Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Information fusion”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10

Construction and Resource Utilization Explorer: Regolith Characterization Using a Modular Instrument Suite and Analysis Tools

The Construction Resource Utilization Explorer (CRUX) is a technology maturation project for the U.S. National Aeronautics and Space Administration to provide enabling technology for lunar and planetary surface operations (LPSO). The CRUX will have 10 instruments, a data handling function (Mapper - with features of data subscription, fusion, interpretation, and publication through geographical information system [GIs] displays), and a decision support system DSS) to provide information needed to plan and conduct LPSO. Six CRUX instruments are associated with an instrumented drill to directly measure regolith properties (thermal, electrical, mechanical, and textural) and to determine the presence of water and other hydrogen sources to a depth of about 2 m (Prospector). CRUX surface and geophysical instruments (Surveyor) are designed to determine the presence of hydrogen, delineate near subsurface properties, stratigraphy, and buried objects over a broad area through the use of neutron and seismic probes, and ground penetrating radar. Techniques to receive data from existing space qualified stereo pair cameras to determine surface topography will also be part of the CRUX. The Mapper will ingest information from CRUX instruments and other lunar and planetary data sources, and provide data handling and display features for DSS output. CRUX operation will be semi-autonomous and near real-time to allow its use for either planning or operations purposes.

Moon↗

A System to Provide Real-Time Collaborative Situational Awareness by Web Enabling a Distributed Sensor Network

In this paper, we describe the architecture of both the PATS and SAP systems and how these two systems interoperate with each other forming a unified capability for deploying intelligence in hostile environments with the objective of providing actionable situational awareness of individuals. The SAP system works in concert with the UICDS information sharing middleware to provide data fusion from multiple sources. UICDS can then publish the sensor data using the OGC's Web Mapping Service, Web Feature Service, and Sensor Observation Service standards. The system described in the paper is able to integrate a spatially distributed sensor system, operating without the benefit of the Web infrastructure, with a remote monitoring and control system that is equipped to take advantage of SWE.

Visualization↗

Achieving Accuracy Requirements for Forest Biomass Mapping: A Spaceborne Data Fusion Method for Estimating Forest Biomass and Lidar Sampling Error

The synergistic use of active and passive remote sensing (i.e., data fusion) demonstrates the ability of spaceborne light detection and ranging (LiDAR), synthetic aperture radar (SAR) and multispectral imagery for achieving the accuracy requirements of a global forest biomass mapping mission (+/-20 Mg/ha or 20%, the greater of the two, for at least 80% of grid cells). A data fusion approach also provides a means to extend 3D information from discrete spaceborne LiDAR measurements of forest structure across scales much larger than that of the LiDAR footprint. For estimating biomass, these measurements mix a number of errors including those associated with LiDAR footprint sampling over regional-global extents. A general framework for mapping above ground live forest biomass density (AGB) with a data fusion approach is presented and verified using data from NASA field campaigns near Howland, ME, USA, to assess AGB and LiDAR sampling errors across a regionally representative landscape. We combined SAR and Landsat-derived optical (passive optical) image data to identify contiguous areas (>0.5 ha) that are relatively homogenous in remote sensing metrics (forest patches). We used this image-derived data with simulated spaceborne LiDAR derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (LVIS) to compute AGB and estimate LiDAR sampling error for forest patches and 100 m, 250 m, 500 m, and 1 km grid cells. At both the patch and grid scales, we evaluated differences in AGB estimation and sampling error from the combined use of LiDAR with both SAR and passive optical and with either SAR or passive optical alone. First, this data fusion approach demonstrates that incorporating forest patches into the AGB mapping framework can provide sub-grid forest information for coarser grid-level AGB reporting. Second, a data fusion approach for estimating AGB using simulated spaceborne LiDAR with SAR and passive optical image combinations reduced forest AGB sampling errors 12%-38% from those where LiDAR is used with SAR or passive optical alone. In absolute terms, sampling errors were reduced from 14-40 Mg/ha to 11-28 Mg/ha across all grid scales and prediction methods, where minimum sampling errors were 11, 15, 18, and 22 Mg/ha for 1 km, 500 m, 250 m, and 100 m grid scales, respectively. Third, spaceborne global scale accuracy requirements were achieved whereby at least 80% of the grid cells at 100 m, 250 m, 500 m, and 1 km grid levels met AGB accuracy requirements using a combination of passive optical and SAR along with machine learning methods to predict vegetation structure metrics for forested areas without LiDAR samples. Finally, using either passive optical or SAR, accuracy requirements were met at the 500 m and 250 m grid level, respectively..

