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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Measurement of the W W + W Z cross section and limits on anomalous triple gauge couplings using final states with one lepton, missing transverse momentum, and two jets with the ATLAS detector at s = 7 TeV

CERN-LHC. The production of a W boson decaying to (e nu) or (mu nu) in association with a W or Z boson decaying to two jets is studied using 4.6 fb-1 of proton-proton collision data at sqrt(s) = 7 TeV recorded with the ATLAS detector at the LHC. The combined WW+WZ cross section is measured with a significance of 3.4 sigma. Two cross sections are measured, a fiducial and a total cross section. The cross sections are measured assuming the SM ratio between the WW and WZ cross sections.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Searching for pulsars using image pattern recognition

In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful data-mining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by using image pattern recognition with deep neural nets—the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ∼9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The performance of this system can be improved over time as more training data are accumulated. This AI system has been integrated into the PALFA survey pipeline and has discovered six new pulsars to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Search for single production of a vector-like T quark decaying to a top quark and a Z boson in the final state with jets and missing transverse momentum at $ \sqrt{s} $ = 13 TeV

A search is presented for single production of a vector-like T quark with charge 2/3 e, in the decay channel featuring a top quark and a Z boson, with the top quark decaying hadronically and the Z boson decaying to neutrinos. The search uses data collected by the CMS experiment in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 137 fb -1 recorded at the CERN LHC in 2016–2018. The search is sensitive to a T quark mass between 0.6 and 1.8 TeV with decay widths ranging from negligibly small up to 30% of the T quark mass. Reconstruction strategies for the top quark are based on the degree of Lorentz boosting of its final state. At 95% confidence level, the upper limit on the product of the cross section and branching fraction for a T quark of small decay width varies between 15 and 602 fb, depending on its mass. For a T quark with decay widths between 10 and 30% of its mass, this upper limit ranges between 16 and 836 fb. For most of the studied range, the results provide the best limits to date. This is the first search for single T quark production based on the full Run 2 data set of the LHC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

MO-G-BRE-09: Validating FMEA Against Incident Learning Data: A Study in Stereotactic Body Radiation Therapy

Purpose: Though FMEA (Failure Mode and Effects Analysis) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge it has never been validated against actual incident learning data. The objective of this study was to perform an FMEA analysis of an SBRT (Stereotactic Body Radiation Therapy) treatment planning process and validate this against data recorded within an incident learning system. Methods: FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, and dosimetrists. Potential failure modes were identified through a systematic review of the workflow process. Failure modes were rated for severity, occurrence, and detectability on a scale of 1 to 10 and RPN (Risk Priority Number) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that had been active for two years. Differences were identified. Results: FMEA identified 63 failure modes. RPN values for the top 25% of failure modes ranged from 60 to 336. Analysis of the incident learning database identified 33 reported near-miss events related to SBRT planning. FMEA failed to anticipate 13 of these events, among which 3 were registered with severity ratings of severe or critical in the incident learning system. Combining both methods yielded a total of 76 failure modes, and when scored for RPN the 13 events missed by FMEA ranked within the middle half of all failure modes. Conclusion: FMEA, though valuable, is subject to certain limitations, among them the limited ability to anticipate all potential errors for a given process. This FMEA exercise failed to identify a significant number of possible errors (17%). Integration of FMEA with retrospective incident data may be able to render an improved overview of risks within a process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Comparative Evaluation of Preprocessing Freeware on Chromatography/Mass Spectrometry Data for Signature Discovery

