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At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

NASA NTRS2000

Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence Tools

In this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.

Rubaai, Ahmed↗

NASA NTRS2003

Method for Constructing Composite Response Surfaces by Combining Neural Networks with other Interpolation or Estimation Techniques

A method and system for design optimization that incorporates the advantages of both traditional response surface methodology (RSM) and neural networks is disclosed. The present invention employs a unique strategy called parameter-based partitioning of the given design space. In the design procedure, a sequence of composite response surfaces based on both neural networks and polynomial fits is used to traverse the design space to identify an optimal solution. The composite response surface has both the power of neural networks and the economy of low-order polynomials (in terms of the number of simulations needed and the network training requirements). The present invention handles design problems with many more parameters than would be possible using neural networks alone and permits a designer to rapidly perform a variety of trade-off studies before arriving at the final design.

Rai, Man Mohan↗

NASA NTRS1995

Distributed state-space generation of discrete-state stochastic models

High-level formalisms such as stochastic Petri nets can be used to model complex systems. Analysis of logical and numerical properties of these models of ten requires the generation and storage of the entire underlying state space. This imposes practical limitations on the types of systems which can be modeled. Because of the vast amount of memory consumed, we investigate distributed algorithms for the generation of state space graphs. The distributed construction allows us to take advantage of the combined memory readily available on a network of workstations. The key technical problem is to find effective methods for on-the-fly partitioning, so that the state space is evenly distributed among processors. In this paper we report on the implementation of a distributed state-space generator that may be linked to a number of existing system modeling tools. We discuss partitioning strategies in the context of Petri net models, and report on performance observed on a network of workstations, as well as on a distributed memory multi-computer.

Ciardo, Gianfranco↗

NASA NTRS1997

High-Performance Parallel Analysis of Coupled Problems for Aircraft Propulsion

Applications are described of high-performance computing methods to the numerical simulation of complete jet engines. The methodology focuses on the partitioned analysis of the interaction of the gas flow with a flexible structure and with the fluid mesh motion driven by structural displacements. The latter is treated by a ALE technique that models the fluid mesh motion as that of a fictitious mechanical network laid along the edges of near-field elements. New partitioned analysis procedures to treat this coupled three-component problem were developed. These procedures involved delayed corrections and subcycling, and have been successfully tested on several massively parallel computers, including the iPSC-860, Paragon XP/S and the IBM SP2. The NASA-sponsored ENG10 program was used for the global steady state analysis of the whole engine. This program uses a regular FV-multiblock-grid discretization in conjunction with circumferential averaging to include effects of blade forces, loss, combustor heat addition, blockage, bleeds and convective mixing. A load-balancing preprocessor for parallel versions of ENG10 was developed as well as the capability for the first full 3D aeroelastic simulation of a multirow engine stage. This capability was tested on the IBM SP2 parallel supercomputer at NASA Ames.

Felippa, C. A.↗

NASA NTRS1992

A mean field neural network for hierarchical module placement

This paper proposes a mean field neural network for the two-dimensional module placement problem. An efficient coding scheme with only O(N log N) neurons is employed where N is the number of modules. The neurons are evolved in groups of N in log N iteration steps such that the circuit is recursively partitioned in alternating vertical and horizontal directions. In our simulations, the network was able to find optimal solutions to all test problems with up to 128 modules.

Unaltuna, M. Kemal↗

NASA NTRS2011

A Self-Stabilizing Distributed Clock Synchronization Protocol for Arbitrary Digraphs

This report presents a self-stabilizing distributed clock synchronization protocol in the absence of faults in the system. It is focused on the distributed clock synchronization of an arbitrary, non-partitioned digraph ranging from fully connected to 1-connected networks of nodes while allowing for differences in the network elements. This protocol does not rely on assumptions about the initial state of the system, other than the presence of at least one node, and no central clock or a centrally generated signal, pulse, or message is used. Nodes are anonymous, i.e., they do not have unique identities. There is no theoretical limit on the maximum number of participating nodes. The only constraint on the behavior of the node is that the interactions with other nodes are restricted to defined links and interfaces. We present an outline of a deductive proof of the correctness of the protocol. A model of the protocol was mechanically verified using the Symbolic Model Verifier (SMV) for a variety of topologies. Results of the mechanical proof of the correctness of the protocol are provided. The model checking results have verified the correctness of the protocol as they apply to the networks with unidirectional and bidirectional links. In addition, the results confirm the claims of determinism and linear convergence. As a result, we conjecture that the protocol solves the general case of this problem. We also present several variations of the protocol and discuss that this synchronization protocol is indeed an emergent system.

