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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Synthesis and evaluation of electroactive CO2 carriers

The development of redox CO2 carrier molecules capable of binding CO2 and releasing it will provide a basis for efficient CO2 removal system for possible applications on long-duration manned space missions. The objective of research being conducted is the utilization of these carriers to develop membranes to selectively and efficiently pump CO2 when a potential is applied across the membranes. The topics covered include the effects of pumping rate and CO2 solubility on carrier efficiency, chemical requirements of redox active carriers, and the screening and evaluation of potential carriers.

Bell, W. L.↗
NASA NTRS2020

Names Don't Fly: Smart Filters for Profanity Detection and Classification in User-Generated Content

Generally, names associate with a person’s identity. But what if in the pretext of a legitimate name and given the opportunity, users of software provide names to online web forms that carry along offensive language, slurs, and other profanity that is then sent to Mars ? The answer is simple: they don’t fly. In this paper,we perform model explorations to detect and classify inappropriate content in the names submitted from people across the world to ‘Send Your Names to MARS’ public engagement campaign.We propose a novel pipeline approach, that can effectively overcome the issues of lack of negative samples, noisy labels by gathering expert knowledge over time with human(s) in the loop and data augmentation, and achieve high accuracy in classifying inappropriate names with very little or no context. We describe cloud-based infrastructure to deploy our application and run predictions on large-scale data through our pipeline and achieve significant speedup over offline processes, with enhanced reliability and security.

Soderstrom, Tomas↗
NASA NTRSDate not supplied

An Overview of High Temperature Venus Surface Lander and Smart Systems Technologies

The extreme surface conditions of Venus, including high temperature/pressure and reactive chemistry, have previously limited the lifetime of surface missions to ~2 hours. Recent advances in high temperature technologies suggest the possibility of extended duration missions on the Venus surface. This presentation gives a brief overview of a range of high temperature technologies to enable a possible extended duration Venus surface mission, as well as an overview of the motivation and challenges of Venus surface exploration. The technology development under the Long-Lived In Situ Solar System Explorer (LLISSE) project is aimed towards developing a full lander system operational for 60 days on the Venus surface and included a power source, electronics, communications, sensors, and the structure, each at a different level of maturity. This presentation briefly describes these and other lander system technologies, their relative level of maturity, and planned future development for Venus surface exploration. Relevance to Aeronautic applications is briefly discussed.

Venus high temperature technologies vehicle health↗
DOE OSTI2024

Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).

14 SOLAR ENERGY↗
NASA NTRS2005

Development, Implementation and Application of Micromechanical Analysis Tools for Advanced High Temperature Composites

This document contains the final report to the NASA Glenn Research Center (GRC) for the research project entitled Development, Implementation, and Application of Micromechanical Analysis Tools for Advanced High-Temperature Composites. The research supporting this initiative has been conducted by Dr. Brett A. Bednarcyk, a Senior Scientist at OM in Brookpark, Ohio from the period of August 1998 to March 2005. Most of the work summarized herein involved development, implementation, and application of enhancements and new capabilities for NASA GRC's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) software package. When the project began, this software was at a low TRL (3-4) and at release version 2.0. Due to this project, the TRL of MAC/GMC has been raised to 7 and two new versions (3.0 and 4.0) have been released. The most important accomplishments with respect to MAC/GMC are: (1) A multi-scale framework has been built around the software, enabling coupled design and analysis from the global structure scale down to the micro fiber-matrix scale; (2) The software has been expanded to analyze smart materials; (3) State-of-the-art micromechanics theories have been implemented and validated within the code; (4) The damage, failure, and lifing capabilities of the code have been expanded from a very limited state to a vast degree of functionality and utility; and (5) The user flexibility of the code has been significantly enhanced. MAC/GMC is now the premier code for design and analysis of advanced composite and smart materials. It is a candidate for the 2005 NASA Software of the Year Award. The work completed over the course of the project is summarized below on a year by year basis. All publications resulting from the project are listed at the end of this report.

