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Results for “Double-well potential”

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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Rotational and vibrational effects in the E 1Sigma(+)g-X 1Sigma(+)g two-photon transitions of H2, HD, and D2

This paper reports a theoretical study of the dependence of the E 1Sigma(+)g v(E) = 0, J-X 1Sigma(+)g, v(X),J transitions in H2, HD, and D2 on the initial vibrational and rotational quantum numbers v(X) and J. The results demonstrate the concerted nature of two-photon transitions. The magnitude of the two-photon transition moment increases with v(X) between 0 and 2, then decreases rapidly at higher v(X). The J dependence of the two-photon transition moment is most strongly affected by the changes in vibrational wave functions caused by centrifugal distortion of the vibrational potential. It is shown that the J variation can be used as a probe of resonant tunneling in the double-well potential of the E,F state.

Huo, Winifred M.↗
NASA NTRS2006

An Ensemble-Based Smoother with Retrospectively Updated Weights for Highly Nonlinear Systems

Monte Carlo computational methods have been introduced into data assimilation for nonlinear systems in order to alleviate the computational burden of updating and propagating the full probability distribution. By propagating an ensemble of representative states, algorithms like the ensemble Kalman filter (EnKF) and the resampled particle filter (RPF) rely on the existing modeling infrastructure to approximate the distribution based on the evolution of this ensemble. This work presents an ensemble-based smoother that is applicable to the Monte Carlo filtering schemes like EnKF and RPF. At the minor cost of retrospectively updating a set of weights for ensemble members, this smoother has demonstrated superior capabilities in state tracking for two highly nonlinear problems: the double-well potential and trivariate Lorenz systems. The algorithm does not require retrospective adaptation of the ensemble members themselves, and it is thus suited to a streaming operational mode. The accuracy of the proposed backward-update scheme in estimating non-Gaussian distributions is evaluated by comparison to the more accurate estimates provided by a Markov chain Monte Carlo algorithm.

Monte Carlo↗