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Results for “State Variable”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Slow and fast state variables for three-dimensional flight dynamics

The identification of a new fast variable for three-dimensional flight dynamics is discussed. It is shown that the variable gives a value of the flightpath angle which is consistent with the velocity-altitude state equations and that the accuracy of single-perturbation analyses is increased by decreasing the magnitude of boundary-layer corrections. The values of the flightpath angle along the three-dimensional energy-state extremal are found to be lower by approximately an order of magnitude than those along the two-dimensional energy-state solution.

Ardema, M.↗
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 NTRS1971

A Transformation Approach to Optimal Control Problems with Bounded State Variables

A technique is described and utilized in the study of the solutions to various general problems in optimal control theory, which are converted in to Lagrange problems in the calculus of variations. This is accomplished by mapping certain properties in Euclidean space onto closed control and state regions. Nonlinear control problems with a unit m cube as control region and unit n cube as state region are considered.

Hanafy, Lawrence Hanafy↗
DOE OSTI2018

A state variable for crumpled thin sheets

Despite the apparent ease with which sheets of paper are crumpled and tossed away, crumpling dynamics are often considered a paradigm of complexity. This arises from the infinite number of configurations that disordered, crumpled sheets can take. Here we experimentally show that key aspects of axially confined crumpled Mylar sheets have a very simple description; evolution of damage in crumpling dynamics can largely be described by a single global quantity—the total length of creases. We follow the evolution of the damage network in repetitively crumpled elastoplastic sheets, and show that the dynamics are deterministic, depending only on the instantaneous state of the crease network and not on the crumpling history. We also show that this global quantity captures the crumpling dynamics of a sheet crumpled for the first time. This leads to a remarkable reduction in complexity, allowing a description of a highly disordered system by a single state parameter.

42 ENGINEERING↗
DOE OSTI2025

Quantification of Oxygen Vacancies in SrFe0.5Cr0.5O3−δ Thin Films: Correlating Lattice Expansion with Oxidation State Variability

Oxygen vacancies (VO), even in the dilute regime, can significantly impact the physical and chemical properties of complex oxides. It is however challenging to reliably detect and adequately quantify such defects in thin film samples where the VO concentration and distribution may further vary in response to interfacial strain. Here, we present a method to quantify VO concentrations in SrFe0.5Cr0.5O3−δ (SFCO) epitaxial thin films. Through coherent Bragg rod analysis, X-ray absorption spectroscopy, and theoretical modeling, we systematically correlate the c-axis lattice expansion detected in SFCO films with a VO concentration gradient. Our results reveal a nearly linear relationship between oxygen off-stoichiometry and out-of-plane lattice expansion in coherently strained SFCO films, with δ increasing from 0.32 to 0.52 and the lattice expanding by ∼1.0%. Moreover, a significant decrease in Cr oxidation state (from Cr5+ to Cr3+) is observed as the VO concentration increases, while the Fe oxidation state remains fixed at Fe3+. Our findings provide a reliable way to quantify oxygen stoichiometry in complex oxides, offering a pathway to design materials with precisely tailored structure–property relationships.

Hossain, Mohammad Delower↗
DOE OSTI2025

Machine-Learning-Based Multiscale Methods for 3D Modelling of Granular Materials by Incorporating History-Dependent State Variables

Over the past decades, the prevalence of machine learning (ML) methods has made the development of ML-based constitutive models for granular materials undoubtedly a popular subject. Numerous studies have been made to feature the loading path or history-dependent stress-strain response of granular media using neural networks. In this work, a novel finite element method (FEM)–ML multiscale approach was developed by incorporating internal variables to improve the simulation accuracy of 3D history-dependent granular materials for the first time. To this end, a surrogate constitutive model based on the single-step-based multi-layer perceptron (MLP) neural network was used to replace representative volume element (RVE) simulations conducted by the discrete element method (DEM) in the multiscale FEM–DEM approach. Although the prediction principle of the MLP aligns with the FEM algorithm, artificially added internal variables are required to differentiate the loading history. To address this issue, history variables associated with the Frobenius norm are proposed to be fed into the MLP coupled with the strain tensor to extract the history-dependent behaviour of granular assemblies. The developed FEM–ML approach was demonstrated in 3D conventional triaxial compression (CTC) simulations. Compared to the multiscale FEM–DEM approach, the proposed FEM–ML method exhibits a significantly improved computational efficiency.

granular materials↗
NASA NTRS1995

Robust integration schemes for generalized viscoplasticity with internal-state variables. Part 1: Theoretical developments and applications