Source record↗
NASA NTRS2013

SpaceCube Version 1.5

SpaceCube 1.5 is a high-performance and low-power system in a compact form factor. It is a hybrid processing system consisting of CPU (central processing unit), FPGA (field-programmable gate array), and DSP (digital signal processor) processing elements. The primary processing engine is the Virtex- 5 FX100T FPGA, which has two embedded processors. The SpaceCube 1.5 System was a bridge to the SpaceCube 2.0 and SpaceCube 2.0 Mini processing systems. The SpaceCube 1.5 system was the primary avionics in the successful SMART (Small Rocket/Spacecraft Technology) Sounding Rocket mission that was launched in the summer of 2011. For SMART and similar missions, an avionics processor is required that is reconfigurable, has high processing capability, has multi-gigabit interfaces, is low power, and comes in a rugged/compact form factor. The original SpaceCube 1.0 met a number of the criteria, but did not possess the multi-gigabit interfaces that were required and is a higher-cost system. The SpaceCube 1.5 was designed with those mission requirements in mind. The SpaceCube 1.5 features one Xilinx Virtex-5 FX100T FPGA and has excellent size, weight, and power characteristics [4×4×3 in. (approx. = 10×10×8 cm), 3 lb (approx. = 1.4 kg), and 5 to 15 W depending on the application]. The estimated computing power of the two PowerPC 440s in the Virtex-5 FPGA is 1100 DMIPS each. The SpaceCube 1.5 includes two Gigabit Ethernet (1 Gbps) interfaces as well as two SATA-I/II interfaces (1.5 to 3.0 Gbps) for recording to data drives. The SpaceCube 1.5 also features DDR2 SDRAM (double data rate synchronous dynamic random access memory); 4- Gbit Flash for storing application code for the CPU, FPGA, and DSP processing elements; and a Xilinx Platform Flash XL to store FPGA configuration files or application code. The system also incorporates a 12 bit analog to digital converter with the ability to read 32 discrete analog sensor inputs. The SpaceCube 1.5 design also has a built-in accelerometer. In addition, the system has 12 receive and transmit RS- 422 interfaces for legacy support. The SpaceCube 1.5 processor card represents the first NASA Goddard design in a compact form factor featuring the Xilinx Virtex- 5. The SpaceCube 1.5 incorporates backward compatibility with the Space- Cube 1.0 form factor and stackable architecture. It also makes use of low-cost commercial parts, but is designed for operation in harsh environments.

Geist, Alessandro↗
NASA NTRS1996

Recent Advances in High-Performance Direct Methanol Fuel Cells

Direct methanol fuel cells for portable power applications have been advanced significantly under DARPA- and ARO-sponsored programs over the last five years. A liquid-feed, direct methanol fuel cell developed under these programs, employs a proton exchange membrane as electrolyte and operates on aqueous solutions of methanol with air or oxygen as the oxidant.

fuel cells methanol methanol fuel cells DARPA ARO↗
NASA NTRS2003

The NASA Smart Probe Project for real-time multiple microsensor tissue recognition

BACKGROUND: Remote surgery requires automated sensors, effectors and sensor-effector communication. The NASA Smart Probe Project has focused on the sensor aspect. METHODS: The NASA Smart Probe uses neural networks and data from multiple microsensors for a unique tissue signature in real time. Animal and human trials use several probe configurations: (1) 8-microsensor probe (2.5 mm in diameter) for rodent studies (normal and subcutaneous mammary tumor tissues), and (2) 21-gauge needle probe with 3 spectroscopic fibers and an impedance microelectrode for breast cancer diagnosis in humans. Multisensor data are collected in real time (update 100 times/s) using PCs. RESULTS: Human data (collected by NASA licensee BioLuminate) from 15 women undergoing breast biopsy distinguished normal tissue from both benign tumors and breast carcinoma. Tumor margins and necrosis are rapidly detected. CONCLUSION: Real-time tissue identification is achievable. Potential applications, including probes incorporating nanoelectrode arrays, are presented. Copyright 2003 S. Karger AG, Basel.

