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FY2019 Analysis Program Annual Progress Report

The VTO Analysis Program supports mission-critical technology, economic, and interdisciplinary analyses to assist in prioritizing VTO technology investments and to inform research portfolio planning. These efforts provide essential vehicle and market data, modeling and simulation, and integrated and applied analyses, using the unique capabilities, analytical tools, and expertise resident in the national laboratory system.

33 ADVANCED PROPULSION SYSTEMS↗

Fractal Characterization of Multitemporal Scaled Remote Sensing Data

Scale is an "innate" concept in geographic information systems. It is recognized as something that is intrinsic to the ingestion, storage, manipulation, analysis, modeling, and output of space and time data within a GIS purview, yet the relative meaning and ramifications of scaling spatial and temporal data from this perspective remain enigmatic. As GISs become more sophisticated as a product of more robust software and more powerful computer systems, there is an urgent need to examine the issue of scale, and its relationship to the whole body of spatiotemporal data, as imparted in GISS. Scale is fundamental to the characterization of geo-spatial data as represented in GISS, but we have relatively little insight on the effects of, or how to measure the effects of, scale in representing multiscaled data; i.e., data that are acquired in different formats (e.g., map, digital) and exist in varying spatial, temporal, and in the case of remote sensing data, radiometric, configurations. This is particularly true in the emerging era of Integrated GISs (IGIS), wherein spatial data in a variety of formats (e.g., raster, vector) are combined with multiscaled remote sensing data, capable of performing highly sophisticated space-time data analyses and modeling. Moreover, the complexities associated with the integration of multiscaled data sets in a multitude of formats are exacerbated by the confusion of what the term "scale" is from a multidisciplinary perspective; i.e., "scale" takes on significantly different meanings depending upon one's disciplinary background and spatial perspective which can lead to substantive confusion in the input, manipulation, analyses, and output of IGISs (Quattrochi, 1993). Hence, we must begin to look at the universality of scale and begin to develop the theory, methods, and techniques necessary to advance knowledge on the "Science of Scale" across a wide number of spatial disciplines that use GISs.

