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At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Astronomical Software Directory Service

With the support of NASA's Astrophysics Data Program (NRA 92-OSSA-15), we have developed the Astronomical Software Directory Service (ASDS): a distributed, searchable, WWW-based database of software packages and their related documentation. ASDS provides integrated access to 56 astronomical software packages, with more than 16,000 URLs indexed for full-text searching. Users are performing about 400 searches per month. A new aspect of our service is the inclusion of telescope and instrumentation manuals, which prompted us to change the name to the Astronomical Software and Documentation Service. ASDS was originally conceived to serve two purposes: to provide a useful Internet service in an area of expertise of the investigators (astronomical software), and as a research project to investigate various architectures for searching through a set of documents distributed across the Internet. Two of the co-investigators were then installing and maintaining astronomical software as their primary job responsibility. We felt that a service which incorporated our experience in this area would be more useful than a straightforward listing of software packages. The original concept was for a service based on the client/server model, which would function as a directory/referral service rather than as an archive. For performing the searches, we began our investigation with a decision to evaluate the Isite software from the Center for Networked Information Discovery and Retrieval (CNIDR). This software was intended as a replacement for Wide-Area Information Service (WAIS), a client/server technology for performing full-text searches through a set of documents. Isite had some additional features that we considered attractive, and we enjoyed the cooperation of the Isite developers, who were happy to have ASDS as a demonstration project. We ended up staying with the software throughout the project, making modifications to take advantage of new features as they came along, as well as influencing the software development. The Web interface to the search engine is provided by a gateway program written in C++ by a consultant to the project (A. Warnock).

Hanisch, Robert J.↗
NASA NTRS1999

Ice Flow in the Northeast Greenland Ice Stream

As the use of satellite data for icesheet research has accelerated, new discoveries have changed the way that we think about ice discharge, and new tools have changed the way that we measure it. We have begun to apply a combination of remote sensing and ice sheet modeling techniques to study ice flow in the recently identified northeast Greenland ice stream. This ice stream shows evidence of organized flow far into the interior and has low-slope areas of rapid flow and regions of enhanced shear that resemble the ice streams of West Antarctica. The onset of rapid flow close to the ice divide and the evolution of its flow pattern, however, make this ice stream unique. Our investigation is aimed at increasing our understanding of this ice stream and its origins and role in the discharge of ice from the ice sheet and the effects that discharge may have on the history and evolution of the ice in northern Greenland. The ice flow data comes from satellite radar interferometry tied to reference points. We have applied this technique to measure the velocity over the entire ice stream, which is more than 700 km in length. The velocity contours reveal distinct, well-developed shear margins, which are coincident with linear features in the Synthetic Aperture Radar (SAR) amplitude imagery. It is surprising to see such defined, enhanced flow so far inland and so close to the ice divide. The velocity data and topography data are being used to feed a modeling effort that allows us to study the dynamics and character rapid flow. Additional information is contained in the original.

Joughin, Ian↗
NASA NTRS1997

Carl Sagan and the Exploration of Mars and Venus

Inspired by childhood readings of books by Edgar Rice Burroughs, Carl Sagan's first interest in planetary science focused on Mars and Venus. Typical of much of his career he was skeptical of early views about these planets. Early in this century it was thought that the Martian wave of darkening, a seasonal albedo change on the planet, was biological in origin. He suggested instead that it was due to massive dust storms, as was later shown to be the case. He was the first to recognize that Mars has huge topography gradients across its surface. During the spacecraft era, as ancient river valleys were found on the planet, he directed studies of Mars' ancient climate. He suggested that changes in the planets orbit were involved in climate shifts on Mars, just as they are on Earth. Carl had an early interest in Venus. Contradictory observations led to a controversy about the surface temperature, and Carl was one of the first to recognize that Venus has a massive greenhouse effect at work warming its surface. His work on radiative transfer led to an algorithm that was extensively used by modelers of the Earth's climate and whose derivatives still dominate the calculation of radiative transfer in planetary atmospheres today. Carl inspired a vast number of young scientists through his enthusiasm for new ideas and discoveries, his skeptical approach, and his boundless energy. I had the privilege to work in Carl's laboratory during the peak of the era of Mars' initial exploration. It was an exciting time, and place. Carl made it a wonderful experience.

