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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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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Towards Misregistration-Tolerant Change Detection using Deep Learning Techniques with Object-Based Image Analysis

Co-registrating is a common pre-processing step for existing change detection algorithms, but registering bi-temporal images is nontrivial. The use of image patch as input for deep learning techniques provides a natural avenue to apply them in the OBIA framework, and have shown successful performance in the object-based land cover mapping and change detection applications. Even though attempts of applying deep learning techniques for change detection applications have been made with varying success, its application under OBIA framework for change detection have not been conducted and its tolerance for misregistration among temporal images are neither known. This study performed change detection under OBIA framework using deep learning techniques for the first time, and evaluated its performance regarding their tolerance of image misregistration on training and testing dataset. Our results demonstrate the proposed change detection scheme is surprisingly robust to image misregistration on the testing dataset, while classifiers trained with the training dataset containing image misregistration errors suffer from slight decrease of overall accuracy.

Liu, Tao↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
DOE OSTI2018

Managing Through Continuous Improvement: If it doesn’t challenge you, it won’t change you

The past 24 months has been one of non-stop continuous improvement – every month, every week, every day, and every hour. It has been filled with continuously re-examining and challenging business systems, work processes, and legacy culture. The journey was difficult, but well worth it. It began by examining the existing conditions in each organization and building the case for change. When we stepped back and looked at our processes, systems, tools, and performance objectively, we realized we were struggling with frequent organizational, resource, and schedule conflicts, work processes that were not well-defined or expert based, project delays were common, management methods were predominantly reactive, advanced planning was limited to the near-term, no integration in scheduling across organizations, limited to no performance metrics to measure and manage performance, and internal and external customers did not feel like we were meeting their quality and performance expectations. It was a wake-up call. Change began at the top with a change leadership with where a new vision, expectations, and commitment were established. Leaving no stone left unturned, every aspect of the business was challenged which opened up communication and exposed the gaps. This was done in a ‘safe’ environment allowing for honest and solution-oriented discussion and action. The leadership team committed to managing through continuous improvement. The organizational structure was realigned to better align scope, responsibilities, execution, and customer focus. Visibility and transparency became the norm as higher levels of leadership and supporting organizations were invited to be part of regular strategy meetings and partnership discussion, effectively opening up the dialogue. This required a tremendous amount of trust and faith in a short period of time. Health assessments were conducted on each organization with specific lines of inquiry. These self-assessments helped to identify better define the scope of the organization and where targeted improvements were needed. Frequent organizational reviews were implemented to answer three fundamental questions: 1) What is your organization paid to do (scope)?, 2) How does your organization, or you, demonstrate that you are doing the job effectively?, and 3) What are you doing to improve your performance? To answer the first question, leaders defined the scope of the organization. Leaders were required to provide a path forward, with specific actions, where performance was not at expected levels internally at first and then from the stakeholders. Where business processes were not clearly understood, process maps were developed to provide this clarity for both those performing the work and those utilizing the process – the customers. To help answer the second question, numerous automated or semi-automated real-time, dynamic performance metrics were developed and implement to help leaders gain the visibility and demonstrate performance in six areas of operational excellence: safety, security, quality, effectiveness, productivity, and sustainability. Analysis of the performance provided windows of transparency and trends of work performance, work delays, cycle times, utilization, and much more that revealed many areas of improvement that were either not visible or desired to be visible. At the same time, they provided real-time progress which generated initial performance improvements by just beginning to measure. To address the third question, where systems did not support effectiveness of an existing or new process, then systems and tools were developed and implemented to drive the right behaviors and reinforce the processes. Tools and metrics were one of the major catalysts for culture shift and change. Risk assessments were leveraged into continuous improvement strategies, plans, and schedules. Targeted initiatives were identified and performed. Change and improvement was not, and is not, without significant and common challenges including limited/fixed budgets, resource availability, data access, natural resistance to change, and numerous other reasons or excuses – we called them ‘off-ramps’ and began to close them one-by-one. The results and outputs were significant and impactful in each organization and across the business with increased productivity, efficiency, visibility, accountability, management, and performance. Stakeholder satisfaction also increased. Cost and time savings allowed to more continuous improvement efforts to be performed. The effort clearly provided clear and measurable return on investment and value. Efforts that once seemed impossible, not feasible, or not achievable were performed. Decades-long issues were solved. ‘Impossible things are simply those which so far have never been done’ said Elbert Hubbard. So what will the next year be for you and your organization? Are you satisfied with the ‘same ‘ol, same ‘ol?’ Will you fall into the complacency trap and believe that change is too difficult, too expensive, and not necessary (or any number of possible excuses) or will you make this year your year or continuous improvement (and every year thereafter…)? Remember – what you do today can improve all your tomorrows’ (Ralph Marston).