Quattrochi, Dale A.↗

Contributions of GRACE to Climate Monitoring

The NASA/German Gravity Recovery and Climate Experiment (GRACE) was launched in March 2002. Rather than looking downward, GRACE continuously monitors the locations of and precise distance between twin satellites which orbit in tandem about 200 km apart. Variations in mass near Earth's surface cause heterogeneities in its gravity field, which in turn affect the orbits of satellites. Thus scientists can use GRACE data to map Earth's gravity field with enough accuracy to discern month to month changes caused by ocean circulation and redistribution of water stored on and in the land. Other gravitational influences, such as atmospheric circulation, post-glacial rebound, and solid earth movements are either independently determined and removed or are negligible on a monthly to sub-decadal timescale. Despite its coarse spatial (>150,000 sq km at mid-latitudes) and temporal (approx monthly) resolutions, GRACE has enabled significant advancements in the oceanic, hydrologic, and cryospheric science, and has great potential for climate monitoring, because it is the only global observing system able to measure ocean bottom pressures, total terrestrial water storage, and ice mass changes. The best known GRACE results are estimates of Greenland and Antarctic ice sheet loss rates. Previously, scientists had estimated ice mass losses using ground and satellite based altimetry and surface mass balance estimates based on snowfall accumulation and glacier discharge. While such measurements are still very useful for their spatial detail, they are imperfectly correlated with large-scale ice mass changes, due to snow and ice compaction and incomplete spatial coverage. GRACE enables scientists to generate monthly time series of Greenland and Antarctic ice mass, which have confirmed the shrinking of the polar ice sheets, one of the most obvious and indisputable manifestations of climate change. Further, GRACE has located and quantified hot spots of ice loss in southeastern Greenland and western Antarctica. For 2002 to present, the rate of ice mass loss has been 200 to 300 GT/yr in Greenland and 70 to 210 GT/yr in Antarctica, and some scientists are suggesting that the rates are accelerating. Similarly, GRACE has been used to monitor mass changes in alpine glaciers. Tamisiea et al. first characterized glacier melt along the southern coast of Alaska, more recently estimated to be occurring at a rate of 84 GT/yr. Chen et al. estimated that Patagonian glaciers are melting at a rate of 28 GT/yr, and estimated that the high mountains of central Asia lose ice at a rate of 47 GT/yr. Tapley et al. and Wahr et al. presented the first GRACE based estimates of changes in column-integrated terrestrial water storage (TWS; the sum of ground-water, soil moisture, surface waters, snow, ice, and water stored in vegetation) at continental scales. Since then, dozens of studies have shown that GRACE based estimates of regional to continental scale TWS variations agree with independent information, and some innovative uses of GRACE data have been developed. Rodell et al. (2004) and Swenson and Wahr (2006) demonstrated that by combining GRACE derived terrestrial water storage changes with observations of precipitation and runoff in a river basin scale water budget, it was possible to produce new estimates of evapotranspiration and atmospheric moisture convergence, essential climate variables that are difficult to estimate accurately. Similarly, GRACE has been used to constrain estimates of global river discharge and the contribution of changes in TWS to sea level rise. Crowley et al. observed a negative correlation between interannual TWS anomalies in the Amazon and the Congo River basin. Yeh et al. and Rodell et al. estimated regionally averaged groundwater storage variations based on GRACE and auxiliary observations. Rodell et al. and Tiwari et al. applied that method to quantify massive groundwater depletion in northern India caused by over reliance on aquifers for irration, and Famiglietti et al. found a similar situation in California's Central Valley. Zaitchik et al. and Lo et al. described approaches to use GRACE to constrain hydrological models, enabling integration of GRACE data with other observations and achieving much higher spatial and temporal resolutions than GRACE alone. Such approaches are now supporting applications including drought and water resources monitoring. Oceanography has likewise benefitted from the independent nature of GRACE observations. One application is measurement of the mass component of sea level rise, which complements radar altimetry and in situ measurements. GRACE also measures ocean bottom pressures (OBP), which help to refine understanding and modeling of ocean circulation and the ocean's fresh water budget, among other things. For example, Hayakawa et al. showed that GRACE observes OBP patterns absent from the background models of oceanic variability. Morison et al. used GRACE to describe important decadal scale shifts in circulation and an ongoing trend of freshening of the western Arctic, important indicators of climate variability. The research of Song and Zlotnicki and Chambers and Willis on GRACE-derived ocean bottom pressures in the sub-polar gyre led to the discovery of an ENSO teleconnection and a long-term change in OBP in the North Pacific sub-polar gyre that was not predicted by an ocean model. Further, Chambers and Willis were able to identify an internal redistribution of mass between Atlantic and Pacific Oceans lasting at least six years, which was not predicted by ocean models and was the first direct evidence of sustained mass transport from one ocean basin to another on periods longer than a year. Boening et al. observed a record increase in OBP over part of the southeastern Pacific in late 2009 and early 2010, primarily caused by wind stress curl associated with a strong and persistent anticyclone and likely related to the concurrent Central Pacific El Nino. GRACE has far surpassed its 5-year design lifetime, but it will likely succumb to the aging of batteries and instrument systems sometime in the next few years. NASA has begun initial development of a follow-on to GRACE with very similar design, which could launch as soon as 2016 and would provide continuity in the data record while improving resolution slightly. Higher resolution time variable gravity missions are also on the drawing board .

