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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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At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

GLOBULAR CLUSTER FORMATION EFFICIENCIES FROM BLACK HOLE X-RAY BINARY FEEDBACK

We investigate a scenario in which feedback from black hole X-ray binaries (BHXBs) sometimes begins inside young star clusters before strong supernova (SN) feedback. Those BHXBs could reduce the gas fraction inside embedded young clusters while maintaining virial equilibrium, which may help globular clusters (GCs) to stay bound when SN-driven gas ejection subsequently occurs. Adopting a simple toy model with parameters guided by BHXB population models, we produce GC formation efficiencies consistent with empirically inferred values. The metallicity dependence of BHXB formation could naturally explain why GC formation efficiency is higher at lower metallicity. For reasonable assumptions about that metallicity dependence, our toy model can produce a GC metallicity bimodality in some galaxies without a bimodality in the field-star metallicity distribution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

AGES OF MASSIVE GALAXIES AT 0.5 > z > 2.0 FROM 3D-HST REST-FRAME OPTICAL SPECTROSCOPY

We present low-resolution near-infrared stacked spectra from the 3D–HST survey up to z = 2.0 and fit them with commonly used stellar population synthesis models: BC03, FSPS10 (Flexible Stellar Population Synthesis), and FSPS-C3K. The accuracy of the grism redshifts allows the unambiguous detection of many emission and absorption features and thus a first systematic exploration of the rest-frame optical spectra of galaxies up to z = 2. We select massive galaxies (log(M{sub ∗}/M{sub ⊙})>10.8), we divide them into quiescent and star-forming via a rest-frame color–color technique, and we median-stack the samples in three redshift bins between z = 0.5 and z = 2.0. We find that stellar population models fit the observations well at wavelengths below the 6500 Å rest frame, but show systematic residuals at redder wavelengths. The FSPS-C3K model generally provides the best fits (evaluated with χ {sup 2} {sub red} statistics) for quiescent galaxies, while BC03 performs the best for star-forming galaxies. The stellar ages of quiescent galaxies implied by the models, assuming solar metallicity, vary from 4 Gyr at z ∼ 0.75 to 1.5 Gyr at z ∼ 1.75, with an uncertainty of a factor of two caused by the unknown metallicity. On average, the stellar ages are half the age of the universe at these redshifts. We show that the inferred evolution of ages of quiescent galaxies is in agreement with fundamental plane measurements, assuming an 8 Gyr age for local galaxies. For star-forming galaxies, the inferred ages depend strongly on the stellar population model and the shape of the assumed star-formation history.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2016

Modeling Antarctic Subglacial Lake Filling and Drainage Cycles

The growth and drainage of active subglacial lakes in Antarctica has previously been inferred from analysis of ice surface altimetry data. We use a subglacial hydrology model applied to a synthetic Antarctic ice stream to examine internal controls on the filling and drainage of subglacial lakes. Our model outputs suggest that the highly constricted subglacial environment of our idealized ice stream, combined with relatively high rates of water flow funneled from a large catchment, can combine to create a system exhibiting slow-moving pressure waves. Over a period of years, the accumulation of water in the ice stream onset region results in a buildup of pressure creating temporary channels, which then evacuate the excess water. This increased flux of water beneath the ice stream drives lake growth. As the water body builds up, it steepens the hydraulic gradient out of the overdeepened lake basin and allows greater flux. Eventually this flux is large enough to melt channels that cause the lake to drain. Lake drainage also depends on the internal hydrological development in the wider system and therefore does not directly correspond to a particular water volume or depth. This creates a highly temporally and spatially variable system, which is of interest for assessing the importance of subglacial lakes in ice stream hydrology and dynamics.

drainage↗
NASA NTRS1986

The Comet Giacobini-Zinner magnetotail: Axial stresses and inferred near-nucleus properties

