SEARCH · Engineering Papers

Results for “Multi-modal testing”

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.

SourceResearch connectionFrom yearThrough year

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

At least 55 records · Page 3

All sources NASA NTRS DOE OSTI

NASA NTRS1980

Multi-modal information processing for visual workload relief

The simultaneous performance of two single-dimensional compensatory tracking tasks, one with the left hand and one with the right hand, is discussed. The tracking performed with the left hand was considered the primary task and was performed with a visual display or a quickened kinesthetic-tactual (KT) display. The right-handed tracking was considered the secondary task and was carried out only with a visual display. Although the two primary task displays had afforded equivalent performance in a critical tracking task performed alone, in the dual-task situation the quickened KT primary display resulted in superior secondary visual task performance. Comparisons of various combinations of primary and secondary visual displays in integrated or separated formats indicate that the superiority of the quickened KT display is not simply due to the elimination of visual scanning. Additional testing indicated that quickening per se also is not the immediate cause of the observed KT superiority.

Burke, M. W.↗

NASA NTRS2017

Online Multi-Modal Learning and Adaptive Information Trajectory Planning for Autonomous Exploration

In robotic information gathering missions, scientists are typically interested in understanding variables which require proxy measurements from specialized sensor suites to estimate. However, energy and time constraints limit how often these sensors can be used in a mission. Robots are also equipped with cheaper to use navigation sensors such as cameras. In this paper, we explore a challenging planning problem in which a robot is required to learn about a scientific variable of interest in an initially unknown environment by planning informative paths and deciding when and where to use its sensors. To tackle this we present two innovations: a Bayesian generative model framework to automatically learn correlations between expensive science sensors and cheaper to use navigation sensors online, and a sampling based approach to plan for multiple sensors while handling long horizons and budget constraints. Our approach does not grow in complexity with data and is anytime making it highly applicable to field robotics. We tested our approach extensively in simulation and validated it with real data collected during the 2014 Mojave Volatiles Prospector Mission. Our planning algorithm performs statistically significantly better than myopic approaches and at least as well as a coverage-based algorithm in an initially unknown environment while having added advantages of being able to exploit prior knowledge and handle other intricacies of the real world without further algorithmic modifications.

learning↗

DOE OSTI2026

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗

DOE OSTI2026

Throughput Estimation of Data Transport Networks From Digital Twin Measurements

Digital twins of networked infrastructures, known as Virtual Infrastructure Twins (VITs), are increasingly used for software development, pre-deployment testing, and design space exploration. While VITs avoid the costs and potential disruptions associated with experiments on operational networks, their throughput measurements are typically not sufficiently accurate for performance profiling of wide-area networks that they emulate. Here, machine learning (ML) methods are developed to transform these inaccurate VIT network throughput measurements to closely match in peak and overall profile of those from a physical testbed or production network. First, a micro kernel network reflecting a physical network is utilized to collect one-time measurements on a host to support this ML transformation. Then, a generic multi-modal ML method is developed to learn a map that transforms measurements from subsequent VITs on the same host to match past, current and follow-on testbed and cloud networks. ML generalization equations are derived to establish its correctness and probabilistically guarantee its generalization accuracy. Experimental results are presented for a variety of VIT hosts with target testbed and cloud networks; they include a case study of a four-site science ecosystem wherein inaccurate convex VIT measurement profiles are transformed into accurate concave profiles of target networks.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2018

A 3D Citizen Science Video Game for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network aimed at accurately assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. We present here the active learning component of the project, which consists of an interactive video game prototype for tablet and mobile devices where players are able to intuitively label morphology classifications over mm-scale 3D coral reef imagery. Active learning applications present a novel methodology for engaging the public while efficiently providing large-scale training and test data for increasingly complex and data-intensive machine learning algorithms. NeMO-Net trains players on domain-specific knowledge through interactive tutorials and periodically checks players' input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide personalized classification training. Players can rate the classifications of other players, unlock rewards and join a global community as they explore and classify coral reefs and other shallow marine environments.