Rodell, Matthew↗

Model-data synthesis for the next generation of forest free-air CO 2 enrichment (FACE) experiments

The first generation of forest free-air CO 2 enrichment (FACE) experiments has successfully provided deeper understanding about how forests respond to an increasing CO 2 concentration in the atmosphere. Located in aggrading stands in the temperate zone, they have provided a strong foundation for testing critical assumptions in terrestrial biosphere models that are being used to project future interactions between forest productivity and the atmosphere, despite the limited inference space of these experiments with regards to the range of global ecosystems. Now, a new generation of FACE experiments in mature forests in different biomes and over a wide range of climate space and biodiversity will significantly expand the inference space. These new experiments are: EucFACE in a mature Eucalyptus stand on highly weathered soil in subtropical Australia; AmazonFACE in a highly diverse, primary rainforest in Brazil; BIFoR-FACE in a 150-yr-old deciduous woodland stand in central England; and SwedFACE proposed in a hemiboreal, Pinus sylvestris stand in Sweden. We now have a unique opportunity to initiate a model–data interaction as an integral part of experimental design and to address a set of cross-site science questions on topics including responses of mature forests; interactions with temperature, water stress, and phosphorus limitation; and the influence of biodiversity.

54 ENVIRONMENTAL SCIENCES↗

An Advanced Open-Source Platform for Air Quality Analysis, Visualization, and Prediction

Ambient air pollution is the largest environmental health risk factor, leading to several million premature deaths globally per year. The challenge of combating poor air quality is exacerbated by growing urban populations, changing emissions, and a warming climate. While there have been many advances monitoring and modeling of atmospheric composition, reflected in the dramatic increase in archived Earth Observations, there is no single measurement or method that alone can provide an accurate depiction of the entire atmosphere. The rapidly growing collections of observational and modeling data require us to be smarter about what data to include, and how such data is used. In recent years, NASA has invested significantly in advancing the concepts for Analytics Collaborative Framework (ACF) [5] and New Observing Strategies (NOS) [4] to tackle our software infrastructure need for harmonized data management and dynamic acquisition of diverse measurements for on-demand, interactive, multivariate analysis, and access [3]. It is not enough to have a big data, standalone analytics solution; it is critical that we start integrating data from remote sensing, modeling, and in-situ networks in a harmonized manner that enables timely and data-driven decision-making for air quality management. This work presents the design and development of an Air Quality Analytics Collaborative Framework (AQ ACF), as part of NASA’s Advanced Information Systems Technology (AIST) effort, to establish a data, machine-learning, and numerically driven platform for air quality analysis, visualization, and prediction.

Liu, Qian↗

Analysis Program (2023 Annual Progress Report)

This document summarizes the progress of VTO Analysis projects supported during the fiscal year 2023. The VTO Analysis Program supports mission-critical technological, economic, and interdisciplinary analyses to assist in prioritizing VTO technology investments and to inform research portfolio planning. These efforts provide essential vehicle and market data, modeling and simulation, and integrated and applied analyses, using the unique capabilities, analytical tools, and expertise resident in the DOE’s national laboratory system. VTO Analysis projects also demonstrate additional capabilities and expertise provided by research partnerships that may include academia, the private sector, and non-profit organizations.

33 ADVANCED PROPULSION SYSTEMS↗

Normalizing Radionuclide Concentration Exposure Rate and Dose Rate Data for the Effects of Radioactive Decay and Weathering Processes

This report describes the procedure that will be implemented to normalize radiological data, collected at various times, to one point in time. Raw radiological data is adjusted for the effects of radioactive decay and weathering processes. Normalizing the radiological data to one point in time enables data that has been collected at various times to be directly compared and utilized in radiological assessment, statistical analysis, and atmospheric dispersion models. This report fulfills a deliverable requirement for the U.S. Department of Energy’s, NA-84 Technology Integration Program for the FY17 project, SNL17-CM-635, Normalizing Radiological Data for Analysis and Integration into Models.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

The FaceBase Consortium: A comprehensive resource for craniofacial researchers

The FaceBase Consortium, funded by the National Institute of Dental and Craniofacial Research, National Institutes of Health, is designed to accelerate understanding of craniofacial developmental biology by generating comprehensive data resources to empower the research community, exploring high-throughput technology, fostering new scientific collaborations among researchers and human/computer interactions, facilitating hypothesis-driven research, and translating science into improved health care to benefit patients. The resources generated by the FaceBase projects include a number of dynamic imaging modalities, genome-wide association studies, software tools for analyzing human facial abnormalities, detailed phenotyping, anatomical and molecular atlases, global and specific gene expression patterns, and transcriptional profiling over the course of embryonic and postnatal development in animal models and humans. The integrated data visualization tools, faceted search infrastructure, and curation provided by the FaceBase Hub offer flexible and intuitive ways to interact with these multidisciplinary data. In parallel, the datasets also offer unique opportunities for new collaborations and training for researchers coming into the field of craniofacial studies. Here we highlight the focus of each spoke project and the integration of datasets contributed by the spokes to facilitate craniofacial research.

