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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Enhancing Distribution Grid Resilience Through Model Predictive Controller Enabled Prioritized Load Restoration Strategy

Effective resilience improvement strategies enable the power grid to cope with disruptive extreme events. Most power grid outages are caused by disruptions in distribution grids. Motivated by the urgent need for power system resilience research, this paper proposes a priority-weighted optimal load restoration technique to enhance the resilience of distribution grids against extreme events. The proposed technique is based on smart distribution technology and framed as sequential multi-step decision process (MDP) and mixed integer linear program (MILP). It is formulated as optimal control problem with a model predictive control (MPC) approach. We applied the devised MILP-MPC-based load restoration technique to a simplified single-bus version of the IEEE 13-bus distribution system with integrated distributed energy resources (DERs) such as wind turbine, photovoitaic array, microturbine, and energy storage device. The technique executes a reducing and rolling horizon optimization in each control step in real-time using the forecasted information of the renewables, the fuel status of the microturbine and the state of charge of the energy storage device. We consider an extreme event which triggered outage of the upstream utility grid and caused islanded operation of the distribution grid. We demonstrated the effectiveness of the proposed MPC approach in restoring the distribution grid loads based on their priority during the main grid outage-caused islanded operation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

PCOR Partnership Best Practices Manual for Subsurface Technical Risk Assessment of Geologic CO 2 Storage Projects

In 2003, the U.S. Department of Energy established the Regional Carbon Sequestration Partnerships (RCSP) Initiative to help develop technology, infrastructure, and regulations needed to facilitate large-scale carbon dioxide (CO 2 ) geologic storage (herein “storage”) and support deployment of commercial carbon capture and storage projects. The Plains CO 2 Reduction (PCOR) Partnership, led by the Energy & Environmental Research Center (EERC), is one of seven partnerships created by this program. The PCOR Partnership is publishing a series of best practices manuals for each of the four PCOR Partnership-defined primary technical elements of a storage site: site characterization; modeling and simulation; risk assessment; and monitoring, verification, and accounting. This document describes the risk assessment process for evaluating subsurface technical risks associated with a CO 2 storage project. This best practices manual identifies the key elements comprising a risk assessment for a CO 2 storage complex and defines important risk management terminology and technical factors that are unique to the geologic storage of CO 2 . It also provides best practices for implementing a risk assessment based on lessons learned from conducting risk assessments for storage complexes within the PCOR Partnership region. Case studies of these real-world examples, which highlight key aspects of applying the risk assessment process to storage projects, are provided to support the proposed best practices. The development of a best practice requires the execution of multiple projects where the knowledge gained and lessons learned are accumulated over time and integrated to yield a best practice. This development progression is an adaptive management process whereby best practices constantly evolve over time in response to knowledge gained and lessons learned. This document encompasses the current body-of-knowledge and best practices for applying a standardized risk assessment approach within risk management for storage projects. Application of these best practices will provide reliable and consistent standards for identifying project-related risks, analyzing the probabilities and potential impacts of these risks, evaluating which risks require treatment, and determining priority for treatment implementation. These best practices will continue to evolve and be refined over time as commercialization of the CO 2 storage industry proceeds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence

Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.

36 MATERIALS SCIENCE↗
NASA NTRS1969

Analysis of a Multiprocessor Guidance Computer

The design of the next generation of spaceborne digital computers is described. It analyzes a possible multiprocessor computer configuration. For the analysis, a set of representative space computing tasks was abstracted from the Lunar Module Guidance Computer programs as executed during the lunar landing, from the Apollo program. This computer performs at this time about 24 concurrent functions, with iteration rates from 10 times per second to once every two seconds. These jobs were tabulated in a machine-independent form, and statistics of the overall job set were obtained. It was concluded, based on a comparison of simulation and Markov results, that the Markov process analysis is accurate in predicting overall trends and in configuration comparisons, but does not provide useful detailed information in specific situations. Using both types of analysis, it was determined that the job scheduling function is a critical one for efficiency of the multiprocessor. It is recommended that research into the area of automatic job scheduling be performed.

