale, expressive non-monotonic reasoning (NMR): especially, 'learning agents' that store, and revise, a body of conclusions while continually acquiring new, possibly defeasible, premise beliefs. Currently available procedures for forward inference and belief revision are exhaustive, and thus impractical: they compute the entire non-monotonic theory, then re-compute from scratch upon updating with new axioms. These methods are thus badly intractable. In most theories of interest, even backward reasoning is combinatoric (at least NP-hard). Here, we give theoretical results for prioritized circumscription that show how to reformulate default theories so as to make forward inference be selective, as well as concurrent; and to restrict belief revision to a part of the theory. We elaborate a detailed divide-and-conquer strategy. We develop concepts of structure in NM theories, by showing how to reformulate them in a particular fashion: to be conjunctively decomposed into a collection of smaller 'part' theories. We identify two well-behaved special cases that are easily recognized in terms of syntactic properties: disjoint appearances of predicates, and disjoint appearances of individuals (terms). As part of this, we also definitionally reformulate the global axioms, one by one, in addition to applying decomposition. We identify a broad class of prioritized default theories, generalizing default inheritance, for which our results especially bear fruit. For this asocially monadic class, decomposition permits reasoning to be localized to individuals (ground terms), and reduced to propositional. Our reformulation methods are implementable in polynomial time, and apply to several other NM formalisms beyond circumscription.

Grosof, Benjamin N.↗
DOE OSTI2026

A scalable variational method for estimating the latent infection-rate field of an outbreak

In this paper, we explore whether the infection-rate of a disease can serve as a robust monitoring variable in epidemiological surveillance algorithms. The infection-rate is dependent on population mixing patterns that do not vary erratically day-to-day; in contrast, daily case-counts used in contemporary surveillance algorithms are corrupted by reporting errors. The technical challenge lies in estimating the latent infection-rate from case-counts. Here we devise a Bayesian method to estimate the infection-rate across multiple adjoining areal units, and then use it, via an anomaly detector, to discern a change in epidemiological dynamics. We extend an existing model for estimating the infection-rate in an areal unit by incorporating a Markov random field model, so that we may estimate infection-rates across multiple areal units, while preserving spatial correlations observed in the epidemiological dynamics. To carry out the high-dimensional Bayesian inverse problem, we develop an implementation of mean-field variational inference specific to the infection model and integrate it with the random field model to incorporate correlations across counties. The method is tested on estimating the COVID-19 infection-rates across all 33 counties in New Mexico using data from the summer of 2020, and then employing them to detect the arrival of the Fall 2020 COVID-19 wave. We perform the detection using a temporal algorithm that is applied county-by-county. We also show how the infection-rate field can be used to cluster counties with similar epidemiological dynamics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Non-autoregressive time-series methods for stable parametric reduced-order models

Advection-dominated dynamical systems, characterized by partial differential equations, are found in applications ranging from weather forecasting to engineering design where accuracy and robustness are crucial. There has been significant interest in the use of techniques borrowed from machine learning to reduce the computational expense and/or improve the accuracy of predictions for these systems. These rely on the identification of a basis that reduces the dimensionality of the problem and the subsequent use of time series and sequential learning methods to forecast the evolution of the reduced state. Often, however, machine-learned predictions after reduced-basis projection are plagued by issues of stability stemming from incomplete capture of multiscale processes as well as due to error growth for long forecast durations. To address these issues, we have developed a non-autoregressive time series approach for predicting linear reduced-basis time histories of forward models. In particular, we demonstrate that non-autoregressive counterparts of sequential learning methods such as long short-term memory (LSTM) considerably improve the stability of machine-learned reduced-order models. Further, we evaluate our approach on the inviscid shallow water equations and show that a non-autoregressive variant of the standard LSTM approach that is bidirectional in the principal component directions obtains the best accuracy for recreating the nonlinear dynamics of partial observations. Moreover-and critical for many applications of these surrogates-inference times are reduced by three orders of magnitude using our approach, compared with both the equation-based Galerkin projection method and the standard LSTM approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Online PMU Missing Value Replacement Via Event-Participation Decomposition

