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At least 163 records · Page 9

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DOE OSTI2022

Search for Solar Flare Neutrinos with the KamLAND Detector

Abstract We report the result of a search for neutrinos in coincidence with solar flares from the GOES flare database. The search was performed on a 10.8 kton-year exposure of KamLAND collected from 2002 to 2019. This large exposure allows us to explore previously unconstrained parameter space for solar flare neutrinos. We found no statistical excess of neutrinos and established 90% confidence level upper limits of 8.4 × 10 7 cm −2 (3.0 × 10 9 cm −2 ) on the electron antineutrino (electron neutrino) fluence at 20 MeV normalized to the X12 flare, assuming that the neutrino fluence is proportional to the X-ray intensity.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Evolution of Main-sequence-like Surviving Companions in Type Ia Supernova Remnants

Recent theoretical and numerical studies of Type Ia supernovae (SNe Ia) explosions within the single-degenerate scenario suggest that the nondegenerate companions could survive during the supernova impact and could be detectable in nearby supernova remnants. However, observational efforts show less promising evidence of the existence of surviving companions from the standard single-degenerate channels. The spin-up/spin-down models are possible mechanisms to explain the nondetection of surviving companions. In these models, the spin-up phase could increase the critical mass for explosion, leading to a super-Chandrasekhar-mass explosion, and the spin-down phase could lead to extra mass loss and angular momentum redistribution. Since the spin-down timescale for the delayed explosion of a rotating white dwarf is unclear, in this paper we explore a vast parameter space of main-sequence-like surviving companions via two-dimensional hydrodynamic simulations of supernova impact and the subsequent stellar evolution of surviving companions. Tight universal relations to describe the mass-stripping effect, supernova kick, and depth of supernova heating are provided. Our results suggest that the not-yet-detected surviving companions from observations of nearby SN Ia remnants might favor low-mass companions, short binary separation, or stronger supernova explosion energies than the standard single-degenerate channels.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

BICEP/Keck. XVI. Characterizing Dust Polarization through Correlations with Neutral Hydrogen

We characterize Galactic dust filaments by correlating BICEP/Keck and Planck data with polarization templates based on neutral hydrogen (H i) observations. Dust polarization is important for both our understanding of astrophysical processes in the interstellar medium (ISM) and the search for primordial gravitational waves in the cosmic microwave background (CMB). In the diffuse ISM, H i is strongly correlated with the dust and partly organized into filaments that are aligned with the local magnetic field. We analyze the deep BICEP/Keck data at 95, 150, and 220 GHz, over the low-column-density region of sky where BICEP/Keck has set the best limits on primordial gravitational waves. We separate the H i emission into distinct velocity components and detect dust polarization correlated with the local Galactic H i but not with the H i associated with Magellanic Stream i. We present a robust, multifrequency detection of polarized dust emission correlated with the filamentary H i morphology template down to 95 GHz. For assessing its utility for foreground cleaning, we report that the Hi morphology template correlates in B modes at a ~10%–65% level over the multipole range 20 < ℓ < 200 with the BICEP/Keck maps, which contain contributions from dust, CMB, and noise components. We measure the spectral index of the filamentary dust component spectral energy distribution to be β = 1.54 ± 0.13. We find no evidence for decorrelation in this region between the filaments and the rest of the dust field or from the inclusion of dust associated with the intermediate velocity H i. Finally, we explore the morphological parameter space in the H i-based filamentary model.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

From Feast to Famine: A Systematic Study of Accretion onto Oblique Pulsars with 3D GRMHD Simulations

Disk-fed accretion onto neutron stars can power a wide range of astrophysical sources ranging from X-ray binaries, to accretion-powered millisecond pulsars, ultraluminous X-ray sources, and gamma-ray bursts. A crucial parameter controlling the gas–magnetosphere interaction is the strength of the stellar dipole. In addition, coherent X-ray pulsations in many neutron star systems indicate that the star's dipole moment is oblique relative to its rotation axis. Therefore, it is critical to systematically explore the 2D parameter space of the star's magnetic field strength and obliquity, which is what this work does, for the first time, in the framework of 3D general-relativistic magnetohydrodynamics. If the accretion disk carries its own vertical magnetic field, this introduces an additional factor: the relative polarity of the disk and stellar magnetic fields. We find that depending on the strength of the stellar dipole and the star–disk relative polarity, the neutron star's jet power can either increase or decrease with increasing obliquity. For weak dipole strength (equivalently, high accretion rate), the parallel polarity results in a positive correlation between jet power and obliquity, whereas the antiparallel orientation displays the opposite trend. For stronger dipoles, the relative-polarity effect disappears, and jet power always decreases with increasing obliquity. The influence of the relative polarity gradually disappears as obliquity increases. Highly oblique pulsars tend to have an increased magnetospheric radius, a lower mass accretion rate, and enter the propeller regime at lower magnetic moments than aligned stars.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1991

The origin of the moon and the single impact hypothesis. IV

The smoothed-particle hydrodynamics method developed to simulate the conditions under which a major planetary collision may have led to the formation of the moon is presently used in 41 simulations exploring the relevant parameter space. Two possible scenarios emerge: one in which the moon is formed directly in the collision or only after dissipation and evolution of the disk. Attention is given to the Giant Blowoff hypothesis, in which not only the primitive atmosphere but large amounts of mantle material flow over the surface of the orbiting disk and blow off in the forward direction. Large amounts of angular momentum can also be lost.