Toon, Owen B.↗
NASA NTRS2009

RXTE Monitoring of the Anomalous X-ray Pulsar 1E 1048.1-5937: Long-Term Variability and the 2007 March Event

After three years of no unusual activity, Anomalous X-ray Pulsar 1E 1048.1-5937 reactivated in 2007 March. We report on the detection of a large glitch (deltav/v = 1.63(2) x 10(exp -5)) on 2007 March 26 (MJD 54185.9), contemporaneous with the onset of a pulsed-flux flare, the third flare observed from this source in 10 years of monitoring with the Rossi X-ray Timing Explorer. Additionally, we report on a detailed study of the evolution of the timing properties, the pulsed flux, and the pulse profile of this source as measured by RXTE from 1996 July to 2008 January. In our timing study, we attempted phase coherent timing of all available observations. We show that in 2001, a timing anomaly of uncertain nature occurred near the rise of the first pulsed flux flare; we show that a likely glitch (deltav/v = 2.91(9) x 10(exp -6)) occurred in 2002, near the rise of the second flare, and we present a detailed description of the variations in the spin-down. In our pulsed flux study, we compare the decays of the three flares and discuss changes in the hardness ratio. In our pulse profile study, we show that the profile exhibited large variations near the peak of the first two flares, and several small short-term profile variations during the most recent flare. Finally, we report on the discovery of a small burst 27 days after the peak of the last flare, the fourth burst discovered from this source. We discuss the relationships between the observed properties in the framework of the magnetar model.

Dib, Rim↗
NASA NTRSDate not supplied

Modeling Atmospheric Science Knowledge from Research Publications

NASA Earth Science Data Centers contain enormous amounts of remote sensing digital data. It is often a significant challenge for users to find data suitable for their research topic in these vast archives. One of the approaches is the usage-driven dataset discovery, where users seek publications on projects similar to their intended study. For this approach to be effective, users need a clear connection between the underlying data in the publications and the study objectives; this is not often apparent to non-expert users. Tools and methodologies that can help facilitate and organize these connections are therefore valuable for creating improved knowledge mappings, which can be further used by search engines to suggest data or publications best tailored to a user’s specific research goal. As an illustration of these challenges, in this work we focus on the atmospheric chemistry processes related to Earth environmental impacts such as ozone depletion, aerosols, smog formation, acid rain, and radiative forcing. We further limit our study to publications that use data from the Microwave Limb Sounder (MLS) instrument flown on the Aura Earth Observing System. To create knowledge representations of science carried out in these publications, we use existing ontologies such as the Global Change Master Directory (GCMD) and Semantic Web for Earth and Environmental Terminology (SWEET). These ontologies together encompass term dictionaries that include measured variables, names of molecules or radicals, mission and instrument names, locations, action words, among many others. Based on these terms acknowledge graph database was populated with the terms retrieved from scientific publications that study atmospheric chemistry. These databases can be used to further enhance the automation of knowledge discovery and facilitate machine learning and artificial intelligence algorithms or applications. These tools and methods can also be extended to apply to content from other related Earth science domains.

Irina Gerasimov↗
NASA NTRS2021

Discovery and Timing of Three Millisecond Pulsars in Radio and Gamma-Rays with the Giant Metrewave Radio Telescope and Fermi Large Area Telescope

We performed deep observations to search for radio pulsations in the directions of 375 unassociated Fermi Large Area Telescope γ-ray sources using the Giant Metrewave Radio Telescope (GMRT) at 322 and 607 MHz. In this paper we report the discovery of three millisecond pulsars (MSPs), PSR J0248+4230, PSR J1207–5050, and PSR J1536–4948. We conducted follow-up timing observations for ∼5 yr with the GMRT and derived phase-coherent timing models for these MSPs. PSR J0248+4230 and J1207–5050 are isolated MSPs having periodicities of 2.60 ms and 4.84 ms. PSR J1536–4948 is a 3.07 ms pulsar in a binary system with an orbital period of ∼62 days about a companion of a minimum mass of 0.32 M⊙. We also present multifrequency pulse profiles of these MSPs from the GMRT observations. PSR J1536–4948 is an MSP with an extremely wide pulse profile having multiple components. Using the radio timing ephemeris we subsequently detected γ-ray pulsations from these three MSPs, confirming them as the sources powering the γ-ray emission. For PSR J1536–4948 we performed combined radio–γ-ray timing using ∼11.6 yr of γ-ray pulse times of arrival (TOAs) along with the radio TOAs. PSR J1536–4948 also shows evidence for pulsed γ-ray emission out to above 25 GeV, confirming earlier associations of this MSP with a ≥10 GeV point source. The multiwavelength pulse profiles of all three MSPs offer challenges to models of radio and γ-ray emission in pulsar magnetospheres.

