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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 433 records · Page 24

Search for muon neutrino disappearance with multi-topologically splitted Inclusive events at SBN

The Short-Baseline Neutrino (SBN) program at Fermilab searches for signatures of sterile neutrinos with mass-squared splittings at the O(1) eV2 scale, motivated by anomalies previously reported by the LSND and MiniBooNE experiments. We present a search for such signatures using the muon neutrino disappearance channel. This analysis exploits the large statistics of fully inclusive charged-current interactions collected by the SBND and ICARUS detectors to maximize sensitivity. The inclusive samples in both detectors are further subdivided into multiple exclusive topological channels, a strategy designed to enhance sensitivity by isolating differences in interaction-mode composition and reconstructed energy response. This multi-topology approach represents a novel analysis technique within SBN, enabling improved constraints on systematic uncertainties arising from neutrino flux, interaction modeling, and detector response. We will present comparisons of data with detailed predictions incorporating a comprehensive treatment of systematic uncertainties and constraints on the systematics obtained by the multi-topology method.

Yadav, Shweta [Texas U., Arlington]↗

The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO 2 , temperature, water, and nitrogen (version 1.0)

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

54 ENVIRONMENTAL SCIENCES↗

The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

James A Franke↗

Nanoindentation of Micrograins in Polycrystals under Multiaxial Stress: Control of Abrupt & Stochastic Plastic Events

Deforming objects in the world of the small has proven a much more complex task than just bending a common-sized object like a spoon. Over the last decade, it has become clear that the application of stress on micron-sized objects, such as micropillars, leads to abrupt and stochastic plastic strain jumps that do not satisfy Weibull statistics (as expected for uncorrelated defect yielding) but instead seem to follow highly wide, power-law distributions, pointing towards cooperative dislocation plasticity. Are power-law distributed plastic events the rule or the exception in crystals? If the rule, can we design protocols to control and suppress them in order to achieve deterministic and smooth microforming processes? We propose to combine experiments and theory to unravel the mysteries of abrupt microplastic deformation in a natural dislocation environment: micron-sized grains (\micrograins") of polycrystals. We propose a novel experimental design that utilizes nanoindentation and 4-point bending techniques on the substrate of polycrystalline thin films of two typical FCC metals (Al and Cu), and make concrete theoretical conjectures, based on intuition built from previous theoretical and experimental efforts in micropillar compression and nanoindentation. Based on a wide set of observations over the last decade, we put forward two distinct hypotheses that we propose to investigate in both theory and experiments: Our first conjecture is that crystal plasticity in the central region of micron-sized grains has micropillar-like stochastic features: power-law plastic strain jumps at stresses above the macroscale yield strength, displaying strong size and rate effects. We will demonstrate this similarity in spherical nanoindentation experiments in the central-region of multi-orientation grains of Al and Cu thin films. Al and Cu are selected for their disparity in stacking fault energy and consequently, their different propensity for cross-slip relaxation; however, our protocol may be applied to any crystalline material of technological interest. We will test our hypothesis by performing two-dimensional extended discrete dislocation plasticity (2.5D-DDP) simulations, inspired by comparable successful studies of micropillar plasticity, and extended to model nanoindentation with two slip systems and on a surface of a bicrystal with microhard grain-boundaries (GB), ie. not allowing dislocation absorption (116). Our model will include effective three dimensional dislocation mechanisms such as dislocation multiplication and double-cross-slip assisted glide. Second, we hypothesize that the application of uniform in-plane stress on our thin films below the yield point can smoothen the micrograin plastic response during nanoindentation. On a technological level, we propose that indentation-induced localization (sink-in) may be diminished by the application of concurrent in-plane stress that activates slow, viscoplastic relaxations near GBs. We aim to demonstrate our hypothesis through a 4-point bending apparatus that introduces a homogeneous in-plane stress state before indentation. We will perform simulations of a coarse-grained mesoscale theory of plasticity in a system with microhard GBs, benchmarked through 2.5D-DDP simulations, which will capture the effects of slow viscoplastic relaxations. Our nanoindentation abrupt event statistics studies, either in the micrograin interior or near GBs, will be correlated to direct observations of induced surface dislocation microstructures. For we will use TEM-based methods (PACOM/TKD) in combination with EBSD. Indents placed in the center of grains will be analyzed based on the orientation of the grain, while those near to GBs will be analyzed based on the orientation of each grain, the distance from the GB and the 5 degrees of freedom of the GB.

