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DOE OSTI2025

The Analysis Description Language Ecosystem: Latest developments and physics applications

We present latest developments in Analysis Description Language (ADL), a declarative domain-specific language describing the physics algorithm of a HEP data analysis decoupled from software frameworks. Analyses written in ADL can be integrated into any framework for various tasks. ADL is a multipurpose construct with uses ranging from analysis design to preservation, reinterpretation, queries, visualisation, combination, etc. The most advanced infrastructure to execute ADL on events is the CutLang runtime interpreter. Recent technical developments include an automated interface with different data types, generation of the abstract syntax tree, a visualization tool that that auto-converts analysis flows to graphs, incorporation of trained machine learning models and a Jupyter-based plotting tool. We also report physics implications including a large scale LHC analysis implementation and validation effort for beyond the standard model reinterpretation purposes and studies with ATLAS and CMS open data.

Sekmen, Sezen [Kyungpook National Univ., Daegu (Ko↗

NASA NTRS2005

A Multi-scale Approach to Urban Thermal Analysis

An environmental consequence of urbanization is the urban heat island effect, a situation where urban areas are warmer than surrounding rural areas. The urban heat island phenomenon results from the replacement of natural landscapes with impervious surfaces such as concrete and asphalt and is linked to adverse economic and environmental impacts. In order to better understand the urban microclimate, a greater understanding of the urban thermal pattern (UTP), including an analysis of the thermal properties of individual land covers, is needed. This study examines the UTP by means of thermal land cover response for the Salt Lake City, Utah, study area at two scales: 1) the community level, and 2) the regional or valleywide level. Airborne ATLAS (Advanced Thermal Land Applications Sensor) data, a high spatial resolution (10-meter) dataset appropriate for an environment containing a concentration of diverse land covers, are used for both land cover and thermal analysis at the community level. The ATLAS data consist of 15 channels covering the visible, near-IR, mid-IR and thermal-IR wavelengths. At the regional level Landsat TM data are used for land cover analysis while the ATLAS channel 13 data are used for the thermal analysis. Results show that a heat island is evident at both the community and the valleywide level where there is an abundance of impervious surfaces. ATLAS data perform well in community level studies in terms of land cover and thermal exchanges, but other, more coarse-resolution data sets are more appropriate for large-area thermal studies. Thermal response per land cover is consistent at both levels, which suggests potential for urban climate modeling at multiple scales.

Gluch, Renne↗

DOE OSTI2015

Numerical and experimental analysis of an in-scale masonry cross-vault prototype up to failure

A heterogeneous full 3D non-linear FE approach is validated against experimental results obtained on an in-scale masonry cross vault assembled with dry joints, and subjected to various loading conditions consisting on imposed displacement combinations to the abutments. The FE model relies into a discretization of the blocks by means of few rigid-infinitely resistant parallelepiped elements interacting by means of planar four-noded interfaces, where all the deformation (elastic and inelastic) occurs. The investigated response mechanisms of vault are the shear in-plane distortion and the longitudinal opening and closing mechanism at the abutments. After the validation of the approach on the experimentally tested cross-vault, a sensitivity analysis is conducted on the same geometry, but in real scale, varying mortar joints mechanical properties, in order to furnish useful hints for safety assessment, especially in presence of seismic action.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Scale and the representation of human agency in the modeling of agroecosystems

Human agency is an essential determinant of the dynamics of agroecosystems. However, the manner in which agency is represented within different approaches to agroecosystem modeling is largely contingent on the scales of analysis and the conceptualization of the system of interest. While appropriate at times, narrow conceptualizations of agroecosystems can preclude consideration for how agency manifests at different scales, thereby marginalizing processes, feedbacks, and constraints that would otherwise affect model results. Modifications to the existing modeling toolkit may therefore enable more holistic representations of human agency. Model integration can assist with the development of multi-scale agroecosystem modeling frameworks that capture different aspects of agency. In addition, expanding the use of socioeconomic scenarios and stakeholder participation can assist in explicitly defining context-dependent elements of scale and agency. Finally, such approaches, however, should be accompanied by greater recognition of the meta agency of model users and the need for more critical evaluation of model selection and application.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Observability Analysis of a Power System Stochastic Dynamic Model Using a Derivative-Free Approach

