SEARCH · Engineering Papers

Results for “data inference”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,729 records · Page 96

All sources NASA NTRS DOE OSTI

NASA NTRS1990

Intercomparison of standard resolution and high resolution TOVS soundings with radiosonde, lidar, and surface temperature/humidity data

One objective of the FIRE Cirrus IFO is to characterize relationships between cloud properties inferred from satellite observations at various scales to those obtained directly or inferred from very high resolution measurements. Satellite derived NOAA-9 high and standard resolution Tiros Operational Vertical Sounder (TOVS) soundings are compared with directly measured lidar, surface temperature, humidity, and vertical radiosonde profiles associated with the Ft. McCoy site. The results of this intercomparison should be useful in planning future cloud experiments.

Wheeler, R. J.↗

NASA NTRS1989

Segmentation of multifrequency polarimetric radar images to facilitate the inference of geophysical parameters

An unsupervised clustering algorithm is used to segment multifrequency polarimetric radar data from the NASA/JPL airborne SAR (synthetic aperture radar). Twenty-two parameters are evaluated for their discriminatory capability for each pixel of an image. A clustering analysis is then performed using different subsets of these parameters. This analysis relies on data taken as part of an intensive field experiment during the summer of 1988 in the vicinity of the Pisgah lava flow in the Mojave Desert in southern California. As part of the experiment, extensive ground truth was acquired, including dielectric constant and topography measurements. Segmentation results show good agreement with these measurements.

Burnette, C F.↗

NASA NTRS1991

Inferring solar structure variations from photometric and helioseismic observations

Present large scale photometric and helioseismic data may be related to spatial and temporal deviations from an otherwise static, spherical solar convection zone (SCZ). New space experiments combine precise photometric, helioseismic, and magnetic observational capabilities (like SOHO and MDI) and will provide data needed to understand the interaction of magnetic fields and global scale circulation in the SCZ. Even simple physical arguments make it clear that the anticipated accuracy of these new experiments virtually guarantees new constraints on models of the SCZ.

Kuhn, J. R.↗

NASA NTRSDate not supplied

Creating Seasonal Climatologies of Aerosol Lidar Ratios Over Ocean Using MODIS AOD Constrained Retrievals and GOCART Model Simulations

The current CALIPSO algorithms assign one lidar ratio (i.e., extinction-to-backscatter ratio; LR) value globally for each of the seven tropospheric aerosol types. In a future data products release, the CALIPSO project aims to improve these algorithms through the development of regional and seasonal LR climatologies. In this work, aerosol LRs are inferred through CALIOP backscatter profiles constrained by collocated aerosol optical depth (AOD) from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data. This analysis is subsampled for those profiles that are cloud-free and contain only one CALIOP-classified aerosol type (e.g., marine). The CALIOP profiles are then collocated with aerosol volume fractions obtained through Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulations. In this poster, we show twelve-year (June 2006-August 2018) mean spatial distributions of inferred aerosol LRs for CALIOP-classified marine aerosols and how they correspond inversely with patterns of GOCART sea salt volume fraction (SSVF). Near land masses, smaller SSVFs and larger LRs are found (due to the influence of over-land aerosols). In the remote ocean regions (likely less impacted by over-land aerosols), larger SSVFs and smaller LRs are found. The developed relationship between the GOCART model SSVFs and MODIS AOD constrained LRs is used to create model-assisted seasonal LR maps. Additionally, we show maps of inferred LRs from constrained retrievals using the CALIPSO Ocean Derived Column Optical Depth (ODCOD) product and comparisons with those from the MODIS analyses. The technique demonstrated here benefits CALIPSO in the near-term, but similar methods can also be applied to the next generation space-based elastic backscatter lidars with collocated passive sensors, such as those of the upcoming NASA Atmosphere Observing System (AOS).

