A modern power system is characterized by an increasing penetration of wind power, which results in large uncertainties in its states. These uncertainties must be quantified properly; otherwise, the system security may be threatened. Facing this challenge, we propose a cost-effective, data-driven approach for the probabilistic load-margin assessment problem. Using actual wind data, a kernel-density-estimator is applied to infer the nonparametric wind speed distributions, which are further merged into the framework of a vine copula. The latter enables us to simulate complex multivariate and highly dependent model inputs with a variety of bivariate copulae that precisely represent the tail dependence in the correlated samples. Furthermore, to reduce the prohibitive computational time of the traditional Monte-Carlo simulations processing a large amount of samples, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model through a Bayesian learning framework. To accelerate the convergence rate of this Bayesian algorithm, a truncated polynomial chaos surrogate is developed, which serves as a highly efficient, parametric Bayesian prior. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. Simulation results reveal the impressive performances of the proposed method.
more »
« less
Bayes goes fast: Uncertainty quantification for a covariant energy density functional emulated by the reduced basis method
A covariant energy density functional is calibrated using a principled Bayesian statistical framework informed by experimental binding energies and charge radii of several magic and semi-magic nuclei. The Bayesian sampling required for the calibration is enabled by the emulation of the high-fidelity model through the implementation of a reduced basis method (RBM)—a set of dimensionality reduction techniques that can speed up demanding calculations involving partial differential equations by several orders of magnitude. The RBM emulator we build—using only 100 evaluations of the high-fidelity model—is able to accurately reproduce the model calculations in tens of milliseconds on a personal computer, an increase in speed of nearly a factor of 3,300 when compared to the original solver. Besides the analysis of the posterior distribution of parameters, we present model calculations for masses and radii with properly estimated uncertainties. We also analyze the model correlation between the slope of the symmetry energy L and the neutron skin of 48 Ca and 208 Pb. The straightforward implementation and outstanding performance of the RBM makes it an ideal tool for assisting the nuclear theory community in providing reliable estimates with properly quantified uncertainties of physical observables. Such uncertainty quantification tools will become essential given the expected abundance of data from the recently inaugurated and future experimental and observational facilities.
more »
« less
- Award ID(s):
- 2004601
- NSF-PAR ID:
- 10433202
- Date Published:
- Journal Name:
- Frontiers in Physics
- Volume:
- 10
- ISSN:
- 2296-424X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
e. With recent advances in online sensing technology and high-performance computing, structural health monitoring (SHM) has begun to emerge as an automated approach to the real-time conditional monitoring of civil infrastructure. Ideal SHM strategies detect and characterize damage by leveraging measured response data to update physics-based finite element models (FEMs). When monitoring composite structures, such as reinforced concrete (RC) bridges, the reliability of FEM based SHM is adversely affected by material, boundary, geometric, and other model uncertainties. Civil engineering researchers have adapted popular artificial intelligence (AI) techniques to overcome these limitations, as AI has an innate ability to solve complex and ill-defined problems by leveraging advanced machine learning techniques to rapidly analyze experimental data. In this vein, this study employs a novel Bayesian estimation technique to update a coupled vehicle-bridge FEM for the purposes of SHM. Unlike existing AI based techniques, the proposed approach makes intelligent use of an embedded FEM model, thus reducing the parameter space while simultaneously guiding the Bayesian model via physics-based principles. To validate the method, bridge response data is generated from the vehicle-bridge FEM given a set of “true” parameters and the bias and standard deviation of the parameter estimates are analyzed. Additionally, the mean parameter estimates are used to solve the FEM model and the results are compared against the results obtained for “true” parameter values. A sensitivity study is also conducted to demonstrate methods for properly formulating model spaces to improve the Bayesian estimation routine. The study concludes with a discussion highlighting factors that need to be considered when leveraging experimental data to update FEMs of concrete structures using AI techniques.more » « less
-
Abstract The next generation of advanced combustion devices is being developed to operate under ultra-high-pressure conditions. However, under such extreme conditions, flame tends to become unstable and measurement of fundamental properties such as the laminar flame speed becomes challenging. One potential method to resolve this issue is measuring the ignition-affected region during spherically expanding flame experiments. The flame in this region is more resistant to perturbations and remains smooth due to the high stretch rates (i.e. small radii). Stable flame propagation allows for improved flame measurement, however, the experimentally observed kernel propagation is a function of both inflammation and ignition plasma. Therefore, the goal of the present study is to better understand the plasma formation and propagation during the ignition process, which would allow for reliable laminar flame speed measurements. To accomplish this goal, thermal plasma operating at high pressures is studied with emphasis on the spark energy effects on the formation of the ignition kernel. The thermal effect of the plasma is experimentally observed using a high-speed Schlieren imaging system. The energy dissipated within the plasma is measured with the use of voltage and current probes with a measurement of plasma sheath voltage drop as an input to numerical modeling. The measured kernel propagation rate is used to assess the accuracy of the model. The experiments and modeling are conducted in dry air at 1, 3, and 5 atm as well as in CH 4 -N 2 mixtures at 1 atm, and kernel radius, temperature, and mass are reported. The voltage-drop (as a non-thermal loss) is measured to be approximately 330 ± 5 V (dry air at 1 atm) for glow plasma with a large dependency on pressure, gas composition, electrode surface quality, electrode geometry, electrode shape, and current density. The same loss within the arc plasma is measured to be 15 ± 5 V, however the arc phase loss which agrees with arc propagation is significantly higher (∼45 V) which suggest additional unaccounted for phenomena occurring during the arc phase. With these losses, the modeling results are shown to predict the final kernel radius within 10%–20% of the observed kernel size. The difference found between the modeling and experimental results is determined to be a result of assuming that the primary loss mechanism (voltage drop across sheath formation) remains constant for the duration of glow discharge. The discrepancy for arc discharge is discussed with several potential sources, however, additional studies are required to better understand how the arc formation affects the kernel propagation.more » « less
-
more » « less
Abstract Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson‐Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1‐BCC‐GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1‐BCC (Austin Model 1‐bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types,
on oi hn1 hn2 hn3 -
This work is devoted to deriving and implementing analytic second- and third-order energy derivatives with respect to the nuclear coordinates and external electric field within the framework of the hybrid quantum mechanics/molecular mechanics method with induced charges and dipoles (QM/DIM). Using these analytic energy derivatives, one can efficiently compute the harmonic vibrational frequencies, infrared (IR) and Raman scattering (RS) spectra of the molecule in the proximity of noble metal clusters/nanoparticles. The validity and accuracy of these analytic implementations are demonstrated by the comparison of results obtained by the finite-difference method and the analytic approaches and by the full QM and QM/DIM calculations. The complexes formed by pyridine and two sizes of gold clusters (Au18 and Au32) at varying intersystem distances of 3, 4, and 5 Å are used as the test systems, and Raman spectra of 4,4′-bipyridine in the proximity of Au2057 and Ag2057 metal nanoparticles (MNP) are calculated by the QM/DIM method and compared with experimental results as well. We find that the QM/DIM model can well reproduce the IR spectra obtained from full QM calculations for all the configurations, while although it properly enhances some of the vibrational modes, it artificially overestimates RS spectral intensities of several modes for the systems with very short intersystem distance. We show that this could be improved, however, by incorporating the hyperpolarizability of the gold metal cluster in the evaluation of RS intensities. Additionally, we address the potential impact of charge migration between the adsorbate and MNPs.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fphy.2022.1054524
