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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation

We evaluate the performance of the Karhunen-Loève Deep Neural Network (KL-DNN) framework for surrogate modeling and approximate Bayesian parameter estimation in partial differential equation models. In the surrogate model, the Karhunen-Loève (KL) expansions are used for the dimensionality reduction of the number of unknown parameters and variables, and a deep neural network is employed to relate the reduced space of parameters to that of the state variables. The KL-DNN surrogate model is used to formulate a maximum-a-posteriori-like least-squares problem, which is randomized to draw samples of the posterior distribution of the parameters. We test the proposed framework for a hypothetical unconfined aquifer via comparison with the forward MODFLOW and inverse PEST++ iterative ensemble smoother (IES) solutions as well as the state-of-the-art Fourier neural operator (FNO) and deep operator networks (DeepONets) operator learning surrogate models. Our results show that the KL-DNN surrogate model outperforms FNO and DeepONet for forward predictions. For solving inverse problems, the randomized algorithm provides the same or more accurate Bayesian predictions of the parameters than IES as evidenced by the higher log-predictive probability of both the estimated parameter field and the forecast hydraulic head. The posterior mean obtained from the randomized algorithm is closer to the reference parameter field than that obtained with FNO as the maximum a posteriori estimate.

Approximate Bayesian inference↗
DOE OSTI2022

Millimeter Light Curves of Sagittarius A* Observed during the 2017 Event Horizon Telescope Campaign

The Event Horizon Telescope (EHT) observed the compact radio source, Sagittarius A* (Sgr A*), in the Galactic Center on 2017 April 5–11 in the 1.3 mm wavelength band. At the same time, interferometric array data from the Atacama Large Millimeter/submillimeter Array and the Submillimeter Array were collected, providing Sgr A* light curves simultaneous with the EHT observations. These data sets, complementing the EHT very long baseline interferometry, are characterized by a cadence and signal-to-noise ratio previously unattainable for Sgr A* at millimeter wavelengths, and they allow for the investigation of source variability on timescales as short as a minute. While most of the light curves correspond to a low variability state of Sgr A*, the April 11 observations follow an X-ray flare and exhibit strongly enhanced variability. All of the light curves are consistent with a red-noise process, with a power spectral density (PSD) slope measured to be between −2 and −3 on timescales between 1 minute and several hours. Our results indicate a steepening of the PSD slope for timescales shorter than 0.3 hr. The spectral energy distribution is flat at 220 GHz, and there are no time lags between the 213 and 229 GHz frequency bands, suggesting low optical depth for the event horizon scale source. We characterize Sgr A*’s variability, highlighting the different behavior observed just after the X-ray flare, and use Gaussian process modeling to extract a decorrelation timescale and a PSD slope. We also investigate the systematic calibration uncertainties by analyzing data from independent data reduction pipelines.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1965

Contributions to linear system theory.

Linear dynamic systems governed by first-order linear differential equations in state variables, emphasizing time reversal and differential controllability

STATE EQUATION↗
NASA NTRS1990

A review of nonlinear constitutive models for metals

Over the past two decades a number of thermomechanical constitutive theories have been proposed for viscoplastic metals. These models are in most cases similar in that they utilize a set of internal state variables which provide locally averaged representations of microphysical phenomena such as dislocation rearrangement and grain boundary sliding. The state of development of several of these models is now at the point where accurate theoretical solutions can be obtained for a wide variety of structural problems at elevated temperatures. The fundamentals of viscoplasticity are briefly reviewed and a general framework is outlined. Several of the more prominent models are reviewed, and predictions from models are compared to experimental results.