Travis Toth↗

DOE OSTI2025

A small core in Vesta inferred from Dawn’s observations

Vesta’s large-scale interior structure had previously been constrained primarily using the gravity and shape data from the Dawn mission. However, these data alone still allow a wide range of possibilities for the differentiation state of the body. The moment of inertia is arguably the most diagnostic parameter related to the radial density distribution of a planetary body, making it crucial for assessing the body’s state of internal differentiation. Determining the moment of inertia requires additional measurements of the amplitudes of small rotational motions, such as precession and nutation. Here we report an updated estimate of the moment of inertia of Vesta inferred from Dawn’s Doppler tracking via the Deep Space Network and onboard imaging data. The recovered value for Vesta’s normalized polar moment of inertia is $\overline{C}$/MR 2 = 0.4208 ± 0.0047 (where M is the mass of Vesta and R is the reference radius), which is only 6.6% lower than the homogeneous value of 0.4505. This value, combined with the gravity field and global shape, suggests that Vesta’s interior has limited density stratification beneath its howardite–eucrite–diogenite-dominated crust. We propose two possible origin scenarios that are consistent with the observed constraints. In the first scenario, Vesta’s interior did not undergo full differentiation due to late accretion. In the second scenario, Vesta originated as an impact remnant of a larger differentiated body re-accreted with non-chondritic bulk composition produced from a catastrophic impact. Vesta did not experience complete differentiation in either scenario, suggesting that its current state reflects a complex interplay between its accretion timing, thermal evolution, redistribution of 26 Al bearing melt and/or impact processes.

CNEOS 2014-01-08 bolide↗

DOE OSTI2021

B-PINNs: Bayesian Physics-informal Neural Networks for Forward and Inverse PDE Problems with Noisy Data

We propose a Bayesian physics-informed neural network (B-PINN) to solve both forward and inverse nonlinear problems described by partial differential equations (PDEs) and noisy data. In this Bayesian framework, the Bayesian neural network (BNN) combined with a PINN for PDEs serves as the prior while the Hamiltonian Monte Carlo (HMC) or the variational inference (VI) could serve as an estimator of the posterior. B-PINNs make use of both physical laws and scattered noisy measurements to provide predictions and quantify the aleatoric uncertainty arising from the noisy data in the Bayesian framework. Compared with PINNs, in addition to uncertainty quantification, B-PINNs obtain more accurate predictions in scenarios with large noise due to their capability of avoiding overfitting. We conduct a systematic comparison between the two different approaches for the B-PINNs posterior estimation (i.e., HMC or VI), along with dropout used for quantifying uncertainty in deep neural networks. Our experiments show that HMC is more suitable than VI with mean field Gaussian approximation for the B-PINNs posterior estimation, while dropout employed in PINNs can hardly provide accurate predictions with reasonable uncertainty. Finally, we replace the BNN in the prior with a truncated Karhunen-Loève (KL) expansion combined with HMC or a deep normalizing flow (DNF) model as posterior estimators. The KL is as accurate as BNN and much faster but this framework cannot be easily extended to high-dimensional problems unlike the BNN based framework.

Non linear PDEs, Noisy data, Bysian physics inform↗

NASA NTRS1999

Satellite Altimetry, Ocean Circulation, and Data Assimilation

Ocean circulation is a critical factor in determining the Earth's climate. Satellite altimetry has been proven a powerful technique for measuring the height of the sea surface for the study of global ocean circulation dynamics. A major objective of my research is to investigate the utility of altimeter data for ocean circulation studies. The 6 years' data record of TOPEX/POSEIDON have been analyzed to study the spatial and temporal characteristics of large-scale ocean variability. A major result obtained in 1998 is the discovery of large-scale oscillations in sea level with a period of 25 days in the Argentine Basin of the South Atlantic Ocean (see diagram). They exhibit a dipole pattern with counterclockwise rotational propagation around the Zapiola Rise (centered at 45S and 317E), a small seamount in the abyssal plain of the basin. The peak-to-trough amplitude is about 10 cm over a distance of 500-1000 km. The amplitude of these oscillations has large seasonal-to-interannual variations. The period and rotational characteristics of these oscillations are remarkably similar to the observations made by two current meters deployed near the ocean bottom in the region. What TOPEX/POSEIDON has detected apparently are manifestations of the movement of the entire water column (barotropic motion). The resultant transport variation is estimated to be about 50 x 10(exp 6) cubic M/S, which is about 50% of the total water transport in the region. Preliminary calculations suggest that these oscillations are topographically trapped waves. A numerical model of the South Atlantic is used to investigate the nature of and causes for these waves. A very important property of sea surface height is that it is directly related to the surface geostrophic velocity, which is related to deep ocean circulation through the density field. Therefore altimetry observations are not only useful for determining the surface circulation but also for revealing information about the deep ocean. Another objective of my research is to investigate the extent of information one can infer about the deep ocean from altimetric observations. The approach is data assimilation by ocean models. The objective is to develop a modeling/data assimilation system that will produce estimates of the 3-dimensional state of the ocean for the entire duration of the TOPEX/POSEIDON Mission.