Preprocessing software is crucial for the discovery of chemical signatures in metabolomics, chemical forensics, and other signature-focused disciplines that involve analyzing large data sets from chemical instruments. Here, four freely available and published preprocessing tools known as metAlign, MZmine, SpectConnect, and XCMS were evaluated for impurity profiling using nominal mass GC/MS data and accurate mass LC/MS data. Both data sets were previously collected from the analysis of replicate samples from multiple stocks of a nerve-agent precursor. Each of the four tools had their parameters set for the untargeted detection of chromatographic peaks from impurities present in the stocks. The peak table generated by each preprocessing tool was analyzed to determine the number of impurity components detected in all replicate samples per stock. A cumulative set of impurity components was then generated using all available peak tables and used as a reference to calculate the percent of component detections for each tool, in which 100% indicated the detection of every component. For the nominal mass GC/MS data, metAlign performed the best followed by MZmine, SpectConnect, and XCMS with detection percentages of 83, 60, 47, and 42%, respectively. For the accurate mass LC/MS data, the order was metAlign, XCMS, and MZmine with detection percentages of 80, 45, and 35%, respectively. SpectConnect did not function for the accurate mass LC/MS data. Larger detection percentages were obtained by combining the top performer with at least one of the other tools such as 96% by combining metAlign with MZmine for the GC/MS data and 93% by combining metAlign with XCMS for the LC/MS data. In terms of quantitative performance, the reported peak intensities had average absolute biases of 41, 4.4, 1.3 and 1.3% for SpectConnect, metAlign, XCMS, and MZmine, respectively, for the GC/MS data. For the LC/MS data, the average absolute biases were 22, 4.5, and 3.1% for metAlign, MZmine, and XCMS, respectively. In summary, metAlign performed the best in terms of peak discovery; however, more than one preprocessing tool should be considered to avoid missing potential chemical signatures.

chemical forensics, chemometrics↗
DOE OSTI2019

Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery

Zeolites are microporous crystalline materials with well-defined cavities and pores, which can be prepared under different pore topologies and chemical compositions. Their preparation is typically defined by multiple interconnected variables (e.g., reagent sources, molar ratios, aging treatments, reaction time and temperature, among others), but unfortunately their distinctive influence, particularly on the nucleation and crystallization processes, is still far from being understood. Thus, the discovery and/or optimization of specific zeolites is closely related to the exploration of the parametric space through trial-and-error methods, generally by studying the influence of each parameter individually. In the past decade, machine learning (ML) methods have rapidly evolved to address complex problems involving highly nonlinear or massively combinatorial processes that conventional approaches cannot solve. Considering the vast and interconnected multiparametric space in zeolite synthesis, coupled with our poor understanding of the mechanisms involved in their nucleation and crystallization, the use of ML is especially timely for improving zeolite synthesis. Indeed, the complex space of zeolite synthesis requires drawing inferences from incomplete and imperfect information, for which ML methods are very well-suited to replace the intuition-based approaches traditionally used to guide experimentation. In this Account, we contend that both existing and new ML approaches can provide the “missing link” needed to complete the traditional zeolite synthesis workflow used in our quest to rationalize zeolite synthesis. Within this context, we have made important efforts on developing ML tools in different critical areas, such as (1) data-mining tools to process the large amount of data generated using high-throughput platforms; (2) novel complex algorithms to predict the formation of energetically stable hypothetical zeolites and guide the synthesis of new zeolite structures; (3) new “ab initio” organic structure directing agent predictions to direct the synthesis of hypothetical or known zeolites; (4) an automated tool for nonsupervised data extraction and classification from published research articles. ML has already revolutionized many areas in materials science by enhancing our ability to map intricate behavior to process variables, especially in the absence of well-understood mechanisms. Undoubtedly, ML is a burgeoning field with many future opportunities for further breakthroughs to advance the design of molecular sieves. For this reason, this Account includes an outlook of future research directions based on current challenges and opportunities. We envision this Account will become a hallmark reference for both well-established and new researchers in the field of zeolite synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Simulations of muon imaging with the LANL GMT detector for spent nuclear fuel cask content verification