Cameron, A. G. W.↗

NASA NTRS1992

Multidisciplinary design optimization using response surface analysis

Aerospace conceptual vehicle design is a complex process which involves multidisciplinary studies of configuration and technology options considering many parameters at many values. NASA Langley's Vehicle Analysis Branch (VAB) has detailed computerized analysis capabilities in most of the key disciplines required by advanced vehicle design. Given a configuration, the capability exists to quickly determine its performance and lifecycle cost. The next step in vehicle design is to determine the best settings of design parameters that optimize the performance characteristics. Typical approach to design optimization is experience based, trial and error variation of many parameters one at a time where possible combinations usually number in the thousands. However, this approach can either lead to a very long and expensive design process or to a premature termination of the design process due to budget and/or schedule pressures. Furthermore, one variable at a time approach can not account for the interactions that occur among parts of systems and among disciplines. As a result, vehicle design may be far from optimal. Advanced multidisciplinary design optimization (MDO) methods are needed to direct the search in an efficient and intelligent manner in order to drastically reduce the number of candidate designs to be evaluated. The payoffs in terms of enhanced performance and reduced cost are significant. A literature review yields two such advanced MDO methods used in aerospace design optimization; Taguchi methods and response surface methods. Taguchi methods provide a systematic and efficient method for design optimization for performance and cost. However, response surface method (RSM) leads to a better, more accurate exploration of the parameter space and to estimated optimum conditions with a small expenditure on experimental data. These two methods are described.

Unal, Resit↗

NASA NTRS1995

Gas-phase chemistry in dense interstellar clouds including grain surface molecular depletion and desorption

We present time-dependent models of the chemical evolution of molecular clouds which include depletion of atoms and molecules onto grain surfaces and desorption, as well as gas-phase interactions. We have included three mechanisms to remove species from the grain mantles: thermal evaporation, cosmic-ray-induced heating, and photodesorption. A wide range of parameter space has been explored to examine the abundance of species present both on the grain mantles and in the gas phase as a function of both position in the cloud (visual extinction) and of evolutionary state (time). The dominant mechanism that removes molecules from the grain mantles is cosmic-ray desorption. At times greater than the depletion timescale, the abundances of some simple species agree with abundances observed in the cold dark cloud TMC-1. Even though cosmic-ray desorption preserves the gas-phase chemistry at late times, molecules do show significant depletions from the gas phase. Examination of the dependence of depletion as a function of density shows that when the density increases from 10(exp 3)/cc to 10(exp 5)/cc several species including HCO(+), HCN, and CN show gas-phase abundance reductions of over an order of magnitude. The CO: H2O ratio in the grain mantles for our standard model is on the order of 10:1, in reasonable agreement with observations of nonpolar CO ice features in rho Ophiuchus and Serpens. We have also examined the interdependence of CO depletion with the space density of molecular hydrogen and binding energy to the grain surface. We find that the observed depletion of CO in Taurus in inconsistent with CO bonding in an H2O rich mantle, in agreement with observations. We suggest that if interstellar grains consist of an outer layer of CO ice, then the binding energies for many species to the grain mantle may be lower than commonly used, and a significant portion of molecular material may be maintained in the gas phase.

Bergin, E. A.↗

NASA NTRS1995

The Dripping Handrail Model: Transient Chaos in Accretion Systems

We define and study a simple dynamical model for accretion systems, the "dripping handrail" (DHR). The time evolution of this spatially extended system is a mixture of periodic and apparently random (but actually deterministic) behavior. The nature of this mixture depends on the values of its physical parameters - the accretion rate, diffusion coefficient, and density threshold. The aperiodic component is a special kind of deterministic chaos called transient chaos. The model can simultaneously exhibit both the quasiperiodic oscillations (QPO) and very low frequency noise (VLFN) that characterize the power spectra of fluctuations of several classes of accretion systems in astronomy. For this reason, our model may be relevant to many such astrophysical systems, including binary stars with accretion onto a compact object - white dwarf, neutron star, or black hole - as well as active galactic nuclei. We describe the systematics of the DHR's temporal behavior, by exploring its physical parameter space using several diagnostics: power spectra, wavelet "scalegrams," and Lyapunov exponents. In addition, we note that for large accretion rates the DHR has periodic modes; the effective pulse shapes for these modes - evaluated by folding the time series at the known period - bear a resemblance to the similarly- determined shapes for some x-ray pulsars. The pulsing observed in some of these systems may be such periodic-mode accretion, and not due to pure rotation as in the standard pulsar model.