Allen, David H.↗
NASA NTRS1990

Earth observing satellite: Understanding the Earth as a system

There is now a plan for global studies which include two very large efforts. One is the International Geosphere/Biosphere Program (IGBP) sponsored by the International Council of Scientific Unions. The other initiative is Mission to Planet Earth, an unbrella program for doing three kinds of space missions. The major one is the Earth Observation Satellite (EOS). EOS is large polar orbiting satellites with heavy payloads. Two will be placed in orbit by NASA, one by the Japanese and one or two by ESA. The overall mission measurement objectives of EOS are summarized: (1) the global distribution of energy input to and energy output from the Earth; (2) the structure, state variables, composition, and dynamics of the atmosphere from the ground to the mesopause; (3) the physical and biological structure, state, composition, and dynamics of the land surface, including terrestrial and inland water ecosystems; (4) the rates, important sources and sinks, and key components and processes of the Earth's biogeochemical cycles; (5) the circulation, surface temperature, wind stress, sea state, and the biological activity of the oceans; (6) the extent, type, state, elevation, roughness, and dynamics of glaciers, ice sheets, snow and sea ice, and the liquid equivalent of snow in the global cryosphere; (7) the global rates, amounts, and distribution of precipitation; and (8) the dynamic motions of the Earth (geophysics) as a whole, including both rotational dynamics and the kinematic motions of the tectonic plates.

Soffen, Gerald↗
NASA NTRS1989

A controller design for multi-body large angle maneuvers

Active large angle slewing maneuvers of a multi-body flexible dynamic system are investigated. An appropriate state variable transformation and a feedback linearization technique are employed to transform the dynamics of the nonlinear system to a new state that is more amenable to control design procedures. Closed-loop feedback algorithms are implemented to perform slewing maneuvers, while simultaneously suppressing flexural vibrations of the system. Stability of this class of nonlinear systems is also investigated, whereby a sufficient condition for asymptotic stability of the system is established. Numerical examples are presented to demonstrate the proposed active control algorithms.

Ghaemmaghami, Peiman↗
DOE OSTI2017

Dependence of calculated postshock thermodynamic variables on vibrational equilibrium and input uncertainty

The purpose of this article is to explore the dependence of calculated postshock thermodynamic properties in shock tube experiments upon the vibrational state of the test gas and upon the uncertainties inherent to calculation inputs. This paper first offers a comparison between state variables calculated according to a Rankine–Hugoniot–equation-based algorithm, known as FROSH, and those derived from shock tube experiments on vibrationally nonequilibrated gases. It is shown that incorrect vibrational relaxation assumptions could lead to errors in temperature as large as 8% for 25% oxygen/argon mixtures at 3500 K. Following this demonstration, this article employs the algorithm to show the importance of correct vibrational equilibration assumptions, noting, for instance, that errors in temperature of up to about 2% at 3500 K may be generated for 10% nitrogen/argon mixtures if vibrational relaxation is not treated properly. Lastly, this article presents an extensive uncertainty analysis, showing that postshock temperatures can be calculated with root-of-sum-of-square errors of better than ±1% given sufficiently accurate experimentally measured input parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Interacting mesons as degrees of freedom in a chiral model

Here, we study the equation of state of hot and dense hadronic matter using an extended chiral mean field (CMF) model framework where the addition is the inclusion of interactions of thermally excited mesons. This is implemented by calculating the in-medium masses of pseudoscalar and vector mesons, obtained through the explicit chiral symmetry-breaking and vector-interaction terms in the Lagrangian, respectively, prior to applying the mean-field approximation. As a result, the in-medium meson contributions generate a feedback term to the CMF’s equations of motion, which then modifies the equation of state. With this improvement, we quantify the effect on the equation of state of strongly interacting matter through comparisons with state-of-the-art lattice QCD results and other hadronic models like the hadron resonance gas model. We find that the results of the updated hadronic CMF model with an improved meson description (mCMF) provide a better agreement with lattice-QCD data for thermodynamic state variables across a wide range of temperatures and baryon chemical potentials.

phenomenology↗
DOE OSTI2023

Variable gap thermal conductivity apparatus and method

An apparatus and a method for determining the thermal conductivity of a fluid specimen are provided. The apparatus and the method include determining thermal conductivity using a quasi-steady state variable gap axial flow technique. The fluid specimen is heated on one side by a heat source with a known power output and cooled on the other side. After reaching steady state, a resulting temperature drop through the fluid specimen exists. This temperature drop, the known fluid specimen thickness (or gap distance), and the known power output are used to calculate the thermal resistance of the fluid specimen. The thermal conductivity of the fluid specimen is then determined using a curve fit of thermal resistance with respect to gap distance.

