Search NASA⌕ Search

SEARCH · Search NASA

Results for “Operator 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.

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

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

The effect of perturbation of convective energy transport on the luminosity and radius of the sun

The response of solar models to perturbations of the efficiency of convective energy transport is studied. Such perturbations primarily affect the shallow superadiabatic layer of the convective envelope. Independent of the details of the perturbation scheme, the resulting change in the solar radius is always very small compared to the change in luminosity. This appears to be true for any physical mechanism of solar variability which operates in the outer layers of the convection zone. Changes of the solar radius have been inferred from historical observations of solar eclipses in 1715 and 1925. Considering the constraints on concurrent luminosity changes, this type of solar variability must be indicative of changes in the solar structure at substantial depths below the superadiabatic layer of the convective envelope.

Endal, A. S.↗
DOE OSTI2024

Approach for Inferring Full-Scope Human Reliability Data Based on Simplified Simulator Data

This paper proposes a method for inferring full-scope human reliability data based on the Simplified Human Error Experimental Program (SHEEP) data. It mainly focuses on the human errors observed when using simulators with different complexity levels. In the proposed method, the manner in which human error probabilities (HEPs) change as a result of increasing simulator complexity and how simulator complexity levels are quantified represent key information for inferring full-scope data. In the present study, SHEEP error data pertaining to actual professional operators using Rancor Microworld (Rancor) (i.e., a more simplified simulator) and Compact Nuclear Simulator (CNS) (i.e., a less simplified simulator) were compared with the HuREX error data. An approach to quantifying simulator complexity levels was then proposed based on information theory and acquired eye-tracker data.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Development of Whole System Digital Twins for Advanced Reactors: Leveraging Graph Neural Networks and SAM Simulations

Here, in this work, we introduce a novel method to develop whole system digital twins (DTs) for advanced nuclear reactors. This method treats a complex reactor system as a heterogeneous graph: with the system components as different types of graph nodes and their physical interconnections as edges. Based on the heterogeneous graph, a graph neural network combining graph convolution and temporal node attention is developed as the DT, facilitating a comprehensive understanding of the system's dynamic behavior. By utilizing the System Analysis Module (SAM) code for simulating various operational transients, we develop a graph-based database that trains the DT. This DT is characterized by two primary functions: It can infer the entire system's status using sparse node information, and it can predict the progress of transients based on current and historical system information. Our approach is validated through case studies on the Experimental Breeder Reactor II (EBR-II) system and a generic Fluoride-salt-cooled High-temperature Reactor (gFHR), demonstrating the DT's accuracy in forecasting operational transients. The DT's rapid computation capabilities enhance its potential for supporting advanced reactor operations, offering benefits in intelligent simulation, autonomous control, and anomaly detection, paving the way for improved safety analysis and intelligent component health management for advanced reactor systems and reducing their operations and maintenance cost.

EBR-II↗
DOE OSTI2025

A GPU‐Accelerated Generative Adversarial Model for Causal Inference

We develop a GPU-accelerated machine learning generative adversarial model designed to facilitate causal inferences from observational data. Our model's theoretical framework is conceptualized in a manner that is amenable to being operable and scalable for high-performance computing platforms. We leverage GPU acceleration to develop a parallel evolutionary algorithm to achieve large-scale parallel computation of the model within a now widely accessible computing platform. This capability both enhances computational speedup and efficiency and also extends the use of the model to a broader range of substantive research domains while maintaining the underlying theoretical properties of the model.

GPU↗
NASA NTRS1988

A multiprocessing architecture for real-time monitoring

A multitasking architecture for performing real-time monitoring and analysis using knowledge-based problem solving techniques is described. To handle asynchronous inputs and perform in real time, the system consists of three or more distributed processes which run concurrently and communicate via a message passing scheme. The Data Management Process acquires, compresses, and routes the incoming sensor data to other processes. The Inference Process consists of a high performance inference engine that performs a real-time analysis on the state and health of the physical system. The I/O Process receives sensor data from the Data Management Process and status messages and recommendations from the Inference Process, updates its graphical displays in real time, and acts as the interface to the console operator. The distributed architecture has been interfaced to an actual spacecraft (NASA's Hubble Space Telescope) and is able to process the incoming telemetry in real-time (i.e., several hundred data changes per second). The system is being used in two locations for different purposes: (1) in Sunnyville, California at the Space Telescope Test Control Center it is used in the preflight testing of the vehicle; and (2) in Greenbelt, Maryland at NASA/Goddard it is being used on an experimental basis in flight operations for health and safety monitoring.