LiDAR↗

Fusion product measurements by nuclear diagnostics in the Joint European Torus deuterium–tritium 2 campaign (invited)

We report a new deuterium–tritium experimental, DTE2, campaign has been conducted at the Joint European Torus (JET) between August 2021 and late December 2021. Motivated by significant enhancements in the past decade at JET, such as the ITER-like wall and enhanced auxiliary heating power, the campaign achieved a new fusion energy world record and performed a broad range of fundamental experiments to inform ITER physics scenarios and operations. New capabilities in the area of fusion product measurements by nuclear diagnostics were available as a result of a decade long enhancement program. These have been tested for the first time in DTE2 and a concise overview is provided here. Confined alpha particle measurements by gamma-ray spectroscopy were successfully demonstrated, albeit with limitations at neutron rates higher than some 10 17 n/s. High resolution neutron spectroscopy measurements with the magnetic proton recoil instrument were complemented by novel data from a set of synthetic diamond detectors, which enabled studies of the supra-thermal contributions to the neutron emission. In the area of escaping fast ion diagnostics, a lost fast ion detector and a set of Faraday cups made it possible to determine information on the velocity space and poloidal distribution of the lost alpha particles for the first time. This extensive set of data provides unique information for fundamental physics studies and validation of the numerical models, which are key to inform the physics and scenarios of ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Engineering workstation: Sensor modeling

The purpose of the engineering workstation is to provide an environment for rapid prototyping and evaluation of fusion and image processing algorithms. Ideally, the algorithms are designed to optimize the extraction of information that is useful to a pilot for all phases of flight operations. Successful design of effective fusion algorithms depends on the ability to characterize both the information available from the sensors and the information useful to a pilot. The workstation is comprised of subsystems for simulation of sensor-generated images, image processing, image enhancement, and fusion algorithms. As such, the workstation can be used to implement and evaluate both short-term solutions and long-term solutions. The short-term solutions are being developed to enhance a pilot's situational awareness by providing information in addition to his direct vision. The long term solutions are aimed at the development of complete synthetic vision systems. One of the important functions of the engineering workstation is to simulate the images that would be generated by the sensors. The simulation system is designed to use the graphics modeling and rendering capabilities of various workstations manufactured by Silicon Graphics Inc. The workstation simulates various aspects of the sensor-generated images arising from phenomenology of the sensors. In addition, the workstation can be used to simulate a variety of impairments due to mechanical limitations of the sensor placement and due to the motion of the airplane. Although the simulation is currently not performed in real-time, sequences of individual frames can be processed, stored, and recorded in a video format. In that way, it is possible to examine the appearance of different dynamic sensor-generated and fused images.

Pavel, M↗

Screening of metal complexes and organic solvents using the COSMOSAC-LANL model to enhance the energy density in a non-aqueous redox flow cell: an insight into the solubility

In this paper, we have proposed a first-principles methodology to screen transition metal complexes against a particular organic solvent and organic solvents against a particular transition metal complex based on their solubility information without the knowledge of heat of fusion and melting temperature. The energy density of a non-aqueous redox flow cell directly depends on the solubility of the redox active species in the non-aqueous medium. Here, we have used the “COSMOSAC-LANL” activity coefficient model which is based on first-principles COSMO calculations where the microscopic information is passed to the macroscopic world via a dielectric continuum solvation model, followed by a post-statistical thermodynamic treatment of the self-consistent properties of the solute particle to calculate the solubility. To model the activity coefficient at infinite dilution for the binary mixtures, a 3-suffix Margules (3sM) function is introduced for the quantitative estimation of the asymmetric interactions and, for the combinatorial term, the Staverman–Guggenheim (SG) form is used. The new activity coefficient model is separately called the “LANL” activity coefficient model. The metal complex and the organic solvent have been treated as a simple binary mixture. The present model has been applied to a set of 14 different organic solvents and 16 different transition metal complexes. Using the new LANL activity coefficient model in combination with the ADF-COSMOSAC-2013 model, we have shown how one can improve the solubility of a transition metal complex in an organic solvent. We applied our model to screen 84 binary mixtures to predict the compatible pair of redox active species and organic solvent to increase the energy density. The solvation mechanism of the transition metal complexes in the organic solvents was obtained using the new model. The results have been compared with the experimental and theoretical results where they are available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion

Dense time-series remote sensing data with detailed spatial information are highly desired for the monitoring of dynamic earth systems. Due to the sensor tradeoff, most remote sensing systems cannot provide images with both high spatial and temporal resolutions. Spatiotemporal image fusion models provide a feasible solution to generate such a type of satellite imagery, yet existing fusion methods are limited in predicting rapid and/or transient phenological changes. Additionally, a systematic approach to assessing and understanding how varying levels of temporal phenological changes affect fusion results is lacking in spatiotemporal fusion research. The objective of this study is to develop an innovative hybrid deep learning model that can effectively and robustly fuse the satellite imagery of various spatial and temporal resolutions. The proposed model integrates two types of network models: super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM). SRCNN can enhance the coarse images by restoring degraded spatial details, while LSTM can learn and extract the temporal changing patterns from the time-series images. To systematically assess the effects of varying levels of phenological changes, we identify image phenological transition dates and design three temporal phenological change scenarios representing rapid, moderate, and minimal phenological changes. The hybrid deep learning model, alongside three benchmark fusion models, is assessed in different scenarios of phenological changes. Results indicate the hybrid deep learning model yields significantly better results when rapid or moderate phenological changes are present. It holds great potential in generating high-quality time-series datasets of both high spatial and temporal resolutions, which can further benefit terrestrial system dynamic studies. The innovative approach to understanding phenological changes’ effect will help us better comprehend the strengths and weaknesses of current and future fusion models.

spatiotemporal fusion↗

Parallelized real-time physics codes for plasma control on DIII-D

A real-time safe multi-threading library was developed on the DIII-D plasma control system to optimize the real-time TORBEAM and real-time STRIDE physics codes. These physics codes are crucial for future fusion power plant operation as they provide information about electron cyclotron wave propagation and heating as well as inform about ideal plasma stability limits. The real-time TORBEAM code executed consistently in under 20 ms while the real-time STRIDE code computes in 100 ms. The multi-threading library developed in this work can be applied to other real-time physics-based codes that will be crucial for the next generation of fusion devices.

DIII-D↗

Construction and Resource Utilization Explorer (CRUX): Implementing Instrument Suite Data Fusion to Characterize Regolith Hydrogen Resources

CRUX is a modular suite of geophysical and borehole instruments combined with display and decision support system (MapperDSS) tools to characterize regolith resources, surface conditions, and geotechnical properties. CRUX is a NASA-funded Technology Maturation Program effort to provide enabling technology for Lunar and Planetary Surface Operations (LPSO). The MapperDSS uses data fusion methods with CRUX instruments, and other available data and models, to provide regolith properties information needed for LPSO that cannot be determined otherwise. We demonstrate the data fusion method by showing how it might be applied to characterize the distribution and form of hydrogen using a selection of CRUX instruments: Borehole Neutron Probe and Thermal Evolved Gas Analyzer data as a function of depth help interpret Surface Neutron Probe data to generate 3D information. Secondary information from other instruments along with physical models improves the hydrogen distribution characterization, enabling information products for operational decision-making.

in situ resource utilization (ISRU)↗

Laser Powder Bed Fusion Additive Manufacturing In-Process Monitoring and Optimization Using Thermionic Emission Detection

In this project, the emission of electrons during laser powder bed fusion additive manufacturing was identified and investigated to resolve the underlying dynamics driven by laser-material interactions. Laser powder bed fusion involves the selective laser melting of metal powder particles layer by layer which fuse together forming a larger 3D structure. Laser-based additive manufacturing approaches such as laser powder bed fusion hold the potential to revolutionize manufacturing of complex metal components in the aerospace, medical, and automotive industries. However, the difficulty in producing defect-free components by metal 3D printing is a major hurdle for widespread commercial adoption. This project resolved important dynamics in laser powder bed fusion including the identification of plasma formation mechanisms, transitions between laser-induced melting modes and stochastic precursors to defect formation. These findings were realized through implementation of electronic sensing diagnostics into an existing testbed system and harnessing capabilities developed for the time dependent analysis of laser powder bed fusion datasets. Importantly, the results advance our understanding of laser-material interactions and reveal that detection of thermionic emission can also resolve information critical to optimization of the laser powder bed fusion fabrication process. The findings are critical for advancing our fundamental understanding of the laser powder bed fusion process and can help assure the requisite fidelity of fabricated components.

