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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Mentoring SFRM: A New Approach to International Space Station Flight Controller Training

The Mission Operations Directorate (MOD) of the Johnson Space Center is responsible for providing continuous operations support for the International Space Station (ISS). Operations support requires flight controllers who are skilled in team performance as well as the technical operations of the ISS. Space Flight Resource Management (SFRM), a NASA adapted variant of Crew Resource Management (CRM), is the competency model used in the MOD. ISS flight controller certification has evolved to include a balanced focus on development of SFRM and technical expertise. The latest challenge the MOD faces is how to certify an ISS flight controller (operator) to a basic level of effectiveness in 1 year. SFRM training uses a two-pronged approach to expediting operator certification: 1) imbed SFRM skills training into all operator technical training and 2) use senior flight controllers as mentors. This paper focuses on how the MOD uses senior flight controllers as mentors to train SFRM skills. Methods: A mentor works with an operator throughout the training flow. Inserted into the training flow are guided-discussion sessions and on-the-job observation opportunities focusing on specific SFRM skills, including: situational leadership, conflict management, stress management, cross-cultural awareness, self care and team care while on-console, communication, workload management, and situation awareness. The mentor and operator discuss the science and art behind the skills, cultural effects on skills applications, recognition of good and bad skills applications, recognition of how skills application changes subtly in different situations, and individual goals and techniques for improving skills. Discussion: This mentoring program provides an additional means of transferring SFRM knowledge compared to traditional CRM training programs. Our future endeavors in training SFRM skills (as well as other organization s) may benefit from adding team performance skills mentoring. This paper explains our mentoring approach and discusses its effectiveness and future applicability in promoting SFRM/CRM skills.

Huning, Therese↗
DOE OSTI2024

Geometric Measures of Trustworthiness for Machine Learning Predictions

his report details the findings from the research and investigation of Geometric Measures of Trustworthiness for Machine Learning Predictions. We explored the trustworthiness of machine learning (ML) models’ predictions using geometric measures to quantify the similarity of a query point with the training data. Predictive uncertainty in ML can originate from at least three sources: (1) Model uncertainty, which represents the uncertainty in model form (e.g. decision tree, vs neural network) and estimating the model parameters from the training data, (2) Data uncertainty, which represents the natural complexities of the data such as class overlap and inherent noise, and (3) Distributional uncertainty, which represents the mismatch between the training and operational distributions. The proposed measures focus on measuring and explaining the data and distributional uncertainties by measuring the relationships of operational data with the training data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Procedure Visualization to Augment Space Mission Training

The Intelligent Virtual Station (IVS) has been developed by the Smart Systems Research Laboratory at the NASA Ames Research Center as a solution to some of the training and operations challenges faced by organizations like the International Space Station training facilities and Mission Control engineering teams. At present, astronaut crews are constrained by limited access to physical mockups, which themselves have a built-in 1-g limitation. Mission operations team are faced with the daunting task of controlling the operations and maintenance of an ever-changing Station in space. Many operations teams create and follow textual procedures without the ability to visualize the given actions or alternatives. The NS allows users to easily generate and view procedures to enhance training and operations. Because training and mission operations are of crucial importance to the International Space Station and other similarly sophisticated programs, this paper is focused on the NS integrated procedure tool.