Schmidt, James L.↗
NASA NTRS1995

Simultaneous Extreme-Ultraviolet Explorer and Optical Observations of Ad Leonis: Evidence for Large Coronal Loops and the Neupert Effect in Stellar Flares

We report on the first simultaneous Extreme-Ultraviolet Explorer (EUVE) and optical observations of flares on the dMe flare star AD Leonis. The data show the following features: (1) Two flares (one large and one of moderate size) of several hours duration were observed in the EUV wavelength range; (2) Flare emission observed in the optical precedes the emission seen with EUVE; and (3) Several diminutions (DIMs) in the optical continuum were observed during the period of optical flare activity. To interpret these data, we develop a technique for deriving the coronal loop length from the observed rise and decay behavior of the EUV flare. The technique is generally applicable to existing and future coronal observations of stellar flares. We also determine the pressure, column depth, emission measure, loop cross-sectional area, and peak thermal energy during the two EUV flares, and the temperature, area coverage, and energy of the optical continuum emission. When the optical and coronal data are combined, we find convincing evidence of a stellar 'Neupert effect' which is a strong signature of chromospheric evaporation models. We then argue that the known spatial correlation of white-light emission with hard X-ray emission in solar flares, and the identification of the hard X-ray emission with nonthermal bremsstrahlung produced by accelerated electrons, provides evidence that flare heating on dMe stars is produced by the same electron precipitation mechanism that is inferred to occur on the Sun. We provide a thorough picture of the physical processes that are operative during the largest EUV flare, compare and contrast this picture with the canonical solar flare model, and conclude that the coronal loop length may be the most important factor in determining the flare rise time and energetics.

Hawley, Suzanne L.↗
NASA NTRS2012

The Kinematic and Microphysical Control of Storm Integrated Lightning Flash Extent

Objective: To investigate the kinematic and microphysical control of lightning properties, particularly those that may govern the production of nitrogen oxides (NOx) in thunderstorms, such as flash rate, type (intracloud [IC] vs. cloud-to-ground [CG] ) and extent. Data and Methodology: a) NASA MSFC Lightning Nitrogen Oxides Model (LNOM) is applied to North Alabama Lightning Mapping Array (NALMA) and Vaisala National Lightning Detection Network(TradeMark) (NLDN) observations following ordinary convective cells through their lifecycle. b) LNOM provides estimates of flash type, channel length distributions, lightning segment altitude distributions (SADs) and lightning NOx production profiles (Koshak et al. 2012). c) LNOM lightning characteristics are compared to the evolution of updraft and precipitation properties inferred from dual-Doppler (DD) and polarimetric radar analyses of UAHuntsville Advanced Radar for Meteorological and Operational Research (ARMOR, Cband, polarimetric) and KHTX (S-band, Doppler).

Carey, Lawrence D.↗
DOE OSTI2026

WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs With Physics‐Informed Neural Networks

Accurate representation of pumping wells is essential for reliable reservoir characterization and simulation of operational scenarios in subsurface flow models. Physics-informed neural networks (PINNs) are emerging as a promising alternative to numerical models for reservoir modeling, offering seamless integration of monitoring data and governing physical equations. However, existing PINN-based studies face major challenges in capturing fluid pressure near wells when using a source/sink term, particularly during the early stages after pumping begins. We address this problem by introducing WellPINN, a workflow in which an initially trained PINN infers fluid pressure across the entire reservoir domain using a large equivalent well radius. This initial PINN solution is then locally refined around the well by a set of subdomain PINNs that are trained for smaller equivalent well radii. Continuity across these subdomain interfaces as well as at the initial condition is ensured by hard-constraining each PINN on its subdomain boundary. Our results demonstrate WellPINN as the first workflow of its kind to focus on accurate inference of fluid pressure from pumping rates throughout the entire injection period, significantly advancing the potential of PINNs for inverse modeling and operational scenario simulations. All data and code for this paper are openly available at https://doi.org/10.20350/DIGITALCSIC/17260.

58 GEOSCIENCES↗
NASA NTRS2004

On Bayesian Inductive Inference & Predictive Estimation

We investigate Bayesian inference and the Principle of Maximum Entropy (PME) as methods for doing inference under uncertainty. This investigation is primarily through concrete examples that have been previously investigated in the literature. We find that it is possible to do Bayesian inference and PME inference using the same information, despite claims to the contrary, but that the results are not directly comparable. This is because Bayesian inference yields a probability density function (pdf) over the unknown model parameters, whereas PME yields point estimates. If mean estimates are extracted from the Bayesian pdfs, the resulting parameter estimates can differ radically from the PME values and also from the Maximum Likelihood values. We conclude that these differences are due to the Bayesian inference not assuming anything beyond the given prior probabilities and the data, whereas PME implicitly assumes that the given constraints are the only constraints that are operating. Since this assumption can be wrong, PME values may have to be revised when subsequent data shows evidence for more constraints. The entropy concentration previously "proved" by E. T. Jaynes is shown to be in error. Further, we show that PME is a generalized form of independence assumption, and so can be a very powerful method of inference when the variables being investigated are largely independent of each other.

