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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Prognostics for Ground Support Systems: Case Study on Pneumatic Valves

Prognostics technologies determine the health (or damage) state of a component or sub-system, and make end of life (EOL) and remaining useful life (RUL) predictions. Such information enables system operators to make informed maintenance decisions and streamline operational and mission-level activities. We develop a model-based prognostics methodology for pneumatic valves used in ground support equipment for cryogenic propellant loading operations. These valves are used to control the flow of propellant, so failures may have a significant impact on launch availability. Therefore, correctly predicting when valves will fail enables timely maintenance that avoids launch delays and aborts. The approach utilizes mathematical models describing the underlying physics of valve degradation, and, employing the particle filtering algorithm for joint state-parameter estimation, determines the health state of the valve and the rate of damage progression, from which EOL and RUL predictions are made. We develop a prototype user interface for valve prognostics, and demonstrate the prognostics approach using historical pneumatic valve data from the Space Shuttle refueling system.

Daigle, Matthew↗
NASA NTRS2012

Requirements Flowdown for Prognostics and Health Management

Prognostics and Health Management (PHM) principles have considerable promise to change the game of lifecycle cost of engineering systems at high safety levels by providing a reliable estimate of future system states. This estimate is a key for planning and decision making in an operational setting. While technology solutions have made considerable advances, the tie-in into the systems engineering process is lagging behind, which delays fielding of PHM-enabled systems. The derivation of specifications from high level requirements for algorithm performance to ensure quality predictions is not well developed. From an engineering perspective some key parameters driving the requirements for prognostics performance include: (1) maximum allowable Probability of Failure (PoF) of the prognostic system to bound the risk of losing an asset, (2) tolerable limits on proactive maintenance to minimize missed opportunity of asset usage, (3) lead time to specify the amount of advanced warning needed for actionable decisions, and (4) required confidence to specify when prognosis is sufficiently good to be used. This paper takes a systems engineering view towards the requirements specification process and presents a method for the flowdown process. A case study based on an electric Unmanned Aerial Vehicle (e-UAV) scenario demonstrates how top level requirements for performance, cost, and safety flow down to the health management level and specify quantitative requirements for prognostic algorithm performance.

video acuity↗
NASA NTRS2018

Atmosphere-Ocean Coupled Data Assimilation Using NASA GEOS: Estimation of Air-Sea Interface State Variables

Air-sea interface variables, such as the skin Sea Surface Temperature (SST) are essential for atmosphere-ocean coupling. In the NASA GMAO Data Assimilation System (DAS), the skin SST and 3-D atmospheric state are jointly estimated [1]. This presentation is focused on the prior or background error covariance that is used in this analysis. The GEOS DAS uses an ensemble-variational assimilation strategy. In that, specification of a climatological background (CB) error covariance for SST relies on the NOAA's OI SST, with estimates of standard deviation and correlation length scales based on weekly analyses of the bulk SST at 1 degree resolution. However, present analysis system is striving to resolve SST diurnal variability with six hourly analyses and assimilates a vast number of in-situ and satellite observations. The first part of this presentation re-derives the CB error covariance using OSTIA SST analyses and illustrates the impact of this update on assimilating satellite observations. In a hybrid assimilation system the CB error covariances are appended with a flow-dependent background error covariance estimate implied by the underlying ensemble. The second part of this presentation refers to: a. treatment of the skin SST in the ensemble members, b. corresponding ensemble spread, and c. impact of these additions on the data assimilation system. [1] S. Akella, et al. (2017), doi:10.1002/qj.2988

Akella, Santha↗
NASA NTRS1994

The sea state bias in altimeter estimates of sea level from collinear analysis of TOPEX data

The wind speed and significant wave height (H(sub 1/3)) dependencies of the sea state bias in altimeter estimates of sea level, expressed in the form (Delta)h(sub SSB) = bH(sub 1/3), are examined from least squares analysis of 21 cycles of collinear TOPEX data. The bias coefficient b is found to increase in magnitude with increasing wind speed up to about 12 m/s and decrease monotonically in magnitude with increasing H(sub 1/3). A parameterization of b as a quadratic function of wind speed only, as in the formation used to produce the TOPEX geophysical data records (GDRs), is significantly better than a parameterization purely in terms of H(sub 1/3). However, a four-parameter combined wind speed and wave height formulation for b (quadratic in wind speed plus linear in H(sub 1/3)) significantly improves the accuracy of the sea state bias correction. The GDR formulation in terms of wind speed only should therefore be expanded to account for a wave height dependence of b. An attempt to quantify the accuracy of the sea state bias correction (Delta)h(sub SSB) concludes that the uncertainty is a disconcertingly large 1% of H(sub 1/3).