McIntosh, Dawn M.↗
DOE OSTI2024

A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics

Abstract Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully‐resolved climate simulations remain computationally intractable, policy makers must rely on coarse‐models to quantify risk for extremes. However, coarse models suffer from inherent bias due to the ignored “sub‐grid” scales. We propose a framework to non‐intrusively debias coarse‐resolution climate predictions using neural‐network (NN) correction operators. Previous efforts have attempted to train such operators using loss functions that match statistics. However, this approach falls short with events that have longer return period than that of the training data, since the reference statistics have not converged. Here, the scope is to formulate a learning method that allows for correction of dynamics and quantification of extreme events with longer return period than the training data. The key obstacle is the chaotic nature of the underlying dynamics. To overcome this challenge, we introduce a dynamical systems approach where the correction operator is trained using reference data and a coarse model simulation nudged toward that reference. The method is demonstrated on debiasing an under‐resolved quasi‐geostrophic model and the Energy Exascale Earth System Model (E3SM). For the former, our method enables the quantification of events that have return period two orders longer than the training data. For the latter, when trained on 8 years of ERA5 data, our approach is able to correct the coarse E3SM output to closely reflect the 36‐year ERA5 statistics for all prognostic variables and significantly reduce their spatial biases.

Barthel Sorensen, B.↗
DOE OSTI2025

Direct interpolative construction of the discrete Fourier transform as a matrix product operator

The quantum Fourier transform (QFT), which can be viewed as a reindexing of the discrete Fourier transform (DFT), has been shown to be compressible as a low-rank matrix product operator (MPO) or quantized tensor train (QTT) operator. However, the original proof of this fact does not furnish a construction of the MPO with a guaranteed error bound. Meanwhile, the existing practical construction of this MPO, based on the compression of a quantum circuit, is not as efficient as possible. We present a simple closed-form construction of the QFT MPO using the interpolative decomposition, with guaranteed near-optimal compression error for a given rank. This construction can speed up the application of the QFT and the DFT, respectively, in quantum circuit simulations and QTT applications. We also connect our interpolative construction to the approximate quantum Fourier transform (AQFT) by demonstrating that the AQFT can be viewed as an MPO constructed using a different interpolation scheme.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2009

Mentoring SFRM: A New Approach to International Space Station Flight Control Training

The Mission Operations Directorate (MOD) of the Johnson Space Center is responsible for providing continuous operations support for the International Space Station (ISS). Operations support requires flight controllers who are skilled in team performance as well as the technical operations of the ISS. Space Flight Resource Management (SFRM), a NASA adapted variant of Crew Resource Management (CRM), is the competency model used in the MOD. ISS flight controller certification has evolved to include a balanced focus on development of SFRM and technical expertise. The latest challenge the MOD faces is how to certify an ISS flight controller (Operator) to a basic level of effectiveness in 1 year. SFRM training uses a twopronged approach to expediting operator certification: 1) imbed SFRM skills training into all Operator technical training and 2) use senior flight controllers as mentors. This paper focuses on how the MOD uses senior flight controllers as mentors to train SFRM skills.

Huning, Therese↗
NASA NTRS1993

Advanced Technology Training System on Motor-Operated Valves

This paper describes how features from the field of Intelligent Tutoring Systems are applied to the Motor-Operated Valve (MOV) Advanced Technology Training System (ATTS). The MOV ATTS is a training system developed at Galaxy Scientific Corporation for the Central Research Institute of Electric Power Industry in Japan and the Electric Power Research Institute in the United States. The MOV ATTS combines traditional computer-based training approaches with system simulation, integrated expert systems, and student and expert modeling. The primary goal of the MOV ATTS is to reduce human errors that occur during MOV overhaul and repair. The MOV ATTS addresses this goal by providing basic operational information of the MOV, simulating MOV operation, providing troubleshooting practice of MOV failures, and tailoring this training to the needs of each individual student. The MOV ATTS integrates multiple expert models (functional and procedural) to provide advice and feedback to students. The integration also provides expert model validation support to developers. Student modeling is supported by two separate student models: one model registers and updates the student's current knowledge of basic MOV information, while another model logs the student's actions and errors during troubleshooting exercises. These two models are used to provide tailored feedback to the student during the MOV course.

