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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Charaterization of Emerging Computing Architectures for Dynamic Simulation of Future Power Grids with Large-Scale Power Electronics

The increasing penetration of power electronics in power grids significantly raises the computing requirements in a real-time (and/or fast) simulation of the power grid. The real-time simulation is an enabler for evaluating controllers, protection systems, new equipment, and twinning. In this paper, emerging computing architectures such as tensor processing units (TPU), neural/neuromorphic processing units (NPU), and quantum processing units (QPU) are introduced and characterized for the real-time (and/or fast) simulation of power electronics-dominated power grids. The metrics and the process to characterize emerging computing architectures to perform real-time (and/or fast) simulations of future power grids with power electronics are discussed. Three of the emerging computing units are characterized based on these metrics and the process developed. This characterization will enable identification and comparison of emerging computing architectures that can perform real-time (and/or fast) simulation of future power grids.

Choi, Jongchan↗
NASA NTRS2012

Identification of Time-Varying Pilot Control Behavior in Multi-Axis Control Tasks

Recent developments in fly-by-wire control architectures for rotorcraft have introduced new interest in the identification of time-varying pilot control behavior in multi-axis control tasks. In this paper a maximum likelihood estimation method is used to estimate the parameters of a pilot model with time-dependent sigmoid functions to characterize time-varying human control behavior. An experiment was performed by 9 general aviation pilots who had to perform a simultaneous roll and pitch control task with time-varying aircraft dynamics. In 8 different conditions, the axis containing the time-varying dynamics and the growth factor of the dynamics were varied, allowing for an analysis of the performance of the estimation method when estimating time-dependent parameter functions. In addition, a detailed analysis of pilots adaptation to the time-varying aircraft dynamics in both the roll and pitch axes could be performed. Pilot control behavior in both axes was significantly affected by the time-varying aircraft dynamics in roll and pitch, and by the growth factor. The main effect was found in the axis that contained the time-varying dynamics. However, pilot control behavior also changed over time in the axis not containing the time-varying aircraft dynamics. This indicates that some cross coupling exists in the perception and control processes between the roll and pitch axes.

Zaal, Peter M. T.↗
NASA NTRS1985

High-speed, automatic controller design considerations for integrating array processor, multi-microprocessor, and host computer system architectures

Modern control systems must typically perform real-time identification and control, as well as coordinate a host of other activities related to user interaction, online graphics, and file management. This paper discusses five global design considerations which are useful to integrate array processor, multimicroprocessor, and host computer system architectures into versatile, high-speed controllers. Such controllers are capable of very high control throughput, and can maintain constant interaction with the nonreal-time or user environment. As an application example, the architecture of a high-speed, closed-loop controller used to actively control helicopter vibration is briefly discussed. Although this system has been designed for use as the controller for real-time rotorcraft dynamics and control studies in a wind tunnel environment, the controller architecture can generally be applied to a wide range of automatic control applications.

Jacklin, S. A.↗
DOE OSTI2022

Device Classification for Industrial Control Systems Using Predicted Traffic Features

To achieve a secure interconnected Industrial Control System (ICS) architecture, security practitioners depend on accurate identification of network host behavior. However, accurate machine learning based host identification methods depends on the availability of significant quantities of network traffic data, which can be difficult to obtain due to system constraints such as network security, data confidentiality, and physical location. In this work, we propose a network traffic feature prediction method based on a generative model, which achieves high host identification accuracy. Furthermore, we develop a joint training algorithm to improve host identification performance compared to separate training of the generative model and the classifier responsible for host identification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1985

Space Station data system analysis/architecture study. Task 1: Functional requirements definition, DR-5

The initial task in the Space Station Data System (SSDS) Analysis/Architecture Study is the definition of the functional and key performance requirements for the SSDS. The SSDS is the set of hardware and software, both on the ground and in space, that provides the basic data management services for Space Station customers and systems. The primary purpose of the requirements development activity was to provide a coordinated, documented requirements set as a basis for the system definition of the SSDS and for other subsequent study activities. These requirements should also prove useful to other Space Station activities in that they provide an indication of the scope of the information services and systems that will be needed in the Space Station program. The major results of the requirements development task are as follows: (1) identification of a conceptual topology and architecture for the end-to-end Space Station Information Systems (SSIS); (2) development of a complete set of functional requirements and design drivers for the SSIS; (3) development of functional requirements and key performance requirements for the Space Station Data System (SSDS); and (4) definition of an operating concept for the SSIS. The operating concept was developed both from a Space Station payload customer and operator perspective in order to allow a requirements practicality assessment.

