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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.
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The NEPP mission is to provide guidance to NASA for the selection and application of microelectronics technologies, to improve understanding of the risks related to the use of these technologies in the space environment and to ensure that appropriate research is performed to meet NASA mission assurance needs.
This study focuses on the comparative study of deep learning (DL) models for pore space and discrete fracture networks (DFNs) segmentation in synthetic fractured digital rocks, specifically targeting low-permeability rock formations, such as shale and tight sandstones. Accurate characterization of pore space and DFNs is critical for subsequent property analysis and fluid flow modeling. Four DL models, SegNet, U-Net, U-Net-wide, and nested U-Net (i.e., U-Net++), were trained, validated, and tested using synthetic datasets, including input and label image pairs with varying properties. The model performance was assessed regarding pixel-wise metrics, including the F1 score and pixel-wise difference maps. In addition, the physics-based metrics were considered for further analysis, including sample porosity and absolute permeability. Particularly, We first simulated the permeability of porous media containing only pore space and then simulated the permeability of porous media with DFNs added. The difference between these two values is used to quantify the connectivity of segmented DFNs, which is an important parameter for low-permeability rocks. The pixel-wise metrics showed that the nested U-Net model outperformed the rest of the DL models in pore space and DFNs segmentation, with the SegNet model exhibiting the second-best performance. Particularly, nested U-Net enhanced segmentation accuracy for challenging boundary pixels affected by partial volume effects. The U-Net-wide model achieved improved accuracy compared to the U-Net model, which indicated the influence of parameter numbers. Similarly, nested U-Net has the closest match to the ground truth of physics-based metrics, including the porosity of pore space and DFNs, and the permeability difference quantifying the connectivity of DFNs. The findings highlight the effectiveness of DL models, especially the U-Net++ model with nested architecture and redesigned skip connections, in accurately segmenting pore spaces and DFNs, which are crucial for pore-scale fluid flow and transport simulation in low-permeability rocks.
We present the results of single event effects (SEE) testing and analysis investigating the effects of radiation on electronics.This paper is a summary of test results.
Spatiotemporal mobility data are available in massive quantities, but large quantities of data typically include fewer variables or data fields. Often, the only available fields are User ID, Longitude, Latitude, Timestamp (ULLT). This raises an important question: how much can we infer about human mobility patterns using only these four fields? With ULLT data, we do not know individuals' socioeconomic status information or when they are visiting their anchor points (AP) or locations (such as homes, places of employment, or schools), and it is a modern challenge to use this data to infer these characteristics. When detecting anchor locations with limited input information, verification and validation (VV) are significant challenges. This paper addresses the problem of identifying individuals' anchor locations using only temporal information from spatiotemporal datasets with limited attributes. Our approach does not explicitly use latitude and longitude during analysis. Locationbased information is only employed in the preprocessing stage to identify periods of movement (trips) and stops (dwelling). Beyond this step, all analysis is based on temporal patterns. In theory, if stops and dwell times could be detected through alternative means, our method could function entirely without location-based input. We demonstrate this methodology on the 2017 National Household Travel Survey (NHTS) data, because it includes a carefully designed and collected time use survey with representative sampling and labeled ground truth. The high-quality survey data allows us to test the accuracy of our methods because NHTS contains intended place labels and agent/user characteristics. We have also applied our validated AP identification algorithm on very large-scale GPS based trajectory data for Patterns-of-Life (PoL) assessment and other applications, but due to space limit that could not be presented here.
Abstract The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions. We address this by employing a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator, which augments simulated data with realistic spatial frequency information. This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set, collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention, marking a crucial step towards fully autonomous harnessing of microscopy big data.
We have irradiated engineering samples of Freescale 4M nonvolatile memories with heavy ions. They use Silicon nanocrystals as the storage element, rather than the more common floating gate. The irradiations were performed using the Texas A&M University cyclotron Single Event Effects Test Facility. The chips were tested in the static mode, and in the dynamic read mode, dynamic write (program) mode, and dynamic erase mode. All the errors observed appeared to be due to single, isolated bits, even in the program and erase modes. These errors appeared to be related to the micro-dose mechanism. All the errors corresponded to the loss of electrons from a programmed cell. The underlying physical mechanisms will be discussed in more detail later. There were no errors, which could be attributed to malfunctions of the control circuits. At the highest LET used in the test (85 MeV/mg/sq cm), however, there appeared to be a failure due to gate rupture. Failure analysis is being conducted to confirm this conclusion. There was no unambiguous evidence of latchup under any test conditions. Generally, the results on the nanocrystal technology compare favorably with results on currently available commercial floating gate technology, indicating that the technology is promising for future space applications, both civilian and military.