Citizen Science↗

NASA NTRSDate not supplied

Summary of Technical Interchange Meetings (TIMs) Designed to Enable Earth Independent Medical Operations (EIMO)

The Exploration Medical Capability Element (ExMC) in NASA’s Human Research Program hosted a series of TIMs in 2023-2024 designed to stimulate discussion around specific topics with the goal of enabling EIMO. In context of the thematic constituent elements of EIMO, namely pre-mission planning, acute/emergent/prolonged medical decision making, supply/resource management and task load management, subject matter experts from industry, academia and government (NASA and other Agencies) provided valuable and actionable guidance and recommendations. Earth-based medical experts will remain indispensable for pre-mission planning, however, management of acute/emergent medical contingencies will require a gradual transition of medical care and decision making from terrestrial to space-based assets to enable support of astronaut health and performance and reduce overall mission risk. Key to achieving these enhancements is providing an integrated data system platform capable of utilizing multiple data streams in concert with a variety of on-board databases and passive monitoring of video and wearable sensors to enable a multi-modal, agentic AI-based clinical decision support system (CDSS) to support crew medical officer (CMO) medical decision-making. The EIMO series of TIMs (I-V) have proven to be instructive and portend a significant paradigm shift will be necessary to maintain crew health and performance on exploration class missions. Importantly, since the expected paradigm shift will be significantly different from the methods of operation that have been employed for the majority of missions from the inception of human spaceflight to date, any proposed methods must be deployed in the setting of ongoing operations early and be “tested, reviewed and practiced” while reliable back-up is available to facilitate an Enterprise-wide level of comfort and acceptance. Serious constraints on data transmission coupled with a large and expanding universe of on-board medical informatics data streams will necessitate implementation of a CDSS to supplant the current reliance on support provided by ground-based SMEs. Establishment of trust in the system by CMO/crew and the ground-based medical support team will be essential. Co-development of a CDSS with industry partners will assure that state of the art tools can be employed, and industry efficiencies can be leveraged. Training regimens, materials and tools must evolve to be responsive (just-in-time training) and facilitate autonomous execution of procedures. Proficiency metrics should be established and be based on validated competencies or milestones as opposed to a prescribed number of training hours. Training should be prioritized for broad, translatable skills that have universal application across a variety of medical conditions. Repetition was deemed to be the key to achieving proficiency and emphasis should lie in procedural training which is known to extinguish more rapidly than diagnostic skills. Advanced tools, e.g., extended reality, can provide more realistic and effective training. Use of advanced probabilistic risk assessment tools will be essential to optimize the medical system capability while carefully balancing risk relative to mass/power/volume limitations. Importance of factoring use-life of medical supplies and maintaining awareness of redundancy and opportunity to re-purpose under off nominal situations was emphasized. Consideration of adopting optimized performance standards vs. “good-enough” performance thresholds is warranted. The use of legacy systems as opposed to creating new systems may be preferable. Managing task load and associated cognitive load will be essential to maintain operational safety and behavioral health. ExMC aspires to create a shared EIMO paradigm and strategic vision for advancing medical system design through novel technologies, training, protocols, and support capabilities, built upon the spirit of successful strategies and innovations over the past six decades of space medicine operations.

Jay Lemery↗

NASA NTRSDate not supplied

Objective Structured Clinical Evaluation (OSCE) of an Artificial Intelligence (AI) Clinical Decision Support System (CDSS) Tool