Utilizing the electron and magnetic field data from the ICE tail traversal of comet Giacobini-Zinner along with the MHD equations, a steady state, stress balance model of the cometary magnetotail was developed, and used to infer important but unmeasured ion properties within the magnetotail at ICE and upstream at the average point along each streamline where cometary ions are picked-up. The derived tailward ion flow speed at ICE is quite constant at approx. -20 to -30 km/sec across the entire tail. The flow velocity, ion temperature, density, and ion source rates upstream from the lobes (current sheet) at the average pick-up locations are approx. -75 km/sec (approx. -12), approx. 4 million K (approx. 100,000), approx. 20 cc (approx. 400), and approx. 15 cu cm/sec. Gradients in the plasma properties between the two regions are quite strong. Implications of inferred plasma properties for the near-nucleus region and for cometary magnetotail formation are examined.

Mccomas, D. J.↗
DOE OSTI2024

Masses of Sunyaev-Zel’dovich galaxy clusters detected by the Atacama Cosmology Telescope: Stacked lensing measurements with Subaru HSC year 3 data

We present a stacked lensing analysis of 96 galaxy clusters selected by the thermal Sunyaev-Zel’dovich (SZ) effect in maps of the cosmic microwave background (CMB). We select foreground galaxy clusters with a 5 σ -level SZ threshold in CMB observations from the Atacama Cosmology Telescope, while we define background source galaxies for the lensing analysis with secure photometric redshift cuts in Year 3 data of the Subaru Hyper Suprime Cam survey. We detect the stacked lensing signal in the range of 0.1 < R [ h - 1 Mpc ] < 100 in each of three cluster redshift bins, 0.092 < z ≤ 0.445 , 0.445 < z ≤ 0.695 , and 0.695 < z ≤ 1.180 , with 32 galaxy clusters in each bin. The cumulative signal-to-noise ratios of the lensing signal are 14.6, 12.0, and 6.6, respectively. Using a halo-based forward model, we then constrain statistical relationships between the mass inferred from the SZ observation (i.e. SZ mass) and the total mass derived from our stacked lensing measurements. At the average SZ mass in the cluster sample ( 2.1 - 2.4 × 10 14 h - 1 M ⊙ ), our likelihood analysis shows that the average total mass differs from the SZ counterpart by a factor of 1.3 ± 0.2 , 1.6 ± 0.2 , and 1.6 ± 0.3 (68%) in the aforementioned redshift ranges, respectively. Our limits are consistent with previous lensing measurements, and we find that the cluster modeling choices can introduce a 1 σ -level difference in our parameter inferences.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1996

A Comparison of Measurements from ATMOS and Instruments Aboard the ER-2 Aircraft: Halogenated Gases

We compare volume mixing ratio profiles of N2O, CFC-11, CFC-12, CCl4, SF6, and HCl in the mid-latitude lower stratosphere measured by the ATMOS Fourier transform spectrometer on the ATLAS-3 Space Shuttle Mission with in situ measurements acquired from the NASA ER-2 aircraft during Nov. 1994. Good agreement is found between ATMOS and in situ correlations of [CFC-11], [CFC-12], and [SF6] with [N2O]. ATMOS measurements of [CCl4] are 15% high compared to ER-2 data, but agree within the systematic uncertainties. ATMOS observations of [HCl] vs [N2O] are within approximately 10% of ER-2 data for [HCl] > 1 ppbv, but exceed in situ measurements by larger fractional amounts for smaller [HCl]. ATMOS measurements of [ClONO2] agree well with values inferred from in situ observations of [ClO], [NO], and [O3]. The sum of [HCl] and [ClONO2] observed by ATMOS, supplemented by a minor contribution from [ClO] estimated with a photochemical model, is consistent with the levels of inorganic chlorine inferred from in situ measurements of chlorine source gases.