36 MATERIALS SCIENCE↗

Heterogeneous catalysis: Optimal performance at a phase boundary?

Most of the industrially used heterogeneous catalysts have been discovered by trial and error, and despite decades of experience, the discovery of new catalysts continues to be extremely challenging. The drive to uncover guiding principles in catalyst design is more present than ever. We share a series of observations indicating that optimal catalysts typically function at characteristic phase boundaries (e.g., abrupt changes in adsorbate coverage, catalyst structure, etc.) accessed in the reaction conditions. The catalyst exploits the associated instability—the desire to exist in multiple states simultaneously—as a driving force for chemical transformations. In other words, phase boundaries are good places to start the catalyst search, and indeed, we should focus on at least two phases at once rather than just one. Here, we substantiate this claim with several studies that combine statistical operando modeling and experiments. Transpiring from these observations is a hitherto unrecognized vector in catalyst discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The National Aviation Operational Monitoring Service (NAOMS): A Documentation of the Development of a Survey Methodology

The National Aviation Operational Monitoring Service (NAOMS) was a research project under NASA s Aviation Safety Program during the years from 2000 to 2005. The purpose of this project was to develop a methodology for gaining reliable information on changes over time in the rates-of-occurrence of safety-related events as a means of assessing the safety of the national airspace. The approach was a scientifically designed survey of the operators of the aviation system concerning their safety-related experiences. This report presents the results of the methodology developed and a demonstration of the NAOMS concept through a survey of nearly 20,000 randomly selected air-carrier pilots. Results give evidence that the NAOMS methodology can provide a statistically sound basis for evaluating trends of incidents that could compromise safety. The approach and results are summarized in the report and supporting documentation and complete analyses of results are presented in 14 appendices.

Connors, Mary M.↗

Optimizing detection of RDX vapors using designed experiments for remote sensing

Abstract: This paper presents results of experiments performed to study the effect of four factors on the detection of RDX vapors from desorption into an atmospheric flow tube mass spectrometer (AFT-MS). The experiments initially included four independent factors: gas flow rate, desorption current, solvent evaporation time and RDX mass. The values of three detection responses, peak height, peak width, and peak area were recorded but only the peak height response was analyzed. Results from the first block of experiments indicated that solvent evaporation time was not statistically significant. A second round of experiments was performed using flow rate, current, and RDX mass as factors and the results were used to create a model to predict conditions resulting in maximum peak height. Those conditions were confirmed experimentally and used to obtain data for a calibration model. The calibration model represented RDX amounts ranging from 1 to 25 pg desorbed into an air flow of 7 L/min. Air samples from a shipping container that held 2 closed explosive storage magazines were collected on metal filaments for varying amounts for time ranging from 5 to 90 minutes. RDX was detected from all of the filaments sampled by desorption into the AFT-MS. From the calibration model, RDX vapor concentrations within the shipping container were calculated to be in the range of 1 to 50 parts-per-quadrillion from data collected on 2 separate days.

