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The Material Test Reactors Fifth Workshop reviews the successes and failures of material test protocols and lessons learned. This presentation introduces the Annular Core Research Reactor (ACRR), the history of nuclear reactors at Sandia National Labs and the experiment review process. Included are actual examples of experimental activities at ACRR.
Wildfires are projected to increase in severity and frequency due to climate change, and the electric grid is both a cause of wildfires and is vulnerable to wildfires. Equipment from the electric grid accounts for 10% of fires burned in California and 3% of fires nationally. Recent catastrophic wildfires, such as the Lahaina Fire, Camp Fire, Marshall Fire, and Smokehouse Creek fires, were all started by electrical equipment and show how devastating these events can be because they threaten lives and structures. Vegetation structure, weather and winds, climate and vegetation response, land use, and human activities all impact the likelihood of severe wildfires. We explore the relationship between the built environment, electric grid infrastructure specifically, and its role in causing catastrophic wildfires to find lessons learned for increasing resilience. Electric grid utility companies currently employ multiple methods to mitigate fire, including (1) early detection, (2) grid hardening, (3) vegetation management, and (4) pre-emptive shutoffs. Utility companies need to consider the conditions for wildfire and the impact that each mitigation strategy has on drivers of wildfire behavior, as a single solution will not be adequate. Utility companies need to work with stakeholders to develop a holistic strategy to reduce ignition likelihood and spread likelihood to reduce catastrophic wildfires and improve resiliency.
In 2010, NASA?s Space Life Sciences Directorate announced the successful results from pilot experiments with open innovation methodologies. Specifically, utilization of internet based external crowd sourcing platforms to solve challenging problems in human health and performance related to the future of spaceflight. The follow-up to this success was an internal crowd sourcing pilot program entitled NASA@work, which was supported by the InnoCentive@work software platform. The objective of the NASA@work pilot was to connect the collective knowledge of individuals from all areas within the NASA organization via a private web based environment. The platform provided a venue for NASA Challenge Owners, those looking for solutions or new ideas, to pose challenges to internal solvers, those within NASA with the skill and desire to create solutions. The pilot was launched in 57 days, a record for InnoCentive and NASA, and ran for three months with a total of 20 challenges posted Agency wide. The NASA@work pilot attracted over 6000 participants throughout NASA with a total of 183 contributing solvers for the 20 challenges posted. At the time of the pilot?s closure, solvers provided viable solutions and ideas for 17 of the 20 posted challenges. The solver community provided feedback on the pilot describing it as a barrier breaking activity, conveying that there was a satisfaction associated with helping co-workers, that it was "fun" to think about problems outside normal work boundaries, and it was nice to learn what challenges others were facing across the agency. The results and the feedback from the solver community have demonstrated the power and utility of an internal collaboration tool, such as NASA@work.
In 2010, NASA's Space Life Sciences Directorate announced the successful results from pilot experiments with open innovation methodologies. Specifically, utilization of internet based external crowdsourcing platforms to solve challenging problems in human health and performance related to the future of spaceflight. The follow-up to this success was an internal crowdsourcing pilot program entitled NASA@work, which was supported by the InnoCentive@work software platform. The objective of the NASA@work pilot was to connect the collective knowledge of individuals from all areas within the NASA organization via a private web based environment. The platform provided a venue for NASA Challenge Owners, those looking for solutions or new ideas, to pose challenges to internal solvers, those within NASA with the skill and desire to create solutions. The pilot was launched in 57 days, a record for InnoCentive and NASA, and ran for three months with a total of 20 challenges posted Agency wide. The NASA@work pilot attracted over 6,000 participants throughout NASA with a total of 183 contributing solvers for the 20 challenges posted. At the time of the pilot's closure, solvers provided viable solutions and ideas for 17 of the 20 posted challenges. The solver community provided feedback on the pilot describing it as a barrier breaking activity, conveying that there was a satisfaction associated with helping co-workers, that it was fun to think about problems outside normal work boundaries, and it was nice to learn what challenges others were facing across the agency. The results and the feedback from the solver community have demonstrated the power and utility of an internal collaboration tool, such as NASA@work.
