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Results for “Guaranteed performance”

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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

The Goddard High Resolution Spectrograph Scientific Support Contract

In 1988, Computer Sciences Corporation (CSC) was selected as the Goddard High Resolution Spectrograph (GHRS) Scientific Support Contractor (SSC). This was to have been a few months before the launch of NASA's first Great Observatory, the Hubble Space Telescope (HST). As one of five scientific instruments on HST, the GHRS was designed to obtain spectra in the 1050-3300 A ultraviolet wavelength region with a resolving power, lambda/Delta(lambda) , of up to 100,000 and relative photometric accuracy to 1%. It was built by Ball AeroSpace Systems Group under the guidance of the GHRS Investigation Definition Team (IDT), comprised of 16 scientists from the US and Canada. After launch, the IDT was to perform the initial instrument calibration and execute a broad scientific program during a five-year Guaranteed Time Observation (GTO) period. After a year's delay, the launch of HST occurred in April 1990, and CSC participated in the in-orbit calibration and first four years of GTO observations with the IDT. The HST primary mirror suffered from spherical aberration, which reduced the spatial and spectral resolution of Large Science Aperture (LSA) observations and decreased the throughput of the Small Science Aperture (SSA) by a factor of two. Periodic problems with the Side 1 carrousel electronics and anomalies with the low-voltage power supply finally resulted in a suspension of the use of Side 1 less than two years after launch. At the outset, the GHRS SSC task involved work in four areas: 1) to manage and operate the GHRS Data Analysis Facility (DAF); 2) to support the second Servicing Mission Observatory Verification (SMOV) program, as well as perform system engineering analysis of the GHRS as nesessary; 3) to assist the GHRS IDT with their scientific research programs, particularly the GSFC members of the team, and 4) to provide administrative and logistic support for GHRS public information and educational activities.

Source record↗
DOE OSTI2025

The rigorous upscaling of advection-dominated transport in heterogeneous porous media via the Method of Finite Averages

Systems involving advection-dominated transport through heterogeneous porous and fractured media are ubiquitous in subsurface engineering applications. However, upscaling such systems continues to challenge rigorous modeling efforts, particularly when advection is stronger than diffusion at fine spatial scales (i.e., when the Péclet number is greater than one at length scales that characterize a system’s unit-cells, representative elementary volumes, or averaging regions). Here, in this work, we propose and validate a strategy for extending the Method of Finite Averages (MoFA), a rigorous upscaling methodology for heterogeneous porous media, to upscale transport systems experiencing stronger advection than diffusion at fine scales (i.e., fine-scale Péclet numbers greater than one). We detail the strategy, the physical conditions under which it can be applied while retaining a priori modeling error guarantees, and implement the strategy to obtain a MoFA model for advective-diffusive transport that accommodates advective physics at fine spatial scales. We then perform two numerical experiments considering systems with system-scale Péclet numbers of 300 and 1000 — which correspond to fine-scale Péclet numbers of 30 and 100, respectively — to verify that the error guarantees are met under the strategy. After, we conduct a numerical study to demonstrate the strategy’s advantages over the original MoFA methodology. The results suggest that rigorously-upscaled transport models for heterogeneous porous media experiencing advective physics at finer spatial scales can be derived through MoFA and resolved orders of magnitude faster than their pore-scale counterparts. The results also suggest that the presented strategy is limited to modeling shallow concentration gradients when there are large differences between the time scales related to advection and a system’s temporally-varying boundary conditions. This limitation hinders the strategy’s practicality in modeling more advective systems, and as such, opportunity exists for developing additional strategies that accommodate rapidly-varying boundary conditions — and consequentially, steeper concentration gradients — while modeling advective systems with MoFA.

36 MATERIALS SCIENCE↗
NASA NTRS1997

Design of Neural Networks for Fast Convergence and Accuracy: Dynamics and Control

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Maghami, Peiman G.↗
DOE OSTI2022

Learning high-dimensional parametric maps via reduced basis adaptive residual networks

