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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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CERN-LHC-ATLAS. Measurements of the inclusive and differential fiducial cross-sections for ttbar+photon production using leptonic channels in proton-proton collisions at a centre-of-mass energy of 13 TeV with 36.1 fb^-1 of data collected in 2015 and 2016.
CERN-LHC. Measurements of the absolute and fiducial differential cross-sections for single leptons and the dilepton system in ttbar production at sqrt(s)=13 TeV, decaying to the e-mu bbbar nnubar final state.
A dedicated sample of Large Hadron Collider proton-proton collision data at centre-of-mass energy s√=8 TeV is used to study inclusive single diffractive dissociation, pp→Xp. The intact final-state proton is reconstructed in the ATLAS ALFA forward spectrometer, while charged particles from the dissociated system X are measured in the central detector components. Cross sections are measured differentially as functions of the proton fractional energy loss ξ, the squared four-momentum transfer t, and Δη, a variable that characterises the rapidity gap separating the proton and the system X. The data are consistent with an exponential t dependence, dσ/dt∝eBt with slope parameter B=7.65±0.34 GeV−2. Interpreted in the framework of Regge phenomenology, the ξ dependence leads to a pomeron intercept of α(0)=1.07±0.09.
Absolute location of the announced Democratic People’s Republic of Korea (DPRK) nuclear test on 6 January 2016 is constrained by fitting associated, Interferometric Synthetic Aperture Radar (InSAR)-based ground displacement with elastic finite-element modeling of an underground explosion source, including the effects of topography. The other five announced nuclear tests are located using arrival times and differential arrival times of regional and teleseismic body waves and constraints on the 6 January 2016 event location. Nuclear tests on 6 January 2016, 9 September 2016, and 3 September 2017 are under the summit ridge of Mt. Mantap, and tests on 25 May 2009 and 12 February 2013 are south of the topographic crest under the steep southern face of the mountain. The first test on 9 October 2006 was located near the crest of a separate topographic ridge approximately 2.87 km east of the 6 January 2016 event. Several unannounced events have occurred in the vicinity of the test site. The location uncertainty ellipse of an event approximately 8 min 30 s after the 2017 announced test covers the DPRK test site and is likely to have occurred there. Finally, additional events on 27 September 2017 and 12 October 2017 are 4–8 km northwest of the test site, and location probability regions do not overlap the test site.
Modern particle accelerator optimization requires sophisticated computational methods to address the inherently stochastic nature of beam dynamics. This research develops a framework applying AD to SDEs that specifically addresses beam dynamics challenges in particle accelerators, focusing on accurately modeling and optimizing beam behavior in regimes dominated by stochastic processes. By incorporating key physical phenomena such as synchrotron radiation, wakefield effects, and quantum excitation, the framework aims to provide auto differentiation on the figure of merit of the phase space evolution and beam dynamics. The methodology will enable effective optimization method in a dynamic system with stochastic process.
We introduce the SmoQyDEAC.jl package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the SmoQyDEAC.jl package. It also provides detailed benchmarks of the package's output against the popular maximum entropy and stochastic analytic continuation methods.
The Brechtel Manufacturing Inc. (BMI) Humidified Tandem Differential Mobility Analyzer (HT-DMA Model 3002) (Brechtel and Kreidenweis 2000a,b, Henning et al. 2005, Xerxes et al. 2014) measures how aerosol particles of different initial dry sizes grow or shrink when exposed to changing relative humidity (RH) conditions. It uses two different mobility analyzers (DMA) and a humidification system to make the measurements. One DMA selects a narrow size range of dry aerosol particles, which are exposed to varying RH conditions in the humidification system. The second (humidified) DMA scans the particle size distribution output from the humidification system. Scanning a wide range of particle sizes enables the second DMA to measure changes in size or growth factor (growth factor = humidified size/dry size), due to water uptake by the particles. A Condensation Particle Counter (CPC) downstream of the second DMA counts particles as a function of selected size in order to obtain the number size distribution of particles exposed to different RH conditions.
