Search NASA⌕ Search

SEARCH · Search NASA

Results for “model structural error”

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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Primary and Low-Strain Creep Models for 9Cr Tempered Martensitic Steels Including the Effects of Irradiation Softening and High-Helium Re-Hardening

Primary and low-strain creep represents a very important integrity challenge to large, complex structures, like fusion reactors. Here, we develop a predictive empirical primary creep model for 9Cr tempered martensitic steels (TMS), relating the applied stress (σ) to strain (ε), time (t) and temperature (T). The most accurate model is based on the applied σ normalized by the steel’s T-dependent ultimate tensile stress (σo), σ/σo(T). The model, fit to 17 heats of 9Cr TMS, yielded a σ root mean square error (RMSE) of ≈±11 MPa. Notably, the model also provides robust predictions for all the other TMS, when calibrated only by the fusion candidate Eurofer97 database. The model was extended to explore two possible effects of neutron irradiation, which produces both displacements per atom (dpa) and helium (He in atomic parts per million, appm) damage. These effects, which have not been previously considered, include: (a) softening, as a function of dpa, at T > ≈400–450 °C, in low-He fission environments (<1 He/dpa); and (b) subsequent re-hardening in high-He (≥10 He/dpa) fusion first-wall environments. The irradiation effect models predict (a) accelerated primary creep due to irradiation softening; and (b) fully arrested creep due to high-He re-hardening.

Alam, Md Ershadul (ORCID:0000000345968026)↗
NASA NTRS2016

Supplement of: The Influence of Volcanic Eruptions on the Climate of Tropical South America During the Last Millennium in an Isotope-Enabled General Circulation Model

Currently, little is known on how volcanic eruptions impact large-scale climate phenomena such as South American paleo-intertropical Convergence Zone (ITCZ) position and summer monsoon behavior. In this paper, an analysis of observations and model simulations is employed to assess the influence of large volcanic eruptions on the climate of tropical South America. This problem is first considered for historically recent volcanic episodes for which more observations are available but where fewer events exist and the confounding effects of El NioSouthern Oscillation (ENSO) lead to inconclusive interpretation of the impact of volcanic eruptions at the continental scale. Therefore, we also examine a greater number of reconstructed volcanic events for the period 850CE to present that are incorporated into the NASA GISS ModelE2-R simulation of the last millennium.An advantage of this model is its ability to explicitly track water isotopologues throughout the hydrologic cycle and simulating the isotopic imprint following a large eruption. This effectively removes a degree of uncertainty associated with error-prone conversion of isotopic signals into climate variables, and allows for a direct comparison between GISS simulations and paleoclimate proxy records.Our analysis reveals that both precipitation and oxygen isotope variability respond with a distinct seasonal and spatial structure across tropical South America following an eruption. During austral winter, the heavy oxygen isotope in precipitation is enriched, likely due to reduced moisture convergence in the ITCZ domain and reduced rainfall over northern South America. During austral summer, however, more negative values of the precipitation isotopic composition are simulated over Amazonia, despite reductions in rainfall, suggesting that the isotopic response is not a simple function of the amount effect. During the South American monsoon season, the amplitude of the temperature response to volcanic forcing is larger than the rather weak and spatially less coherent precipitation signal, complicating the isotopic response to changes in the hydrologic cycle.

Colose, Christopher↗
NASA NTRSDate not supplied

Evaluation of the LOFTID Flight Thermocouple Measurements

The Flexible Thermal Protection System (FTPS) on NASA’s Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) vehicle was instrumented with thermocouples (TC) to measure the in-depth thermal response during entry into Earth’s atmosphere. Accurate flight temperature measurements are critical for verifying vehicle performance during the flight test and reducing uncertainties in the thermal models. The deployable nature of inflatable decelerator technology presents challenges, namely the TCs need to be compactable, cannot damage the FTPS nor the inflatable structure, and need to be able to withstand high temperatures and large thermal gradients over significant running lengths. As previously reported, an extensive ground-test campaign was conducted prior to flight to inform the selection of the LOFTID FTPS TCs which minimized all known potential error sources. Overall, the resulting flight FTPS TC measurements were exceptionally clean throughout the atmospheric entry heat pulse. However, a few of the FTPS TCs still exhibited electrical shorting or other anomalous behavior. This paper presents the flight measurements made by the LOFTID FTPS TCs, identifies anomalous behavior, and discusses potential explanations for the causes of the anomalous behavior. Lastly, recommendations to further mitigate TC measurement error on future Hypersonic Inflatable Aerodynamic Decelerator (HIAD) missions are provided.