High energy astrophysics↗
NASA NTRS2020

Trident: The Path to Triton on a Discovery Budget

This paper describes Trident, a proposed Discovery mission to Neptune’s moon Triton, 30 AU from the Sun. Triton formed in the Kuiper Belt but was captured by Neptune into a highly-inclined retrograde orbit, where tidal forces thawed its interior, forming an ocean that likely persists to the present day. Recent outer solar system missions like Cassini and New Horizons have yielded completely new models for processes on ocean worlds, active worlds, and KBOs. Triton isn’t just a key to solar system science, it’s a whole keyring: a singular captured KBO and evolved ocean world, with active plumes, an energetic ionosphere, and a young unique surface. The NASA OPAG Roadmap to Ocean Worlds identifies Triton as the highest priority candidate ocean world [1], ripe for investigation. The Trident mission concept is an excellent case study in “design to cost”: we show how exploration of Triton under Discovery is made possible by radioisotope power combined with a rare, extremely efficient Jupiter gravity assist, enabling a simple, lowmass spacecraft design on a ballistic trajectory. The Triton encounter sequence probes for an ocean, measures the ionosphere, and views nearly the whole of Triton as it traverses a single orbit around Neptune, mapping the >60% of the surface that is as yet unseen. The Triton encounter concludes with fullframe imaging illuminated by “Neptune-shine” when Trident is in Neptune eclipse, for direct comparison with Voyager 2’s observations nearly 50 years prior. Trident carries a mature complement of instruments: a Magnetometer, IR Spectrometer & Narrow Angle Camera, Wide-Angle Camera, Plasma Spectrometer, and telecom hardware-enabled Radio Science. The flight system design integrates heritage components from Ball with JPL leadership and expertise in key specialty areas to provide a Voyager-like, robust spacecraft commensurate with a Discovery cost and risk tolerance. Cost avoidance features include: use of existing instrument designs, small blowdown monopropellant propulsion system, a contributed X-band telecommunications system that also performs radio science, and a simple power system. Additionally, the passive thermal design accommodates the large solar dynamic range from Venus to Neptune by using the HGA for shade when close to the sun, and MMRTG excess heat, modulated by louvers.

Frazier, William↗
NASA NTRSDate not supplied

Modeling and Simulation for DAVINCI Entry, Descent, and Landing

The Deep Atmosphere Venus Investigation of Noble gases, Chemistry, and Imaging (DAVINCI) mission will launch in June 2029 and explore Venus via two flybys and a probe landing currently scheduled for June 2031. DAVINCI is the 16th selection of the NASA Discovery Class program and its objectives include quantifying the chemical composition of the Venusian atmosphere, taking infrared descent imagery of the surface, and conducting remote observations of the dynamic atmosphere and cloud-deck. The goals are to study the origin, evolution, and current state of Venus, to understand if it was habitable at a point in the past, and to create an analog to hot terrestrial exo-planets similar to Venus.

Soumyo Dutta↗
DOE OSTI2021

Tutorial: Machine Learning and Artificial Intelligence in Batteries

Machine learning (ML) promises to compress the time needed to characterize battery performance, lifetime and safety. By coupling ML with physical models and metrics, that learning can bridge across materials, chemistries and cell designs. This tutorial will discuss the most popular ML techniques and resources and review recent work in the electrochemical literature. Applications include materials discovery, image recognition for quantitative microscopy analysis, fast charge algorithm development and life prediction.

