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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
DOE OSTI2023

United Kingdom's Action Plan for Power Sector Decarbonisation

A collaborative report from the Clean Energy Ministerial (CEM) on Lessons Learned for Rapid Decarbonization of Power Sectors was delivered to energy ministers and presented at CEM13 in the United States in September 2022. In light of these lessons learned and discussed at CEM13, several countries signaled intent to develop Action Plans for power sector decarbonization that will be presented at CEM14 in India. These action plans complement but are differentiated from other international power sector initiatives such as the Breakthrough Agenda whose broad purpose is to raise collective ambition and the Global Power System Transformation (G-PST) Consortium whose goals are to convene power system operators to accelerate research innovations and foster peer learning. These action plans, supported by the 21st Century Power Partnership (21CPP) and other CEM workstreams via direct technical assistance and capacity building, are instead intended to focus on implementation actions given each country's existing power sector goals and activities and are an opportunity for countries to display leadership in power sector decarbonization. These action plans are voluntary, developed by each country individually, and can be viewed as a living document that is subject to change.

decarbonization↗
NASA NTRSDate not supplied

ATD-2 Digital Services / DIP

The path to NAS-wide implementation for ATD-2 multi-airport IADS is as a digital service for flight operators that builds on and supplements FAA TBO investments. This session will focus on a suite of Machine Learning (ML) services the ATD-2 team developed to model airport surface operations. The approach used to leverage SWIM data sets will be described and a preliminary analysis of ML model accuracy will be presented. NASA’s plans to build on this work under the ATM-X Digital Information Platform (DIP) sub-project will be discussed.

ATD TIM, ATD-2 Digital Services, DIP↗
DOE OSTI2024

Conceptual Design of Integrated Energy Systems with Market Interaction Surrogate Models

Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with market and can result in overestimated economic values. In this work, we pro-pose a machine learning surrogate-assisted optimization framework to quantify the IES/market interactions and thus go beyond price taker. We use time series clustering to generate representative IES operation profiles for the IES optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.

Chen, Xinhe↗
DOE OSTI2023

Generalized Quantum Convolution for Multidimensional Data

The convolution operation plays a vital role in a wide range of critical algorithms across various domains, such as digital image processing, convolutional neural networks, and quantum machine learning. In existing implementations, particularly in quantum neural networks, convolution operations are usually approximated by the application of filters with data strides that are equal to the filter window sizes. One challenge with these implementations is preserving the spatial and temporal localities of the input features, specifically for data with higher dimensions. In addition, the deep circuits required to perform quantum convolution with a unity stride, especially for multidimensional data, increase the risk of violating decoherence constraints. In this work, we propose depth-optimized circuits for performing generalized multidimensional quantum convolution operations with unity stride targeting applications that process data with high dimensions, such as hyperspectral imagery and remote sensing. We experimentally evaluate and demonstrate the applicability of the proposed techniques by using real-world, high-resolution, multidimensional image data on a state-of-the-art quantum simulator from IBM Quantum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

SAGE III on ISS: Mission Update and Science Milestone Results and Future Plans

The Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS) has been collecting data on the stratosphere almost continuously for 4 years since the first light milestone was achieved on March 17, 2017. In that time, the SAGE III/ISS data products have withstood robust assessments both internal and external, using comparisons to data sets both in-situ and spaceborne, and incorporated improvements into the retrieval algorithm and the science data products themselves. With the new v5.2 release of the SAGE III/ISS science data product algorithm, improvements made to the aerosol and water vapor products have produced better correlations with both MLS and balloonsonde data. At this 4-year milestone, we will look back on the last 4 years of data collection highlighting SAGE III/ISS ozone and aerosol data from 2017-2020 in a 6-minute video highlighting the SAGE III mission. An overview of mission operations and the challenges of operating a science mission on the ISS will highlight lessons learned and the mission science objectives completed during the first three years of the mission. The future goals for the SAGE III/ISS mission, as we continue operations and science collection, will round out the presentation.

