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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Near-wall similarity in the three-dimensional turbulent boundary layers. III - Shear-driven flow results

Ten of eleven proposed three-dimensional similarity models identified in the literature are evaluated with direct wall shear, velocity field, and pressure gradient data from a three-dimensional shear-driven boundary layer flow. Results define an upper limit on velocity vector skewing for each model's predictive ability. When combined with earlier results for pressure-driven flows, each model's predictive ability with and without pressure gradients is summarized. The utility of some two-dimensional type indirect wall shear measurement methods and wall shear inference methods from near-wall velocity measurements for three-dimensional flows is also discussed.

Pierce, F. J.↗
NASA NTRS1993

Development of methods for inferring cloud thickness and cloud-base height from satellite radiance data

Cloud-top height is a major factor determining the outgoing longwave flux at the top of the atmosphere. The downwelling radiation from the cloud strongly affects the cooling rate within the atmosphere and the longwave radiation incident at the surface. Thus, determination of cloud-base temperature is important for proper calculation of fluxes below the cloud. Cloud-base altitude is also an important factor in aircraft operations. Cloud-top height or temperature can be derived in a straightforward manner using satellite-based infrared data. Cloud-base temperature, however, is not observable from the satellite, but is related to the height, phase, and optical depth of the cloud in addition to other variables. This study uses surface and satellite data taken during the First ISCCP Regional Experiment (FIRE) Phase-2 Intensive Field Observation (IFO) period (13 Nov. - 7 Dec. 1991, to improve techniques for deriving cloud-base height from conventional satellite data.

Smith, William L., Jr.↗
DOE OSTI2025

Protein-ligand binding affinity prediction using multi-instance learning with docking structures

Recent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Despite recent advances and improved predictive performance, most methods in this category primarily rely on utilizing co-crystal complex structures and experimentally measured binding affinities as both input and output data for model training. Nevertheless, co-crystal complex structures are not readily available and the inaccurate predicted structures from molecular docking can degrade the accuracy of the machine learning methods. We introduce a novel structure-based inference method utilizing multiple molecular docking poses for each complex entity. Our proposed method employs multi-instance learning with an attention network to predict binding affinity from a collection of docking poses. We validate our method using multiple datasets, including PDBbind and compounds targeting the main protease of SARS-CoV-2. The results demonstrate that our method leveraging docking poses is competitive with other state-of-the-art inference models that depend on co-crystal structures. This method offers binding affinity prediction without requiring co-crystal structures, thereby increasing its applicability to protein targets lacking such data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

The Intercalibration of Geostationary Visible Imagers Using Operational Hyperspectral SCIAMACHY Radiances

Spectral band differences between sensors can complicate the process of intercalibration of a visible sensor against a reference sensor. This can be best addressed by using a hyperspectral reference sensor whenever possible because they can be used to accurately mitigate the band differences. This paper demonstrates the feasibility of using operational Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) large-footprint hyperspectral radiances to calibrate geostationary Earth-observing (GEO) sensors. Near simultaneous nadir overpass measurements were used to compare the temporal calibration of SCIAMACHY with Aqua Moderate Resolution Imaging Spectroradiometer band radiances, which were found to be consistent to within 0.44% over seven years. An operational SCIAMACHY/GEO ray-matching technique was presented, along with enhancements to improve radiance pair sampling. These enhancements did not bias the underlying intercalibration and provided enough sampling to allow up to monthly monitoring of the GEO sensor degradation. The results of the SCIAMACHY/GEO intercalibration were compared with other operational four-year Meteosat-9 0.65-μm calibration coefficients and were found to be within 1% of the gain, and more importantly, it had one of the lowest temporal standard errors of all the methods. This is more than likely that the GEO spectral response function could be directly applied to the SCIAMACHY radiances, whereas the other operational methods inferred a spectral correction factor. This method allows the validation of the spectral corrections required by other methods.