Ruth A Miller↗
NASA NTRS2006

What Happened, and Why: Toward an Understanding of Human Error Based on Automated Analyses of Incident Reports

The objective of the Aviation System Monitoring and Modeling (ASMM) project of NASA s Aviation Safety and Security Program was to develop technologies that will enable proactive management of safety risk, which entails identifying the precursor events and conditions that foreshadow most accidents. This presents a particular challenge in the aviation system where people are key components and human error is frequently cited as a major contributing factor or cause of incidents and accidents. In the aviation "world", information about what happened can be extracted from quantitative data sources, but the experiential account of the incident reporter is the best available source of information about why an incident happened. This report describes a conceptual model and an approach to automated analyses of textual data sources for the subjective perspective of the reporter of the incident to aid in understanding why an incident occurred. It explores a first-generation process for routinely searching large databases of textual reports of aviation incident or accidents, and reliably analyzing them for causal factors of human behavior (the why of an incident). We have defined a generic structure of information that is postulated to be a sound basis for defining similarities between aviation incidents. Based on this structure, we have introduced the simplifying structure, which we call the Scenario as a pragmatic guide for identifying similarities of what happened based on the objective parameters that define the Context and the Outcome of a Scenario. We believe that it will be possible to design an automated analysis process guided by the structure of the Scenario that will aid aviation-safety experts to understand the systemic issues that are conducive to human error.

Maille, Nicolas P.↗
DOE OSTI2025

Perspectives on Systematic Cloud Microphysics Scheme Development With Machine Learning

Cloud microphysics—the collection of processes that govern the small‐scale formation, evolution, and interactions of liquid droplets and ice crystals in clouds and precipitation—remains a major source of uncertainty in weather and climate models. Although too small in scale to be explicitly resolved in any large‐eddy simulation, weather, or climate model, the representation of cloud microphysical processes has significant impact at the climate scale. Current microphysical schemes are limited by both parametric uncertainty, linked to uncertainty in physical parameter values, and structural uncertainty, arising from incomplete physical understanding of the processes at play or approximations made for computational efficiency. Recent advances in the application of machine learning (ML) to the physical sciences show significant potential for minimizing these limitations by leveraging high‐fidelity simulations and observations. Here we outline the challenges that must be addressed to apply ML toward cloud microphysics scheme development. This perspectives paper synthesizes recent progress in using data‐driven methods, including ML, to improve cloud microphysics parameterizations and highlights opportunities to address key uncertainties. We discuss the roles of aleatoric (irreducible, or statistical) and epistemic (reducible, or systematic) errors in contributing to microphysics parameterization uncertainty. ML can leverage observations to improve microphysical schemes via bottom‐up and top‐down constraints. Methods such as differentiable programming and ML‐enhanced sampling strategies and the creation of large scale benchmark data sets promise to bridge the gap between observations and models and to improve the consistency of cloud microphysical representation across temporal and spatial scales.

Lamb, Kara D. [Columbia Univ., New York, NY (Unite↗
DOE OSTI2023

Quantum simulations of SO(5) many-fermion systems using qudits

The structure and dynamics of many-body systems are the result of a delicate interplay between underlying interactions. Fermionic pairing, for example, plays a central role in various physical systems, ranging from condensed matter to nuclear systems, where it can lead to collective phenomena such as superconductivity and superfluidity. In atomic nuclei, the interplay between pairing and particle-hole interactions leads to a high degree of complexity and intricate entanglement structures. Despite this apparent complexity, symmetries emerge and manifest themselves in observable regular patterns. These symmetries and their breakings have long been used to determine relevant degrees of freedom and simplify classical descriptions of many-body systems. Here, this work explores the potential utility of quantum computers with arrays of qudits in simulating interacting fermionic systems, when the qudits can naturally map the relevant degrees of freedom determined by an underlying symmetry group. The Agassi model of fermions interacting via particle-hole and pairing interactions is based on an underlying so(5) algebra. Such systems can intuitively be partitioned into pairs of modes with five basis states, which thus naturally map to arrays of d = 5 qudits (qu5its). Classical noiseless simulations of the time evolution of systems with up to twelve qu5its are performed, by implementing quantum circuits that are developed herein, using PYTHON codes invoking Google's CIRQ software. The resource requirements of the qu5it circuits are analyzed and compared with two different mappings to qubit systems: a physics-aware Jordan-Wigner mapping requiring four qubits per mode pair and a state-to-state mapping requiring three qubits per mode pair. While the dimensionality of Hilbert spaces in mappings to qu5it systems are less than those for the corresponding qubit systems, the number of entangling operations, depending on the available hardware, can either be greater or smaller than for the physics-aware Jordan-Wigner mapping. The state-to-state mapping, while having a smaller Hilbert space than Jordan-Wigner mappings, appears to be the least efficient in gate counts. Further, a previously unknown sign problem has been identified from Trotterization errors in time evolving high-energy excitations. There appear to be advantages in employing quantum computers with arrays of qudits to perform simulations of many-body dynamics that exploit the role of underlying symmetries, specifically in lowering the required quantum resources and in reducing anticipated errors that take the simulation out of the physical space. If the necessary entangling gates are not directly supported by the hardware, physics-aware mappings to qubits may, however, be advantageous for other aspects.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Spectral evidence of solar neighborhood analogs in CALIFA galaxies