47 OTHER INSTRUMENTATION↗
NASA NTRS2019

Orbital Debris Ontology, Terminology, and Knowledge Modeling

The looming threat orbital debris poses to assets in orbit demands solutions. As the orbital population grows, so does this hazard, but so does the sea of data. The problem is also an opportunity for interdisciplinary innovation and cooperation. This paper focuses on the data and information management aspect of developing solutions for a sustainable and safe orbital space environment. The corresponding author’s in-progress work to develop an orbital debris domain ontology is summarized in order to discuss knowledge modeling for this domain. Methodological approaches of this effort can also contribute to standards efforts and address terminological and policy questions. Leveraging the growing volumes of orbital debris and space situational awareness (SSA) data will create a more complete picture of the orbital space environment. Part of the solution will be: consistent and correct data interpretation, sharing orbital debris and SSA data in one form or another, terminology development & harmonization, and knowledge or domain modeling. To facilitate this, [Rovetto, 2015/16] discussed ontology development for the orbital debris domain. This paper lists concepts from that paper, and subsequently developed concepts [2-9]. Ontology engineering is an interdisciplinary field related to knowledge representation and reasoning in artificial intelligence, semantic technologies and the so-called semantic web. An ontology is effectively a computable and semantically rich terminology that presents a knowledge or domain model for a topic area. Expressions of knowledge or assertions are stored using formally defined term. This knowledge base is reasoned over to yield answers to queries, among other things. Ontologies have been developed in knowledge-based projects across various disciplines, and used for such things as search engines, chatbots, enterprise knowledge graphs, etc. Ontologies support: interoperability, automated reasoning, data sharing and integration, data search and retrieval, and communicating the meaning of data. The Orbital Debris Ontology (ODO), and related ontologies [Rovetto & Kelso 2016] [Rovetto 2016, 2017], were proposed to help achieve this. ODO, for instance, is intended as a domain ontology that can be used across federated databases, offering an explicitly specified set of concepts describing the orbital debris domain. Its meaning-rich taxonomy will provide a sharable semantics for orbital debris data to, in part, consistently communicate the meaning of data to both humans and machines, and tag data elements in space object catalogs to help afford inference tasks, decision support, knowledge discovery, and information integration. ODO and the SSA ontology (SSAO) is part of the overall Orbital Space Domain Ontology concept, which is conceived as a broader domain reference ontology. It aims to provide a knowledge representation structure of the orbital space environment, a common semantic model, and develop a sharable terminology. Collectively this will provide common meaning for datasets, a high-level taxonomy or classification for orbital space objects, and thus means to characterize space objects. Ongoing efforts have included using visualizations, R, JSON-LD, and contemporary semantic technologies. Potential applications and interdisciplinary partnerships include web-based platforms, web apps, visualizations, and academia projects. Community input and participation may yield a more widely understood domain model as well as facilitate terminological standards. For example, the proposed conceptual, terminological and ontological analysis may contribute to such efforts as the Space Debris Mitigation Requirements in the International Standards Organization by developing more precise, consistent and coherent terms and definitions. Projects that seek to develop in-house ontologies can use ODO and related ontologies as domain reference ontologies. This paper was developed independent of author affiliations. Readers are encouraged to contact corresponding author(1) with general interest and potential opportunities to support or realize the described project.

Robert J. Rovetto↗
NASA NTRS2022

Direct-imaging Discovery and Dynamical Mass of a Substellar Companion Orbiting an Accelerating Hyades Sun-like Star with SCExAO/CHARIS

We present the direct-imaging discovery of a substellar companion in orbit around a Sun-like star member of the Hyades open cluster. So far, no other substellar companions have been unambiguously confirmed via direct imaging around main-sequence stars in Hyades. The star HIP 21152 is an accelerating star as identified by the astrometry from the Gaia and Hipparcos satellites. We detected the companion, HIP 21152 B, in multiple epochs using the high-contrast imaging from SCExAO/CHARIS and Keck/NIRC2. We also obtained the stellar radialvelocity data from the Okayama 188 cm telescope. The CHARIS spectroscopy reveals that HIP 21152 B’s spectrum is consistent with the L/T transition, best fit by an early T dwarf. Our orbit modeling determines the semimajor axis and the dynamical mass of HIP 21152 B to be 17 +7.2 −3.8 and 27.8 +8.4 −5.4 M Jub , respectively. The mass ratio of HIP 21152 B relative to its host is ≈2%, near the planet/brown dwarf boundary suggested by recent surveys. Mass estimates inferred from luminosity-evolution models are slightly higher (33–42 MJup). With a dynamical mass and a well-constrained age due to the system’s Hyades membership, HIP 21152 B will become a critical benchmark in understanding the formation, evolution, and atmosphere of a substellar object as a function of mass and age. Our discovery is yet another key proof of concept for using precision astrometry to select direct-imaging targets.

