Search NASA⌕ Search

SEARCH · Search NASA

Results for “Learning systems”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning

Internal atmospheric variability fundamentally limits predictability of climate and obscures evidence of anthropogenic climate change regionally and on time scales of up to a few decades. Dynamical adjustment techniques estimate and subsequently remove the influence of atmospheric circulation variability on temperature or precipitation. The residual component is expected to contain the thermodynamical signal of the externally forced response but with less circulation-induced noise. Existing techniques have led to important insights into recent trends in regional (hydro-) climate and their drivers, but the variance explained by circulation is often low. Here, we develop a novel dynamical adjustment technique by implementing principles from statistical learning. We demonstrate in an ensemble of Community Earth System Model (CESM) simulations that statistical learning methods, such as regularized linear models, establish a clearer relationship between circulation variability and atmospheric target variables, and need relatively short periods of record for training (around 30 years). The method accounts for, on average, 83% and 78% of European monthly winter temperature and precipitation variability at gridcell level, and around 80% of global mean temperature and hemispheric precipitation variability. We show that the residuals retain forced thermodynamical contributions to temperature and precipitation variability. Accurate estimates of the total forced response can thus be recovered assuming that forced circulation changes are gradual over time. Overall, forced climate response estimates can be extracted at regional or global scales from approximately 3–5 times fewer ensemble members, or even a single realization, using statistical learning techniques. We anticipate the technique will contribute to reducing uncertainties around internal variability and facilitating climate change detection and attribution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Technical note: Deep learning for creating surrogate models of precipitation in Earth system models

Abstract. We investigate techniques for using deep neural networks to produce surrogatemodels for short-term climate forecasts. A convolutional neural network istrained on 97 years of monthly precipitation output from the 1pctCO2 run (theCO 2 concentration increases by 1 % per year) simulated by the second-generation Canadian Earth System Model (CanESM2). The neural network clearly outperforms a persistence forecast anddoes not show substantially degraded performance even when the forecast lengthis extended to 120 months. The model is prone to underpredicting precipitationin areas characterized by intense precipitation events. Scheduled sampling(forcing the model to gradually use its own past predictions rather than groundtruth) is essential for avoiding amplification of early forecasting errors.However, the use of scheduled sampling also necessitates preforecasting(generating forecasts prior to the first forecast date) to obtain adequateperformance for the first few prediction time steps. We document the trainingprocedures and hyperparameter optimization process for researchers who wish toextend the use of neural networks in developing surrogate models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Automatic acquisition of domain and procedural knowledge

The design concept and performance of AKAS, an automated knowledge-acquisition system for the development of expert systems, are discussed. AKAS was developed using the FLES knowledge base for the electrical system of the B-737 aircraft and employs a 'learn by being told' strategy. The system comprises four basic modules, a system administration module, a natural-language concept-comprehension module, a knowledge-classification/extraction module, and a knowledge-incorporation module; details of the module architectures are explored.

Ferber, H. J.↗
NASA NTRS1995

Object-oriented approach for gas turbine engine simulation

An object-oriented gas turbine engine simulation program was developed. This program is a prototype for a more complete, commercial grade engine performance program now being proposed as part of the Numerical Propulsion System Simulator (NPSS). This report discusses architectural issues of this complex software system and the lessons learned from developing the prototype code. The prototype code is a fully functional, general purpose engine simulation program, however, only the component models necessary to model a transient compressor test rig have been written. The production system will be capable of steady state and transient modeling of almost any turbine engine configuration. Chief among the architectural considerations for this code was the framework in which the various software modules will interact. These modules include the equation solver, simulation code, data model, event handler, and user interface. Also documented in this report is the component based design of the simulation module and the inter-component communication paradigm. Object class hierarchies for some of the code modules are given.

Curlett, Brian P.↗
NASA NTRS2008

NASA's Evolutionary Xenon Thruster: The NEXT Ion Propulsion System for Solar System Exploration

This viewgraph presentation reviews NASA s Evolutionary Xenon Thruster (NEXT) Ion Propulsion system. The NEXT project is developing a solar electric ion propulsion system. The NEXT project is advancing the capability of ion propulsion to meet NASA robotic science mission needs. The NEXT system is planned to significantly improve performance over the state of the art electric propulsion systems, such as NASA Solar Electric Propulsion Technology Application Readiness (NSTAR). The status of NEXT development is reviewed, including information on the NEXT Thruster, the power processing unit, the propellant management system (PMS), the digital control interface unit, and the gimbal. Block diagrams NEXT system are presented. Also a review of the lessons learned from the Dawn and NSTAR systems is provided. In summary the NEXT project activities through 2007 have brought next-generation ion propulsion technology to a sufficient maturity level.