Chelton, Dudley B.↗
DOE OSTI2025

Standard Analysis Report INV-SAR-81, Revision 0 Chemical and Cement Components 2023 Inventory Estimates for the Interim State Performance Assessment of the Waste Isolation Pilot Plant

This standard analysis report provides the estimates for the chemical (oxyanions and complexing agents) and cement components with a data collection cut-off date of December 31, 2023. These estimates will be included in a Performance Assessment Inventory Report developed for the U.S. Department of Energy (DOE) interim state repository configuration performance assessment (PA) for the 2026 Compliance Recertification Application (CRA).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years. The requirement of extending the license of reactor under LTO requires hundreds of fatigue tests to be conducted to understand the mechanism associated with the above-mentioned interdependent failure causes. However, conducting large number of fatigue tests is not a feasibility due to the cost involved. To address this issues Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program trying to develop a hybrid predictive modeling approach. This is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics based modeling tools. The hybrid approach not-only can improve the accuracy of the existing stress analysis and fatigue modeling approach but also can reduce the over-dependency on test-based approach. Towards this goal following are some of the major contributions based on ANL’s FY-20 environmental fatigue modeling activities: 1) A cyclic plasticity material model database for 82/182 dissimilar metal weld, which can be readily shared with US nuclear industry and regulatory agency on request. 2) A well validated analytical modeling methodology to perform cycle-by-cycle stress prediction under both constant amplitude fatigue loading and variable amplitude fatigue loading (with load-sequence effect). 3) An AI/ML/DL based methodology to predict unmeasurable cyclic strain based on other available sensor signals. This type of approach can be used for estimating strain in real reactor components from other sensor readings. 4) An AI/ML based approach to improve the US capability on environmental fatigue testing. This is by improving ANL’s existing environmental fatigue testing capacity to conduct ASME required strain-controlled tests (by controlling strain amplitudes and its rate), while not measuring the strain (due to the difficulty of placing an extensometer in a narrow autoclave in a PWR-water-test system). 5) A simulation and experiment based probabilistic modeling methodology for time-series fatigue state and life estimation of reactor metal such as dissimilar metal weld.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

State-Space Model to Estimate Salmon Escapement Using Multiple Data Sources

Abstract Accurate estimates of salmonids passing Lower Granite Dam on the Snake River, by species and origin, are a critical input to assessing the status and trends of various populations as well as successful management of fisheries in the Snake River basin. Here, we describe a state-space model that estimates such escapement past a dam by using window counts, PIT tag observations, and data from an adult fish trap, accounting for issues such as nighttime passage, fallback and reascension, potential observation error at the window, and uncertainty in the adult trap rate. We tested the approach using a simulation framework that mimicked several levels of observation error, differences between nighttime passage and reascension rates, and the possibility of the adult trap being closed for some period of time. Our results demonstrate that the model produced unbiased estimates across all tested scenarios. We also applied this model to empirical data from Lower Granite Dam to produce estimates of wild, clipped hatchery, and unclipped hatchery spring/summer-run Chinook Salmon Oncorhynchus tshawytscha and steelhead O. mykiss from spawn years 2010–2019.

See, Kevin E. (ORCID:0000000297626442)↗
DOE OSTI2023

State of battery health estimation based on swelling characteristics

There is disclosed an electrical device including a battery, and a battery management system. The battery management system includes a controller in electrical communication with a pressure sensor to monitor the state of health of the battery. The controller applies a method for determining the state of health that uses a non-electrical (mechanical) signal of force measurements combined with incremental capacity analysis to estimate the capacity fading and other health indicators of the battery with better precision than existing methods. The pressure sensor may provide the force measurement signal to the controller, which may determine which incremental capacity curve based on force to use for the particular battery. The controller then executes a program utilizing the data from the pressure sensor and the stored incremental capacity curves based on force to estimate the capacity fading and signal a user with the state of health percentage.

