Real-time Data Fusion to Guide Disease Forecasting Models
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Materials Assessment for FLiBe Fusion Blankets
This evaluation was prepared at the request of DOE-FES and expands on an earlier evaluation prepared by Wilson, Wiffen and Keiser in 2015 (attached as Appendices A and B). Additional useful information for materials in fission molten salt reactor systems that use FLiBe can be found in a prior NRC report [Busby 2019]. The purpose of this report is to review the considerations that will determine the fusion blanket structural materials that will be suitable for use in a FLiBe breeder-coolant fusion power plant (FPP). Identifying a fusion-relevant structural material that is sufficiently compatible with FLiBe is a key issue to be addressed. The constraints and trade-offs made in structural materials selection include the usual power generation requirements such as mechanical properties, fabricability and durability, which assumes reasonable compatibility with the working fluid. For an FPP, additional constraints include neutron economy (breeding ratio), resistance to irradiation damage and low activation (i.e. waste disposal) characteristics.
36 MATERIALS SCIENCE↗
Model fusion with physics-guided machine learning: Projection-based reduced-order modeling
The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatiotemporal scales has enabled the rapid advancement of data-driven and especially deep learning models in the field of fluid mechanics. Although these methods are proven successful for many applications, there is a grand challenge of improving their generalizability. This is particularly essential when data-driven models are employed within outer-loop applications like optimization. In this work, we put forth a physics-guided machine learning (PGML) framework that leverages the interpretable physics-based model with a deep learning model. Leveraging a concatenated neural network design from multi-modal data sources, the PGML framework is capable of enhancing the generalizability of data-driven models and effectively protects against or inform about the inaccurate predictions resulting from extrapolation. We apply the PGML framework as a novel model fusion approach combining the physics-based Galerkin projection model and long- to short-term memory (LSTM) network for parametric model order reduction of fluid flows. We demonstrate the improved generalizability of the PGML framework against a purely data-driven approach through the injection of physics features into intermediate LSTM layers. Our quantitative analysis shows that the overall model uncertainty can be reduced through the PGML approach, especially for test data coming from a distribution different than the training data. Moreover, we demonstrate that our approach can be used as an inverse diagnostic tool providing a confidence score associated with models and observations. The proposed framework also allows for multi-fidelity computing by making use of low-fidelity models in the online deployment of quantified data-driven models.
42 ENGINEERING↗
Localized Defect Detection from Spatially Mapped, In-Situ Process Data With Machine Learning
In powder bed fusion additive manufacturing, machines are often equipped with in-situ sensors to monitor the build environment as well as machine actuators and subsystems. The data from these sensors offer rich information about the consistency of the fabrication process within a build and across builds. This information may be used for process monitoring and defect detection; however, little has been done to leverage this data from the machines for more than just coarse-grained process monitoring. In this work we demonstrate how these inherently temporal data may be mapped spatially by leveraging scan path information. We then train a XGBoost machine learning model to predict localized defects—specifically soot–using only the mapped process data of builds from a laser powder bed fusion process as input features. The XGBoost model offers a feature importance metric that will help to elucidate possible relationships between the process data and observed defects. Finally, we analyze the model performance spatially and rationalize areas of greater and lesser performance.
3D printing↗
Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas
Understanding complex physical systems often requires integrating data from multiple diagnostics, each with limited resolution or coverage. We present a machine learning framework that reconstructs synthetic high-temporal-resolution data for a target diagnostic using information from other diagnostics, without direct target measurements during the inference. This multimodal super-resolution technique improves diagnostic robustness and enables monitoring even in case of measurement failures or degradation. Applied to fusion plasmas, our method targets edge-localized modes (ELMs), which can damage plasma-facing materials. By reconstructing super-resolution Thomson Scattering data from complementary diagnostics, we uncover fine-scale plasma dynamics and validate the role of resonant magnetic perturbations (RMPs) in ELM suppression through magnetic island formation. The approach provides new observation supporting the plasma profile flattening due to these islands. Our results demonstrate the framework’s ability to generate high-fidelity synthetic diagnostics, offering a powerful tool for ELM control development in future reactors like ITER. The approach is broadly transferable to other domains facing sparse, incomplete, or degraded diagnostic data, opening new avenues for discovery.
