Resilience testing device Patent

Automated ball rebound resilience test equipment for determining viscoelastic properties of polymer

Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data

While nonlinear stochastic partial differential equations arise naturally in spatiotemporal modeling, inference for such systems often faces two major challenges: sparse noisy data and ill-posedness of the inverse problem of parameter estimation. To overcome the challenges, we introduce a strongly regularized posterior by normalizing the likelihood and by imposing physical constraints through priors of the parameters and states. We investigate joint parameter-state estimation by the regularized posterior in a physically motivated nonlinear stochastic energy balance model (SEBM) for paleoclimate reconstruction. The high-dimensional posterior is sampled by a particle Gibbs sampler that combines MCMC with an optimal particle filter exploiting the structure of the SEBM. In tests using either Gaussian or uniform priors based on the physical range of parameters, the regularized posteriors overcome the ill-posedness and lead to samples within physical ranges, quantifying the uncertainty in estimation. Due to the ill-posedness and the regularization, the posterior of parameters presents a relatively large uncertainty, and consequently, the maximum of the posterior, which is the minimizer in a variational approach, can have a large variation. In contrast, the posterior of states generally concentrates near the truth, substantially filtering out observation noise and reducing uncertainty in the unconstrained SEBM

High precision cryogenic thermal conductivity standards

New apparatus allows accurate simultaneous measurement of thermal conductivity, electrical resistivity, and thermopower for technically important materials, such as new or uncommon alloys. A list of materials investigated is presented. Sources for obtaining data on these materials, as well as the source giving a description of the apparatus, are cited

Hydrogen slush density reference system

A hydrogen slush density reference system was designed for calibration of field-type instruments and/or transfer standards. The device is based on the buoyancy principle of Archimedes. The solids are weighed in a low-mass container so arranged that solids and container are buoyed by triple-point liquid hydrogen during the weighing process. Several types of hydrogen slush density transducers were developed and tested for possible use as transfer standards. The most successful transducers found were those which depend on change in dielectric constant, after which the Clausius-Mossotti function is used to relate dielectric constant and density

Instrumentation for hydrogen slush storage containers

Hydrogen liquid and slush tank continuous inventory during ground storag

Robustness of the stochastic parameterization of sub-grid scale wind variability in sea-surface fluxes

High-resolution numerical models have been used to develop statistical models of the enhancement of sea surface fluxes resulting from spatial variability of sea-surface wind. In particular, studies have shown that the flux enhancement is not a deterministic function of the resolved state. Previous studies focused on single geographical areas or used a single high-resolution numerical model. This study extends the development of such statistical models by considering six different high-resolution models, four different geographical regions, and three different ten-day periods, allowing for a systematic investigation of the robustness of both the deterministic and stochastic parts of the data-driven parameterization. Results indicate that the deterministic part, based on regressing the unresolved normalized flux onto resolved scale normalized flux and precipitation, is broadly robust across different models, regions, and time periods. The statistical features of the stochastic part of the model (spatial and temporal autocorrelation and parameters of a Gaussian process fit to the regression residual) are also found to be robust and not strongly sensitive to the underlying model, modelled geographical region, or time period studied. Best-fit Gaussian process parameters display robust spatial heterogeneity across models, indicating potential for improvements to the statistical model. These results illustrate the potential for the development of a generic, explicitly stochastic parameterization of sea-surface flux enhancements dependent on wind variability