Cheeseman, Peter↗
DOE OSTI2026

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗
DOE OSTI2024

CHMMPY: A python package for constrained Hidden Markov Models

SAND2025-11909O chmmpy software analyzes multivariate timeseries data to detect patterns. It uses a Hidden Markov Model (HMM) and application-specific constraints that reflect known relationships among hidden states to accomplish this. The chmmpy software provides a generic framework for expressing application-specific constraints and supporting constrained HMM inference using optimization solvers. chmmpy is available on GitHub. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hart, William↗
NASA NTRS1983

The International Satellite Cloud Climatology Project (ISCCP) - The first project of the World Climate Research Programme

The first project of the World Climate Research Program is the International Satellite Cloud Climatology Project, (ISCCP) whose objective is the collection and analysis of satellite radiance data in order to infer the global distribution of cloud radiative properties and improve the modeling of cloud effects on climate. The operational component of ISCCP takes advantage of the global coverage provided by the current and planned international array of geostationary and polar-orbiting meteorological satellites in the 1980s. It will produce a five-year global radiance and cloud data set. The research component of ISCCP will coordinate studies to validate climatology, improve cloud analysis algorithms, improve cloud effects modelling, and investigate the role of clouds in the atmospheric radiation budget and hydrologic cycle.

Schiffer, R. A.↗
NASA NTRS1985

Simulation studies related to the design of post-FGGE observing systems

The results of three detailed simulation studies are presented. The first study consists of a comparative assessment of the performance of an advanced moisture/temperature sounder (AMTS) being proposed by NASA as a follow-on replacement to the current HIRS-2 operational sounder aboard the NOAA weather satellites. The second study was concerned with assessing the relative accuracies of inferred atmospheric states for idealized lidar wind profiling systems, temperature profiling systems, temperature profiling systems, surface pressure systems, and composite systems. The third study incorporated the above systems into a highly realistic data analysis/forecast cycle from which a series of forecast impact studies were conducted. These studies, taken together, give us a picture of the potential that emerging technoloies can offer in the determination of the basic atmospheric variables required for long-range numerical weather and climate prediction.

Halem, M.↗
NASA NTRS1995

Dynamics of Vapour Bubbles in Nucleate Boiling: Evolution of Thermally Controlled Bubbles - 2

The previously developed dynamic theory of growth and detachment of vapour bubbles under conditions of nucleate pool boiling is applied to study motion and deformation of a bubble evolving at a single nucleation site. The bubble growth is presumed to be thermally controlled, and two components of heat transfer to the bubble are accounted of: the one from the bulk of surrounding liquid and the one due to heat conduction across a liquid microlayer formed underneath the bubble. Bubble evolution is governed by the buoyancy and an effective surface tension force, both the forces making the bubble centre of mass move away from the wall and, thus, assisting its detachment. Buoyancy-controlled and surface-tension-controlled regimes are considered separately in a meticulous way. The duration of the whole process of bubble evolution till detachment, the rate of growth, and the bubble departure size are found as functions of time and physical and operating parameters. Some repeatedly observed phenomena, such as an influence of gravity on the growth rate, are explained. Inferences of the model agree qualitatively with available experimental evidence, and conclusions pertaining to the dependence on gravity of the bubble radius at detachment and the whole time of the bubble development when being attached to the wall are confirmed quantitatively.

Buyevich, Yu A.↗
NASA NTRS2026

A Multi-Tier Autonomous Aerial Architecture for Wildfire Detection, Characterization, and Communication in Infrastructure-Denied Environments

Wildfire response depends on how fast an ignition can be confirmed and located, especially in remote regions where ground-based communication and monitoring may be limited. Geostationary sensors provide frequent observations but at kilometer-scale resolution, which is too coarse to resolve small fires in remote terrain. Ground camera networks require sightlines and infrastructure that back-country areas lack. To address these limitations, this work proposes a Multi-Tier Autonomous Wildfire Intelligence System that combines wide-area monitoring with targeted, high-resolution sensing. A solar-powered high-altitude long endurance (HALE) platform operating at approximately 60,000 ft provides persistent wide-area thermal and optical surveillance, running onboard edge inference to screen candidate ignitions and reduce false positives and downlink bandwidth. When a candidate ignition is detected, low-altitude uncrewed aircraft systems (UAS) can be deployed to conduct localized observations, including high-resolution imaging and atmospheric measurements such as wind and plume observation. By combining persistent detection with local sensing, the proposed architecture is designed to provide first responders with timely, high-resolution information about fire location and behavior to aid in emergency decision making.

