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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Resilient Computing with Dynamical Systems

We reformulate fundamental numerical problems to run on novel hardware inspired by the brain. Such "neuromorphie hardware consumes less energy per computation, promising a means to augment next-generation exascale computers. However, their programming model is radically different from floating-point machines, with fewer guarantees about precision and communication. The approach is to pass each given problem through a sequence of transformations (algorithmic "reductions") which change it from conventional form into a dynamical system, then ultimately into a spiking neural network. Results for the eigenvalue problem are presented, showing that the dynamical system formulation is feasible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Coupling Power System Dynamics and Building Dynamics to Enable Building-to-Grid Integration

The interactions between power system dynamics and building dynamics are usually ignored or over-simplified in existing power systems and building modeling and simulation tools, which limits how system modeling can sup-port Building-to-Grid integration. This paper discusses a new approach to consider those interactions by model-ing motor-driven building devices or systems. The motor-driven model is based on simplified mechanical rotation equations and allows us to study the coupling relationship between frequency/voltage in the power system and motor-driven device operation. This model is validated by performing one proof-of-concept case study with Modelica. The simulation results suggest that the proposed model can yield better representations of these interactions than the existing simplified models, especially the ones with the fast transient dynamics.

Buildings-to-grid integration, motor, coupling sim↗
NASA NTRS1985

Polynomic nonlinear dynamical systems - A residual sensitivity method for model reduction

The motivation for using polynomic combinations of system states and inputs to model nonlinear dynamics systems is founded upon the classical theories of analysis and function representation. A feature of such representations is the need to make available all possible monomials in these variables, up to the degree specified, so as to provide for the description of widely varying functions within a broad class. For a particular application, however, certain monomials may be quite superfluous. This paper examines the possibility of removing monomials from the model in accordance with the level of sensitivity displayed by the residuals to their absence. Critical in these studies is the effect of system input excitation, and the effect of discarding monomial terms, upon the model parameter set. Therefore, model reduction is approached iteratively, with inputs redesigned at each iteration to ensure sufficient excitation of remaining monomials for parameter approximation. Examples are reported to illustrate the performance of such model reduction approaches.

Yurkovich, S.↗
NASA NTRS1988

Stabilization of computational procedures for constrained dynamical systems

A new stabilization method of treating constraints in multibody dynamical systems is presented. By tailoring a penalty form of the constraint equations, the method achieves stabilization without artificial damping and yields a companion matrix differential equation for the constraint forces; hence, the constraint forces are obtained by integrating the companion differential equation for the constraint forces in time. A principal feature of the method is that the errors committed in each constraint condition decay with its corresponding characteristic time scale associated with its constraint force. Numerical experiments indicate that the method yields a marked improvement over existing techniques.

Park, K. C.↗
NASA NTRS1990

Organizing Performance Requirements For Dynamical Systems

Paper describes methodology for establishing performance requirements for complicated dynamical systems. Uses top-down approach. In series of steps, makes connections between high-level mission requirements and lower-level functional performance requirements. Provides systematic delineation of elements accommodating design compromises.

Malchow, Harvey L.↗
DOE OSTI2014

Ensemble Kalman filters for dynamical systems with unresolved turbulence

Ensemble Kalman filters are developed for turbulent dynamical systems where the forecast model does not resolve all the active scales of motion. Coarse-resolution models are intended to predict the large-scale part of the true dynamics, but observations invariably include contributions from both the resolved large scales and the unresolved small scales. The error due to the contribution of unresolved scales to the observations, called ‘representation’ or ‘representativeness’ error, is often included as part of the observation error, in addition to the raw measurement error, when estimating the large-scale part of the system. It is here shown how stochastic superparameterization (a multiscale method for subgridscale parameterization) can be used to provide estimates of the statistics of the unresolved scales. In addition, a new framework is developed wherein small-scale statistics can be used to estimate both the resolved and unresolved components of the solution. The one-dimensional test problem from dispersive wave turbulence used here is computationally tractable yet is particularly difficult for filtering because of the non-Gaussian extreme event statistics and substantial small scale turbulence: a shallow energy spectrum proportional to k{sup −5/6} (where k is the wavenumber) results in two-thirds of the climatological variance being carried by the unresolved small scales. Because the unresolved scales contain so much energy, filters that ignore the representation error fail utterly to provide meaningful estimates of the system state. Inclusion of a time-independent climatological estimate of the representation error in a standard framework leads to inaccurate estimates of the large-scale part of the signal; accurate estimates of the large scales are only achieved by using stochastic superparameterization to provide evolving, large-scale dependent predictions of the small-scale statistics. Again, because the unresolved scales contain so much energy, even an accurate estimate of the large-scale part of the system does not provide an accurate estimate of the true state. By providing simultaneous estimates of both the large- and small-scale parts of the solution, the new framework is able to provide accurate estimates of the true system state.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

