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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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NASA NTRS1988

Launch team training system

A new approach to the training, certification, recertification, and proficiency maintenance of the Shuttle launch team is proposed. Previous training approaches are first reviewed. Short term program goals include expanding current training methods, improving the existing simulation capability, and scheduling training exercises with the same priority as hardware tests. Long-term goals include developing user requirements which would take advantage of state-of-the-art tools and techniques. Training requirements for the different groups of people to be trained are identified, and future goals are outlined.

Webb, J. T.↗

NASA NTRS1985

Eye-scan behavior in a flight simulation task as a function of level of training

The present study explored eye-scan behavior as a function of level of subject training. Oculometric (eye-scan) measures were recorded from each of ten subjects during training trials on a CRT-based flight simulation task. The task developed for the study incorporated subtasks representative of specific activities performed by pilots, but which could be performed at asymptotic levels within relatively short periods of training. Changes in eye-scan behavior were examined as initially untrained subjects developed skill in the task. Eye-scan predictors of performance on the task were found. Examination of eye-scan in proximity to selected task events revealed differences in the distribution of looks at the instruments as a function of level of training.

Comstock, J. R., Jr.↗

DOE OSTI2024

Enhanced Carbon Storage Forecasting via Cross-Geology Transfer Learning

Rapid simulation of the spatiotemporal evolution of pressure & saturation for SACROC 1. Neural operator was trained on only 153 simulation runs 2. Trained to account for heterogeneity and variations/uncertainties in engineering, fluids, and geology 3. Pressure forecast has less than 1% error 4. Saturation forecast has less than 2% error 5. Traditional simulator takes 1 hour for a single scenario, while neural operator takes less than 1 minute. Rapid simulation of the spatiotemporal evolution of pressure & saturation for IBDP 1. Transfer Learning was implemented on the SACROC-based Neural Operator that was trained on only 20 simulation runs for IBDP Site 2. SACROC and IBDP Sites have several significant differences in geology and engineering parameters. 3. Pressure forecast has less than 5 psi error 4. Saturation forecast has less than 7% error 5. Traditional simulator takes 1 hour for a single scenario, while neural operator takes less than 1 minute and only 20 simulations for training/validation.

Misra, Siddharth↗

NASA NTRS2017

Time-Varying Loads of Co-Axial Rotor Blade Crossings

The blade crossing event of a coaxial counter-rotating rotor is a potential source of noise and impulsive blade loads. Blade crossings occur many times during each rotor revolution. In previous research by the authors, this phenomenon was analyzed by simulating two airfoils passing each other at specified speeds and vertical separation distances, using the compressible Navier-Stokes solver OVERFLOW. The simulations explored mutual aerodynamic interactions associated with thickness, circulation, and compressibility effects. Results revealed the complex nature of the aerodynamic impulses generated by upperlower airfoil interactions. In this paper, the coaxial rotor system is simulated using two trains of airfoils, vertically offset, and traveling in opposite directions. The simulation represents multiple blade crossings in a rotor revolution by specifying horizontal distances between each airfoil in the train based on the circumferential distance between blade tips. The shed vorticity from prior crossing events will affect each pair of upperlower airfoils. The aerodynamic loads on the airfoil and flow field characteristics are computed before, at, and after each airfoil crossing. Results from the multiple-airfoil simulation show noticeable changes in the airfoil aerodynamics by introducing additional fluctuation in the aerodynamic time history.