Marilee M Roell↗
DOE OSTI2020

Enhancement of microbiome management by machine learning for biological wastewater treatment

Here we propose to develop microbiome-based machine learning models to predict the response of biological wastewater treatment systems to environmental or operational disturbances or to design specific microbiomes to achieve a desired system function. These machine learning models can be used to enhance the stability of microbiome-based biological systems and warn against the failure of these systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Dynamic Validation of CNN-Based Surrogate Models for Inverter-Based Resources in Open-Source Solvers

Traditionally, distribution system planning has focused on steady-state analyses, with limited consideration of dynamic behavior. However, as large or medium-scale inverter-based resources (IBRs), particularly grid-following (GFL) inverters in commercial or industry buildings, become more prevalent, understanding their dynamic impact is essential for grid planning and operation. This article presents an innovative deep-learning (DL)-approach using convolutional neural networks technique to model the GFL inverters. Developed from real grid-tied commercial IBR transient data, these dynamic DL models overcome proprietary constraints by requiring minimal knowledge of internal converter physics while maintaining high accuracy and flexibility. To demonstrate their applicability, the models were incorporated into GridLAB-D, an open-source, three-phase distribution analysis tool. This integration enables dynamic simulations of large-scale distribution networks with high IBR penetration stability analysis. Rigorous testing and validation, aligned with industry standards, confirmed the reliability and efficiency of this approach, paving the way for enhanced planning and operational assessments of modern power systems.

Deep-learning↗
NASA NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗
NASA NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗
DOE OSTI2024

Fuel Cell Inverter Dataset

This data set contains the three phase AC voltage, three phase AC current, DC voltage and DC current. These data sets were captured during fuel cell inverter operation in grid-connected dispatch, islanded load changes, transition from grid-connected mode to islanded mode and vice-versa.

25 ENERGY STORAGE↗
DOE OSTI2024

TRANSFER LEARNING FOR FIELD EMISSION MITIGATION IN CEBAF SRF CAVITIES

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab operates hundreds of super-conducting radio frequency (SRF) cavities in its two linear accelerators (linacs). Field emission (FE) is an ongoing operational challenge in higher gradient SRF cavities. FE generates high levels of neutron and gamma radiation leading to damaged accelerator hardware and a radiation hazard environment. During machine development periods, we performed gradient scans to record data capturing the relationship between cavity gradients and radiation levels measured throughout the linacs. However, the field emission environment at CEBAF varies considerably over time as the configuration of the radio frequency (RF) gradients changes and due to the changing behaviour of field emitters. An artificial intelligence/machine learning (AI/ML) approach with transfer learning could be a valuable tool to mitigate FE and lower the radiation levels. In this work, we mainly focus on leveraging the RF trip data gathered during CEBAF operations. We develop a transfer learning-based surrogate model for radiation detector readings given RF cavity gradients to track the CEBAF?s changing configuration and environment. Then, we could use the developed model as an optimization process for redistributing the RF gradients within a linac to minimize radiation levels.

Ahammed, K.↗
DOE OSTI2024

TRANSFER LEARNING FOR FIELD EMISSION MITIGATION IN CEBAF SRF CAVITIES

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab operates hundreds of super-conducting radio frequency (SRF) cavities in its two linear accelerators (linacs). Field emission (FE) is an ongoing operational challenge in higher gradient SRF cavities. FE generates high levels of neutron and gamma radiation leading to damaged accelerator hardware and a radiation hazard environment. During machine development periods, we performed gradient scans to record data capturing the relationship between cavity gradients and radiation levels measured throughout the linacs. However, the field emission environment at CEBAF varies considerably over time as the configuration of the radio frequency (RF) gradients changes and due to the changing behaviour of field emitters. An artificial intelligence/machine learning (AI/ML) approach with transfer learning could be a valuable tool to mitigate FE and lower the radiation levels. In this work, we mainly focus on leveraging the RF trip data gathered during CEBAF operations. We develop a transfer learning-based surrogate model for radiation detector readings given RF cavity gradients to track the CEBAF?s changing configuration and environment. Then, we could use the developed model as an optimization process for redistributing the RF gradients within a linac to minimize radiation levels.

