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DOE OSTI2024

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

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Climate Nowcasting

The climate is changing so rapidly that climatologies based on historical statistics cannot reliably capture the current risk of extreme weather events hazardous to society. Decision relevant projections of weather extreme probability over the next 10–15 years are needed to enable adaptation and resilience in the face of this evolving risk. Current weather forecasts/predictions and long-term climate projections for decades into the future are inadequate for providing this information to stakeholders that need it, targeting forecast horizons either too short or too far into the future. We argue that a new approach is needed: climate nowcasting. Climate nowcasting would focus on user-inspired extreme metrics over the next 10–15 year time frame, targeting specific impacts and locations down to a local scale, by engaging with stakeholders to understand their needs and provide information in a format relevant for decision making. Importantly, climate nowcasting will not consist of a single approach or data set, involving rather data fusion from different sources of information: simulations, observations and data driven methods, likely through different weights depending on the metric of interest. Predictions must be accompanied by serious engagement with stakeholders facing climate risks, and clearly present uncertainties and limitations of any prediction. Such a vision is very different from how typical climate or weather forecasts are applied today.

Gettelman, Andrew↗
DOE OSTI2020

Simulations of Laser Experiments to Study the Origin of Cosmic Magnetic Fields

Magnetic fields are ubiquitous in the universe; however, their origin is not fully understood. Cosmologists and astrophysicists have proposed a variety of ways in which small seed magnetic fields could be created. It is widely thought that the much larger values of the cosmic magnetic fields that we observe are a result of the amplification of these seed fields by the nonlinear turbulent dynamo mechanism. Such a mechanism had not yet been demonstrated in a controlled laboratory environment. We conceived experiments designed to demonstrate and study the nonlinear turbulent dynamo mechanism in the laboratory. These experiments characterize the distribution of turbulent energy among the velocity, magnetic field, and density fluctuations, providing a comprehensive picture of the energy cascade in a magnetized, turbulent plasma. The experiments build on the pathfinder experiments we have conducted on the Vulcan laser at the Rutherford-Appleton Laboratory in the UK and the OMEGA laser at Laboratory for Laser Energetics at Rochester. They utilize the high-intensity lasers at the Omega Laser Facility, the National Ignition Facility (NIF) at LLNL and the Laser Megajoule (LMJ) Facility in France – the largest laser facilities in the world. The goal of this project was to design and model the highly demanding experiments through simulation campaigns using FLASH, a highly capable radiation-MHD code we have developed, and large-scale 3D simulations on the Mira supercomputer at ANL. The simulations were vital to ensuring the experiments achieve the strong turbulence and large magnetic Reynolds numbers required for the nonlinear turbulent dynamo mechanism to operate. The simulations were also critical to determining when to fire the diagnostics, since the experiments last tens of nanoseconds but the signals of the strongly amplified magnetic fields last only a few nanoseconds. Finally, high-fidelity, validated FLASH simulations were crucial to interpreting the results of the experiments. By combining theory, experiments, and simulations, we were able to succeed in demonstrating and characterizing the nonlinear turbulent dynamo mechanism. The effort leveraged (1) the emergence of the Flash Center for Computational Science as a leader in numerical modeling of academic High Energy Density Physics (HEDP) experiments; (2) the FLASH code, which has been developed by the Flash Center and is a leadership-class, multi- physics, publicly available community code that has been applied successfully in many laser-driven experiments; (3) the expertise acquired through pathfinder experiments we performed on the Vulcan laser at the Rutherford-Appleton Laboratory in the UK and on the Omega laser facility; (4) the experience we have gained in designing and interpreting these experiments using validated simulations done with FLASH; and (5) the close collaborations we have established with outstanding experimental groups in the US and abroad. The broader impacts of the project are two-fold: (1) the work furthered the transformation of the academic community’s ability to design and analyze HEDP experiments at large laser facilities that is happening through the availability of FLASH and its widespread adoption by the academic community – a community that previously had had limited access to open and validated hydro/MHD simulation tools for HEDP experiments; and (2) the work trained young scientists to design and interpret HEDP experiments using validated simulations – a critical national need.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Final Scientific/Technical Report: Correlations and Fluctuations in Weakly Collisional Plasma

