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At least 1,495 records · Page 83

Light Output and Thermal Blast Analysis of Nuclear Fireballs

During the 1940s, 50s, and 60s, there were 210 atmospheric nuclear tests. Every test was extensively recorded with high quality motion picture films and photographs, providing a unique historic record, containing ~10,000 films, with a total of ~22 million frames of information. Much of what is known about the effects of nuclear weapons has been obtained from these tests. There are approximately 6500 scientific films that are in the process of being scanned by Lawrence Livermore National Laboratory and re-analyzed using modern computer techniques. The four major effects of a nuclear detonation are prompt radiation, shockwave, thermal blast, and fallout. The objective of this interim report is to describe recent efforts to extract benchmark data from the existing streak films using modern Figure-processing techniques. These new and, hopefully, greatly-improved data will provide our code developers with the benchmark data necessary to validate our modern computer codes that simulate nuclear weapon effects.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Oak Ridge National Laboratory's Strategic Research and Development Insights for Digital Twins

Oak Ridge National Laboratory (ORNL) is pleased to provide our response to the NITRD RFI on Digital Twins Research and Development. Digital twins are virtual representations of physical systems, leveraging real-time data to simulate and predict behaviors. ORNL is advancing digital twin technology across various disciplines, including neutron scattering, networking, science ecosystems, supercomputing, secure facilities, mobility technologies, materials design and discovery, power systems, fusion reactors, biological sciences, and earth observation. These efforts aim to enhance scientific research, operational efficiency, and decision-making processes. ORNL facilities, such as the High Flux Isotope Reactor (HFIR), Grid-C, Spallation Neutron Source (SNS), and Oak Ridge Leadership Computing Facility (OLCF), provide the infrastructure to develop and demonstrate these digital twin technologies. In this document, we lay out key challenges, research gaps, and future opportunities based on our experience with digital twins that aim to serve as useful contributions towards a National Digital Twins R&D Strategic Plan. In the remaining document, we address nine of the thirteen topic areas specified in the RFI.

97 MATHEMATICS AND COMPUTING↗

Use of Machine Learning for Signature Development in a Multi-Sensor Environment for Safeguard Applications of Solvent Extraction Processes

Idaho National Laboratory is constructing the Special Nuclear Material test bed (Beartooth) for research and development pertaining to nuclear fuel processing operations using centrifugal contactors. Beartooth will enable give researchers the opportunity to study the dissolution, separation, and conversion of special nuclear materials (e.g., plutonium, enriched uranium, and thorium). As a lead-in to Beartooth, this project constructed a test bed to study the process and equipment behavior with real time series data collection using a variety of non-traditional sensors, in order to identify process failures and label them as either accidental or purposeful, thus enhancing nuclear nonproliferation stewardship. The measurement sources in this test bed include vibration, acoustic, current, flow, colorimetric, and thermal data. These measurements vary in terms of magnitude, frequency, and location. This information will be combined and used within data-driven machine-learning methods to detect operation condition changes that are either intentional (i.e., changes in operating set points) or malicious (i.e., flow diversion) in nature. This presentation covers the data processing and signature development activities necessary for utilizing this heterogeneous dataset to classify process anomalies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

CROCUS Air Quality Data at Northeastern Illinois University Rooftop

This dataset is from the Department of Energy Office of Science funded project, Community Research on Urban and Climate Science (CROCUS) (https://crocus-urban.org/). The AQT (Vaisala AQT530) instrument provides observations on meteorological conditions, including particulate matter (PM2.5, PM10), gas species concentrations (NO, NO2, O3, CO), and environment temperature and moisture. These measurements are critical for understanding air quality. These measurements are useful for understanding changes in aerosol properties, air quality research, and comparing to model experiments especially in urban environments.Datasets are stored in the netCDF data format, and we we encourage users to make use the associated toolkits available from Unidata (https://www.unidata.ucar.edu/software/netcdf/), Project Pythia (https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html), and our “Instrument Cookbooks” (https://crocus-urban.github.io/instrument-cookbooks) for more information on how to process the metadata-rich datasets.