Experimental Design↗

Investigation of 3D printed lightweight hybrid composites via theoretical modeling and machine learning

Hybrid composites combine two or more different fillers to achieve multifunctional or advanced material properties, such as lightweight and enhanced mechanical properties. The properties of the composites significantly depend on their microstructures, which can be tailored via advanced 3D printing processes. Understanding the process-structure-property relationships is critical to enable the design and engineering of novel hybrid composites for applications in aerospace, automotive, and protective coatings. Here, for this work, we develop 3D printable and lightweight hybrid composites and leverage the conventional design of experiments, a theoretical hybrid model, and an image-driven machine learning (ML) method to investigate their mechanical behaviors. The hybrid composites are formulated with elastomer matrix, microfillers, and thin-shell particles, enabling a significant degree of design freedom of microstructures with densities and mechanical properties varying up to 70% and 91%, respectively. Our statistical analysis indicates that the 3D printing path direction and the microfibers fraction are dominating process parameters with contribution percentages of 45.3% and 57.7% on the specific stiffness and strength, respectively. A hybrid mechanics model is developed based on a simple Weibull distribution function and classical single-filler models to effectively capture the variations in mechanical properties, however, it overestimates the values due to its statistical constraints and idealization of experimental uncertainty. The image-driven ML model leverages the microscale images directly without losing the structural details, shows more accurate predictions with experimental data, and has 48.6% lower root mean square error than the theoretical model.

3D printing↗

Dark Matter and Track Triggering with the CMS Experiment (Final Report)

This report summarizes the progress from DOE Early Career Award entitled “Dark Matter and Track Triggering with the CMS Experiment”. The first major goal of the project was to establish a sensitive Dark Matter (DM) search program for the CMS experiment in Run-2 of the LHC. Two related strategies were developed to accomplish this goal. First, we designed and executed searches for DM produced in association with heavy flavor quark pairs, top/anti-top (ttbar) and bottom/anti-bottom (bbbar). In addition, we sought to maximize the power of Run-2 DM searches through the development of a statistical combination of all major search channels within a consistent theoretical framework. The results of these aspects of the project are detailed in Section 1. The second major goal of the project was to develop a real-time “Level-1” tracking trigger system for the high-luminosity LHC (HL-LHC) CMS upgrade. Charged particle tracking in the first stage of the CMS trigger will be crucial for surviving the high-pileup environment expected at the HL-LHC. The L1 tracking trigger must process all front-end hit data sent from the inner Tracker and will have just 4us to output track primitives to the downstream L1 trigger without data loss. Our development of the CMS tracking trigger is described in Section 2. The team supported by the award is given in Section 3. The project resulted in 10 peer reviewed publications, a graduate student thesis, and contributions to the CMS Phase-2 Tracker Technical Design Report, as is detailed in Section 4. Our development work for the CMS tracking trigger has been integrated in the backend system architecture for the Tracker upgrade.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Density reconstruction in convergent high-energy-density systems using x-ray radiography and Bayesian inference

X-ray radiography is a technique frequently used to diagnose convergent high-energy-density (HED) systems, such as inertial confinement fusion implosion, and provides unique information that is not available through self-emission measurements. In this work, we investigate the scope and limits of that information using a radiography simulation combined with the Bayesian inference workflow. The accuracy of density reconstruction from simulated radiographs of spherical implosions driven with 27 kJ laser energy is assessed, including the increase or decrease in accuracy due to the addition of Lagrangian marker layers, Poisson noise, and improved prior information. This work is the first to present the full uncertainty distributions inferred from radiography analysis in HED systems and demonstrates the importance of constructing the full posterior probability density, as opposed to a point estimate, due to the modal structure of the likelihood surface introduced by typical experimental noise sources. This general methodology can be used both for robust analysis of radiographic data and for an improved design of radiography experiments by modeling the full experimental system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Laser and Electron Beam Technology for Parity Violating Electron Scattering Measurements