Plasma is a state of matter that exhibits a very rich range of phenomena. To begin with, plasma is both electrical and mechanical - bringing together theories of particle motion and the electromagnetic field. Furthermore, and especially important for this project, a weakly-collisional plasma, such as is found in high-temperature (fusion energy) experiments on earth and the majority of contexts in space and astrophysics, has many moving parts. For example, sitting in earth’s atmosphere we are immersed in a mechanical wave field (sound), a possibly turbulent fluid motion (wind), and an electromagnetic vector wave field with two polarizations (light). This is already enough to produce a rich range of possibilities. In plasma, the electromagnetic field is coupled to the mechanical motion of the medium because it is ionized. Furthermore, a weakly-collisional plasma supports an infinite number of mechanically independent fluids. Thus, plasmas support an infinite number of independent electromechanical waves. Much has been done to describe plasmas with "reduced models" of various kinds. The goal of this project was to both explore the validity of reduced plasma models that are in use, and to propose and validate new models of plasma motion. The primary means to his end was laboratory experiments employing both electrical probes and laser spectroscopy. Laser spectroscopy enables many techniques which can separate the spectrum of independent fluid motions in the ion phase-space. The choice was to focus on low frequency electrostatic waves because the electron motion is relatively simple, the experiments can be on a spatial scale of a few meters, and all the relevant parameters can be measured with a few lasers systems. No study of this kind had previously been undertaken for the study of plasmas. The validation of theories required that the experimental descriptions be compared with theory and simulation in detail. It was found that even multi-fluid theories leave out a large part of the complexity of plasma motion. Reduced descriptions were found to fail under most circumstances. A new technique was developed that enabled a measurement of the phase-space resolved ion correlation function for the first time. The wide range of plasma dynamics possible became clear through this technique. It was found that collisionless (Vlasov) theory has a large field of application even when the plasma is weakly-collisional. A new approach, the kinetic wave expansion, was proposed, tested and found to be very useful for describing electrostatic ion waves. This project demonstrated a new way of looking at the "degrees-of-freedom" of plasmas and provided significant validation tests of fluid and kinetic plasma descriptions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Development of Eulerian Code Modeling for ICF Experiments

One of the most pressing unexplained phenomena standing in the way of ICF ignition is understanding mix and how it interacts with burn. Experiments were being designed and fielded as part of the Defect-Induced Mix Experiment (DIME) project to obtain data about the extent of material mix and how this mix influenced burn. Experiments on the Omega laser and National Ignition Facility (NIF) provided detailed data for comparison to the Eulerian code RAGE1. The Omega experiments were able to resolve the mix and provide “proof of principle” support for subsequent NIF experiments, which were fielded from July 2012 through June 2013. The Omega shots were fired at least once per year between 2009 and 2012. RAGE was not originally designed to model inertial confinement fusion (ICF) implosions. It still lacks lasers, so the code has been validated using an energy source. To test RAGE, the simulation output is compared to data and by means of postprocessing tools that were developed. Here, the various postprocessing tools are described with illustrative examples.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Radiation damage effects in beryllium for next generation neutrino beam targetry (Final Technical Report)