Gallagher, Ryan C.↗
DOE OSTI2022

Autoencoder Neural Network for chemically reacting systems

Incorporating detailed chemical kinetic models is critical for accurate simulations of reacting flows. However, detailed models involve a large number of thermochemical (TC) state variables. Solving the governing equations to evolve these TC variables becomes impractical for real-world applications. In this work, we propose an autoencoder (AE) neural network (NN)-based reduced model to accelerate such simulations. The AE NN is first trained to find a low-dimensional latent representation of the TC states. Then, the evolving state of a chemical system can be tracked by solving the equations of the latent variables instead of the original TC equations. We demonstrate the reduced model in a syngas CO/H 2 combustion system, using training data collected from canonical perfectly stirred reactors (PSRs). It is found that the AE model can reduce the dimension of the combustion system from 12 to 2 while maintaining low reconstruction error and excellent elemental mass conservation for the test dataset. In the a posteriori test, the combustion states obtained from solving the two latent equations are compared to those from solving the 12 equations of the full model. The AE reduced method is found to be able to capture the diverse combustion states on the top two branches of the S-curve well including the extinction turning point, but with higher prediction errors for states near the ignition turning point.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

Time domain modelling and stability analysis of an integral pulse frequency modulated dc to dc power converter

Using state variable representation a nonlinear, discrete-time system is derived that models the converter exactly. This system is linearized about its steady state solution, and converter stability, transient response and audio susceptibility are studied. The steady state solution of the converter is stable if and only if all the roots of the linearized system are absolutely less than unity. Excellent agreement with laboratory test data has been observed.

Iwens, R. P.↗
NASA NTRS1977

The epoch state navigation filter

The formulation of a recursive maximum likelihood navigation system employing reference position and velocity vectors as state variables is presented. Convenient forms of the required variational equations of motion are developed together with an explicit form of the associated state transition matrix needed to refer measurement data from the measurement time to the epoch time. Computational advantages accrue from this design in that the usual forward extrapolation of the covariance matrix of estimation errors can be avoided without incurring unacceptable system errors. Simulation data for earth orbiting satellites are provided to substantiate this assertion.

Battin, R. H.↗
NASA NTRS1973

Low-thrust trajectory analysis for the geosynchronous mission

Methodology employed in development of a computer program designed to analyze optimal low-thrust trajectories is described, and application of the program to a Solar Electric Propulsion Stage (SEPS) geosynchronous mission is discussed. To avoid the zero inclination and eccentricity singularities which plague many small-force perturbation techniques, a special set of state variables (equinoctial) is used. Adjoint equations are derived for the minimum time problem and are also free from the singularities. Solutions to the state and adjoint equations are obtained by both orbit averaging and precision numerical integration; an evaluation of these approaches is made.