Young, Karl↗

NASA NTRS2004

Temporal Large-Eddy Simulation

In 1999, Stolz and Adams unveiled a subgrid-scale model for LES based upon approximately inverting (defiltering) the spatial grid-filter operator and termed .the approximate deconvolution model (ADM). Subsequently, the utility and accuracy of the ADM were demonstrated in a posteriori analyses of flows as diverse as incompressible plane-channel flow and supersonic compression-ramp flow. In a prelude to the current paper, a parameterized temporal ADM (TADM) was developed and demonstrated in both a priori and a posteriori analyses for forced, viscous Burger's flow. The development of a time-filtered variant of the ADM was motivated-primarily by the desire for a unifying theoretical and computational context to encompass direct numerical simulation (DNS), large-eddy simulation (LES), and Reynolds averaged Navier-Stokes simulation (RANS). The resultant methodology was termed temporal LES (TLES). To permit exploration of the parameter space, however, previous analyses of the TADM were restricted to Burger's flow, and it has remained to demonstrate the TADM and TLES methodology for three-dimensional flow. For several reasons, plane-channel flow presents an ideal test case for the TADM. Among these reasons, channel flow is anisotropic, yet it lends itself to highly efficient and accurate spectral numerical methods. Moreover, channel-flow has been investigated extensively by DNS, and a highly accurate data base of Moser et.al. exists. In the present paper, we develop a fully anisotropic TADM model and demonstrate its utility in simulating incompressible plane-channel flow at nominal values of Re(sub tau) = 180 and Re(sub tau) = 590 by the TLES method. The TADM model is shown to perform nearly as well as the ADM at equivalent resolution, thereby establishing TLES as a viable alternative to LES. Moreover, as the current model is suboptimal is some respects, there is considerable room to improve TLES.

Pruett, C. D.↗

NASA NTRS2010

Visualization of Global Sensitivity Analysis Results Based on a Combination of Linearly Dependent and Independent Directions

A useful technique for the validation and verification of complex flight systems is Monte Carlo Filtering -- a global sensitivity analysis that tries to find the inputs and ranges that are most likely to lead to a subset of the outputs. A thorough exploration of the parameter space for complex integrated systems may require thousands of experiments and hundreds of controlled and measured variables. Tools for analyzing this space often have limitations caused by the numerical problems associated with high dimensionality and caused by the assumption of independence of all of the dimensions. To combat both of these limitations, we propose a technique that uses a combination of the original variables with the derived variables obtained during a principal component analysis.

Davies, Misty D.↗

NASA NTRS2012

LEGION: Lightweight Expandable Group of Independently Operating Nodes

LEGION is a lightweight C-language software library that enables distributed asynchronous data processing with a loosely coupled set of compute nodes. Loosely coupled means that a node can offer itself in service to a larger task at any time and can withdraw itself from service at any time, provided it is not actively engaged in an assignment. The main program, i.e., the one attempting to solve the larger task, does not need to know up front which nodes will be available, how many nodes will be available, or at what times the nodes will be available, which is normally the case in a "volunteer computing" framework. The LEGION software accomplishes its goals by providing message-based, inter-process communication similar to MPI (message passing interface), but without the tight coupling requirements. The software is lightweight and easy to install as it is written in standard C with no exotic library dependencies. LEGION has been demonstrated in a challenging planetary science application in which a machine learning system is used in closed-loop fashion to efficiently explore the input parameter space of a complex numerical simulation. The machine learning system decides which jobs to run through the simulator; then, through LEGION calls, the system farms those jobs out to a collection of compute nodes, retrieves the job results as they become available, and updates a predictive model of how the simulator maps inputs to outputs. The machine learning system decides which new set of jobs would be most informative to run given the results so far; this basic loop is repeated until sufficient insight into the physical system modeled by the simulator is obtained.

Burl, Michael C.↗

NASA NTRS2015

Atmosphere Resource Recovery and Environmental Monitoring

Atmosphere Resource Recovery and Environmental Monitoring (ARREM) is a project focused on evolving existing and maturing emerging 'closed loop' atmosphere revitalization (AR) life support systems that produce clean, breathable air for crewmembers, and developing a suite of low mass, low power environmental monitors to detect and measure air- and waterborne constituents and contaminants. The objective is to improve reliability and efficiency, reduce mass and volume, and increase recovery of oxygen from carbon dioxide created by human metabolism from 43% to greater than 90%. The technology developments under ARREM are vital to extending human space missions from low-Earth orbit like the International Space Station to destinations deeper into space such as Mars where dependency on Earth for resupply of maintenance items and critical life support elements such as water and oxygen is not possible. The primary goal of the ARREM project is to demonstrate that systems meet the more stringent performance parameters for deep space exploration and are compatible with other systems within closed loop life support through a series of integrated tests performed in an environmental test chamber capable of simulating human metabolic activities and measuring systems outputs.

