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Bulaevskaya, Vera

Publications and source records attributed to Bulaevskaya, Vera.

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Joint Bayesian Inference for Near-surface Explosion Yield

Geophysical observations of an explosion scale with the yield of the explosion. Guided by the principal of yield-scaling and observed relationships in the data, we defined and parameterized seismoacoustic forward models in Ford et al. [2014]. This work extends the model with additional data features of maximum velocity and positive phase duration for the seismic and acoustic data types, respectively, and a new optic data type. Parameters for the seismoacoustic models are estimated via Bayesian inference. The parameterized models can then be used to infer the yield of a new explosion.

58 GEOSCIENCES↗
DOE OSTI2019

Inferring Atmospheric Release Characteristics in a Large Computer Experiment using Bayesian Adaptive Splines

An atmospheric release of hazardous material, whether accidental or intentional, can be catastrophic for those in the path of the plume. Predicting the path of a plume based on characteristics of the release (location, amount and duration) and meteorological conditions is an active research area highly relevant for emergency and long-term response to these releases. As a result, researchers have developed particle dispersion simulators to provide plume path predictions that incorporate release characteristics and meteorological conditions. However, since release characteristics and meteorological conditions are often unknown, the inverse problem is of great interest, that is, based on all the observations of the plume so far, what can be inferred about the release characteristics? This is the question we seek to answer using plume observations from a controlled release at the Diablo Canyon Nuclear Power Plant in Central California. With access to a large number of evaluations of a computationally expensive particle dispersion simulator that includes continuous and categorical inputs and spatio-temporal output, building a fast statistical surrogate model (or emulator) presents many statistical challenges, but is an essential tool for inverse modeling and sensitivity analysis. We achieve accurate emulation using Bayesian adaptive splines to model weights on empirical orthogonal functions. Here, we use this emulator as well as appropriately identifiable simulator discrepancy and observational error models to calibrate the simulator, thus finding a posterior distribution of characteristics of the release. Since the release was controlled, these characteristics are known, making it possible to compare our findings to the truth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Forward Model Parameter Estimation and Inversion in the Presence of Correlated Measurements

The technical report by Johannesson et al. discussed the case of one quantity being extracted from an air pressure or a seismic time profile. Suppose that we now measure several quantities from each of these profiles. While the seismic measurements are still assumed to be independent from the air pressure measurements, the multiple quantities from a given time profile are potentially correlated. For example, for the air pressure data, the original quantity measured and used in the inference about the explosion yield and HOB/DOB was the integral of the air pressure profile in a certain time interval, but we can also measure the peak air pressure value as an additional source of information in the inverse modeling. The seismic quantity considered in the technical report was the vertical displacement of the first P arrival, but other kinds of seismic measurements can be extracted from the seismic profile and can be potentially useful when inferring the yield and HOB/DOB. In the present document, we limit the discussion to the case of just two correlated measurements for a given data type (air pressure or seismic) as it is computationally easier to handle, but in the future we plan to generalize this to three or more such measurements.

58 GEOSCIENCES↗
DOE OSTI2016

Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model

We evaluate the sensitivity of simulated turbine-height winds to 26 parameters applied in a planetary boundary layer (PBL) scheme and a surface layer scheme of the Weather Research and Forecasting (WRF) model over an area of complex terrain during the Columbia Basin Wind Energy Study. An efficient sampling algorithm and a generalized linear model are used to explore the multiple-dimensional parameter space and quantify the parametric sensitivity of modeled turbine-height winds. The results indicate that most of the variability in the ensemble simulations is contributed by parameters related to the dissipation of the turbulence kinetic energy (TKE), Prandtl number, turbulence length scales, surface roughness, and the von Kármán constant. The relative contributions of individual parameters are found to be dependent on both the terrain slope and atmospheric stability. The parameter associated with the TKE dissipation rate is found to be the most important one, and a larger dissipation rate can produce larger hub-height winds. A larger Prandtl number results in weaker nighttime winds. Increasing surface roughness reduces the frequencies of both extremely weak and strong winds, implying a reduction in the variability of the wind speed. All of the above parameters can significantly affect the vertical profiles of wind speed, the altitude of the low-level jet and the magnitude of the wind shear strength. The wind direction is found to be modulated by the same subset of influential parameters. Remainder of abstract is in attachment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A Bayesian inversion framework for yield and height-of-burst/depth-of-burial for near-surface explosions

A Bayesian inversion framework is presented to estimate the yield of an explosion and height-of-burst/depth-of-burial (HOB/DOB) using seismic and air pressure data. This is accomplished by first calibrating the parameters in the forward models that relate the observations to the yield and HOB/DOB and then using the calibrated model to estimate yield and HOB/DOB associated with a new set of seismic and air pressure observations. The MCMC algorithms required to perform these steps are outlined, and the results with real data are shown. Finally, an extension is proposed for a case when clustering in the seismic displacement occurs as a function of different types of rock and other factors.

58 GEOSCIENCES↗
DOE OSTI2014

A Comprehensive Evaluation of Robust Unit Commitment

This report summarizes the procedure to compare the robust UC approach with the deterministic counterpart. The procedure involves solving the robust and deterministic UC problems under various scenario configurations, and then performing system dispatch simulations under each UC solution to generate economic and operational performance measures. The report describes the parallelization process for solving UC problems as well as for running system dispatch simulations. The report also summarizes the statistical model used for generating Monte Carlo simulation samples and forming forecast ranges used in the robust UC approach. Some key findings are discussed as well. In particular, the economic and operational performance of the robust and deterministic approaches is compared under different 1) conservatism levels or the sizes of forecast range, 2) the wind penetration levels, and 3) the mixture of load and wind behaviors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