54 ENVIRONMENTAL SCIENCES↗

Process Simulation Role in the Development of New Alloys Based on Integrated Computational Material Science and Engineering

To accelerate the introduction of new materials and components, the development of metal casting processes requires the teaming between different disciplines, as multi-physical phenomena have to be considered simultaneously for the process design and optimization of mechanical properties. The required models for physical phenomena as well as their validation status for metal casting are reviewed. The data on materials properties, model validation, and relevant microstructure for materials properties are highlighted. One vehicle to accelerate the development of new materials is through combined experimental-computational efforts. Integrated computational/experimental practices are reviewed; strengths and weaknesses are identified with respect to metal casting processes. Specifically, the examples are given for the knowledge base established at Oak Ridge National Laboratory and computer models for predicting casting defects and microstructure distribution in aluminum alloy components.

Sabau, Adrian S↗

Data-Driven Exploration of Climate Attractor Manifolds For Long-Term Predictability

Focal Area: This white paper responds to Focal Area 3. We seek to gain insight into decadal-scale climate predictability by applying novel manifold-finding probabilistic AI techniques to the complex data produced by Earth System models (ESMs) such as E3SM. The associated portfolio of research activities leverages DOE’s asset mix of HPC platforms, climate expertise, climate simulation codes, and AI expertise. Science Challenge: Climate and climate models are dynamical systems exhibiting properties that are interpretable through chaos theory. The theory contains an important concept that is relevant to multi-decade-scale climate prediction: a chaotic attractor. While the space containing all the possible states of the Earth’s atmosphere and ocean, the possible weather, is large, the realized states tend to stay near the smaller-dimensioned attractor. This behavior is responsible for the “order behind the irregularity” [1] of climate phenomena. Climate change can be thought of as a change in the properties of the attractor, and predicting the climate over years to decades is equivalent to predicting how those properties will change. To date, the attractor has been a useful conceptual tool, but has not been amenable to direct characterization. A new development is the advent of efficient high-dimensional manifold-finding probabilistic AI techniques, which permit a data-driven characterization of the ESM attractor and its probability distribution over weather states. Such a characterization would result in a natural dimensional reduction — a “non-linear Principal Components Analysis (PCA) adapted to climate simulation data” — leading to important advances in scenario-based long-term climate prediction, long-term prediction of water cycle extremes, ESM verification, inter-model comparison, and process model development.

54 ENVIRONMENTAL SCIENCES↗

Primary transcripts: From the discovery of RNA processing to current concepts of gene expression ‐ Review