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1987

Optimal pre-scheduling of problem remappings

A large class of scientific computational problems can be characterized as a sequence of steps where a significant amount of computation occurs each step, but the work performed at each step is not necessarily identical. Two good examples of this type of computation are: (1) regridding methods which change the problem discretization during the course of the computation, and (2) methods for solving sparse triangular systems of linear equations. Recent work has investigated a means of mapping such computations onto parallel processors; the method defines a family of static mappings with differing degrees of importance placed on the conflicting goals of good load balance and low communication/synchronization overhead. The performance tradeoffs are controllable by adjusting the parameters of the mapping method. To achieve good performance it may be necessary to dynamically change these parameters at run-time, but such changes can impose additional costs. If the computation's behavior can be determined prior to its execution, it can be possible to construct an optimal parameter schedule using a low-order-polynomial-time dynamic programming algorithm. Since the latter can be expensive, the performance is studied of the effect of a linear-time scheduling heuristic on one of the model problems, and it is shown to be effective and nearly optimal.

Nicol, David M.↗
NASA NTRS1977

A hot wire radiant energy source for mapping the field of view of a radiometer

The design and performance of a calibration device that allows the measurement of a radiometer's field of view are described. The heart of the device is a heated 0.0254-mm (0.001-inch) diameter filament that provides a variable, isothermal line source of radiant energy against a cold background. By moving this discrete line source across the field of view of a radiometer, the radiometer's spatial response can be completely mapped. The use of a platinum filament provides a durable radiation source whose temperature is stable and repeatable to 10 K over the range of 600 to 1200 K. By varying the energy emitted by the filament, the field of view of radiometers with different sensitivities (or multiple channel radiometers) can be totally mapped.

Edwards, S. F.↗
NASA NTRS1978

Geologic application of thermal-inertia mapping from satellite

The author has identified the following significant results. A theoretical evaluation of the proportional and linear relationship between absolute and relative thermal inertia was performed, and a potentially more accurate expression for absolute thermal inertia mapping was proposed.

Offield, T. W.↗
NASA NTRS1982

Cartographic mapping study

The errors associated with planimetric mapping of the United States using satellite remote sensing techniques are analyzed. Assumptions concerning the state of the art achievable for satellite mapping systems and platforms in the 1995 time frame are made. An analysis of these performance parameters is made using an interactive cartographic satellite computer model, after first validating the model using LANDSAT 1 through 3 performance parameters. An investigation of current large scale (1:24,000) US National mapping techniques is made. Using the results of this investigation, and current national mapping accuracy standards, the 1995 satellite mapping system is evaluated for its ability to meet US mapping standards for planimetric and topographic mapping at scales of 1:24,000 and smaller.

