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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 613 records · Page 34

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NASA NTRS2012

Simulation Modeling Requirements for Loss-of-Control Accident Prevention of Turboprop Transport Aircraft

In-flight loss of control remains the leading contributor to aviation accident fatalities, with stall upsets being the leading causal factor. The February 12, 2009. Colgan Air, Inc., Continental Express flight 3407 accident outside Buffalo, New York, brought this issue to the forefront of public consciousness and resulted in recommendations from the National Transportation Safety Board to conduct training that incorporates stalls that are fully developed and develop simulator standards to support such training. In 2010, Congress responded to this accident with Public Law 11-216 (Section 208), which mandates full stall training for Part 121 flight operations. Efforts are currently in progress to develop recommendations on implementation of stall training for airline pilots. The International Committee on Aviation Training in Extended Envelopes (ICATEE) is currently defining simulator fidelity standards that will be necessary for effective stall training. These recommendations will apply to all civil transport aircraft including straight-wing turboprop aircraft. Government-funded research over the previous decade provides a strong foundation for stall/post-stall simulation for swept-wing, conventional tail jets to respond to this mandate, but turboprops present additional and unique modeling challenges. First among these challenges is the effect of power, which can provide enhanced flow attachment behind the propellers. Furthermore, turboprops tend to operate for longer periods in an environment more susceptible to ice. As a result, there have been a significant number of turboprop accidents as a result of the early (lower angle of attack) stalls in icing. The vulnerability of turboprop configurations to icing has led to studies on ice accumulation and the resulting effects on flight behavior. Piloted simulations of these effects have highlighted the important training needs for recognition and mitigation of icing effects, including the reduction of stall margins. This paper addresses simulation modeling requirements that are unique to turboprop transport aircraft and highlights the growing need for aerodynamic models suitable for stall training for these configurations. A review of prominent accidents that involved aerodynamic stall is used to illustrate various modeling features unique to turboprop configurations and the impact of stall behavior on susceptibility to loss of control that has led to new training requirements. This is followed by an overview of stability and control behavior of straight-wing turboprops, the related aerodynamic characteristics, and a summary of recent experimental studies on icing effects. In addition, differences in flight dynamics behavior between swept-wing jets and straight-wing turboprop configurations are discussed to compare and contrast modeling requirements. Specific recommendations for aerodynamic models along with further research needs and data measurements are also provided. 1

Crider, Dennis↗

DOE OSTI2021

Adaptive machine learning for time-varying systems: low dimensional latent space tuning

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a novel method of adaptive ML for time-varying systems. Our approach is to map very high (N ≈ 100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N ≈ 2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly large parameter space systems and to track their evolution in real time based on feedback without massive new data sets for re-training. We demonstrate that our method can accurately predict and track the phase space of charged particle beams at various locations in a particle accelerator by adaptively adjusting in real-time while the unknown input beam distribution of the accelerator is changing in shape, charge, and offset and while the RF system of the accelerator itself is also changing in an unpredictable way. For FACET-II we demonstrate that such an approach has the potential to use transverse deflecting cavity and energy spread spectrum beam measurements to accurately predict 2D projections of the 6D phase space of the electron beam at the plasma wakefield acceleration interaction point where such diagnostics are unavailable.

47 OTHER INSTRUMENTATION↗

NASA NTRS1990

Shuttle rendezvous and proximity operations

Shuttle rendezvous and proximity operations trajectory control techniques are reviewed and it is noted that they have been affected by many factors including Shuttle system design constraints such as limited forward RCS and single point radar failures. Crew training requirements and mission operations constraints such as large launch windows, flexibility, and contingency profile interrupts are also integral factors. The resulting trajectory control design primarily uses ground support for the launch and orbit adjust phases, standardized crew techniques utilizing onboard software for the relative navigation flight phase, and mission unique manual control for a flexible proximity operations phase.