On the Stability of Toroidal Manifold for One Class of Dynamical Systems

We study the problem of stability of an invariant toroidal manifold for one class of linear extensions of a dynamical system on a torus. The obtained result is used to investigate the problem of existence of invariant manifold for a nonlinear system of differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Metriplectic four-bracket algorithm for constructing thermodynamically consistent dynamical systems

A unified thermodynamic algorithm is presented for constructing thermodynamically consistent dynamical systems, i.e., systems that have Hamiltonian and dissipative parts that conserve energy while producing entropy. The algorithm is based on the metriplectic 4-bracket given in Morrison and Updike [Phys. Rev. E 109, 045202 (2024)]. A feature of the unified thermodynamic algorithm is the force-flux relation 𝐉 𝛼 =−𝐿 𝛼⁢𝛽 𝛁(𝛿⁢𝐻⁡/𝛿⁢𝜉 𝛽 ) for phenomenological coefficients 𝐿 𝛼⁢𝛽 , Hamiltonian 𝐻, and dynamical variables 𝜉 𝛽 . The algorithm is applied to the Navier-Stokes-Fourier, the Cahn-Hilliard-Navier-Stokes, and Brenner-Navier-Stokes-Fourier systems, and significant generalizations of these systems are obtained.

Multiphase flows↗
DOE OSTI2019

Helicity in Hamiltonian dynamical systems

Helicity plays a unique role as an integral invariant of a dynamical system. In this paper, the concept of helicity in the general setting of Hamiltonian dynamics is discussed. It is shown, through examples, how the conservation of overall helicity can imply a bound on other quantities of the motion in a nontrivial way.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Bayesian reduced-order deep learning surrogate model for dynamic systems described by partial differential equations

We propose a reduced-order deep-learning surrogate model for dynamic systems described by time-dependent partial differential equations. This method employs space–time Karhunen–Loève expansions (KLEs) of the state variables and space-dependent KLEs of space-varying parameters to identify the reduced (latent) dimensions. Subsequently, a deep neural network (DNN) is used to map the parameter latent space to the state variable latent space. An approximate Bayesian method is developed for uncertainty quantification (UQ) in the proposed KL-DNN surrogate model. The KL-DNN method is tested for the linear advection–diffusion and nonlinear diffusion equations, and the Bayesian approach for UQ is compared with the deep ensembling (DE) approach, commonly used for quantifying uncertainty in DNN models. It was found that the approximate Bayesian method provides a more informative distribution of the PDE solutions in terms of the coverage of the reference PDE solutions (the percentage of nodes where the reference solution is within the confidence interval predicted by the UQ methods) and log predictive probability. The DE method is found to underestimate uncertainty and introduce bias. For the nonlinear diffusion equation, we compare the KL-DNN method with the Fourier Neural Operator (FNO) method and find that KL-DNN is 10% more accurate and needs less training time than the FNO method.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Testing relativity with solar system dynamics

A major breakthrough is described in the accuracy of Solar System dynamical tests of relativistic gravity. The breakthrough was achieved by factoring in ranging data from Viking Landers 1 and 2 from the surface of Mars. Other key data sources included optical transit circle observations, lunar laser ranging, planetary radar, and spacecraft (Mariner 9 to Mars and Mariner 10 to Mercury). The Solar System model which is used to fit the data and the process by which such fits are performed are explained and results are discussed. The results are fully consistent with the predictions of General Relativity.

Hellings, R. W.↗
NASA NTRS2007

A Decision Tool that Combines Discrete Event Software Process Models with System Dynamics Pieces for Software Development Cost Estimation and Analysis

The development process for a large software development project is very complex and dependent on many variables that are dynamic and interrelated. Factors such as size, productivity and defect injection rates will have substantial impact on the project in terms of cost and schedule. These factors can be affected by the intricacies of the process itself as well as human behavior because the process is very labor intensive. The complex nature of the development process can be investigated with software development process models that utilize discrete event simulation to analyze the effects of process changes. The organizational environment and its effects on the workforce can be analyzed with system dynamics that utilizes continuous simulation. Each has unique strengths and the benefits of both types can be exploited by combining a system dynamics model and a discrete event process model. This paper will demonstrate how the two types of models can be combined to investigate the impacts of human resource interactions on productivity and ultimately on cost and schedule.

Mizell, Carolyn Barrett↗