Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Bayesian analysis”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

MOA-2007-BLG-400 A Super-Jupiter-mass Planet Orbiting a Galactic Bulge K-dwarf Revealed by Keck Adaptive Optics Imaging

We present Keck/NIRC2 adaptive optics imaging of planetary microlensing event MOA-2007-BLG-400 that resolves the lens star system from the source. We find that the MOA-2007-BLG-400L planetary system consists of a 1.71 ± 0.27M(sub Jup) planet orbiting a 0.69 ± 0.04M⨀ K-dwarf host star at a distance of 6.89 ± 0.77 kpc from the Sun. So, this planetary system probably resides in the Galactic bulge. The planet–host star projected separation is only weakly constrained due to the close-wide light-curve degeneracy; the 2σ projected separation ranges are 0.6–1.0 au and 4.7–7.7 au for close and wide solutions, respectively. This host mass is at the top end of the range of masses predicted by a standard Bayesian analysis. Our Keck follow-up program has now measured lens-source separations for six planetary microlensing events, and five of these six events have host star masses above the median prediction under the assumption that assumes that all stars have an equal chance of hosting planets detectable by microlensing. This suggests that more massive stars may be more likely to host planets of a fixed mass ratio that orbit near or beyond the snow line. These results also indicate the importance of host star mass measurements for exoplanets found by microlensing. The microlensing survey imaging data from NASA’s Nancy Grace Roman Space Telescope (formerly WFIRST) mission will be doing mass measurements like this for a huge number of planetary events.

Exoplanets↗
NASA NTRS2021

Systematic KMTNet Planetary Anomaly Search. I. OGLE-2019-BLG-1053Lb, a BuriedTerrestrial Planet

In order to exhume the buried signatures of “missing planetary caustics” in Korea Microlensing Telescope Network (KMTNet) data, we conducted a systematic anomaly search of the residuals from point-source point-lens fits, based on a modified version of the KMTNet Event Finder algorithm. This search revealed the lowest-mass-ratio planetary caustic to date in the microlensing event OGLE-2019-BLG-1053, for which the planetary signal had not been noticed before. The planetary system has a planet–host mass ratio ofq= (1.25±0.13) × 10−5. A Bayesian analysis yielded estimates of the mass of the host star, Mhost =-0.61+0.29 -0.24 Mo, the mass of its planet, Mplanet =-2.48 +1.19 -0.98 Mo, the projected planet – host separation, a^= 3.4 +0.5/-0.5 au, and the lens distance, DL =-6.8 +0.6 -0.90kpc.The discovery of this very-low-mass-ratio planet illustrates the utility of our method and opens a new window for a large and homogeneous sample to study the microlensing planet–host mass ratio function down to q∼ 10−5.

Exoplanet detection methods↗
NASA NTRS2021

KMT-2021-BLG-0322: Severe Degeneracy Between Triple-lens and Higher-order Binary-lens Interpretations

We investigate the microlensing event KMT-2021-BLG-0322, for which the light curve exhibits three distinctive sets of caustic-crossing features. It is found that the overall features of the light curve are approximately described by a binary-lens (2L1S) model, but the model leaves substantial residuals. We test various interpretations with the aim of explaining the residuals. Methods. We find that the residuals can be explained either by considering a nonrectilinear lens-source motion caused by the microlens-parallax and lens-orbital effects or by adding a low-mass companion to the binary lens (3L1S model). The degeneracy between the higher-order 2L1S model and the 3L1S model is very severe, making it difficult to single out a correct solution based on the photometric data. This degeneracy was known before for two previous events (MACHO-97-BLG-41 and OGLE-2013-BLG-0723),which led to the false detections of planets in binary systems, and thus the identification of the degeneracy for KMT-2021-BLG-0322illustrates that the degeneracy can be not only common but also very severe, emphasizing the need to check both interpretations of deviations from 2L1S models. Results. From the Bayesian analysis conducted with the measured lensing observables of the event timescale, angular Einstein radius, and microlens parallax, it was estimated that the binary lens components have masses(M1,M2)=(0.62+0.25−0.26M,0.07+0.03−0.03M), for both2L1S and 3L1S solutions, and the mass of the tertiary lens component according to the 3L1S solution isM3=6.40+2.64−2.78MJ.