Stefanopoulou, Anna G.↗
NASA NTRS2017

Remediating Non-Positive Definite State Covariances for Collision Probability Estimation

The NASA Conjunction Assessment Risk Analysis team estimates the probability of collision (Pc) for a set of Earth-orbiting satellites. The Pc estimation software processes satellite position+velocity states and their associated covariance matri-ces. On occasion, the software encounters non-positive definite (NPD) state co-variances, which can adversely affect or prevent the Pc estimation process. Inter-polation inaccuracies appear to account for the majority of such covariances, alt-hough other mechanisms contribute also. This paper investigates the origin of NPD state covariance matrices, three different methods for remediating these co-variances when and if necessary, and the associated effects on the Pc estimation process.

Hall, Doyle T.↗
DOE OSTI2023

Alternative Fuel Vehicle Usage and Owner Demographics in New York State

With mounting concerns over climate change and the environmental impact of fossil fuels, the United States has witnessed a growing interest in alternative fuel vehicles (AFVs). In 2021, approximately 1.5 million battery EVs (BEVs), 0.8 million plug-in hybrid EVs (PHEVs), and 5.5 million hybrid EVs (HEVs) were registered in the United States. In the state of New York, a total of 51,900 BEVs, 44,600 PHEVs, and 221,600 HEVs were registered in 2021. The current report presents the results of an analysis of AFV adoption patterns in New York State and the rest of the United States based on data from the 2017 National Household Travel Survey (NHTS). Overall, the report reveals the demographics and mobility factors (e.g., household income, homeownership, and trip length) that contribute to the adoption of AFVs. This study provides insights that can inform policy decisions aimed at promoting sustainable transportation solutions.The 2017 NHTS data showed that the percentage of households owning at least one AFV is lower in New York City compared with that in other regions of New York State. From the NHTS samples, of the 25 households that owned at least one BEV in New York State, 15 households (60%) lived within a 5-mile radius, based on the great circle distance, of the nearest charging station, and 23 households (92%) lived within a 10-mile radius of the nearest charging station. Furthermore, among the 40 households in New York State that own at least one PHEV, 48% (19 households) lived within a 5-mile radius of the closest EV charging station, and 83% (33 households) lived within a 10-mile radius of the nearest charging station. The rest of the United States had a higher percentage of households that own at least one AFV compared with that of New York State. A comparison was made between EV adoption levels using NHTS and EValuateNY, which is a tool that gathers statistics on the electric car market in New York State. The estimates obtained from New York State household samples in NHTS were slightly lower than the data provided by EValuateNY. In New York State and the rest of the United States, households with higher incomes tended to have a higher proportion of AFV ownership compared with those with lower incomes. For example, households in New York State earning $\$ $150,000 or more had an approximately 6% share of owning at least one AFV, which was markedly higher than those earning less than $\$ $100,000 (less than 3%). Additionally, homeowners in New York State and the rest of the United States also exhibited a significantly higher share of AFV ownership compared with that of renters. In New York State, households that own at least one AFV tended to travel farther and had longer travel times compared with their counterparts without an AFV. In terms of households with at least one AFV, households with HEVs tended to have more person trips, longer person miles of travel, and more vehicle miles traveled, resulting in longer travel times than that of households with BEVs or PHEVs. Notably, households with AFVs had a slightly lower share of family and personal business trips but a higher share of social and recreational trips compared with households without AFVs. Additionally, households with at least one AFV tended to have a slightly higher share of walking trips than their counterparts without an AFV. However, the comparisons were not statistically significant. These travel patterns observed in New York State were consistent with those observed in other regions of the United States.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Constrained Block Nonlinear Neural Dynamical Models

Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for data-efficient learning of deep control-oriented nonlinear dynamical models by embedding local model structure and constraints. The proposed method consists of neural network blocks that represent input, state, and output dynamics with constraints placed on the network weights and system variables. For handling partially observable dynamical systems, we utilize a state observer neural network to estimate the states of the system's latent dynamics. We evaluate the performance of the proposed architecture and training methods on system identification tasks for three nonlinear systems: a continuous stirred tank reactor, a two tank interacting system, and an aerodynamics body. Models optimized with a few thousand system state observations accurately represent system dynamics in open loop simulation over thousands of time steps from a single set of initial conditions. Experimental results demonstrate an order of magnitude reduction in open-loop simulation mean squared error for our constrained, block-structured neural models when compared to traditional unstructured and unconstrained neural network models.