Biopsy/methods↗
DOE OSTI2021

A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops a machine learning based approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurement. The machine learning model is developed by using random forest algorithm. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.

advanced metering infrastructure↗
DOE OSTI2025

Implementation of Advanced Grid Support Functionalities by Smart Operation of Residential Loads with low Cost Converter Interface

This paper investigates a grid-supportive load concept for small-scale residential appliances, focusing on a residential refrigerator. Power consumption is adjusted based on grid conditions to achieve IEEE-1547 grid support functions. Two key aspects are presented: a low-cost refrigerator converter with Lyapunov energy function-based local controllers for speed control, and the impact on a standard microgrid system, demonstrating advanced grid support from the load side. This method enhances grid resilience and reliability and can be extended to other residential loads. The study contributes to efficient and robust grid-supportive load management systems, showing promising performance. This approach has the potential to improve overall grid stability and can be adapted for various types of residential appliances. The modeling and simulations in MATLAB/Simulink and PLECS confirm the feasibility and effectiveness of the proposed solution. Future work will explore real-world implementation and scalability of this concept for broader applications.

grid supportive loads (GSL)↗
DOE OSTI2023

A Siamese CNN + KNN-Based Classification Framework for Non-intrusive Load Monitoring

Through the development of smart grids, programs such as demand side response, have been presented as auxiliary services to the real-time operation of distributed networks. In order to provide consumers information on their energy consumption, so that a modulation in consumption is possible, non-intrusive load monitoring has been introduced as an solution to this pattern recognition problem. Non-intrusive load monitoring enables the modeling of electrical loads connected to the low-voltage system, considering only a single measurement point. Presented state-of-the-art solutions though, consider availability of data as well as representation of all possible classes of the environment. This is of course a most conservative hypothesis, since in real-life applications availability of such data is much difficult, as well as the dynamic behavior of models is implicitly evolving in time. Here, a framework that uses neural Siamese networks with k-nearest neighbor clustering is presented toward non-intrusive load monitoring. Online learning feature is implemented, which relaxes the hypothesis of data requirements as well addresses the evolving nature of load profile. k-nearest clustering allows nonlinear characteristic space modelling. Test results using synthetics and real-life data show that the solution, besides obtaining a good generalizability in the classification, also obtained results with an accuracy of 95.77%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Frequency Resilience Enhancement for Power Systems with High Penetration of Grid-Forming Inverters

Abstract—Grid following inverter-based renewable generation has replaced conventional generation in recent years, resulting in lower system inertia. The frequency resilience in such a lower inertia system is critical for emergency mitigation. In this paper, we propose a resilience metric based on frequency recovery to quantitatively represent system resilience in terms of the rate of change of frequency. Application of grid-forming converters provides a means to improve system resilience by providing virtual inertia. An under-frequency load shedding strategy is applied to further support frequency recovery in cases with high penetration of grid-forming inverters. Case studies are designed and performed in a modified IEEE 9-bus test system using the PSCAD/EMTDC platform. Simulation results demonstrate the validity of the proposed resilience metric and the effectiveness of the strategy for reducing frequency excursion in inverter-based power systems. citation: J. Gui, H. Lei, T. R. McJunkin and B. K. Johnson, "Frequency Resilience Enhancement for Power Systems with High Penetration of Grid-Forming Inverters," 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2023, pp. 1-5, doi: 10.1109/ISGT51731.2023.10066357.

14 SOLAR ENERGY↗
DOE OSTI2022

Real-time Distribution Simulation and Application Development for Power Systems Education

To help bridge the gap between traditional power system engineering instruction and emerging industry needs, a new set of classroom and research tools are needed. Real-time simulation tools emulating power system control room software present an opportunity to introduce students to the array of operational considerations, technical challenges, and decision-making associated with power system operations. The GridAPPS-D platform is proposed to support coursework and academic research as it provides an open-source platform for simulation, application development, and software integration. The GridAPPS-D platform, simulation capabilities, development environment, and interactive training are discussed in the context of lessons-learned from implementation for undergraduate and graduate students in the US and India. A series of potential GridAPPS-D supported academic capabilities are introduced.

