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DOE OSTI2023

Nuclear data for space exploration

Understanding the harmful effects of galactic cosmic rays (GCRs) on space exploration requires a substantial amount of nuclear data. Specifically, the interaction of energetic GCR charged particles with spacecraft materials generates secondary radiations that, through energy deposition, can harm astronauts and electronic systems. By identifying the gaps in our knowledge of the relevant nuclear data—such as interaction cross sections—and identifying ways to fill those gaps—with measurements, compilations, evaluations, dissemination, reaction modeling, sensitivity studies, and uncertainty quantification—the safety and viability of space exploration can be improved. This work surveys the state of the art in this interdisciplinary field and identifies promising collaborative research topics that have significant potential to advance our understanding of the effects of the space radiation environment on space exploration.

42 ENGINEERING↗

DOE OSTI2024

Methodology for Exploring Water and Hydropower Operating Criteria That Simultaneously Improve Economic and Environmental Considerations

Despite the reliability and flexibility of hydropower, the operation of hydroelectric power plants may have significant impacts on the downstream river system, including its water stage, sediment transport, and water temperature, ultimately affecting the ecology. To address these challenges, there is a need to identify water scheduling patterns that improve both hydropower economics and the environment relative to current operations. This paper presents a new methodology to explore promising operational criteria/rules that can achieve such improvements. Typical environmental impact statements and relicensing processes generally perform detailed site-specific analyses of a few alternatives that focus on reservoir water release operating rules and their associated environmental impacts. In contrast, the methodology presented in this article uses a widely applicable approach that explores a much larger solution space. This large set of potential alternatives can be represented in a multidimensional space for which one axis represents the economic value and the other axis quantify individual environmental impacts (e.g., sediment transport and fish growth), and they are explored via two approaches: a Monte Carlo simulation that identifies “win–win” alternatives and a multi-objective optimization problem that identifies Pareto-optimal alternatives.

13 HYDRO ENERGY↗

DOE OSTI2021

The Instrument of the Imaging X-Ray Polarimetry Explorer

While X-ray spectroscopy, timing, and imaging have improved much since 1962 when the first astronomical nonsolar source was discovered, especially wi the launch of the Newton/X-ray Multi-Mirror Mission, Rossi/X-ray Timing Explorer, and Chandra/Advanced X-ray Astrophysics Facility, the progress of X-ray polarimetry has been meager. This is in part due to the lack of sensitive polarization detectors, which in turn is a result of the fate of approved missions and because celestial X-ray sources appear less polarized than expected. Only one positive measurement has been available until now: the Orbiting Solar Observatory measured the polarization of the Crab Nebula in the 1970s. The advent of microelectronics techniques has allowed for designing a detector based on the photoelectric effect of gas in an energy range where the optics are efficient at focusing in X-rays. Here we describe the instrument, which is the major contribution of the Italian collaboration to the Small Explorer mission called IXPE, the Imaging X-ray Polarimetry Explorer, which will launch in late 2021. The instrument is composed of three detector units based on this technique and a detector service unit. Three mirror modules provided by Marshall Space Flight Center focus X-rays onto the detectors. We show the technological choices, their scientific motivation, and results from the calibration of the instrument. IXPE will perform imaging, timing, and energy-resolved polarimetry in the 2–8 keV energy band opening this window of X-ray astronomy to tens of celestial sources of almost all classes.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Prospects for a Precise Equation of State Measurement from Advanced LIGO and Cosmic Explorer

Gravitational-wave observations of neutron star mergers can probe the nuclear equation of state by measuring the imprint of the neutron star's tidal deformability on the signal. We investigate the ability of future gravitational-wave observations to produce a precise measurement of the equation of state from binary neutron star inspirals. Because measurability of the tidal effect depends on the equation of state, we explore several equations of state that span current observational constraints. We generate a population of binary neutron stars as seen by a simulated Advanced LIGO–Virgo network, as well as by a planned Cosmic Explorer observatory. We perform Bayesian inference to measure the parameters of each signal, and we combine measurements across each population to determine R 1.4 , the radius of a 1.4 M ⊙ neutron star. We find that, with 321 signals, the LIGO–Virgo network is able to measure R 1.4 to better than 2% precision for all equations of state we consider; however, we also find that achieving this precision could take decades of observation, depending on the equation of state and the merger rate. On the other hand, we find that with one year of observation, Cosmic Explorer will measure R 1.4 to better than 0.6% precision. In both cases, we find that systematic biases, such as from an incorrect mass prior, can significantly impact measurement accuracy, and efforts will be required to mitigate these effects.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Exploring Bioeconomy Growth through the Public Release of the Biomass Scenario Model