36 MATERIALS SCIENCE↗

Investigating explainable transfer learning for battery lifetime prediction under state transitions

Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development. Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions. However, the internal mechanisms of batteries are sensitive to many factors, such as charging/discharging protocols, manufacturing/storage conditions, and usage patterns. These factors will induce state transitions, thereby decreasing the prediction accuracy of data-driven approaches. Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources. Hence, we develop two transfer learning methods, Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit, to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance. From our results, our transfer learning methods reduce root-mean-squared error by 41% through adapting to the target domain. Furthermore, the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining. These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.

25 ENERGY STORAGE↗

Diagnosing hot-spot symmetry in surrogate ignition experiments via secondary DT-neutron spectroscopy at the NIF

The directional energy spectrum of neutrons generated from the in-flight fusion reaction of 1-MeV tritons contains information about the hot-spot symmetry. The National Ignition Facility (NIF) fields Symmetry Capsule (Symcap) implosions, which have historically measured the symmetry of the radiation, drive by measuring the hot-spot shape via x-ray self-emission. Symcaps are used to tune the hot-spot symmetry for ignition experiments at the NIF. This work shows the relationship between directional secondary DT-n spectra and x-ray imaging data for a large database of Symcap implosions. A correlation is observed between the relative widths of the DT-n spectra measured with nTOFs and the shape measured with x-ray imaging. A Monte Carlo model, which computes the directional secondary DT-n spectrum, is used to interpret the results. A comparison of the x-ray and secondary DT-n data with the Monte Carlo model indicates that 56% of the variance between the two datasets is explained by a P2 asymmetry. More advanced simulations using HYDRA suggest that the unaccounted variance is due to P1 and P4 asymmetries present in the hot spot. The comparison of secondary DT-n data and x-ray imaging data to the modeling shows the DT-n data contain important information that supplements current P2 measurements and contain new information about the P1 asymmetry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Physics and technology considerations for the deuterium–tritium fuel cycle and conditions for tritium fuel self sufficiency