Chang, A. Y.↗
NASA NTRS2024

Masses of Sunyaev-Zel’dovich Galaxy Clusters Detected by the Atacama Cosmology Telescope: Stacked Lensing Measurements with Subaru HSC Year 3 Data

We present a stacked lensing analysis of 96 galaxy clusters selected by the thermal Sunyaev-Zel’dovich (SZ) effect in maps of the cosmic microwave background (CMB). We select foreground galaxy clusters with a 5σ-level SZ threshold in CMB observations from the Atacama Cosmology Telescope, while we define background source galaxies for the lensing analysis with secure photometric redshift cuts in Year 3 data of the Subaru Hyper Suprime Cam survey. We detect the stacked lensing signal in the range of 0.1 < R[h −1 Mpc] < 100 in each of three cluster redshift bins, 0.092 < z ≤ 0.445, 0.445 < z ≤ 0.695, and 0.695 < z ≤ 1.180, with 32 galaxy clusters in each bin. The cumulative signal-to-noise ratios of the lensing signal are 14.6, 12.0, and 6.6, respectively. Using a halo-based forward model, we then constrain statistical relationships between the mass inferred from the SZ observation (i.e. SZ mass) and the total mass derived from our stacked lensing measurements. At the average SZ mass in the cluster sample (2.1 − 2.4 × 10 14 h −1 M⊙), our likelihood analysis shows that the average total mass differs from the SZ counterpart by a factor of 1.3 ± 0.2, 1.6 ± 0.2, and 1.6 ± 0.3 (68%) in the aforementioned redshift ranges, respectively. Our limits are consistent with previous lensing measurements, and we find that the cluster modeling choices can introduce a 1σ-level difference in our parameter inferences.

Masato Shirasaki↗
DOE OSTI2017

Phylodynamic Inference across Epidemic Scales

Within-host genetic diversity and large transmission bottlenecks confound phylodynamic inference of epidemiological dynamics. Conventional phylodynamic approaches assume that nodes in a time-scaled pathogen phylogeny correspond closely to the time of transmission between hosts that are ancestral to the sample. However, when hosts harbor diverse pathogen populations, node times can substantially pre-date infection times. Imperfect bottlenecks can cause lineages sampled in different individuals to coalesce in unexpected patterns. To address realistic violations of standard phylodynamic assumptions we developed a new inference approach based on a multi-scale coalescent model, accounting for nonlinear epidemiological dynamics, heterogeneous sampling through time, non-negligible genetic diversity of pathogens within hosts, and imperfect transmission bottlenecks. We apply this method to HIV-1 and Ebola virus (EBOV) outbreak sequence data, illustrating how and when conventional phylodynamic inference may give misleading results. Within-host diversity of HIV-1 causes substantial upwards bias in the number of infected hosts using conventional coalescent models, but estimates using the multi-scale model have greater consistency with reported number of diagnoses through time. In contrast, we find that within-host diversity of EBOV has little influence on estimated numbers of infected hosts or reproduction numbers, and estimates are highly consistent with the reported number of diagnoses through time. The multi-scale coalescent also enables estimation of within-host effective population size using single sequences from a random sample of patients. We find within-host population genetic diversity of HIV-1 p17 to be 2Nμ=0.012 (95% CI 0.0066–0.023), which is lower than estimates based on HIV envelope serial sequencing of individual patients.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1984

Friction- and mountain-torque estimates from global atmospheric data

Seasonal, zonal surface torques between the atmosphere and the earth are estimated and compared, using data from a number of independent sources. The mountain torque is computed both from surface pressure data and from isobaric height data. The friction torque is estimated from the oceanic stress data of Hellerman and Rosenstein. Results for the total torque are inferred from atmospheric angular momentum data. Finally, the globally integrated total torque is compared with astronomical observations of the earth's rotation rate. These comparisons help us to assess the quality of the different results. Zonal torques are also computed using results from a GFDL general circulation model of the atmosphere. A comparison with the corresponding results inferred from real data is presented and interpreted in terms of model accuracy.