Current and future high-power accelerators put severe requirements on materials used for target and beam windows and target facilities have been recognized as a critical challenge in development of future particle accelerators. In accelerators, window and target materials are exposed to extreme conditions, which include bombardment with very high energy protons (1- 100 GeV) and thermomechanical shock waves. Radiation can cause direct damage in the material, and it leads to production of transmutation products (especially helium), both phenomena having a potential adverse effect on the stability and durability of the target/window material. At high enough temperatures, He can aggregate to form gas bubbles, which in turn cause significant dimensional changes (swelling), enable easy crack propagation, and eventually cause failure by fracture. On the other hand, if the temperature is too low, radiation damage accumulates in the form of internal defects (e.g., dislocations), leading to hardening and a decreased ductility of the material. In this project, we will focus on beryllium since it is considered to be one of the candidate materials for beam windows and targets in the next-generation proton accelerators, e.g., the Long Baseline Neutrino Facility (LBNF). Radiation effects in Be have been studied in the context of nuclear fusion reactor applications. However, key differences exist between reactor and accelerator conditions, including neutron vs. proton irradiation, continuous vs. pulsed beam flux, much higher energies of bombarding particles in accelerators, and higher operating temperatures for typical reactors. For example, the impact of beam pulsing on the radiation damage and the He bubble kinetics is largely unknown. While results obtained on Be from fusion research might not be directly transferrable to understanding target materials, there is an opportunity to bring state-of-the-art tools from materials research in nuclear reactors to aid design of target and beam window materials in high-power accelerators. To this end, the overarching goal of this project are to develop an experimentally-validated computational framework capable of predicting radiation damage evolution in beryllium relevant to beam window and target conditions, focusing on He bubble formation and growth as a function of irradiation temperature. Our model will be based on the cluster dynamics formalism, where size distribution of defects and He bubbles is simulated as a function of time, temperature, and radiation dose. Parameters for the model will be taken from published experiments and from high-fidelity atomistic simulations proposed in this project. In addition, we will carry out a series of targeted ex-situ and in-situ dual-beam experiments using low-energy protons to provide critical data for validation of the model on the effects of radiation on He clustering, He bubble distribution, and dislocation loop density/size in proton irradiated Be.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Decomposition of plasma kinetic entropy into position and velocity space and the use of kinetic entropy in particle-in-cell simulations

In this work, we describe a systematic development of kinetic entropy as a diagnostic in fully kinetic particle-in-cell (PIC) simulations and use it to interpret plasma physics processes in heliospheric, planetary, and astrophysical systems. In the beginning, we calculate kinetic entropy in two forms—the “combinatorial” form related to the logarithm of the number of microstates per macrostate and the “continuous” form related to flnf, where f is the particle distribution function. We discuss the advantages and disadvantages of each and discuss subtleties about implementing them in PIC codes. Using collisionless PIC simulations that are two-dimensional in position space and three-dimensional in velocity space, we verify the implementation of the kinetic entropy diagnostics and discuss how to optimize numerical parameters to ensure accurate results. We show the total kinetic entropy is conserved to three percent in an optimized simulation of antiparallel magnetic reconnection. Kinetic entropy can be decomposed into a sum of a position space entropy and a velocity space entropy, and we use this to investigate the nature of kinetic entropy transport during collisionless reconnection. We find the velocity space entropy of both electrons and ions increases in time due to plasma heating during magnetic reconnection, as the position space entropy decreases due to plasma compression. This project uses collisionless simulations, so it cannot address physical dissipation mechanisms; nonetheless, the infrastructure developed here should be useful for studies of collisional or weakly collisional heliospheric, planetary, and astrophysical systems. Beyond reconnection, the diagnostic is expected to be applicable to plasma turbulence and collisionless shocks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

12th IAEA Technical Meeting on Energetic Particles in Magnetic Confinement Systems (7–11 September 2011) and 5th IAEA Technical Meeting on Theory of Plasma Instabilities (5–7 September 2011)