The tritium aspects of the DT fuel cycle embody some of the most challenging feasibility and attractiveness issues in the development of fusion systems. The review and analyses in this paper provide important information to understand and quantify these challenges and to define the phase space of plasma physics and fusion technology parameters and features that must guide a serious R&D in the world fusion program. We focus in particular on components, issues and R&D necessary to satisfy three 'principal requirements': (1) achieving tritium self-sufficiency within the fusion system, (2) providing a tritium inventory for the initial start-up of a fusion facility, and (3) managing the safety and biological hazards of tritium. A primary conclusion is that the physics and technology state-of-the-art will not enable DEMO and future power plants to satisfy these principal requirements. We quantify goals and define specific areas and ideas for physics and technology R&D to meet these requirements. A powerful fuel cycle dynamics model was developed to calculate time-dependent tritium inventories and flow rates in all parts and components of the fuel cycle for different ranges of parameters and physics and technology conditions. Dynamics modeling analyses show that the key parameters affecting tritium inventories, tritium start-up inventory, and tritium self-sufficiency are the tritium burn fraction in the plasma (f b ), fueling efficiency (η f ), processing time of plasma exhaust in the inner fuel cycle (t p ), reactor availability factor (AF), reserve time (tr) which determines the reserve tritium inventory needed in the storage system in order to keep the plant operational for time t r in case of any malfunction of any part of the tritium processing system, and the doubling time (t d ). Results show that η f f b > 2% and processing time of 1–4 h are required to achieve tritium self-sufficiency with reasonable confidence. For η f f b = 2% and processing time of 4 h, the tritium start-up inventory required for a 3 GW fusion reactor is ~11 kg, while it is <5 kg if η f f b = 5% and the processing time is 1 h. To achieve these stringent requirements, a serious R&D program in physics and technology is necessary. The EU-DEMO direct internal recycling concept that carries fuel directly from the plasma exhaust gas to the fueling systems without going through the isotope separation system reduces the overall processing time and tritium inventories and has positive effects on the required tritium breeding ratio (TBR R ). A significant finding is the strong dependence of tritium self-sufficiency on the reactor availability factor. Simulations show that tritium self-sufficiency is: impossible if AF < 10% for any η f f b , possible if AF > 30% and 1% ≤ η f f b ≤ 2%, and achievable with reasonable confidence if AF > 50% and η f f b > 2%. These results are of particular concern in light of the low availability factor predicted for the near-term plasma-based experimental facilities (e.g. FNSF, VNS, CTF), and can have repercussions on tritium economy in DEMO reactors as well, unless significant advancements in RAMI are made. There is a linear dependency between the tritium start-up inventory and the fusion power. The required tritium start-up inventory for a fusion facility of 100 MW fusion power is as small as 1 kg. Since fusion power plants will have large powers for better economics, it is important to maintain a 'reserve' tritium inventory in the tritium storage system to continue to fuel the plasma and avoid plant shutdown in case of malfunctions of some parts of the tritium processing lines. But our results show that a reserve time as short as 24 h leads to unacceptable reserve and start-up inventory requirements. Therefore, high reliability and fast maintainability of all components in the fuel cycle are necessary in order to avoid the need for storing reserve tritium inventory sufficient for continued fusion facility operation for more than a few hours. The physics aspects of plasma fueling, tritium burn fraction, and particle and power exhaust are highly interrelated and complex, and predictions for DEMO and power reactors are highly uncertain because of lack of experiments with burning plasma. Fueling by pellet injection on the high field side of tokamak has evolved to be the preferred method to fuel a burning plasma. Extrapolation from the DIII-D penetration scaling shows fueling efficiency expected in DEMO to be <25%, but such extrapolations are highly uncertain. The fueling efficiency of gas in a reactor relevant regime is expected to be extremely poor and not very useful for getting tritium into the core plasma efficiently. Gas fueling will nonetheless be useful for feedback control of the divertor operating parameters. Extensive modeling has been carried out to predict burn fraction, fueling requirements, and fueling efficiency for ITER, DEMO, and beyond. The fueling rate required to operate Q = 10 ITER plasmas in order to provide the required core fueling, helium exhaust and radiative divertor plasma conditions for acceptable divertor power loads was calculated. If this fueling is performed with a 50–50 DT mix, the tritium burn fraction in ITER would be ~0.36%, which is too low to satisfy the self-sufficiency conditions derived from the dynamics modeling for fusion reactors. Extrapolation to DEMO using this approach would also yield similarly low burn fraction. Extensive analysis presented shows that specific features of edge neutral dynamics in ITER and fusion reactors, which are different from present experiments, open possibilities for optimization of tritium fueling and thus to improve the burn fraction. Using only tritium in pellet fueling of the plasma core, and only deuterium for edge density, divertor power load and ELM control results in significant increase of the burn fraction to 1.8–3.6%. These estimates are performed with physics models whose results cannot be fully validated for ITER and DEMO plasma conditions since these cannot be achieved in present tokamak experiments. Thus, several uncertainties remain regarding particle transport and scenario requirements in ITER and DEMO. The safety standard requirements for protection of the public and release guidelines for tritium have been reviewed. General safety approaches including minimizing tritium inventories, reducing tritium permeation through materials, and decontaminating material for waste disposal have been suggested.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE Challenges and Opportunities Associated with Accountable Nuclear Material Needs for Development and Commercialization of Fusion Nuclear Energy

Fusion energy represents a transformative opportunity to deliver a safe, plentiful, and carbon-free source of reliable primary power. In recent years, fusion research and development have accelerated significantly, particularly within the US, driven by decades of foundational public investment. Notably, in 2024 the US Department of Energy (DOE) established a comprehensive Fusion Energy Strategy aimed at collaborating with industry partners to enable the deployment of fusion power plants and grid integration by the 2030s. This strategy is chiefly implemented through the DOE Office of Science (SC) Fusion Energy Sciences program. This project was initiated to identify potential approaches for the Office of Environment, Safety, and Health (NA-ESH-12) within DOE’s National Nuclear Security Administration to begin engagement with the fusion community on future accountable material needs. The goal of the project is to inform and influence the supply of and demand for accountable nuclear materials as fusion energy is developed and commercialized. NA-ESH-12 must proactively engage with the fusion community regarding the production and management of accountable nuclear materials. Given the complexity and scale of materials required for research, pilot projects, and eventual commercial reactors, early coordination is vital. The project’s objective is to provide insights that will shape the supply and demand landscape for critical nuclear materials, ensuring that DOE is prepared to effectively support fusion energy development and commercialization. In FY 2025, an initial limited review was conducted to identify the status of the fusion energy community’s progress toward full-scale energy production and the need for accountable nuclear material. This included communications with SC, NA-ESH-12, Savannah River National Laboratory, and Oak Ridge National Laboratory, and attending the Rutgers University–sponsored Supply Chain Workshop: Scaling the Fusion Industry and the International Atomic Energy Agency’s Ninth DEMO Programme Workshop. The review to date indicates that the amounts of tritium, lithium-6, and deuterium required by the fusion industry will be dependent on fuel type, breeding technology, blankets, and R&D improvements. One concern is that the commercial sector does not have a sufficient supply chain to meet the demand for development and commercialization for fusion energy production. The supply and demand estimates for these materials should be routinely reviewed as fusion technologies mature.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Height-resolved emission spectroscopy and high-speed imaging of the TiAl6V4 vapor plume under laser powder bed fusion conditions