BACKGROUND Objective Structured Clinical Evaluations (OSCEs) have long been established as a robust methodology for summative assessment of clinical skills and decision-making during medical education. The recent integration of Artificial Intelligence (AI) into clinical decision-making processes has prompted the need for novel evaluation frameworks to assess the efficacy and reliability of AI clinical decision support system (CDSS) tools. This abstract outlines the process of quantitatively evaluating a novel CDSS (“Doc in a Box” Google 2024) trained on curated medical spaceflight data in the psychomotor domain as it interfaces with a human volunteer acting as the crew medical officer (CMO). PURPOSE The AI CDSS under review was developed as part of the Lunar Command and Control Interoperability (LuCCI) project, which is intended to address a gap in how Lunar Surface Systems (LSS) would interoperate across multiple programs, commercial partners, and international partners. The project objective is to define, prototype, integrate, and evaluate an interoperable lunar command, control, data, and software reference architecture to enable autonomy and informatics capability through common standards across LSS. A multi-modal AI-based CDSS compatible with Federated LSS will assist clinicians in diagnosing and managing complex medical conditions by providing evidence-based recommendations through predictive analytics. Given the critical role of decision-support as NASA continues to evolve its Earth-independent medical operations (EIMO), it is imperative to ensure that such AI tools perform reliably and align with clinical standards during progressive lunar and Martian exploration class missions. METHODS The OSCE framework, traditionally used for evaluating human clinicians, was adapted to assess the AI tool's decision-making capabilities in simulated clinical scenarios. In this adapted OSCE, the AI CDSS was tested across a series of structured clinical scenarios designed to mimic real-life spaceflight patient cases. These scenarios included a range of conditions and complexities, allowing for comprehensive assessment of the tool's performance. Key evaluation metrics included accuracy of diagnosis, timeliness of decision-making, and appropriate recommendations for therapies. The OSCE was scored by human physician evaluators who assessed the AI's recommendations in comparison with expert clinicians' medical decision making to ensure alignment with best practices and the standard of care. RESULTS Preliminary results indicate that the AI CDSS demonstrated high accuracy in diagnostic recommendations and decision support across various scenarios. However, certain limitations were noted, such as occasional discrepancies in handling complex or nuanced cases that required a more contextual understanding. Additionally, the tool scored higher on the diagnostic portion of the rubric, with lower scores in the therapeutic recommendations. These findings highlight the importance of continuous refinement and validation of AI tools through rigorous evaluation frameworks like the OSCE. The adaptation of OSCEs for AI tools presents several advantages, including a structured and reproducible approach to evaluation, the ability to test AI systems in diverse clinical scenarios, and the opportunity to benchmark AI performance against established clinical standards to permit charting of future progress as aerospace medicine evolves as a discipline. Remaining challenges include ensuring that these evaluations capture the full spectrum of clinical decision-making scenarios that will be confronted by CMOs during missions and adequately reflecting real-world variability of the austere spaceflight environment. CONCLUSION Employing OSCEs to evaluate AI clinical decision support tools offers a promising approach to validating their clinical utility and efficacy. This methodology not only provides insights into the tool's performance but also fosters ongoing improvement and alignment with standard of care practices. Future research should focus on refining these evaluation processes and addressing limitations to enhance the integration of AI tools in clinical spaceflight settings. REFERENCES Scott S, Hearns V, Barker MA. Testing Clinical Skills: A Look at the OSCE and USMLE Clinical Skills Exams. S D Med. 2019 Oct;72(10):451-453. Majumder MAA, Kumar A, Krishnamurthy K, Ojeh N, Adams OP, Sa B. An evaluative study of objective structured clinical examination (OSCE): students and examiners perspectives. Adv Med Educ Pract. 2019 Jun 5;10:387-397. Karam VY, Park YS, Tekian A, Youssef N. Evaluating the validity evidence of an OSCE: results from a new medical school. BMC Med Educ. 2018 Dec 20;18(1):313.

Ariana M Nelson↗

NASA NTRSDate not supplied

Comparison of OVERFLOW Computational and Experimental Results for a Blunt Mars Entry Vehicle Concept during Supersonic Retropropulsion

Simulations of unsteady supersonic retropropulsion (SRP) flow over a Hypersonic Inflatable Aerodynamic Decelerator (HIAD) blunt-body vehicle were performed using the OVERFLOW Computational Fluid Dynamics (CFD) solver. High-fidelity flow solver techniques, including Detached Eddy Simulation (DES) turbulence modeling and Adaptive Mesh Refinement (AMR), were employed to obtain improved realism in CFD predictions. Simulation conditions and geometry configurations were designed to match specific runs in the Descent System Study (DSS) wind tunnel testing (WTT) campaign. The accuracy of each simulation is assessed by direct comparison to experimental data. Comparisons of computational predictions of the SRP flowfield and bow shock shape to experimental schlieren imaging show reasonable prediction of mean shock shape, with approximately 10% similarity in shock standoff distance for selected conditions, as well as similarity in local, time-varying fluctuations of the shock-plume interaction. Comparisons of discrete measurements of surface pressure coefficient (Cp) indicate CFD accuracy within approximately 10% of the experiment across the majority of the model heatshield, with larger variations at some of the heatshield edge locations with stronger flow unsteadiness. Simulated unsteadiness of these chaotic flows, which were highly dynamic and multi-modal, was shown to be within 20-40% of experimentally-measured pressure standard deviation (SD) for the majority of the sampled locations.