The Biomass Scenario Model (BSM) is an important tool for exploring vibrant future bioeconomy scenarios that leverage domestic resources. Developed by NREL and BETO, this model of the domestic biofuels supply chain has been used to explore success strategies for BETO's activities towards bioeconomy growth. The BSM offers a robust test bed for detailed exploration of effects of BETO activities within the complex context of resource availability; physical, technological, and economic constraints; behavior; and policy. The public release of the model in 2017 will allow broad engagement with the theme of the conference as model users can analyze bioeconomy growth, domestic biomass resource use, and associated effects. The BSM is a carefully validated, state-of-the-art, dynamic model of the biomass to biofuels supply chain. Using a system dynamics simulation modeling approach, the model tracks long-term deployment of biofuels given technology development and investment, considering land availability, the competing oil market, consumer demand, and government policies over time. Sample outputs include biofuels production, feedstock use, capital investment, incentives, and costs of feedstocks and fuels. BSM scenarios reveal technological, economic, and policy challenges, as well as opportunities for dynamic growth of the bioeconomy with strategic public and private investment at key points in the system. The model logic and results have been reviewed extensively, through collaborative analysis, expert reviews and external publications (https://www.zotero.org/groups/bsm_publications/).

09 BIOMASS FUELS↗

DOE OSTI2019

Poster and Slide: Energy Loss: Exploring Dissipation Pathways from Ions to In-reactor

Young Researcher Association is hosting an early career poster and networking session on July 25th, 2019. The poster session is designed to provide an overview of the researcher’s experience prior to employment at INL, and their current area of research and interests. The following is a brief description of the major sections of the poster. Before starting at INL in May of this year, my research focused on exploring the effects of electronic and nuclear energy loss on the damage evolution of Ti3SiC2 MAX phase during energetic irradiations. A series of ion irradiations revealed a threshold in electronic energy loss (Se), above which the formation of a FCC-MAX structure is enhanced, accommodating point defects and alleviating lattice strain. However, systematically separating the effects reveals that increasing Se/Sn suppresses this phase formation, thus allowing the strain from point defects to increase and saturate. Presently my research supports Post Irradiation Examination (PIE) of U-Mo fuel for the USHPRR and KJRR programs. The PIE evaluation currently consists of non-destructive examination (NDE) of fuel plates irradiated in the Advanced Test Reactor (ATR), focusing on plate profilometry and fuel swelling analysis. NDE will culminate in finalizing the destructive examination (DE) test plans and execution of said DE. Additionally, I am interested in exploring the roles that different energy dissipation pathways play in the damage evolution of the U-Mo fuel, exploring the mechanisms by which the fuel remains in its meta-stable phase under irradiation at high temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Design Space Exploration of Emerging Memory Technologies for Machine Learning Applications

Memory design space exploration methods study memory systems’ performances and limitations before implementation. The computer memory design space has grown exponentially because of the enormous growth of memory types, memory controllers, and application software. Computer simulators are commonly used for memory design space exploration. However, complex memory simulations take an enormous amount of time. Hence, in this paper, we proposed a machine learning-based design space exploration method for dynamic random-access memory and non-volatile memory systems. We applied our method to the CosmoGAN and LeNet applications to predict the following six memory response parameters: (i) bandwidth, (ii) power, (iii) average latency, (iv) average total latency, (v) memory reads, and (vi) memory writes. Our experimental results show that machine learning models can predict memory response parameter values faster than simulations. We used support vector machine, random forest, and gradient boosting machine learning models. We observed that the support vector machine provides better performance for bandwidth, average latency, and average total latency. The random forest model works better for memory reads and writes. The gradient boosting model provides superior prediction performance for power. We provide a detailed discussion on learning curve characteristics, error analysis, and memory type recommendation.