We introduce a novel nonparametric method to find solar neighborhood analogs (SNAs) in extragalactic integral field spectroscopic surveys. The main ansatz is that the physical properties of the solar neighborhood (SN) should be encoded in its optical stellar spectrum. We assume that our best estimate of such a spectrum is the one extracted from the analysis performed by the Code for Stellar properties Heuristic Assignment (CoSHA) from the MaStar stellar library. It follows that finding SNAs in other galaxies consist in matching, in a χ 2 sense, the SN reference spectrum across the optical extent of the observed galaxies. We applied this procedure to a selection of CALIFA galaxies, by requiring a close to face-on projection, relative isolation, and non-active galactic nucleus. We explore how the local and global properties of the SNAs (stellar age, metallicity, dust extinction, mass-to-light ratio, stellar surface mass density, star-formation density, and galactocentric distance) and their corresponding host galaxies (morphological type, total stellar mass, star-formation rate, and effective radius) compared with those of the SN and the Milky Way (MW). We find that SNAs are located preferentially in S(B)a–S(B)c galaxies, in a ring-like structure, which radii seem to scale with the galaxy size. Despite the known sources of systematics and errors, most properties present a considerable agreement with the literature on the SN. We conclude that the solar neighborhood is relatively common in our sample of SNAs. Our results warrant a systematic exploration of correlations among the physical properties of the SNAs and their host galaxies. We reckon that our method should inform current models of the galactic habitable zone in our MW and other galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Helical core reconstruction of a DIII-D hybrid scenario tokamak discharge

Our paper presents the first fully 3-dimensional (3D) equilibrium reconstruction of a helical core in a tokamak device. Using a new parallel implementation of the Variational Moments Equilibrium Code (PARVMEC) coupled to V3FIT, 3D reconstructions can be performed at resolutions necessary to produce helical states in nominally axisymmetric tokamak equilibria. In a flux pumping experiment performed on DIII-D, an external n=1 field was applied while a 3/2 neoclassical tearing mode was suppressed using ECCD. The externally applied field was rotated past a set of fixed diagnostics at a 20 Hz frequency. Furthermore, the modulation, were found to be strongest in the core SXR and MSE channels, indicates a localized rotating 3D structure locked in phase with the applied field. Signals from multiple time slices are converted to a virtual rotation of modeled diagnostics adding 3D signal information. In starting from an axisymmetric equilibrium reconstruction solution, the reconstructed broader current profile flattens the q-profile, resulting in an m=1, n=1 perturbation of the magnetic axis that is $\sim 50\times $ larger than the applied n=1 deformation of the edge. Error propagation confirms that the displacement of the axis is much larger than the uncertainty in the axis position validating the helical equilibrium.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Data-driven material models for atomistic simulation

The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and find accurate parametrizations for potentials using traditional approaches. Machine learning has emerged as an effective alternative approach to develop accurate and robust interatomic potentials. Beginning with a very general model form, the potential is learned directly from a database of electronic structure calculations and therefore can be viewed as a multiscale link between quantum and classical atomistic simulations. Risk of inaccurate extrapolation exists outside the narrow range of time and length scales where the two methods can be directly compared. Here, we use the spectral neighbor analysis potential (SNAP) and show how a fit can be produced with minimal interpolation errors which is also robust in extrapolating beyond training. To demonstrate the approach, we have developed a tungsten-beryllium potential suitable for the full range of binary compositions. Subsequently, large-scale molecular dynamics simulations were performed of high energy Be atom implantation onto the (001) surface of solid tungsten. The machine learned W-Be potential generates a population of implantation structures consistent with quantum calculations of defect formation energies. A very shallow ( < 2 nm ) average Be implantation depth is predicted which may explain ITER diverter degradation in the presence of beryllium.