Wiederholt, Bradley J.↗
NASA NTRS2014

A Historical Analysis of Crane Mishaps at Kennedy Space Center

Cranes and hoists are widely used in many areas. Crane accidents and handling mishaps are responsible for injuries, costly equipment damage, and program delays. Most crane accidents are caused by preventable factors. Understanding these factors is critical when designing cranes and preparing lift plans. Analysis of previous accidents provides insight into current recommendations for crane safety. Cranes and hoists are used throughout Kennedy Space Center to lift everything from machine components to critical flight hardware. Unless they are trained crane operators, most NASA employees and contractors do not need to undergo specialized crane training and may not understand the safety issues surrounding the use of cranes and hoists. A single accident with a crane or hoist can injure or kill people, cause severe equipment damage, and delay or terminate a program. Handling mishaps can also have a significant impact on the program. Simple mistakes like bouncing or jarring a load, or moving the crane down when it should go up, can damage fragile flight hardware and cause major delays in processing. Hazardous commodities (high pressure gas, hypergolic propellants, and solid rocket motors) can cause life safety concerns for the workers performing the lifting operations. Most crane accidents are preventable with the correct training and understanding of potential hazards. Designing the crane with human factors taken into account can prevent many accidents. Engineers are also responsible for preparing lift plans where understanding the safety issues can prevent or mitigate potential accidents. Cranes are widely used across many areas of KSC. Failure of these cranes often leads to injury, high damage costs, and significant delays in program objectives. Following a basic set of principles and procedures during design, fabrication, testing, regular use, and maintenance can significantly minimize many of these failures. As the accident analysis shows, load drops are often caused or influenced by human factors. Therefore, proper training and understanding of crane safety throughout the workforce is critical. It is important that the engineers designing the cranes, lift planners preparing the lift plans, operators performing the lifts, and training officers conducting the operator training all understand the problems that can happen with cranes and how to ensure the safety of the workforce and equipment being lifted.

Materials Handling↗
NASA NTRS1989

Work, exercise, and space flight. 1: Operations, environment, and effects of spaceflight

The selection, training, and operations of space flight impose significant physical demands which seem to be adequately met by the existing physical training facilities and informal individual exercise programs. The professional astronaut population has, by selection, better than average health and physical capacity. The essentials of life on earth are adequately met by the spacecraft. However, as the human body adapts to weightlessness, it is compromised for the usual life on earth, but readaptation is rapid. Long term flight without countermeasures will produce major changes in the cardiovascular, respiratory, musculoskeletal and neuromuscular systems. There is strong theoretical and experimental evidence from 1-g studies and limited in-flight evidence to believe that exercise is a key counter-measure to many of these adaptations.

Thornton, William↗
NASA NTRS2011

The Final Count Down: A Review of Three Decades of Flight Controller Training Methods for Space Shuttle Mission Operations

Operations of human spaceflight systems is extremely complex; therefore, the training and certification of operations personnel is a critical piece of ensuring mission success. Mission Control Center (MCC-H), at the Lyndon B. Johnson Space Center in Houston, Texas, manages mission operations for the Space Shuttle Program, including the training and certification of the astronauts and flight control teams. An overview of a flight control team s makeup and responsibilities during a flight, and details on how those teams are trained and certified, reveals that while the training methodology for developing flight controllers has evolved significantly over the last thirty years the core goals and competencies have remained the same. In addition, the facilities and tools used in the control center have evolved. Changes in methodology and tools have been driven by many factors, including lessons learned, technology, shuttle accidents, shifts in risk posture, and generational differences. Flight controllers share their experiences in training and operating the space shuttle. The primary training method throughout the program has been mission simulations of the orbit, ascent, and entry phases, to truly train like you fly. A review of lessons learned from flight controller training suggests how they could be applied to future human spaceflight endeavors, including missions to the moon or to Mars. The lessons learned from operating the space shuttle for over thirty years will help the space industry build the next human transport space vehicle.