Masayuki Kuzuhara↗
DOE OSTI2024

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to enable and understanding of their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transfected with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produce. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of novel DB positions and enzymatic specificity.

mass spectrometry, deep learning, Lipidomics, doub↗
DOE OSTI2024

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN)-based deep learning models is a powerful technique to detect features from material micrographs in an automated manner. In contrast to microstructural classification, supervised CNN models for segmentation tasks require pixel-wise annotation labels. However, manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. Microstructural characterization especially needs to be expedited for faster material discovery by changing alloy compositions. Here, in this study, we attempt to overcome such limitations by utilizing multimodal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy images of 347H stainless steel as training data and electron backscatter diffraction micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. The viability of our method is evaluated by considering a set of deep CNN architectures. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that naïve pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Using Aggregation to Chaperone Nanoparticles Across Fluid Interfaces

Nanoparticles (NPs) transfer is usually induced by adding ligands to modify NP surfaces, but aggregation of NPs oftentimes hampers the transfer. Here, we show that aggregation during NP phase transfer does not necessarily result in transfer failure. Using a model system comprising gold NPs and amphiphilic polymers, we demonstrate an unusual mechanism by which NPs can undergo phase transfer from the aqueous phase to the organic phase via a single-aggregation-single pathway. Our discovery challenges the conventional idea that aggregation inhibits NP transfer and provides an unexpected pathway for transferring larger-sized NPs (>20 nm). The charged amphiphilic polymers effectively act as chaperons for the NP transfer and offer a unique way to manipulate the dispersion and distribution of NPs in two immiscible liquids. Moreover, by intentionally jamming the NP-polymer assembly at the liquid/liquid interface, the transfer process can be inhibited.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. Here, we finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Advances in Kriging-Based Autonomous X-Ray Scattering Experiments

Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multivariate chemogenomic screening prioritizes new macrofilaricidal leads

Development of direct acting macrofilaricides for the treatment of human filariases is hampered by limitations in screening throughput imposed by the parasite life cycle. In vitro adult screens typically assess single phenotypes without prior enrichment for chemicals with antifilarial potential. We developed a multivariate screen that identified dozens of compounds with submicromolar macrofilaricidal activity, achieving a hit rate of >50% by leveraging abundantly accessible microfilariae. Adult assays were multiplexed to thoroughly characterize compound activity across relevant parasite fitness traits, including neuromuscular control, fecundity, metabolism, and viability. Seventeen compounds from a diverse chemogenomic library elicited strong effects on at least one adult trait, with differential potency against microfilariae and adults. Our screen identified five compounds with high potency against adults but low potency or slow-acting microfilaricidal effects, at least one of which acts through a novel mechanism. We show that the use of microfilariae in a primary screen outperforms model nematode developmental assays and virtual screening of protein structures inferred with deep learning. These data provide new leads for drug development, and the high-content and multiplex assays set a new foundation for antifilarial discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

RAVEN Theory Manual

RAVEN is a software framework able to perform parametric and stochastic analysis based on the response of complex system codes. The initial development was aimed at providing dynamic risk analysis capabilities to the thermohydraulic code RELAP-7, currently under development at Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose stochastic and uncertainty quantification platform, capable of communicating with any system code. In fact, the provided Application Programming Interfaces (APIs) allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating system response and explore input space using various sampling schemes such as Monte Carlo, grid, or Latin hypercube. However, RAVEN strength lies in its system feature discovery capabilities such as: constructing limit surfaces, separating regions of the input space leading to system failure, and using dynamic supervised learning techniques. The development of RAVEN started in 2012 when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework arose. RAVEN’s principal assignment is to provide the necessary software and algorithms in order to employ the concepts developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just to identify the frequency of an event potentially leading to a system failure, but the proximity (or lack thereof) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. peak pressure in a pipe) is exceeded under certain conditions. Most of the capabilities, implemented having RELAP-7 as a principal focus, are easily deployable to other system codes. For this reason, several side activates have been employed (e.g. RELAP5-3D, any MOOSE-based App, etc.) or are currently ongoing for coupling RAVEN with several different software. The aim of this document is to provide a set of commented examples that can help the user to become familiar with the RAVEN code usage.

97 MATHEMATICS AND COMPUTING↗