Doelling, David R.↗
DOE OSTI2019

Approximate Bayesian Model Inversion for PDEs with Heterogeneous and State-Dependent Coefficients

We present two approximate Bayesian inference methods for parameter estimation in partial differential equation (PDE) models with space-dependent and state-dependent parameters. We demonstrate that these methods provide accurate and cost-effective alternatives to Markov Chain Monte Carlo simulation. We assume a parameterized Gaussian prior on the unknown functions, and approximate the posterior density by a parameterized multivariate Gaussian density. The parameters of the prior and posterior are estimated from sparse observations of the PDE model's states and the unknown functions themselves by maximizing the evidence lower bound (ELBO), a lower bound on the log marginal likelihood of the observations. The first method, Laplace-EM, employs the expectation maximization algorithm to maximize the ELBO, with a Laplace approximation of the posterior on the E-step, and minimization of a Kullback-Leibler divergence on the M-step. The second method, DSVI-EB, employs the doubly stochastic variational inference (DSVI) algorithm, in which the ELBO is maximized via gradient-based stochastic optimization, with nosiy gradients computed via simple Monte Carlo sampling and Gaussian backpropagation. We apply these methods to identifying diffusion coefficients in linear and nonlinear diffusion equations, and we find that both methods provide accurate estimates of posterior densities and the hyperparameters of Gaussian priors. While the Laplace-EM method is more accurate, it requires computing Hessians of the physics model. The DSVI-EB method is found to be less accurate but only requires gradients of the physics model.

approximate Bayesian inference, model inversion, v↗
NASA NTRS1991

Maximum entropy method applied to deblurring images on a MasPar MP-1 computer

A statistical inference method based on the principle of maximum entropy is developed for the purpose of enhancing and restoring satellite images. The proposed maximum entropy image restoration method is shown to overcome the difficulties associated with image restoration and provide the smoothest and most appropriate solution consistent with the measured data. An implementation of the method on the MP-1 computer is described, and results of tests on simulated data are presented.

Bonavito, N. L.↗
NASA NTRS2015

Advanced Bayesian Method for Planetary Surface Navigation

Autonomous Exploration, Inc., has developed an advanced Bayesian statistical inference method that leverages current computing technology to produce a highly accurate surface navigation system. The method combines dense stereo vision and high-speed optical flow to implement visual odometry (VO) to track faster rover movements. The Bayesian VO technique improves performance by using all image information rather than corner features only. The method determines what can be learned from each image pixel and weighs the information accordingly. This capability improves performance in shadowed areas that yield only low-contrast images. The error characteristics of the visual processing are complementary to those of a low-cost inertial measurement unit (IMU), so the combination of the two capabilities provides highly accurate navigation. The method increases NASA mission productivity by enabling faster rover speed and accuracy. On Earth, the technology will permit operation of robots and autonomous vehicles in areas where the Global Positioning System (GPS) is degraded or unavailable.

Center, Julian↗
DOE OSTI2022

Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy

In this work, we develop several inference methods to estimate the position of dislocations from images generated using dark-field X-ray microscopy (DFXM)—achieving superresolution accuracy and principled uncertainty quantification. Using the framework of Bayesian inference, we incorporate models of the DFXM contrast mechanism and detector measurement noise, along with initial position estimates, into a statistical model coupling DFXM images with the dislocation position of interest. We motivate several position estimation and uncertainty quantification algorithms based on this model. We then demonstrate the accuracy of our primary estimation algorithm on synthetic realistic DFXM images of edge dislocations in single-crystal aluminum. We conclude with a discussion of our methods’ impact on future dislocation studies and possible future research avenues.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Deep Koopman operators for causal discovery

Causal discovery aims to identify cause-effect mechanisms for better scientific understanding, explainable decision-making, and more accurate modeling. Standard statistical frameworks, such as Granger causality, lack the ability to quantify causal relationships in nonlinear dynamics due to the presence of complex feedback mechanisms, timescale mixing, and nonstationarity. Thus, applying these methods to study causal dynamics in real-world systems, such as the Earth, is a major challenge. Addressing this shortcoming, we leverage deep learning and a Koopman operator-theoretic formalism to present a class of causal discovery algorithms. Kausal uses deep Koopman operator methods to approximate nonlinear dynamics in a linearized vector space in which traditional causal inference methods such as Granger causality can be more easily applied. Our idealized experiments demonstrate Kausal’s superior ability in discovering and characterizing causal signals compared to existing deep learning and non-deep learning state-of-the-art approaches. Finally, the successful identification of major El Niño and La Niña events in observations showcases Kausal’s skill to handle real-world applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Learning Latent Interactions for Event Identification via Graph Neural Networks and PMU Data

