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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

NASA management of the Space Shuttle Program

The management system and management technology described have been developed to meet stringent cost and schedule constraints of the Space Shuttle Program. Management of resources available to this program requires control and motivation of a large number of efficient creative personnel trained in various technical specialties. This must be done while keeping track of numerous parallel, yet interdependent activities involving different functions, organizations, and products all moving together in accordance with intricate plans for budgets, schedules, performance, and interaction. Some techniques developed to identify problems at an early stage and seek immediate solutions are examined.

Peters, F.↗
NASA NTRS1983

Factors influencing aircraft ground handling performance

Problems associated with aircraft ground handling operations on wet runways are discussed and major factors which influence tire/runway braking and cornering traction capability are identified including runway characteristics, tire hydroplaning, brake system anomalies, and pilot inputs. Research results from tests with instrumented ground vehicles and aircraft, and aircraft wet runway accident investigation are summarized to indicate the effects of different aircraft, tire, and runway parameters. Several promising means are described for improving tire/runway water drainage capability, brake system efficiency, and pilot training to help optimize aircraft traction performance on wet runways.

Yager, T. J.↗
NASA NTRS1983

Review of factors affecting aircraft wet runway performance

Problems associated with aircraft operations on wet runways are discussed and major factors which influence tire/runway braking and cornering traction capability are identified including runway characteristics, tire hydroplaning, brake system anomalies, and pilot inputs. Research results from investigations conducted at the Langley Aircraft Landing Loads and Traction Facility and from tests with instrumented ground vehicles and aircraft are summarized to indicate the effects of different aircraft, tire, and runway parameters. Several promising means are described for improving tire/runway water drainage capability, brake system efficiency, and pilot training to help optimize aircraft traction performance on wet runways.

Yager, T. J.↗
NASA NTRS2000

National General Aviation Roadmap for a Small Aircraft Transportation System (SATS)

The National Aeronautics and Space Administration (NASA), Federal Aviation Administration, as well as state, industry, and academia partners have joined forces to pursue the NASA National General Aviation Roadmap leading to a Small Aircraft Transportation System (SATS). This long-term strategic undertaking has a goal to bring next-generation technologies and improve air access to small communities. The envisioned outcome is to improve travel between remote communities and transportation centers in urban areas by utilizing a new generation of single-pilot light planes for personal and business transportation between the nation's 5,400 public use general aviation airports. Current NASA investments in aircraft technologies are enabling industry to bring affordable, safe, and easy-to-use features to the marketplace, including "Highway in the Sky" glass cockpit operating capabilities, affordable crash worthy composite airframes, more efficient IFR flight training, and revolutionary engines. To facilitate this initiative, a comprehensive upgrade of public infrastructure must be planned, coordinated, and implemented within the framework of the national air transportation system. State partnerships are proposed to coordinate research support in key public infrastructure areas. Ultimately, SATS may permit more than tripling aviation system throughput capacity by tapping the under-utilized general aviation facilities to achieve the national goal of doorstep-to-destination travel at four times the speed of highways for the nation's suburban, rural, and remote communities.

Holmes, Bruce J.↗
NASA NTRS2018

Characterizing Aerodynamic Damping of a Supersonic Missile with CFD

Time accurate solutions of the Euler and Navier{Stokes equations are used as an approach to elucidate aerodynamic coefficients that include rigid body motion effects. The Army-Navy Finner geometry is used for work flow development due to its simple shape, inexpensive grid generation, and available literature that include aerodynamic damping derivatives obtained from ight test, wind tunnel tests, and computational fluid dynamics. Supersonic conditions for pitch and roll damping include angles of attack up to 90 deg. Aerodynamic responses due to rigid body maneuvers with prescribed wind incidence angles and body rates are computed using the DoD CREATE Kestrel and NASA FUN3D flow solvers. First, reference numerical and experimental results provide validation of aero- dynamic damping terms computed by traditional periodic motion in roll and pitch. Next, individual, impulse motion inputs provide the canonical responses for general input-output modeling based on classical superposition and convolution concepts. Finally, simultaneous impulse excitation of all inputs provides an efficient system identification training scenario for accurate aerodynamic model construction in state space via the NASA Sys- tem/Observer/Controller Identification Toolbox.

