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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Psychological training of NASA astronauts for extended missions

The success of operational teams working in remote and hostile environments rests in large part on adequate preparation of those teams prior to emplacement in field settings. Psychological training, directed at the maintenance of crew health and performance becomes increasingly important as space missions grow in duration and complexity. Methods: Topics to be discussed include: the conceptual framework of psychological training; needs analysis; content and delivery options; methods of assessing training efficacy; use of testbeds and analogies and the relationship of training to crew selection and real-time support activities. Results and Conclusions: This paper will discuss the psychological training approach being developed at the NASA/JSC Behavior and Performance Laboratory. This approach will be compared and contrasted with those underway in the U.S. Department of Defense and in other space agencies.

Holland, A. W.↗
NASA NTRS2003

Computer Program Recognizes Patterns in Time-Series Data

A computer program recognizes selected patterns in time-series data like digitized samples of seismic or electrophysiological signals. The program implements an artificial neural network (ANN) and a set of N clocks for the purpose of determining whether N or more instances of a certain waveform, W, occur within a given time interval, T. The ANN must be trained to recognize W in the incoming stream of data. The first time the ANN recognizes W, it sets clock 1 to count down from T to zero; the second time it recognizes W, it sets clock 2 to count down from T to zero, and so forth through the Nth instance. On the N + 1st instance, the cycle is repeated, starting with clock 1. If any clock has not reached zero when it is reset, then N instances of W have been detected within time T, and the program so indicates. The program can readily be encoded in a field-programmable gate array or an application-specific integrated circuit that could be used, for example, to detect electroencephalographic or electrocardiographic waveforms indicative of epileptic seizures or heart attacks, respectively.

Hand, Charles↗
NASA NTRS1991

Intelligent computer-aided training and tutoring

Specific autonomous training systems based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground-based support personnel that demonstrate an alternative to current training systems are described. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer-Aided Training (ICAT) systems would provide, for the trainee, much of the same experience that could be gained from the best on-the-job training. By integrating domain expertise with a knowledge of appropriate training methods, an ICAT session should duplicate, as closely as possible, the trainee undergoing on-the-job training in the task environment, benefitting from the full attention of a task expert who is also an expert trainer. Thus, the philosophy of the ICAT system is to emulate the behavior of an experienced individual devoting his full time and attention to the training of a novice - proposing challenging training scenarios, monitoring and evaluating the actions of the trainee, providing meaningful comments in response to trainee errors, responding to trainee requests for information, giving hints (if appropriate), and remembering the strengths and weaknesses displayed by the trainee so that appropriate future exercises can be designed.

Loftin, R. Bowen↗
NASA NTRS2012

Command Center Training Tool (C2T2)

This abstract presents the training approach taken to create a management-centered, experiential learning solution for the Virginia Port Authority's Port Command Center. The resultant tool, called the Command Center Training Tool (C2T2), follows a holistic approach integrated across the training management cycle and within a single environment. The approach allows a single training manager to progress from training design through execution and AAR. The approach starts with modeling the training organization, identifying the organizational elements and their individual and collective performance requirements, including organizational-specific performance scoring ontologies. Next, the developer specifies conditions, the problems, and constructs that compose exercises and drive experiential learning. These conditions are defined by incidents, which denote a single, multi-media datum, and scenarios, which are stories told by incidents. To these layered, modular components, previously developed meta-data is attached, including associated performance requirements. The components are then stored in a searchable library An event developer can create a training event by searching the library based on metadata and then selecting and loading the resultant modular pieces. This loading process brings into the training event all the previously associated task and teamwork material as well as AAR preparation materials. The approach includes tools within an integrated management environment that places these materials at the fingertips of the event facilitator such that, in real time, the facilitator can track training audience performance and resultantly modify the training event. The approach also supports the concentrated knowledge management requirements for rapid preparation of an extensive AAR. This approach supports the integrated training cycle and allows a management-based perspective and advanced tools, through which a complex, thorough training event can be developed.

Jones, Phillip↗
NASA NTRS2010

Gait Adaptability Training Improves Both Postural Stability and Dual-Tasking Ability

After spaceflight, the process of readapting to Earth's gravity commonly presents crewmembers with a variety of locomotor challenges. Our recent work has shown that the ability to adapt to a novel discordant sensorimotor environment can be increased through preflight training, so one focus of our laboratory has been the development of a gait training countermeasure to expedite the return of normal locomotor function after spaceflight. We used a training system comprising a treadmill mounted on a motion base facing a virtual visual scene that provided a variety of sensory challenges. As part of their participation in a larger retention study, 10 healthy adults completed 3 training sessions during which they walked on a treadmill at 1.1 m/s while receiving discordant support-surface and visual manipulations. After a single training session, subjects stride frequencies improved, and after 2 training sessions their auditory reaction times improved, where improvement was indicated by a return toward baseline values. Interestingly, improvements in reaction time came after stride frequency improvements plateaued. This finding suggests that postural stability was given a higher priority than a competing cognitive task. Further, it demonstrates that improvement in both postural stability and dual-tasking can be achieved with multiple training exposures. We conclude that, with training, individuals become more proficient at walking in discordant sensorimotor conditions and are able to devote more attention to competing tasks.