Source record↗
NASA NTRS2015

Mechanical Properties of Air Plasma Sprayed Environmental Barrier Coating (EBC) Materials

Development work in Environmental Barrier Coatings (EBCs) for Ceramic Matrix Composites (CMCs) has focused considerably on the identification of materials systems and coating architectures to meet application needs. The evolution of these systems has occurred so quickly that modeling efforts and requisite data for modeling lag considerably behind development. Materials property data exists for many systems in the bulk form, but the effects of deposition on the critical properties of strength and fracture behavior are not well studied. We have plasma sprayed bulk samples of baseline EBC materials (silicon, ytterbium disilicate) and tested the mechanical properties of these materials to elicit differences in strength and toughness. We have also endeavored to assess the mixed-mode fracture resistance, Gc, of silicon in a baseline EBC applied to SiCSiC CMC via four point bend test. These results are compared to previously determined properties of the comparable bulk material.

Ceramic Matrix Composites (CMC's)↗
DOE OSTI2021

Constrained Block Nonlinear Neural Dynamical Models

Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for data-efficient learning of deep control-oriented nonlinear dynamical models by embedding local model structure and constraints. The proposed method consists of neural network blocks that represent input, state, and output dynamics with constraints placed on the network weights and system variables. For handling partially observable dynamical systems, we utilize a state observer neural network to estimate the states of the system's latent dynamics. We evaluate the performance of the proposed architecture and training methods on system identification tasks for three nonlinear systems: a continuous stirred tank reactor, a two tank interacting system, and an aerodynamics body. Models optimized with a few thousand system state observations accurately represent system dynamics in open loop simulation over thousands of time steps from a single set of initial conditions. Experimental results demonstrate an order of magnitude reduction in open-loop simulation mean squared error for our constrained, block-structured neural models when compared to traditional unstructured and unconstrained neural network models.

Skomski, Elliott↗
NASA NTRS1988

Innovative architectures for dense multi-microprocessor computers

The results of a Phase I Small Business Innovative Research (SBIR) project performed for the NASA Langley Computational Structural Mechanics Group are described. The project resulted in the identification of a family of chordal-ring interconnection architectures with excellent potential to serve as the basis for new multimicroprocessor (MMP) computers. The paper presents examples of how computational algorithms from structural mechanics can be efficiently implemented on the chordal-ring architecture.

Donaldson, Thomas↗
NASA NTRS1996

On-Line Tracking Controller for Brushless DC Motor Drives Using Artificial Neural Networks

A real-time control architecture is developed for time-varying nonlinear brushless dc motors operating in a high performance drives environment. The developed control architecture possesses the capabilities of simultaneous on-line identification and control. The dynamics of the motor are modeled on-line and controlled using an artificial neural network, as the system runs. The control architecture combines the experience and dependability of adaptive tracking systems with potential and promise of the neural computing technology. The sensitivity of real-time controller to parametric changes that occur during training is investigated. Such changes are usually manifested by rapid changes in the load of the brushless motor drives. This sudden change in the external load is simulated for the sigmoidal and sinusoidal reference tracks. The ability of the neuro-controller to maintain reasonable tracking accuracy in the presence of external noise is also verified for a number of desired reference trajectories.

Rubaai, Ahmed↗
DOE OSTI2021

The Architecture of Area 12 Camp - Nevada's Atomic Ghost Town. An Architectural Survey of Area 12 Camp, Nevada National Security Site, Nye County, Nevada

The U.S. Department of Energy, National Nuclear Security Administration Nevada Field Office (NNSA/NFO) plans to demolish 10 buildings and one structure in Area 12 Camp at the Nevada National Security Site (NNSS) in Nye County, Nevada, to meet current and future National Weapons Science, Global and Homeland Security Program, and Environmental Management mission requirements and to reduce dangers to site workers from some of these resources. These demolitions constitute an undertaking subject to review under Title 54 U.S.C. § 306108, commonly known as Section 106 of the National Historic Preservation Act, Title 54 U.S.C. § 300101, et seq., and its implementing regulations, 36 C.F.R. Part 800. These resources were recorded and evaluated as individually ineligible for the National Register of Historic Places (National Register or NRHP) by Menocal and Shaw (2019). Upon review by the Nevada State Historic Preservation Office (SHPO) and internal review by Desert Research Institute (DRI) and NNSA/NFO staff, it was determined that these resources needed to be rerecorded and evaluated in relation to what appeared to be a National Register-eligible Historic District made up of the entirety of Area 12 Camp. Based on preliminary information, it appeared that all 11 resources would likely be contributing elements of that historic district. With that assumption in mind, recording of the entire proposed Area 12 Camp Historic District was done for three purposes. First, it would result in a definitive significance evaluation of the 11 resources in question. Second, recording of the entire camp would be offered as an appropriate mitigation measure for the demolition of these resources. Third, it would present an essential background for future management of resources within the district boundaries. Prior to the present survey, Area 12 Camp had not yet been systematically recorded; therefore, DRI surveyed an area of approximately 229 acres for architectural resources. This effort resulted in the identification, recording, and evaluation of the Area 12 Camp Historic District (SHPO Resource No. D372), including the identification of its contributing components. This district is recommended eligible for the National Register of Historic Places under Criteria A and C. It is unevaluated under Criteria B and D. The Area 12 Historic District contains 71 Principal Resources, which include landscapes, buildings, and structures. Of these resources, 69 (including 10 of the 11 resources to be removed) are recommended as contributing elements of the historic district during its period of significance corresponding to nuclear testing from 1960 through 1992. During most of the Cold War, the government reservation now called the NNSS was named the Nevada Test Site (NTS).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