Jalalvand, Azarakhsh [Princeton Univ., NJ (United ↗
Laser‐Powder Melt Pool Solidification Dynamics and Microstructural Engineering of Ti‐5553 Microlattices
The fine geometric and topological control afforded by additive manufacturing technologies has enabled the manufacture of architected materials across length scales, and enabling tunable mechanical performance as a function of local and global design. Progress has been made to tune the mechanical response of architected materials through geometry, but understanding how the geometry and processing conditions will inform the microstructure remains a challenge due to the rapid solidification in laser powder bed fusion. This study uses in situ X-ray imaging and electron backscatter diffraction microscopy to demonstrate that the melt pool size, microstructure morphology, and elastic strain distribution is influenced by a combination of lattice geometry and laser processing conditions. These results indicate that within larger melt pools the local thermal gradients are sufficient to enable a columnar-to-equiaxed transition across the melt pool. Furthermore, the solidification mechanisms producing these microstructures are examined across the first 5 ms of melting and solidification, described via in situ high-speed X-ray imaging and mirrored via multiphysics simulation.
additive manufacturing↗
Image fusion for neutron tomography of nuclear fuel
Image fusion, the process of combining different images together, can be useful to create a more complete picture. In this work, image fusion is applied to neutron tomography of nuclear fuel with the goal of enhancing the information obtained about the fuel. Different reconstruction methods, such as Feldkamp, Davis and Kress filtered back projection and Simultaneous Reconstruction Technique, were combined to enhance image quality. This methodology was shown to reduce noise and ring artifacts without sacrificing sharp edges, allowing for a more accurate representation of sample geometry. Technique enhancements and future applications for the neutron imaging community are also discussed.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Data Fusion to Enhance Quality Control and Analysis with Instruments at the Marine and Coastal Research Laboratory
Deploying environmental monitoring instruments in the marine environment can be challenging, facing challenges around device survivability, biofouling and corrosion, and consistent data collection. This project explores the use of data fusion – the process of integrating multiple data sources to produce more consistent, accurate, and useful information – to build a consistent long-term monitoring system at the Marine and Coastal Research Laboratory (MCRL) in Sequim, Washington. Unused instruments that had been acquired from past projects were inventoried and deployments planned on the MCRL pier and floating dock. A total of 8 instruments were deployed including a tide gauge, hydrophone, acoustic Doppler current profiler (ADCP), photosynthetically active radiation (PAR) sensors, meteorological station, and three water quality sensors. Deployments were planned to be well-protected around the pier structure and a maintenance schedule was created for cleaning and recalibration. An automated data pipeline was created to aggregate data on edge computers that push data to Amazon Web Services (AWS) cloud storage every 15 minutes, performing automated quality control and data transformations using the Time Series Data Analytical Toolkit (TSDAT). Continued efforts are underway to maintain this system into the future, take a data-driven approach to maintenance scheduling, improve the reliability of the system, and share the data with a variety of end-users.
54 ENVIRONMENTAL SCIENCES↗
Limiting Data Friction by Reducing Data Download Using Spatiotemporally Aligned Data Organization Through STARE
Current data processing practice limits the volume and variety of relevant geoscience data that can practically be applied to important problems. File archives in centralized data centers are the principal means by which Earth Science data are accessed. This approach, however, requires laborious search, retrieval, and eventual customization/adaptation for the data to be used. Such fractionation makes it even more difficult to share outcomes, i.e. research artifacts and data products, hampering reusability and repeatability, since end users generally have their own research agenda and preferences as well as scarce resources. Thus, while finding and downloading data files from central data centers are already costly for end users working in their own field, using data products from other disciplines rapidly becomes prohibitive. This curtails scientific productivity, limits avenues of study, and endangers quality and reproducibility. The Spatio-Temporal Adaptive Resolution Encoding (STARE) is a unifying scheme that facilitates the indexing, access, and fusion of diverse Earth Science data. STARE implements an innovative encoding of geo-spatiotemporal information, originally developed for aligning datasets with diverse spatiotemporal characteristics in an array database. The spatial component of STARE recursively quadfurcates a root polyhedron, producing a hierarchical scheme for addressing geographic locations and regions. The temporal component of STARE uses conventional date-time units as an indexing hierarchy. The additional encoding of spatial and temporal resolution information in STARE enables comparisons and conditional selections across diverse datasets. Moreover, spatiotemporal set-operations, e.g. union and intersection, are mapped to efficient integer operations with STARE. Applied to existing data models (point, grid, spacecraft swath) and corresponding granules, STARE indexes provide a streamlined description usable as geo-spatiotemporal metadata. When coupled with large scale, distributed hardware and software, STARE-based data access reduces pre-analysis data preparation costs by offering a convenient means to align different datasets spatiotemporally without specialized effort in parallel computing or distributed data management.