60 APPLIED LIFE SCIENCES↗

Data-Enabled Fusion Technology (Final Scientific/Technical Report)

Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Safeguards and security modeling for electrochemical plants

Safeguards and security design for reprocessing plants can lead to excessive costs if not incorporated early in the design process. The design for electrochemical plants is somewhat uncertain since these plants have not been built at a commercial scale in the past. The Separation and Safeguards Performance Model (SSPM), developed at Sandia National Laboratories, has been used for safeguards design and evaluation for multiple reprocessing plant types. The SSPM includes the following capabilities: -) spent fuel source term library, -) mass tracking of elements 1-99 and bulk solid/liquids, -) tracking of heat load and activity, -) customisable measurement points, -) automated calculation of ID and error propagation, -) alarm conditions and statistical tests, and -) user-defined diversion scenarios. Materials accountancy and process monitoring data can provide more timely detection of material loss specifically to protect against the insider threat. While the SSPM is capable of determining detection probabilities and examining detection times for material loss scenarios, it does not model the operations or spatial effects for a plant design. The STAGE software was chosen to model the physical protection system. STAGE provides a framework to create end-to-end scalable force-on-force combat simulations. It allows for a complete 3D model of a facility to be designed along with the design of physical protection elements. This software, then, can be used to model operations and response for various material loss scenarios. The future integration of the SSPM model data with the STAGE software will provide a more complete analysis of diversion scenarios to assist plant designers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Towards Z-Next: The Integration of Theory, Experiments, and Computational Simulation in a Bayesian Data Assimilation Framework

Making reliable predictions in the presence of uncertainty is critical to high-consequence modeling and simulation activities, such as those encountered at Sandia National Laboratories. Surrogate or reduced-order models are often used to mitigate the expense of performing quality uncertainty analyses with high-fidelity, physics-based codes. However, phenomenological surrogate models do not always adhere to important physics and system properties. This project develops surrogate models that integrate physical theory with experimental data through a maximally-informative framework that accounts for the many uncertainties present in computational modeling problems. Correlations between relevant outputs are preserved through the use of multi-output or co-predictive surrogate models; known physical properties (specifically monotoncity) are also preserved; and unknown physics and phenomena are detected using a causal analysis. By endowing surrogate models with key properties of the physical system being studied, their predictive power is arguably enhanced, allowing for reliable simulations and analyses at a reduced computational cost.

97 MATHEMATICS AND COMPUTING↗

Global Scale Diagnosis of FGGE Data

The objective was to perform descriptive global-scale diagnoses of the Goddard Laboratory for Atmospheric Sciences (GLAS) First GARP Global Experiment (GLAS) SOP-1 analyses and to compare these diagnoses against controlled, real-data integrations of the GLAS General Circulation Model (GCM) as well as other data sets. The effects of critical latitudes, the diagnoses of the influence of tropical wind data and latent heating upon the GLAS GCM, investigations of planetary wave structure on various time scales from the diurnal to the monthly, and comparison of the GLAS analyses with other analyses are discussed.