Ahammed, K.↗
NASA NTRS1993

Telerobotic control of a mobile coordinated robotic server, executive summary

This interim report continues with the research effort on advanced adaptive controls for space robotics systems. In particular, previous results developed by the principle investigator and his research team centered around fuzzy logic control (FLC) in which the lack of knowledge of the robotic system as well as the uncertainties of the environment are compensated for by a rule base structure which interacts with varying degrees of belief of control action using system measurements. An on-line adaptive algorithm was developed using a single parameter tuning scheme. In the effort presented, the methodology is further developed to include on-line scaling factor tuning and self-learning control as well as extended to the multi-input, multi-output (MIMO) case. Classical fuzzy logic control requires tuning input scale factors off-line through trial and error techniques. This is time-consuming and cannot adapt to new changes in the process. The new adaptive FLC includes a self-tuning scheme for choosing the scaling factors on-line. Further the rule base in classical FLC is usually produced by soliciting knowledge from human operators as to what is good control action for given circumstances. This usually requires full knowledge and experience of the process and operating conditions, which limits applicability. A self-learning scheme is developed which adaptively forms the rule base with very limited knowledge of the process. Finally, a MIMO method is presented employing optimization techniques. This is required for application to space robotics in which several degrees-of-freedom links are commonly used. Simulation examples are presented for terminal control - typical of robotic problems in which a desired terminal point is to be reached for each link. Future activities will be to implement the MIMO adaptive FLC on an INTEL microcontroller-based circuit and to test the algorithm on a robotic system at the Mars Mission Research Center at North Carolina State University.

Lee, Gordon↗
NASA NTRSDate not supplied

Operating a Crewed Spacecraft in the Age of Commercial Space Using Private/Government Partnership

Fifty years after humans completed a large-scale United States government-funded and government-engineered effort of landing humans on the moon, human spaceflight has entered a new paradigm. Private companies are now investing their own money and taking on an ever-increasing role in human spaceflight, in partnership with the U.S. government. This paper will describe the development of one of these partnerships through the lens of its mission operations team. As part of the Commercial Crew Program (CCP), NASA selected Boeing’s CST-100 Starliner as one of the next generation of crewed vehicles. Boeing opted to partner with the US Government for their Starliner operations by contracting with NASA’s Mission Control teams in the Flight Operations Directorate (FOD) at the Johnson Space Center to create its own Mission Operations (MO) flight controllers. Partnering with FOD provided benefits to both Boeing and NASA while also creating new challenges. MO brought over 60 years of crewed spaceflight experience and infrastructure to Boeing’s new program. Within certain legal constraints, MO was able to work closely and efficiently with their FOD counterparts who were performing both integration duties and, under the auspices of the CCP, insight of the contractors, in this case Boeing. Involvement of NASA as the Boeing operations agent did lead to what management deemed a ‘healthy tension’ within FOD, challenging old processes and often creating better, more robust teamwork. Successful development of the framework and boundaries of both the legal aspects and the oversight tensions has been one of the keys to developing a successful corporate/government partnership. Due to the highly automated nature of the Starliner, the MO organization was designed to be much smaller than previous NASA’s flight control teams for past programs. NASA has learned through the decades that spacecraft design and operations need to be as flexible and forgiving as possible. NASA’s Commercial Crew Program was established to sponsor corporate development of economical vehicles that could get humans to and from low Earth orbit. These companies, of course, need to meet contractual obligations in providing a safe means of transporting astronauts to the International Space Station (ISS), but also need to do so in a manner that leads to the venture resulting in a profit at the same time. Through MO’s involvement in the development of this spaceflight paradigm shift, there are ample lessons to be conveyed to future teams and programs working to develop similar missions.

Robert C Dempsey↗
NASA NTRSDate not supplied

Operating a Crewed Spacecraft in the Age of Commercial Space Using Private/Government Partnership