36 MATERIALS SCIENCE↗
NASA NTRS1972

On the stability and pointing of an attached double-gimbal experiment package

The pointing capability of a double gimbal experiment isolation and control system of the Apollo telescope mount (ATM) type was investigated. Three composite structural models of an experiment package connected through frictionless gimbals to a carrier vehicle (including a rigid package-rigid carrier model, a rigid package-flexible carrier model, and a flexible package-flexible carrier model) were used, and a linear model of the nominal ATM gimbal control system is considered. A linear stability analysis was performed to verify stability of the control system with nominal gains. Transform techniques were used to compute pointing errors onboard the experiment package due to random crew motions input into the carrier vehicle. Results of the investigation indicate that there is no stability problem due to flexible coupling of the experiment package and the carrier, and that the ATM-type system is capable of pointing accuracies better than 0.1 arc second.

Groom, N. J.↗
NASA NTRS1987

A simulation study of the flight dynamics of elastic aircraft. Volume 1: Experiment, results and analysis

The simulation experiment described addresses the effects of structural flexibility on the dynamic characteristics of a generic family of aircraft. The simulation was performed using the NASA Langley VMS simulation facility. The vehicle models were obtained as part of this research. The simulation results include complete response data and subjective pilot ratings and comments and so allow a variety of analyses. The subjective ratings and analysis of the time histories indicate that increased flexibility can lead to increased tracking errors, degraded handling qualities, and changes in the frequency content of the pilot inputs. These results, furthermore, are significantly affected by the visual cues available to the pilot.

Waszak, Martin R.↗
NASA NTRS1989

Predicting The Propagation Of Cracks

Advanced Crack Propagation Predictive Analysis Program, FLAGR04, developed as aid in predicting growth of preexisting flaws and cracks in structural components. Provides fracture-mechanics analyst with computerized method of evaluation of "safe crack-growth life" capabilities of structural components. Also used to evaluate tolerance-to-damage aspects of given structural design. Predicts growth of crack by use of two-dimensional model independently predicting growth of flaw in two directions based on calculation of stress-intensity factors. Input to FLAGR04 consists of initial definition of crack, type of rate solution, type and geometry of flaw, properties of material, data on load spectrum, load-stress functions, and design-limit stress levels. Output includes echo of input with any error or warning message and history of propagation of crack. Written in FORTRAN IV.

Tianlai, HU↗
NASA NTRS1991

The determination of time-stationary two-dimensional convection patterns with single-station radars

A critical examination of the accuracy of ionospheric vector velocity determinations, using realistic modeled flow patterns that are time-stationary but not spatially uniform, is presented. Under certain circumstances the actual and inferred flow fields are found to exhibit considerable discrepancy, sometimes not even agreeing in the sense of flow direction. It is shown that the natural curvature present in ionospheric convection on varying spatial scales can introduce significant error in the velocity estimate, particularly when the radius of curvature of the flow structure is less than or equal to the radar range to the scattering volume. It is argued that the ionospheric convection should be measured by bidirectional or multidirectional observations of a common ionospheric volume and that a synthesis of coherent and incoherent radar observations from different sites is preferable to multidirectional single-station observations using either radar alone.

Freeman, M. P.↗
NASA NTRS1993

The Middeck Active Control Experiment (MACE)

The Middeck Active Control Experiment (MACE) is a NASA In-Step and Control Structure Interaction (CSI) Office funded Shuttle middeck experiment. The objective is to investigate the extent to which closed-loop behavior of flexible spacecraft in zero-gravity (0-g) can be predicted. This prediction becomes particularly difficult when dynamic behavior during ground testing exhibits extensive suspension and direct gravity coupling. On-orbit system identification and control reconfiguration is investigated to improve performance which would otherwise be limited due to errors in prediction. The program is presently in its preliminary design phase with launch expected in the summer of 1994. The MACE test article consists of three attitude control torque wheels, a two axis gimballing payload, inertial sensors and a flexible support structure. With the acquisition of a second payload, this will represent a multiple payload platform with significant structural flexibility. This paper presents on-going work in the areas of modelling and control of the MACE test article in the zero and one-gravity environments. Finite element models, which include suspension and gravity effects, and measurement models, derived from experimental data, are used as the basis for Linear Quadratic Gaussian controller designs. Finite element based controllers are analytically used to study the differences in closed-loop performance as the test article transitions between the 0-g and 1-g environments. Measurement based controllers are experimentally applied to the MACE test article in the 1-g environment and achieve over an order of magnitude improvement in payload pointing accuracy when disturbed by a broadband torque disturbance. The various aspects of the flight portion of the experiment are also discussed.

