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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Laser Angle of Incidence Effects on in-Situ Consolidation of Automated Fiber Placement of Polyaryletherketone Composites

NASA and Electroimpact, Inc. ® in conjunction with other U.S. industry partners are performing research as a part of the NASA High-rate Composites for Aircraft Manufacturing (HiCAM) Project to fabricate thermoplastic panels using automated fiber placement (AFP) to increase manufacturing rates of aircraft structural composites. This work focuses on evaluating the in-situ consolidation AFP of thermoplastics (ICAT) process. Previous studies of the ICAT process using semi-crystalline, polyaryletherketone (PAEK) slit-tape have resulted in an adequate degree of intimate contact between plies; however, the resulting interlaminar strength have been less than laminates fabricated in an autoclave. To improve these properties, the effect of reducing the laser angle of incidence (AoI) during placement to increase the degree of auto-hesion was evaluated. The AoI of the laser assisted AFP head was varied between 12° and 16° to fabricate multiple quasi-isotropic and unidirectional test panels. Physics-based thermal models developed at the NASA Langley Research Center were utilized to predict the temperature profile. Laminate processing temperature was measured experimentally, and panel quality was evaluated by both non-destructive evaluation (NDE) as well as destructively by photo-microscopy. The effect on interlaminar strength was determined by short beam strength testing. Test results of carbon fiber laminates fabricated by ICAT using polyetheretherketone (PEEK), polyetherketoneketone (PEKK), and low-melt polyaryletherketone (LM-PAEK) at various laser angles of incidence, placement temperatures and placement speeds are presented.

Thermoplastic Composites↗
NASA NTRS2016

FluidCam 1 & 2 - UAV-based Fluid Lensing Instruments for High-Resolution 3D Subaqueous Imaging and Automated Remote Biosphere Assessment of Reef Ecosystems

We present NASA ESTO FluidCam 1 & 2, Visible and NIR Fluid-Lensing-enabled imaging payloads for Unmanned Aerial Vehicles (UAVs). Developed as part of a focused 2014 earth science technology grant, FluidCam 1&2 are Fluid-Lensing-based computational optical imagers designed for automated 3D mapping and remote sensing of underwater coastal targets from airborne platforms. Fluid Lensing has been used to map underwater reefs in 3D in American Samoa and Hamelin Pool, Australia from UAV platforms at sub-cm scale, which has proven a valuable tool in modern marine research for marine biosphere assessment and conservation. We share FluidCam 1&2 instrument validation and testing results as well as preliminary processed data from field campaigns. Petabyte-scale aerial survey efforts using Fluid Lensing to image at-risk reefs demonstrate broad applicability to large-scale automated species identification, morphology studies and reef ecosystem characterization for shallow marine environments and terrestrial biospheres, of crucial importance to improving bathymetry data for physical oceanographic models and understanding climate change’s impact on coastal zones, global oxygen production, carbon sequestration.

Chirayath, Ved↗
DOE OSTI2026

Ionic Liquids for Direct Air Capture of CO 2 using Electric‐Field‐Mediated Moisture Gradient Process (Final Technical Report)

The final report provides executive summary, a list of publications, and information on training graduate students and postdoctoral researchers. We carried out computational and experimental research on understanding molecular-level mechanism of how CO 2 is absorbed in a solution containing ethylene glycol as the solvent and KOH as the salt in the presence of ionic liquids and under the influence of electric field. In doing so, we developed an automated high-throughput method which allowed us to measure the solubility of CO 2 in a large number of ionic liquids, considerably speeding up the CO 2 solubility measurement. We also demonstrated how varying the concentration of ionic liquids in ethylene glycol can result in a maximum in ionic conductivity. Reaction of CO 2 and subsequent release results in a 50% reduction when the process is operated at an ionic liquid-ethylene glycol concentration yielding maximum ionic conductivity amongst all the ionic liquid-ethylene glycol combinations studied as a part of this research. We utilized machine learning models to identify unique ionic liquid-solvent combinations with ionic conductivity much higher than that measured for ionic liquid-ethylene glycol combinations. We demonstrated that the rate of CO 2 reaction with KOH in ethylene glycol can be optimized with the type of ionic liquid and its concentration. Overall, the research led to publication of 10 peer-reviewed research articles and several presentations at national conferences. We are also in the process of developing additional manuscripts based on the research carried out as a part of this project. Two graduate students and two postdoctoral researchers were supported on the funding.