Roman, Monsi↗

NASA NTRSDate not supplied

Quantifying Emergent Fluid Dynamics Using Reynolds-Interpolated Fluid Reduced-order Models

Fluid reduced-order models (ROMs) which capture the flow physics within the problem's physical domain are usually constrained in accuracy to only the parameter points, e.g. Reynolds and Mach numbers, at which reference data was provided. Interpolation-focused quantity-of-interest ROMs are often structured differently and fail to provide flow volume data with the same quality - if at all. In this paper, techniques which reside at the intersection of these two ROM schools - flow physics ROMs which can be interpolated within a parameter space of interest - are explored. Using a combination of existing and novel techniques, emergent physics are identified using a fluid ROM at parameter points which are not provided in the ROM's training data.

uncertainty quantification↗

NASA NTRSDate not supplied

Quantifying Emergent Fluid Dynamics Using Reynolds-Interpolated Fluid Reduced-order Models

uncertainty quantification↗

DOE OSTI2022

Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process

Abstract Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces to discover optimal experimental conditions and regions of interest in the image space or parameter space of computational models. The direct grid search of the parameter space tends to be extremely time-consuming, leading to the development of strategies balancing exploration of unknown parameter spaces and exploitation towards required performance metrics. However, classical Bayesian optimization (BO) strategies based on the Gaussian process (GP) do not readily allow for the incorporation of the known physical behaviors or past knowledge. Here we explore a hybrid optimization/exploration algorithm created by augmenting the standard GP with a structured probabilistic model of the expected system’s behavior. This approach balances the flexibility of the non-parametric GP approach with a rigid structure of physical knowledge encoded into the parametric model. The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of a standard univariate test function used to evaluate optimization algorithms and further extended to physical lattice models. This methodology is expected to be universally suitable for injecting prior knowledge in the form of physical models and past data in the BO framework.

42 ENGINEERING↗

DOE OSTI2025

Computationally efficient subglacial drainage modelling using Gaussian process emulators: GlaDS-GP v1.0

Subglacial drainage models represent water flow at the ice–bed interface through coupled distributed and channelized systems to determine water pressure, discharge, and drainage system geometry. While they are used to understand processes such as the relationship between surface melt and ice flow, the number of uncertain model parameters and the computational cost of running models makes it difficult to adequately explore the high-dimensional parameter space and evaluate uncertainty in model predictions. Here, we develop Gaussian process (GP) emulators that make fast predictions with associated uncertainty of subglacial drainage model outputs. Using a truncated principal component (PC) basis representation, we construct a GP emulator for diurnally averaged subglacial water pressure. We also explore emulation of scalar variables describing drainage efficiency and configuration. We train the emulators using ensembles of up to 512 simulations varying eight parameters of the Glacier Drainage System (GlaDS) model on a synthetic domain intended to represent an ice-sheet margin. The emulators make predictions ∼ 1000 times faster than GlaDS simulations, with errors <3 % for the water pressure field and ∼ 5 %–9 % for drainage efficiency and configuration. We apply the emulators to explore the eight-dimensional parameter space by computing variance-based parameter sensitivity indices, finding that three parameters (ice flow coefficient, bed bump aspect ratio, and the subglacial cavity system conductivity) explain 90 % of the variance in modelled water pressure in response to parameter changes. The GP emulator approach described here is well suited to integrating observational data with models to make calibrated, credible predictions of subglacial drainage.

58 GEOSCIENCES↗

DOE OSTI2021

Navigator function for the conformal bootstrap

Current numerical conformal bootstrap techniques carve out islands in theory space by repeatedly checking whether points are allowed or excluded. We propose a new method for searching theory space that replaces the binary information "allowed"/"excluded" with a continuous "navigator" function that is negative in the allowed region and positive in the excluded region. Such a navigator function allows one to efficiently explore high-dimensional parameter spaces and smoothly sail towards any islands they may contain. The specific functions we introduce have several attractive features: they are well-defined in large regions of parameter space, can be computed with standard methods, and evaluation of their gradient is immediate due to an SDP gradient formula that we provide. The latter property allows for the use of efficient quasi-Newton optimization methods, which we illustrate by navigating towards the 3d Ising island.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. Here, the associated notebooks allow to reproduce the reported analyses and apply them to other systems.

97 MATHEMATICS AND COMPUTING↗