Mission Statement: The NASA Electronic Parts and Packaging (NEPP) Program provides NASA's leadership for developing and maintaining guidance for the screening, qualification, test. and usage of EEE parts by NASA as well as in collaboration with other government Agencies and industry. NASA Space Technology Mission Directorate (STMD) "STMD rapidly develops, demonstrates, and infuses revolutionary, high-payoff technologies through transparent, collaborative partnerships, expanding the boundaries of the aerospace enterprise." Mission Statement: The Space Environments Testing Management Office (SETMO) will identify, prioritize, and manage a select suite of Agency key capabilities/assets that are deemed to be essential to the future needs of NASA or the nation, including some capabilities that lack an adequate business base over the budget horizon. NESC mission is to perform value-added independent testing, analysis, and assessments of NASA's high-risk projects to ensure safety and mission success. NASA Space Environments and Avionics Fellows as well as Radiation and EEE Parts Community of Practice (CoP) leads.
Electronic coarse-graining (ECG) methods predict quantum-mechanical electronic properties directly from coarse-grained (CG) molecular configurations, enabling electronic predictions at mesoscale length scales. Here, we present a diagnostic assessment of the feasibility of chemically transferable ECG models across a broad polymer-relevant chemical space using all-atom, united-atom, and Martini-scale representations. While high-resolution ECG models achieve near-quantitative accuracy, we show that chemically transferable ECG at the Martini resolution fails because the CG force field does not sample the same configurational distribution of local molecular structure as that underlying the DFT-parameterized ECG model. We demonstrate that our proposed Element-Count-Label (ECL) representation, which augments Martini beads with explicit stoichiometric data, significantly improves chemical generalization across diverse polymer chemistries. However, we find that even with improved chemical resolution, the model cannot recover electronic property distributions that are absent from the configurational space sampled by the CG force field. These results demonstrate that chemically transferable ECG requires future Martini-like force fields to explicitly preserve quantum chemistry–compatible local molecular structure in addition to thermodynamic and structural fidelity.
Throughout the past few weeks I have learned a great amount of information about many interesting aspects that go on here at NASA Glenn Research Center Branch 7820. Branch 7820 is the Systems and Analysis Branch. The people involved in this Branch deal with in a nutshell the analysis of propulsion systems for Earth to orbit and space transportation systems. The first project that I had worked on was helping my mentor learn more about lunar geography and the most recommended way to maintain communication for our future lunar missions. During this time I studied the craters of the moon, especially the South Pole, to provide her with information so that she can make decisions. I also researched to provide her with contact information on those people who are specialized in lunar geography so that she may talk to them to find out more in depth information. Most of my time spent here has been helping to develop a comprehensive explanation and background of the different projects our Branch has supported. When I first came to NASA Glenn and started working with the 7820 Branch there website had many holes that needed to be filled in. I have spent numerous weeks researching information about topics such as Project Prometheus itself and one of its components Jupiter s Icy Moons Orbiter (JIMO). I have also done a large amount of research on propulsion systems and how different kinds work. I have learned many facts about Nuclear Electric Propulsion (NEP) all the way to Nuclear Thermal Propulsion (NTP) systems. I will continue to do this until all the holes are filled and find out about Global Integrated Design Environment (GLIDE) and Next Generation Launch technology (NGLT). Since most of my job was providing information to go onto a website I has to learn how to put my information into a HTML format. I had no previous knowledge on how to do that kind of task and had to study how to do it and am now able to create a document in HTML format. There has been reorganizing done here at NASA Glenn and our Branch was moved to another building. Therefore, our library had to be moved with us. I spent time helping to put together the boxes, pack the library, and label them accordingly. This was not an easy task but was an experience in itself. I was able to see old posters that NASA had produced about different space missions and look at Russian map of the US and books on space missions. I was also about to see what was in the library in terms of reference material helped because now I can make use of the information for my research on the website. Throughout my internship my mentor will provide me with graphs to analyze and recreate so that she may use them to her advantage. I will learn from every piece of data that comes my way. Later, I will study and analysis gravity-loss for Earth departure trajectories. Since I haven't done that yet I cannot really describe what 1 will learn or what exactly the project entails. The whole experience has been great and I have no doubt that it will exceed every expectation previously thought.