The main purpose of this review is to recall for investigators - and in particular students -, some of the early data and concepts in molecular genetics and biology that are rarely cited in the current literature and are thus invariably overlooked. There is a growing tendency among editors and reviewers to consider that only data produced in the last 10–20 years or so are pertinent. However this is not the case. In exact science, sound data and lucid interpretation never become obsolete, and even if forgotten, will resurface sooner or later. In the field of gene expression, covered in the present review, recent post-genomic data have indeed confirmed many of the earlier results and concepts developed in the mid-seventies, well before the start of the recombinant DNA revolution. Human brains and even the most powerful computers, have difficulty in handling and making sense of the overwhelming flow of data generated by recent high-throughput technologies. This was easier when low throughput, more integrative methods based on biochemistry and microscopy dominated biological research. Nowadays, the need for organising concepts is ever more important, otherwise the mass of available data can generate only {sup b}uilding ruins{sup }- the bricks without an architect. Concepts such as pervasive transcription of genomes, large genomic domains, full domain transcripts (FDTs) up to 100 kb long, the prevalence of post-transcriptional events in regulating eukaryotic gene expression, and the 3D-genome architecture, were all developed and discussed before 1990, and are only now coming back into vogue. Thus, to review the impact of earlier concepts on later developments in the field, I will confront former and current data and ideas, including a discussion of old and new methods. Whenever useful, I shall first briefly report post-genomic developments before addressing former results and interpretations. Equally important, some of the terms often used sloppily in scientific discussions will be clearly defined. As a basis for the ensuing discussion, some of the issues and facts related to eukaryotic gene expression will first be introduced. In chapter 2 the evolution in perception of biology over the last 60 years and the impact of the recombinant DNA revolution will be considered. Then, in chapter 3 data and theory concerning the genome, gene expression and genetics will be reviewed. The experimental and theoretical definition of the gene will be discussed before considering the 3 different types of genetic information - the {sup T}riad{sup }- and the importance of post-transcriptional regulation of gene expression in the light of the recent finding that 90% of genomic DNA seems to be transcribed. Some previous attempts to provide a conceptual framework for these observations will be recalled, in particular the {sup C}ascade Regulation Hypothesis{sup }(CRH) developed in 1967–85, and the {sup G}ene and Genon{sup }concept proposed in 2007. A knowledge of the size of primary transcripts is of prime importance, both for experimental and theoretical reasons, since these molecules represent the primary units of the {sup R}NA genome{sup }on which most of the post-transcriptional regulation of gene expression occurs. In chapter 4, I will first discuss some current post-genomic topics before summarising the discovery of the high Mr-RNA transcripts, and the investigation of their processing spanning the last 50 years. Since even today, a consensus concerning the real form of primary transcripts in eukaryotic cells has not yet been reached, I will refer to the viral and specialized cellular models which helped early on to understand the mechanisms of RNA processing and differential splicing which operate in cells and tissues. As a well-studied example of expression and regulation of a specific cellular gene in relation to differentiation and pathology, I will discuss the early and recent work on expression of the globin genes in nucleated avian erythroblasts. An important concept is that the primary transcript not only embodies protein-coding information and regulation of its expression, but also the 3D-structure of the genomic DNA from which it was derived. The wealth of recent post-genomic data published in this field emphasises the importance of a fundamental principle of genome organisation and expression that has been overlooked for years even though it was already discussed in the 1970–80ties. These issues are addressed in chapter 5 which focuses on the involvement of the nuclear matrix and nuclear architecture in DNA and RNA biology. This section will make reference to the Unified Matrix Hypothesis (UMH), which was the first molecular model of the 3D organisation of DNA and RNA. The chapter on the {sup R}NA-genome and peripheral memories{sup }discusses experimental data on the ribonucleoprotein complexes containing pre-mRNA (pre-mRNPs) and mRNA (mRNPs) which are organised in nuclear and cytoplasmic spaces respectively. Finally, {sup O}utlook {sup }will enumerate currently unresolved questions in the field, and will propose some ideas that may encourage further investigation, and comprehension of available experimental data still in need of interpretation. In chapter 8, some propositions and paradigms basic to the authors own analysis are discussed. {sup I}n conclusion{sup }the raison d'être of this review is recalled and positioned within the overall framework of scientific endeavour.

60 APPLIED LIFE SCIENCES↗

Different approaches to modeling the LANSCE H{sup −} ion source filament performance

An overview of different approaches to modeling of hot tungsten filament performance in the Los Alamos Neutron Science Center (LANSCE) H{sup −} surface converter ion source is presented. The most critical components in this negative ion source are two specially shaped wire filaments heated up to the working temperature range of 2600 K–2700 K during normal beam production. In order to prevent catastrophic filament failures (creation of hot spots, wire breaking, excessive filament deflection towards source body, etc.) and to improve understanding of the material erosion processes, we have simulated the filament performance using three different models: a semi-empirical model, a thermal finite-element analysis model, and an analytical model. Results of all three models were compared with data taken during LANSCE beam production. The models were used to support the recent successful transition from the beam pulse repetition rate of 60 Hz–120 Hz.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03)

This dataset contains layer-wise powder bed images from three different powder bed printing technologies – laser powder bed fusion, electron beam powder bed fusion, and binder jetting. This dataset was collected and annotated using the internally-developed Peregrine software tool and is designed primarily to facilitate research into anomaly defect detection using image segmentation or similar techniques. A total of 20 layers are provided for each printing technology, with each layer of data consisting of one or more calibrated images and an annotation file containing pixel-wise ground truth labels. The ground truths were labeled by domain experts, typically printer technicians. Data in this release were collected at Oak Ridge National Laboratory between 2016 and 2020 and were compiled in March 2021.

36 MATERIALS SCIENCE↗

A Comparative Study of the Efficacy of Local/Global and Parametric/Nonparametric Machine Learning Methods for Establishing Structure–Property Linkages in High-Contrast 3D Elastic Composites

Reduced-order structure–property (S-P) linkages play a pivotal role in the tailored design of materials for advanced engineering components. There is a critical need to distill these from the simulation datasets aggregated using sophisticated, computationally expensive, physics-based numerical tools (e.g., finite element methods). The recent emergence of materials data science approaches has opened new avenues for addressing this challenge. In this paper, we critically compare the relative merits of the application of four distinct machine learning approaches for their efficacy in extracting microstructure-property linkages from the finite element simulation data aggregated on high-contrast elastic composites with different microstructures. The machine learning approaches selected for the study have included different combinations of local/global and parametric/nonparametric approaches. Furthermore, the nonparametric approaches selected for this study are based on Gaussian Process (GP) models that allow for a formal treatment of uncertainty quantification in the predicted values. The predictive performances of these different approaches have been compared against each other using rigorous cross-validation error metrics. Furthermore, their sensitivity to both the dataset size and dimensionality has been investigated.