The 12th IAEA Technical Meeting on Energetic Particles in Magnetic Confinement Systems took place in Austin, Texas (7–11 September 2011). This meeting was organized jointly with the 5th IAEA Technical Meeting on Theory of Plasma Instabilities (5–7 September 2011). The two meetings shared one day (7 September 2011) with presentations relevant to both groups. Some of the work reported at these meetings was then published in a special issue of Nuclear Fusion [Nucl. Fusion 52 (2012)]. Summaries of the Energetic Particle Conference presentations were given by Kazuo Toi and Boris Breizman. They respectively discussed the experimental and theoretical progress presented at the meeting. Highlights of this meeting include the tremendous progress that has been achieved in the development of diagnostics that enables the ‘viewing’ of internal fluctuations and allows comparison with theoretical predictions, as demonstrated, for example, in the talks of P. Lauber and M. Osakabe. The need and development of hardened diagnostics in the severe radiation environment, such as those that will exist in ITER, was discussed in the talks of V. Kiptily and V.A. Kazakhov. In theoretical studies, much of the effort is focused on nonlinear phenomena. For example, detailed comparison of theory and experiment on D-III-D on the n = 0 geodesic mode was reported in separate papers by R. Nazikian and G. Fu. A large number of theoretical papers were presented on wave chirping including a paper by B.N. Breizman, which notes that wave chirping from a single frequency may emanate continuously once marginal stability conditions have been established. Another area of wide interest was the detailed study of alpha orbits in a burning plasma, where losses can come from symmetry breaking due to finite coil number or magnetic field imperfections introduced by diagnostic or test modules. An important area of development, covered by M.A. Hole and D.A. Spong, is concerned with the self-consistent treatment of the induced fields that accounts for toroidally asymmetric MHD response. In addition, a significant number of studies focused on understanding nonlinear behavior by means of computer simulation of energetic particle driven instability. An under-represented area of investigation was the study of electron runaway formation during major tokamak disruptions. It was noted in an overview by S. Putvinski that electron energies in the 10–20 MeV range is to be expected during projected major disruptions in ITER and that reliable methods for mitigation of the runaway process needs to be developed. Significant recent work in the field of the disruption induced electron runaway, which was reported by J. Riemann, had been submitted to Physics of Plasmas. Overall it is clear that reliable mitigation of electron runaway is an extremely important topic that is in need of better understanding and solutions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Magnetic Reconnection in High Energy Density Laboratory Plasmas

Understanding the physics of collisionless, magnetized plasmas is key to unlocking a number of important problems in space and astrophysics. Magnetic reconnection is a fundamental mechanism which can explosively release stored magnetic energy, convert it to plasma heat and flows, and accelerate particles. In this project we developed technique and conducted experiments and simulations of magnetic reconnection in laser produces plasmas. Through this project, we also developed experimental techniques to generate magnetized collisionless shocks, and we therefore expanded the scope of the project to develop these experiments. These projects make fundamental progress on understanding the dynamics of magnetized laser-produced plasmas. In additional to plasma astrophysics, these results have broad interest for laboratory high energy density plasmas, since magnetic fields can be used to confine and control plasmas in many contexts and may offer new capabilities toward laboratory ignited plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Accelerated kinetic model for global macro stability studies of high-beta fusion reactors

The field reversed configuration (FRC), such as studied in the C-2W experiment at TAE Technologies, is an attractive candidate for realizing a nuclear fusion reactor. In an FRC, kinetic ion effects play the majority role in macroscopic stability, which allows global stability studies to make use of fluid-kinetic hybrid (also referred to as Ohm's law) models wherein ions are treated kinetically while electrons are treated as a fluid. The development and validation of such a hybrid particle-in-cell algorithm in the Exascale Computing Project code WarpX are reported here. Implementation of this model in the WarpX framework benefits from the numerical efficiency of WarpX as well as its scalability on large HPC systems and portability to different architectures. Performance benchmarks of the new algorithm for large, 3-dimensional, full device simulations from the Perlmutter supercomputer are presented. Results of a series of FRC simulations are discussed in which the impact of two-fluid effects on the tilt-mode growth rate was studied. It was observed that, in agreement with previous Hall-MHD studies, two-fluid effects have a stabilizing impact on the tilt mode.

Physics↗
DOE OSTI2024

Nested Pebble Bed Blanket (NesPeB)