Dittermore, Gary↗
DOE OSTI2020

U.S. Indirect and Direct Potable Water Reuse Systems (2020)

Collection of proposed, operating, demonstration, and pilot study potable reuse systems across the U.S. as of 2020. Information includes project name, location, augmentation type, capacity, year operational, treatment train, and source water type. References are included in this submission. As potable reuse facilities undergo expansions or upgrades, treatment trains and capacities are subject to change. Operational status may also change. Treatment trains listed are reported to the detail that the source provides. Type and doses of chemical post-treatment or chlorination is often not specified. Please see README section in spreadsheet for definitions of acronyms used.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1994

Interactive orbital proximity operations planning system instruction and training guide

This guide instructs users in the operation of a Proximity Operations Planning System. This system uses an interactive graphical method for planning fuel-efficient rendezvous trajectories in the multi-spacecraft environment of the space station and allows the operator to compose a multi-burn transfer trajectory between orbit initial chaser and target trajectories. The available task time (window) of the mission is predetermined and the maneuver is subject to various operational constraints, such as departure, arrival, spatial, plume impingement, and en route passage constraints. The maneuvers are described in terms of the relative motion experienced in a space station centered coordinate system. Both in-orbital plane as well as out-of-orbital plane maneuvering is considered. A number of visual optimization aids are used for assisting the operator in reaching fuel-efficient solutions. These optimization aids are based on the Primer Vector theory. The visual feedback of trajectory shapes, operational constraints, and optimization functions, provided by user-transparent and continuously active background computations, allows the operator to make fast, iterative design changes that rapidly converge to fuel-efficient solutions. The planning tool is an example of operator-assisted optimization of nonlinear cost functions.

Grunwald, Arthur J.↗
DOE OSTI2023

Methods for Analysis and Quantification of Power System Resilience

This paper summarizes the report prepared by an IEEE PES Task Force. Resilience is a fairly new technical concept for power systems, and it is important to precisely delineate this concept for actual applications. As a critical infrastructure, power systems have to be prepared to survive rare but extreme incidents (natural catastrophes, extreme weather events, physical/cyber-attacks, equipment failure cascades, etc.) to guarantee power supply to the electricity-dependent economy and society. Thus, resilience needs to be integrated into planning and operational assessment to design and operate adequately resilient power systems. Quantification of resilience as a key performance indicator is important, together with costs and reliability. Quantification can analyze existing power systems and identify resilience improvements in future power systems. Given that a 100% resilient system is not economic (or even technically achievable), the degree of resilience should be transparent and comprehensible. Several gaps are identified to indicate further needs for research and development.

42 ENGINEERING↗
NASA NTRS2014

LADEE Multi-Domain Simulation

The Lunar Atmosphere Dust Environment Explorer (LADEE) was a small explorer class spacecraft that was launched on Sept 7, 2013 and that was de-orbited and successfully impacted the Moons surface on April 17, 2014 after completing all of the mission objectives. The low-cost rapidly prototyped hardware design used for the spacecraft was extend to the development of the software base. To achieve this goal, a Model Based Design approach was utilized to develop the onboard flight software, and out of this development a model based multipurpose simulator was created of the LADEE spacecraft and its mission environment. This simulator extended the traditional function of propagating the vehicle's kinematic and rotational states and included the electrical and thermal states propagation. Traditionally, these domains are handled by domain specific high fidelity simulations that use the states histories from other domains as input. By reducing the fidelity and abstracting the relevant features being monitored and controlled by the flight software, it was possible to model the coupling across these domains resulting in more accurate overall system behavior. A faster than real-time workstation (WSIM) version of the LADEE simulator was used to develop and test the software control algorithms in the Simulink environment. To maximize the performance of the simulation, modeling knobs were introduced to reduce the resolution of the some of the domains models when the effects of that domain were not significant for the scope of that simulation. The automatic code generation feature in Simulink was used to port the simulation to several real-time environments to support Processor-in-the-Loop (PIL) and Hardware-in-the-Loop (HIL) testing, verification and validation. The real-time environment required that the design of each of the domain models be deterministic as possible in the time required to perform all of the calculations to update its states. The simulation interface was designed to be compatible with the command interface employed by the LADEE mission operation team. The WSIM, PIL, and HIL simulators thus used a common interface and thus were used for flight software testing, for mission operations personnel training (nominal and off-nominal operations) prior to the mission and to perform command sequence verification during the mission. This presentation will look at the modeling strategies used to create a common interface to the simulator and to model and couple multiple domains within the simulation, the results of those strategies, and the lessons learned.