36 MATERIALS SCIENCE↗

Atmospheric Radiation Measurement Climate Research Facility Operations Quarterly Report October 1–December 31, 2012

Individual datastreams from instrumentation at the Atmospheric Radiation Measurement (ARM) Climate Research Facility fixed and mobile research sites are collected and routed to the Data Management Facility (DMF) for processing in near-real-time. Instrument and processed data are then delivered approximately daily to the ARM Data Archive, where they are made freely available to the research community. For each instrument, we calculate the ratio of the actual number of processed data records received daily at the Data Archive to the expected number of data records. The results are tabulated by (1) individual datastream, site, and month for the current year and (2) site and fiscal year dating back to 1998.

54 ENVIRONMENTAL SCIENCES↗

Scanning structural mapping at the Life Science X-ray Scattering Beamline

This work describes the instrumentation and software for microbeam scattering and structural mapping at the Life Science X-ray Scattering (LiX) beamline at NSLS-II. Using a two-stage focusing scheme, an adjustable beam size between a few micrometres and a fraction of a millimetre is produced at the sample position. Scattering data at small and wide angles are collected simultaneously on multiple Pilatus detectors. A recent addition of an in-vacuum Pilatus 900k detector, with the detector modules arranged in a C-shaped configuration, has improved the azimuthal angle coverage in the wide-angle data. As an option, fluorescence data can be collected simultaneously. Fly scans have been implemented to minimize the time interval between scattering patterns and to avoid unnecessary radiation damage to the sample. For weakly scattering samples, an in-vacuum sample environment has been developed here to minimize background scattering. Data processing for these measurements is highly sample-specific. To establish a generalized data process workflow, first the data are reduced to reciprocal coordinates at the time of data collection. The users can then quantify features of their choosing from these intermediate data and construct structural maps. As examples, results from in-vacuum mapping of onion epidermal cell walls and 2D tomographic sectioning of an intact poplar stem are presented.

36 MATERIALS SCIENCE↗

Augmented Topological Descriptors of Pore Networks for Material Science

One potential solution to reduce the concentration of carbon dioxide in the atmosphere is the geologic storage of captured CO 2 in underground rock formations, also known as carbon sequestration. There is ongoing research to guarantee that this process is both efficient and safe. We describe tools that provide measurements of media porosity, and permeability estimates, including visualization of pore structures. Existing standard algorithms make limited use of geometric information in calculating permeability of complex microstructures. This quantity is important for the analysis of biomineralization, a subsurface process that can affect physical properties of porous media. This paper introduces geometric and topological descriptors that enhance the estimation of material permeability. Our analysis framework includes the processing of experimental data, segmentation, and feature extraction and making novel use of multiscale topological analysis to quantify maximum flow through porous networks. We illustrate our results using synchrotron-based X-ray computed microtomography of glass beads during biomineralization. As a result, we also benchmark the proposed algorithms using simulated data sets modeling jammed packed bead beds of a monodispersive material.

36 MATERIALS SCIENCE↗

Lidar - ESRL WindCube 200s, Wasco Airport - Processed Data

The available "Readme" file introduces the basics of the Doppler lidar data and offers a detailed description of the variables present in the data files. For those with any further questions about the data and its interpretation, contact either Alan Brewer ( ) or Sunil Baidar ( ). It is highly recommended to discuss any planned use of the data with National Oceanic and Atmospheric Administration-Chemical Sciences Division (NOAA-CSD) scientists. For more information, refer to the Readme file: "noaa-esrl-wascolidar-readme.docx."