Hasan, S M Shamimul↗

DOE OSTI2022

The Geothermal Artificial Intelligence for geothermal exploration

Exploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. In this paper, we present a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas namely mineral markers, surface temperature, faults and deformation. We demonstrated the implementation of the method in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). Here, we processed various satellite images and geospatial data for mineral markers, temperature, faults and deformation and then implemented ML methods to obtain pattern of surface manifestation of geothermal sites. We developed an AI that uses patterns from surface manifestations to predict geothermal potential of each pixel. We tested the Geothermal AI using independent data sets obtaining accuracy of 92-95%; also tested the Geothermal AI trained on one site by executing it for the other site to predict the geothermal / non-geothermal delineation, the Geothermal AI performed quite well in prediction with 72-76% accuracy.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Programs and Code for Geothermal Exploration Artificial Intelligence

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including: - Land Surface Temperature K-Means classifier - Labeling AI using Self Organizing Maps (SOM) - Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM - Mineral marker summarizing - Artificial Intelligence (AI) Data splitting: creates data set from a single raster file - Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets - AI Mapper: creates a classification map based on a raster file

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Characterizing gravitational wave detector networks: from A # to cosmic explorer

Gravitational-wave observations by the laser interferometer gravitational-wave observatory (LIGO) and Virgo have provided us a new tool to explore the Universe on all scales from nuclear physics to the cosmos and have the massive potential to further impact fundamental physics, astrophysics, and cosmology for decades to come. In this paper we have studied the science capabilities of a network of LIGO detectors when they reach their best possible sensitivity, called A # , given the infrastructure in which they exist and a new generation of observatories that are factor of 10 to 100 times more sensitive (depending on the frequency), in particular a pair of L-shaped cosmic explorer (CE) observatories (one 40 km and one 20 km arm length) in the US and the triangular Einstein telescope with 10 km arms in Europe. We use a set of science metrics derived from the top priorities of several funding agencies to characterize the science capabilities of different networks. The presence of one or two A # observatories in a network containing two or one next generation observatories, respectively, will provide good localization capabilities for facilitating multimessenger astronomy (MMA) and precision measurement of the Hubble parameter. Two CE observatories are indispensable for achieving precise localization of binary neutron star events, facilitating detection of electromagnetic counterparts and transforming MMA. Their combined operation is even more important in the detection and localization of high-redshift sources, such as binary neutron stars, beyond the star-formation peak, and primordial black hole mergers, which may occur roughly 100 million years after the Big Bang. The addition of the Einstein Telescope to a network of two CE observatories is critical for accomplishing all the identified science metrics including the nuclear equation of state, cosmological parameters, the growth of black holes through cosmic history, but also make new discoveries such as the presence of dark matter within or around neutron stars and black holes, continuous gravitational waves from rotating neutron stars, transient signals from supernovae, and the production of stellar-mass black holes in the early Universe. For most metrics the triple network of next generation terrestrial observatories are a factor 100 better than what can be accomplished by a network of three A # observatories.

Einstein telescope↗

DOE OSTI2020

eFESTA: Ensemble Feature Exploration with Surface Density Estimates

We propose surface density estimate (SDE) to model the spatial distribution of surface features-isosurfaces, ridge surfaces, and streamsurfaces-in 3D ensemble simulation data. The inputs of SDE computation are surface features represented as polygon meshes, and no field datasets are required (e.g., scalar fields or vector fields). The SDE is defined as the kernel density estimate of the infinite set of points on the input surfaces and is approximated by accumulating the surface densities of triangular patches. We also propose an algorithm to guide the selection of a proper kernel bandwidth for SDE computation. An ensemble Feature Exploration method based on Surface densiTy EstimAtes (eFESTA) is then proposed to extract and visualize the major trends of ensemble surface features. For an ensemble of surface features, each surface is first transformed into a density field based on its contribution to the SDE, and the resulting density fields are organized into a hierarchical representation based on the pairwise distances between them. The hierarchical representation is then used to guide visual exploration of the density fields as well as the underlying surface features. We demonstrate the application of our method using isosurface in ensemble scalar fields, Lagrangian coherent structures in uncertain unsteady flows, and streamsurfaces in ensemble fluid flows.