Serving as a prerequisite to power system dynamic state estimation, the observability analysis of a power system dynamic model has recently attracted the attention of many power engineers. However, because this model is typically nonlinear and large-scale, the analysis of its observability is a challenge to the traditional derivative-based methods. Indeed, the linear-approximation-based approach may provide unreliable results while the nonlinear-technique-based approach inevitably faces extremely complicated derivations. Furthermore, because power systems are intrinsically stochastic, the traditional deterministic approaches may lead to inaccurate observability analyses. In this work, facing these challenges, we propose a novel polynomial-chaos-based derivative-free observability analysis approach that not only is free of any linear approximations, but also accounts for the stochasticity of the dynamic model while bringing a low implementation complexity. Furthermore, this approach enables us to quantify the degree of observability of a stochastic model, what conventional deterministic methods cannot do. The excellent performance of the proposed method has been demonstrated by performing extensive simulations using a synchronous generator model with IEEE-DC1A exciter and the TGOV1 turbine governor.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2012

Development of a Scale Model for High Flux Isotope Reactor Cycle 400

The development of a comprehensive SCALE computational model for the High Flux Isotope Reactor (HFIR) is documented and discussed in this report. The SCALE model has equivalent features and functionality as the reference MCNP model for Cycle 400 that has been used extensively for HFIR safety analyses and for HFIR experiment design and analyses. Numerical comparisons of the SCALE and MCNP models for the multiplication constant, power density distribution in the fuel, and neutron fluxes at several locations in HFIR indicate excellent agreement between the results predicted with the two models. The SCALE HFIR model is presented in sufficient detail to provide the users of the model with a tool that can be easily customized for various safety analysis or experiment design requirements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2023

Structural Chokepoints Determine the Resilience of Agri-Food Supply Chains in the United States

The agricultural and food systems of the United States are critical for ensuring the stability of both domestic and global food systems. Thus, it is essential to understand the structural resilience of the country’s agri-food supply chains to a suite of threats. Here we employ complex network statistics to identify the spatially resolved structural chokepoints in the agri-food supply chains of the United States. We identify seven chokepoints at county scale: Riverside CA, San Bernardino CA, Los Angeles CA, Shelby TN, Maricopa AZ, San Diego CA and Cook IL; as well as seven chokepoints at freight analysis framework scale: Los Angeles–Long Beach CA, Chicago– Naperville IL, New York–New Jersey NJ, New York–New Jersey NY, Remainder of Texas, Remainder of Pennsylvania, and San Jose–San Francisco–Oakland CA. These structural chokepoints are generally consistent through time (2007, 2012, 2017), particularly for processed food commodities. This study improves our understanding of agri-food supply-chain security and may aid policies aimed at enhancing its resilience.

Agriculture↗

DOE OSTI2012

Detection of Coherent Structures in Extreme-Scale Simulations

The analysis of coherent structures is a common problem in many scientific domains ranging from astrophysics to combustion, fusion, and materials science. The data from three-dimensional simulations are analyzed to detect the structures, extract statistics on them, and track them over time to gain insights into the phenomenon being modeled. This analysis is typically done off-line, using data that have been written out by the simulations. However, the move towards extreme scale architectures, with multi-core processors and graphical processing units, will affect how such analysis is done as it is unlikely that the systems will support the I/O bandwidth required for off-line analysis. Moving the analysis in-situ is a solution only if we know a priori what analysis will be done, as well as the algorithms used and their parameter settings. Even then, we need to ensure that this will not substantially increase the memory requirements or the data movement as the former will be limited and the latter will be expensive. In the Exa-DM project, a collaboration between Lawrence Livermore National Laboratory and University of Minnesota, we are exploring ways in which we can address the conflicting demands of coherent structure analysis of simulation data and the architecture of modern parallel systems, while enabling scientific discovery at the exascale. In this paper, we describe our work in two areas: the in situ implementation of an existing algorithm for coherent structure analysis and the use of graph-based techniques to efficiently compress the data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Steam Condensation Scaled Experiment in the Presence of Non-condensable Gas for Reactor Containment Passive Safety Analysis