Atmospheric muons are typically high energy, highly penetrating charged particles. They interact with matter primarily through multiple Coulomb scatterings. Muon scattering intensities can be used to characterize the density and atomic number of the matter that they pass through. Previously, the Los Alamos National Laboratory (LANL) muon tomography team performed muon imaging of the partially filled MC-10 spent nuclear fuel (SNF) cask at Idaho National Laboratory (INL). This experiment demonstrated the feasibility of muon imaging for the verification of spent fuel container contents. That original effort used the mini muon tracker array, consisting of two arrays of drift tubes on either side of the SNF cask. The reconstructed image quality was limited by statistics, largely due to low muon flux at high zenith angles. A LANL led team will perform new measurements with a larger array, the Giant Muon Tracker (GMT), to improve data collection rates and statistics. In this work, simulations were performed with the GMT near the partially filled INL MC-10 cask. For more general fuel diversion detection, a full MC-10 cask and casks with a singular missing fuel bundle were also simulated. To understand minimum measurement times needed for missing bundle identification, 100 000 to millions of tracked muons (corresponding to 1.4 days to several weeks measurement time) were analyzed. Simulated images were then analyzed visually and numerically to explore techniques designed to minimize the collection time needed to identify the diversion of fuel in each scenario.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

CALIBRATING STELLAR POPULATION MODELS WITH MAGELLANIC CLOUD STAR CLUSTERS

Stellar population models are commonly calculated using star clusters as calibrators for those evolutionary stages that depend on free parameters. However, discrepancies exist among different models, even if similar sets of calibration clusters are used. With the aim of understanding these discrepancies, and of improving the calibration procedure, we consider a set of 43 Magellanic Cloud (MC) clusters, taking age and photometric information from the literature. We carefully assign ages to each cluster based on up-to-date determinations, ensuring that these are as homogeneous as possible. To cope with statistical fluctuations, we stack the clusters in five age bins, deriving for each of them integrated luminosities and colors. We find that clusters become abruptly red in optical and optical-infrared colors as they age from {approx}0.6 to {approx}1 Gyr, which we interpret as due to the development of a well-populated thermally pulsing asymptotic giant branch (TP-AGB). We argue that other studies missed this detection because of coarser age binnings. Maraston and Girardi et al. models predict the presence of a populated TP-AGB at {approx}0.6 Gyr, with a correspondingly very red integrated color, at variance with the data; Bruzual and Charlot and Conroy models run within the error bars at all ages. The discrepancy between the synthetic colors of Maraston models and the average colors of MC clusters results from the now obsolete age scale adopted. Finally, our finding that the TP-AGB phase appears to develop between {approx}0.6 and 1 Gyr is dependent on the adopted age scale for the clusters and may have important implications for stellar evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

MO-PIS-Exhibit Hall-01: Tools for TG-142 Linac Imaging QA I

Partners in Solutions is an exciting new program in which AAPM partners with our vendors to present practical “hands-on” information about the equipment and software systems that we use in our clinics. The therapy topic this year is solutions for TG-142 recommendations for linear accelerator imaging QA. Note that the sessions are being held in a special purpose room built on the Exhibit Hall Floor, to encourage further interaction with the vendors. Automated Imaging QA for TG-142 with RIT Presentation Time: 2:45 – 3:15 PM This presentation will discuss software tools for automated imaging QA and phantom analysis for TG-142. All modalities used in radiation oncology will be discussed, including CBCT, planar kV imaging, planar MV imaging, and imaging and treatment coordinate coincidence. Vendor supplied phantoms as well as a variety of third-party phantoms will be shown, along with appropriate analyses, proper phantom setup procedures and scanning settings, and a discussion of image quality metrics. Tools for process automation will be discussed which include: RIT Cognition (machine learning for phantom image identification), RIT Cerberus (automated file system monitoring and searching), and RunQueueC (batch processing of multiple images). In addition to phantom analysis, tools for statistical tracking, trending, and reporting will be discussed. This discussion will include an introduction to statistical process control, a valuable tool in analyzing data and determining appropriate tolerances. An Introduction to TG-142 Imaging QA Using Standard Imaging Products Presentation Time: 3:15 – 3:45 PM Medical Physicists want to understand the logic behind TG-142 Imaging QA. What is often missing is a firm understanding of the connections between the EPID and OBI phantom imaging, the software “algorithms” that calculate the QA metrics, the establishment of baselines, and the analysis and interpretation of the results. The goal of our brief presentation will be to establish and solidify these connections. Our talk will be motivated by the Standard Imaging, Inc. phantom and software solutions. We will present and explain each of the image quality metrics in TG-142 in terms of the theory, mathematics, and algorithms used to implement them in the Standard Imaging PIPSpro software. In the process, we will identify the regions of phantom images that are analyzed by each algorithm. We then will discuss the process of the creation of baselines and typical ranges of acceptable values for each imaging quality metric.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2017