Pearson, Don J.↗

NASA NTRS1992

DSN acquisition of Magellan high-rate telemetry data

The Magellan Project levied the stringent requirement of a 98 percent high-rate telemetry data capture rate on the Deep Space Network (DSN) during the Magellan Prime Mapping Mission. To meet this requirement, the DSN undertook extensive development of the DSN Telemetry System, as well as extensive DSN operation planning and test and training. In actuality, the DSN substantially exceeded the requirement by achieving a Prime Mapping Mission high-rate telemetry data capture rate of 99.14 percent. This article details the DSN telemetry system development, and DSN operations planning and test and training. In addition, the actual high-rate telemetry data outages are comprehensively presented and analyzed.

Berman, A. L.↗

NASA NTRS1996

Translating Research Into Airline Practice: Case Studies In Collaboration

Airline training departments are avid customers for research that will help them enhance the effectiveness of training and the safety of flight operations. However, various factors often make it difficult for training department managers to draw upon the large body of human factors research, e.g.: research may not address the specific questions facing the training departments, the research literature may not be in a form that training managers can readily interpret, researchers' recommendations may be too expensive or impractical to implement, etc. This panel will discuss ways in which researchers can work with training departments to design research and translate findings into products that airlines can use readily. This collaboration is most effective when it is an integral part of the study from its inception. To illustrate the process of collaboration we will use as a case study the recently completed LOFT (Line Oriented Flight Training) Debriefing research project. We will summarize the findings from that study and discuss how we translated those findings into two training tools: a manual on how to facilitate LOFT debriefings and a video that illustrates facilitation techniques in a realistically enacted debriefing. In some cases, instead of starting a new research project, training department needs can be addressed by reviewing the existing research literature and using expert opinion to develop products that specifically address those needs. To illustrate this approach we will discuss a recent informal working group of scientists and airline personnel that met to develop training material to enhance situation awareness. This group reviewed scientific literature and ASRS (Aviation Safety Reporting System) reports, analyzed contributing factors, and produced a model for managing situation awareness.

Dismukes, R. Key↗

NASA NTRS2002

Method and Apparatus for Encouraging Physiological Self-Regulation Through Modulation of an Operator's Control Input to a Video Game or Training Simulator

Apparatus and methods for modulating the control authority (i.e., control function) of a computer simulation or game input device (e.g., joystick, button control) using physiological information so as to affect the user's ability to impact or control the simulation or game with the input device. One aspect is to use the present invention, along with a computer simulation or game, to affect physiological state or physiological self-regulation according to some programmed criterion (e.g., increase, decrease, or maintain) in order to perform better at the game task. When the affected physiological state or physiological self-regulation is the target of self-regulation or biofeedback training, the simulation or game play reinforces therapeutic changes in the physiological signal(s).

Olafur S Palsson↗

DOE OSTI2024

Application of Partial Least Squares Approaches to Pyroprocessing ER Data

Multivariate approaches show promise for application to process monitoring for safeguards of pyroprocessing. Past MPACT work explored the application of Principal Component Analysis (PCA) to detect off-normal conditions in pyroprocessing electrorefiner (ER) data from in the Hot Fuel Examination Facility (HFEF) at Idaho National Laboratory (INL) known as the Scalable Pyrochemical Recycling testbed (SPyRe) ER. PCA, however, does not consider the output variables. In FY24, multivariate analysis was extended from PCA to Partial Least Squares (PLS) analysis. PLS maximizes the variance between both the input signals and output variables. In the case of this work, PLS was applied in two different manners: Predictive PLS and Discriminant PLS. Predictive PLS maximizes the covariance between the process variables of the ER and the measured U concentration from in-situ voltammetry. Discriminant PLS maximizes the covariance between the process variables and a set of training process “states” such as known off-normal conditions. By projecting into the latent variable space in PLS, the process variables can be regressed onto the outputs and predictions can be made for new data sets. In this work, by applying predictive PLS, a penalized non-linear PLS approach was able to make predictions of concentration based on test and training data and detect when operations were off-normal. However, the predictive PLS does not classify the signals to which off-normal operations are attributable. Discriminant PLS can be used to classify off-normal operations but is inadequate to properly classify specific off-normal classes like power supply faults when the Discriminant PLS model is only specifically trained to detect that off-normal class. When all faults are trained against the observation data, all three operational classes are accurately classified and distinguished. Thus, future application of latent variable techniques should not select any given method, but should use a mixture of PCA, Predictive PLS, and Discriminant PLS.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2004