Dynamic neural networks based on-line identification and control of high performance motor drives

In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

Rubaai, Ahmed↗
NASA NTRS1991

Parameter estimation in space systems using recurrent neural networks

The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

Parlos, Alexander G.↗
NASA NTRS1985

Space station on-orbit identification and performance monitor

This paper describes the generic applications of on-orbit identification to the reference Space Station configuration currently under consideration by NASA. Identification functions are categorized, and the various methods for extracting parameter estimates are correlated with the sensing of specific characteristics of interest to both engineering subsystems and users of the Station's commercial and scientific facilities. A case study of crew motion detection and identification is included to illustrate the application to the Station's disturbance environment and plant characterization using accelerometer sensing. Onboard implementation architecture is discussed from the viewpoint of maximizing integration of the identification process with the flight subsystem's data and signal flow.

Mettler, E.↗
NASA NTRS2011

DCS: A Case Study of Identification of Knowledge and Disposition Gaps Using Principles of Continuous Risk Management

The Human Research Program (HRP) is formulated around the program architecture of Evidence-Risk-Gap-Task-Deliverable. Review of accumulated evidence forms the basis for identification of high priority risks to human health and performance in space exploration. Gaps in knowledge or disposition are identified for each risk, and a portfolio of research tasks is developed to fill them. Deliverables from the tasks inform the evidence base with the ultimate goal of defining the level of risk and reducing it to an acceptable level. A comprehensive framework for gap identification, focus, and metrics has been developed based on principles of continuous risk management and clinical care. Research towards knowledge gaps improves understanding of the likelihood, consequence or timeframe of the risk. Disposition gaps include development of standards or requirements for risk acceptance, development of countermeasures or technology to mitigate the risk, and yearly technology assessment related to watching developments related to the risk. Standard concepts from clinical care: prevention, diagnosis, treatment, monitoring, rehabilitation, and surveillance, can be used to focus gaps dealing with risk mitigation. The research plan for the new HRP Risk of Decompression Sickness (DCS) used the framework to identify one disposition gap related to establishment of a DCS standard for acceptable risk, two knowledge gaps related to DCS phenomenon and mission attributes, and three mitigation gaps focused on prediction, prevention, and new technology watch. These gaps were organized in this manner primarily based on target for closure and ease of organizing interim metrics so that gap status could be quantified. Additional considerations for the knowledge gaps were that one was highly design reference mission specific and the other gap was focused on DCS phenomenon.

Norcross, Jason↗
NASA NTRS1986

Mars to earth optical communication link for the proposed Mars Sample Return mission roving vehicle

The Mars Sample Return (MSR) mission planed for 1989 will deploy a rover from its landing craft to survey the Martian surface. During traversals of the rover from one site to the next in search of samples, three-dimensional images from a pair of video cameras will be transmitted to earth; the terrestrial operators will then send back high level direction commands to the rover. Attention is presently given to the effects of wind and dust on communications, the architecture of the optical communications package, and the identification of technological areas requiring further development for MSR incorporation.

Sipes, Donald L., Jr.↗
NASA NTRS1993

Structure identification in fuzzy inference using reinforcement learning

In our previous work on the GARIC architecture, we have shown that the system can start with surface structure of the knowledge base (i.e., the linguistic expression of the rules) and learn the deep structure (i.e., the fuzzy membership functions of the labels used in the rules) by using reinforcement learning. Assuming the surface structure, GARIC refines the fuzzy membership functions used in the consequents of the rules using a gradient descent procedure. This hybrid fuzzy logic and reinforcement learning approach can learn to balance a cart-pole system and to backup a truck to its docking location after a few trials. In this paper, we discuss how to do structure identification using reinforcement learning in fuzzy inference systems. This involves identifying both surface as well as deep structure of the knowledge base. The term set of fuzzy linguistic labels used in describing the values of each control variable must be derived. In this process, splitting a label refers to creating new labels which are more granular than the original label and merging two labels creates a more general label. Splitting and merging of labels directly transform the structure of the action selection network used in GARIC by increasing or decreasing the number of hidden layer nodes.

Berenji, Hamid R.↗