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Results for “Parametric bootstrap”

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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7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Iron-Chromium-Aluminum Accident Tolerant Fuel Concept Source Term Accident Sequence Analysis - High Burnup Fuel Source Term Accident Sequence Analysis Supplement

To extend NUREG-1465 and high burnup fuel source term (SAND2023-01313) recommendations, representative radiological releases to containment – patterned after NUREG-1465 – have been evaluated for LWRs utilizing iron-chromium-aluminum (FeCrAl) alloys in place of zirconium-based alloys in major core structures (cladding and fuel canisters) and high burnup fuel with enrichments of 8% and 10% for PWRs and BWRs, respectively. Representative radionuclide releases are generated for this accident tolerant fuel concept by applying non-parametric bootstrap methods to MELCOR simulation results. Accident scenarios considered in this analysis include principle contributors to historical core damage frequency estimates for a range of nuclear reactor technologies representative of the operating U.S.A. fleet of nuclear reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Cr-coated Accident Tolerant Fuel Concept Source Term Accident Sequence Analysis - High Burnup Fuel Source Term Accident Sequence Analysis Supplement

To extend NUREG-1465 and high burnup fuel source term (SAND2023-01313) recommendations, representative radiological releases to containment – patterned after NUREG-1465 – have been evaluated for LWRs utilizing the chromium-coating on major zircaloy structures (cladding and fuel canisters) and high burnup fuel with enrichments of 8% and 10% for PWRs and BWRs, respectively. Representative radionuclide releases are generated for this accident tolerant fuel concept by applying non-parametric bootstrap methods to MELCOR simulation results. Accident scenarios considered in this analysis include principle contributors to historical core damage frequency estimates for a range of nuclear reactor technologies representative of the operating U.S.A. fleet of nuclear reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Modeling memoryless degradation under variable stress

Accelerated degradation tests can be used as the basis for predicting the performance or state of health of products and materials at use conditions over time. Measurements acquired at accelerated levels of stress are used to develop models that relate to the degradation of one or more performance measures. Frequently, products/materials of interest are subjected to variable stress levels during their lifetimes. However, testing is usually performed only at a few fixed stress levels. In such cases, cumulative degradation models are developed and assessed by using data acquired under those fixed stress conditions. The degradation rate at any stress condition within the range of the model can be estimated by the derivative of the cumulative model at that stress condition. It follows that, to predict cumulative degradation over variable use conditions, one might integrate the fluctuating degradation rate over time. Existing approaches for doing this consider degradation rates that depend only on the current stress level. Here, we propose to allow the degradation rate to also depend on the current state of health as indicated by the associated performance measure(s). The resulting modeling approach is capable of portraying a broader range of degradation behavior than existing approaches. The assertion of memoryless degradation by using this or any other approach should be assessed experimentally with data acquired under variable stress in order to increase confidence that the integrated rate model is accurate. In this article, we demonstrate the additional capability of the proposed approach by developing empirical memoryless rate-based degradation models to predict resistance increase and capacity decrease in lithium-ion cells that are being evaluated for use in electric vehicles. Here, we then assess the plausibility of these models.

42 ENGINEERING↗
DOE OSTI2018

Handling Skewed Emission Distributions in Natural Gas Systems

This work was presented at the American Center for Life Cycle Assessment (ACLCA) LCAXVIII Conference in Fort Collins, CO on September 26, 2018. The presentation showed how NETL's LCA program has gradually improved its methods for uncertainty characterization for upstream natural gas and associated power systems. An objective of uncertainty characterization is to understand the role of system variability without introducing more uncertainty to the data. Non-parametric statistical bootstrapping is one technique for accomplishing this objective and is especially useful for skewed, irregularly-shaped probability distributions. Stochastic modeling is effective at retaining data dependencies, even if specific correlations are not identified.

Littlefield, James↗
DOE OSTI2025

Uniqueness criteria for the Virasoro-Shapiro amplitude

The scattering amplitudes of string theory exhibit many extraordinary properties. But are they the unique mathematical objects to do so? Recently, it has been shown how the spectrum and amplitudes of open string theory follow directly from the assumptions of faster than power-law falloff at high energies and a property dubbed level truncation. At present there is no analogous principle for closed string scattering, which is famously rigid and naively impervious to modification. In this paper we analytically bootstrap the spectrum and four-point amplitudes of the closed string—together with a parametrized space of deformations—from conditions on high-energy falloff and level truncation. While these deformations exhibit the same Regge scaling as pure gravity, in the tensionless limit they reproduce remarkable extremal amplitudes that have appeared in bottom-up studies of positivity.

Field & string theory models & techniques↗
DOE OSTI2024

Holography and Regge phases with U(1) charge

Abstract We use holography to study the large spinJlimit of the spectrum of low energy states with chargeQunder a U(1) conserved current in CFTs ind> 2 dimensions, with a focus ond= 3 andd= 4. ForQ= 2, the spectrum of such states is known to be universal and properly captured by the long-distance limit of holographic theories, regardless of whether the CFT itself is holographic. We study in detail the holographic description of such states atQ> 2, by considering the contribution to the energies ofQscalar particles coming from single photon and graviton exchange in the bulk of AdS; in some cases, scalar exchange and bulk contact terms are also included. For a range of finite values ofQandJ, we numerically diagonalize the Hamiltonian for such states and examine the resulting spectrum and wavefunctions as a function of the dimension ∆ of the charge-one operator and the central charges$$ {c}_{\mathcal{T}} $$ c T ,$$ {c}_{\mathcal{J}} $$ c J of the stress tensor and U(1) current, finding multiple regions in parameter space with qualitatively different behavior. We discuss the extension of these results to the regime of parametrically large chargeQ, as well as to what extent such results are expected to hold universally, beyond the limit of holographic CFTs. We compare our holographic computations to results from the conformal bootstrap for the 3dO(2) model atQ= 3 andQ= 4 and find excellent agreement.

Physics↗
DOE OSTI2024

Exploration with Scalable Gaussian Process Reinforcement Learning

Exploration is a challenging problem in reinforcement learning (RL), especially in environments with sparse rewards. Quantifying and utilizing the parametric uncertainty has been shown to be paramount for successful exploration [Osband et al., 2018]. Bayesian, or approximately Bayesian, methods present a principled means of estimating the parametric uncertainty in RL problems. Gaussian processes, nonparametric Bayesian models, are often impractical due to poor scalability and computational bottlenecks. We introduce a scalable Gaussian process RL (GPRL) method which directly induces sparsity in the covariance matrix to facilitate faster computation. This is a departure from previous GPRL methods which instead rely on data reduction and subsampling. We compare various covariance-based exploration techniques (Thompson sampling, upper confidence bound, and probabilistic maximum variance) which leverage our scalable GP framework in sparse reward environments. Finally, we show favorable comparison against the bootstrapped deep Q-Network.

97 MATHEMATICS AND COMPUTING↗