Computational modeling↗

DOE OSTI2023

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.

chemical space exploration↗

DOE OSTI2013

Dixie Valley Engineered Geothermal System Exploration Methodology Project, Baseline Conceptual Model Report

The Engineered Geothermal System (EGS) Exploration Methodology Project is developing an exploration approach for EGS through the integration of geoscientific data. The Project chose the Dixie Valley Geothermal System in Nevada as a field laboratory site for methodlogy calibration purposes because, in the public domain, it is a highly characterized geothermal systems in the Basin and Range with a considerable amount of geoscience and most importantly, well data. This Baseline Conceptual Model report summarizes the results of the first three project tasks (1) collect and assess the existing public domain geoscience data, (2) design and populate a GIS database, and (3) develop a baseline (existing data) geothermal conceptual model, evaluate geostatistical relationships, and generate baseline, coupled EGS favorability/trust maps from +1km above sea level (asl) to -4km asl for the Calibration Area (Dixie Valley Geothermal Wellfield) to identify EGS drilling targets at a scale of 5km x 5km. It presents (1) an assessment of the readily available public domain data and some proprietary data provided by Terra-Gen Power, LLC, (2) a re-interpretation of these data as required, (3) an exploratory geostatistical data analysis, (4) the baseline geothermal conceptual model, and (5) the EGS favorability/trust mapping. The conceptual model presented applies to both the hydrothermal system and EGS in the Dixie Valley region.

15 GEOTHERMAL ENERGY↗

DOE OSTI2013

Exploration Gap Assessment (FY13 Update)

This submission contains an update to the previous Exploration Gap Assessment funded in 2012, which identify high potential hydrothermal areas where critical data are needed (gap analysis on exploration data). The uploaded data are contained in two data files for each data category: A shape (SHP) file containing the grid, and a data file (CSV) containing the individual layers that intersected with the grid. This CSV can be joined with the map to retrieve a list of datasets that are available at any given site. A grid of the contiguous U.S. was created with 88,000 10-km by 10-km grid cells, and each cell was populated with the status of data availability corresponding to five data types: 1. well data 2. geologic maps 3. fault maps 4. geochemistry data 5. geophysical data

15 GEOTHERMAL ENERGY↗

DOE OSTI2014

Dixie Valley Engineered Geothermal System Exploration Methodology Project, Baseline Conceptual Model Report

The Engineered Geothermal System (EGS) Exploration Methodology Project is developing an exploration approach for EGS through the integration of geoscientific data. The overall project area is 2500km2 with the Calibration Area (Dixie Valley Geothermal Wellfield) being about 170km2. The Final Scientific Report (FSR) is submitted in two parts (I and II). FSR part I presents (1) an assessment of the readily available public domain data and some proprietary data provided by terra-gen power, llc, (2) a re-interpretation of these data as required, (3) an exploratory geostatistical data analysis, (4) the baseline geothermal conceptual model, and (5) the EGS favorability/trust mapping. The conceptual model presented applies to both the hydrothermal system and EGS in the Dixie Valley region. FSR Part II presents (1) 278 new gravity stations; (2) enhanced gravity-magnetic modeling; (3) 42 new ambient seismic noise survey stations; (4) an integration of the new seismic noise data with a regional seismic network; (5) a new methodology and approach to interpret this data; (5) a novel method to predict rock type and temperature based on the newly interpreted data; (6) 70 new magnetotelluric (MT) stations; (7) an integrated interpretation of the enhanced MT data set; (8) the results of a 308 station soil CO2 gas survey; (9) new conductive thermal modeling in the project area; (10) new convective modeling in the Calibration Area; (11) pseudo-convective modeling in the Calibration Area; (12) enhanced data implications and qualitative geoscience correlations at three scales (a) Regional, (b) Project, and (c) Calibration Area; (13) quantitative geostatistical exploratory data analysis; and (14) responses to nine questions posed in the proposal for this investigation. Enhanced favorability/trust maps were not generated because there was not a sufficient amount of new, fully-vetted (see below) rock type, temperature, and stress data. The enhanced seismic data did generate a new method to infer rock type and temperature (However, in the opinion of the Principal Investigator for this project, this new methodology needs to be tested and evaluated at other sites in the Basin and Range before it is used to generate the referenced maps. As in the baseline conceptual model, the enhanced findings can be applied to both the hydrothermal system and EGS in the Dixie Valley region).

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Brady Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Salton Sea Geothermal Field. It includes all input and output files used with the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files are used with the Geothermal Exploration Artificial Intelligence for the Salton Sea Geothermal Site to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Salton Sea Geothermal Site.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.

15 GEOTHERMAL ENERGY↗