Active distribution networks, open educational res↗
DOE OSTI2022

High-Fidelity Solar Irradiance Data: Simple Access to State-of-the-Art Information Accelerates Southeast Asia's Clean Energy Economic Transformation

High quality, robust, and reliable renewable energy resource data is foundational to climate-smart decision making, evidence-based policy planning, and clean energy investment mobilization. USAID and NREL, through the Advanced Energy Partnership for Asia, are expanding access to this critical resource data by providing free, high-fidelity solar resource data for Southeast Asia through the RE Data Explorer platform. This brief highlights several ways the Southeast Asia solar resource data has been used for power system planning and project development in the region.

Advanced Energy Partnership for Asia↗
DOE OSTI2026

A Systematic Review on Coordinated Restoration Strategies for Power Distribution Grids

Power distribution grids are increasingly exposed to High-Impact Low-Probability (HILP) events, which cause widespread disruptions with severe societal and economic impacts. The growing complexity of modern grids, driven by the integration of distributed energy resources and smart grid technologies, has introduced new challenges to effective service restoration. While significant research has explored individual restoration strategies, such as network reconfiguration and microgrid formation, limited attention has been given to methods in which they can be effectively coordinated. Furthermore, the absence of systematic review papers addressing this issue hampers the development of cohesive restoration frameworks capable of addressing the operational complexities of modern grids. This paper presents a systematic review synthesizing existing knowledge on power grid restoration, identifying key limitations, and highlighting opportunities for coordinated strategies. By addressing research gaps and emphasizing the integration of diverse approaches, this study provides critical insights for advancing grid resiliency and recovery, offering a foundation for future research and practical applications in the face of HILP events.

Systematic review↗
DOE OSTI2025

MIC-DP: A Scalable Correlation-Aware Differential Privacy Framework for High-Dimensional Data

Conventional differential privacy (DP) assumes record independence, limiting effectiveness on real-world datasets with temporal, spatial, or structural correlations. These dependencies undermine privacy guarantees and degrade utility in domains like healthcare, IoT, and smart city analytics. We propose Maximum Information Correlated Differential Privacy (MIC-DP), a novel framework that dynamically calibrates noise based on statistical dependencies. MIC-DP uses the Maximum Information Coefficient (MIC) to capture both linear and nonlinear correlations without explicit modeling, enabling adaptive sensitivity adjustment and improved privacy–utility trade-offs. Evaluations on healthcare (MIMIC), demographic (ACI), and synthetic datasets show that MIC-DP reduces mean absolute error (MAE) by up to 5.2% under strict privacy budgets (ϵ≤1), with aggregate utility improvements reaching 18% across datasets and evaluation metrics. MIC-DP provides formal (ϵ,δ)-privacy guarantees, scales efficiently with feature count, and supports deployment in moderate-scale, privacy-sensitive applications. Its tunable performance and runtime efficiency make MIC-DP suitable for privacy-sensitive applications where low-latency analytics and strong privacy guarantees must coexist. These results demonstrate MIC-DP’s effectiveness as a correlation-aware solution for practical DP.

Yang, Wenjun [Univ. of Washington, Tacoma, WA (Uni↗
DOE OSTI2021

A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2004

The Perfect Mate for Safe Fueling

Referred to as the "lifeline for any space launch vehicle" by NASA Space Launch Initiative Program Manager Warren Wiley, an umbilical is a large device that transports power, communications, instrument readings, and fluids such as propellants, pressurization gases, and coolants from one source to another. Numerous launch vehicles, planetary systems, and rovers require umbilical "mating". This process is a driving factor for dependable and affordable space access. With future-generation space vehicles in mind, NASA recently designed a smart, automated method for quickly and reliably mating and demating electrical and fluid umbilical connectors. The new umbilical concept is expected to replace NASA s traditional umbilical systems that release at vehicle lift-off (T-0). The idea is to increase safety by automatically performing hazardous tasks, thus reducing potential failure modes and the time and labor hours necessary to prepare for launch. The new system will also be used as a test bed for quick disconnect development and for advance control and leak detection. It incorporates concepts such as a secondary mate plate, robotic machine vision, and compliant motor motion control, and is destined to advance usage of automated umbilicals in a variety of aerospace and commercial applications.

Source record↗