In the initial LCP proposal it was expected that experiments on material differentiation would be performed as one of the tasks in FY14. Due to funding being lower than projected, these experiments were put on hold for FY14 but initial planning for future experiments was performed. In parallel with this task, Task 3 continued work on improving the capabilities for correlating muons with secondary particles such as gammas or neutrons. Potential facilities for future experiments were identified and ROM costs were determined. By establishing communications early, this will facilitate a more efficient movement into experimental facilities in the future. The primary component of the planning was to identify possible locations for the experiments along with ROM estimates for use of these facilities.
The rise of low-power neuromorphic hardware has the potential to change high-performance computing; however much of the focus on brain-inspired hardware has been on machine learning applications. A low-power solution for solving partial differential equations could radically change how we approach large-scale computing in the future. The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications. We consider here two neural algorithms that can be used to efficiently implement random walks on spiking neuromorphic hardware. The first method tracks the positions of individual walkers independently by using a modular code inspired by grid cells in the brain. The second method tracks the densities of random walkers at each spatial location directly. We present the scaling complexity of each of these methods and illustrate their ability to model random walkers under different probabilistic conditions. Finally, we present implementations of these algorithms on neuromorphic hardware.
The objectives of this project are to devise and analyze efficient solvers for several classes of PDE- constrained optimization problems. The project was initially focused on, but not restricted to time- dependent PDE-constraints such as parabolic equations, time-dependent fluid flows (Navier-Stokes equations), hyperbolic equations (Burgers equation, shallow water equations). Other constraints considered are PDEs with stochastic coefficients. Additional elements of interest are the handling of problems with control and/or state inequality constraints or non-differentiable regularization terms, and boundary controls. A direction of work not initially listed among the goals, which was triggered by conversations with DOE scientists from the Sandia National Labs, is to develop efficient solvers for optimization- based domain-decomposition PDE-solvers. Also targeted in the project are a number of technical issues necessary for facilitating the integration of the methods resulted from this project with existing high-performance packages developed at DOE national labs; this integration is expected to further enable the scientific community to use the novel methods. An example of such a task is the need to develop algebraic multigrid (AMG) versions of the algorithms. A list of accomplishments in several areas is detailed as well as a list of publications.
The National Center for Atmospheric Research (NCAR) and Montana State University (MSU) are nearing completion of a National Science Foundation (NSF) major research instrumentation grant to build five micropulse differential absorption lidars (MPDs) for water vapor profiling. The first demonstration of the MPD configured as a five-unit network occurred at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) field sites near Lamont, Oklahoma from 17 April–21 July 2019 for a total of 96 days of operation. The field deployment was funded in part by DOE (which provided support for the MPD installation, power at the field sites, personnel to launch 3-hourly radiosondes from the Central Facility, and the cost of the radiosondes themselves), internal NCAR funds (to support the MPD shipping costs and NCAR staff travel expenses), and NSF/Major Research Instrumentation (MRI) funds (to support the MSU travel expenses to participate in the instrument setup).
Modulated Differential Scanning Calorimetry (MDSC) is a thermoanalytical technique that detects changes in intrinsic heat flow by subjecting a material to two simultaneous heating rates, one linear and one sinusoidal. In this way, MDSC overcomes the limitations of its predecessor- conventional DSC-allowing for complex and overlapping thermal transitions to be resolved without sacrificing sensitivity. The advantages of MDSC lend credence to its extensive use in applications that require superior analytical accuracy, such as the food and drug industries.
The overall objectives of the proposal were to dissect the genetics of the ability of switchgrass to interact with arbuscular mycorrhizal fungi (AMF) and to tolerate subzero temperatures, and to investigate the effect of AMF colonization on freezing tolerance and biomass production. In order to achieve this, we generated genetic maps in an F2 mapping population derived from a cross between the lowland genotype AP13 and the upland genotype VS16. In addition to providing a framework for identification of quantitative trait loci (QTL), the genetic maps were used to validate and improve the switchgrass genome assembly. We mapped phenotypic quantitative trait loci (QTL) for AMF colonization and cold tolerance in the F 2 population. We demonstrated that identification of QTL for AMF colonization was achieved only if AMF genus-specific colonization levels were used as phenotypes. No QTL were obtained when the overall level of AMF colonization was used as a trait. We conducted RNASeq on a subset of the F2 progeny following cold acclimation, and developed statistical methods to conduct expression QTL (eQTL) analyses in a polyploid organism. A preliminary analysis of the results identified eQTL for a range of genes that were differentially regulated in the lowland AP13 compared to the upland VS16 under cold acclimation. Once sequencing of the transcriptomes of a temporal replicate of the mapping population that was cold-acclimated using the same conditions has been completed, a final eQTL analysis will be conducted.