Parity violating electron scattering (PVES) is a precision tool used in a broad program of experiments which include studying the structure of protons and nuclei and searching for new Beyond the Standard Model (BSM) physics. In parity violation electron scattering experiments, a longitudinally polarized electron beam is incident on an unpolarized target. The sign of the longitudinal polarization is changed (making a parity transformation), and the fractional rate difference between right and left helicity states, dσ R and dσ L , is measured. An interference between the electromagnetic and weak amplitudes, M γ and M Z respectively, gives rise to a parity violating asymmetry A PV = $\frac{σ_{R}-σ_{L}}{σ_{R}-σ_{L}}$, defined by the fractional rate difference between right and left helicity states. A PV can be measured to extraordinarily high precision and is proportional to the ratio of the weak and electromagnetic amplitudes. This thesis highlights three such PVES experiments as well as a new polarized beam source which will improve their precision. The PVES experiments are PREX-II, MOLLER, and a 12 C transverse asymmetry measurement at Mainz. Chapters 1-3 of this thesis cover introductions to MOLLER and PREX-II, Chapter 4 contains the results of the 12 C transverse asymmetry measurement at Mainz, and Chapters 5-6 contain the original work performed by this author. PREX-II, the Lead Radius Experiment, measures the weak skin of the 208 Pb nucleus, provides a clean measurement of R N , the RMS radius of neutrons in a heavy nucleus, and constrains the equation of state (EOS) of highly dense matter which is important for describing neutron star structure, heavy ion collisions, and atomic parity violation experiments. The 12 C transverse asymmetry measurement at Mainz, and the future Mainz program of measuring the 208 Pb transverse asymmetry as well, addresses an important systematic correction for PREX and other PVES experiments preformed with heavy-nuclei. MOLLER (Measurement Of Lepton Lepton Electroweak Reaction) is an extremely precise PVES experiment searching for new neutral currents in electron-electron scattering. It’s called MOLLER because it will measure A PV in the Moller ee scattering process, and will infer the weak charge of the electron $Q^e_W$ to extremely high precision. MOLLER is sensitive to new Beyond the Standard Model physics at MeV and multi-TeV scales and will serve as an indirect complementary measurement to direct searches at high energy colliders. To achieve high precision measurements on A PV for MOLLER, we have developed an innovative Rubidium Titanyl Phosphate (RTP) Pockels cell in the polarized source to satisfy both statistical and systematic requirements as regards the electron beam produced. This new ultra-fast RTP cell design uses electric field gradients to provide unprecedented control over helicity correlated beam asymmetries and has been demonstrated to be capable of producing precisely controlled polarized electron beam at Jefferson Laboratory, controlling beam steering down to the nm-level. The precision reached with the RTP cell offers sufficient control over and minimization of helicity correlated beam asymmetries to perform PREX II. The RTP Pockels cell system will provide fast flipping and suitable control helicity correlated beam asymmetries and parity quality beam for the future MOLLER experiment, providing an unprecedented precision on the electron weak charge and electroweak mixing angle.

Palatchi, Caryn↗

Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint

Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under different levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.

25 ENERGY STORAGE↗

Measuring the Muon Anomalous Magnetic Moment to High Precision [PowerPoint]

The anomalous magnetic moment of the muon, characterized as the anomaly $a_{\mu} \equiv (g_{\mu}-2)/2$, remains a compelling hint of physics beyond the Standard Model (BSM) in light of the Brookhaven E821 measurement from the early 2000s, which differs with theoretical predictions by $>$3\,$\sigma$. To address this discrepancy, the Muon $g-2$ Experiment at Fermilab (E989) has been designed to determine $a_{\mu}$ to a precision of 140 parts per billion, a four-fold improvement over the E821 measurement. This improved precision presents an excellent opportunity to uncover BSM physics, and at minimum constrain BSM prescriptions. To date, the E989 experiment has completed its first two physics runs, recording more than 3 times the statistics recorded in the E821 experiment. The collaboration is preparing for a third physics run starting in October, with the goal of accumulating a factor of three more data than the previous runs combined, resulting in an order of magnitude more statistics than the Brookhaven measurement. An overview of the experimental technique will be discussed, along with a summary of Run 1 and 2. An update of the analysis of the Run 1 data will be presented; additionally, Run 3 progress will be summarized.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Ceramic processing: Experimental design and optimization

The objectives of this paper are to: (1) gain insight into the processing of ceramics and how green processing can affect the properties of ceramics; (2) investigate the technique of slip casting; (3) learn how heat treatment and temperature contribute to density, strength, and effects of under and over firing to ceramic properties; (4) experience some of the problems inherent in testing brittle materials and learn about the statistical nature of the strength of ceramics; (5) investigate orthogonal arrays as tools to examine the effect of many experimental parameters using a minimum number of experiments; (6) recognize appropriate uses for clay based ceramics; and (7) measure several different properties important to ceramic use and optimize them for a given application.