Jasper, T. P.↗
DOE OSTI2018

Data Driven Temperature Control of Heat Exchangers

The following poster presents a couple of approaches for temperature control at heat exchanger’s exit. One of the approaches is based on using n-step ahead neural network while the other approach utilizes a combination of control + learning module for temperature control. Designing robust controllers for typical heat exchangers is very challenging primarily because of complexities associated with the synthesis of dynamics of industrial heat exchangers. Even if a dynamics model is synthesized, the accuracy of it would be called into question as there can be lot of unmodeled system dynamics and random noise in input parameters. In many situations, the degree of uncertainty in the model of system being controlled limits the utility of optimal control design. Controller can be manually tuned on the field but it is very difficult to determine manual adjustments that result in overall improvement. These practical challenges call for learning based approaches for temperature control that would result in optimal heat exchanger performance. We utilize the existing mathematical model for air-water system synthesized by Underwood and Crawford for testing our algorithms. Underwood and Crawford developed a model of heating coil by fitting a second-order non-linear equations to measurements of air and water temperature and flow rates obtained from the actual coil. The uncertainties in the model include air and water inlet temperatures and air-flow rate. These uncertainties are modeled as random variables that are sampled from a uniform distribution. The control variable is the water flow rate which is controlled for regulating the air-outlet temperature. Neural networks are frequently used in various literatures for learning time-series. We propose a modified version of neural networks known as n-step ahead neural network that take the state variables and output at time t as input to successfully predict output at time t+n. The variable n is a hyper-parameter which can be optimized based on performance constraints. The n-step ahead neural network was then trained on training data generated from experimental model to high training accuracies. This trained model performed appreciably in its objective to regulate outlet temperature of a heat exchanger. The second approach uses a combination of control and learning module for temperature regulation. The control module used is a proportional controller while the learning module used is steady state neural network. The steady state neural network is used to learn the controller output as a function of random variables and temperature set-point. The proportional controller is then used to compensate for transient behavior of linearized system around various operating points. The algorithm works effectively for temperature regulation as neural network captures the non-linear input-output behavior of closed loop system leaving proportional controller to compensate for steady state errors of a linearized plant. The proposed algorithm’s performances were evaluated against the performance of a PI controller. Further, the robustness of the algorithms were also studied in great detail under small perturbations around tuned parameters of the proposed algorithms.

Sundar, Sreenath↗
NASA NTRS1971

Combustion of a premixed system in stagnation flow. I - Theoretical.

The theoretical steady-state and transient characteristics of a premixed combustible system in laminar axisymmetric stagnation flow are studied by way of numerical solution of the governing boundary-layer equations. Computations, which are based on the oxidation reaction of carbon monoxide in humid air, invoke a minimum of simplifying assumptions. Steady-state results include prediction of (1) profiles of the state variables, (2) extinction of blow-off conditions, (3) ignition conditions, and (4) the heat flux to the stagnation point. Computer simulations of the unsteady state provide an indication of the stability of a given steady state to perturbations. Comparisons between some of the predictions of the mathematical model and the results of laboratory experiments will be reported in Part II.

Smith, H. W.↗
NASA NTRS2005

An Optimal Orthogonal Decomposition Method for Kalman Filter-Based Turbofan Engine Thrust Estimation

A new linear point design technique is presented for the determination of tuning parameters that enable the optimal estimation of unmeasured engine outputs such as thrust. The engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters related to each major engine component. Accurate thrust reconstruction depends upon knowledge of these health parameters, but there are usually too few sensors to be able to estimate their values. In this new technique, a set of tuning parameters is determined which accounts for degradation by representing the overall effect of the larger set of health parameters as closely as possible in a least squares sense. The technique takes advantage of the properties of the singular value decomposition of a matrix to generate a tuning parameter vector of low enough dimension that it can be estimated by a Kalman filter. A concise design procedure to generate a tuning vector that specifically takes into account the variables of interest is presented. An example demonstrates the tuning parameters ability to facilitate matching of both measured and unmeasured engine outputs, as well as state variables. Additional properties of the formulation are shown to lend themselves well to diagnostics.

Litt, Jonathan S.↗
NASA NTRS2007

An Optimal Orthogonal Decomposition Method for Kalman Filter-Based Turbofan Engine Thrust Estimation

A new linear point design technique is presented for the determination of tuning parameters that enable the optimal estimation of unmeasured engine outputs, such as thrust. The engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters related to each major engine component. Accurate thrust reconstruction depends on knowledge of these health parameters, but there are usually too few sensors to be able to estimate their values. In this new technique, a set of tuning parameters is determined that accounts for degradation by representing the overall effect of the larger set of health parameters as closely as possible in a least-squares sense. The technique takes advantage of the properties of the singular value decomposition of a matrix to generate a tuning parameter vector of low enough dimension that it can be estimated by a Kalman filter. A concise design procedure to generate a tuning vector that specifically takes into account the variables of interest is presented. An example demonstrates the tuning parameters ability to facilitate matching of both measured and unmeasured engine outputs, as well as state variables. Additional properties of the formulation are shown to lend themselves well to diagnostics.

Litt, Jonathan S.↗