Malekpour, Mahyar R.↗

NASA NTRS2011

Model Checking a Self-Stabilizing Distributed Clock Synchronization Protocol for Arbitrary Digraphs

This report presents the mechanical verification of a self-stabilizing distributed clock synchronization protocol for arbitrary digraphs in the absence of faults. This protocol does not rely on assumptions about the initial state of the system, other than the presence of at least one node, and no central clock or a centrally generated signal, pulse, or message is used. The system under study is an arbitrary, non-partitioned digraph ranging from fully connected to 1-connected networks of nodes while allowing for differences in the network elements. Nodes are anonymous, i.e., they do not have unique identities. There is no theoretical limit on the maximum number of participating nodes. The only constraint on the behavior of the node is that the interactions with other nodes are restricted to defined links and interfaces. This protocol deterministically converges within a time bound that is a linear function of the self-stabilization period.

Malekpour, Mahyar R.↗

NASA NTRS2011

Correctness Proof of a Self-Stabilizing Distributed Clock Synchronization Protocol for Arbitrary Digraphs

This report presents a deductive proof of a self-stabilizing distributed clock synchronization protocol. It is focused on the distributed clock synchronization of an arbitrary, non-partitioned digraph ranging from fully connected to 1-connected networks of nodes while allowing for differences in the network elements. This protocol does not rely on assumptions about the initial state of the system, and no central clock or a centrally generated signal, pulse, or message is used. Nodes are anonymous, i.e., they do not have unique identities. There is no theoretical limit on the maximum number of participating nodes. The only constraint on the behavior of the node is that the interactions with other nodes are restricted to defined links and interfaces. We present a deductive proof of the correctness of the protocol as it applies to the networks with unidirectional and bidirectional links. We also confirm the claims of determinism and linear convergence.

Malekpour, Mahyar R.↗

NASA NTRS2011

A Self-Stabilizing Synchronization Protocol for Arbitrary Digraphs

This paper presents a self-stabilizing distributed clock synchronization protocol in the absence of faults in the system. It is focused on the distributed clock synchronization of an arbitrary, non-partitioned digraph ranging from fully connected to 1-connected networks of nodes while allowing for differences in the network elements. This protocol does not rely on assumptions about the initial state of the system, other than the presence of at least one node, and no central clock or a centrally generated signal, pulse, or message is used. Nodes are anonymous, i.e., they do not have unique identities. There is no theoretical limit on the maximum number of participating nodes. The only constraint on the behavior of the node is that the interactions with other nodes are restricted to defined links and interfaces. This protocol deterministically converges within a time bound that is a linear function of the self-stabilization period. We present an outline of a deductive proof of the correctness of the protocol. A bounded model of the protocol was mechanically verified for a variety of topologies. Results of the mechanical proof of the correctness of the protocol are provided. The model checking results have verified the correctness of the protocol as they apply to the networks with unidirectional and bidirectional links. In addition, the results confirm the claims of determinism and linear convergence. As a result, we conjecture that the protocol solves the general case of this problem. We also present several variations of the protocol and discuss that this synchronization protocol is indeed an emergent system.

Malekpour, Mahyar R.↗

NASA NTRS2012

Model Checking A Self-Stabilizing Synchronization Protocol for Arbitrary Digraphs

Malekpour, Mahyar R.↗

NASA NTRS2014

Fault-Tolerant Self-Stabilizing Distributed Clock Synchronization Protocol for Arbitrary Digraphs

A self-stabilizing network in the form of an arbitrary, non-partitioned digraph includes K nodes having a synchronizer executing a protocol. K-1 monitors of each node may receive a Sync message transmitted from a directly connected node. When the Sync message is received, the logical clock value for the receiving node is set to between 0 and a communication latency value (gamma) if the clock value is less than a minimum event-response delay (D). A new Sync message is also transmitted to any directly connected nodes if the clock value is greater than or equal to both D and a graph threshold (T(sub S)). When the Sync message is not received the synchronizer increments the clock value if the clock value is less than a resynchronization period (P), and resets the clock value and transmits a new Sync message to all directly connected nodes when the clock value equals or exceeds P.

Malekpour, Mahyar R.↗

NASA NTRS1995

High-performance parallel analysis of coupled problems for aircraft propulsion

This research program deals with the application of high-performance computing methods to the numerical simulation of complete jet engines. The program was initiated in 1993 by applying two-dimensional parallel aeroelastic codes to the interior gas flow problem of a by-pass jet engine. The fluid mesh generation, domain decomposition and solution capabilities were successfully tested. Attention was then focused on methodology for the partitioned analysis of the interaction of the gas flow with a flexible structure and with the fluid mesh motion driven by these structural displacements. The latter is treated by an ALE technique that models the fluid mesh motion as that of a fictitious mechanical network laid along the edges of near-field fluid elements. New partitioned analysis procedures to treat this coupled 3-component problem were developed in 1994. These procedures involved delayed corrections and subcycling, and have been successfully tested on several massively parallel computers. For the global steady-state axisymmetric analysis of a complete engine we have decided to use the NASA-sponsored ENG10 program, which uses a regular FV-multiblock-grid discretization in conjunction with circumferential averaging to include effects of blade forces, loss, combustor heat addition, blockage, bleeds and convective mixing. A load-balancing preprocessor for parallel versions of ENG10 has been developed. It is planned to use the steady-state global solution provided by ENG10 as input to a localized three-dimensional FSI analysis for engine regions where aeroelastic effects may be important.