Fifty years after humans completed a large-scale United States government-funded and government-engineered effort of landing humans on the moon, human spaceflight has entered a new paradigm. Private companies are now investing their own money and taking on an ever-increasing role in human spaceflight, in partnership with the U.S. government. This paper will describe the development of one of these partnerships through the lens of its mission operations team. As part of the Commercial Crew Program (CCP), NASA selected Boeing’s CST-100 Starliner as one of the next generation of crewed vehicles. Boeing opted to partner with the US Government for their Starliner operations by contracting with NASA’s Mission Control teams in the Flight Operations Directorate (FOD) at the Johnson Space Center to create its own Mission Operations (MO) flight controllers. Partnering with FOD provided benefits to both Boeing and NASA while also creating new challenges. MO brought over 60 years of crewed spaceflight experience and infrastructure to Boeing’s new program. Within certain legal constraints, MO was able to work closely and efficiently with their FOD counterparts who were performing both integration duties and, under the auspices of the CCP, insight of the contractors, in this case Boeing. Involvement of NASA as the Boeing operations agent did lead to what management deemed a ‘healthy tension’ within FOD, challenging old processes and often creating better, more robust teamwork. Successful development of the framework and boundaries of both the legal aspects and the oversight tensions has been one of the keys to developing a successful corporate/government partnership. Due to the highly automated nature of the Starliner, the MO organization was designed to be much smaller than previous NASA’s flight control teams for past programs. NASA has learned through the decades that spacecraft design and operations need to be as flexible and forgiving as possible. NASA’s Commercial Crew Program was established to sponsor corporate development of economical vehicles that could get humans to and from low Earth orbit. These companies, of course, need to meet contractual obligations in providing a safe means of transporting astronauts to the International Space Station (ISS), but also need to do so in a manner that leads to the venture resulting in a profit at the same time. Through MO’s involvement in the development of this spaceflight paradigm shift, there are ample lessons to be conveyed to future teams and programs working to develop similar missions.

Robert C. Dempsey↗
NASA NTRSDate not supplied

Operating a Crewed Spacecraft in the Age of Commercial Space Using Private/Government Partnership

Fifty years after humans completed a large-scale United States government-funded and government-engineered effort of landing humans on the moon, human spaceflight has entered a new paradigm. Private companies are now investing their own money and taking on an ever-increasing role in human spaceflight, in partnership with the U.S. government. This paper will describe the development of one of these partnerships through the lens of its mission operations team. As part of the Commercial Crew Program (CCP), NASA selected Boeing’s CST-100 Starliner as one of the next generation of crewed vehicles. Boeing opted to partner with the US Government for their Starliner operations by contracting with NASA’s Mission Control teams in the Flight Operations Directorate (FOD) at the Johnson Space Center to create its own Mission Operations (MO) flight controllers. Partnering with FOD provided benefits to both Boeing and NASA while also creating new challenges. MO brought over 60 years of crewed spaceflight experience and infrastructure to Boeing’s new program. Within certain legal constraints, MO was able to work closely and efficiently with their FOD counterparts who were performing both integration duties and, under the auspices of the CCP, insight of the contractors, in this case Boeing. Involvement of NASA as the Boeing operations agent did lead to what management deemed a ‘healthy tension’ within FOD, challenging old processes and often creating better, more robust teamwork. Successful development of the framework and boundaries of both the legal aspects and the oversight tensions has been one of the keys to developing a successful corporate/government partnership. Due to the highly automated nature of the Starliner, the MO organization was designed to be much smaller than previous NASA’s flight control teams for past programs. NASA has learned through the decades that spacecraft design and operations need to be as flexible and forgiving as possible. NASA’s Commercial Crew Program was established to sponsor corporate development of economical vehicles that could get humans to and from low Earth orbit. These companies, of course, need to meet contractual obligations in providing a safe means of transporting astronauts to the International Space Station (ISS), but also need to do so in a manner that leads to the venture resulting in a profit at the same time. Through MO’s involvement in the development of this spaceflight paradigm shift, there are ample lessons to be conveyed to future teams and programs working to develop similar missions.

Robert C. Dempsey↗
NASA NTRS2019

The Global Ecosystem Dynamics Investigation (GEDI) Lidar Laser Transmitter

The Global Ecosystems Dynamics Investigation (GEDI) Lidar, is an Earth Science remote sensing instrument aboard the International Space Station (ISS) and the Japanese Experiment Module (JEM). Its core mission is to measure the global carbon balance of Earth's forests by using a set of three solid state laser transmitters in a multibeam waveform capture lidar technique. GEDI's laser transmitters and precision optical system transmits over 3.4 million laser pulses to the Earth every hour, each pulse producing an individual 3-D biomass column measurement. To enable a successful two-year mission, the lasers had to be reliable, highly repeatable in performance with each measurement power cycle, and designed with minimal part count for reduced manufacture complexity and cost. These transmitters are in-house products; developed, constructed, qualified, and fully integrated into the GEDI instrument at NASA's Goddard Space Flight Center. We will present the lasers' path from initial design to flight operation, with emphasis on the major milestones, critical issues, and lessons learned. Full credit goes to the excellent team effort that led to the successful commissioning and initiation of full-time science operations in March 2019.

Coyle, D. Barry↗