Multi-Domain↗
DOE OSTI2025

Advanced Transmission Technologies – GETs and HPCs Session 2: Advanced Power Flow Control and Transmission Topology Optimization

The INL TADA GETs Cohort Session 2, held on November 7, 2025, conducted in collaboration with ScottMadden, focused on two core Advanced Transmission Technologies (ATTs): Advanced Power Flow Control (APFC) and Transmission Topology Optimization (TTO). These technologies are pivotal in enhancing grid flexibility, reliability, and cybersecurity resilience. APFC, particularly through modular FACTS devices like Modular Static Synchronous Series Compensators (M-SSSCs), enables dynamic voltage injection to reroute power flows. The session highlighted the deployment benefits of APFC, such as rapid installation, minimal civil works, and re-deployability. Regulatory drivers like FERC Order 2023 mandate the inclusion of Grid-Enhancing Technologies (GETs) in interconnection studies. Case studies from Central Hudson, CAISO, and National Grid (UK) demonstrated APFC’s effectiveness in congestion relief and cost savings. The session also addressed cybersecurity concerns, including firmware vulnerabilities, SCADA integration risks, and supply chain dependencies. Participants engaged in interactive exercises to rank cybersecurity and supply chain risks, emphasizing the need for robust digital assurance strategies. TTO involves software-based reconfiguration of transmission networks to optimize power flow without new infrastructure. The session showcased its operational value, with examples from SPP, PJM, and MISO showing significant congestion cost reductions. Cybersecurity vulnerabilities were discussed, particularly in API security and software supply chains, referencing incidents like SolarWinds and attacks on Danish utilities. Digital assurance exercises explored worst-case scenarios, attack paths, and mitigation responsibilities between vendors and utilities. Reliability challenges such as algorithm stability, vendor dependency, and operator trust were also examined. Cross-cutting themes emphasized the importance of digital assurance tools, including Software Bills of Materials (SBOMs) and hardware-in-loop testing. Human performance, training, and operational confidence were identified as critical enablers of technology adoption. The session concluded with a preview of Session 3, which will focus on High Performance Conductors (HPCs) and risk-based cybersecurity tools. Session 2 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Efficient Training of Deep Neural Operator Networks via Randomized Sampling

Neural operators (NOs) employ deep neural networks to learn the mappings between infinitedimensional function spaces. Deep operator network (DeepONet), a popular NO architecture, has demonstrated success in the real-time prediction of complex dynamics across various scientific and engineering applications. In this work, we introduce a random sampling technique to be adopted during the training of DeepONet, aimed at improving the generalization ability of the model, while significantly reducing the computational time. The proposed approach targets the trunk network of the DeepONet model that outputs the basis functions corresponding to the spatiotemporal locations of the bounded domain on which the physical system is defined. While constructing the loss function, DeepONet training traditionally considers a uniform grid of spatiotemporal points at which all the output functions are evaluated for each iteration. This approach leads to a larger batch size, resulting in poor generalization and increased memory demands, due to the limitations of the stochastic gradient descent (SGD) optimizer. The proposed random sampling over the inputs of the trunk net mitigates these challenges, improving generalization and reducing the memory requirements during training, resulting in significant computational gains. We validate our hypothesis through three benchmark examples, demonstrating substantial reductions in training time while achieving comparable or lower overall test errors relative to the traditional training approach. Our results indicate that incorporating randomization in the trunk network inputs during training enhances the efficiency and robustness of DeepONet, offering a promising avenue for improving the framework’s performance in modeling complex physical systems.

Karumuri, Sharmila [Department of Civil & Systems ↗