Time-Varying Loads↗

NASA NTRS2022

Reduced-Gravity Simulator for Field Environments - Drone Augmented System

Astronauts need to go through extensive training on Earth before heading into space, and the more accurate the analogue, the better prepared they will be. NASA currently trains astronauts in simulated lower gravity environments in either the Neutral Buoyancy Laboratory or the Active Response Gravity Offload System (ARGOS), but neither of these systems allow for testing outdoors in field environments. NASA, in partnership with Aquiline Drones, mechanical engineering, biomedical engineering, and the Krenicki Arts and Engineering Institute, are working to develop a gravity offload device compatible with testing in field environments such as Desert Research and Technology Studies (Desert RATS). This report covers the technical requirements of the gravity offload device, the design choices and justifications, options for scalability, current progress, and future goals. Two separate teams were formed to respond to NASA’s solicitation, and this report covers the work of the weather balloon and drone side of the project. Using a weather balloon to passively offload the user’s weight and a drone to actively respond to changes using a series of sensors, this system should create a consistent and well balanced offloading force. The weather balloon and drone system is connected to a harness system to comfortably lift the user, offload at their center of gravity, and provide attachment points for some of the necessary sensors. Final testing proved the comfort of the harness, mobility of the user, and passive stability of the balloon offload to exceed all expectations of the team. The current system is a scaled model of what NASA would be able to implement with a maximum offload of 25 pounds. The ability to scale up to NASA’s desired value of 100 pounds is easily achievable with linear scaling of the current model. All systems should work similarly on scaled versions, but minor tweaking and enhanced controls would be needed for optimal performance.

Jason Lee↗

NASA NTRS1986

A technique for incorporating the NASA spacelab payload dedicated experiment processor software into the simulation system for the payload crew training complex

The feasibility of some off-the-shelf microprocessors and state-of-art software is assessed (1) as a development system for the principle investigator (pi) in the design of the experiment model, (2) as an example of available technology application for future PI's experiments, (3) as a system capable of being interactive in the PCTC's simulation of the dedicated experiment processor (DEP), preferably by bringing the PI's DEP software directly into the simulation model, (4) as a system having bus compatibility with host VAX simulation computers, (5) as a system readily interfaced with mock-up panels and information displays, and (6) as a functional system for post mission data analysis.

Bremmer, D. A.↗

NASA NTRSDate not supplied

Crew Medical Training to Progressively Enable EIMO

Background. Onboard medical capabilities have greatly expanded over the history of the US space program. Newly identified space-related medical conditions, technological advances, and longer mission durations have led to an increasing need for on-demand medical expertise. Lengthy communications delays, lack of resupply and evacuation opportunities on exploration-class missions place an ever-increasing burden on the crew to provide medical care. Having adequate knowledge, skills, and abilities (KSA) available is an essential component of successful Earth Independent Medical Operations (EIMO). Without appropriate crew training and KSA, cutting-edge medical equipment has little value. Presumably, the crew will include a qualified physician; however, if the physician is incapacitated, a non-physician crew medical officer (CMO) will be needed. While more crew time is needed for medical training, there will be concomitant increases in preflight training demands for vehicle system management, operations, science, and contingencies. In truly independent operations, onboard resources such as just-in-time training, mixed reality, decision support tools, and AI-enabled chatbot “consultants” will be needed to augment KSA. Overview. Because of crew time constraints, topical priorities must be determined for preflight training. Curricula should be developed that emphasize management of conditions with relatively high incidence and morbidity/mortality. Defining the required KSA levels to treat each condition is essential, but all crewmembers should have basic lifesaving skills. Procedural and diagnostic training on live patients and simulators should be prioritized over classroom lectures. Crews must be trained with onboard equipment, resources, mixed reality, and AI-based decision support tools. Mission simulations should include medical problems with/without ground support and with appropriate communication delays. Certification guidelines for each level of KSA must be established. Skills rapidly decay for non-physician CMO’s; both pre-flight and in-flight refresher training will be needed. During spaceflight just-in-time training, simulations, and onboard CME with crew physician can help retain skills. Discussion. Medical technology, simulation design, mixed reality, and AI are advancing at a dizzying rate. Recognizing the severe constraints on crew time, it is critical that astronaut training is highly efficient and adapted to keep pace with new innovations both pre-flight and during exploration missions. These challenges will be discussed during this panel session.