Wilson, C.↗
DOE OSTI2023

Uncertain role of clouds in shaping summertime atmosphere-sea ice connections in reanalyses and CMIP6 models

Downwelling longwave radiation (DLR) driven by the atmospheric and cloud conditions in the troposphere is suggested to be a dominant factor to determine the summertime net surface energy budget over the Arctic Ocean and thus plays a key role to shape the September sea ice. We use reanalyses and the self-organizing map (SOM) method to distinguish CMIP6 model performance in replicating the observed strong atmosphere-DLR connection. We find all models can reasonably simulate the linkage between key atmosphere variables and the clear sky DLR but behave differently in replicating the atmosphere-DLR connection due to cloud forcing. In ERA5 and strongly coupled models, tropospheric high pressure is associated with decreased clouds in the mid- and high-levels and increased clouds near the surface. This out-of-phase structure indicates that DLR cloud forcing is nearly neutral, making the clear sky DLR more important to bridge JJA circulation to late-summer sea ice. In MERRA-2 and weakly coupled models, tropospheric clouds display a vertically homogeneous reduction; the cloud DLR is thus strongly reduced due to the cooling effect, which partially cancels out the clear sky DLR and makes the total DLR less efficient to translate circulation forcing to sea ice. The differences of cloud vertical distribution in CMIP6 appear to be differentiated by circulation related relative humidity. Therefore, a better understanding of the discrepancy of different reanalyses and remote sensing products is critical to comprehensively evaluate simulated interactions among circulation, clouds, sea ice and energy budget at the surface in summer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗
DOE OSTI2019

Photoinduced, reversible phase transitions in all-inorganic perovskite nanocrystals

Significant interest exists in lead trihalides that present the perovskite structure owing to their demonstrated potential in photovoltaic, lasing, and display applications. These materials are also notable for their unusual phase behavior often displaying easily accessible phase transitions. In this work, time-resolved X-ray diffraction, performed on perovskite cesium lead bromide nanocrystals, maps the lattice response to controlled excitation fluence. These nanocrystals undergo a reversible, photoinduced orthorhombic-to-cubic phase transition which is discernible at fluences greater than 0.34 mJ cm -2 through the loss of orthorhombic features and shifting of high-symmetry peaks. This transition recovers on the timescale of 510 ± 100 ps. A reversible crystalline-to-amorphous transition, observable through loss of Bragg diffraction intensity, occurs at higher fluences (greater than 2.5 mJ cm -2 ). These results demonstrate that light-driven phase transitions occur in perovskite materials, which will impact optoelectronic applications and enable the manipulation of non-equilibrium phase characteristics of the broad perovskite material class.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

RIFTA: A Robust Iterative Fourier Transform-based dwell time Algorithm for ultra-precision ion beam figuring of synchrotron mirrors

With the rapid evolution of synchrotron X-ray sources, the demand for high-precision X-ray mirrors has greatly increased. Single nanometer profile error is required to keep imaging capability at the diffraction limit. Ion Beam Figuring (IBF), as a highly deterministic surfacing technique, has been used for ultra-precision finishing of mirrors. One crucial step that guides the IBF process is dwell time calculation. A valid dwell time solution should be non-negative and duplicate the shape of the desired removal map. Another important aspect is to minimize the total dwell time. In this study, we propose a Robust Iterative Fourier Transform-based dwell time Algorithm (RIFTA) that automatically fulfills these requirements. First, the thresholded inverse filtering in Fourier transform-based deconvolution is stabilized and automated by optimizing the threshold value using the Nelder-Mead simplex algorithm. Second, a novel two-level iterative scheme is proposed to guarantee the minimized total dwell time with its non-negativity at each dwell point. Third, a bicubic resampling is employed to flexibly adapt the calculated dwell time map to any IBF process intervals. The performance of RIFTA is first studied with simulation, followed by a comparison with the other state-of-the-art dwell time algorithms. We then demonstrate with an experiment that, using the dwell time calculated by the RIFTA, the total dwell time is shortened by a factor of two and the RMS in a 5 × 50 mm clear aperture was reduced from 3.4 nm to 1.1 nm after one IBF run, which proves the effectiveness and the efficiency of the proposed algorithm.

42 ENGINEERING↗
DOE OSTI2025

GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five levels of autonomy, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modelling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, we emphasize that as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future.