17 WIND ENERGY↗

THE MEASUREMENT, TREATMENT, AND IMPACT OF SPECTRAL COVARIANCE AND BAYESIAN PRIORS IN INTEGRAL -FIELD SPECTROSCOPY OF EXOPLANETS

The recovery of an exoplanet’s atmospheric parameters from its spectrum requires accurate knowledge of the spectral errors and covariances. Unfortunately, the complex image processing used in high-contrast integral-field spectrograph (IFS) observations generally produces spectral covariances that are poorly understood and often ignored. In this work, we show how to measure the spectral errors and covariances and include them self-consistently in parameter retrievals. By combining model exoplanet spectra with a realistic noise model generated from the Gemini Planet Imager (GPI) early science data, we show that ignoring spectral covariance in high-contrast IFS data can both bias inferred parameters and lead to unreliable confidence regions on those parameters. This problem is made worse by the common practice of scaling the χ {sup 2} per degree of freedom to unity; the input parameters then fall outside the 95% confidence regions in as many as ∼80% of noise realizations. The biases we observe can approach the typical levels of precision achieved in high-contrast spectroscopy. Accounting for realistic priors in fully Bayesian retrievals can also have a significant impact on the inferred parameters. Plausible priors on effective temperature and surface gravity can vary by an order of magnitude across the confidence regions appropriate for objects with weak age constraints; priors for objects with good age constraints are dominated by modeling uncertainties. Our methods are directly applicable to existing high-contrast IFSs including GPI and SPHERE, as well as upcoming instruments like CHARIS and, ultimately, WFIRST-AFTA.

79 ASTRONOMY AND ASTROPHYSICS↗

Statistical framework to assess long-term spatio-temporal climate changes: East River mountainous watershed case study

Abstract Evaluation of long-term temporal and spatial climatic change in mountainous regions is a critical challenge because of the interactive effects of multiple land and climatic factors and processes. Here we present the application of the statistical framework to the assessment of changes of climatic conditions, using data from 17 meteorological stations across the East River watershed near Crested Butte, Colorado, USA, and spanning the period from 1966 to 2021. The framework is developed based on (1) a time-series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) evaluation and time series analysis of potential evapotranspiration (ET o ), actual evapotranspiration (ET), aridity index (AI), standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) a temporal-spatial climatic zonation of the studied area based on the hierarchical clustering and PCA analysis of the SPEI, because the SPEI can be considered an integrative characteristic of the changes of climatic conditions. The Budyko model, with the application of the Penman–Monteith equation for the estimation of ET o , was used to determine the ET. The time series analysis of the AI is used to identify the periods with energy limited and water limited conditions. Hierarchical clustering of site locations for the three temporal segments of the SPEI showed a significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns. Therefore, the watershed climatic zonation requires periodic re-evaluation based on the structural time series analysis of meteorological and water balance data.

54 ENVIRONMENTAL SCIENCES↗

Dissolution zone model of the oxide structure in additively manufactured dispersion-strengthened alloys

The structural evolution of oxides in dispersion-strengthened superalloys during laser-powder bed fusion is considered in detail. Alloy chemistry and process parameter effects on oxide structure are assessed through a parameter study on the model alloy Ni-20Cr, doped with varying concentrations of Y 2 O 3 and Al. Small angle neutron scattering measurements of the dispersoid size distribution show the dispersoid size increases with higher laser power, slower scan speed, and increasing Y 2 O 3 and Al content. Complementary electron microscopy measurements reveal reactions between Y 2 O 3 and Al, even in nanoscale dispersoids, and the presence of micron-scale oxide slag inclusions in select specimens. A scaling analysis of mass and momentum transport within the melt pool, presented here, establishes that diffusional structural evolution mechanisms dominate for nanoscale dispersoids, while fluid forces and advection become significant for larger slag inclusions. These findings are developed into a theory of dispersoid structural evolution, integrating quantitative models of diffusional processes – dispersoid dissolution, nucleation, growth, coarsening – with a reduced order model of time-temperature trajectories of fluid parcels within the melt pool. Calculations of the dispersoid size in single-pass melting reveal a zone in the center of the melt track in which the oxide feedstock fully dissolves. Within this zone the final Y 2 O 3 size is independent of feedstock size and determined by nucleation and growth kinetics. If the dissolution zones of adjacent melt tracks overlap sufficiently with each other to dissolve large oxides, formed during printing or present in the powder feedstock, then the dispersoid structure throughout the build volume is homogeneous and matches that from a single pass within the dissolution zone. Gaps between adjacent dissolution zones result in oxide accumulation into larger slag inclusions. Predictions of final dispersoid size and slag formation using this dissolution zone model match the present experimental data and explain process-structure linkages speculated in the open literature.

36 MATERIALS SCIENCE↗