We propose a scalable framework for the learning of high-dimensional parametric maps via adaptively constructed residual network (ResNet) maps between reduced bases of the inputs and outputs. When just few training data are available, it is beneficial to have a compact parametrization in order to ameliorate the ill-posedness of the neural network training problem. By linearly restricting high-dimensional maps to informed reduced bases of the inputs, one can compress high-dimensional maps in a constructive way that can be used to detect appropriate basis ranks, equipped with rigorous error estimates. A scalable neural network learning framework is thus to learn the nonlinear compressed reduced basis mapping. Unlike the reduced basis construction, however, neural network constructions are not guaranteed to reduce errors by adding representation power, making it difficult to achieve good practical performance. Inspired by recent approximation theory that connects ResNets to sequential minimizing flows, we present an adaptive ResNet construction algorithm. This algorithm allows for depth-wise enrichment of the neural network approximation, in a manner that can achieve good practical performance by first training a shallow network and then adapting. We prove universal approximation of the associated neural network class for $L^2_v$ functions on compact sets. Our overall framework allows for constructive means to detect appropriate breadth and depth, and related compact parametrizations of neural networks, significantly reducing the need for architectural hyperparameter tuning. Numerical experiments for parametric PDE problems and a 3D CFD wing design optimization parametric map demonstrate that the proposed methodology can achieve remarkably high accuracy for limited training data, and outperformed other neural network strategies we compared against.

42 ENGINEERING↗
DOE OSTI2021

Rotor blade imbalance fault detection for variable-speed marine current turbines via generator power signal analysis

Marine hydrokinetic (MHK) turbines extract renewable energy from oceanic environments. However, due to the harsh conditions that these turbines operate in, system performance naturally degrades over time. Thus, ensuring efficient condition-based maintenance is imperative towards guaranteeing reliable operation and reduced costs for marine hydrokinetic power. This work proposes a novel framework aimed at identifying and classifying the severity of rotor blade pitch imbalance faults experienced by marine current turbines (MCTs). In the framework, a Continuous Morlet Wavelet Transform (CMWT) is first utilized to acquire the wavelet coefficients encompassed within the 1P frequency range of the turbine's rotor shaft. From these coefficients, several statistical indices are tabulated into a six-dimensional feature space. Next, Principle Component Analysis (PCA) is employed on the resulting feature space for dimensionality reduction, and then the application of a K-Nearest Neighbor (KNN) machine learning algorithm is utilized for fault detection and severity classification. The effectiveness of the proposed framework is validated using a high-fidelity MCT numerical simulation platform, where results demonstrate that the presence of a pitch imbalance fault can be accurately detected 100% of the time and correctly classified based upon severity more than 97% of the time.

42 ENGINEERING↗
DOE OSTI2022

Synchronization in electric power networks with inherent heterogeneity up to 100% inverter-based renewable generation

The synchronized operation of power generators is the foundation of electric power network stability and a key to the prevention of undesired power outages and blackouts. Here, we derive the conditions that guarantee synchronization in power networks with inherent generator heterogeneity when subjected to small perturbations, and perform a parametric sensitivity analysis to understand synchronization with varied types of generators. As inverter-based resources, which are the primary interfacing technology for many renewable sources of energy, have supplanted synchronous generators in ever growing numbers, the center of attention on associated integration challenges have resided primarily on the role of declining system inertia. Our results instead highlight the critical role of generator damping in achieving a stable state of synchronization. Additionally, we report the feasibility of operating interconnected electric grids with up to 100% power contribution from inverter-based renewable generation technologies. Our study has important implications as it sets the basis for the development of advanced control architectures and grid optimization methods that ensure synchronization and further pave the path towards the decarbonization of the electric power sector.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Enabling Real-Time Communication in Multi-Agent Systems: A Graph Neural Network Based Approach

Global connectivity enables effective coordination in Multi-Agent Systems (MAS). Solving these connection problems under hardware constraints is an NP-hard non-Euclidean Degree Constrained Minimum Spanning Tree (DCMST) problem. Prior MAS controllers coordinate team movement for task completion and collision avoidance; some considering Line-of-Sight (LOS) maintenance but prioritizing flexibility over guarantees. Evolutionary Algorithms (EA) have been shown to find good solutions for DCMST, but their performance degrades with larger populations required to support a large MAS. We present a method based on edge graph attention networks, trained offline to reduce online computation times. Empirical comparisons with greedy polynomial-time solvers and EA show that our method leverages latent graph information to consistently find constraint-satisfying solutions in less time.

connectivity maintenance↗
DOE OSTI2020

Improved Fireset Acceptance Using Established Evaluation Measurements

As new insight is gained into the relationship between firesets and detonators, tighter control of the circuit parameters is desired to maintain consistent and optimum system performance. An analysis of the established acceptance method reveals that it has insufficient precision to guarantee a uniform population of units. While the production has been historically very uniform, the uniformity has been primarily due to the quality and consistency of the manufacturing process rather than to the stringency of the acceptance criteria. The established method of evaluating and accepting firesets for the stockpile has been to discharge the units into a representative load and to record the output current waveform. Three characteristics of the output waveform (Peak Current, Rise Time, and Pulse Width) are then analyzed and an acceptance window is applied directly to these measurements. The purpose of this paper is to introduce a new approach that incorporates these same established measurements. However, instead of applying acceptance criteria to the three measurements directly, rather these measurements are used to evaluate the circuit values of capacitance, inductance and resistance to provide a more stringent and uniform acceptance criteria.