This study presents scaled experiments using steam condensation with non-condensable gas (NCG)—helium, simulating hydrogen—as these experiments are pivotal for water-cooled reactor passive containment cooling system (PCCS) design and analysis. Research into PCCSs for small modular reactors (SMRs) is especially important in light of SMR system design; however, studies in the literature reflect limitations due to test geometry and operational condition variations, without considering SMR prototypic design. To address these challenges, a scaled test facility was developed to accurately replicate SMR PCCSs. This facility includes vertical down-flow condensing test sections with 1-, 2-, and 4-in.-diameter condensing tubes, accompanied by annular water cooling. Experiments were conducted using both superheated and saturated steam, with steam mass flow rates varying from 55 to 66 kg/hr., in the presence of helium as the NCG mass flow rate ranges from 1.8 to 22 kg/hr. Test data were collected on (a) the axial temperatures of the annular cooling water; (b) the outer wall temperature of the condensers; and (c) the mass flow rate, temperature, and pressure at the test section inlets and outlets. These primary test data were used in conjunction with a standard data reduction methodology to estimate essential thermal parameters such as heat fluxes, heat transfer coefficients, and condensation rates. The effects of NCGs on steam condensation within the geometry of the scaled test sections were then presented in regard to various testing conditions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Presentation: Steam Condensation Scaled Experiment in the Presence of Non-condensable Gas for Reactor Containment Passive Safety Analysis

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

A Scalable Parallel Hypergraph Generator (HyGen)

Graphs are extensively used to model real-world complex systems. An edge in a graph can model pairwise relationships. However, multiway relationships (connections between three or more vertices) are common in many complex systems such as cellular process, image segmentation, and circuit design. A graph edge cannot model multiway relationships. A hypergraph, which can connect more than two vertices, is thus a better option to model multiway relationships. A large-scale hypergraph analysis has the potential to find useful insights from a complex system and assist in knowledge discovery. Currently a limited number of hypergraphs exists that are representative of real-world datasets. Moreover, real-world hypergraph datasets are small in size and inadequate to incorporate future needs. A graph generator that can produce large-scale synthetic hypergraphs can solve the above mentioned problems. In this paper, we present a scalable parallel hypergraph generator (HyGen) based on the Message Passing Interface (MPI) standard. To generate hypergraphs, HyGen takes the following parameter values as inputs: i) number of vertices, ii) number of hyperedges, iii) number of clusters, iv) vertex distribution, v) hyperedge distribution, vi) local cluster cardinality, and vii) global cluster cardinality. We have demonstrated that HyGen can generate hypergraphs of various sizes in a scalable fashion. HyGen takes approximately four minutes to generate a hypergraph with 4.8 million vertices, 1.6 million hyperedges, and 800 clusters using 1,024 processes on a leadership class computing platform. Our strong and weak scaling experiments on supercomputers demonstrate that HyGen can quickly create large-scale hypergraphs in a parallel manner, thus providing a useful capability for hypergraph analysis.

Hasan, S M Shamimul↗

DOE OSTI2023

Heterogeneity or illusion? Track the carbon Kuznets curve of global residential building operations

Residential buildings, the “last mile” sector in the global decarbonization, have become the most significant uncertain factor hindering carbon neutrality with increasing household energy demand. To track the operational carbon in buildings, this study investigates the carbon Kuznets curve (CKC) and the corresponding decoupling status of residential building operations at four emission scales by using the data of 30 countries from 2000 to 2019. The results show that (1) the CKC model can fit more than half of the samples. Most curves have an inverted U-shape, with 76% of emission per household and 82% of total emissions. (2) In the presence of the CKC, over four-fifths of global residential buildings peak regardless of any emission scale. The analysis denotes that the carbon emissions of developed countries reach their peaks earlier. In the total emissions, the samples’ peaking proportion is 20% and 25% with income per capita < 20,000 United States dollars (USD) and 20,000–40,000 USD, respectively. (3) The Tapio decoupling analysis and the threshold regression effectively verify the robustness and the heterogeneity of CKCs, respectively. Strong decoupling effects of CKCs in most countries are demonstrated at the scales of emission per floor space and the total emissions, and the heterogeneity proves the classic inverted U-shaped relationship between economy and emissions doesn’t exist in all emitters. Overall, this study tracks the historical carbon emission trajectories of residential building operations at a global scale, providing reference for different economies to simulate the dynamic of building carbon emissions along with the economic booming.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

The Quest for the Inflationary Spectral Runnings in the Presence of Systematic Errors