Pencil, Eric J.↗
NASA NTRS2016

Adaptive Coding and Modulation Experiment With NASA's Space Communication and Navigation Testbed

National Aeronautics and Space Administration (NASA)'s Space Communication and Navigation Testbed is an advanced integrated communication payload on the International Space Station. This paper presents results from an adaptive coding and modulation (ACM) experiment over S-band using a direct-to-earth link between the SCaN Testbed and the Glenn Research Center. The testing leverages the established Digital Video Broadcasting Second Generation (DVB-S2) standard to provide various modulation and coding options, and uses the Space Data Link Protocol (Consultative Committee for Space Data Systems (CCSDS) standard) for the uplink and downlink data framing. The experiment was con- ducted in a challenging environment due to the multipath and shadowing caused by the International Space Station structure. Several approaches for improving the ACM system are presented, including predictive and learning techniques to accommodate signal fades. Performance of the system is evaluated as a function of end-to-end system latency (round- trip delay), and compared to the capacity of the link. Finally, improvements over standard NASA waveforms are presented.

waveforms↗
NASA NTRS2016

Adaptive Coding and Modulation Experiment With NASA's Space Communication and Navigation Testbed

National Aeronautics and Space Administration (NASA)'s Space Communication and Navigation Testbed is an advanced integrated communication payload on the International Space Station. This paper presents results from an adaptive coding and modulation (ACM) experiment over S-band using a direct-to-earth link between the SCaN Testbed and the Glenn Research Center. The testing leverages the established Digital Video Broadcasting Second Generation (DVB-S2) standard to provide various modulation and coding options, and uses the Space Data Link Protocol (Consultative Committee for Space Data Systems (CCSDS) standard) for the uplink and downlink data framing. The experiment was conducted in a challenging environment due to the multipath and shadowing caused by the International Space Station structure. Several approaches for improving the ACM system are presented, including predictive and learning techniques to accommodate signal fades. Performance of the system is evaluated as a function of end-to-end system latency (round-trip delay), and compared to the capacity of the link. Finally, improvements over standard NASA waveforms are presented.

waveforms↗
NASA NTRS2018

A Learn-To-Fly Approach for Adaptively Tuning Flight Control Systems

A method is presented for adaptively tuning feedback control gains in a ight control sys- tem to achieve desired closed-loop performance. The method combines efficient parameter estimation for identifying closed-loop dynamics models, with online nonlinear optimization for sequentially perturbing and updating control gains to improve performance. Prior in- formation on stability and control derivatives is not needed, nor is any knowledge about the control system architecture. Following convergence, the optimized control gains (with uncertainties), the open-loop dynamics model, and the closed-loop dynamics model are available. The method is demonstrated for tuning a longitudinal stability augmentation system using a realistic nonlinear ight dynamics simulation of the NASA FASER airplane. Convergence was attained using five piloted maneuvers that spanned approximately one minute of ight test time. Although demonstrated for a relatively simple case, the method is general and can be applied to other aircraft, axes, performance metrics, and control systems.

Grauer, Jared A.↗
NASA NTRS2021

A Learn-to-Fly Approach for Adaptively Tuning Flight Control Systems

A method is presented for adaptively tuning feedback control gains in a flight control system to achieve desired closed-loop performance. The method combines efficient aircraft parameter estimation, for identifying closed-loop dynamics models, with online nonlinear optimization, for sequentially perturbing and updating control gains to improve performance. Access to flight measurements and the control gains is required, but no prior information about the aircraft dynamics or the flight control architecture is needed. After the procedure, the optimized control gains (with uncertainties), the open-loop dynamics model, and the closed-loop dynamics model are available. The method is demonstrated for tuning a longitudinal stability augmentation system using a realistic nonlinear flight dynamics simulation of a subscale airplane. Convergence was attained using five maneuvers that spanned approximately one minute of flight test time. Although shown for a relatively simple case, the method is general and can be applied to other aircraft, axes of motion, performance metrics, and control system designs.