Skomski, Elliott↗
DOE OSTI2024

MITgcm-AD v2: Open source tangent linear and adjoint modeling framework for the oceans and atmosphere enabled by the Automatic Differentiation tool Tapenade

The Massachusetts Institute of Technology General Circulation Model (MITgcm) is widely used by the climate science community to simulate planetary atmosphere and ocean circulations. A defining feature of the MITgcm is that it has been developed to be compatible with an algorithmic differentiation (AD) tool, TAF, enabling the generation of tangent-linear and adjoint models. These provide gradient information which enables dynamics-based sensitivity and attribution studies, state and parameter estimation, and rigorous uncertainty quantification. Importantly, gradient information is essential for computing comprehensive sensitivities and performing efficient large-scale data assimilation, ensuring that observations collected from satellites and in-situ measuring instruments can be effectively used to optimize a large uncertain control space. As a result, the MITgcm forms the dynamical core of a key data assimilation product employed by the physical oceanography research community: Estimating the Circulation and Climate of the Ocean (ECCO) state estimate. Although MITgcm and ECCO are used extensively within the research community, the AD tool TAF is proprietary and hence inaccessible to a large proportion of these users. The new version 2 (MITgcm-AD v2) framework introduced here is based on the source-to-source AD tool Tapenade, which has recently been open-sourced. Another feature of Tapenade is that it stores required variables by default (instead of recomputing them) which simplifies the implementation of efficient, AD-compatible code. The framework has been integrated with the MITgcm model’s main branch and is now freely available.

Adjoints↗
NASA NTRS1972

Orbit determination in the presence of unmodeled accelerations.

In the investigation discussed here, the unmodeled acceleration is approximated as a first-order stationary Gauss-Markov process which can be separated into a time wise correlated component and a purely random component. An extended form of the Kalman-Bucy filter is used to estimate the state and the unmodeled accelerations at each observation epoch. Applications of the estimation algorithm to simulated data and to tracking data obtained during the Apollo 10 and 11 missions indicate that a more accurate estimate of the state is obtained using the algorithm and that the estimated values of the unmodeled accelerations can be correlated with physical phenomena neglected by the mathematical model.

Tapley, B. D.↗
NASA NTRS1987

State-space self-tuning controllers for general multivariable stochastic systems

This paper presents a state-space approach for self-tuning control of a more general class of multivariable stochastic systems having a number of inputs equal or different from the number of outputs. The dynamic system is represented in the state-space innovation form with Luenberger's canonical structures. The model parameters and the Kalman gain are identified via either the extended least-squares algorithm or the least-squares ladder algorithm. The Kalman gain matrix and states can be estimated from the identified parameters without utilizing the standard state estimation algorithm. A long division method is introduced for finding the similarity transformation matrix.

Shieh, L. S.↗
NASA NTRS1977

Digital flight control systems

The design of stable feedback control laws for sampled-data systems with variable rate sampling was investigated. These types of sampled-data systems arise naturally in digital flight control systems which use digital actuators where it is desirable to decrease the number of control computer output commands in order to save wear and tear of the associated equipment. The design of aircraft control systems which are optimally tolerant of sensor and actuator failures was also studied. Detection of the failed sensor or actuator must be resolved and if the estimate of the state is used in the control law, then it is also desirable to have an estimator which will give the optimal state estimate even under the failed conditions.

Caglayan, A. K.↗
NASA NTRS1972

Recent results of nonlinear estimators applied to hereditary systems.

An application of the extended Kalman filter to delayed systems to estimate the state and time delay is presented. Two nonlinear estimators are discussed and the results compared with those of the Kalman filter. For all the filters considered, the hereditary system was treated with the delay in the pure form and by using Pade approximations of the delay. A summary of the convergence properties of the filters studied is given. The results indicate that the linear filter applied to the delayed system performs inadequately while the nonlinear filters provide reasonable estimates of both the state and the parameters.

Schiess, J. R.↗
DOE OSTI2024

Enhanced Estimation of Quantum Properties with Common Randomized Measurements

We present a technique for enhancing the estimation of quantum state properties by incorporating approximate prior knowledge about the quantum state. This consists in performing randomized measurements on a quantum processor and comparing the results with those obtained from a classical computer that stores an approximation of the quantum state. We provide unbiased estimators for expectation values of multicopy observables and present performance guarantees in terms of variance bounds that depend on the prior knowledge accuracy. We demonstrate the effectiveness of our approach through experimental and numerical examples detecting mixed-state entanglement, and estimating polynomial approximations of the von Neumann entropy and state fidelities. Published by the American Physical Society 2024

Physics↗