Supersonic Retropropulsion↗

NASA NTRSDate not supplied

Comparison of OVERFLOW Computational and Experimental Results for a Blunt Mars Entry Vehicle Concept during Supersonic Retropropulsion

Supersonic Retropropulsion↗

DOE OSTI2023

A Data-Driven Framework for Direct Local Tensile Property Prediction of Laser Powder Bed Fusion Parts

This article proposes a generalizable, data-driven framework for qualifying laser powder bed fusion additively manufactured parts using part-specific in situ data, including powder bed imaging, machine health sensors, and laser scan paths. To achieve part qualification without relying solely on statistical processes or feedstock control, a sequence of machine learning models was trained on 6299 tensile specimens to locally predict the tensile properties of stainless-steel parts based on fused multi-modal in situ sensor data and a priori information. A cyberphysical infrastructure enabled the robust spatial tracking of individual specimens, and computer vision techniques registered the ground truth tensile measurements to the in situ data. The co-registered 230 GB dataset used in this work has been publicly released and is available as a set of HDF5 files. The extensive training data requirements and wide range of size scales were addressed by combining deep learning, machine learning, and feature engineering algorithms in a relay. The trained models demonstrated a 61% error reduction in ultimate tensile strength predictions relative to estimates made without any in situ information. Lessons learned and potential improvements to the sensors and mechanical testing procedure are discussed.

36 MATERIALS SCIENCE↗

NASA NTRS2019

Next-Generation Optical Sensing Technologies for Exploring Ocean Worlds - NASA FluidCam, MiDAR, and NeMO-Net

We highlight three emerging NASA optical technologies that enhance our ability to remotely sense, analyze, and explore ocean worlds–FluidCam and fluid lensing, MiDAR, and NeMO-Net. Fluid lensing is the first remote sensing technology capable of imaging through ocean waves without distortions in 3D at sub-cm resolutions. Fluid lensing and the purpose-built FluidCam CubeSat instruments have been used to provide refraction-corrected 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs). Results from repeat 2013 and 2016 airborne fluid lensing campaigns over coral reefs in American Samoa present a promising new tool for monitoring fine-scale ecological dynamics in shallow aquatic systems tens of square kilometers in area. MiDAR is a recently-patented active multispectral remote sensing and optical communications instrument which evolved from FluidCam. MiDAR is being tested on UAVs and autonomous underwater vehicles (AUVs) to remotely sense living and non-living structures in light-limited and analog planetary science environments. MiDAR illuminates targets with high-intensity narrowband structured optical radiation to measure an object’s spectral reflectance while simultaneously transmitting data. MiDAR is capable of remotely sensing reflectance at fine spatial and temporal scales, with a signal-to-noise ratio 10-10(exp 3) times higher than passive airborne and spaceborne remote sensing systems, enabling high-framerate multispectral sensing across the ultraviolet, visible, and near-infrared spectrum. Preliminary results from a 2018 mission to Guam show encouraging applications of MiDAR to imaging coral from airborne and underwater platforms whilst transmitting data across the air-water interface. Finally, we share NeMO-Net, the Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment. NeMO-Net is a machine learning technology under development that exploits high-resolution data from FluidCam and MiDAR for augmentation of low-resolution airborne and satellite remote sensing. NeMO-Net is intended to harmonize the growing diversity of 2D and 3D remote sensing with in situ data into a single open-source platform for assessing shallow marine ecosystems globally using active learning for citizen-science based training. Preliminary results from four-class Q17 coral classification have an accuracy of 94.4%. Together, these maturing technologies present promising scalable, practical, and cost-efficient innovations that address current observational and technological challenges in optical sensing of marine systems.

Ved Chirayath↗

NASA NTRS2003

Development of a Dynamically Configurable, Object-Oriented Framework for Distributed, Multi-modal Computational Aerospace Systems Simulation: Second Year Progress Report