Paegle, J.↗

Advanced Technology Lifecycle Analysis System (ATLAS)

Developing credible mass and cost estimates for space exploration and development architectures require multidisciplinary analysis based on physics calculations, and parametric estimates derived from historical systems. Within the National Aeronautics and Space Administration (NASA), concurrent engineering environment (CEE) activities integrate discipline oriented analysis tools through a computer network and accumulate the results of a multidisciplinary analysis team via a centralized database or spreadsheet Each minute of a design and analysis study within a concurrent engineering environment is expensive due the size of the team and supporting equipment The Advanced Technology Lifecycle Analysis System (ATLAS) reduces the cost of architecture analysis by capturing the knowledge of discipline experts into system oriented spreadsheet models. A framework with a user interface presents a library of system models to an architecture analyst. The analyst selects models of launchers, in-space transportation systems, and excursion vehicles, as well as space and surface infrastructure such as propellant depots, habitats, and solar power satellites. After assembling the architecture from the selected models, the analyst can create a campaign comprised of missions spanning several years. The ATLAS controller passes analyst specified parameters to the models and data among the models. An integrator workbook calls a history based parametric analysis cost model to determine the costs. Also, the integrator estimates the flight rates, launched masses, and architecture benefits over the years of the campaign. An accumulator workbook presents the analytical results in a series of bar graphs. In no way does ATLAS compete with a CEE; instead, ATLAS complements a CEE by ensuring that the time of the experts is well spent Using ATLAS, an architecture analyst can perform technology sensitivity analysis, study many scenarios, and see the impact of design decisions. When the analyst is satisfied with the system configurations, technology portfolios, and deployment strategies, he or she can present the concepts to a team, which will conduct a detailed, discipline-oriented analysis within a CEE. An analog to this approach is the music industry where a songwriter creates the lyrics and music before entering a recording studio.

O'Neil, Daniel A.↗

Advancements in 3D Structural Analysis of Geothermal Systems

Robust geothermal activity in the Great Basin, USA is a product of both anomalously high regional heat flow and active fault-controlled extension. Elevated permeability associated with some fault systems provides pathways for circulation of geothermal fluids. Constraining the local-scale 3D geometry of these structures and their roles as fluid flow conduits is crucial in order to mitigate both the costs and risks of geothermal exploration and to identify blind (no surface expression) geothermal resources. Ongoing studies have indicated that much of the robust geothermal activity in the Great Basin is associated with high density faulting at structurally complex fault intersection/interaction areas, such as accommodation/transfer zones between discrete fault systems, step-overs or relay ramps in fault systems, intersection zones between faults with different strikes or different senses of slip, and horse-tailing fault terminations. These conceptualized models are crucial for locating and characterizing geothermal systems in a regional context. At the local scale, however, pinpointing drilling targets and characterizing resource potential within known or probable geothermal areas requires precise 3D characterization of the system. Employing a variety of surface and subsurface data sets, we have conducted detailed 3D geologic analyses of two Great Basin geothermal systems. Using EarthVision (Dynamic Graphics Inc., Alameda, CA) we constructed 3D geologic models of both the actively producing Brady’s geothermal system and a ‘greenfield’ geothermal prospect at Astor Pass, NV. These 3D models allow spatial comparison of disparate data sets in 3D and are the basis for quantitative structural analyses that can aid geothermal resource assessment and be used to pinpoint discrete drilling targets. The relatively abundant data set at Brady’s, ~80 km NE of Reno, NV, includes 24 wells with lithologies interpreted from careful analysis of cuttings and core, a 1:24,000 scale detailed geologic map and cross-sections, 2D seismic reflection profiles and other geophysical data, and downhole temperature data. The 3D geologic model based on these data consists of 61 fault planes, 25 distinct stratigraphic units, and 2 intrusive bodies. Geothermal fluids are produced from a left step-over/relay ramp within the Brady’s Fault Zone (BFZ). Under local stress conditions, fault segments that strike NNE-to-NE are most likely to slip and/or dilate, and therefore transmit geothermal fluids. The 3D model defines the locations of discrete fault intersections within the BFZ and indicates that the densest zones of structurally controlled fracture permeability are ~10-to-10s of meters in diameter and plunge ~55° NW-NNW beneath the heart of the BFZ step over. The locations of high intersection density, high fault slip and dilation tendency, high subsurface temperature, and lithologies known to support high fracture permeability are combined to produce 3D ‘fairway’ maps useful in both assessments of geothermal resource potential and for defining drilling targets. Astor Pass is located on the Pyramid Lake Paiute Reservation, ~80 km north of Reno, NV. It is a prospective ‘greenfield’ geothermal area, and thus subsurface data are relatively sparse. Available data include: two relatively deep wells (~1400 m) and one shallower well (~500 m) with lithologies interpreted from drill cuttings, several 2D seismic reflection profiles, a 1:24,000 scale geologic map and cross-section, a shallow temperature survey, and downhole temperature data. 3D modeling based on these data has defined 19 distinct fault planes and 16 stratigraphic units. Based on the stress field calculated from borehole breakouts, drilling induced tensile cracks and petal-centerline cracks in the two relatively deep wells, 3D slip and dilation tendency analysis indicates that northerly striking fault segments are most likely to slip and/or dilate, and therefore transmit geothermal fluids. Analysis of fault intersection density indicates that the highest density of structurally controlled permeability within the field lies in a narrow (10-to-10s of m) zone plunging moderately (~35°) to the NNW beneath Pleistocene tufa deposits. This zone of increased fracture density, which we interpret as the primary upflow zone, is controlled by the intersection of N-to-NNW striking normal faults and a WNW striking dextral fault zone and represents the most promising target for future drilling. Construction of a 3D geologic model involves integration of a variety of data into an internally consistent framework. A robust model allows for spatial comparison between the various types of data (structural, stratigraphic, geophysical, temperature, etc.) that are commonly used independently to site geothermal wells. Furthermore, highly detailed 3D geologic models provide the basis for additional quantitative analysis, including 3D fault slip and dilation tendency analysis and the precise location of structurally controlled permeability pathways. These analyses provide detailed information relating to the internal dynamics of geothermal systems and can mitigate the costs and risks of geothermal exploration and development by contributing to better well targeting and more accurate evaluations of resource potential.