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

Crowd-Sourced Data and Analysis Tools for Advancing the Chemical Vapor Deposition of Graphene: Implications for Manufacturing

Industrial production of graphene by chemical vapor deposition (CVD) requires more than the ability to synthesize large domain, high-quality graphene in a lab reactor. The integration of graphene in the fabrication process of electronic devices requires the cost-effective and environmentally friendly production of graphene on dielectric substrates, but current approaches can only produce graphene on metal catalysts. Sustainable manufacturing of graphene should also conserve the catalyst and reaction gases, but today the metal catalysts are typically dissolved after synthesis. Progress toward these objectives is hindered by the hundreds of coupled synthesis parameters that can strongly affect CVD of low-dimensional materials and poor communication in the published literature of the rich experimental data that exists in individual laboratories. We report here on a platform, "graphene recipes for synthesis of high quality material" (Gr-ResQ: pronounced graphene rescue), which includes powerful new tools for data-driven graphene synthesis. At the core of Gr-ResQ is a crowd-sourced database of CVD synthesis recipes and associated experimental results. The database captures similar to 300 parameters ranging from synthesis conditions such as a catalyst material and preparation steps, to ambient lab temperature and reactor details, as well as resulting Raman spectra and microscopy images. These parameters are carefully selected to unlock the potential of machine-learning models to advance synthesis. A suite of associated tools enable fast, automated, and standardized processing of Raman spectra and scanning electron microscopy images. To facilitate community-based efforts, Gr-ResQ provides tools for cyber-physical collaborations among research groups, allowing experiments to be designed, executed, and analyzed by different teams. Gr-ResQ also allows publication and discovery of recipes via the Materials Data Facility, which assigns each recipe a unique identifier when published and collects parameters in a search index. We envision that this holistic approach to data-driven synthesis can accelerate CVD recipe discovery and production control and open opportunities for advancing not only graphene but also many other 1D and 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards Auto-Generated Data Systems

After decades of progress, database management systems (DBMSs) are now the backbones of many data applications that we interact with on a daily basis. Yet, with the emergence of new data types and hardware, building and optimizing new data systems remain as difficult as the heyday of relational databases. In this paper, we summarize our work towards automating the building and optimization of data systems. Drawing from our own experience, we further argue that any automation technique must address three aspects: user specification, code generation, and result validation. We conclude by discussing a case study using videos data processing, along with opportunities for future research towards designing data systems that are automatically generated.

Computer Science↗
DOE OSTI2020

Continuous Water Quality Sensing for FGD Wastewater

The coal fired plant power industry requires new solutions for on-line monitoring of flue gas desulfurization (FGD) wastewaters for regulated trace metals to reduce recurring operating and off-site laboratory analysis costs and provide a high level of confidence of compliance with state and federal discharge guidelines. The University of Alabama at Birmingham (UAB) developed an integrated water sensor package for continuous water quality monitoring of flue gas desulfurization (FGD) wastewaters for concentration measurements of Selenium (Se). UAB utilized a diverse team of University Professors/Staff Members/Student(s) (Engineering and Innovative Technology Development Group), a University Affiliated Research Institution from the local area (Southern Research), and a cost sharing Industry Partner (Metrohm). The team developed and validated a proprietary wet chemistry batch process for water sample preparation; and implemented this process as a fully automated on-line monitor prototype system. This prototype system’s performance was evaluated in both the laboratory and during in-field deployment to a coal fired power plant. In both environments it was found that the prototype system was unable to recreate the batch process’s success at preparing FGD water samples for total Se measurements with a commercial-off-the-shelf (COTS) cyclic anodic stripping voltammetry (CSV) system. Through this experience, UAB has provided insight into a number of specific areas where improvements can be made with respect to the COTS systems used in this project to yield a more complete and competitive industry solution for on-line monitoring of total Se in industrial wastewater.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1989

The aerospace energy systems laboratory: Hardware and software implementation

For many years NASA Ames Research Center, Dryden Flight Research Facility has employed automation in the servicing of flight critical aircraft batteries. Recently a major upgrade to Dryden's computerized Battery Systems Laboratory was initiated to incorporate distributed processing and a centralized database. The new facility, called the Aerospace Energy Systems Laboratory (AESL), is being mechanized with iAPX86 and iAPX286 hardware running iRMX86. The hardware configuration and software structure for the AESL are described.

Glover, Richard D.↗
NASA NTRS1992

Framework Programmable Platform for the Advanced Software Development Workstation (FPP/ASDW). Demonstration framework document. Volume 1: Concepts and activity descriptions

The Framework Programmable Software Development Platform (FPP) is a project aimed at effectively combining tool and data integration mechanisms with a model of the software development process to provide an intelligent integrated software development environment. Guided by the model, this system development framework will take advantage of an integrated operating environment to automate effectively the management of the software development process so that costly mistakes during the development phase can be eliminated. The Advanced Software Development Workstation (ASDW) program is conducting research into development of advanced technologies for Computer Aided Software Engineering (CASE).