Software-Only-Simulations have been an emerging but quickly developing field of study throughout NASA. The NASA Independent Verification Validation (IVV) Independent Test Capability (ITC) team has been rapidly building a collection of simulators for a wide range of NASA missions. ITC specializes in full end-to-end simulations that enable developers, VV personnel, and operators to test-as-you-fly. In four years, the team has delivered a wide variety of spacecraft simulations that have ranged from low complexity science missions such as the Global Precipitation Management (GPM) satellite and the Deep Space Climate Observatory (DSCOVR), to the extremely complex missions such as the James Webb Space Telescope (JWST) and Space Launch System (SLS).This paper describes the evolution of ITCs technologies and processes that have been utilized to design, implement, and deploy end-to-end simulation environments for various NASA missions. A comparison of mission simulators are discussed with focus on technology and lessons learned in complexity, hardware modeling, and continuous integration. The paper also describes the methods for executing the missions unmodified flight software binaries (not cross-compiled) for verification and validation activities.
No abstract available
In 2013, the Boeing Company model 787-8 Dreamliner commercial aircraft experienced three catastrophic lithium (Li) battery failures. The cause of each failure resulted in a single-cell thermal runaway (TR) condition, which propagated to adjacent battery cells. Two of the failures involved rechargeable lithium-ion (Li-Ion) batteries, and the third event involved a nonrechargeable lithium-manganese dioxide (Li-MnO2) battery. In response to these Li battery failures, the NASA Engineering and Safety Center (NESC) approved a technical assessment of the International Space Station Simplified Aid for Extra-Vehicular Activity Rescue (SAFER) Li non-rechargeable battery. This assessment was conducted to evaluate the SAFER Li nonrechargeable battery safety design features against Boeing 787 Dreamliner Li battery failure lessons learned. Specifically, this investigation focused on assessing the severity of a SAFER battery TR hazard conditions.
The Trilateral partners — the European Space Agency (ESA), Japan Aerospace Exploration Agency (JAXA), and National Aeronautics and Space Administration (NASA) and the United States Government (USG) in their execution of safe and successful missions, have a long-standing history of sustaining the shared space environment of operational Earth-Moon orbits with their global space-stewardship, or spacekeeping, of these areas. This has driven missions to limit the generation of new and long-lived debris, control debris releases, minimize accidental explosions and collisions, and ensure Post-Mission Disposal of space systems, so that the environment remains safe for future operations and explorations. Given the continued need for space systems to support global mandatory infrastructure and commercial enterprise, spacefarers are extending beyond the Trilateral partners, more and smaller satellites are being deployed, and current assets are being utilized much longer than expected. This is making space more congested than ever; therefore, the Trilateral partners are evaluating potential future debris mitigation strategies, evolving technology, developing on-orbit servicing capabilities, and advancing their abilities to assess disposal and mission extension plans. They are also sharing their lessons learned and identifying opportunities for future state-of-the-art advancements for current and future space enterprises. Sustaining the space environment cannot be ensured by any one agency or country. Thus, the Trilateral authors have shared their lessons learned, insights, and guidance, herein, on disposal and mission extension assessment strategies with the gratitude of each agency. As such, this document is not prescriptive, but was formulated to enhance value-and-risk-balanced operational decision-making, support policy refinement, and guide spacefaring partners beyond these agencies to assess their activities in space with safety and a global space-stewardship, or spacekeeping, in mind. This includes not only the disposal and mission extension assessment addressed herein, but also preserving space history, ensuring collaboration/interoperability of technology, supporting fellow operators without interfering, and the utilization of in-situ resources for the common benefit of humankind. It is the intention of the Trilateral partners that this document evolves based on community lessons learned and the introduction of new assessment methodologies. So, all readers are encouraged to share their insights with the authors from their own application of this guidance or other strategies to ensure each mission has a successful, safe, and judicious life and conclusion.