Jay Lemery↗

NASA NTRS1989

Space station Simulation Computer System (SCS) study for NASA/MSFC. Volume 3: Refined conceptual design report

The results of the refined conceptual design phase (task 5) of the Simulation Computer System (SCS) study are reported. The SCS is the computational portion of the Payload Training Complex (PTC) providing simulation based training on payload operations of the Space Station Freedom (SSF). In task 4 of the SCS study, the range of architectures suitable for the SCS was explored. Identified system architectures, along with their relative advantages and disadvantages for SCS, were presented in the Conceptual Design Report. Six integrated designs-combining the most promising features from the architectural formulations-were additionally identified in the report. The six integrated designs were evaluated further to distinguish the more viable designs to be refined as conceptual designs. The three designs that were selected represent distinct approaches to achieving a capable and cost effective SCS configuration for the PTC. Here, the results of task 4 (input to this task) are briefly reviewed. Then, prior to describing individual conceptual designs, the PTC facility configuration and the SSF systems architecture that must be supported by the SCS are reviewed. Next, basic features of SCS implementation that have been incorporated into all selected SCS designs are considered. The details of the individual SCS designs are then presented before making a final comparison of the three designs.

Source record↗

NASA NTRS1986

Flight simulator requirements for airline transport pilot training - An evaluation of motion system design alternatives

The effect of motion platform systems on pilot behavior is considered with emphasis placed on civil aviation applications. A dynamic model for human spatial orientation based on the physiological structure and function of the human vestibular system is presented. Motion platform alternatives were evaluated on the basis of the following motion platform conditions: motion with six degrees-of-freedom required for Phase II simulators and two limited motion conditions. Consideration was given to engine flameout, airwork, and approach and landing scenarios.

Lee, A. T.↗

DOE OSTI2025

Transfer learning nonlinear plasma dynamic transitions in low dimensional embeddings via deep neural networks

Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel, data-driven model reduction methods, coupled with detection of abnormal modes with plasma physics, opens a unique opportunity to identify plasma instabilities through automated construction of parsimonious models that can be tuned to balance accuracy and cost. Our fusion transfer learning (FTL) model demonstrates success in rapidly reconstructing nonlinear kink mode structures by learning from a limited amount of nonlinear simulation data. The knowledge transfer process leverages a pre-trained neural encoder–decoder network, initially trained on linear simulations, to effectively capture nonlinear dynamics. The low-dimensional embeddings extract the coherent structures of interest, while preserving the inherent dynamics of the complex system. Experimental results highlight FTL’s capacity to capture transitional behaviors and dynamical features in plasma dynamics—a task often challenging for conventional methods. The model developed in this study is generalizable and can be extended broadly through transfer learning to address various magnetohydrodynamics modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2003

Effect of Above Real Time Training and Post Flight Feedback in Training of Novice Pilots in a PC-Based Flight Simulator

The use of Post-Flight Feedback (PFFB) and Above Real-Time Training (ARTT) while training novice pilots to perform a coordinated level turn on a PC-based flight simulator was investigated. One group trained at 1.5 ARTT followed by an equal number of flights at 2.0 ARTT; the second group experienced Real Time Training (RTT). The total number of flights for both groups was equal. Each group was further subdivided into two groups one of which was provided PFFB while the other was not. Then, all participants experienced two challenging evaluation missions in real time. Performance was assessed by comparing root-mean-square error in bank-angle and altitude. Participants in the 1.512.0 ARTT No-PFFB sequence did not show improvement in performance across training sessions. An ANOVA on performance in evaluation flights found that the PFFB groups performed significantly better than those with No-PFFB. Also, the RTT groups performed significantly better than the ARTT groups. Data from two additional groups trained under a 2.011.5 ARTT PFFB and No-PFFB regimes were collected and combined with data from the previously Trainers, Real-time simulation, Personal studied groups and reanalyzed to study the computers, Man-in-the-loop simulation influence of sequence. An ANOVA on test trials found no significant effects between groups. Under training situations involving ARTT we recommend that appropriate PFFB be provided.

Khan, M. Javed↗

NASA NTRSDate not supplied

High Fidelity Aerospace Simulations at NASA Ames SimLabs

An overview of the simulation facilities at SimLabs at NASA Ames. Main focus is on the Vertical Motion Simulator or VMS, also brief descriptions of other simulators. Discussion of the history and general specifications of the VMS. Also, provide an overview of past research and training conducted on the simulators, and some future developments with a focus on AR/VR technologies.

flight simulators↗

DOE OSTI2023

Feature Interpretability

The feature interpretability code is a python module that interprets and analyzes neural networks trained on hydrodynamic simulation output in the form of numpy arrays. The code takes trained neural networks and extracts internal model states in the form of images. Additionally, tools for covariance analysis of network weights and predictions are provided. This code is built on the TensorFlow and PyTorch python libraries, and includes trained networks and example input data for demonstration purposes.