Mayer, Richard J.↗
NASA NTRS1991

The role of behavioral decision theory for cockpit information management

The focus of this report is the consideration of one form of cognition, judgment and decision making, while examining some information management issues associated with the implementation of new forms of automation. As technology matures and more tasks become suitable to automation, human factors researchers will have to consider the effect that increasing automation will have on operator performance. Current technology allows flight deck designers the opportunity to automate activities involving substantially more cognitive processing.

Jonsson, Jon E.↗
NASA NTRS1992

NASA Space Engineering Research Center for utilization of local planetary resources

Reports covering the period from 1 Nov. 1991 to 31 Oct. 1992 and documenting progress at the NASA Space Engineering Research Center are included. Topics covered include: (1) processing of propellants, volatiles, and metals; (2) production of structural and refractory materials; (3) system optimization discovery and characterization; (4) system automation and optimization; and (5) database development.

Source record↗
NASA NTRS1991

NIST: Information Management in the AMRF

The information management strategies developed for the NIST Automated Manufacturing Research Facility (AMRF) - a prototype small batch manufacturing facility used for integration and measurement related standards research are outlined in this video. The five major manufacturing functions - design, process planning, off-line programming, shop floor control, and materials processing are explained and their applications demonstrated.

Callaghan, George↗
NASA NTRS1996

Interdisciplinary Investigations in Support of Project DI-MOD

Various concepts from time series analysis are used as the basis for the development of algorithms to assist in the analysis and interpretation of remote sensed imagery. An approach to trend detection that is based upon the fractal analysis of power spectrum estimates is presented. Additionally, research was conducted toward the development of a software architecture to support processing tasks associated with databases housing a variety of data. An algorithmic approach which provides for the automation of the state monitoring process is presented.

Starks, Scott A.↗
NASA NTRS2016

Achieving Fast Operational Intelligence in NASA's Deep Space Network Through Complex Event Processing

NASA’s Deep Space Network (DSN) is a complex, global project, in which the expertise of human operators remain crucial for its successful operation. To find ways to save costs in operations and to improve its services, a number of modernization efforts are underway in the DSN. One such effort is a research and technology development task at the Jet Propulsion Laboratory that is investigating the use of complex event processing (CEP) for intelligent assessment of situations, trend analysis, and advanced automation. The technology leverages the significant business intelligence (BI) and data science advancements made in the enterprise industries over the last several years. The open source big data processing engine Apache SparkTM and the high-throughput, distributed messaging system Apache Kafka form the core of the DSN Complex Event Processing (DCEP) framework. This paper discusses the system engineering perspective of why achieving efficient, lower-cost operations in the DSN is a challenging problem, how the DCEP system handles the use cases that help realize intelligent operations, and how this solution fits into the overall model of the planned DSN Follow-the- Sun Operations (FtSO).

Choi, Joshua S.↗
DOE OSTI2022

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 3

This project is a collaborative research effort between PKMJ Technical Services LLC, Idaho National Laboratory, and Public Service Enterprise Group (PSEG) Nuclear, LLC. The collaboration, led by PKMJ Technical Services LLC, is part of the industry Funding Opportunity Announcement (FOA) award under Advanced Nuclear Technology Development FOA #DE-FOA-0001817. The pilot demonstration focuses on the Circulating Water System (CWS), an important non-safety-related system that impacts the power generation capability of the plant site. Achieving riskinformed condition-based Predictive Maintenance (PdM) on the CWS will result in significant economic benefits, and the developed methodologies can also be applied to other plant systems. This approach supports an industry goal of ensuring that nuclear power generation remains a viable, economically competitive option in the energy market. Operation and Maintenance (O&M) costs include labor-intensive Preventive Maintenance (PM) programs that involve manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies as well as time-based replacement of assets, irrespective of condition. This project offers an alternative by focusing on riskinformed condition-based maintenance to reduce O&M costs while still maintaining plant health and safety. This report summarizes the progress made toward achieving a risk-informed condition-based maintenance approach. The research and development (R&D) activities presented in this report are associated with development of a nuclear digital platform application, integration of fault signature models, and automated work management processes. The fault signatures and Machine Learning (ML) models are key components in predictive analytics and are heavily leveraged to improve the insights received by existing plant process data sources. Availability of the analysis results within a centralized digital platform enhances efficiency by enabling automation of activities otherwise performed manually. Personnel are presented with enhanced information that can be used to evaluate plant status and risks. Utilizing the enhancements to data analytics supports automated responses, (i.e. issuance of work orders) to address developing equipment faults and thus preventing forced, unplanned shutdowns of components or systems. The R&D activities described within this report lay the foundation for developing and demonstrating a digital automated platform to centralize the implementation of condition monitoring and response to equipment faults. The digital automated platform is cloud-based and designed to enable improved efficiency of plant processes. The digital platform includes content related to maintenance optimization, fault signature analysis, and plant records, which can all be used to support efficiencies when located within a centralized digital platform. These efficiencies could be further enhanced when deployed through industry-wide deployment of the technology to improve insights and processes based upon economies of scale.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2000

