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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 271 records · Page 15

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NASA NTRS1998

Neural Net-Based Redesign of Transonic Turbines for Improved Unsteady Aerodynamic Performance

A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology (RSM) and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The optimization procedure yields a modified design that improves the aerodynamic performance through small changes to the reference design geometry. The computed results demonstrate the capabilities of the neural net-based design procedure, and also show the tremendous advantages that can be gained by including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.

Madavan, Nateri K.↗

NASA NTRSDate not supplied

Predicting Melt Properties Using Atomistic Simulations With A Highly Accurate Physically Informed Neural Network Interatomic Potential

The use of a recently developed machine learning (ML) interatomic potential for molecular dynamics simulations of aluminum melt properties will be presented. Such properties are critical for process modeling in additive manufacturing, including the melt pool size, solidification, and formation of solidification microstructures. Direct first-principles modeling of these processes is computationally prohibitive whereas simulations employing ML potentials combine the high accuracy of quantum-mechanical methods with high computational speeds. The physically-informed neural network (PINN) method used herein, integrates a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. PINN potentials can accurately reproduce many properties of aluminum in both crystalline-solid and liquid phases. We examine the accuracy of a PINN Al potential in predicting the density, self-diffusivity, viscosity, and the tension of the liquid surface and liquid-solid interfaces. Comparison with experimental data and ab initio molecular dynamics calculations shows very good agreement for all properties tested.

molecular dynamics↗

NASA NTRS1993

The proposed HDTV schemes and the NASA network

Results of the simulation of high definition television (HDTV) techniques in the context of their application to transmit HDTV sequences over the NASA network are presented. Various tradeoffs involved in selecting the different services available on the NASA network are discussed. The possibility of transmitting an HDTV-like signal over the CCSDS network is investigated. Some suitable approaches are suggested.

Chen, Y. C.↗

NASA NTRSDate not supplied

Simulation of the Aerosol Size Distribution Using a Neural Network Surrogate for the Modal Aerosol Module (MAM7)

One objective of atmospheric simulations is to quantify the distribution of aerosols and their properties. Accurate parameterizations of the processes governing aerosol mass, particle number, and particle size distribution are important for predicting the Earth’s net radiative balance and aerosol-cloud interactions. The Modal Aerosol Module (MAM7) is a two-moment aerosol model that simulates mass, number, and size distribution of seven modes comprised of internally mixed aerosol species. The two-moment scheme adds significant computational expense but allows for the prediction of varying particle size distribution relative to the bulk method which predicts only total mass. In this work, we developed a neural network surrogate model for MAM7 (MAMnet) to predict the aerosol number concentration in NASA’s Global Earth Observing System (GEOS) without adding prohibitive computational expense. MAMnet, can be driven by output from a single moment, mass-based, aerosol scheme (Goddard Chemistry Aerosol and Radiation model (GOCART)) or from reanalysis products (Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)). MAMnet was trained using number concentrations from a 5-year GEOS/MAM7 simulation at 1-degree horizontal resolution and using the total mass calculated across modes as inputs, as well as temperature and air density. The model architecture for MAMnet was based on AlexNet, the 2012 winner of the ImageNet Large Scale Visual Recognition Challenge. While some modifications were necessary to accommodate our problem, important aspects of the network were preserved. MAMnet was able to reproduce zonal dynamics and spatial distributions of the aerosol number concentration however predictability in the upper troposphere was poor.

Katherine H Breen↗

NASA NTRS1975

Carbon-burning nucleosynthesis with convection

The effect of convection on carbon-burning nucleosynthesis is explored with a limited network of reactions. Convection is simulated by a series of networks at fixed mass points in the core of an evolving 15 solar mass star. Complete mixing is always assumed. Comparison to single network calculations show that the 'half-energy' approximation of Arnett yields reasonable results, although the abundances of nuclei which are created by beta-decays of unstable nuclei tend to be underestimated, by this approximation.

Endal, A. S.↗

NASA NTRS2010

Spacecraft Multiple Array Communication System Performance Analysis

The Communication Systems Simulation Laboratory (CSSL) at the NASA Johnson Space Center is tasked to perform spacecraft and ground network communication system simulations, design validation, and performance verification. The CSSL has developed simulation tools that model spacecraft communication systems and the space and ground environment in which the tools operate. In this paper, a spacecraft communication system with multiple arrays is simulated. Multiple array combined technique is used to increase the radio frequency coverage and data rate performance. The technique is to achieve phase coherence among the phased arrays to combine the signals at the targeting receiver constructively. There are many technical challenges in spacecraft integration with a high transmit power communication system. The array combining technique can improve the communication system data rate and coverage performances without increasing the system transmit power requirements. Example simulation results indicate significant performance improvement can be achieved with phase coherence implementation.