Wildfire management, UAS, drones↗
NASA NTRS1993

Earth observing scanning polarimeter

Climate forcing by tropospheric aerosols is receiving increased attention because of the realization that the climate effects may be large, while our knowledge of global aerosol characteristics and temporal changes is very poor. Tropospheric aerosols cause a direct radiative forcing due simply to their scattering and absorption of solar radiation, as well as an indirect effect as cloud condensation nuclei which can modify the shortwave reflectivity of clouds. Sulfate aerosols tend to increase planetary albedo through both the direct and indirect effects; a cooling due to anthropogenic sulfate aerosols has been estimated of order 1 W/sq m, noting that this is similar in magnitude to the present anthropogenic greenhouse gas warming. Other aerosols, including those from biomass burning and wind-blown desert dust are also of potential climatic importance. At present, the only global monitoring of tropospheric aerosols is a NOAA operational product, aerosol optical thickness, obtained using channel-1 (0.58-0.68 mu m) radiances from the AVHRR. With this single channel radiance data, one must use an approach which is based on the inferred excess of reflected radiance owing to scattering by the aerosols over that expected from theoretical calculations. This approach is suited only for situations where the surface has a low albedo that is well known a priori. Thus, the NOAA operational product is restricted to coverage over the ocean at AVHRR scan angles well away from sun glint, and aerosol changes are subject to confusion with changes caused by either optically thin or subpixel clouds. Because optically thin aerosols have only a small effect on the radiance, accurate measurements for optical thickness less than 0.1 (which is a typical background level) are precluded. Moreover, some of the largest and most important aerosol changes are expected over land. The Earth Observing Scanning Polarimeter (EOSP) instrument, based upon design heritage and analysis techniques developed for planetary missions, will retrieve tropospheric aerosol characteristics from measurements of multispectral radiance and polarization. Moreover, the same radiance and polarization measurements will also provide very precise information on cloud properties and maps of surface characteristics for cloud-free regions. These capabilities also give EOSP the unique ability to discriminate aerosol from clouds and surface.

Travis, Larry↗
NASA NTRS2001

Inference of Global Mean Temperature Trend and Climate Change from MSU and AMSU

Microwave Sounding Unit (MSU) and Advanced MSU (AMSU) radiometers flown on the NOAA operational satellite series are potentially valuable as global temperature monitoring devices. Spencer and Christy pioneered the analysis of mid-tropospheric temperature, given by MSU Channel 2 (Ch 2) at 53.74 GHz, to derive the global temperature trend. Also, in addition to monitoring global temperature, these microwave radiometers have the potential to reveal interannual climate signals in tropics. We have analyzed the data of MSU Ch 2 and AMSU Ch 5 (53.6 GHz) from the NOAA operational satellites for the period 1980 to 2000, utilizing the NOAA calibration procedure. The data are corrected for the satellite orbital drift based on the temporal changes of the on-board warm blackbody temperature. From our analysis, we find that the global temperature increased at a rate of 0.13 +/- 0.05 Kdecade(sup -1) during 1980 to 2000. From an Empirical Orthogonal Function (EOF) analysis of the MSU global data, we find that the mid-tropospheric temperature in middle and high latitudes responds to the ENSO forcing during the Northern Hemisphere Winter in a distinct manner. This mid-latitude response is opposite in phase to that in the tropics. This result is in accord with simulations performed with an ECMWF global spectral model. This study shows a potential use of the satellite observations for climatic change.

Prabhakara, Cuddapah↗
NASA NTRS1991

Plan recognition and generalization in command languages with application to telerobotics

A method for pragmatic inference as a necessary accompaniment to command languages is proposed. The approach taken focuses on the modeling and recognition of the human operator's intent, which relates sequences of domain actions ('plans') to changes in some model of the task environment. The salient feature of this module is that it captures some of the physical and linguistic contextual aspects of an instruction. This provides a basis for generalization and reinterpretation of the instruction in different task environments. The theoretical development is founded on previous work in computational linguistics and some recent models in the theory of action and intention. To illustrate these ideas, an experimental command language to a telerobot is implemented. The program consists of three different components: a robot graphic simulation, the command language itself, and the domain-independent pragmatic inference module. Examples of task instruction processes are provided to demonstrate the benefits of this approach.

Yared, Wael I.↗