Shelton, Andrew↗
NASA NTRS2018

Arog Dual Purpose Habitat

Vision and Mission Statement Commonality of design between a space transit habitat and Mars surface habitat has the potential to reduce mission cost and risk by reducing crew training and increasing efficiency on arrival. Using the diverse array of talent and expertise available at the University of Michigan, BLiSS will explore the concept of commonality on every level of the design. System Definition We will design a habitation system that will house a human crew on either the Martian surface or a deep space transit. The system will be designed with a high degree of commonality between the two configurations. The team will validate the design through analysis and the construction of full scale mockups.

Nilton De Oliveira Renno↗
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 NTRSDate not supplied

ISS Payload Operations Training During the COVID-19 Pandemic: Impacts and Solutions

Introduction: The onset and the protracted duration of the COVID-19 pandemic brought a dramatic and rapid transformation to almost every aspect of humanity in 2020. The world’s space agencies and their missions were not immune to the unpredictable and wide-sweeping changes. One discipline principally affected was mission operations and the various groups supporting that critical function. Mission support teams, especially for complex and crewed missions like the International Space Station (ISS) were forced to rapidly rethink how and where control center staff performed their vital work. Operations training – an essential element to mission ops, had unique hurdles to overcome. Operations training is responsible for preparing astronaut crews for their scientific missions, providing recurring proficiency and currency material to flight controllers, as well as ensuring new team members are ready to join their colleagues on console. Every element of training was impacted by COVID restrictions. From orientation and introductory classes for new controllers, training for scientists and payload developers, to simulations and advanced lessons, critical On the Job Training (OJT) and final evaluations; all aspects faced new challenges. Operations trainers at NASA’s Marshall Space Flight Center (MSFC) in Huntsville, Alabama, were forced to rethink, retool, and become more efficient with trainees and training resources in order to continue supporting payload operations on the ISS. Impacts and Barriers to Operations Training: The pandemic started in the United States in early March 2020. Immediately, NASA mandated that the support for ISS real-time operations was critical. As a result, physical access to the ISS Payload Operations Center was restricted to only those people supporting console operations. Thus, use of on-site NASA facilities and any trainer to trainee face-to-face interaction was not authorized. Trainers and trainees had to quickly shift to 100% remote learning. In the short term, this was not a problem since NASA had already prepared Information Technology (IT) resources that facilitated remote access to mission resources like classes, documents and teleconferences. However, instructors quickly discovered the classes that they were accustomed to delivering in a large classroom environment did not translate well to remote teaching. The lack of non-verbal communication with the class became a major barrier to instruction. Another significant hurdle to operations training was the mandate that all simulations could only be held remotely. The logistics of planning and executing even small simulations proved to be challenging, almost impossible, due to IT technology and public internet limitations. With simulations essentially stopped, as well as the restrictions on most OJT training, trainees were essentially stopped in their advancement towards certification. An additional barrier that had to be overcome was the formal documentation of the curriculum objectives. Even though NASA had been deliberately transitioning to electronic documents for years, some organizations were still relying on “pen and ink” documents to formalize training completion. The logistics of implementing secure and controlled e-signature documents in a standardized, NASA approved format had to be addressed. Lesson Learned and Efficiencies: Once the various limitations were identified, ISS trainers at MSFC discussed, proposed, and prioritized new options and techniques. Transitioning to all electronic learning materials and certifications was a relevantly easy fix. Teaching courses to large and diverse groups took additional shifts in the NASA training paradigm. Likewise, methods and procedures for preparing astronauts for their missions were forced to be revised. Simulation supervisors and their teams were also forced to find more efficient techniques to provide realistic training experience. Communication and approvals from management was essential. In every case, the ISS payload operations instructors found novel and efficient solutions to all functions listed above. This paper will discuss the factors and solutions payloads operations trainers found to keep scientific research on the ISS flying forward to mission success.