Cosmological inflation suggests that the scalar spectral index "runs" with scale. Constraints on the values of the spectral runnings, ${\alpha }_{s}\equiv {{dn}}_{s}/d\,\mathrm{ln}\,k$ and ${\beta }_{s}\equiv d{\alpha }_{s}/d\,\mathrm{ln}\,k$, therefore provide a fundamental test of the physics of inflation. In this work, we review the feasibility of measuring the runnings when information from upcoming large-volume galaxy surveys is used to supplement the information provided by a cosmic microwave background (CMB)-S4 experiment, particularly focusing on the effect of including high-k, nonlinear scales. Since these measurements will be sensitive to modeling uncertainties for the nonlinear power spectrum, we examine how three different ways of parameterizing those systematics—introducing zero, two, or several hundred nuisance parameters—affect constraints and protect against parameter biases. Considering statistical errors alone, we find that including strongly nonlinear scales can substantially tighten constraints. However, these constraints weaken to levels not much better than those from a CMB-S4 experiment alone when we limit our analysis to scales where estimates are not strongly affected by systematic biases. Given these considerations, near-future large-scale structure (LSS) surveys are unlikely to add much information to the CMB-S4 measurement of the first running α s . There is more potential for improvement for the second running, β s , for which LSS information will allow constraints to be improved by a factor of 3–4 relative to using the CMB alone. Though these limitations are still above the value predicted by slow-roll inflation, they do probe regions of parameter space relevant to nonstandard inflationary models with large runnings, for example, those that can generate an appreciable abundance of primordial black holes.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Used Nuclear Fuel-Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS)

This will be licensed via RSICC. A new, integrated data and analysis system has been designed to simplify and automate the performance of accurate and efficient evaluations for characterizing the input to the overall nuclear waste management system -UNF-Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS). A relational database within UNF-ST&DARDS provides a standard means by which UNF-ST&DARDS can succinctly store and retrieve modeling and simulation (M&S) parameters for specific spent nuclear fuel analysis. A library of various analysis model templates provides the ability to communicate the various set of M&S parameters to the most appropriate M&S application. Interactive visualization capabilities facilitate data analysis and results interpretation. UNF-ST&DARDS current analysis capabilities include (1) assembly-specific depletion and decay, (2) and spent nuclear fuel cask-specific criticality and shielding. Currently, UNF-ST&DARDS uses SCALE nuclear analysis code system for performing nuclear analysis.

Banerjee, Kaushik↗

DOE OSTI2016

Large scale nanoparticle screening for small molecule analysis in laser desorption ionization mass spectrometry

Nanoparticles (NPs) have been suggested as efficient matrixes for small molecule profiling and imaging by laser-desorption ionization mass spectrometry (LDI-MS), but so far there has been no systematic study comparing different NPs in the analysis of various classes of small molecules. Here, we present a large scale screening of 13 NPs for the analysis of two dozen small metabolite molecules. Many NPs showed much higher LDI efficiency than organic matrixes in positive mode and some NPs showed comparable efficiencies for selected analytes in negative mode. Our results suggest that a thermally driven desorption process is a key factor for metal oxide NPs, but chemical interactions are also very important, especially for other NPs. Furthermore, the screening results provide a useful guideline for the selection of NPs in the LDI-MS analysis of small molecules.

36 MATERIALS SCIENCE↗

NASA NTRS1999

Sleep-wake differences in scaling behavior of the human heartbeat: analysis of terrestrial and long-term space flight data

We compare scaling properties of the cardiac dynamics during sleep and wake periods for healthy individuals, cosmonauts during orbital flight, and subjects with severe heart disease. For all three groups, we find a greater degree of anticorrelation in the heartbeat fluctuations during sleep compared to wake periods. The sleep-wake difference in the scaling exponents for the three groups is comparable to the difference between healthy and diseased individuals. The observed scaling differences are not accounted for simply by different levels of activity, but appear related to intrinsic changes in the neuroautonomic control of the heartbeat.