Fu, Lee-Lueng↗

DOE OSTI2021

The growth of density perturbations in the last ~10 billion years from tomographic large-scale structure data

In order to investigate the origin of the ongoing tension between the amplitude of matter fluctuations measured by weak lensing experiments at low redshifts and the value inferred from the cosmic microwave background anisotropies, we reconstruct the evolution of this amplitude from z ~ 2 using existing large-scale structure data. To do so, we decouple the linear growth of density inhomogeneities from the background expansion, and constrain its redshift dependence making use of a combination of 6 different data sets, including cosmic shear, galaxy clustering and CMB lensing. We analyze these data under a consistent harmonic-space angular power spectrum-based pipeline. We show that current data constrain the amplitude of fluctuations mostly in the range 0.2 < z < 0.7, where it is lower than predicted by . This difference is mostly driven by current cosmic shear data, although the growth histories reconstructed from different data combinations are consistent with each other, and we find no evidence of systematic deviations in any particular experiment. In spite of the tension with , the data are well-described by the model, albeit with a lower value of S 8 ≡ σ 8 (Ω m /0.3) 0.5 . As part of our analysis, we find constraints on this parameter of S 8 = 0.7781 ± 0.0094 (68% confidence level), reaching almost percent-level errors comparable with CMB measurements, and 3.4σ away from the value found by .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Inferring three-nucleon couplings from multi-messenger neutron-star observations

Understanding the interactions between nucleons in dense matter is an important challenge in theoretical physics. Effective field theories have emerged as the dominant approach to address this problem at low energies, with many successful applications to the structure of nuclei and the properties of dense nucleonic matter. However, how far into the interior of neutron stars these interactions can describe dense matter is an open question. Here, we develop a framework that enables the inference of three-nucleon couplings in dense matter directly from astrophysical neutron star observations. We apply this formalism to the LIGO/Virgo gravitational-wave event GW170817 and the X-ray measurements from NASA’s Neutron Star Interior Composition Explorer and establish direct constraints for the couplings that govern three-nucleon interactions in chiral effective field theory. Furthermore, we demonstrate how next-generation observations of a population of neutron star mergers can offer stringent constraints on three-nucleon couplings, potentially at a level comparable to those from laboratory data. Our work directly connects the microscopic couplings in quantum field theories to macroscopic observations of neutron stars, providing a way to test the consistency between low-energy couplings inferred from terrestrial and astrophysical data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2008

Aerothermodynamic Testing of the Crew Exploration Vehicle in the LaRC 20-Inch Mach 6 and 31-Inch Mach 10 Tunnels

An experimental wind tunnel program is being conducted in support of a NASA wide effort to develop a Space Shuttle replacement and to support the Agency s long term objective of returning to the Moon and Mars. This report documents experimental measurements made on several scaled ceramic heat transfer models of the proposed Crew Exploration Vehicle Crew Module. The experimental data highlighted in this test report are to be used to assess numerical tools that will be used to generate the flight aerothermodynamic database. Global heat transfer images and heat transfer distributions were obtained over a range of freestream Reynolds numbers and angles of attack with the phosphor thermography technique. Heat transfer data were measured on the forebody and afterbody and were used to infer the heating on the vehicle as well as the boundary layer state on the forebody surface. Several model support configurations were assessed to minimize potential support interference. In addition, the ability of the global phosphor thermography method to provide quantitative heating measurements in the low temperature environment of the capsule base region was assessed. While naturally fully developed turbulent levels were not obtained on the forebody, the use of boundary layer trips generated fully developed turbulent flow. Laminar and turbulent computational results were shown to be in good agreement with the data. Backshell testing demonstrated the ability to obtain data in the low temperature region as well as demonstrating the lack of significant model support hardware influence on heating.