Star City, Russia Medical Operations

Since the beginning of the NASA/Mir missions, NASA has had astronauts in training at the Gagarin Cosmonaut Training Center (GCTC), also known as Star City, with crewmembers currently there to train for the International Space Station missions. Agreements have been reached with all International Partners that allow the crewmember's parent agency to provide a flight surgeon to oversee crewmember health and safety during training away from home. NASA Medical Operations through the Bioastronautics Contract employs flight surgeons to provide medical support for U.S. crewmembers and their support staff. This poster presentation reviews the aspects of NASA medical operations at Star City.

Chandler, Michael R.↗

NASA NTRS2003

Surveillance system and method having parameter estimation and operating mode partitioning

A system and method for monitoring an apparatus or process asset including partitioning an unpartitioned training data set into a plurality of training data subsets each having an operating mode associated thereto; creating a process model comprised of a plurality of process submodels each trained as a function of at least one of the training data subsets; acquiring a current set of observed signal data values from the asset; determining an operating mode of the asset for the current set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a current set of estimated signal data values from the selected process submodel for the determined operating mode; and outputting the calculated current set of estimated signal data values for providing asset surveillance and/or control.

Bickford, Randall L.↗

NASA NTRSDate not supplied

Using Earth-based Operational Field Tests as High-Fidelity Analogs for Planetary Surface Exploration

NASA is preparing to land the first woman and first person of color on the Moon within the next decade and establish a permanent sustainable human presence before sending humans onto Mars. To ensure the success of these missions, NASA has performed operational testing in terrestrial, aquatic, and laboratory analog environments that simulate Lunar and Martian environmental characteristics to evaluate exploration concepts of operations (ConOps), engineering design requirements, science support needs, mission operations techniques, and crew training. Terrestrial analogs include Desert Research and Technology Studies (D-RATS), Biologic Analog Science Associated with Lava Terrains (BASALT), and Next Space Technologies for Exploration Partnerships (NextSTEP)Habitat Ground Testing. Aquatic analogs include NASA Extreme Environment Mission Operations (NEEMO) and Pavilion Lake Research Project (PLRP).Laboratory analogs include the Neutral Buoyancy Laboratory (NBL), Active Response Gravity Offload System(ARGOS), rock yards, and virtual and hybrid reality simulation environments. While no single Earth-based analog environment is perfect for simulating all characteristics of other planetary surfaces, testing across multiple locations leverages the strengths of each to provide an integrated understanding of how to best conduct real spaceflight surface exploration missions.

B A Janoiko↗

NASA NTRS2011

Prescribed Travel Schedules for Fatigue Management

The NASA Fatigue Management Team is developing recommendations for managing fatigue during travel and for shift work operations, as Clinical Practice Guidelines for the Management of Circadian Desynchrony in ISS Operations. The Guidelines provide the International Space Station (ISS ) flight surgeons and other operational clinicians with evidence-based recommendations for mitigating fatigue and other factors related to sleep loss and circadian desynchronization. As much international travel is involved both before and after flight, the guidelines provide recommendations for: pre-flight training, in-flight operations, and post-flight rehabilitation. The objective of is to standardize the process by which care is provided to crewmembers, ground controllers, and other support personnel such as trainers, when overseas travel or schedule shifting is required. Proper scheduling of countermeasures - light, darkness, melatonin, diet, exercise, and medications - is the cornerstone for facilitating circadian adaptation, improving sleep, enhancing alertness, and optimizing performance. The Guidelines provide, among other things, prescribed travel schedules that outline the specific implementation of these mitigation strategies. Each travel schedule offers evidence based protocols for properly using the NASA identified countermeasures for fatigue. This presentation will describe the travel implementation schedules and how these can be used to alleviate the effects of jet lag and/or schedule shifts.

