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Results for “parallel state estimation”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Thin-Film Thermal Conductivity Measurements Using Superconducting Nanowires

We present a simple experimental scheme for estimating the cryogenic thermal transport properties of thin films using superconducting nanowires. In a parallel array of nanowires, the heat from one nanowire in the normal state changes the local temperature around adjacent nanowires, reducing their switching current. Calibration of this change in switching current as a function of bath temperature provides an estimate of the temperature as a function of displacement from the heater. This provides a method of determining the contribution of substrate heat transport to the cooling time of superconducting nanowire single-photon detectors. Understanding this process is necessary for successful electrothermal modeling of superconducting nanowire systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2017

Thin Film Thermal Conductivity Measurements using Superconducting Nanowires

We present a simple experimental scheme for estimating the cryogenic thermal transport properties of thin films using superconducting nanowires. In a parallel array of nanowires, the heat from one nanowire in the normal state changes the local temperature around adjacent nanowires, reducing their switching current. Calibration of this change in switching current as a function of bath temperature provides an estimate of the temperature as a function of displacement from the heater. This provides a method of determining the contribution of substrate heat transport to the cooling time of superconducting nanowire single photon detectors. Understanding this process is necessary for successful electrothermal modeling of superconducting nanowire systems.

Shaw, M. D.↗
DOE OSTI2012

Towards Real-Time High Performance Computing For Power Grid Analysis

Real-time computing has traditionally been considered largely in the context of single-processor and embedded systems, and indeed, the terms real-time computing, embedded systems, and control systems are often mentioned in closely related contexts. However, real-time computing in the context of multinode systems, specifically high-performance, cluster-computing systems, remains relatively unexplored. Imposing real-time constraints on a parallel (cluster) computing environment introduces a variety of challenges with respect to the formal verification of the system's timing properties. In this paper, we give a motivating example to demonstrate the need for such a system--- an application to estimate the electromechanical states of the power grid--- and we introduce a formal method for performing verification of certain temporal properties within a system of parallel processes. We describe our work towards a full real-time implementation of the target application--- namely, our progress towards extracting a key mathematical kernel from the application, the formal process by which we analyze the intricate timing behavior of the processes on the cluster, as well as timing measurements taken on our test cluster to demonstrate use of these concepts.

High performance computing, real-time-computing, p↗
DOE OSTI2012

Estimate the Electromechanical States Using Particle Filtering and Smoothing

Accurate knowledge of electromechanical states is critical for efficient and reliable control of a power system. This paper proposes a particle filtering approach to estimate the electromechanical states of power systems from Phasor Measurement Unit (PMU) data. Without having to go through laborious linearization procedure, the proposed particle filtering techniques can estimate states of a complex power system, which is often non-linear and has non-Gaussian noise. The proposed method is evaluated using a multi-machine system with both large and small disturbances. Sensitivity studies of the dynamic state estimation performance are also presented to show the robustness of the proposed method. The inherent decoupling properties of particle filtering make it highly scalable and the potential to reduce computational time through parallel implementation is very promising.

Auxiliary Particle Filter↗
NASA NTRS2005

Parallel Estimators and Communication in Spacecraft Formations

This paper investigates the closed-loop dynamics of systems controlled via parallel estimators. This structure arises in formation flying problems when each spacecraft bases its control action on an internal estimate of the complete formation state. For LTI systems a separation principle shows that the necessary and sufficient conditions for overall system stability are more stringent than the single controller case; the controllers' open-loop dynamics necessarily appear in the closed-loop dynamics. Communication amongst the spacecraft can be used to specify the complete system dynamics and a framework for integrating the design of the communication links into the formation flying control design problem is presented.

communications↗
NASA NTRS1978

On-line parameter estimation using a high sensitivity estimator

An on-line parameter identification method is presented. The method is based on a recursive formulation of the maximum likelihood method, with a significant modification on the gains of the state estimator. In the conventional maximum likelihood method, the Kalman gains are used in the state estimator. This produces unbiased, minimum variance parameter estimates in the presence of process noise and measurement noise, but it also slows the convergence rate when the identification is done on-line. Here we suggest choosing the gains to maximize a measure of the sensitivities of the state estimates to parameter variations. One such criterion is to minimize the trace of the inverse information matrix. This increases the convergence rate significantly. After one or two time constants, the gains are switched to the Kalman values to assure unbiased, minimum-variance estimates. The state estimate will initially be nonoptimal, and may not be adequate for control purposes. In this case, a parallel Kalman filter which uses the identifier's parameter estimates can be used. This method is applied here for the identification of a simple first-order system, and for the identification of short-period stability derivatives of an F-8 aircraft from simulated data.