Autonomous GI↗
DOE OSTI2023

ML-CGRA: An Integrated Compilation Framework to Enable Efficient Machine Learning Acceleration on CGRAs

Coarse-Grained Reconfigurable Arrays (CGRAs) can achieve higher energy-efficiency than general-purpose processors and accelerators or fine-grained reconfigurable devices, while maintaining adaptability to different computational patterns. CGRAs have shown some success as a platform to accelerate machine learning (ML) thanks to their flexibility, which allows them to support new models not considered by fixed accelerators. However, current solutions for CGRAs employ low level instruction-based compiler approaches and lack specialized compilation infrastructures from high-level ML frameworks that could leverage semantic information from the models, limiting the ability to efficiently map them on the recon- figurable substrate. This paper proposes ML-CGRA, an integrated compilation framework based on the MLIR infrastructure that en- ables efficient ML acceleration on CGRAs. ML-CGRA provides an end-to-end solution for mapping ML models on CGRAs that out- performs conventional approaches by 3.15× and 6.02 × on 4×4 and 8×8 CGRAs, respectively.

machine learning↗
DOE OSTI2020

EspressoDB: A scientific database for managing high-performance computing workflows

EspressoDB is a programmatic object-relational mapping (ORM) data management framework implemented in Python and based on the Django web framework. EspressoDB was developed to streamline data management, centralize and promote data integrity, while providing domain flexibility and ease of use. It is designed to directly integrate in utilized software to allow dynamical access to vast amount of relational data at runtime.

Chang, Chia↗
DOE OSTI2025

Bulk segregant and QTL analysis of maltose metabolism in Saccharomyces eubayanus

This study investigates natural variation in maltose utilization among Saccharomyces eubayanus strains isolated from different tree hosts. An F2 population was generated from a cross between strain QC18 (Quercus-associated) and strain CL467.1 (Nothofagus-associated). Bulk segregant analysis (BSA) was performed on the F2 offspring, and QTL mapping was conducted by whole-genome sequencing of two pools of 30 segregants each, representing the lowest and highest growth in high maltose concentrations. For QTL analysis, the parental strains QC18 and CL467.1 were also sequenced.

evolutionary plasticity↗
NASA NTRS1971

Skylab 3 preliminary reference Earth Resources Experiment Package (EREP) pass planning document. Volume 2: EREP sites and S190 swath study of selected revs

The ground tracks and S190 swaths are presented of selected revolutions over areas containing earth resources experiment package (EREP) sites. The following eight EREP disciplines are shown: sensor performance evaluation, forestry, geology, hydrology, land use mapping, oceanography, pollution, and weather. Most of the data reported consists of passes over the continental United States.

Lunde, A. N.↗
NASA NTRS1973

Interpretation of land use and stream order, Pawnee River Basin, Kansas

The author has identified the following significant results. Land use and stream orders were mapped from ERTS-1 imagery covering the Pawnee River Basin. Total area of the basin, irrigated dry land crop area, and rangeland area were determined. The stream order of the basin was calculated by the Strahler method and provided more detail than is available on the comparable 1:250,000 sheets. This study has demonstrated the feasibility of using ERTS-1 imagery to map stream networks in greater detail than is required for 1:250,000 scale. The utility of ERTS-1 has also been demonstrated for mapping and mensuration of data relating to performance of ground cover types and the extent of irrigation.

Morain, S. A.↗
NASA NTRS1974

Study of spin-scan imaging for outer planets missions: Executive summary

The development and characteristics of spin-scan imagers for interplanetary exploration are discussed. The spin-scan imaging photopolarimeter instruments of Pioneer 10 and 11 are described. In addition to the imaging function, the instruments are also used in a faint-light mode to take sky maps in both radiance and polarization. The performance of a visible-infrared spin-scan radiometer (VISSR), which operates in both visible and infrared wavelengths, is reported.

Russell, E. E.↗