Plasma membrane vesicles were isolated from zucchini (Cucurbita pepo) hypocotyl tissue by aqueous phase partitioning and assessed for homogeneity by the use of membrane-specific enzyme assays. The highly pure (ca. 95%) plasma membrane vesicles maintained a pH differential across the membrane and accumulated a tritiated azido analogue of 3-indoleacetic acid (IAA), 5-azido-[7-3H]IAA ([3H]N3IAA), in a manner similar to the accumulation of [3H]IAA. The association of the [3H]N3IAA with membrane vesicles was saturable and subject to competition by IAA and auxin analogues. Auxin-binding proteins were photoaffinity labeled by addition of [3H]N3IAA to plasma membrane vesicles prior to exposure to UV light (15 sec; 300 nm) and detected by subsequent NaDodSO4/PAGE and fluorography. When the reaction temperature was lowered to -196 degrees C, high-specific-activity labeling of a 40-kDa and a 42-kDa polypeptide was observed. Triton X-100 (0.1%) increased the specific activity of labeling and reduced the background, which suggests that the labeled polypeptides are intrinsic membrane proteins. The labeled polypeptides are of low abundance, as expected for auxin receptors. Further, the addition of IAA and auxin analogues to the photoaffinity reaction mixture resulted in reduced labeling that was qualitatively similar to their effects on the accumulation of radiolabeled IAA in membrane vesicles. Collectively, these results suggest that the radiolabeled polypeptides are auxin receptors. The covalent nature of the label should facilitate purification and further characterization of the receptors.
Investigated are five designs of software-based ON/OFF indicators in a hypothetical Space Station Power System monitoring task. The hardware equivalent of the indicators used in the present study is the traditional indicator light that illuminates an ON label or an OFF label. Coding methods used to represent the active state were reverse video, color, frame, check, or reverse video with check. Display background color was also varied. Subjects made judgments concerning the state of indicators that resulted in very low error rates and high percentages of agreement across indicator designs. Response time measures for each of the five indicator designs did not differ significantly, although subjects reported that color was the best communicator. The impact of these results on indicator design is discussed.
Aggregate data objects (such as arrays) are distributed across the processor memories when compiling a data-parallel language for a distributed-memory machine. The mapping determines the amount of communication needed to bring operands of parallel operations into alignment with each other. A common approach is to break the mapping into two stages: an alignment that maps all the objects to an abstract template, followed by a distribution that maps the template to the processors. This paper describes algorithms for solving the various facets of the alignment problem: axis and stride alignment, static and mobile offset alignment, and replication labeling. We show that optimal axis and stride alignment is NP-complete for general program graphs, and give a heuristic method that can explore the space of possible solutions in a number of ways. We show that some of these strategies can give better solutions than a simple greedy approach proposed earlier. We also show how local graph contractions can reduce the size of the problem significantly without changing the best solution. This allows more complex and effective heuristics to be used. We show how to model the static offset alignment problem using linear programming, and we show that loop-dependent mobile offset alignment is sometimes necessary for optimum performance. We describe an algorithm with for determining mobile alignments for objects within do loops. We also identify situations in which replicated alignment is either required by the program itself or can be used to improve performance. We describe an algorithm based on network flow that replicates objects so as to minimize the total amount of broadcast communication in replication.
We present the results of single event effect (SEE) testing and analysis investigating the effects of radiation on electronics. This paper is a summary of test results.
NASA has initiated the Living with a Star (LWS) Program to develop the scientific understanding to address the aspects of the Connected Sun-Earth system that affects life and society. The Program Architecture includes science missions, theory and modeling and Space Environment Testbeds (SET). This current paper discusses the Space Environment Testbeds. The goal of the SET program is to improve the engineering approach to accomodate and/or mitigate the effects of solar variability on spacecraft design and operations. The SET Program will infuse new technologies into the space programs through collection of data in space and subsequent design and validation of technologies. Examples of these technologies are cited and discussed.
Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.
We present the results of single event effects (SEE) testing and analysis investigating the effects of radiation on electronics. This paper is a summary of test results.
Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.
The entropic order parameters measure in a universal geometric way the statistics of non-local operators responsible for generalized symmetries. In this article, we compute entropic order parameters in weakly coupled gauge theories. To perform this computation, the natural route of evaluating expectation values of physical (smeared) non-local operators is prevented by known difficulties in constructing suitable smeared Wilson loops. We circumvent this problem by studying the smeared non-local class operators in the enlarged non-gauge invariant Hilbert space. This provides a generic approach for smeared operators in gauge theories and explicit formulas at weak coupling. In this approach, the Wilson and ’t Hooft loops are labeled by the full weight and co-weight lattices respectively. We study generic Lie groups and discuss couplings with matter fields. Smeared magnetic operators, as opposed to the usual infinitely thin ones, have expectation values that approach one at weak coupling. The corresponding entropic order parameter saturates to its maximum topological value, except for an exponentially small correction, which we compute. On the other hand, smeared ’t Hooft loops and their entropic disorder parameter are exponentially small. We verify that both behaviors match the certainty relation for the relative entropies. In particular, we find upper and lower bounds (that differ by a factor of 2) for the exact coefficient of the linear perimeter law for thin loops at weak coupling. This coefficient is unphysical/non-universal for line operators. We end with some comments regarding the RG flows of entropic parameters through perturbative beta functions.