Mishne, D.↗
NASA NTRS1985

Viscous effects on transonic airfoil stability and response

Viscous effects on transonic airfoil stability and response are investigated using an integral boundary layer model coupled to the inviscid XTRAN2L transonic small disturbance code. Unsteady transonic airloads required for stability analyses are computed using a pulse transfer function analysis including viscous effects. The pulse analysis provides unsteady aerodynamic forces for a wide range of reduced frequency in a single flow field computation. Nonlinear time marching aeroelastic solutions are presented which show the effects of viscosity on airfoil response behavior and flutter. Effects of amplitude on time marching responses are demonstrated. A state space aeroelastic model employing Pade approximants to describe the unsteady airloads is used to study the effects of viscosity on transonic airfoil stability. State space dynamic pressure root loci are in good overall agreement with time marching damping and frequency estimates. Parallel sets of results with and without viscous effects reveal the effects of viscosity on transonic unsteady airloads and aeroelastic characteristics of airfoils.

Berry, H. M.↗
NASA NTRS2021

Collaborative Pose Estimation of An Unknown Target Using Multiple Spacecraft

A reliable method for pose estimation of an unknown and uncooperative space target using monocular vision remains an open problem. Vision-based pose determination can be challenging in case of unfavorable illumination, time-varying conditions due to rotational motion and relative orbit, and scale ambiguity resolution. To address these challenges, we propose a novel collaborative pose determination algorithm called Multi- Spacecraft Simultaneous Estimation of Pose and Shape algorithm or M-SEPS.Within M-SEPS, a team of chaser spacecraft, each equipped with a monocular camera, exchange information over a local network to jointly estimate the relative kinematic state of the target and its sparse shape landmarks. In this approach, each spacecraft processes its own images and observes particular target landmarks in parallel and in a distributed fashion. Then, the local network is exploited by the spacecraft to share their consensus proposals and aggregate them to achieve the joint estimate. We validate our algorithm using simulations of relative orbits and observations, captured by each chaser spacecraft. To the best of the authors’ knowledge, this is the first cooperative, vision-based algorithm for estimating the pose and shape of a space object for an arbitrary number of spacecraft.

Chung, Soon-Jo↗
DOE OSTI2016

Bayesian inference of nonlinear unsteady aerodynamics from aeroelastic limit cycle oscillations

A Bayesian model selection and parameter estimation algorithm is applied to investigate the influence of nonlinear and unsteady aerodynamic loads on the limit cycle oscillation (LCO) of a pitching airfoil in the transitional Reynolds number regime. At small angles of attack, laminar boundary layer trailing edge separation causes negative aerodynamic damping leading to the LCO. The fluid–structure interaction of the rigid, but elastically mounted, airfoil and nonlinear unsteady aerodynamics is represented by two coupled nonlinear stochastic ordinary differential equations containing uncertain parameters and model approximation errors. Several plausible aerodynamic models with increasing complexity are proposed to describe the aeroelastic system leading to LCO. The likelihood in the posterior parameter probability density function (pdf) is available semi-analytically using the extended Kalman filter for the state estimation of the coupled nonlinear structural and unsteady aerodynamic model. The posterior parameter pdf is sampled using a parallel and adaptive Markov Chain Monte Carlo (MCMC) algorithm. The posterior probability of each model is estimated using the Chib–Jeliazkov method that directly uses the posterior MCMC samples for evidence (marginal likelihood) computation. The Bayesian algorithm is validated through a numerical study and then applied to model the nonlinear unsteady aerodynamic loads using wind-tunnel test data at various Reynolds numbers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Effect of surface attractive strength on structural transitions of a confined HP lattice protein

We investigate the influence of surface attractive strength on structural transitions of a hydrophobic-polar (HP) lattice protein confined in a slit formed by two parallel, attractive walls. We apply Wang-Landau sampling together with efficient Monte Carlo updates to estimate the density of states of the system. The conformational transitions, namely, the debridging process and hydrophobic core formation, can be identified by analyzing the specific heat together with several structural observables, such as the numbers of surface contacts, the number of hydrophobic pairs, and radii of gyration in different directions. As temperature decreases, we find that the occurrence of the debridging process is conditional depending on the surface attractive strength. This, in turn, affects the nature of the hydrophobic core formation that takes place at a lower temperature. We illustrate these observations with the aid of a HP protein chain with 48 monomers.

Pattanasiri, Busara↗
NASA NTRS2006

Processing EOS MLS Level-2 Data

A computer program performs level-2 processing of thermal-microwave-radiance data from observations of the limb of the Earth by the Earth Observing System (EOS) Microwave Limb Sounder (MLS). The purpose of the processing is to estimate the composition and temperature of the atmosphere versus altitude from .8 to .90 km. "Level-2" as used here is a specialists f term signifying both vertical profiles of geophysical parameters along the measurement track of the instrument and processing performed by this or other software to generate such profiles. Designed to be flexible, the program is controlled via a configuration file that defines all aspects of processing, including contents of state and measurement vectors, configurations of forward models, measurement and calibration data to be read, and the manner of inverting the models to obtain the desired estimates. The program can operate in a parallel form in which one instance of the program acts a master, coordinating the work of multiple slave instances on a cluster of computers, each slave operating on a portion of the data. Optionally, the configuration file can be made to instruct the software to produce files of simulated radiances based on state vectors formed from sets of geophysical data-product files taken as input.