Search for Dark Matter Produced in Association with a Higgs Boson Decaying to b b ¯ using 36 fb − 1 of p p collisions at s = 13 TeV with the ATLAS Detector

CERN-LHC. Several extensions of the Standard Model predict associated production of dark-matter particles with a Higgs boson. Such processes are searched for in final states with missing transverse momentum and a Higgs boson decaying to a bb pair with the ATLAS detector using 36.1 fb-1 of pp collisions at a center-of-mass energy of 13 TeV at the LHC. The observed data are in agreement with the Standard Model and limits are placed on the associated production of dark-matter particles and a Higgs boson.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Hybrid physics-based and machine learning tools for materials assessment

In this work, we develop novel physics-based and machine learning computational techniques to predict fundamental properties of metallic systems that affect radiation damage behavior in reactor structural materials. Oftentimes, atomistic predictions of engineering alloys simplify chemical compositions to a single element to reduce computational cost and complexity, introducing large sources of uncertainty and potentially missing important behavior. In addition, engineering alloys such as SS 316 are particularly challenging to simulate with first principles methods because of the additional degrees of freedom introduced by magnetic natures of the constituent elements, and very little data of this type exists within the literature. These novel methods aim to improve the qualitative prediction of radiation damage in engineering alloys by more accurately simulating their compositions, both by accelerating the computations and by developing novel analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Enabling Early Transient Discovery in LSST via Difference Imaging with DECam

We present SLIDE, a pipeline that enables transient discovery in data from the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), using archival images from the Dark Energy Camera as templates for difference imaging. We apply this pipeline to the recently released Data Preview 1 (DP1; the first public release of Rubin commissioning data) and search for transients in the resulting difference images. The image subtraction, photometry extraction, and transient detection are all performed on the Rubin Science Platform. We demonstrate that SLIDE effectively extracts clean photometry by circumventing poor or missing LSST templates. We identified 29 previously unreported transients, 12 of which would not have been detected based on the DP1 DiaObject catalog. SLIDE will be especially useful for transient analysis in the early years of LSST, when template coverage will be largely incomplete or when templates may be contaminated by transients present at the time of acquisition. We present multiband light curves for a sample of known transients, along with new transient candidates identified through our search. Finally, we discuss the prospects of applying this pipeline during the main LSST survey. Our pipeline is broadly applicable and will support studies of all transients with slowly evolving phases.

Dong, Yize 一泽董 [Harvard-Smithsonian Center for Ast↗
DOE OSTI2014

Meta-analysis of high-latitude nitrogen-addition and warming studies implies ecological mechanisms overlooked by land models

Accurate representation of ecosystem processes in land models is crucial for reducing predictive uncertainty in energy and greenhouse gas feedbacks with the climate. Here we describe an observational and modeling meta-analysis approach to benchmark land models, and apply the method to the land model CLM4.5 with two versions of belowground biogeochemistry. We focused our analysis on the aboveground and belowground responses to warming and nitrogen addition in high-latitude ecosystems, and identified absent or poorly parameterized mechanisms in CLM4.5. While the two model versions predicted similar soil carbon stock trajectories following both warming and nitrogen addition, other predicted variables (e.g., belowground respiration) differed from observations in both magnitude and direction, indicating that CLM4.5 has inadequate underlying mechanisms for representing high-latitude ecosystems. On the basis of observational synthesis, we attribute the model–observation differences to missing representations of microbial dynamics, aboveground and belowground coupling, and nutrient cycling, and we use the observational meta-analysis to discuss potential approaches to improving the current models. However, we also urge caution concerning the selection of data sets and experiments for meta-analysis. For example, the concentrations of nitrogen applied in the synthesized field experiments (average = 72 kg ha -1 yr -1 ) are many times higher than projected soil nitrogen concentrations (from nitrogen deposition and release during mineralization), which precludes a rigorous evaluation of the model responses to likely nitrogen perturbations. Overall, we demonstrate that elucidating ecological mechanisms via meta-analysis can identify deficiencies in ecosystem models and empirical experiments.