Gravitational lensing: micro↗
NASA NTRS2009

Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis

This document, Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, is intended to provide guidelines for the collection and evaluation of risk and reliability-related data. It is aimed at scientists and engineers familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques. This document provides both: A broad perspective on data analysis collection and evaluation issues. A narrow focus on the methods to implement a comprehensive information repository. The topics addressed herein cover the fundamentals of how data and information are to be used in risk and reliability analysis models and their potential role in decision making. Understanding these topics is essential to attaining a risk informed decision making environment that is being sought by NASA requirements and procedures such as 8000.4 (Agency Risk Management Procedural Requirements), NPR 8705.05 (Probabilistic Risk Assessment Procedures for NASA Programs and Projects), and the System Safety requirements of NPR 8715.3 (NASA General Safety Program Requirements).

Dezfuli, Homayoon↗
NASA NTRS2016

Uncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.

Uncertainty↗
DOE OSTI2025

Bayesian And Human Reliability Analysis (hra)-aided Method For The Reliability Analysis Of Software (bahamas)

The purpose of the BAHAMAS code is to provide a simplified process for performing quantitative evaluations of software reliability. The Bayesian and Human Reliability Analysis (HRA)-Aided method for the Reliability Analysis of software (BAHAMAS) was developed specifically to perform quantification under limited data conditions, i.e., when limited testing or operational data are available, such as during early development stages. BAHAMAS essentially examines the quality of a software development life cycle to determine the probability of specific types of software failure. BAHAMAS will have modules to support user input for detailed and simplified analyses. The user interface will also support software common cause failure analysis.

Wang, Congjian (0000000207789927)↗
NASA NTRS1992

Reliability evaluation methodology for NASA applications

Liquid rocket engine technology has been characterized by the development of complex systems containing large number of subsystems, components, and parts. The trend to even larger and more complex system is continuing. The liquid rocket engineers have been focusing mainly on performance driven designs to increase payload delivery of a launch vehicle for a given mission. In otherwords, although the failure of a single inexpensive part or component may cause the failure of the system, reliability in general has not been considered as one of the system parameters like cost or performance. Up till now, quantification of reliability has not been a consideration during system design and development in the liquid rocket industry. Engineers and managers have long been aware of the fact that the reliability of the system increases during development, but no serious attempts have been made to quantify reliability. As a result, a method to quantify reliability during design and development is needed. This includes application of probabilistic models which utilize both engineering analysis and test data. Classical methods require the use of operating data for reliability demonstration. In contrast, the method described in this paper is based on similarity, analysis, and testing combined with Bayesian statistical analysis.

Taneja, Vidya S.↗
NASA NTRS2012

A Bayesian Framework for Reliability Analysis of Spacecraft Deployments

Deployable subsystems are essential to mission success of most spacecraft. These subsystems enable critical functions including power, communications and thermal control. The loss of any of these functions will generally result in loss of the mission. These subsystems and their components often consist of unique designs and applications for which various standardized data sources are not applicable for estimating reliability and for assessing risks. In this study, a two stage sequential Bayesian framework for reliability estimation of spacecraft deployment was developed for this purpose. This process was then applied to the James Webb Space Telescope (JWST) Sunshield subsystem, a unique design intended for thermal control of the Optical Telescope Element. Initially, detailed studies of NASA deployment history, "heritage information", were conducted, extending over 45 years of spacecraft launches. This information was then coupled to a non-informative prior and a binomial likelihood function to create a posterior distribution for deployments of various subsystems uSing Monte Carlo Markov Chain sampling. Select distributions were then coupled to a subsequent analysis, using test data and anomaly occurrences on successive ground test deployments of scale model test articles of JWST hardware, to update the NASA heritage data. This allowed for a realistic prediction for the reliability of the complex Sunshield deployment, with credibility limits, within this two stage Bayesian framework.

Evans, John W.↗
NASA NTRS2010

Automated Detection of Oscillating Regions in the Solar Atmosphere

Recently observed oscillations in the solar atmosphere have been interpreted and modeled as magnetohydrodynamic wave modes. This has allowed for the estimation of parameters that are otherwise hard to derive, such as the coronal magnetic-field strength. This work crucially relies on the initial detection of the oscillations, which is commonly done manually. The volume of Solar Dynamics Observatory (SDO) data will make manual detection inefficient for detecting all of the oscillating regions. An algorithm is presented that automates the detection of areas of the solar atmosphere that support spatially extended oscillations. The algorithm identifies areas in the solar atmosphere whose oscillation content is described by a single, dominant oscillation within a user-defined frequency range. The method is based on Bayesian spectral analysis of time series and image filtering. A Bayesian approach sidesteps the need for an a-priori noise estimate to calculate rejection criteria for the observed signal, and it also provides estimates of oscillation frequency, amplitude, and noise, and the error in all of these quantities, in a self-consistent way. The algorithm also introduces the notion of quality measures to those regions for which a positive detection is claimed, allowing for simple post-detection discrimination by the user. The algorithm is demonstrated on two Transition Region and Coronal Explorer (TRACE) datasets, and comments regarding its suitability for oscillation detection in SDO are made.