Weiser, Martin W.↗

Spotlight-8 Image Analysis Software

Spotlight is a cross-platform GUI-based software package designed to perform image analysis on sequences of images generated by combustion and fluid physics experiments run in a microgravity environment. Spotlight can perform analysis on a single image in an interactive mode or perform analysis on a sequence of images in an automated fashion. Image processing operations can be employed to enhance the image before various statistics and measurement operations are performed. An arbitrarily large number of objects can be analyzed simultaneously with independent areas of interest. Spotlight saves results in a text file that can be imported into other programs for graphing or further analysis. Spotlight can be run on Microsoft Windows, Linux, and Apple OS X platforms.

Klimek, Robert↗

Distributed Acoustic Sensing to Estimate the Permeability

Optical fiber in a borehole can be interrogated with distributed acoustic sensors (DAS) to capture fracture displacements with the potential to map surrounding fracture networks. We designed a laboratory experiment to test the capability of DAS to determine borehole flow characteristics, and we show that for the first time DAS can be used to remotely estimate permeability. Optical fiber was wrapped around a bead filled pipe and the pressure drop and flow velocity were measured to directly calculate permeability. A machine learning model using statistical features from continuous DAS estimated the bulk permeability. Fluid interactions with the permeable material demonstrate insufficient resolution using DAS amplitude-based measurements for estimating pressure drop to infer permeability. Variations in the spectral domain relate DAS measurements to the pressure drop and provide consistent permeability estimates. Resolution with DAS is sufficient to estimate permeability and provides a reliable method to monitor at depth in borehole conditions.

58 GEOSCIENCES↗

Normalizing flows for likelihood-free inference with fusion simulations

Fluid-based scrape-off layer transport codes, such as UEDGE, are heavily utilized in tokamak analysis and design, but typically require user-specified anomalous transport coefficients to match experiments. Determining the uniqueness of these parameters and the uncertainties in them to match experiments can provide valuable insights to fusion scientists. Here, we leverage recent work in the area of likelihood-free inference (‘simulation-based inference’) to train a neural network, which enables accurate statistical inference of the anomalous transport coefficients given experimental plasma profile input. UEDGE is treated as a black-box simulator and runs multiple times with anomalous transport coefficients sampled from priors, and the neural network is trained on these simulations to emulate the posterior. The neural network is trained as a normalizing flow model for density estimation, allowing it to accurately represent complicated, high-dimensional distribution functions. With a fixed simulation budget, we compare a single-round procedure to a multi-round approach that guides the training simulations toward a specific target observation. Finally, we discuss the future possibilities for use of amortized models, which train on a wide range of simulations and enable fast statistical inference for results during experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

From Nucleons to Nuclei: Enabling Discovery for Neutrinos, Dark Matter and More

Neutrino interaction uncertainties are a limiting factor in current and next generation experiments probing the fundamental physics of neutrinos, a unique window on physics beyond the Standard Model. Our understanding of the elementary neutrino-nucleon amplitudes at the heart of the neutrino interaction program relies on experiments from an earlier age whose statistical and systematic precision are insufficient for current needs. The workshop brings together physicists with a diverse range of expertise to discuss the status of neutrino-nucleus scattering and to explore (i) the quantitative impact of better constraints on the elementary amplitudes; (ii) the scientific impact of a new hydrogen or deuterium target experiment; (iii) the optimal design and technical feasibility of a new hydrogen or deuterium target experiment; and (iv) complementary constraints on the elementary amplitudes from other methods. The tools and techniques required for understanding neutrino-nucleus interactions are common to many problems at the interface of particle and nuclear physics. The workshop will discuss new developments and prospects for related topics and new physics searches, including dark matter interactions with the Standard Model and neutrinoless double beta decay.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