Hwu, Shian U.↗

NASA NTRS1974

Laser network survey and orbit recovery

Simulations were performed for the anticipated GEOS-C laser network stations at Goddard, Bermuda, and Florida to predict how well survey and orbit will be recovered. Lasers were added one at a time at Grand Turk, Antigua, and Panama to estimate the contribution from these additional sites. Time tag biases of 50 microseconds, survey uncertainties of 10 meters in each coordinate, laser range biases and noise estimates of 20 cm each, and conventional gravity uncertainties were included in the simulations. The results indicate that survey can be recovered to about 1 meter and Grand Turk can be recovered better than Antigua or Panama. Reducing the probably pessimistic assumed time tag biases and gravity field uncertainties improves the results. Using these survey recovery estimates, the short arc GEOS-C satellite heights for altimeter intercomparison orbits can be recovered within the calibration area to better than the required two meters.

Berbert, J. H.↗

NASA NTRS1993

Seminar Software

The Society for Computer Simulation International is a professional technical society that distributes information on methodology techniques and uses of computer simulation. The society uses NETS, a NASA-developed program, to assist seminar participants in learning to use neural networks for computer simulation. NETS is a software system modeled after the human brain; it is designed to help scientists exploring artificial intelligence to solve pattern matching problems. Examples from NETS are presented to seminar participants, who can then manipulate, alter or enhance them for their own applications.

Source record↗

NASA NTRS1993

Use of networked workstations for parallel nonlinear structural dynamic simulations of rotating bladed-disk assemblies

The principal objective of this research is to investigate, develop and demonstrate coarse-grained, parallel-processing strategies for nonlinear dynamic simulations for rotating bladed-disk assemblies. The parallel -processing strategies addressed include numerical algorithms for parallel nonlinear solutions and techniques to effect load balancing among processors. The parallel environment employed is a distributed-memory, coarse-grained one consisting of networked workstations. A parallel explicit time integration method has been implemented for transient nonlinear solutions of rotationg bladed-disk assemblies. Automatic domain partitioning techniques have been investigated for load balancing among processors. Advanced computing environments, data structures and interactive computer graphics all contribute to an integrated parallel finite element analysis system to facilitate more efficient and powerful dynamic simulations.

Hsieh, Shang-Hsien↗

NASA NTRS2003

Modelling protein functional domains in signal transduction using Maude

Modelling of protein-protein interactions in signal transduction is receiving increased attention in computational biology. This paper describes recent research in the application of Maude, a symbolic language founded on rewriting logic, to the modelling of functional domains within signalling proteins. Protein functional domains (PFDs) are a critical focus of modern signal transduction research. In general, Maude models can simulate biological signalling networks and produce specific testable hypotheses at various levels of abstraction. Developing symbolic models of signalling proteins containing functional domains is important because of the potential to generate analyses of complex signalling networks based on structure-function relationships.

Signal Transduction↗

NASA NTRS2007

Prototype of NASA's Global Precipitation Measurement Mission Ground Validation System

NASA is developing a Ground Validation System (GVS) as one of its contributions to the Global Precipitation Mission (GPM). The GPM GVS provides an independent means for evaluation, diagnosis, and ultimately improvement of GPM spaceborne measurements and precipitation products. NASA's GPM GVS consists of three elements: field campaigns/physical validation, direct network validation, and modeling and simulation. The GVS prototype of direct network validation compares Tropical Rainfall Measuring Mission (TRMM) satellite-borne radar data to similar measurements from the U.S. national network of operational weather radars. A prototype field campaign has also been conducted; modeling and simulation prototypes are under consideration.

Schwaller, M. R.↗

NASA NTRS1986

The TAVERNS emulator: An Ada simulation of the space station data communications network and software development environment

The Space Station DMS (Data Management System) is the onboard component of the Space Station Information System (SSIS) that includes the computers, networks and software that support the various core and payload subsystems of the Space Station. TAVERNS (Test And Validation Environment for Remote Networked Systems) is a distributed approach for development and validation of application software for Space Station. The TAVERNS concept assumes that the different subsystems will be developed by different contractors who may be geographically separated. The TAVERNS Emulator is an Ada simulation of a TAVERNS on the ASD VAX. The software services described in the DMS Test Bed User's Manual are being emulated on the VAX together with simulations of some of the core subsystems and a simulation of the DCN. The TAVERNS Emulator will be accessible remotely from any VAX that can communicate with the ASD VAX.

Howes, Norman R.↗

NASA NTRS2010

Spacecraft VHF Radio Propagation Analysis in Ocean Environments Including Atmospheric Effects

The Communication Systems Simulation Laboratory (CSSL) at the National Aeronautics and Space Administration (NASA)/Johnson Space Center (JSC) is tasked to perform spacecraft and ground network communication system simulations. The CSSL has developed simulation tools that model spacecraft communication systems and the space/ground environment in which they operate. This paper is to analyze a spacecraft's very high frequency (VHF) radio signal propagation and the impact to performance when landing in an ocean. Very little research work has been done for VHF radio systems in a maritime environment. Rigorous Radio Frequency (RF) modeling/simulation techniques were employed for various environmental effects. The simulation results illustrate the significance of the environmental effects on the VHF radio system performance.