Automated Reflectance Measurement System Designed and Fabricated to Determine the Limits of Atomic Oxygen Treatment of Art Through Contrast Optimization

Atomic oxygen generated in ground-based research facilities has been used to not only test erosion of candidate spacecraft materials but as a noncontact technique for removing organic deposits from the surfaces of artwork. NASA has patented the use of atomic oxygen to remove carbon-based soot contamination from fire-damaged artwork. The process of cleaning soot-damaged paintings with atomic oxygen requires exposures for variable lengths of time, dependent on the condition of a painting. Care must be exercised while cleaning to prevent the removal of pigment. The cleaning process must be stopped as soon as visual inspection or surface reflectance measurements indicate that cleaning is complete. Both techniques rely on optical comparisons of known bright locations against known dark locations on the artwork being cleaned. Difficulties arise with these techniques when either a known bright or dark location cannot be determined readily. Furthermore, dark locations will lighten with excessive exposure to atomic oxygen. Therefore, an automated test instrument to quantitatively characterize cleaning progression was designed and developed at the NASA Glenn Research Center at Lewis Field to determine when atomic oxygen cleaning is complete.

Sechkar, Edward A.↗
DOE OSTI2026

Image processing pipeline for AI-driven nanoparticle megalibrary characterization

Recent innovations have made it possible to produce megalibraries, millions of structurally and compositionally distinct nanoparticles on a chip. These megalibraries yield vast volumes of data that are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image processing step before training can produce significantly higher-performing models in a fraction of the time and make them more robust to different image noise levels and microscope acquisition settings. The image processing pipeline proposed here effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures. These features result in higher performance and significant cost savings. Experiments demonstrate superior performance relative to baseline, including an 18.2% improvement in recall and a 13.1% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives, and our best-performing model reaches a precision of 95.9% and a weighted F-score of 95.1% on an unseen test set. Additionally, model training time is reduced from hours to less than a minute. We also show that, using this custom image processing pipeline, model performance is significantly improved at lower pixel resolutions compared to downsizing alone. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will allow researchers to rapidly and accurately analyze much greater volumes of data, thereby accelerating materials discovery.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Automation Scripts for Feeder Analysis using CymPy

SAND2025-00330O Automation Scripts for Feeder Analysis using CymPy is a software tool that uses Python scripts to automate the analysis of power system feeders using CYME software. Utilities and researchers can use it to perform various types of analyses by leveraging the capabilities of CYME. The scripts use the CymPy library to interface with CYME and automate the analysis process. The software uses standard Python libraries along with the Eaton CymPy library. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525

Reno, Matthew↗
NASA NTRSDate not supplied

A Hybrid Approach to Labeling Datasets in Earth Science Publications

NASA Data Centers provide the public with thousands of datasets that result in published papers, reports, and conference proceedings. Collecting accurate metrics on usage of these datasets is key to connecting different areas of knowledge and evaluating the datasets’ impact. While most of the datasets have Digital Object Identifiers (DOIs) assigned, most publications do not cite them hampering the automated search of these publications. Instead, articles mention attributes like organization, instrument, mission, variable, or a publication describing the dataset. Often only domain experts can deduce the dataset that was used in the publication text. The lack of a citation slows the spread of information and reduces the research’s impact. With thousands of papers produced each year, an automated means of labeling datasets is critical. This paper explores a hybrid approach of heuristics and a Natural Language Processing (NLP) Named Entity Recognition (NER) model to find and label the datasets used within Earth Science papers. Heuristics are used to produce the labelled sentences and any potential dataset candidates that can be derived from a sentence. The heuristic labels the sentences with the names of mission, instrument, re-analysis models, and science keywords taken from the Global Change Master Directory (GCMD) ontology. Additionally, it uses those labels to generate the dataset citation candidates. If the mission, instrument, and variable are sufficient to create the citation for the dataset the citation and the label the domain expert reviews the output without going through the NLP model. If the extracted label is not sufficient to label the dataset on its own, the sentence and its associated dataset labels will be inputted into the NER model. The model outputs the labeled sentence and the potential dataset candidates with their associated probabilities. The domain expert then reviews the NER model’s output and the correct labels are determined. The newly labelled papers can then be used as additional training data. This creates an iterative process for the approach to continuously improve. Because all the possible mentions are gathered by the model, the domain expert can quickly and easily label the papers resulting in large time savings.

Jacob Atkins↗