In this chapter, we review the progress in MHD stability, disruptions and control in magnetic fusion research that has occurred over the past (more than) one and a half decades since the publication by Hender et al in 2007 on the same topic as part of the update of ITER Physics Basis. During this period, remarkable progress has been achieved in the understanding of the basic physics and overall control of MHD instabilities through a wide spectrum of dedicated experiments, theory and modeling. The sawtooth activities are probably today one of the best understood of MHD events and very robust control schemes have been developed for reliable operation of tokamaks through core heating. Similarly, significant improvements have been achieved in understanding and control of neoclassical tearing modes, resistive wall modes or locked modes and their control through ECCD or error field control. The field of disruption prediction through application of artificial intelligence, machine learning or deep learning methods, which had already started at the time of the 2007 review, has progressed significantly due to general progress in these fields and application of newer, more sophisticated algorithms. However, although remarkable progress has been achieved in the field of Disruptions, their understanding, prediction, possible avoidance and mitigation still remain probably the most active fields of R&D globally in this field. This is especially because reactor grade machines like ITER and DEMO will be much less tolerant in respect of disruptions and runaway currents, and their occurrences must be either avoided altogether or minimized to an acceptable value without causing any significant hindrance to robust machine operations. This review is intended to present a broad spectrum of the R&D that has occurred in this field in support of ITER, which will also be of immense significance for all future machines, especially reactors like DEMO.
Desert Research and Technology Studies (Desert RATS) is a multi-year series of hardware and operations tests carried out annually in the high desert of Arizona on the San Francisco Volcanic Field. These activities are designed to exercise planetary surface hardware and operations in conditions where long-distance, multi-day roving is achievable, and they allow NASA to evaluate different mission concepts and approaches in an environment less costly and more forgiving than space.The results from the RATS tests allows election of potential operational approaches to planetary surface exploration prior to making commitments to specific flight and mission hardware development. In previous RATS operations, the Science Support Room has operated largely in an advisory role, an approach that was driven by the need to provide a loose science mission framework that would underpin the engineering tests. However, the extensive nature of the traverse operations for 2010 expanded the role of the science operations and tested specific operational approaches. Science mission operations approaches from the Apollo and Mars-Phoenix missions were merged to become the baseline for this test. Six days of traverse operations were conducted during each week of the 2-week test, with three traverse days each week conducted with voice and data communications continuously available, and three traverse days conducted with only two 1-hour communications periods per day. Within this framework, the team evaluated integrated science operations management using real-time, tactical science operations to oversee daily crew activities, and strategic level evaluations of science data and daily traverse results during a post-traverse planning shift. During continuous communications, both tactical and strategic teams were employed. On days when communications were reduced to only two communications periods per day, only a strategic team was employed. The Science Operations Team found that, if communications are good and down-linking of science data is ensured, high quality science returns is possible regardless of communications. What is absent from reduced communications is the scientific interaction between the crew on the planet and the scientists on the ground. These scientific interactions were a critical part of the science process and significantly improved mission science return over reduced communications conditions. The test also showed that the quality of science return is not measurable by simple numerical quantities but is, in fact, based on strongly non-quantifiable factors, such as the interactions between the crew and the Science Operations Teams. Although the metric evaluation data suggested some trends, there was not sufficient granularity in the data or specificity in the metrics to allow those trends to be understood on numerical data alone.
Abstract The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F 1 score, Matthews correlation coefficient and Brier score provided a more consistent assessment of the overall performance across the 12 datasets. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that SMN significantly outperformed the other three methods. We also found that a strong negative correlation existed between the prediction accuracy and the imbalance ratio (IR), which is defined as the number of inactive compounds divided by the number of active compounds. SMN became less effective when IR exceeded a certain threshold (e.g., > 28). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. This work demonstrates that the performance of SAR-based, imbalanced chemical toxicity classification can be significantly improved through the use of data rebalancing.
The lithium-ion battery (LIB) field is moving towards the direction of investigating spatially resolved physical phenomena in the 3D porous microstructure of electrodes. These pore-scale simulations give new insights into the local dynamics of lithiation/de-lithiation and charge transport, Nevertheless, the computational time of these simulations limits the integration of these models in optimization workflows of cycling conditions or electrode manufacturing processes. Machine learning models present a way of assessing in real-time the performance of materials. While several successful techniques for replicating simulations with machine learning have been proposed, this case study presents a more demanding problem, due to the necessity of understanding the behavior of heterogeneous 3D local data, as it evolves in time: this poses both a scientific and a technical challenge. To this end, we propose an autoregressive multiscale convolutional neural network model to predict relevant quantities at the pore-scale in the solid phase: the lithium concentration (in the active material) and potential (in the active material and carbon binder). Here, these are ultimately used to reconstruct the battery discharge curve. 3D images of the electrode microstructures are the input to the network, trained with a dataset of finite element method simulations to predict the discharge behavior of the cathode side in lithium ion batteries. We propose this machine learning model as a proof-of-concept of the applicability of multiscale networks for time-dependent physics problems. The trained model exhibits very high accuracy (with errors lower than 2 %) in forecasting the discharge behavior of new unseen cathodes.