We introduce a new method for online Phasor Measurement Unit (PMU) missing value replacement. Our approach allows us to decompose PMU event responses into a non-dynamic component (denoted the participation factor) that can be inferred directly from the past and a dynamic component that can be inferred directly from all other PMUs (denoted the event strength). When missing values occur, we can use these two components, which do not rely on the missing index, to estimate the correct value. The method is extremely fast and can easily be used for online applications. Furthermore, extensive testing on real power system event data reveals that our approach achieves state-of-the-art performance in terms of Mean Absolute Percent Errors (MAPEs) for PMU data dropped during event periods. Here, the method also yields an interpretable and simplified view of events for further analysis and applications. The method relies only on PMU data and does not take outside information such as network topology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Localized Cyber Threat Mitigation Approach For Wide Area Control of FACTS

We propose a localized oscillation amplitude monitoring (OAM) method for the mitigation of cyber threats directed at the wide area control (WAC) system used to coordinate control of Flexible AC Transmission Systems (FACTS) for power oscillation damping (POD) of active power flow on inter-area tie lines. The method involves monitoring the inter-area tie line active power oscillation amplitude over a sliding window. We use system instability - inferred from oscillation amplitudes growing instead of damping - as evidence of an indication of a malfunction in the WAC of FACTS, possibly indicative of a cyber attack. Monitoring the presence of such a growth allows us to determine whether any destabilizing behaviors appear after the WAC system engages to control the POD. If the WAC signal increases the oscillation amplitude over time, thereby diminishing the POD performance, the FACTS falls back to POD using local measurements. The proposed method does not require an expansive system-wide view of the network. We simulate replay, control integrity, and timing attacks for a test system and present results that demonstrate the performance of the OAM method for mitigation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Environmental Selection, Dispersal, and Organism Interactions Shape Community Assembly in High-Throughput Enrichment Culturing

A central goal of microbial ecology is to identify and quantify the forces that lead to observed population distributions and dynamics. However, these forces, which include environmental selection, dispersal, and organism interactions, are often difficult to assess in natural environments. Here in this paper, we present a method that links microbial community structures with selective and stochastic forces through highly replicated subsampling and enrichment of a single environmental inoculum. Specifically, groundwater from a well-studied natural aquifer was serially diluted and inoculated into nearly 1,000 aerobic and anaerobic nitrate-reducing cultures, and the final community structures were evaluated with 16S rRNA gene amplicon sequencing. We analyzed the frequency and abundance of individual operational taxonomic units (OTUs) to understand how probabilistic immigration, relative fitness differences, environmental factors, and organismal interactions contributed to divergent distributions of community structures. We further used a most probable number (MPN) method to estimate the natural condition-dependent cultivable abundance of each of the nearly 400 OTU cultivated in our study and infer the relative fitness of each. Additionally, we infer condition-specific organism interactions and discuss how this high-replicate culturing approach is essential in dissecting the interplay between overlapping ecological forces and taxon-specific attributes that underpin microbial community assembly. IMPORTANCE Through highly replicated culturing, in which inocula are subsampled from a single environmental sample, we empirically determine how selective forces, interspecific interactions, relative fitness, and probabilistic dispersal shape bacterial communities. These methods offer a novel approach to untangle not only interspecific interactions but also taxon-specific fitness differences that manifest across different cultivation conditions and lead to the selection and enrichment of specific organisms. Additionally, we provide a method for estimating the number of cultivable units of each OTU in the original sample through the MPN approach.

16S RNA↗
DOE OSTI2017

PyDREAM: high-dimensional parameter inference for biological models in python

Biological models contain many parameters whose values are difficult to measure directly via experimentation and therefore require calibration against experimental data. Markov chain Monte Carlo (MCMC) methods are suitable to estimate multivariate posterior model parameter distributions, but these methods may exhibit slow or premature convergence in high-dimensional search spaces. Here, we present PyDREAM, a Python implementation of the (Multiple-Try) Differential Evolution Adaptive Metropolis [DREAM(ZS)] algorithm developed by Vrugt and ter Braak (2008) and Laloy and Vrugt (2012). PyDREAM achieves excellent performance for complex, parameter-rich models and takes full advantage of distributed computing resources, facilitating parameter inference and uncertainty estimation of CPU-intensive biological models.

59 BASIC BIOLOGICAL SCIENCES↗