Snyder, W. Van↗
DOE OSTI2025

Entanglement-enhanced ac magnetometry in the presence of Markovian noise

Entanglement is a resource to improve the sensitivity of quantum sensors. In an ideal case, using an entangled state as a probe to detect target fields, we can beat the standard quantum limit by which all classical sensors are bounded. However, since entanglement is fragile against decoherence, it is unclear whether entanglement-enhanced metrology is useful in a noisy environment. Its benefit is indeed limited when estimating the amplitude of dc magnetic fields under the effect of parallel Markovian decoherence, where the noise operator is parallel to the target field. In this paper, on the contrary, we show an advantage to using an entanglement over the classical strategy under the effect of parallel Markovian decoherence when we try to detect ac magnetic fields. We consider a scenario to induce a Rabi oscillation of the qubits with the target ac magnetic fields. Although we can, in principle, estimate the amplitude of the ac magnetic fields from the Rabi oscillation, the signal becomes weak if the qubit frequency is significantly detuned from the frequency of the ac magnetic field. We show that, by using the Greenberger-Horne-Zeilinger (GHZ) states, we can significantly enhance the signal of the detuned Rabi oscillation even under the effect of parallel Markovian decoherence. Further, our method is based on the fact that the interaction time between the GHZ states and ac magnetic fields scales as 1/L to mitigate the decoherence effect, where L is the number of qubits, which contributes to improving the bandwidth of the detectable frequencies of the ac magnetic fields. Our results pave the way for new applications of entanglement-enhanced ac magnetometry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1999

Massively Parallel Assimilation of TOGA/TAO and Topex/Poseidon Measurements into a Quasi Isopycnal Ocean General Circulation Model Using an Ensemble Kalman Filter

A massively parallel ensemble Kalman filter (EnKF)is used to assimilate temperature data from the TOGA/TAO array and altimetry from TOPEX/POSEIDON into a Pacific basin version of the NASA Seasonal to Interannual Prediction Project (NSIPP)ls quasi-isopycnal ocean general circulation model. The EnKF is an approximate Kalman filter in which the error-covariance propagation step is modeled by the integration of multiple instances of a numerical model. An estimate of the true error covariances is then inferred from the distribution of the ensemble of model state vectors. This inplementation of the filter takes advantage of the inherent parallelism in the EnKF algorithm by running all the model instances concurrently. The Kalman filter update step also occurs in parallel by having each processor process the observations that occur in the region of physical space for which it is responsible. The massively parallel data assimilation system is validated by withholding some of the data and then quantifying the extent to which the withheld information can be inferred from the assimilation of the remaining data. The distributions of the forecast and analysis error covariances predicted by the ENKF are also examined.

Keppenne, Christian L.↗
DOE OSTI2020

Automated calculation and convergence of defect transport tensors

Defect diffusion is a key process in materials science and catalysis, but as migration mechanisms are often too complex to enumerate a priori, calculation of transport tensors typically have no measure of convergence and require significant end-user intervention. These two bottlenecks prevent high-throughput implementations essential to propagate model-form uncertainty from interatomic interactions to predictive simulations. In order to address these issues, we extend a massively parallel accelerated sampling scheme, autonomously controlled by Bayesian estimators of statewide sampling completeness, to build atomistic kinetic Monte Carlo models on a state-space irreducible under exchange and space group symmetries. Focusing on isolated defects, we derive analytic expressions for drift and diffusion coefficients, providing a convergence metric by calculating the Kullback–Leibler divergence across the ensemble of diffusion processes consistent with the sampling uncertainty. The autonomy and efficacy of the method is demonstrated on surface trimers in tungsten and Hexa-interstitials in magnesium oxide, both of which exhibit complex, correlated migration mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Time-periodic steady-state solution of fluid-structure interaction and cardiac flow problems through multigrid-reduction-in-time

In this study, a time-periodic MGRIT algorithm is proposed as a means to reduce the time-to-solution of numerical algorithms by exploiting the time periodicity inherent to many applications in science and engineering. The time-periodic MGRIT algorithm is applied to a variety of linear and nonlinear single- and multiphysics problems that are periodic-in-time. It is demonstrated that the proposed parallel-in-time algorithm can obtain the same time-periodic steady-state solution as sequential time-stepping. It is shown that the required number of MGRIT iterations can be estimated a priori and that the new MGRIT variant can significantly and consistently reduce the time-to-solution compared to sequential time-stepping, irrespective of the number of dimensions, linear or nonlinear PDE models, single-physics or coupled problems and the employed computing resources. The numerical experiments demonstrate that the time-periodic MGRIT algorithm enables a greater level of parallelism yielding faster turnaround, and thus, facilitating more complex and more realistic problems to be solved.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

Space Shuttle flight control system

The Space Shuttle is a control stabilized vehicle with control provided by an all digital, fly-by-wire flight control system. This paper gives a description of the several modes of flight control which correspond to the Shuttle mission phases. These modes are ascent flight control (including open loop first stage steering, the use of four computers operating in parallel and inertial guidance sensors), on-orbit flight control (with a discussion of reaction control, phase plane switching logic, jet selection logic, state estimator logic and OMS thrust vector control), entry flight control and TAEM (terminal area energy management to landing). Also discussed are redundancy management and backup flight control.

Klinar, W. J.↗