Recent advances in magnetic confinement fusion technology have attracted billions of dollars of investments in startups from venture capitals and corporations, resulting in the development of devices aiming to demonstrate net energy gain in a self-heated burning plasma, such as SPARC (under construction) and others. However, future fusion power plants must operate in regimes that will require technologies far beyond current experience. According to a National Academies of Science, Engineering, and Medicine report, to have nuclear fusion power plants contributing in a timely manner to the planned reduction of atmospheric carbon dioxide, a pilot plant should be built by 2035, and it should demonstrate fusion power production and the performance of the tritium fuel system (requiring a high enough tritium breeding) by 2040. A recognized key technology gap by [26] is the fusion first wall and blanket since no current blanket concept is considered satisfactory or has been built and proven. The first wall and blanket in magnetic fusion reactors form a vital and complex system, as it must satisfy different functions such as power extraction, tritium breeding, plasma containment, radiation shielding, and safety. The list of design requirements is even longer: high enough tritium production for fusion self-sufficiency, low material activation, decay heat and shutdown dose rates, high thermal efficiency, high-capacity factor, high magnets-divertor-vacuum vessel-first wall life, low corrosion, low cost, and intrinsically safe (requiring minimal licensing). Despite fifty-plus years of research, the first wall and blanket concepts proposed suffer from fundamental technical problems and immaturity (TRL=2-3) that jeopardize the timely delivery of a commercial fusion power plant. A fusion first-wall blanket has never been built nor tested, and a "winning", practical functioning design requires enough engineering margins (high enough tritium breeding considering the uncertainty, etc.), manufacturing simplicity, ease of continuous operation, maintenance, and low cost. A new, groundbreaking blanket concept called "Nested Pebble Bed Blanket" (NesPeB) was developed at ORNL under the successful ARPA-E GAMOW FERMI project (patent application allowed by the USPTO). The NesPeB blanket concept addresses current blanket concepts' shortcomings and technical immaturity, paving the way for accelerated delivery of fusion power plants. NesPeB is based on nested pebbles, which are binary-sized lithium-ceramic pebbles enclosed in "Beryllide" perforated and coated spherical shells, which are also binary-sized, stacked on top of each other, forming a "bed" and cooled by Nitrogen gas also "sweeping" the Helium and Tritium generated by the neutron irradiation of Lithium; the vacuum vessel plasma facing material is Molybdenum-96 and -97 with the first wall cooled by Helium while the divertor armor is made of Tungsten. The simulations of the NesPeB blanket using Fusion Reactors Models Integrator (FERMI) are encouraging as they estimate a tritium breeding ratio (TBR) greater than 1.2 using natural Lithium, acceptable pressure drop, and excellent heat transfer properties. Furthermore, the NesPeB blanket is not limited by magneto-hydro-dynamics (MHD) effects, is designed for online refueling, relies on existing tritium extraction technologies, has a simple construction, and limits the corrosion and chemical reactivity problems. NesPeB has the potential to be transformational and disruptive since it can solve all the main, challenging technical problems of fusion device blankets and accelerate a pilot plant delivery for 10 or more years.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Automated metadata, provenance cataloging and navigable interfaces: ensuring the usefulness of extreme-scale data

The MPO (Metadata, Provenance, Ontology) Project successfully addressed the goal of improving the usefulness and traceability of scientific data by building a system that could capture and display all steps in the process of creating, analyzing and disseminating that data. Throughout history, scientists have generated handwritten logbooks to keep track of data, their hypotheses, assumptions, experimental setup, and computational processes as well as reflections on observations and issues encountered. Over the last several decades, with the growth of personal computers, handheld devices, and the World Wide Web, the handwritten logbook has begun to be replaced by electronic logbooks. This transition has brought increased capability such as supporting multi-media, hypertext, and fast searching. However, content creation and metadata (a set of data that describes and gives information about other data) capturing has for the most part remained a manual activity just as it was with handwritten logbooks. This has led to a fragmentation of data, processing, and annotation that has only accelerated as scientific workflows continue to increase in complexity. From a scientific perspective, it is very important to be able to understand the lineage of any piece of data: who, what, when, how, and why. This is typically referred to as data provenance. The fragmentation discussed previously often means that data provenance is lost. As scientific workflows move to powerful computers and become more complex, the ability to track all of the steps involved in creating a piece of data become even more difficult. It was the goal of the MPO (Metadata, Provenance, Ontology) Project to create a system (the MPO System) that allows for automatic provenance and metadata capturing in such a way to allow easy searching and browsing. This goal needed to be accomplished in a general way so that it may be used across a broad range of scientific domains, yet allow the addition of vocabulary (Ontology) that is domain specific as is required for intelligent searching and browsing in the scientific context. Through the creation and deployment of the MPO system, the goals of the project were achieved. An enhanced metadata, provenance, and ontology storage system was created. This was combined with innovative methodologies for navigating and exploring these data using a web browser for both experimental and simulation-based scientific research. In addition, a system to allow scientists to instrument their existing workflows for automatic metadata and provenance is part of the MPO system. In that way, a scientist can continue to use their existing methodology yet easily document their work. Workflows and data provenance can be displayed either graphically or in an electronic notebook format and support advanced search features including via ontology. The MPO system was successfully used in both Climate and Magnetic Fusion Energy Research. The software for the MPO system is located at https://github.com/MPO-Group/MPO and is open source distributed under the Revised BSD License. A demonstration site of the MPO system is open to the public and is available at https://mpo.psfc.mit.edu/. A Docker container release of the command line client is available for public download using the command docker pull jcwright/mpo-cli at https://hub.docker.com/r/jcwright/mpo-cli.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Radiation Belt Remediation: A Complex Engineered System (RBR-ACES) (Institutional Computing Report)