Whitmire, Alexandra↗

NASA NTRS1997

The Challenges of Field Testing the Traffic Management Advisor (TMA) in an Operational Air Traffic Control Facility

The Traffic Management Advisor (TMA), the sequence and schedule tool of the Center/TRACON Automation System (CTAS), was evaluated at the Fort Worth Center (ZFW) in the summer of 1996. This paper describes the challenges encountered during the various phases of the TMA field evaluation, which included system (hardware and software) installation, personnel training, and data collection. Operational procedures were developed and applied to the evaluation process that would ensure air safety. The five weeks of field evaluation imposed minimal impact on the hosting facility and provided valuable engineering and human factors data. The collection of data was very much an opportunistic affair, due to dynamic traffic conditions. One measure of the success of the TMA evaluation is that, rather than remove TMA after the evaluation until it could be fully implemented, the prototype TMA is in continual use at ZFW as the fully operational version is readied for implementation.

Hoang, Ty↗

NASA NTRS2002

Crew Training STS-110

The crewmembers are shown being suited for the STS-110 flight. The STS-110 crews are shown in training for four EVA's on the International Space Station. The crewmembers consist of: Michael J. Bloomfield, mission commander; Stephen N. Frick, pilot; and mission specialists: Ellen Ochoa, Lee M.E. Morin, Rex J. Walheim, Steven L. Smith, and Jerry Ross. Crew ascent middeck operations and Orbiter Skills Training in a fixed Based Simulator are the training areas shown. The STS-110 crew and Expedition four are seen during training at the Johnson Space Center Space Station Training Facility (SSTF). A photo session of the crew is also presented.

Source record↗

DOE OSTI2022

Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network

The study of solute transport in porous media is of interest in many chemical engineering systems. Some example applications include packed bed catalytic reactors, filtration devices, and batteries. The pore scale modeling of these systems is time consuming and may require large computing resources, for this reason computational fluid dynamics (CFD) simulations are not practical if a large number of simulations is required, like in multiscale modeling, where a model at a large scale calls for pore scale simulations. It has been shown that neural networks can be trained with a dataset of flow simulations and then predict fields orders of magnitude faster, and with less computational resources, in new domains. However, it is crucial to provide the neural network with an effective description of the domain and the undergoing operating conditions to be able to train models that generalize accurately in unseen samples. Therefore, research is needed to employ neural networks in new complex systems. The appropriate training of a network for predicting coupled flow and solute transport processes is an outstanding problem due to the complex interplay between geometry and operating conditions. In this work, we train a multi scale convolutional neural network (MSNet) with a diverse dataset of simulations of transport and chemical reaction in porous media to predict the local concentration fields in images of porous media. Our dataset contains a wide diversity of sphere pack arrangements under different operating conditions (Péclet and Reynolds numbers). Further, we train a robust model by employing different input descriptors that represent the medium and the different operating conditions of each system. Our trained model is able to provide nearly instantaneous predictions, compared to around twenty hours of the CFD workflow, with less than 3.5% error on new geometries and transport conditions. Thus the model could be easily integrated in a multiscale workflow where fast response is needed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2017

Autonomous Diagnostic Imaging Performed by Untrained Operators using Augmented Reality as a Form of "Just-in-Time" Training