Maltach, E. G.↗
NASA NTRS1976

Advanced space system concepts and their orbital support needs (1980 - 2000). Volume 1: Executive summary

The likely system concepts which might be representative of NASA and DoD space programs in the 1980-2000 time period were studied along with the programs' likely needs for major space transportation vehicles, orbital support vehicles, and technology developments which could be shared by the military and civilian space establishments in that time period. Such needs could then be used by NASA as an input in determining the nature of its long-range development plan. The approach used was to develop a list of possible space system concepts (initiatives) in parallel with a list of needs based on consideration of the likely environments and goals of the future. The two lists thus obtained represented what could be done, regardless of need; and what should be done, regardless of capability, respectively. A set of development program plans for space application concepts was then assembled, matching needs against capabilities, and the requirements of the space concepts for support vehicles, transportation, and technology were extracted. The process was pursued in parallel for likely military and civilian programs, and the common support needs thus identified.

Bekey, I.↗
NASA NTRS1984

Hidden-Line Computer Code

New, efficient solution minimizes run time. Approach based on approved theorem provides formal basis for assuring generality and rapid execution. Theorem does not directly address nuisance of square law growth. Analysis of algorithm shows it tends to avoid square-law growth, and rigorous testing verified algorithm tends to enjoy almost linear growth.

Hedgley, D. R., Jr.↗
NASA NTRS1983

Accelerometer-enhanced orbit control near the sun-earth L1 libration point

Because the halo-orbit about the sun-earth L(1) libration point in which the satellite ISEE-3 was maintained for nearly four years is unstable, a loose control scheme about a precomputed nominal path was implemented which required orbit maneuvers approximately every three months. Execution errors were minimized by processing on-board accelerometer telemetry data in real time, and adjusting the maneuvers. Because spacecraft vibrations caused oscillations in the accelerometer data, smoothing techniques were applied to provide accurate estimates of the performance of pulse mode thrusters employed in the spin stabilization of the spacecraft. It has been found that the processed accelerometer data has an average error of only + or - 1.3 per cent.

Muhonen, D. P.↗
NASA NTRS1987

Reverse time migration: A seismic processing application on the connection machine

The implementation of a reverse time migration algorithm on the Connection Machine, a massively parallel computer is described. Essential architectural features of this machine as well as programming concepts are presented. The data structures and parallel operations for the implementation of the reverse time migration algorithm are described. The algorithm matches the Connection Machine architecture closely and executes almost at the peak performance of this machine.

Fiebrich, Rolf-Dieter↗
NASA NTRS1987

Long term prediction of roll phase for an undisturbed spinning spacecraft

This paper describes the attitude control of the Pioneer 10 spacecraft since the loss of the sun-sensor signal in late 1983. It is necessary to control the attitude of the spin-stablized spacecraft so as to maintain communications with earth. Roll phase is calculated on earth using data from a science instrument on-board Pioneer 10, the imaging-photopolarimeter, which, along with its other functions, was designed to collect images of Jupiter during encounter in 1973. With calculation of instantaneous roll phase performed only once per week, the spacecraft roll angle can be predicted more than a week ahead for timing reorientation impulses. Attitude reorientation maneuvers based on roll phase predictions have been successfully executed for several years on Pioneer 10. Of 10 maneuvers analyzed in this paper, predictions were made for as many as 10 days in the future based on a roll-phase measurements spanning only 12 days of data. The average maneuver was planned by projecting the roll phase for 3 days (22,000 spacecraft revolutions) and resulted in a maneuver execution phase error of only 11 deg.

Smith, M. A.↗
NASA NTRS1993

Driving a car with custom-designed fuzzy inferencing VLSI chips and boards

Vehicle control in a-priori unknown, unpredictable, and dynamic environments requires many calculational and reasoning schemes to operate on the basis of very imprecise, incomplete, or unreliable data. For such systems, in which all the uncertainties can not be engineered away, approximate reasoning may provide an alternative to the complexity and computational requirements of conventional uncertainty analysis and propagation techniques. Two types of computer boards including custom-designed VLSI chips were developed to add a fuzzy inferencing capability to real-time control systems. All inferencing rules on a chip are processed in parallel, allowing execution of the entire rule base in about 30 microseconds, and therefore, making control of 'reflex-type' of motions envisionable. The use of these boards and the approach using superposition of elemental sensor-based behaviors for the development of qualitative reasoning schemes emulating human-like navigation in a-priori unknown environments are first discussed. Then how the human-like navigation scheme implemented on one of the qualitative inferencing boards was installed on a test-bed platform to investigate two control modes for driving a car in a-priori unknown environments on the basis of sparse and imprecise sensor data is described. In the first mode, the car navigates fully autonomously, while in the second mode, the system acts as a driver's aid providing the driver with linguistic (fuzzy) commands to turn left or right and speed up or slow down depending on the obstacles perceived by the sensors. Experiments with both modes of control are described in which the system uses only three acoustic range (sonar) sensor channels to perceive the environment. Simulation results as well as indoors and outdoors experiments are presented and discussed to illustrate the feasibility and robustness of autonomous navigation and/or safety enhancing driver's aid using the new fuzzy inferencing hardware system and some human-like reasoning schemes which may include as little as six elemental behaviors embodied in fourteen qualitative rules.