Brady, Rachel A.↗
NASA NTRS1993

Effect of exercise training on cardiopulmonary baroreflex control of forearm vascular resistance in humans

We studied the stimulus-response characteristics of cardiopulmonary baroreflex control of forearm vascular resistance (FVR) in four groups of male volunteer subjects: i) unfit, ii) physically fit, iii) before and after 10 wk of endurance training (chronic blood volume expansion), and iv) before and after acute blood volume expansion. We assessed the relationship between reflex stimulus, i.e., changes in central venous pressure and response, i.e., FVR, during unloading of cardiopulmonary mechanoreceptors with lower body negative pressure (LBNP, 0 to -20 mm Hg). The slope of the linear relationship between FVR and CVP, the index of the responsiveness of this baroreflex, was significantly diminished (> 50%) in the fit subjects compared with the unfit. The slope of the FVR-CVP relationship was inversely correlated with the subject's total blood volume, suggesting that blood volume expansion was related to the attenuated CP baroreflex. In the exercise training study, maximal oxygen consumption and blood volume increased following 10 wk of endurance training (N = 14) but were unchanged in the time control group (N = 7). The slope of the FVR-CVP relationship was significantly reduced (32%) following 10 wk of training but was unchanged in the time control group. The reduction in slope of the FVR-CVP relationship was inversely related to the increase in blood volume associated with exercise training. Acute blood volume expansion 8 ml.kg-1 body weight with 5% human serum albumin solution) significantly reduced the slope of the FVR-CVP relationship. These data support the hypothesis that the attenuated forearm vascular reflex in physically fit individuals is related to a training-induced hypervolemia.(ABSTRACT TRUNCATED AT 250 WORDS).

Review↗
NASA NTRS1952

Time-History Data of Maneuvers Performed by an F-86A Airplane During Squadron Operational Training

Preliminary results of one phase of a control-motion study program are presented in the form of plots of load factor.and angular acceleration against indicated airspeed and of time histories of several measured quantities. The results were obtained from 197 maneuvers performed by an F-86A jet-fighter airplane during normal squadron operational training. Most of the tactical maneuver8 of which the F-86A is capable were performed at pressure altitudes ranging from 0 to 32,000 feet and at indicated airspeeds ranging from 95 to 650 miles per hour.

Henderson, Campbell↗
DOE OSTI2025

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. Furthermore, we demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

Data-driven model reduction↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

Presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

This is the conference paper accompanying an oral presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗
DOE OSTI2025

A Reinforcement Learning Approach to Augment Conventional PID Control in Nuclear Power Plant Transient Operation

The ability of nuclear reactors to operate their power conversion cycles more flexibly will enhance their value to energy grids with variable pricing. Current nuclear control systems are typically classical controllers that are often based on proportional-integral-derivative (PID) control. This paper presents a method of augmenting the existing PID control for difficult transient operations in nuclear power plants using a reinforcement learning–derived feedforward signal applied in real time. The agents, which are trained on a test thermal load-following problem, are designed to improve steam generator outlet temperature control for a range of fast load-following scenarios covering ramp rates from 9%/min to 15%/min. Several reinforcement learning algorithms were initially investigated for the training of the feedforward agents with deep Q-learning (DQN) and proximal policy optimization (PPO) networks, which were found to be the most promising. The DQN controllers utilize discrete actions, giving them a better disturbance rejection at steady state but inconsistent response to initial temperature deviations. In contrast, PPO-trained agents, which take continuous actions except for a dead zone around zero, were shown to have the best combination of high disturbance rejection at steady state and good tracking of the desired temperature value. The ability of the PPO agent was also examined, with the average time of decision making found to be on the order of 1 ms. The fault properties of the controller under the loss of the reinforcement learning agent feedforward signal were also examined. The controller showed strong performance in situations of “no-signal” faults. but was less good at handling “stuck-at” faults, where the feedforward signal remains at a set value. In both cases, however, the PID was able to successfully maintain stability, eventually returning the system to a steady state. It is hoped that this work will allow for the proposed control architecture to be examined for more difficult control problems such that it may eventually be used to adapt existing nuclear plants for more aggressive load-following on grids of the future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid

A dynamic neural network (NN) based multi-class classifier is proposed for improving online prediction of coherent generator groups (CGGs), following the occurrences of various contingencies in the power grid. This is motivated by the increasing availability of the measurements from phasor measurement units (PMUs) and the number of grouping schemes is limited. The proposed method consists of three steps. First, by performing offline simulations, a library of system dynamic responses characterized by post-contingency rotor angles and speeds of individual generators is obtained. To generate sufficient data, up to N-2 contingencies and the uncertain parameters associated with the power grid including type and location of disturbance and fault clearing times are modeled. Secondly, the training data-set is produced by generating labels for individual contingencies using a hierarchical clustering method based on rotor angle and speed data. Lastly, the dynamic NN models are trained for online applications such as emergency controls and controlled islanding. The proposed method is tested on the standard 16-generator 68-bus system to demonstrate its performance. Furthermore, the impact of the sample data lengths on the CGG numbers is evaluated. It is interesting to observe that the time domain stability behaviors can be determined by examining the changes in the CGG numbers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1997