We will address the Human Factors and Performance Team, "Risk of performance errors due to training deficiencies" by improving the JIT training materials for ultrasound and OCT imaging by providing advanced guidance in a detailed, timely, and user-friendly manner. Specifically, we will (1) develop an audio-visual tutorial using AR that guides non-experts through an abdominal trauma ultrasound protocol; (2) develop an audio-visual tutorial using AR to guide an untrained operator through the acquisition of OCT images; (3) evaluate the quality of abdominal ultrasound and OCT images acquired by untrained operators using AR guidance compared to images acquired using traditional JIT techniques (laptop-based training conducted before image acquisition); and (4) compare the time required to complete imaging studies using AR tutorials with images acquired using current JIT practices to identify areas for time efficiency improvements. Two groups of subjects will be recruited to participate in this study. Operator-subjects, without previous experience in ultrasound or OCT, will be asked to perform both procedures using either the JIT training with AR technology or the traditional JIT training via laptop. Images acquired by inexperienced operator-subjects will be scored by experts in that imaging modality for diagnostic and research quality; experts will be blinded to the form of JIT used to acquire the images. Operator-subjects also will be asked to submit feedback to improve the training modules used during the scans to improve future training modules. Scanned-subjects will be a small group individuals from whom all images will be acquired.

Martin, D. S.↗

NASA NTRS2017

Autonomous Diagnostic Imaging Performed by Untrained Operator Using Augmented Reality as a Form of "Just-in-Time" Training

Martin, David S.↗

DOE OSTI2020

PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-Efficient ReRAM

The wide adoption of deep neural networks has been accompanied by ever-increasing energy and performance demands due to the expensive nature of training them. Additionally, numerous special-purpose architectures have been proposed to accelerate training: both digital and hybrid digital-analog using resistive RAM (ReRAM) crossbars. ReRAM-based accelerators have demonstrated the effectiveness of ReRAM crossbars at performing matrix-vector multiplication operations that are prevalent in training. However, they still suffer from inefficiency due to the use of serial reads and writes for performing the weight gradient and update step. A few works have demonstrated the possibility of performing outer products in crossbars, which can be used to realize the weight gradient and update step without the use of serial reads and writes. However, these works have been limited to low precision operations which are not sufficient for typical training workloads. Moreover, they have been confined to a limited set of training algorithms for fully-connected layers only. To address these limitations, we propose a bit-slicing technique for enhancing the precision of ReRAM-based outer products, which is substantially different from bit-slicing for matrix-vector multiplication only. We incorporate this technique into a crossbar architecture with three variants catered to different training algorithms. To evaluate our design on different types of layers in neural networks (fully-connected, convolutional, etc.) and training algorithms, we develop PANTHER, an ISA-programmable training accelerator with compiler support. Our design can also be integrated into other accelerators in the literature to enhance their efficiency. Our evaluation shows that PANTHER achieves up to 8.02×, 54.21×, and 103× energy reductions as well as 7.16×, 4.02×, and 16× execution time reductions compared to digital accelerators, ReRAM-based accelerators, and GPUs, respectively.

42 ENGINEERING↗

NASA NTRS1994

Autogenic-feedback training as a treatment for airsickness in high-performance military aircraft: Two case studies

The purpose of this paper is to present a detailed description of the physiological and performance responses of two military pilots undergoing a treatment for motion sickness. The treatment used, Autogenic-Feedback Training (AFT), is an operant conditioning procedure where subjects are taught to control several of their autonomic responses and thereby suppress their motion sickness symptoms. Two male, active duty military pilots (U.S. Navy and U. S. Marine Corps), ages 30 and 35, were each given twelve 30-minute training sessions. The primary criterion for success of training was the subject's ability to tolerate rotating chair motion sickness tests for progressively longer periods of time and at higher rotational velocities. A standardized diagnostic scale was used during motion sickness to assess changes in the subject's perceived malaise. Physiological data were obtained from one pilot during tactical maneuvers in an F-18 aircraft after completion of his training. A significant increase in tolerance to laboratory-induced motion sickness tests and a reduction in autonomic nervous system (ANS) response variability was observed for both subjects after training. Both pilots were successful in applying AFT for controlling their airsickness during subsequent qualification tests on F-18 and T-38 aircraft and were returned to active duty flight status.

Cowings, Patricia S.↗