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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 sources NASA NTRS DOE OSTI

DOE OSTI2022

Deterministic and Monte Carlo Nuclear Data Adjustment Methods [Slides]

For the Bayesian Monte Carlo methodology, a need to understand convergence of the posterior moments as a function of the number of parameter realizations is required. In high-dimensional systems, it can be very costly to sample entire parameter space and perform functional evaluation for every realization. Bayesian Monte Carlo allows one to relax the GLLS approximations of model linearity and prior/posterior PDF shape. The Bayesian Stochastic Collocation Method is a deterministic approach to “sample” the parameter space. It allows one to relax the GLLS approximations of model linearity and posterior PDF shape. Higher-order posterior moments (i.e., skewness, kurtosis, etc.) can be studied through polynomial expansion. Tensor product quadrature scales poorly and can use sparse grid quadrature methods.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1973

A suboptimal stochastic controller for an N-body spacecraft

Considerable attention, in the open literature, is being focused on the problem of developing a suitable set of deterministic dynamical equations for a complex spacecraft. This paper considers the problem of determining a stochastic optimal controller for an n-body spacecraft. The approach used in obtaining the stochastic controller involves the application, interpretation, and combination of advanced dynamical principles and the theoretical aspects of modern control theory. The stochastic controller obtained herein for a complicated model of a spacecraft uses sensor angular measurements associated with the base body to obtain smoothed estimates of the entire state vector. It can be easily implemented, and it enables system performance to be significantly improved.

Larson, V.↗

NASA NTRS1986

Effects of structural error on the estimates of parameters of dynamical systems

In this paper, the notion of 'near-equivalence in probability' is introduced for identifying a system in the presence of several error sources. Following some basic definitions, necessary and sufficient conditions for the identifiability of parameters are given. The effects of structural error on the parameter estimates for both the deterministic and stochastic cases are considered.

Hadaegh, F. Y.↗

DOE OSTI2022

Fracton Hydrodynamics without Time-Reversal Symmetry

Here, we present an effective field theory for the nonlinear fluctuating hydrodynamics of a single conserved charge with or without time-reversal symmetry, based on the Martin-Siggia-Rose formalism. Applying this formalism to fluids with only charge and multipole conservation, and with broken time-reversal symmetry, we predict infinitely many new dynamical universality classes, including some with arbitrarily large upper critical dimensions. Using large scale simulations of classical Markov chains, we find numerical evidence for a breakdown of hydrodynamics in quadrupole-conserving models with broken time-reversal symmetry in one spatial dimension. Our framework can be applied to the hydrodynamics around stationary states of open systems, broadening the applicability of previously developed ideas and methods to a wide range of systems in driven and active matter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1991

Probabilistic Structural Analysis Methods (PSAM) for select space propulsion system components, part 2

The technical effort and computer code enhancements performed during the sixth year of the Probabilistic Structural Analysis Methods program are summarized. Various capabilities are described to probabilistically combine structural response and structural resistance to compute component reliability. A library of structural resistance models is implemented in the Numerical Evaluations of Stochastic Structures Under Stress (NESSUS) code that included fatigue, fracture, creep, multi-factor interaction, and other important effects. In addition, a user interface was developed for user-defined resistance models. An accurate and efficient reliability method was developed and was successfully implemented in the NESSUS code to compute component reliability based on user-selected response and resistance models. A risk module was developed to compute component risk with respect to cost, performance, or user-defined criteria. The new component risk assessment capabilities were validated and demonstrated using several examples. Various supporting methodologies were also developed in support of component risk assessment.

Source record↗

NASA NTRS1992

A probabilistic fracture mechanics approach for structural reliability assessment of space flight systems

A probabilistic fracture mechanics approach for predicting the failure life distribution due to subcritical crack growth is presented. A state-of-the-art crack propagation method is used in a Monte Carlo simulation to generate a distribution of failure lives. The crack growth failure model expresses failure life as a function of stochastic parameters including environment, loads, material properties, geometry, and model specification errors. A stochastic crack growth rate model that considers the uncertainties due to scatter in the data and mode misspecification is proposed. The rationale for choosing a particular type of probability distribution for each stochastic input parameter and for specifying the distribution parameters is presented. The approach is demonstrated through a probabilistic crack growth failure analysis of a welded tube in the Space Shuttle Main Engine. A discussion of the results from this application of the methodology is given.