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

Decker, Arthur J.↗
NASA NTRS1998

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

Decker, Arthur J.↗
DOE OSTI2021

Machine learning based rate optimization under geologic uncertainty

We propose a novel approach for rate optimization during a waterflood under geologic uncertainty in reservoir properties such as permeability and porosity. The traditional approach typically involves several runs of the forward simulator. This may not scale well when the optimization is to be performed at the full field-level and over multiple geologic realizations. A machine-learning (ML) based approach which is quick and scalable for rate optimization over multiple geologic realizations is proposed instead. The training data for the model is generated by running the forward simulator with randomly assigned well rates using multiple geologic realizations. A reduced order representation of the permeability heterogeneity in each of the realizations is derived using a grid connectivity transformation (GCT). This step involves finding basis functions corresponding to the different modal frequencies of the grid connectivity represented by the grid Laplacian. The projection of the heterogeneous property field along these basis functions gives the basis coefficients that form the reduced order representation. Subsequently, for each training datapoint, streamlines are traced and the minimum time of flight (TOF) representing the tracer breakthrough time at each producer is recorded. The basis coefficients and well rates are fed to a machine learning model as input and the minimum TOF at the producers forms the output of the model. This trained model can then be used along with an optimizer for computing the optimal injection rates to maximize the injection sweep efficiency. This corresponds to minimizing the variance in the minimum TOF within each well group. Different architectures of neural network are tested using 5-fold cross validation to decide the best ML model to compute the streamline time of flight. The trained model is used to perform well rate optimization over multiple realizations of geology by using a risk tolerance penalty. The optimal well rates thus obtained are compared with two cases: a) equal well rates assigned to all injectors and producers and b) well rates obtained by optimizing over a single realization without considering the uncertainty in geology. The optimal well rates are seen to offer better oil recovery and sweep efficiency than both cases.

02 PETROLEUM↗
NASA NTRS2005

Contingency Operations Support to NASA Johnson Space Center Medical Operations Division

The Wyle Laboratories Contingency Operations Group provides support to the NASA Johnson Space Center (JSC) Medical Operations Division in the event of a space flight vehicle accident or JSC mishap. Support includes development of Emergency Medical System (EMS) requirements, procedures, training briefings and real-time support of mishap investigations. The Contingency Operations Group is compliant with NASA documentation that provides guidance in these areas and maintains contact with the United States Department of Defense (DOD) to remain current on military plans to support NASA. The contingency group also participates in Space Operations Medical Support Training Courses (SOMSTC) and represents the NASA JSC Medical Operations Division at contingency exercises conducted worldwide by the DOD or NASA. The events of September 11, 2001 have changed how this country prepares and protects itself from possible terrorist attacks on high-profile targets. As a result, JSC is now considered a high-profile target and thus, must prepare for and develop a response to a Weapons of Mass Destruction (WMD) incident. The Wyle Laboratories Contingency Operations Group supports this plan, specifically the medical response, by providing expertise and manpower.

Stepaniak, Philip↗
DOE OSTI2025

ORNL Portable Diagnostic Package for Evaluating Mirror Performance

Oak Ridge National Laboratory (ORNL) and Realta Fusion successfully executed a one-year collaborative effort to deploy the Portable Diagnostic Package (PDP) on the Wisconsin HTS Axisymmetric Mirror (WHAM) and to assess future diagnostic needs. The PDP consists of an optical emission spectroscopy (OES) subsystem and a Thomson scattering (TS) subsystem. Within the first two months, the PDP OES subsystem was installed, integrated with WHAM timing, and handed over to trained Realta personnel. It immediately delivered multi-channel impurity surveys and the first spatially resolved plasma-rotation profiles inferred from C III Doppler shifts. The TS subsystem was commissioned within the first six months; transportation of the class IV laser system, approval of the safety plan, and the integration with WHAM timing with 100 ns resolution. The TS subsystem provided the first localized electron temperature measurements in WHAM commissioning shots. Leveraging these results, the team has discussed a diagnostic concept map for Realta’s next-step facility, Anvil, identifying line-of-sight constraints, required temporal resolution, and hardware upgrades.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Operator inference with roll outs for learning reduced models from scarce and low-quality data

Data-driven modeling has become a key building block in computational science and engineering. However, data that are available in science and engineering are typically scarce, often polluted with noise and affected by measurement errors and other perturbations, which makes learning the dynamics of systems challenging. Here, in this work, we propose to combine data-driven modeling via operator inference with the dynamic training via roll outs of neural ordinary differential equations. Operator inference with roll outs inherits interpretability, scalability, and structure preservation of traditional operator inference while leveraging the dynamic training via roll outs over multiple time steps to increase stability and robustness for learning from low-quality and noisy data. Numerical experiments with data describing shallow water waves and surface quasi-geostrophic dynamics demonstrate that operator inference with roll outs provides predictive models from training trajectories even if data are sampled sparsely in time and polluted with noise of up to 10%.

97 MATHEMATICS AND COMPUTING↗