Pin, Francois G.↗
NASA NTRS2005

Java-based Graphical User Interface for MAVERIC-II

A computer program entitled "Marshall Aerospace Vehicle Representation in C II, (MAVERIC-II)" is a vehicle flight simulation program written primarily in the C programming language. It is written by James W. McCarter at NASA/Marshall Space Flight Center. The goal of the MAVERIC-II development effort is to provide a simulation tool that facilitates the rapid development of high-fidelity flight simulations for launch, orbital, and reentry vehicles of any user-defined configuration for all phases of flight. MAVERIC-II has been found invaluable in performing flight simulations for various Space Transportation Systems. The flexibility provided by MAVERIC-II has allowed several different launch vehicles, including the Saturn V, a Space Launch Initiative Two-Stage-to-Orbit concept and a Shuttle-derived launch vehicle, to be simulated during ascent and portions of on-orbit flight in an extremely efficient manner. It was found that MAVERIC-II provided the high fidelity vehicle and flight environment models as well as the program modularity to allow efficient integration, modification and testing of advanced guidance and control algorithms. In addition to serving as an analysis tool for techno logy development, many researchers have found MAVERIC-II to be an efficient, powerful analysis tool that evaluates guidance, navigation, and control designs, vehicle robustness, and requirements. MAVERIC-II is currently designed to execute in a UNIX environment. The input to the program is composed of three segments: 1) the vehicle models such as propulsion, aerodynamics, and guidance, navigation, and control 2) the environment models such as atmosphere and gravity, and 3) a simulation framework which is responsible for executing the vehicle and environment models and propagating the vehicle s states forward in time and handling user input/output. MAVERIC users prepare data files for the above models and run the simulation program. They can see the output on screen and/or store in files and examine the output data later. Users can also view the output stored in output files by calling a plotting program such as gnuplot. A typical scenario of the use of MAVERIC consists of three-steps; editing existing input data files, running MAVERIC, and plotting output results.

Seo, Suk Jai↗
NASA NTRS2010

Space Shuttle Program Primary Avionics Software System (PASS) Success Legacy - Quality and Reliability Date

Thsi slide presentation reviews the avionics software system on board the space shuttle, with particular emphasis on the quality and reliability. The Primary Avionics Software System (PASS) provides automatic and fly-by-wire control of critical shuttle systems which executes in redundant computers. Charts given show the number of space shuttle flights vs time, PASS's development history, and other charts that point to the reliability of the system's development. The reliability of the system is also compared to predicted reliability.

Orr, James K.↗
NASA NTRS2015

An Innovative Solution to NASA's NEO Impact Threat Mitigation Grand Challenge and Flight Validation Mission Architecture Development

This paper presents the results of a NASA Innovative Advanced Concept (NIAC) Phase 2 study entitled "An Innovative Solution to NASA's Near-Earth Object (NEO) Impact Threat Mitigation Grand Challenge and Flight Validation Mission Architecture Development." This NIAC Phase 2 study was conducted at the Asteroid Deflection Research Center (ADRC) of Iowa State University in 2012-2014. The study objective was to develop an innovative yet practically implementable mitigation strategy for the most probable impact threat of an asteroid or comet with short warning time (< 5 years). The mitigation strategy described in this paper is intended to optimally reduce the severity and catastrophic damage of the NEO impact event, especially when we don't have sufficient warning times for non-disruptive deflection of a hazardous NEO. This paper provides an executive summary of the NIAC Phase 2 study results. Detailed technical descriptions of the study results are provided in a separate final technical report, which can be downloaded from the ADRC website (www.adrc.iastate.edu).