Sutharshana, S.↗

DOE OSTI2020

Evidence of Skewness and Sub-Gaussian Character in Temperature-Dependent Distributions of One Million Electronic Excitation Energies in PbS Quantum Dots

Obtaining statistical distributions by sampling a large number of conformations is vital for an accurate description of temperature-dependent properties of chemical systems. However, constructing distributions with 105??106 samples is computationally challenging because of the prohibitively high computational cost of performing rst-principles quantum-mechanical calculations. In this work, we present a new technique called the Eective Stochastic Potential Conguration Interaction Singles (ESP-CIS) method to obtain excitation energies. The ESP-CIS method uses random matrix theory for the construction of an eective stochastic representation of the Fock operator and combines it with linear-response theory. Here, we perform excitation calculations on PbS quantum dots (0.75-1.75 nm) at temperatures of 200-400 K. Results from a total of 15 million excitation energy calculations (1 million samples for each of the three dots at ve dierent temperatures) revealed the distributions to be sub-Gaussian in nature with negative skewness. The distributions progressively became red-shifted with increasing temperature. The study shows that random distortions in structure from the minimum-energy geometry of these quantum dots are more likely to lower the excitation energies.

Scher, Jeremy A.↗

DOE OSTI2025

Testing convolutional neural network based deep learning systems: a statistical metamorphic approach

Machine learning technology spans many areas and today plays a significant role in addressing a wide range of problems in critical domains,i.e., healthcare, autonomous driving, finance, manufacturing, cybersecurity,etc. Metamorphic testing (MT) is considered a simple but very powerful approach in testing such computationally complex systems for which either an oracle is not available or is available but difficult to apply. Conventional metamorphic testing techniques have certain limitations in verifying deep learning-based models (i.e., convolutional neural networks (CNNs)) that have a stochastic nature (because of randomly initializing the network weights) in their training. In this article, we attempt to address this problem by using a statistical metamorphic testing (SMT) technique that does not require software testers to worry about fixing the random seeds (to get deterministic results) to verify the metamorphic relations (MRs). We propose seven MRs combined with different statistical methods to statistically verify whether the program under test adheres to the relation(s) specified in the MR(s). We further use mutation testing techniques to show the usefulness of the proposed approach in the healthcare space and test two CNN-based deep learning models (used for pneumonia detection among patients). The empirical results show that our proposed approach uncovers 85.71% of the implementation faults in the classifiers under test (CUT). Furthermore, we also propose an MRs minimization algorithm for the CUT, thus saving computational costs and organizational testing resources.

Computer Science↗

NASA NTRS1990

A probabilistic approach to the dynamic analysis of ducts subjected to multibase harmonic and random excitation

The dynamic behavior of the discharge duct of the high-pressure oxidizer turbopump of a cryogenic rocket motor is investigated analytically. The probabilistic analysis program NESSUS (Numerical Evaluation of Stochastic Structures Under Stress; Cruse et al., 1988) is used to treat the uncertainties due to random and harmonic excitation (e.g., pump noise, pump-induced harmonics, and combustion noise), variations in engine inlet pressure, and changes in system damping. The load modeling procedure, the variation in power-spectral density in different zones of the engine structure, and the dynamic structural-analysis technique are described, and the numerical results of the NESSUS analysis are presented in extensive tables and graphs and discussed in detail.

Debchaudhury, Amit↗

NASA NTRS1993

The quasi-periodic oscillations and very low frequency noise of Scorpius X-1 as transient chaos - A dripping handrail?

We present evidence that the quasi-periodic oscillations (QPO) and very low frequency noise (VLFN) characteristic of many accretion sources are different aspects of the same physical process. We analyzed a long, high time resolution EXOSAT observation of the low-mass X-ray binary (LMXB) Sco X-1. The X-ray luminosity varies stochastically on time scales from milliseconds to hours. The nature of this variability - as quantified with both power spectrum analysis and a new wavelet technique, the scalegram - agrees well with the dripping handrail accretion model, a simple dynamical system which exhibits transient chaos. In this model both the QPO and VLFN are produced by radiation from blobs with a wide size distribution, resulting from accretion and subsequent diffusion of hot gas, the density of which is limited by an unspecified instability to lie below a threshold.