Berger, Karen T.↗

NASA NTRS2009

Aerothermodynamic Testing of the Crew Exploration Vehicle in the LaRC 20-Inch Mach 6 and 31-Inch Mach 10 Tunnels

An experimental wind tunnel program is being conducted in support of a NASA wide effort to develop a Space Shuttle replacement and to support the Agency s long term objective of returning to the Moon and Mars. This article documents experimental measurements made on several scaled ceramic heat transfer models of the proposed Crew Exploration Vehicle Crew Module. The experimental data highlighted in this article are to be used to assess numerical tools that will be used to generate the flight aerothermodynamic database. Global heat transfer images and heat transfer distributions were obtained over a range of freestream Reynolds numbers and angles of attack with the phosphor thermography technique. Heat transfer data were measured on the forebody and afterbody and were used to infer the heating on the vehicle as well as the boundary layer state on the forebody surface. Several model support configurations were assessed to minimize potential support interference. In addition, the ability of the global phosphor thermography method to provide quantitative heating measurements in the low temperature environment of the capsule base region was assessed. While naturally fully developed turbulent levels were not obtained on the forebody, the use of boundary layer trips generated fully developed turbulent flow. Laminar and turbulent computational results were shown to be in good agreement with the data. Backshell testing demonstrated the ability to obtain data in the low temperature region as well as demonstrating the lack of significant model support hardware influence on heating.

Berger, Karen T.↗

NASA NTRS1999

A Methodology for Evaluating the Fidelity of Ground-Based Flight Simulators

An analytical and experimental investigation was undertaken to model the manner in which pilots perceive and utilize visual, proprioceptive, and vestibular cues in a ground-based flight simulator. The study was part of a larger research effort which has the creation of a methodology for determining flight simulator fidelity requirements as its ultimate goal. The study utilized a closed-loop feedback structure of the pilot/simulator system which included the pilot, the cockpit inceptor, the dynamics of the simulated vehicle and the motion system. With the exception of time delays which accrued in visual scene production in the simulator, visual scene effects were not included in this study. The NASA Ames Vertical Motion Simulator was used in a simple, single-degree of freedom rotorcraft bob-up/down maneuver. Pilot/vehicle analysis and fuzzy-inference identification were employed to study the changes in fidelity which occurred as the characteristics of the motion system were varied over five configurations i The data from three of the five pilots that participated in the experimental study were analyzed in the fuzzy inference identification. Results indicate that both the analytical pilot/vehicle analysis and the fuzzyinference identification can be used to reflect changes in simulator fidelity for the task examined.

Zeyada, Y.↗

DOE OSTI2021

Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation

The more easily available system performance data and advances in data analytics have provided us with opportunities to optimize maintenance programs for engineered systems, for example nuclear power plants. One key task in maintenance optimization is to obtain an accurate model for system degradation. Here, in this research, we propose a Bayesian method to address this problem. Noting that systems usually exhibit multiple states and that the actual state of a system usually is not directly observable, in the method we first model the system degradation process and the observation process based on a hidden Markov model. Then we develop a sequential Bayesian inference algorithm based on importance sampling and the forward algorithm to infer the posterior distributions of the transition rates in the hidden Markov model based on available observations. The proposed Bayesian method allows us to take advantage of evidence from multiple sources, and also allows us to perform Bayesian inference sequentially, without the need to use the entire history of observations every time new observations are collected. We demonstrate the proposed method using both synthetic data for a nuclear power plant feedwater pump and realistic data for a nuclear power plant chemistry analytical device.