Wahr, J. M.↗
DOE OSTI2022

Neural Ordinary Differential Equations for Nonlinear System Identification

Neural ordinary differential equations (NODE) have been recently proposed as a promising approach for nonlinear system identification tasks. In this work, we systematically compare their predictive performance with current state-of-the-art nonlinear and classical linear methods. In particular, we present a quantitative study comparing NODE's performance against neural state-space models and classical linear system identification methods and evaluate their inference speed and prediction performance on open-loop errors across eight different dynamical systems. The experiments show that NODEs can consistently improve the prediction accuracy by order of magnitude compared to benchmark methods. Besides improved accuracy, we also observed that NODEs are less sensitive to hyperparameters compared to neural state-space models by paying the cost of increased computation at the inference time.

machine leaning, system identification, physics in↗
DOE OSTI2024

Collapsar Gamma-Ray Bursts Grind Their Black Hole Spins to a Halt

Abstract The spin of a newly formed black hole (BH) at the center of a massive star evolves from its natal value due to two competing processes: accretion of gas angular momentum that increases the spin and extraction of BH angular momentum by outflows that decreases the spin. Ultimately, the final, equilibrium spin is set by a balance between both processes. In order for the BH to launch relativistic jets and power aγ-ray burst (GRB), the BH magnetic field needs to be dynamically important. Thus, we consider the case of a magnetically arrested disk (MAD) driving the spin evolution of the BH. By applying the semianalytic MAD BH spin evolution model of Lowell et al. to collapsars, we show that if the BH accretes ∼20% of its initial mass, its dimensionless spin inevitably reaches small values,a≲ 0.2. For such spins, and for mass accretion rates inferred from collapsar simulations, we show that our semianalytic model reproduces the energetics of typical GRB jets,L jet ∼ 10 50 erg s −1 . We show that our semianalytic model reproduces the nearly constant power of typical GRB jets. If the MAD onset is delayed, this allows powerful jets at the high end of the GRB luminosity distribution,L jet ∼ 10 52 erg s −1 , but the final spin remains low,a≲ 0.3. These results are consistent with the low spins inferred from gravitational wave detections of binary BH mergers. In a companion paper by Gottlieb et al., we use GRB observations to constrain the natal BH spin to bea≃ 0.2.

Astronomy & Astrophysics↗
DOE OSTI2019

Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models

Sensitivity analysis is a vital tool in numerical modeling to identify important parameters and processes that contribute to the overall uncertainty in model outputs. We developed a new sensitivity analysis method to quantify the relative importance of uncertain model processes that contain multiple uncertain parameters. The method is based on the concepts of Bayesian networks (BNs) to account for complex hierarchical uncertainty structure of a model system. We derived a new set of sensitivity indices using the methodology of variance-based global sensitivity analysis with the Bayesian inference. The framework is capable of representing the detailed uncertainty information of a complex model system using BNs and affords flexible grouping of different uncertain inputs given their characteristics and dependency structures. We have implemented the method on a real-world biogeochemical model at the groundwater-surface water interface within the Hanford Site’s 300 Area. The uncertainty sources of the model were first grouped into forcing scenario and three different processes based on our understanding of the complex system. The sensitivity analysis results indicate that both the reactive transport and groundwater flow processes are important sources of uncertainty for carbon-consumption predictions. Within the groundwater flow process, the structure of geological formations is more important than the permeability heterogeneity within a given geological formation. Our new sensitivity analysis framework based on BNs offers substantial flexibility for investigating the importance of combinations of interacting uncertainty sources in a hierarchical order, and it is expected to be applicable to a wide range of multi-physics models for complex systems.

Dai, Heng↗
DOE OSTI2023

Adaptive mitigation of time-varying quantum noise

Current quantum computers suffer from non-stationary noise channels with high error rates, which undermines their reliability and reproducibility. We propose a Bayesian inference based adaptive algorithm that can learn and mitigate quantum noise in response to changing channel conditions. Our study emphasizes the need for dynamic inference of critical channel parameters to improve program accuracy. We use the Dirichlet distribution to model the stochasticity of the Pauli channel. This allows us to perform Bayesian inference, which can improve the performance of probabilistic error cancellation (PEC) under time-varying noise. Our work demonstrates the importance of characterizing and mitigating temporal variations in quantum noise, which is crucial for developing more accurate and reliable quantum technologies. Our results demonstrate that Bayesian PEC can outperform non-adaptive approaches by a factor of 4.5x when measured using Hellinger distance from the ideal distribution.