Scargle, Jeffrey D.↗

DOE OSTI2023

Nucleation-mediated reshaping of facetted metallic nanocrystals: Breakdown of the classical free energy picture

Shape stability is key to avoiding degradation of performance for metallic nanocrystals synthesized with facetted non-equilibrium shapes to optimize properties for catalysis, plasmonics, and so on. Reshaping of facetted nanocrystals is controlled by the surface diffusion-mediated nucleation and growth of new outer layers of atoms. Here, Kinetic Monte Carlo (KMC) simulation of a realistic stochastic atomistic-level model is applied to precisely track the reshaping of Pd octahedra and nanocubes. Unexpectedly, separate constrained equilibrium Monte Carlo analysis of the free energy profile during reshaping reveals a fundamental failure of the classical nucleation theory (CNT) prediction for the reshaping barrier and rate. Why? Nucleation barriers can be relatively low for these processes, so the system is not locally equilibrated before crossing the barrier, as assumed in CNT. This claim is supported by an analysis of a first-passage problem for reshaping within a master equation framework for the model that reasonably captures the behavior in KMC simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1994

Stochastic Star Identification

An approach to star identification based on comparing observed pattern statistics with the precomputed star catalogued statistics is suggested.

spacecraft↗

DOE OSTI2020

Adaptive Recovery Model: Designing Systems for Testing Tracing and Vaccination to Support COVID-19 Recovery Planning.

This report documents a new approach to designing disease control policies that allocate scarce testing, contact tracing, and vaccination resources to better control community transmission of COVID19 or similar diseases. The Adaptive Recovery Model (ARM) combines a deterministic compartmental disease model with a stochastic network disease propagation model to enable us to simulate COVID-19 community spread through the lens of two complementary modeling motifs. ARM contact networks are derived from cell-phone location data that have been anonymized and interpreted as individual arrivals to specic public locations. Modeling disease spread over these networks allows us to identify locations within communities conducive to rapid disease spread. ARM applies this model- and data-derived abstractions of community transmission to evaluate the effectiveness of disease control measures including targeted social distancing, contact tracing, testing and vaccination. The architecture of ARM provides a unique capacity to help decision makers understand how best to deploy scarce testing, tracing and vaccination resources to minimize disease-spread potential in a community. This document details the novel mathematical formulations underlying ARM, presents a dynamical stability analysis of the deterministic model components, a sensitivity analysis of control parameters and network structure, and summarizes a process for deriving contact networks from cell-phone location data. An example use case steps through applying ARM to evaluate three targeted social distancing policies using Bernalillo County, New Mexico as an exemplar test locale. This step-by-step analysis demonstrates how ARM can be used to measure the relative performance of competing public health policies. Initial scenario tests of ARM shows that ARMs design focus on resource utilization rather than simple incidence prediction can provide decision makers with additional quantitative guidance for managing ongoing public health emergencies and planning future responses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence

Particle-laden turbulent flows subject to radiative heating are relevant in many applications, for example concentrated solar power receivers. Efficient and accurate simulations provide valuable insights and enable optimization of such systems. However, as there are many uncertainties inherent in such flows, uncertainty quantification is fundamental to improve the predictive capabilities of the numerical simulations. For large-scale, multi-physics problems exhibiting high-dimensional uncertainty, characterizing the stochastic solution presents a significant computational challenge as most strategies require a large number of high-fidelity solves. This requirement might result in an infeasible number of simulations when a typical converged high-fidelity simulation requires intensive computational resources. To reduce the cost of quantifying high-dimensional uncertainties, we investigate the application of a non-intrusive, bi-fidelity approximation to estimate statistics of quantities of interest associated with an irradiated particle-laden turbulent flow. This method exploits the low-rank structure of the solution to accelerate the stochastic sampling and approximation processes by means of cheaper-to-run, lower fidelity representations. Here, the application of this bi-fidelity approximation results in accurate estimates of the quantities of interest statistics, while requiring a small number of high-fidelity model evaluations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Fast ion confinement in the presence of core magnetic islands in Wendelstein 7-X

The effect of magnetic islands in the core region of Wendelstein 7-X (W7-X) on fast ion confinement is explored through simulations with the BEAMS3D code. A magnetic configuration where the n/m = 5/5 island chain is shifted to r/a ~ 0.7 allows the exploration of core island physics in W7-X. The control coil system on W7-X allows the tuning of the island size either increasing the island width or decreasing it. A coupling of the BEAMS3D code to the FIELDLINES code provides a versatile mechanism for incorporating magnetic islands and stochastic regions into the BEAMS3D code. Collisionless simulations suggest that the presence of core islands degrade the confinement of passing particles in the region of the island chain. Full neutral beam simulations of W7-X show a similar behavior with confinement decreasing as the island width is increased. Comparisons between a vacuum magnetic field and low beta HINT2 simulation are made showing similar fast ion behavior. Measurements of lost fast ions in W7-X confirm this trend with the control coil suppressed island configuration showing lower losses than that with no control coils applied. Simulations of fast ion wall loads are performed suggesting no drastic change in loss pattern and a slight reduction in losses with minimized islands.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Mitigating the Impacts of Uncertain Geomagnetic Disturbances on Electric Grids: A Distributionally Robust Optimization Approach