15 GEOTHERMAL ENERGY↗

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗

The representation of manipulable solid objects in a relational database

This project is concerned with the interface between database management and solid geometric modeling. The desirability of integrating computer-aided design, manufacture, testing, and management into a coherent system is by now well recognized. One proposed configuration for such a system uses a relational database management system as the central focus; the various other functions are linked through their use of a common data repesentation in the data manager, rather than communicating pairwise to integrate a geometric modeling capability with a generic relational data managemet system in such a way that well-formed questions can be posed and answered about the performance of the system as a whole. One necessary feature of any such system is simplification for purposes of anaysis; this and system performance considerations meant that a paramount goal therefore was that of unity and simplicity of the data structures used.

Bahler, D.↗

Directions of Moving Plaids is Biased by Asymmetric Viewing Windows

Directionally selective V1 neurons are tuned to particular spatio-temporal frequencies and respond to local 1-D edge motion. At least some MT neurons however appear to respond to the actual velocity of moving 2D patterns. To better understand how the 1D local motion information available from VI is integrated to derive a 2D velocity signal we investigated human perception of moving plaids, 2-D patterns composed of the sum of two 1-D sinusoidal gratings of different orientations. We measured the effect of the shape of the viewing window on the perceived direction of plaid motion. The plaids were spatially windowed by 2-D Gaussians with unequal standard deviations (sigma 1, sigma 2). Four observers indicated perceived direction by adjusting a pointer. Direction errors were measured as a function of the difference between window orientation and true plaid direction (DELTA THETA) for several grating spatial frequencies (SF = 0.3, 0.6, 1.2 c/d) and window aspect ratios (AR = sigma 1/sigma 2 = 1, 1.4, 2, 4). Observers showed systematic errors in perceived direction (approx. 15 for AR = 4 and SF = 0.6 c/d) that peaked at DELTA THETA approx. 40. The errors increased for increasing aspect ratio and decreased for increasing spatial frequencies (or number of cycles). These results show that despite the unambiguous motion of the plaids, under these conditions human misperceive the motion. These data constrain models of motion integration from V1 to MT and, in particular, are inconsistent with algorithms that use either the Intersection of Constraints rule or cross correlation to compute the perceived direction of motion.

Beutter, B. R.↗