Callis, Skylar↗

NASA NTRS1980

Development of a multi-media crew-training program for the terminal configured vehicle mission simulator

This paper describes the work being done at the National Aeronautics and Space Administration's (NASA) Langley Research Center on the development of a multi-media crew-training program for the Terminal Configured Vehicle (TCV) Mission Simulator. Brief descriptions of the goals and objectives of the TCV Program and of the TCV Mission Simulator are presented. A detailed description of the training program is provided along with a description of the performance of the first group of four commercial pilots to be qualified in the TCV Mission Simulator.

Rhouck, J. A.↗

NASA NTRS1980

Development of a multi-media crew-training program for the Terminal Configured Vehicle Mission Simulator

Houck, J. A.↗

NASA NTRS1989

Slow shock characteristics as a function of distance from the X-line in the magnetotail

Both particle and MHD simulations are performed to study the characteristics of slow shocks in the magnetotail. The particle simulations indicate that switch-off shocks exhibit large amplitude rotational wave trains, while magnetotail slow shocks with an intermediate Mach number M(An) less than M(c) of about 0.98 do not display such rotational wave trains. The MHD simulations show that the spontaneous reconnection process in the near-earth plasma sheet leads to the formation of a pair of slow shocks tailward of the reconnection line (X-line). The properties of slow shocks are found to vary as a function of the distance from X-line due to the formation of plasmoid. Slow shocks in most regions of magnetotail are found to be nonswitch-off shocks with M(An) of less than 0.98. The present results are used to discuss the lack of large amplitude rotational wave trains at slow shocks in the deep magnetotail.

Lee, L. C.↗

DOE OSTI2025

Advancing AI-Driven Analysis in X-ray Absorption Spectroscopy: Spectral Domain Mapping and Universal Models

In recent years, rapid progress has been made in developing artificial intelligence (AI) and machine learning (ML) methods for X-ray absorption spectroscopy (XAS) analysis. Compared to traditional XAS analysis methods, AI/ML approaches offer dramatic improvements in efficiency and help eliminate human bias. To advance this field, we advocate an AI-driven XAS analysis pipeline that features several interconnected key building blocks: benchmarks, workflows, databases, and AI/ML models. Specifically, we present two case studies for XAS ML. In the first study, we demonstrate the importance of reconciling the discrepancies between simulation and experiment using spectral domain mapping (SDM). Our ML model, which is trained solely on simulated spectra, predicts an incorrect oxidation state trend for Ti atoms in a combinatorial zinc titanate film. After transforming the experimental spectra into a simulation-like representation using SDM, the same model successfully recovers the correct oxidation state trend. In the second study, we explore the development of universal XAS ML models that are trained on the entire periodic table, which enables them to leverage common trends across elements. Looking ahead, we envision that an AI-driven pipeline can unlock the potential of real-time XAS analysis to accelerate scientific discovery.

36 MATERIALS SCIENCE↗

NASA NTRS2013

Basic Operational Robotics Instructional System

The Basic Operational Robotics Instructional System (BORIS) is a six-degree-of-freedom rotational robotic manipulator system simulation used for training of fundamental robotics concepts, with in-line shoulder, offset elbow, and offset wrist. BORIS is used to provide generic robotics training to aerospace professionals including flight crews, flight controllers, and robotics instructors. It uses forward kinematic and inverse kinematic algorithms to simulate joint and end-effector motion, combined with a multibody dynamics model, moving-object contact model, and X-Windows based graphical user interfaces, coordinated in the Trick Simulation modeling environment. The motivation for development of BORIS was the need for a generic system for basic robotics training. Before BORIS, introductory robotics training was done with either the SRMS (Shuttle Remote Manipulator System) or SSRMS (Space Station Remote Manipulator System) simulations. The unique construction of each of these systems required some specialized training that distracted students from the ideas and goals of the basic robotics instruction.

Todd, Brian Keith↗