Hwu, Shian↗

NASA NTRS2025

Localization of Ad-Hoc Lunar Constellations in Communication Failure Modes for Distributed Spacecraft Autonomy

As Lunar missions increase in complexity, inspired by NASA’s Artemis Program, they will require reliable and sufficient Position, Navigation, and Timing (PNT) capability to support the upcoming Lunar users. The navigation service should also be compatible with the smaller platforms, like CubeSats, being sent by the public and private sectors. A non-dedicated, ad-hoc Lunar navigation constellation can provide PNT services on-demand using the non-dedicated swarm assets. Swarm members cooperatively and autonomously localize themselves with minimal interaction from Earth, freeing up valuable bandwidth and ground segment resources. The autonomous localization of Lunar constellations utilizes neighbor two-way intersatellite link (ISL) measurements in a distributed extended Kalman filter (DEKF) system to minimize operating costs. Because the decentralized Lunar PNT system relies on relay communication amongst the agents, network failures or loss of assets among ad-hoc Lunar constellations may impact localization performance. This study presents an evaluation of localization performance under increasing levels of network degradation. A simulation of an ad-hoc Lunar PNT swarm is augmented to include system faults and the impacts of intermittent and permanent failures on localization performance are evaluated. We investigate three potential causes of network degradation: single spacecraft loss, multiple spacecraft loss, and antenna failure. The numerical assessments from the simulation show that the LPNT system under study, based on an autonomous decentralized concept of operation, is highly robust and resilient to communication failures. Minor faults, such as single spacecraft loss, solar interference, technical malfunctions, message delays, and antenna outages, have minimal impact on state estimation, with only a 4.47% and 3.75% degradation in median position error for assets and a representative ground user, respectively, compared to an ideal communication scenario. However, major faults, such as hardware failures or meteor strikes leading to the loss of multiple spacecrafts, are more concerning. The permanent loss of three spacecraft results in a more severe performance degradation, with median position error increasing by 23.3% for assets and 11.7% for a representative ground user, despite the Lunar PNT system remaining functional.

Yeji Kim↗

NASA NTRS1994

An intelligent control system for failure detection and controller reconfiguration

We present an architecture of an intelligent restructurable control system to automatically detect failure of system components, assess its impact on system performance and safety, and reconfigure the controller for performance recovery. Fault detection is based on neural network associative memories and pattern classifiers, and is implemented using a multilayer feedforward network. Details of the fault detection network along with simulation results on health monitoring of a dc motor have been presented. Conceptual developments for fault assessment using an expert system and controller reconfiguration using a neural network are outlined.

Biswas, Saroj K.↗

NASA NTRS1999

Gene Regulation Networks for Modeling Drosophila Development

This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila Melanogaster.

gene regulation morphogenesis network model↗

NASA NTRS2001

Integrating LMINET with TAAM and SIMMOD: A Feasibility Study

LMINET is a queuing network air traffic simulation model implemented at 64 large airports and the entire National Airspace System in the United States. TAAM and SIMMOD are two widely used air traffic event-driven simulation models mostly for airports. Based on our proposed Progressive Augmented window approach, TAAM and SIMMOD are integrated with LMINET though flight schedules. In the integration, the flight schedules are modified through the flight delays reported by the other models. The benefit to the local simulation study is to let TAAM or SIMMOD take the modified schedule from LMINET, which takes into account of the air traffic congestion and flight delays at the national network level. We demonstrate the value of the integrated models by the case studies at Chicago O'Hare International Airport and Washington Dulles International Airport. Details of the integration are reported and future work for a full-blown integration is identified.

Long, Dou↗

NASA NTRS2018

New Neural Network Cloud Mask Algorithm Based on Radiative Transfer Simulations

Cloud detection and screening constitute critically important first steps required to derive many satellite data products. Traditional threshold-based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and they have difficulties over areas partially covered with snow/ice. Exploiting advances in machine learning techniques and radiative transfer modeling of coupled environmental systems, we have developed a new, threshold-free cloud mask algorithm based on a neural network classifier driven by extensive radiative transfer simulations. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over snow-covered areas in the mid-latitudes. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors. Comparedto threshold-based methods and previous machine-learning approaches, this new cloud mask (i) does not rely on thresholds, (ii) needs fewer satellite channels, (iii) has superior performance during winter seasons in mid-latitude areas, and (iv) can easily be applied to different sensors.

cloud mask algorithms↗