NEO Impact Threat Mitigation↗
NASA NTRS2015

An Optimal Mitigation Strategy Against the Asteroid Impact Threat with Short Warning Time

This paper presents the results of a NASA Innovative Advanced Concept (NIAC) Phase 2 study entitled "An Innovative Solution to NASA's Near-Earth Object (NEO) Impact Threat Mitigation Grand Challenge and Flight Validation Mission Architecture Development." This NIAC Phase 2 study was conducted at the Asteroid Deflection Research Center (ADRC) of Iowa State University in 2012-2014. The study objective was to develop an innovative yet practically implementable mitigation strategy for the most probable impact threat of an asteroid or comet with short warning time (less than 5 years). The mitigation strategy described in this paper is intended to optimally reduce the severity and catastrophic damage of the NEO impact event, especially when we don't have sufficient warning times for non-disruptive deflection of a hazardous NEO. This paper provides an executive summary of the NIAC Phase 2 study results.

NEO Impact threat↗
NASA NTRS2017

Juno Orbit Determination Experience During First Year At Jupiter

The Juno spacecraft successfully inserted into a polar orbit around Jupiter on 5- July-2016. Since the Jupiter Orbit Insertion (JOI) maneuver, Juno has completed six orbits around Jupiter. The mission plan at the time of JOI was for Juno to perform two 53.5-day capture orbits before executing a Period Reduction Maneuver (PRM) to place the spacecraft into its intended 14-day science orbit. This maneuver was canceled due to a concern with the propulsion system. As a result, the Juno spacecraft will remain in its longer orbit period for rest of its mission. This paper discusses the Navigation Team’s experience: the orbit determination strategy and how it changed due to the cancellation of the PRM, challenges fitting the data during perijove, and how we reconstructed the trajectory during Juno’s first year in orbit.

Thompson, Paul↗
NASA NTRS2018

Radio-hosted Flight / Ground Interface for Operations Standardization

The interface between a spacecraft and its ground operations segment includes the flow of commands, configuration, and sequencing elements to the spacecraft, and the flow of telemetry and data products from the spacecraft. Creating and implementing a complete definition of this interface simplifies and standardizes mission operations, allowing easy sharing of operations personnel across missions. Early spacecraft featured a simple flight / ground interface (FGI) using hardware command decoding in the radio, driven by technological limitations of the time. Modern spacecraft use command and data handling (CDH) avionics on which flight software executes, which in turn controls and configures the mission, executes subsystem and instrument instructions, and implements critical fault protection actions. Deep space missions feature advanced operations software for running sequenced activities over a period of weeks, which allows them to function with only infrequent ground contact. This approach comes at the cost of increased complexity in the FGI, requiring expensive modifications to heritage flight software and ground systems. By hosting the interface in the radio instead of the CDH avionics, modern missions can approximate the FGI design simplicity of early spacecraft, with significant advantages for vendor competition, lowered costs, standardization of operations, and reduction of implementation risk.

Lock, Patricia D.↗
NASA NTRS2026

Integrated Computational Materials Engineering (ICME) Capability Maturity Levels for Ecosystems Enabling Digital Transformation

Digital engineering (DE) and integrated computational materials engineering (ICME) are widely recognized as critical enablers of faster, more affordable, and more reliable aerospace systems. However, many organizations have struggled to realize the promised return on investment (ROI) from digital initiatives. A primary reason is the absence of a shared, decision-focused framework that distinguishes simple digitization of existing workflows from true digital transformation that fundamentally changes how engineering decisions are made. This paper introduces an ICME capability maturity framework that fills this gap. The framework defines six cumulative ICME capability maturity levels (CMLs), explicitly tied to decision authority, engineering integration, optimization, and uncertainty management across material, process, structure, and performance scales. It is designed to complement established readiness metrics such as technology readiness levels (TRLs), manufacturing readiness levels (MRLs), and integration readiness levels (IRLs), by addressing a missing dimension: the conditions required for model-informed decision authority across scales. A unifying figure and capability table illustrate the six-level ICME Capability Maturity Framework, showing how organizations progress from digitization—with limited or negative ROI—to true digital transformation, where ICME-enabled workflows deliver measurable improvements in decision quality, cycle time, risk reduction, and reuse. The framework is intended for both technical practitioners and executive leadership, providing a common language to assess current state, guide roadmaps, align software ecosystem investments, and set realistic expectations for digital transformation outcomes. A regulatory-relevant statement clarifying the relationship between ICME capability and existing certification frameworks is provided.

ICME↗
NASA NTRS2026

Hydrology Copilot: A Cloud-Native Ai System for Hydrological Data Analysis

The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.

Data virtualization↗