Flight Experiment↗

NASA NTRS2013

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data: 1. an EOF Approach to the Spatial-Temporal Variability of Aerosol Optical Depth Using Multiple Remote Sensing Data Sets

Many remote sensing techniques and passive sensors have been developed to measure global aerosol properties. While instantaneous comparisons between pixel-level data often reveal quantitative differences, here we use Empirical Orthogonal Function (EOF) analysis, also known as Principal Component Analysis, to demonstrate that satellite-derived aerosol optical depth (AOD) data sets exhibit essentially the same spatial and temporal variability and are thus suitable for large-scale studies. Analysis results show that the first four EOF modes of AOD account for the bulk of the variance and agree well across the four data sets used in this study (i.e., Aqua MODIS, Terra MODIS, MISR, and SeaWiFS). Only SeaWiFS data over land have slightly different EOF patterns. Globally, the first two EOF modes show annual cycles and are mainly related to Sahara dust in the northern hemisphere and biomass burning in the southern hemisphere, respectively. After removing the mean seasonal cycle from the data, major aerosol sources, including biomass burning in South America and dust in West Africa, are revealed in the dominant modes due to the different interannual variability of aerosol emissions. The enhancement of biomass burning associated with El Niño over Indonesia and central South America is also captured with the EOF technique.

variability↗

DOE OSTI2017

New approaches for metabolomics by mass spectrometry

Small molecules constitute a large part of the world around us, including fossil and some renewable energy sources. Solar energy harvested by plants and bacteria is converted into energy rich small molecules on a massive scale. Some of the worst contaminants of the environment and compounds of interest for national security also fall in the category of small molecules. The development of large scale metabolomic analysis methods lags behind the state of the art established for genomics and proteomics. This is commonly attributed to the diversity of molecular classes included in a metabolome. Unlike nucleic acids and proteins, metabolites do not have standard building blocks, and, as a result, their molecular properties exhibit a wide spectrum. This impedes the development of dedicated separation and spectroscopic methods. Mass spectrometry (MS) is a strong contender in the quest for a quantitative analytical tool with extensive metabolite coverage. Although various MS-based techniques are emerging for metabolomics, many of these approaches include extensive sample preparation that make large scale studies resource intensive and slow. New ionization methods are redefining the range of analytical problems that can be solved using MS. This project developed new approaches for the direct analysis of small molecules in unprocessed samples, as well as pushed the limits of ultratrace analysis in volume limited complex samples. The projects resulted in techniques that enabled metabolomics investigations with enhanced molecular coverage, as well as the study of cellular response to stimuli on a single cell level. Effectively individual cells became reaction vessels, where we followed the response of a complex biological system to external perturbation. We established two new analytical platforms for the direct study of metabolic changes in cells and tissues following external perturbation. For this purpose we developed a novel technique, laser ablation electrospray ionization (LAESI), for metabolite profiling of functioning cells and tissues. The technique was based on microscopic sampling of biological specimens by mid-infrared laser ablation followed by electrospray ionization of the plume and MS analysis. The two main shortcomings of this technique had been limited specificity due to the lack of a separation step, and limited molecular coverage, especially for nonpolar chemical species. To improve specificity and the coverage of the metabolome, we implemented the LAESI ion source on a mass spectrometer with ion mobility separation (IMS). In this system, the gas phase ions produced by the LAESI source were first sorted according to their collisional cross sections in a mobility cell. These separated ion packets were then subjected to MS analysis. By combining the atmospheric pressure ionization with IMS, we improved the metabolite coverage. Further enhancement of the non-polar metabolite coverage resulted from the combination of laser ablation with vacuum UV irradiation of the ablation plume. Our results indicated that this new ionization modality provided improved detection for neutral and non-polar compounds. Based on rapid progress in photonics, we had introduced another novel ion source that utilized the interaction of a laser pulse with silicon nanopost arrays (NAPA). In these nanophotonic ion sources, the structural features were commensurate with the wavelength of the laser light. The enhanced interaction resulted in high ion yields. This ultrasensitive analytical platform enabled the MS analysis of single yeast cells. We extended these NAPA studies from yeast to other microorganisms, including green algae (Chlamydomonas reinhardtii) that captured energy from sunlight on a massive scale. Combining cellular perturbations, e.g., through environmental changes, with the newly developed single cell analysis methods enabled us to follow dynamic changes induced in the cells. In effect, we were able to use individual cells as a “laboratory,” and approached the long-standing goal of establishing a “lab-in-a-cell.” Model systems for these studies included cells of cyanobacteria (Anabaena), yeast (Saccharomyces cerevisiae), green algae (C. reinhardtii) and Arabidopsis thaliana.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