Felippa, C. A.↗

NASA NTRS1996

High-Performance Parallel Analysis of Coupled Problems for Aircraft Propulsion

This research program dealt with the application of high-performance computing methods to the numerical simulation of complete jet engines. The program was initiated in January 1993 by applying two-dimensional parallel aeroelastic codes to the interior gas flow problem of a bypass jet engine. The fluid mesh generation, domain decomposition and solution capabilities were successfully tested. Attention was then focused on methodology for the partitioned analysis of the interaction of the gas flow with a flexible structure and with the fluid mesh motion driven by these structural displacements. The latter is treated by a ALE technique that models the fluid mesh motion as that of a fictitious mechanical network laid along the edges of near-field fluid elements. New partitioned analysis procedures to treat this coupled three-component problem were developed during 1994 and 1995. These procedures involved delayed corrections and subcycling, and have been successfully tested on several massively parallel computers, including the iPSC-860, Paragon XP/S and the IBM SP2. For the global steady-state axisymmetric analysis of a complete engine we have decided to use the NASA-sponsored ENG10 program, which uses a regular FV-multiblock-grid discretization in conjunction with circumferential averaging to include effects of blade forces, loss, combustor heat addition, blockage, bleeds and convective mixing. A load-balancing preprocessor tor parallel versions of ENG10 was developed. During 1995 and 1996 we developed the capability tor the first full 3D aeroelastic simulation of a multirow engine stage. This capability was tested on the IBM SP2 parallel supercomputer at NASA Ames. Benchmark results were presented at the 1196 Computational Aeroscience meeting.

Felippa, C. A.↗

NASA NTRS1971

Irradiation of linear polyethylene - Partitioning between sol and gel.

Investigation of the importance of chain-scission processes and of the applicability of the general theory of network formation to polyethylene with respect to critical conditions for gelation, using molecular weight fractions of linear polyethylene irradiated at 133 C. The partitioning between sol and gel was found to adhere to the theory just beyond the gel point. Deviations from theory occurred as the irradiation dosage was increased. It was concluded that main-chain scission at the temperatures concerned is not a significant process.

Rijke, A. M.↗

DOE OSTI2025

Quantifying Message Aggregation Optimisations for Energy Savings in PGAS Models

Upon breaking past the exascale barrier, HPC systems are facing their greatest challenge yet - a power wall that must be addressed through new methods in both hardware and software. While energy costs are becoming a major issue at all levels, of particular concern is that of the network, as the relative cost of moving data is increasing faster than ever. The partitioned global address space (PGAS) model is critical within certain HPC domains, but is known to suffer from the small message problem, where irregular many-to-many access patterns result in congesting the network with excessive numbers of small messages. To address this, the conveyor aggregation library was developed to defer individual messages and group them for subsequent bulk processing. In this paper, we investigate its impact on energy use related to the network, with a focus on the Slingshot 11 interconnect. We will demonstrate that this strategy is not only highly performant, but also crucial to reducing energy footprints to remain within target power envelopes.

Welch, Aaron [ORNL]↗

NASA NTRS2000

Reliability Modeling of Microelectromechanical Systems Using Neural Networks

Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.

Perera. J. Sebastian↗

DOE OSTI2023

A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks

As a result of climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution utilities and system operators to ensure that there is uninterrupted power supply to critical loads in their networks; thus, the level of proactive preparation of the distribution system to be able to handle severe impacts of extreme weather events represents the system's resilience. One method that distribution system planners can use to prepare for future extreme events is to plan multiple microgrids which can use local generation as much as possible to supply critical loads. But partitioning an existing distribution system such that multiple feasible islands are planned and which are capable of supporting critical loads is still challenging for distribution systems - first, because of the size of the network graph partitioning problem and, second, because of the difficulty in properly formulating the desired attributes of such islands or microgrids. Therefore, this paper presents a genetic algorithm based approach that facilitates incorporating multiple objectives for grid partitioning by formulating two types of problems - node allocation and edge elimination - and it considers multiple topological and resilience-enhancing objectives. The performance of the proposed genetic algorithm-based approach is numerically evaluated on multiple test systems as well as on a real distribution feeder in Colorado, United States.

genetic algorithm↗

DOE OSTI2026

Evapotranspiration partitioning estimates from 8 methods from 47 NEON sites, 2019-2021

This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.

EARTH SCIENCE > ATMOSPHERE↗