Kuo, Kwo-Sen↗
NIAC Phase-2 Final Report for Astrophysics and Technical Lab Studies of a Solar Neutrino Spacecraft Detector
The Sun provides all the energy that our planet needs for life and has been doing so for five billion years. Understanding our Sun and its interior is one of the major goals of the NASA Science program. Still this is a very difficult task because very little makes it directly out of the Sun’s interior. The energy we see today, that warms the Earth, was made 50,000 to 80,000 years ago and is only now coming to the surface to make light. However, neutrinos penetrate matter almost without interaction and make it to Earth in only eight minutes from creation. Since neutrinos interact only weakly they are hard to detect; never-the-less within the last ten years neutrino detectors on Earth have started to reliably detect neutrinos from the fusion reactions in the interior of the Sun and scientists have started to use this information to investigate the Sun’s nuclear furnace. Changes in solar neutrino flux make it advantageous to take a neutrino detector into space since the solar neutrino intensity changes dramatically as the inverse square of the distance from the Sun, by five orders of magnitude when going from the Earth to the Sun. Launch of a neutrino detector into space toward the Sun will: a) aim to significantly increase the neutrino flux 10,000x allowing for a smaller detector which improves detector energy resolution and performance, b) attempt to completely eliminate background terrestrial neutrino sources for improved measurement accuracy, and c) conduct unique science experiments near the Sun not achievable with much larger detectors on the Earth. NASA's interest in deep space exploration has been a key factor in its unmanned spacecraft development and launch of exploration science satellites and spacecraft. NASA has done exceptional experiments in space where science benefits from the unique platform of spacecraft that provides unprecedented views. For example, the Hubble Space Telescope is really a small and very common instrument, but when it is put into an orbit high above the Earth, it becomes one of the most powerful optical observatories man has ever made. Moving neutrino observations to space is the next obvious step. The concept of putting a neutrino detector in close orbit of the sun is completely unexplored and innovative. Its scientific return is to vastly enhance the understanding of the solar interior which is a NASA major goal as stated in the decadal survey. Preliminary calculations show that such a spacecraft if properly shielded, can operate in this environment both taking data of neutrino interactions which can be distinguished from random background rates of solar Electromagnetic emissions, Galactic charged cosmic-ray, and gamma-rays by using a double pulsed signature. The NIAC Phase-1 simulations have shown this idea to be very successful in eliminating background and identifying the neutrino interaction signal, hence this spacecraft detector concept once demonstrated to be Technical Readiness Level 7 and flight mission-ready would enable a whole new type of mission to explore and study our Sun, in details that could neither be done with the largest neutrino detectors on Earth nor other types of space-craft measurements that are not using neutrino detection. Our goal in this NIAC Phase-2 was to take the detector simulation ideas and construct a prototype for testing in the lab with tagged sources to evaluate and demonstrate that the performance in the simulations are borne out by a prototype.
Neutrino↗
Ion concentration ratio measurements of ion beams generated by a commercial microwave electron cyclotron resonance plasma source
A commercially available electron cyclotron resonance (ECR) plasma source (GenII Plasma Source, tectra GmbH) is widely used for surface processing. This plasma source is compatible with ultrahigh vacuum systems, and its working pressure is relatively low, around 10 –6 –10 –4 Torr even without differential pumping. Here, we report ion flux concentration ratios for each ion species in an ion beam from this source, as measured by a mass/energy analyzer that is a combination of a quadrupole mass spectrometer, an electrostatic energy analyzer, and focusing ion optics. In this study, the examined beams were those arising from plasmas produced from feed gases of H 2 , D 2 , N 2 , O 2 , Ar, and dry air over a range of input power and working pressures. H 2 (D 2 ) plasmas are widely used for nuclear fusion applications and, hence, the ion concentration ratios of H + , H 2 + , and H 3 + reported here will be useful information for research that applies this plasma source to well-controlled plasma-material interaction studies. Ion energy distributions, stability of operation, and impurity concentrations were also assessed for each of the plasma species investigated.
47 OTHER INSTRUMENTATION↗
pvOps: a Python package for empirical analysis of photovoltaic field data
The purpose of pvOps is to support empirical evaluations of data collected in the field related to the operations and maintenance (O&M) of photovoltaic (PV) power plants. pvOps presently contains modules that address the diversity of field data, including text-based maintenance logs, current-voltage (IV) curves, and timeseries of production information. The package functions leverage machine learning, visualization, and other techniques to enable cleaning, processing, and fusion of these datasets. These capabilities are intended to facilitate easier evaluation of field patterns and extraction of relevant insights to support reliability-related decision-making for PV sites. The open-source code, examples, and instructions for installing the package through PyPI can be accessed through the GitHub repository.
14 SOLAR ENERGY↗
Hierarchical multisensor analysis for robotic exploration
Robotic vehicles for lunar and Mars exploration will carry an array of complex instruments requiring real-time data interpretation and fusion. The system described here uses hierarchical multiresolution analysis of visible and multispectral images to extract information on mineral composition, texture and object shape. This information is used to characterize the site geology and choose interesting samples for acquisition. Neural networks are employed for many data analysis steps. A decision tree progressively integrates information from multiple instruments and performs goal-driven decision making. The system is designed to incorporate more instruments and data types as they become available.