This is a presentation on the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation by the University of Utah, presented by No'am Zach Dvory. This video slide presentation, by the University of Utah, discussed the technical objectives of developing a real-time decision-making platform to enhance seismic monitoring and risk management during stimulation activities. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.
Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society.
A capability for mapping meter-level resolution soil moisture with frequent temporal sampling over large regions is essential for quantifying local-scale environmental heterogeneity and eco-hydrologic behavior. However, available surface soil moisture (SSM) products generally involve much coarser grain sizes ranging from 30 m to several 10s of kilometers. Hence a new method is proposed to estimate 3-m resolution SSM using a combination of multi-sensor fusion, machine- learning (ML) and Cumulative Distribution Function (CDF) matching approaches. This method established favorable SSM correspondence between 3-m pixels and overlying 9-km grid cells from overlapping Planet SuperDove (PSD) observations and NASA Soil Moisture Active-Passive (SMAP) mission products. The resulting 3-m SSM predictions showed improved accuracy by reducing ab- solute bias and RMSE by ~0.01 cm3/cm3 over the original SMAP data in relation to in-situ soil moisture measurements for the Australian Yanco region, while preserving the high sampling frequency (1-3 day global revisit) and sensitivity to surface wetness (R 0.865) from SMAP. Heterogeneous soil moisture distributions varying with vegetation biomass gradients and irrigation regimes were generally captured within a selected study area. Further algorithm refinement and implementation for regional applications will allow for improvement in water resources management, precision agriculture, and disaster forecasts and responses.
The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence (XRF) techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role in critical biological activities. The elemental mappings and the morphological aspects of the biological samples, in this instance, the bacterium Escherichia coli ( E. Coli ), also serve as label-free biological fingerprints to identify E. coli cells that have been treated differently. The key limitations of achieving good identification performance are the extraction of cells from raw XRF measurements via binary conversion, definition of features, noise floor and proportion of cells treated differently in the measurement. Automating cell extraction from raw XRF measurements across different types of chemical treatment and the implementation of machine-learning models to distinguish cells from the background and their differing treatments are described. Principal components are calculated from domain knowledge specific features and clustered to distinguish healthy and poisoned cells from the background without manual annotation. The cells are ranked via fuzzy clustering to recommend regions of interest for automated experimentation. The effects of dwell time and the amount of data required on the usability of the software are also discussed.
The accuracy of distribution system state estimation may be significantly impacted by the existence of bad measure-ments and unexpected topology errors. This paper proposes a data-driven Graph Convolutional Network (GCN) for anomaly detection, including bad measurements and topology change events. Compared to many existing machine learning approaches, the proposed approach embeds both spatial-temporal measure-ment correlations, which allows us to detect and distinguish different anomalies. Numerical results carried out on the IEEE 37-node system demonstrate that the proposed-based method can obtain high accuracy in detecting bad data and topology changes as compared to other approaches, even in the presence of high PV penetrations.
We introduce HAMscope, a compact, snapshot hyperspectral autofluorescence miniscope that enables real-time, label-free molecular imaging in a wide range of biological systems. By integrating a thin polymer diffuser into a widefield miniscope, HAMscope spectrally encodes each frame and employs a probabilistic deep learning framework to reconstruct 30-channel hyperspectral stacks (452-703 nm) or directly infer molecular composition maps from single images. A scalable multi-pass U-Net architecture with transformer-based attention and per pixel uncertainty estimation enables high spatio-spectral fidelity (mean absolute error ∼0.0048) at video rates. While initially demonstrated in plant systems, including lignin, chlorophyll, and suberin imaging in intact poplar and cork tissues, the platform is readily adaptable to other applications such as neural activity mapping, metabolic profiling, and histopathology. We show that the system generalizes to out-of-distribution tissue types and supports direct molecular mapping without the need for spectral unmixing. HAMscope establishes a general framework for compact, uncertainty-aware spectral imaging that combines minimal optics with advanced deep learning, offering broad utility for real-time biochemical imaging across neuroscience, environmental monitoring, and biomedicine.