Dasgupta, Samudra↗
NASA NTRS1972

Representations of the language recognition problem for a theorem prover

Two representations of the language recognition problem for a theorem prover in first order logic are presented and contrasted. One of the representations is based on the familiar method of generating sentential forms of the language, and the other is based on the Cocke parsing algorithm. An augmented theorem prover is described which permits recognition of recursive languages. The state-transformation method developed by Cordell Green to construct problem solutions in resolution-based systems can be used to obtain the parse tree. In particular, the end-order traversal of the parse tree is derived in one of the representations. An inference system, termed the cycle inference system, is defined which makes it possible for the theorem prover to model the method on which the representation is based. The general applicability of the cycle inference system to state space problems is discussed. Given an unsatisfiable set S, where each clause has at most one positive literal, it is shown that there exists an input proof. The clauses for the two representations satisfy these conditions, as do many state space problems.

Minker, J.↗
NASA NTRS2022

Searching for Quasi-periodic Oscillations in Astrophysical Transients Using Gaussian Processes

Analyses of quasi-periodic oscillations(QPOs)are important to understanding the dynamic behavior in manyastrophysical objects during transient events like gamma-ray bursts, solarflares, magnetarflares, and fast radiobursts. Astrophysicists often search for QPOs with frequency-domain methods such as(Lomb–Scargle)periodograms, which generally assume power-law models plus some excess around the QPO frequency. Time-series data can alternatively be investigated directly in the time domain using Gaussian process(GP)regression.While GP regression is computationally expensive in the general case, the properties of astrophysical data andmodels allow fast likelihood strategies. Heteroscedasticity and nonstationarity in data have been shown to causebias in periodogram-based analyses. GPs can take account of these properties. Using GPs, we model QPOs as astochastic process on top of a deterministicflare shape. Using Bayesian inference, we demonstrate how to infer GPhyperparameters and assign them physical meaning, such as the QPO frequency. We also perform model selectionbetween QPOs and alternative models such as red noise and show that this can be used to reliablyfind QPOs. Thismethod is easily applicable to a variety of different astrophysical data sets. We demonstrate the use of this methodon a range of short transients: a gamma-ray burst, a magnetarflare, a magnetar giantflare, and simulated solarflare data.

Moritz Hubner↗
DOE OSTI2024

Neural lumped parameter differential equations with application in friction-stir processing

Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a “lumped” element representative of the physical scales of the modeled system. For systems where the definition of a lumped element or its associated physics may be unknown, modeling tasks may be restricted to full-fidelity physics simulations. Here, in this work, we consider data-driven modeling tasks with limited point-wise measurements of otherwise continuous systems. We build upon the notion of the Universal Differential Equation (UDE) to construct data-driven models for reducing dynamics to that of a lumped parameter and inferring its properties. The flexibility of UDEs allow for composing various known physical priors suitable for application-specific modeling tasks, including lumped parameter methods. The motivating example for this work is the plunge and dwell stages for friction-stir welding; specifically, (i) mapping power input into the tool to a point-measurement of temperature and (ii) using this learned mapping for process control.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Neural networks for parameter estimation in intractable models

The goal is to use deep learning models to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible. For instance, inference for max-stable processes is exceptionally challenging even with small datasets, but simulation is straightforward. Data from model simulations are used to train deep neural networks and learn statistical parameters from max-stable models. The proposed neural network-based method provides a competitive alternative to current approaches, as demonstrated by considerable accuracy and computational time improvements. Finally, it serves as a proof of concept for deep learning in statistical parameter estimation and can be extended to other estimation problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON’s efficiency and potential in robust predictive modeling for large-scale Earth system computations.

Bai, Zhe↗