Severe geomagnetic disturbances (GMDs) increase the magnitude of the electric field on the Earth’s surface (E-field) and drive geomagnetically-induced currents (GICs) along the transmission lines in electric grids. These additional currents can pose severe risks, such as current distortions, transformer saturation and increased reactive power losses, each of which can lead to system unreliability. Today several mitigation actions (e.g., changing grid topology) exist that can reduce the harmful GIC effects on the grids. Making such decisions can be challenging, however, because the magnitude and direction of the E-field are uncertain and non-stationary. In this paper, we model uncertain E-fields using the distributionally robust optimization (DRO) approach that determines optimal transmission grid operations such that the worst-case expectation of the system cost is minimized. We also capture the effect of GICs on the nonlinear AC power flow equations. For solution approaches, we develop an accelerated column-and-constraint generation (CCG) algorithm by exploiting a special structure of the support set of uncertain parameters representing the E-field. Extensive numerical experiments based on “epri-21” and “uiuc-150” systems, designed for GMD studies, demonstrate (i) the computational performance of the accelerated CCG algorithm, (ii) the superior performance of distributionally robust grid operations that satisfy nonlinear, nonconvex AC power flow equations and GIC constraints, in comparison with standard stochastic programming-based methods during the out-of-sample testing.

42 ENGINEERING↗

DOE OSTI2022

The WRF-Solar Ensemble Prediction System: Development, Test, and Validation

Providing reliable probabilistic solar radiation information is needed to improve management of the uncertainty and variability of solar generation. Thus, guidance on how to develop skillful and accurate ensemble forecasts is essential and it will ultimately contribute to integration of high amounts of solar energy on the grid. A team from the National Renewable Energy Laboratory and the National Center for Atmospheric Research had been collaborating to develop the WRF-Solar ensemble prediction system (WRF-Solar EPS) in the past three years to produce probabilistic solar irradiance forecasts and better predict solar energy by quantifying forecast uncertainty. The WRF-Solar EPS basically generates ensemble members for solar irradiance based on stochastic perturbations to provide intraday and day-ahead probabilistic forecasts. This study will present main research steps in developing the WRF-Solar EPS including: (a) tangent linear analysis for identifying key input variables of six WRF-Solar modules significantly related to predicting of cloud and solar irradiance, (b) combining stochastic perturbation technique with the WRF-Solar model, and (c) ensemble calibration method to decrease error and uncertainty of ensemble-based solar forecasts. The capability of WRF-Solar EPS is now updated to the most recent version of standard WRF model. This presentation will summarize comprehensive results from the evaluation of forecasts against the National Solar Radiation Data Base as well as ground-measured observations. Moreover, we will introduce the user's guide for WRF-Solar EPS (e.g., parameters to configure stochastic perturbations) and future extension of this research.

day-ahead forecast↗

DOE OSTI2022

Insensitivity of a turbulent laser-plasma dynamo to initial conditions

It has recently been demonstrated experimentally that a turbulent plasma created by the collision of two inhomogeneous, asymmetric, weakly magnetized, laser-produced plasma jets can generate strong stochastic magnetic fields via the small-scale turbulent dynamo mechanism, provided the magnetic Reynolds number of the plasma is sufficiently large. In this paper, we compare such a plasma with one arising from two pre-magnetized plasma jets whose creation is identical save for the addition of a strong external magnetic field imposed by a pulsed magnetic field generator. We investigate the differences between the two turbulent systems using a Thomson-scattering diagnostic, x-ray self-emission imaging, and proton radiography. The Thomson-scattering spectra and x-ray images suggest that the external magnetic field has a limited effect on the plasma dynamics in the experiment. Although the external magnetic field induces collimation of the flows in the colliding plasma jets and although the initial strengths of the magnetic fields arising from the interaction between the colliding jets are significantly larger as a result of the external field, the energies and morphologies of the stochastic magnetic fields post-amplification are indistinguishable. We conclude that, for turbulent laser-plasmas with supercritical magnetic Reynolds numbers, the dynamo-amplified magnetic fields are determined by the turbulent dynamics rather than the seed fields or modest changes in the initial flow dynamics of the plasma, a finding consistent with theoretical expectations and simulations of turbulent dynamos.

47 OTHER INSTRUMENTATION↗