Phasor measurement units (PMUs) are being widely installed on power systems, providing a unique opportunity to enhance wide-area situational awareness. One essential application is the use of PMU data for real-time event identification. However, how to take full advantage of all PMU data in event identification is still an open problem. Thus, we propose a novel method that performs event identification by mining interaction graphs among different PMUs. The proposed interaction graph inference method follows an entirely data-driven manner without knowing the physical topology. Moreover, unlike previous works that treat interactive learning and event identification as two different stages, our method learns interactions jointly with the identification task, thereby improving the accuracy of graph learning and ensuring seamless integration between the two stages. Moreover, to capture multi-scale event patterns, a dilated inception-based method is investigated to perform feature extraction of PMU data. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. We report numerical results validate that our method has higher classification accuracy compared to previous methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals

We introduce new modules in the open-source PyCBC gravitational-wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. Here, we demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the parameters' posterior distributions obtained using our new code agree well with the published estimates for binary black holes in the first Advanced LIGO–Virgo observing run.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2001

Robust Strategy for Rocket Engine Health Monitoring

Monitoring the health of rocket engine systems is essentially a two-phase process. The acquisition phase involves sensing physical conditions at selected locations, converting physical inputs to electrical signals, conditioning the signals as appropriate to establish scale or filter interference, and recording results in a form that is easy to interpret. The inference phase involves analysis of results from the acquisition phase, comparison of analysis results to established health measures, and assessment of health indications. A variety of analytical tools may be employed in the inference phase of health monitoring. These tools can be separated into three broad categories: statistical, rule based, and model based. Statistical methods can provide excellent comparative measures of engine operating health. They require well-characterized data from an ensemble of "typical" engines, or "golden" data from a specific test assumed to define the operating norm in order to establish reliable comparative measures. Statistical methods are generally suitable for real-time health monitoring because they do not deal with the physical complexities of engine operation. The utility of statistical methods in rocket engine health monitoring is hindered by practical limits on the quantity and quality of available data. This is due to the difficulty and high cost of data acquisition, the limited number of available test engines, and the problem of simulating flight conditions in ground test facilities. In addition, statistical methods incur a penalty for disregarding flow complexity and are therefore limited in their ability to define performance shift causality. Rule based methods infer the health state of the engine system based on comparison of individual measurements or combinations of measurements with defined health norms or rules. This does not mean that rule based methods are necessarily simple. Although binary yes-no health assessment can sometimes be established by relatively simple rules, the causality assignment needed for refined health monitoring often requires an exceptionally complex rule base involving complicated logical maps. Structuring the rule system to be clear and unambiguous can be difficult, and the expert input required to maintain a large logic network and associated rule base can be prohibitive.

Santi, L. Michael↗
NASA NTRS2005

Faults Discovery By Using Mined Data

Fault discovery in the complex systems consist of model based reasoning, fault tree analysis, rule based inference methods, and other approaches. Model based reasoning builds models for the systems either by mathematic formulations or by experiment model. Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. The rule based inference build the model based on the expert knowledge. Those models and methods have one thing in common; they have presumed some prior-conditions. Complex systems often use fault trees to analyze the faults. Fault diagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space Station (ISS) control center operates on the data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, this paper present an approach that uses decision trees to discover fault from data in real-time and capture the contents of fault trees as the initial state of the trees.

Lee, Charles↗
DOE OSTI2023

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Monitoring the State-of-Charge of a Vanadium Redox Flow Battery with the Acoustic Attenuation Coefficient: An In Operando Noninvasive Method

Redox flow battery technology has been increasingly recognized as a promising option for large-scale grid energy storage. Access to high-fidelity information on the health status of the electrolyte, including the state-of-charge (SOC), is vital to maintaining optimal and economical battery operation. In this study, an ultrasonic probing cell that can be used to measure SOC in real time is designed. This unprecedented, new measurement approach overcomes the influence of varying temperatures by measuring the acoustic attenuation coefficient of the redox flow battery electrolyte online and noninvasively. The new approach is used to estimate the SOC of a vanadium redox flow battery (VRFB) in operando from measured acoustic properties. The accuracy of the SOC estimated from the acoustic properties is validated against SOC calculated by the titration method. The results show that the acoustic attenuation coefficient is a robust parameter for SOC monitoring, with a maximum error of 4.8% and extremely low sensitivity to temperature, while sound speed appears to be less accurate in the benchmark-inference method, with a maximum error of 22.5% and high sensitivity to temperature. The acoustic measurement approach has great potential for inexpensive real-time SOC monitoring of redox flow battery operations.

25 ENERGY STORAGE↗