This two-part report is concerned with the development of a general framework for the implicit time-stepping integrators for the flow and evolution equations in generalized viscoplastic models. The primary goal is to present a complete theoretical formulation, and to address in detail the algorithmic and numerical analysis aspects involved in its finite element implementation, as well as to critically assess the numerical performance of the developed schemes in a comprehensive set of test cases. On the theoretical side, the general framework is developed on the basis of the unconditionally-stable, backward-Euler difference scheme as a starting point. Its mathematical structure is of sufficient generality to allow a unified treatment of different classes of viscoplastic models with internal variables. In particular, two specific models of this type, which are representative of the present start-of-art in metal viscoplasticity, are considered in applications reported here; i.e., fully associative (GVIPS) and non-associative (NAV) models. The matrix forms developed for both these models are directly applicable for both initially isotropic and anisotropic materials, in general (three-dimensional) situations as well as subspace applications (i.e., plane stress/strain, axisymmetric, generalized plane stress in shells). On the computational side, issues related to efficiency and robustness are emphasized in developing the (local) interative algorithm. In particular, closed-form expressions for residual vectors and (consistent) material tangent stiffness arrays are given explicitly for both GVIPS and NAV models, with their maximum sizes 'optimized' to depend only on the number of independent stress components (but independent of the number of viscoplastic internal state parameters). Significant robustness of the local iterative solution is provided by complementing the basic Newton-Raphson scheme with a line-search strategy for convergence. In the present first part of the report, we focus on the theoretical developments, and discussions of the results of numerical-performance studies using the integration schemes for GVIPS and NAV models.

Saleeb, Atef F.↗
NASA NTRS1995

Robust integration schemes for generalized viscoplasticity with internal-state variables. Part 2: Algorithmic developments and implementation

This two-part report is concerned with the development of a general framework for the implicit time-stepping integrators for the flow and evolution equations in generalized viscoplastic models. The primary goal is to present a complete theoretical formulation, and to address in detail the algorithmic and numerical analysis aspects involved in its finite element implementation, as well as to critically assess the numerical performance of the developed schemes in a comprehensive set of test cases. On the theoretical side, the general framework is developed on the basis of the unconditionally-stable, backward-Euler difference scheme as a starting point. Its mathematical structure is of sufficient generality to allow a unified treatment of different classes of viscoplastic models with internal variables. In particular, two specific models of this type, which are representative of the present start-of-art in metal viscoplasticity, are considered in applications reported here; i.e., fully associative (GVIPS) and non-associative (NAV) models. The matrix forms developed for both these models are directly applicable for both initially isotropic and anisotropic materials, in general (three-dimensional) situations as well as subspace applications (i.e., plane stress/strain, axisymmetric, generalized plane stress in shells). On the computational side, issues related to efficiency and robustness are emphasized in developing the (local) interative algorithm. In particular, closed-form expressions for residual vectors and (consistent) material tangent stiffness arrays are given explicitly for both GVIPS and NAV models, with their maximum sizes 'optimized' to depend only on the number of independent stress components (but independent of the number of viscoplastic internal state parameters). Significant robustness of the local iterative solution is provided by complementing the basic Newton-Raphson scheme with a line-search strategy for convergence. In the present second part of the report, we focus on the specific details of the numerical schemes, and associated computer algorithms, for the finite-element implementation of GVIPS and NAV models.

Li, Wei↗
DOE OSTI2015

Relationships among top‐of‐atmosphere radiation and atmospheric state variables in observations and CESM

Abstract A detailed examination is made in both observations and the Community Earth System Model (CESM) of relationships among top‐of‐atmosphere radiation, water vapor, temperatures, and precipitation for 2000–2014 to assess the origins of radiative perturbations and climate feedbacks empirically. The 30‐member large ensemble coupled runs are analyzed along with one run with specified sea surface temperatures for 1994 to 2005 (to avoid volcanic eruptions). The vertical structure of the CESM temperature profile tends to be top heavy in the model, with too much deep convection and not enough lower stratospheric cooling as part of the response to tropospheric heating. There is too much absorbed solar radiation (ASR) over the Southern Oceans and not enough in the tropics, and El Niño–Southern Oscillation (ENSO) is too large in amplitude in this version of the model. However, the covariability of monthly mean anomalies produces remarkably good replication of most of the observed relationships. There is a lot more high‐frequency variability in radiative fluxes than in temperature, highlighting the role of clouds and transient weather systems in the radiation statistics. Over the Warm Pool in the tropical western Pacific and Indian Oceans, where nonlocal effects from the Walker circulation driven by the ENSO events are important, several related biases emerge: in response to high SST anomalies there is more precipitation, water vapor, and cloud and less ASR and outgoing longwave radiation in the model than observed. Different model global mean trends are evident, however, possibly hinting at too much positive cloud feedback in the model.

Trenberth, Kevin E.↗
DOE OSTI2023

A Deep Learning-Based Workflow for Fast Prediction of 3D State Variables in Geological Carbon Storage: A Dimension Reduction Approach

In this study, we used deep learning techniques, which are a form of artificial intelligence, to create fast and effective models for predicting how fluids flow in underground geological formations. This is important for managing geological carbon storage, a method used to fight climate change by storing carbon dioxide underground. The challenge lies in the complex nature of these underground spaces and the large amount of data needed to accurately simulate them. To overcome these issues, we developed a new workflow that reduces the data’s complexity before training the deep learning model and then reconstructs the predicted results in their original form. We also proposed a unique approach to handle the specific complexities found in 3D saturation fields, a crucial aspect of fluid flow prediction. We tested our method using real-world data from the Gulf of Mexico. Our results show that our approach not only accurately predicts fluid behavior but also significantly reduces computation time. This will greatly improve real-time decision-making and risk assessment in large-scale geological carbon storage operations.

Wang, Hongsheng↗