Mesh generation has long been recognized as a bottleneck in the CFD process. While much research on automating the volume mesh generation process have been relatively successful,these methods rely on appropriate initial surface triangulation to work properly. Surface discretization has been one of the least automated steps in computational simulation due to its dependence on implicitly defined CAD surfaces and curves. Differences in CAD peometry engines manifest themselves in discrepancies in their interpretation of the same entities. This lack of "good" geometry causes significant problems for mesh generators, requiring users to "repair" the CAD geometry before mesh generation. The problem is exacerbated when CAD geometry is translated to other forms (e.g., IGES )which do not include important topological and construction information in addition to entity geometry. One technique to avoid these problems is to access the CAD geometry directly from the mesh generating software, rather than through files. By accessing the geometry model (not a discretized version) in its native environment, t h s a proach avoids translation to a format which can deplete the model of topological information. Our approach to enable models developed in the Denali software environment to directly access CAD geometry and functions is through an Application Programming Interface (API) known as CAPRI. CAPRI provides a layer of indirection through which CAD-specific data may be accessed by an application program using CAD-system neutral C and FORTRAN language function calls. CAPRI supports a general set of CAD operations such as truth testing, geometry construction and entity queries.

Afjeh, Abdollah A.↗

DOE OSTI2026

Anomaly Detection for Online Monitoring of Thermocouple Sensors in the Advanced Test Reactor

This study explores data-driven anomaly detection methods to analyze sensor fail- ures in the Advanced Gas Reactor (AGR) nuclear fuel irradiation experiments. Specifically, we examine failures of thermocouples (TCs), which are critical for mon- itoring and controlling in-reactor temperatures during operation. Failures were pri- marily observed during abrupt power transitions and manifested as sensor drop-outs, drifts, or unexplained behavior. We applied three time-series analysis techniques— rolling mean smoothing, matrix profile, and vector auto-regression (VAR)—to de- tect anomalies in TC data prior to failure events. The rolling mean method effec- tively highlighted deviations aligned with reported failures, while the matrix profile provided partial early warning but sometimes flagged normal fluctuations during power-down periods. VAR shows potential in capturing multivariate dependencies but requires further calibration. A rare case of TC drift was also documented, which did not result in failure, underscoring the challenge of building predictive models with sparse positive examples. Our findings demonstrate that traditional statistical tools can aid anomaly detection but have limited predictive power without richer training data. We propose future directions including synthetic data generation, real- time surrogate modeling, and multi-modal feature integration. This work provides a foundation for applying robust anomaly detection frameworks to mission-critical sensor systems in experimental settings.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations

Predictive modeling typically relies on Bayesian model calibration to provide uncertainty quantification. Variational inference utilizing fully independent (“mean-field”) Gaussian distributions are often used as approximate probability density functions. This simplification is attractive since the number of variational parameters grows only linearly with the number of unknown model parameters. However, the resulting diagonal covariance structure and unimodal behavior can be too restrictive to provide useful approximations of intractable Bayesian posteriors that exhibit highly non-Gaussian behavior, including multimodality. High-fidelity surrogate posteriors for these problems can be obtained by considering the family of Gaussian mixtures. Gaussian mixtures are capable of capturing multiple modes and approximating any distribution to an arbitrary degree of accuracy, while maintaining some analytical tractability. Unfortunately, variational inference using Gaussian mixtures with full-covariance structures suffers from a quadratic growth in variational parameters with the number of model parameters. The existence of multiple local minima due to strong nonconvex trends in the loss functions often associated with variational inference present additional complications, These challenges motivate the need for robust initialization procedures to improve the performance and computational scalability of variational inference with mixture models. In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference. The procedure begins with a global optimization stage in model parameter space. In this step, local gradient-based optimization, globalized through multistart, is used to determine a set of local maxima, which we take to approximate the mixture component centers. Around each mode, a local Gaussian approximation is constructed via the Laplace approximation. Finally, the mixture weights are determined through constrained least squares regression. The robustness and scalability of the proposed methodology is demonstrated through application to an ensemble of synthetic tests using high-dimensional, multimodal probability density functions. Here, the practical aspects of the approach are demonstrated with inversion problems in structural dynamics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗

DOE OSTI2024

Synchrotron micro-computed tomography analysis of neutron-irradiated U-Mo fuel

The three-dimensional (3D) microstructure of neutron-irradiated uranium-10 wt.% molybdenum (U-10Mo) fuel with a burn-up of 9.8 × 10 21 fissions/cm 3 was characterized using a novel, multi-modal synchrotron micro-computed tomography approach combining propagation-based phase-contrast enhanced and absorption contrast techniques. The porosity development, porosity interconnectedness, swelling, composition, local thickness of the zirconium (Zr) diffusion barrier, and the influence of the fuel–cladding interaction on the local composition and pore morphology, were uniquely determined in 3D. Two cuboids were produced using a focused ion beam-scanning electron microscope at the Zr diffusion barrier–fuel interface and in the bulk fuel. The bulk fuel sample swelled by 53.3 [+9.7/−3.1]%, while the fuel near the Zr–fuel interface swelled by 63.3 [+14.7/−7.2]%. The average local thickness of the Zr diffusion barrier decreased by 53 %, compared to the expected pre-irradiated thickness. Four pore morphology regions were identified initiating parallel to the fuel–Zr interaction region: (1) an interaction layer of suppressed porosity, (2) a layer of elongated and interconnected porosity, (3) a transition zone of low porosity, and (4) a layer of unoriented porosity representative of the bulk fuel behavior. The increase in porosity near the diffusion barrier corresponded to a higher U concentration compared to that in the bulk fuel. The interconnected porosity in the fuel near the diffusion barrier was extensive and oriented parallel to the diffusion barrier, while the bulk fuel had more compact and isolated pore networks. The interaction layer, despite having suppressed porosity, was nearly 100 wt.% U. Porosity suppression at the diffusion barrier corresponds to the expected reduction in radiation-driven diffusion of Xe at the interface despite the anticipated increase in fission product nucleation originating from a higher U concentration. In conclusion, the novel 3D insights of the porosity, swelling, and compositional variations characterized herein can improve the fidelity of fuel performance codes for proliferation-resistant fuels for research and test reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

Human Capabilities Assessments for Autonomous Missions: A Multi-Team Research Effort to Reduce Risk in the Human-System Integration Architecture for Future Deep-Space Missions

In future exploration missions beyond low earth-orbit, crew will have to execute complex operations and respond to off-nominal events, without real-time support from Mission Control. It is anticipated that increased reliance on automated systems, including human-centric vehicle and information architecture, will need to be designed to support the crew; increased risk to performance, health, and safety may occur if these are not implemented appropriately. The Human Factors and Behavioral Performance Element (HFBP) in the NASA Human Research Program supports research to characterize and mitigate such human health and performance risks, including the Risk of Adverse Outcome Due to Inadequate Human Systems Integration Architecture (HSIA). The HSIA risk addresses the integration of onboard capability and the crew roles and responsibilities necessary to enable the crew to respond effectively and efficiently in the increasingly autonomous mission operations environment. In 2017, HFBP released the “Human Capabilities Assessments for Autonomous Missions” (HCAAM) research topic to address HSIA related questions. HCAAM is a major NASA research effort that has assembled a multidisciplinary team from seven institutions to work closely with design and engineering efforts on research towards developing and refining human performance standards, guidelines and automation tools. The scientific focus is on quantitative assessment of human capabilities relevant to future deep-space missions during which earth/spacecraft communication is so delayed and intermittent that the crew must be able to function autonomously. The integrated strategy of the HCAAM project characterizes human capabilities and limitations related to potential performance decrements during long duration exploration mission spaceflight as relevant to both routine and complex task performance; defines system characteristics that reduce the likelihood or impact of potential decrements in human performance capabilities; performs integrated assessment of intelligent system responses within the context of an operational environment with relevant NASA tools, systems, and data structures in order to determine positive or negative interactions and validate recommended approaches; and proposes specific updates to existing standards and guidelines for inclusion in NASA handbooks for the design of future spacecraft intelligent systems that provide crew performance assessment/feedback, and to also serve as decision-support aids for the onboard crew (i.e., NASA-STD-3001, and NASA/SP-Human Integration Design Handbook (HIDH)). The scientific research vectors being addressed by the seven HCAAM teams include: - crew task performance (accuracy, efficiency) (crew + automation) - crew performance (accuracy, efficiency) - crew Situation Awareness - procedure design and multi-modal enhancement - concurrent tasking (mixed manual + some level of autonomy) - task handover - crew self-planning and time-lining - task design - trust in automation, real-time calibration - human multi-sensory feedback and guidance - human trust in on-board software-based intelligent assistants - virtual assistants The presentation will highlight plans and progress made in each of these research areas as well as the methods by which surrogate astronaut crews in the NASA JSC HERA spaceflight analog facility will function as human test subjects for all of the HCAAM research projects.

HCAAM VNSCOR↗