We have performed the simulation campaigns in support of LDRD-DR project Radiation Belt Remediation: A Complex Engineered System (RBR-ACES). This project involves modeling wave generation and wave propagation in plasma environment and studying the effect of waves on the distribution of high energy electrons with the focus on electrons appeared after a high-altitude nuclear explosion (HANE). One of the major goal of this research is strong validation of the numerical models with laboratory experiments and space-based experiments and observations.

42 ENGINEERING↗
DOE OSTI2019

Investigation of Marangoni convection with high-fidelity simulations for metal melt pool dynamics

The goal of this research is to investigate and characterize the dynamics of a molten metal pool through modeling and simulation. Two manufacturing processes are of interest, laser spot welding and selective laser melting. These are laser-based techniques for joining parts together and for building up parts. The laser power impacts the melt pool temperature and therefore the dynamics. Moreover, the metals used are often alloys, containing impurities, where the concentration of these impurities can have a large effect on surface tension and therefore influence the melt pool dynamics. The application space considered in this project is for laser spot welding as a case study, and is also relevant to 3D metal printing. The thermal evolution and fluid dynamics in a 2D metal block exposed to a Gaussian distributed laser source is numerically simulated using a high-order, fully-implicit code. Results are compared to published experimental data and other published numerical simulations for laser spot welding. Results confirm that unstable structures appear at higher laser powers with higher impurity concentrations compared to simulations with no impurities and lower laser powers.

42 ENGINEERING↗
DOE OSTI2022

Analysis of wave mode content in fully turbulent, moderately collisional plasma laboratory experiment and kinetic simulation

Our major goal for the project was to make magnetic field and plasma density fluctuation measurements using identical diagnostic and acquisition setups on two different laboratory-based turbulent plasma devices: SSX at Swarthmore College and a new plasma source in development at Bryn Mawr College (BMX). We used single-point correlation and k-filtering metrics using these experimental measurements. These metrics were compared to kinetic simulations of the experimental environments in order to determine wave mode content of the laboratory turbulence.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Global 3D Simulations of the Tokamak Edge Region

The goal of this project is to better understand magnetized plasma dynamics at the edge region of the tokamak, including both closed-flux-surface region and the scrape-off-layer (SOL), through analytical analysis and advanced numerical simulations. Under the auspices of the U.S. DOE, the PI, along with students and collaborators have made a significant progress. These results were disseminated by 13 peer-reviewed article and many presentations and posters in domestic and international conferences.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Orbital Debris IC Report

We started a simulation campaign in support of our ongoing plasma signatures of small orbital debris project SINTRA program funded by IARPA. This project aims to identify plasma signatures generated by the interaction of small orbital debris (>10cm) with space plasma that can be used for detection, tracking and eventually, develop technologies to remove these small but lethal objects. The small orbital debris problem is increasing exponentially threaten the current and future use of space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

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

The project is motivated by the challenges, risks, and costs associated with geothermal exploration and production. Many processes and parameters impacting geothermal conditions are poorly understood. Diverse datasets are available to help characterize subsurface geothermal conditions (public and proprietary; satellite, airborne surveys, vegetation/water sampling, geological, geophysical, etc.). Yet, it is not clear how to properly leverage these datasets for geothermal exploration due to an incomplete understanding of how physical processes impacting subsurface geothermal conditions are represented in these observations. Recent advancements in machine learning (ML) provide great promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. Our goals and work under Phases 1 and 2 (as proposed) of this project address all these needs.

15 GEOTHERMAL ENERGY↗