42 ENGINEERING↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

space biology↗

DOE OSTI2020

Hydrodynamic and Radiographic Toolbox (HART)

With a multi-lab and university team, we propose to develop new methods for a Hydrodynamic and Radiographic Toolbox (HART) that will enable a fuller and more extensive use of experimental radiographic data towards better characterizing and reducing uncertainties in predictive modeling of weapons performance. We will achieve this by leveraging recent developments in areas of computational imaging, statistical and machine learning, and reduced order modeling of hydrodynamics. In terms of software practices, by partnering with XCP (RISTRA Project) we will conform to recent XCP standards that are inline with modern software practices and standards and ensure compatibility and ease of inter-operability with existing codes. Three new activities under this proposal include (a) the development and use of deep learning-based surrogates to accelerate reconstruction and variational inference of density fields from radiographs of hydrotests, (b) a model-data fusion strategy that couples deep learning-based density reconstructions with fast hydrodynamics simulators to better constrain the reconstruction, and (c) a method for treating asymmetries using techniques adopted from limited view tomography. All three new activities will be based on improved treatment of scatter, noise, beam spot movement, detector blur, and flat fielding in the forward model, and a use of sophisticated priors to aid in the re construction. The improvements to the forward model and improved algorithmic design of the reconstruction when complete will be contained in the iterative reconstruction code SHIVA—a code project that we have recently initiated. The many ways in which machine learning can be used in the reconstruction work will be contained in a code HERMES that has been initiated with DTRA support. For example, the significant levels of acceleration that will likely be achieved by the use of machine learning techniques will permit us (and are required) to quantify uncertainties in density retrievals. Next, the two-way coupling between density reconstruction and model-based simulation of the hydrodynamics will be contained in code EREBUS, and will permit a fuller realization of the potential of the data to constrain the hydrodynamic model and better address issues related to asymmetries in the problem. Finally, we anticipate that the better consistency with physics achieved in our reconstructions will allow them to be used by X-Division more so than today.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

NASA NTRS1984

Lu-Hf and Sm-Nd evolution in lunar mare basalts

Existing cumulate remelting models for mare basalt genesis are evaluated in light of Lu-Hf, Rb-Sr, Sm-Nd data and overall REE characteristics in order to determine the simplest model that can account for these data. A data base for comparing Lu-Hf evolution in the lunar mantle as inferred from Lu-Hf analyses of oceanic basalts is presented along with a preliminary comparison of Lu-Hf and Sm-Nd evolution betwee mare basalts and terrestrial oceanic basalts. It is found that Lu/Hf characteristics of mare basalts cannot be explained in terms of modal melting of cumulate sources formed from a magma ocean with chondritic Lu/Hf. The data are consistent with a model in which the cumulate sources formed from a light REE + HF-enriched magma ocean. Nonmodal melting of ilmenite in the sources is also required. The Lu-Hf data suggest that even the high-Ti basalt sources contained no more than about 3 percent ilmenite.

Unruh, D. M.↗

DOE OSTI2021

Bayesian inference for plasmonic nanometrology

Here, we introduce a Bayesian method for the characterization of plasmonic nanoparticles, which is applicable to both near- and far-field problems. Designed to combine data generated from any photon-plasmon interaction experiment with physically motivated theoretical models, our approach leverages state-of-the-art Markov chain Monte Carlo sampling techniques and returns parameter estimates on nanometric scales. Simulated spectral data sets, describing resonant scattering of photons from ellipsoidal and toroidal nanoparticles, are explored as concrete examples of our approach, with the resulting Bayesian estimates showing excellent agreement with the ground truth, even under conditions of high statistical noise. By incorporating Bayes factors into the method as well, we reveal how model selection can determine which one of competing geometric shapes better explains the observed data. Our comprehensive nanometrology procedure can be tailored to a variety of light-particle interaction models, and its reliance on Bayesian inference furnishes automatic uncertainty quantification. In addition to applicability to a host of plasmonic configurations such as nanoparticle dimers, trimers, and array studies, it is proposed that the presented analysis can be extended to the quantum regime, where nonclassical photon statistics may provide additional insight for inference of scatterer properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