Ireland, J.↗
DOE OSTI2024

Bayesian Fit for the NOvA Three Flavor Oscillation Analysis

NOvA is a long baseline neutrino oscillation experiment, using Fermilab's NuMI beam and a functionally identical near and far detector. NOvA measures muon neutrino disappearance and electron neutrino appearance to probe neutrino oscillation parameters, including the large neutrino mixing angle, the mass ordering, and the CP-violating phase. NOvA has developed a Bayesian analysis in addition to its Frequentist analysis, using Markov Chain Monte Carlo. This Bayesian framework allows for measurements previously difficult to make with the Frequentist framework, such as the Jarlskog invariant and the reactor mixing angle. The details and status of the Bayesian Framework will be presented, as well as latest NOvA results on measurements of three-flavor oscillation parameters.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2013

Estimating the Properties of Hard X-Ray Solar Flares by Constraining Model Parameters

We wish to better constrain the properties of solar flares by exploring how parameterized models of solar flares interact with uncertainty estimation methods. We compare four different methods of calculating uncertainty estimates in fitting parameterized models to Ramaty High Energy Solar Spectroscopic Imager X-ray spectra, considering only statistical sources of error. Three of the four methods are based on estimating the scale-size of the minimum in a hypersurface formed by the weighted sum of the squares of the differences between the model fit and the data as a function of the fit parameters, and are implemented as commonly practiced. The fourth method is also based on the difference between the data and the model, but instead uses Bayesian data analysis and Markov chain Monte Carlo (MCMC) techniques to calculate an uncertainty estimate. Two flare spectra are modeled: one from the Geostationary Operational Environmental Satellite X1.3 class flare of 2005 January 19, and the other from the X4.8 flare of 2002 July 23.We find that the four methods give approximately the same uncertainty estimates for the 2005 January 19 spectral fit parameters, but lead to very different uncertainty estimates for the 2002 July 23 spectral fit. This is because each method implements different analyses of the hypersurface, yielding method-dependent results that can differ greatly depending on the shape of the hypersurface. The hypersurface arising from the 2005 January 19 analysis is consistent with a normal distribution; therefore, the assumptions behind the three non- Bayesian uncertainty estimation methods are satisfied and similar estimates are found. The 2002 July 23 analysis shows that the hypersurface is not consistent with a normal distribution, indicating that the assumptions behind the three non-Bayesian uncertainty estimation methods are not satisfied, leading to differing estimates of the uncertainty. We find that the shape of the hypersurface is crucial in understanding the output from each uncertainty estimation technique, and that a crucial factor determining the shape of hypersurface is the location of the low-energy cutoff relative to energies where the thermal emission dominates. The Bayesian/MCMC approach also allows us to provide detailed information on probable values of the low-energy cutoff, Ec, a crucial parameter in defining the energy content of the flare-accelerated electrons. We show that for the 2002 July 23 flare data, there is a 95% probability that Ec lies below approximately 40 keV, and a 68% probability that it lies in the range 7-36 keV. Further, the low-energy cutoff is more likely to be in the range 25-35 keV than in any other 10 keV wide energy range. The low-energy cutoff for the 2005 January 19 flare is more tightly constrained to 107 +/- 4 keV with 68% probability.