This report summarizes work completed under the Laboratory Directed Research and Development (LDRD) project "Uncertainty Quantification of Geophysical Inversion Using Stochastic Differential Equations." Geophysical inversions often require computationally expensive algorithms to find even one solution, let alone propagating uncertainties through to the solution domain. The primary purpose of this project was to find more computationally efficient means to approximate solution uncertainty in geophysical inversions. We found multiple computationally efficient methods of propagating Earth model uncertainty into uncertainties in solutions of full waveform seismic moment tensor inversions. However, the optimum method of approximating the uncertainty in these seismic source solutions was to use the Karhunen-Love theorem with data misfit residuals. This method was orders of magnitude more computationally efficient than traditional Monte Carlo methods and yielded estimates of uncertainty that closely approximated those of Monte Carlo. We will summarize the various methods we evaluated for estimating uncertainty in seismic source inversions as well as work toward this goal in the realm of 3-D seismic tomographic inversion uncertainty.
Effect of image charges on reconstruction of focusing elements calibration with differential trajectory method is estimated and found negligible for measurements at the PIP2IT MEBT.
Modeling real-world phenomena to any degree of accuracy is a challenge that the scientific research community has navigated since its foundation. Lack of information and limited computational and observational resources necessitate modeling assumptions which, when invalid, lead to model-form error (MFE). The work reported herein explored a novel method to represent model-form uncertainty (MFU) that combines Bayesian statistics with the emerging field of universal differential equations (UDEs). The fundamental principle behind UDEs is simple: use known equational forms that govern a dynamical system when you have them; then incorporate data-driven approaches – in this case neural networks (NNs) – embedded within the governing equations to learn the interacting terms that were underrepresented. Utilizing epidemiology as our motivating exemplar, this report will highlight the challenges of modeling novel infectious diseases while introducing ways to incorporate NN approximations to MFE. Prior to embarking on a Bayesian calibration, we first explored methods to augment the standard (non-Bayesian) UDE training procedure to account for uncertainty and increase robustness of training. In addition, it is often the case that uncertainty in observations is significant; this may be due to randomness or lack of precision in the measurement process. This uncertainty typically manifests as “noisy” observations which deviate from a true underlying signal. To account for such variability, the NN approximation to MFE is endowed with a probabilistic representation and is updated using available observational data in a Bayesian framework. By representing the MFU explicitly and deploying an embedded, data-driven model, this approach enables an agile, expressive, and interpretable method for representing MFU. In this report we will provide evidence that Bayesian UDEs show promise as a novel framework for any science-based, data-driven MFU representation; while emphasizing that significant advances must be made in the calibration of Bayesian NNs to ensure a robust calibration procedure.
This presentation discusses the Bayesian Monte Carlo evaluation framework of differential and integral data, specifically in relation to two methods that are based on Bayes theorem, which assumes that all PDFs are normal (Gaussian) and that they use a linear approximation for IBEs. The presentation states that basic components of the MC evaluation framework include the computation of random MC ensemble from ENDF File 32, the simulation of IBEs and R-matrix cross section compared to experimental data, and the computation of weighted averages. It also states that the application to U-233 indicates deviation from the conventional linear approximation. In conclusion, evaluation framework will require MCMC method e.g., Metropolis-Hastings (M.-H.).
This presentation is on Bayesian evaluation framework for imperfect differential and integral data or models. This presentation includes a particular focus on common assumptions used in Bayesian ND evaluations. Additionally, this presentation highlights the benefits of a generalized form of the Bayes' theorem.