Eberlein, Susan↗
Inertial Electrostatic Confinement Fusion: The Laser Elevator Solar System Survey for Propellants Abstract
Dr. Wayne Pryor worked on three projects this summer. These were: 1) Inertial Electrostatic Confinement; 2) The Laser Elevator; and 3) Solar System Survey for Propellants Abstract. We Assisted Jon Nadler from Richland Community College in assembling and operating a table-top nuclear fusion reactor. We successfully demonstrated neutron production in a deuterium plasma. Pryor also obtained basic spectroscopic information on the atomic and molecular emissions in the plasma. The second project consisted of the completion of a paper on a novel propulsion concept (due to Tom Meyer of Colorado, the first author): a laser sail that bounces light back to the laser source. Recycling the photons from source to sail perhaps 100-1000 times dramatically improves the energy efficiency of this system, which may become very important for high-velocity missions in the future. Lastly, we compiled a very basic inventory of solar system propellant resources, their locations, and their accessibility. This initial inventory concentrates on sunlight availability, water availability, and the difficulty (delta-velocity requirement and radiation environment) in getting there.
Pryor, Wayne↗
MagNetUS 2022 Proceedings
2022 MagNetUS meeting is open for anyone toparticipate either virtually or in-person. The in-personcomponent will be held at The College of William &Mary inWilliamsburg, VA. The purpose of this meeting is (1)to spotlight exciting research in and adjacent to theMagNetUS community and (2) to foster andmaintaincollaborations in basic magnetized plasmaresearch.Intended participants include anyone researching the basic science of magnetized plasmas. More specifically, this could include, but is not limited to, those using spacecraft data, simulationists, theoreticians, and those working with experiments rangingfrom collaborative research facilities (e.g. BaPSF, WiPPL, MPRL, and Frontier experiments on DIII-D) to single-PI college and university-scale labs. Membersof the HED, LTP, and fusion plasma communities areencouraged to participate with an eye toward developing potentially collaborative projects for magnetizedmidscalefacilities. Information about the meeting and MagNetUS in general can be found at http://MagNetUS.net.
Mordijck, Saskia↗
Performance and transport in the ARC tokamak
The ARC TM tokamak, a high-field (𝐵 𝑇 = 11.4 T) fusion power plant, under development by Commonwealth Fusion Systems, is studied using a suite of integrated modelling tools to predict its fusion power generation (𝑃𝑓𝑢𝑠), transport and confinement properties. Analysis is based off an ARC operational point scoped first with zero-dimensional (0-D) plasma operational contour (POPCON) modelling to produce 1.13 GW of fusion power. A suite of integrated modelling tools (TRANSP, ASTRA and TORAX) were applied to predict the performance and kinetic profiles of the ARC design point, yielding a range of predicted performance spanning from ∼900 to 1300 MW in rough quantitative agreement with POPCON predictions. The sensitivity of these results to uncertain modelling inputs was probed using scans of pedestal boundary conditions around EPED-predicted values (total pressure and temperature ratios), tungsten concentration and seperatrix density around their nominal assumptions. Pedestal pressure and pedestal top (𝑇 𝑖 /𝑇 𝑒 ) play a large role in 1.5-dimensional performance predictions, able to modify the predicted 𝑃 𝑓𝑢𝑠 by a factor of 2 within reasonable assumptions. High-fidelity core nonlinear gyrokinetic profile predictions, performed using CGYRO (Candy et al. 2016 J. Comput. Phys., vol. 324, pp. 73–93) coupled with the PORTALS (Rodriguez-Fernandez et al. 2024 Nucl. Fusion, vol. 64, 076034; Phys. Plasmas, vol. 31, 2024, 062501) framework, yield substantially lower performance (𝑃 𝑓𝑢𝑠 =677 MW) compared with 0-D and medium-fidelity modelling for nominal assumptions, showing that there is non-negligible uncertainty between models and that future work on SPARC may help resolve discrepancies. Lower overall performance results from significantly reduced volume-averaged densities and temperatures, along with reduced levels of density and temperature peaking. Turbulence and transport are largely dominated by ion temperature gradient across the profile, confirmed by both linear stability and the response of the nonlinear fluxes to changes in gradients, with some impact of kinetic ballooning modes in the deep core. This work represents one of the most complete scoping of potential fusion power plant conditions performed to date. The extensive integrated modelling provides confidence in ARC performance approaching 1 GW, while nonlinear gyrokinetic modelling results in open questions into the physics of density and temperature peaking in fusion-power-plant-relevant operational space. A discussion of results and the role that the SPARC tokamak (Creely et al. 2020 J. Plasma Phys., vol. 86, 865860502) will play in informing ARC design, performance and operation is presented.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine
High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.
Carl Malings↗
Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine
High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.
Carl Malings↗