X-rays↗
DOE OSTI2024

A Measurement of νe Appearance and νµ Disappearance Using 10 Years of Data from the NOvA Experiment

NOvA is a long baseline neutrino experiment with an 810 km baseline, using the NuMIbeam at Fermilab, and a functionally identical near and far detector operating at an angle14 mrad off axis from the beam. NOvA jointly measures muon neutrino (and antineutrino)disappearance and electron neutrino (and antineutrino) appearance to make a measurementof sin2θ23, δCP, and ∆m232, including its sign, the mass ordering.This dissertation reports a new measurement from NOvA, using 10 years of data, with a totalexposure of 26.6 ×1020 POT of neutrino beam and 12.5 ×1020 POT of antineutrino beam.This represents 95.6% more neutrino beam exposure since the last NOvA analysis. Aspects ofthe analysis are discussed in detail, including neutrino energy estimation, analysis systematicuncertainties, including the implementation of systematics new to the NOvA analysis, andthe Bayesian fit infrastructure using Markov Chain Monte Carlo (MCMC).The analysis yields the following credible intervals for the oscillation parameters assumingthe normal ordering: δCP = 0.930+0.210−0.290 π ∪0.150+0.150−0.110 π,∆m232 = 2.424+0.035−0.040 × 10−3eV2,and sin2θ23 = 0.55+0.02−0.06, with a 76% preference for normal ordering using a prior for sin2(2θ13)using Daya Bay’s measurement. If the Daya Bay sin2(2θ13) vs ∆m232 constraint is used as aprior instead, an 87% preference for normal ordering emerges.

43 PARTICLE ACCELERATORS↗
NASA NTRSDate not supplied

3D Representation of UAV-obstacle Collision Risk Under Off-nominal Conditions

Safe operations of autonomous unmanned aerial vehicles (UAVs) in low-altitude airspace with beyond visual line-of-sight (BVLOS) flights demand robust risk monitoring of airspace as well as of people and property on ground. One of the safety critical factors for UAV flights is the risk of collision with static and dynamic obstacles in proximity to its flight path. This paper presents a detailed formulation of risk of obstacle collision incorporating the effects of off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts. The risk is represented in terms of a matrix with rows corresponding to the likelihood of occurrence of collision and columns representing severity of collision to the vehicle and surrounding structures. Risk likelihood is generated using a Bayesian Belief Network (BBN) that compiles knowledge from related Failure Modes and Effects Analysis (FMEAs) and Subject Matter Experts (SMEs) to determine the probability of collision based on on-board sensor measurements indicative of vehicle health and controllability. Risk severity is computed utilizing a point-mass 3D kinematic model of the vehicle in presence of wind. The proposed risk factor is demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in presence of simulated obstacles and wind conditions. Effect of varying wind conditions, level of controllability and obstacle measurement noise on the risk factor is demonstrated. The proposed approach enables risk-informed decision making for timely mitigation of current and future unsafe events in autonomous systems.

risk analysis↗
NASA NTRS1989

A Bayesian approach to reliability and confidence

The historical evolution of NASA's interest in quantitative measures of reliability assessment is outlined. The introduction of some quantitative methodologies into the Vehicle Reliability Branch of the Safety, Reliability and Quality Assurance (SR and QA) Division at Johnson Space Center (JSC) was noted along with the development of the Extended Orbiter Duration--Weakest Link study which will utilize quantitative tools for a Bayesian statistical analysis. Extending the earlier work of NASA sponsor, Richard Heydorn, researchers were able to produce a consistent Bayesian estimate for the reliability of a component and hence by a simple extension for a system of components in some cases where the rate of failure is not constant but varies over time. Mechanical systems in general have this property since the reliability usually decreases markedly as the parts degrade over time. While they have been able to reduce the Bayesian estimator to a simple closed form for a large class of such systems, the form for the most general case needs to be attacked by the computer. Once a table is generated for this form, researchers will have a numerical form for the general solution. With this, the corresponding probability statements about the reliability of a system can be made in the most general setting. Note that the utilization of uniform Bayesian priors represents a worst case scenario in the sense that as researchers incorporate more expert opinion into the model, they will be able to improve the strength of the probability calculations.

Barnes, Ron↗
NASA NTRS2015

Enhancements of Bayesian Blocks; Application to Large Light Curve Databases

Bayesian Blocks are optimal piecewise linear representations (step function fits) of light-curves. The simple algorithm implementing this idea, using dynamic programming, has been extended to include more data modes and fitness metrics, multivariate analysis, and data on the circle (Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations, Scargle, Norris, Jackson and Chiang 2013, ApJ, 764, 167), as well as new results on background subtraction and refinement of the procedure for precise timing of transient events in sparse data. Example demonstrations will include exploratory analysis of the Kepler light curve archive in a search for "star-tickling" signals from extraterrestrial civilizations. (The Cepheid Galactic Internet, Learned, Kudritzki, Pakvasa1, and Zee, 2008, arXiv: 0809.0339; Walkowicz et al., in progress).

Kepler light curve archive↗