Optical emission spectroscopy is increasingly used as an in situ monitoring technique during laser powder bed fusion (LPBF) because plume emission holds elemental information not found in other in situ sensing techniques. This work explores the shape, stability, and temperature of the emission plume above the melt pool of Ti6Al4 V undergoing laser scans under LPBF-like processing conditions, using high-speed video and height-resolved spectroscopy to study the details of plume dynamics. Optical emission spectroscopy is conducted in the 480 nm to 525 nm region, where Ti emission is strong, with 0.3 mm vertical resolution above the baseplate. The Boltzmann plot method is used to determine temperature at each probed elevation, which indicates that the hottest location of the plume is occasionally elevated 0.3 mm to 0.6 mm above the scanning surface. The results show that the plume shape, stability, temperature, and spectra are highly dependent on the processing conditions. We highlight some of the complexities of optical emission spectroscopy and discuss potential challenges for implementing optical emission spectroscopy across an entire build.

36 MATERIALS SCIENCE↗

Data Fusion With Uncertainty Quantification for Sub-City-Scale Air Quality Assessment and Forecasting

Many information sources can support air quality assessment and forecasting, including atmospheric chemistry model outputs, satellite retrievals of column chemical and aerosol constituents, and surface-based air quality monitoring data from both regulatory and low-cost instruments. Systematic integration of these data sources provides a major opportunity to improve understanding and management of air quality, but also presents technical barriers, especially in resource- and data-constrained settings in the Global South. This presentation describes a data fusion system, currently under development using the Google Earth Engine platform, which aims to integrate the information sources listed above to support comprehensive sub-city-scale assessment and management of air quality. Furthermore, the data fusion framework includes provisions for the quantification of uncertainties in the resulting fused estimates based on the variability of and among the input data sources. These capabilities will allow air quality managers to better understand their local air quality situation, including relative confidence in the fused estimates for different constituents, locations, and times, leading to better informed air quality management decisions. This presentation covers the underlying methodology of the data fusion and uncertainty quantification approaches, provides an update on the status of its implementation, and presents early qualitative and quantitative results.

Carl Malings↗

Theoretical Modeling of Reactor Relevant Conditions for Plasma Jet Driven Magneto-Inertial Fusion

The Charger Advanced Power and Propulsion Laboratory (CAPP), a laboratory within the Propulsion Research Center (PRC) at the University of Alabama in Huntsville (UAH) is working with Los Alamos National Laboratory (LANL). to develop models and inform on promising paths for high gain magneto-inertial fusion (MIF) conditions. This report provides a framework for identifying promising conditions for achieving ignition in plasma-jet-driven magneto-inertial fusion (PJMIF)[1]. As proposed, for the first part of the contract, UAH proposes to develop a gain over unity set of stagnation conditions to provide a state of plasma conditions to achieve to set long terms goals for the PJMIF program. Specifically, UAH will model PJMIF stagnation conditions to include radiation, heat transfer, two temperature energy equations, fusion reactivity and nonlocal fusion product deposition, but no hydrodynamics for these purposes. These calculations will use a stationary plasma model to reduce simulation complexity—focusing on a DT target at 10 keV. Subsequent work will include a DD plasma layer acting as an afterburner. UAH will assume an initial magnetic field without any consideration of the topology, just assume a field strength, most likely scaled with consideration of the local hall parameter. This effort will inform the team on the tradeoff between mass, peak target field, etc and the achievable gain.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