ISS Operations↗
DOE OSTI2021

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.

Ma, Zhan↗
DOE OSTI2024

An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry

Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with ∼ 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers ∼ 20% shorter training time without any loss of accuracy.

Wang, Tianle [Brookhaven National Laboratory (BNL)↗
DOE OSTI2024

Lion Cub: Minimizing Communication Overhead in Distributed Lion

Communication overhead is a key challenge in distributed deep learning, especially on slower Ethernet intercon nects, and given current hardware trends, communication is likely to become a major bottleneck. While gradient compression techniques have been explored for SGD and Adam, the Lion optimizer has the distinct advantage that its update vectors are the output of a sign operation, enabling straightforward quantization. However, simply compressing updates for communication and using techniques like majority voting fails to lead to end-to-end speedups due to inefficient communication algorithms and reduced convergence. We analyze three factors critical to distributed learning with Lion: optimizing communication methods, identifying effective quantization methods, and assessing the necessity of momentum synchronization. Our findings show that quantization techniques adapted to Lion and selective momentum synchronization can significantly reduce communication costs while maintaining convergence. We combine these into Lion Cub, which enables up to 5x speedups in end-to-end training compared to Lion. This highlights Lion’s potential as a communication-efficient solution for distributed training.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2007

Development of Training Programs to Optimize Planetary Ambulation

Astronauts experience disturbances in functional mobility following their return to Earth due to adaptive responses that occur during exposure to the microgravity conditions of space flight. Despite significant time spent performing in-flight exercise routines, these training programs have not been able to mitigate postflight alterations in postural and locomotor function. Therefore, the goal of our two inter-related projects (NSBRI-ground based and ISS flight study, "Mobility") is to develop and test gait training programs that will serve to optimize functional mobility during the adaptation period immediately following space flight, thereby improving the safety and efficiency of planetary ambulation. The gait training program entails manipulating the sensory conditions of treadmill exercise to systematically challenge the balance and gait control system. This enhances the overall adaptability of locomotor function enabling rapid reorganization of gait control to respond to ambulation in different gravitational environments. To develop the training program, we are conducting a series of ground-based studies evaluating the training efficacy associated with variation in visual flow, body loading, and support surface stability during treadmill walking. We will also determine the optimal method to present training stimuli within and across training sessions to maximize both the efficacy and efficiency of the training procedure. Results indicate that variations in both visual flow and body unloading during treadmill walking leads to modification in locomotor control and can be used as effective training modalities. Additionally, the composition and timing of sensory challenges experienced during each training session has significant impact on the ability to rapidly reorganize locomotor function when exposed to a novel sensory environment. We have developed the capability of producing support surface variation during gait training by mounting a treadmill on a six-degree-of-freedom motion device. This hardware development will allow us to evaluate the efficacy of this type of training in conjunction with variation in visual flow and body unloading.

Bloomberg, J. J.↗
NASA NTRS1993

Self-attitude awareness training: An aid to effective performance in microgravity and virtual environments

This paper describes ongoing development of training procedures to enhance self-attitude awareness in astronaut trainees. The procedures are based on observations regarding self-attitude (perceived self-orientation and self-motion) reported by astronauts. Self-attitude awareness training is implemented on a personal computer system and consists of lesson stacks programmed using Hypertalk with Macromind Director movie imports. Training evaluation will be accomplished by an active search task using the virtual Spacelab environment produced by the Device for Orientation and Motion Environments Preflight Adaptation Trainer (DOME-PAT) as well as by assessment of astronauts' performance and sense of well-being during orbital flight. The general purpose of self-attitude awareness training is to use as efficiently as possible the limited DOME-PAT training time available to astronauts prior to a space mission. We suggest that similar training procedures may enhance the performance of virtual environment operators.