Bouskill, N. J.↗
NASA NTRS2007

Lessons Learned in the Specification, Purchase, Validation and Final Installation Process of a Replacement PCM Bit Synchronizer

This paper intends to describe the lessons learned while specifying validating and installing a bit sync to replace the 30 year old Aydin Model 335a PCM bit sync used in the Space Shuttle Launch Control Center. The engineer had to analyze the original requirements and specifications and then create new requirements documentation that more correctly described our needs. One issue to consider was the removal of unnecessary requirements such as various data formats when only one format is used. The conversion to a system that no longer has an assortment of analog rotary switches required retraining of the operators. Finally, post-procurement corrections for undisclosed user requirements and missed design requirements required close contact with a manufacturer who was willing to accommodate the changes.

Price, Richard N.↗
NASA NTRS2015

Directed Design of Experiments for Validating Probability of Detection Capability of NDE Systems (DOEPOD)

Directed Design of Experiments for Validating Probability of Detection Capability of NDE Systems (DOEPOD) Manual v.1.2 The capability of an inspection system is established by applications of various methodologies to determine the probability of detection (POD). One accepted metric of an adequate inspection system is that there is 95% confidence that the POD is greater than 90% (90/95 POD). Design of experiments for validating probability of detection capability of nondestructive evaluation (NDE) systems (DOEPOD) is a methodology that is implemented via software to serve as a diagnostic tool providing detailed analysis of POD test data, guidance on establishing data distribution requirements, and resolving test issues. DOEPOD demands utilization of observance of occurrences. The DOEPOD capability has been developed to provide an efficient and accurate methodology that yields observed POD and confidence bounds for both Hit-Miss or signal amplitude testing. DOEPOD does not assume prescribed POD logarithmic or similar functions with assumed adequacy over a wide range of flaw sizes and inspection system technologies, so that multi-parameter curve fitting or model optimization approaches to generate a POD curve are not required. DOEPOD applications for supporting inspector qualifications is included.

Generazio, Edward R.↗
NASA NTRS2024

COLUTA: Custom 8-Channel 15-bit 40-MSPS ADC for the ATLAS Liquid Argon Calorimeter Readout

The stringent radiation-tolerance, quantization requirements, and the need for seamless integration of the on-detector readout electronics chain require the design of a full-custom analog-to-digital converter (ADC) for the 182,468 channels in the ATLAS Liquid Argon (LAr) calorimeter at the Large Hadron Collider (LHC) at CERN, as part of the High-Luminosity LHC (HL-LHC) upgrade. Each of the 8 channels in the prototype 65 nm CMOS ADC with 15-bit resolution and >68 dB signal-to-noise-and-distortion ratio (SNDR), or equivalently >11 effective number of bits (ENOB), consists of a Multiplying-DAC (MDAC) and a successive-approximation (SAR) ADC. A digital data processing unit (DDPU) calibrates and serially transmits the quantized data. Initial lab characterization of 18 chips shows a minimum SNDR of 69.5 dB at full-scale at about 5 MHz and a maximum power consumption of 1.17 W; differential non-linearity (DNL) measurements show no missing codes. Additional validation of the ADC is ongoing before approximately 70k chips can be mass-produced for the upgrade.

Rui Xu↗
DOE OSTI2021

Geographic Mapping Tool for Power Systems

Power systems models are continuously updated to reflect changing infrastructure and modeling techniques. Open source geographic locations of power systems infrastructure do not include attributes that directly tie to electrical models. Moreover, relationships established between electric models and geospatial data sets do not apply to a future model because of nomenclature changes and architecture changes. The software titled “Geographic Mapping Tool for Power Systems” starts with a known set of locations, then runs multiple iterations to identify missing locations with a triangulation algorithm based on the branch connections and distances to the known locations.”

Snyder, Isabelle B↗
DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