Miller, David W.↗
NASA NTRS1990

Analysis of MMU FDIR expert system

This paper describes the analysis of a rulebase for fault diagnosis, isolation, and recovery for NASA's Manned Maneuvering Unit (MMU). The MMU is used by a human astronaut to move around a spacecraft in space. In order to provide maneuverability, there are several thrusters oriented in various directions, and hand-controlled devices for useful groups of them. The rulebase describes some error detection procedures, and corrective actions that can be applied in a few cases. The approach taken in this paper is to treat rulebases as symbolic objects and compute correctness and 'reasonableness' criteria that use the statistical distribution of various syntactic structures within the rulebase. The criteria should identify awkward situations, and otherwise signal anomalies that may be errors. The rulebase analysis agorithms are derived from mathematical and computational criteria that implement certain principles developed for rulebase evaluation. The principles are Consistency, Completeness, Irredundancy, Connectivity, and finally, Distribution. Several errors were detected in the delivered rulebase. Some of these errors were easily fixed. Some errors could not be fixed with the available information. A geometric model of the thruster arrangement is needed to show how to correct certain other distribution nomalies that are in fact errors. The investigations reported here were partially supported by The Aerospace Corporation's Sponsored Research Program.

Landauer, Christopher↗
NASA NTRS2016

The Influence of Volcanic Eruptions on the Climate of Tropical South America During the Last Millennium in an Isotope-Enabled General Circulation Model

Currently, little is known on how volcanic eruptions impact large-scale climate phenomena such as South American paleo-intertropical Convergence Zone (ITCZ) position and summer monsoon behavior. In this paper, an analysis of observations and model simulations is employed to assess the influence of large volcanic eruptions on the climate of tropical South America. This problem is first considered for historically recent volcanic episodes for which more observations are available but where fewer events exist and the confounding effects of El Niño-Southern Oscillation (ENSO) lead to inconclusive interpretation of the impact of volcanic eruptions at the continental scale. Therefore, we also examine a greater number of reconstructed volcanic events for the period 850 CE to present that are incorporated into the NASA GISS ModelE2-R simulation of the last millennium. An advantage of this model is its ability to explicitly track water isotopologues throughout the hydrologic cycle and simulating the isotopic imprint following a large eruption. This effectively removes a degree of uncertainty associated with error-prone conversion of isotopic signals into climate variables, and allows for a direct comparison between GISS simulations and paleoclimate proxy records. Our analysis reveals that both precipitation and oxygen isotope variability respond with a distinct seasonal and spatial structure across tropical South America following an eruption. During austral winter, the heavy oxygen isotope in precipitation is enriched, likely due to reduced moisture convergence in the ITCZ domain and reduced rainfall over northern South America. During austral summer, however, more negative values of the precipitation isotopic composition are simulated over Amazonia, despite reductions in rainfall, suggesting that the isotopic response is not a simple function of the "amount effect". During the South American monsoon season, the amplitude of the temperature response to volcanic forcing is larger than the rather weak and spatially less coherent precipitation signal, complicating the isotopic response to changes in the hydrologic cycle.

Colose, Christopher M.↗
DOE OSTI2026

Maximizing efficiency of dataset compression for machine learning potentials with information theory

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

36 MATERIALS SCIENCE↗
DOE OSTI2019

An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework

Complex systems are susceptible to many types of anomalies, faults, and abnormal behavior caused by a variety of off-nominal conditions that may ultimately result in major failures or catastrophic events. Early and accurate detection of these anomalies using system inputs and outputs collected from sensors and smart devices has become a challenging problem and an active area of research in many application domains. In this article, we present a new Bayesian hierarchical framework that is able to model the relationship between system inputs (sensor measurements) and outputs (response variables) without imposing strong distributional/parametric assumptions while using only a subset of training samples and sensor attributes. Then, an optimal cost-sensitive anomaly detection framework is proposed to determine whether a sample is an anomalous one taking into consideration the trade-off between misclassification errors and detection rates. The model can be used for both supervised and unsupervised settings depending on the availability of data regarding the behavior of the system under anomaly conditions. The unsupervised model is particularly useful when it is prohibitive to identify in advance the anomalies that a system may present and where no data are available regarding the behavior of the system under anomaly conditions. A Bayesian hierarchical setting is used to structure the proposed framework and help with accommodating uncertainty, imposing interpretability, and controlling the sparsity and complexity of the proposed anomaly detection framework. A Markov chain Monte Carlo algorithm is also developed for model training using past data. Furthermore, the numerical experiments conducted using a simulated data set and a wind turbine data set demonstrate the successful application of the proposed work for system response modeling and anomaly detection.

17 WIND ENERGY↗