Parker, Donald E.↗
DOE OSTI2025

Efficient online quantum circuit learning with no upfront training

Optimization is a promising candidate for studying the utility of variational quantum algorithms (VQAs). However, evaluating cost functions using quantum hardware introduces runtime overheads that limit exploration. Surrogate-based methods can reduce calls to a quantum computer, yet existing approaches require hyperparameter pre-training and have been tested only on small problems. Here, we show that surrogate-based methods can enable successful optimization at scale, without pre-training, by using radial basis function interpolation (RBF) to construct an adaptive, hyperparameter-free surrogate. Using the surrogate as an acquisition function drives hardware queries to the vicinity of the true optima. For 16-qubit random 3-regular Max-Cut instances with the Quantum Approximate Optimization Algorithm (QAOA), our method outperforms state-of-the-art approaches, without considering their upfront training costs. Furthermore, we successfully optimize QAOA circuits for 127-qubit random Ising models on an IBM processor using 10 4 −10 5 measurements. Strong empirical performance demonstrates the promise of automated surrogate-based learning for large-scale VQA applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An Efficient Checkpointing System for Large Machine Learning Model Training

As machine learning models increase in size and complexity rapidly, the cost of checkpointing in ML training became a bottleneck in storage and performance (time). For example, the latest GPT-4 model has massive parameters at the scale of 1.76 trillion. It is highly time and storage consuming to frequently writes the model to checkpoints with more than 1 trillion floating point values to storage. This work aims to understand and attempt to mitigate this problem. First, we characterize the checkpointing interface in a collection of representative large machine learning/language models with respect to storage consumption and performance overhead. Second, we propose the two optimizations: i) A periodic cleaning strategy that periodically cleans up outdated checkpoints to reduce the storage burden; ii) A data staging optimization that coordinates checkpoints between local and shared file systems for performance improvement.

machine learning, artificial intelligence↗
NASA NTRS1994

Presentation of a Swedish study program concerning recruitment, selection and training of student air traffic controllers: The MRU project phase 1

The Director of the ANS Department has set up an objective for the efficiency of screening and training procedures for air traffic controller students which implies that all students admitted 'shall be considered to have the qualification for - and be given the means of - completing the training'. As a consequence, a study project has been established. It is run by the ANS Department with members from the Swedish CAA, in close cooperation with Uppsala University.

Haglund, Rune↗
NASA NTRS1995

Locomotion training of legged robots using hybrid machine learning techniques

In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible patent by NASA, Johnson Space Center. An alternative modular approach is also developed which uses separate controllers for each stage of the running stride. A self-organizing fuzzy-neural controller controls the height, distance and angular momentum of the stride. A CMAC-based controller controls the movement of the leg from the time the foot leaves the ground to the time of landing. Because the leg joints are controlled at each time step during flight, movement is smooth and obstacles can be avoided. Initial results indicate that this approach can yield fast, accurate results.

Simon, William E.↗
DOE OSTI2022

Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage

In this work, we present deep-learning-based surrogate models for CCUS developed with four different algorithms and a physics-framed two-phase flow problem involving displacement of water by CO 2 . The deep-learning models were trained using 3D datasets describing the pressure plume, CO 2 saturation plume, and water extraction rate generated by numerical simulation. The hyperparameters defining the architecture of the neural networks were optimized to determine the slimmest network size and training parameters that give the most efficient performance at the least training cost. To develop a robust model that closely mimics the governing physical laws, the discretized form of the two-phase fluid transport equation was used to formulate the supervised deep-learning task. The algorithms investigated in this study predicted the data to above 95% accuracy, with the multi-layer perceptron model demonstrating the best performance by balancing training speed, prediction time, and prediction accuracy with lean network capacity. Furthermore, the surrogate models simultaneously predict reservoir pressure and CO 2 saturation in every grid block, including the surface well extraction rate and bottomhole pressure, at all simulation times for a given static model realization in just a few seconds on a standard desktop computer. A key outcome of this study is that limits can be placed on network design parameters to avoid over designing neural networks, with associated efficiencies in training and prediction times. This is very useful because large volumes of data may be generated in CCUS projects and over-design of neural network architectures imposes penalties that are antithetical to the goal of near-real time forecasting.

58 GEOSCIENCES↗