Adaptive tuning↗
NASA NTRSDate not supplied

Lessons Learned in the Implementation of a Training Knowledge Management System for the SERVIR Network

The SERVIR program is a unique partnership between NASA, the U.S. Agency for International Development (USAID), focusing on building capacity to use Earth observations for addressing development challenges. In that context, between 2004 and 2020, the program delivered approximately 365 trainings to almost 10,000 professionals. More recently, between November 2020 and August 2021, the SERVIR network executed some 55 training events addressing SERVIR’s 4 priority thematic areas, and roughly a quarter of SERVIR trainings overall have focused on themes related to Ecological Forecasting. Due to the ongoing COVID-19 pandemic, almost four-fifths of recent training events have been virtual, with the remainder being in-person under limited circumstances. The large number of training events delivered represents both an opportunity and a challenge in terms of knowledge management. While the training materials developed can later be reused in other parts of the SERVIR network, prior to recently, the lack of a central repository for those materials has prevented wider dissemination and use. The recently developed Training Knowledge Management System (TKMS) is now becoming an integral part of the SERVIR Capacity Building Framework, supporting the exchange of resources and methods for conducting training activities across the network. This presentation focuses on the structure of this system, as well as on the anticipated benefits for the User Communities for Earth Observations of Terrestrial Systems.

training↗
DOE OSTI2025

Potential of deep learning methods to enhance satellite-based monitoring of nuclear power plants focusing on remote operation evaluations

The anticipated expansion of the nuclear industry and the deployment of new nuclear reactors (200 + GW of new nuclear capacity by 2050) require the development of monitoring systems that align with safety and security concerns, providing enhanced evaluation capabilities. A remote monitoring system using satellites and deep learning techniques was evaluated for its ability to detect anomalies and capture various features of nuclear reactors independently of the conditions on the ground. Satellite images of current operational and under-construction nuclear power plants were collected from Google Earth Pro as a surrogate database. Subsequently, five datasets were created from the collected images. Transfer learning technique was used for several classification tasks utilizing VGG16, ResNet50V2, Xception, DenseNet121, and MobileNetV2 pre-trained models. In the first task, the capability of the monitoring system to detect abnormal conditions or processes in a nuclear power plant was investigated. In the second task, the ability to capture operational features remotely was examined. As an example, for the purposes of this study, these features included classifying reactors based on type, power range, or onsite condition. Several evaluation metrics were used to compare the performance of the pre-trained models and the overall monitoring system. Here, the evaluation results demonstrated that deep learning techniques and pre-trained models applied to satellite images have the potential to facilitate further and expand capabilities in monitoring systems to assess plant operation details.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2000

POwer WithOut Wire (POWOW): A SEP Concept for Space Exploration

Electric propulsion has emerged as a cost-effective solution to a wide range of satellite applications. Deep Space 1 demonstrated electric propulsion as a primary propulsion source for a spacecraft. The POwer WithOut Wires (POWOW) concept has been developed as a solar electric propelled spacecraft that would travel to Mars, for example, enter selenosynchronous orbit and then use lasers to beam power to surface installations. This concept has been developed with industrial expertise in high efficiency solar cells, advanced concentrator modules, innovative arrays, and high power electric propulsion systems. The paper will present the latest version of the spacecraft, the technologies involved, possible missions and trip times to Mars and laser beaming options. The POWOW spacecraft is a general purpose solar electric propulsion system that includes technologies that are directly applicable to commercial and government spacecraft with power levels ranging from 4 kW in Low Earth Orbits (LEO) to about 1 MW. The system is modular and expandable. Learning curve costing methodologies are used to demonstrate cost effectiveness of a modular system.

Brandhorst, Henry W., Jr.↗
NASA NTRS2004

SDM Virtualness Project

In the summer of 2001, NASA and Ford Motor Company co-sponsored a research project aimed at improving the education process and application of lessons learned through distant education. The System Design & Management Program (SDM) was used as a laboratory for improving distance learning capabilities. The research project was a follow up to previous research on globally dispersed teams.

Lim, Christopher Yit-Seong↗
NASA NTRS1986

Designing with Ada for satellite simulation: A case study

A FORTRAN-operated and an Ada-oriented design for the same system are compared to learn whether an essentially different design was produced using Ada. The designs were produced by an experiment that involves the parallel development of software for a spacecraft dynamics simulator. Design differences are identified in the use of abstractions, system structure, and simulator operations. Although the designs were significantly different, this result may be influenced by some special characteristics discussed.

Agresti, William W.↗