42 ENGINEERING↗
DOE OSTI2022

Application of Margin-Based Methods to Assess System Health

Health management of complex systems such as nuclear power plants is an essential task to guarantee system reliability. This task can be greatly enhanced by constantly monitoring asset status and performances and process such data (through anomaly detection, diagnostic, and prognostic computational algorithms) to identify asset degradation trends and faulty states. While such information and data are typically available for many of the assets, they are not propagated from the asset to the system level in order to identify the most critical assets and prioritize maintenance and surveillance activities. The main reason is driven by the fact that current reliability modeling techniques are inadequate to process such information/data. This is due to the nature of these techniques which are based on the concept of failure rate/probability that do not serve an operational context where quantitative asset health information is available. Simply stated, current reliability techniques serve a run-to-failure operational setting and not a predictive maintenance one where the goal is to perform maintenance and surveillance activities only when they are needed based on asset health. The risk informed asset management (RIAM) project is focusing on the development of a different kind of reliability modeling techniques designed to adequately serve a predictive operational setting. Such reliability techniques move aways from a failure rate/probability to a margin-based mindset where margin is here used as a metric to quantify asset health based only on current and past operational experience of the asset under consideration. In addition, margin-based reliability techniques are able to propagate asset health information from the component to system level and provide importance measure to each asset. This report summarizes a recent activity performed in collaboration with plant modernization pathway designed to integrate monitoring data into margin-based reliability models. Such activity focuses on a specific system of an existing nuclear power plant where large amount of historic monitoring data is used to monitor asset and system health.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Analysis of Niobium Electropolishing Using a Generalized Distribution of Relaxation Times Method

Using electrochemical impedance spectroscopy, we have devised a method of sensing the microscopic surface conditions on the surface of niobium as it is undergoing an electrochemical polishing (EP) treatment. The method uses electrochemical impedance spectroscopy (EIS) to gather information on the surface state of the electrode without disrupting the polishing reaction. The EIS data is analyzed using a so-called distribution of relaxation times (DRT) method. Using DRT, the EIS data can be deconvolved into discrete relaxation time peaks without any a priori knowledge of the electrode dynamics. By analyzing the relaxation time peaks, we are able to distinguish two distinct modes of the EP reaction. As the polishing voltage is increased, the electrode transitions from the low voltage EP mode, characterized by a single relaxation time peaks, to the high voltage EP mode, characterized by two relaxation time peaks. We theorize that this second peak is caused by the formation of an oxide layer on the electrode. We also find that this oxide induced peak transitions from to a negative relaxation time, which is indicative of a blocking electrode process. By analyzing EPed samples, we show that samples polished in the low voltage mode have significantly higher surface roughness due to grain etching and faceting. We find that the surface roughness of the samples only improves when the oxide film peak is present and in the negative relaxation time region. This shows that EIS combined with DRT analysis can be used to predict etching on EPed Nb. This method can also be performed before or during the EP, which could allow for adjustment of polishing parameters to guarantee a smooth cavity surface finish.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Mitigating Inter-Job Interference via Process-Level Quality-of-Service

Jobs on most high-performance computing (HPC) systems share the network with other concurrently executing jobs. Network sharing leads to contention that can severely degrade performance. Here we investigate the use of Quality of Service (QoS) mechanisms to reduce the negative impacts of network contention. QoS allows users to manage resource sharing between network flows and to provide bandwidth guarantees to specific flows. Our results show that careful use of QoS reduces the impact of network contention for specific jobs, resulting in up to a 40% performance improvement. In some cases, it completely eliminates the impact of contention. It achieves these improvements with limited negative impact to other jobs; any job that experiences performance loss typically degrades less than 5%, and often much less. Our approach can help ensure that HPC machines maintain high levels of throughput as per-node compute power continues to increase faster than network bandwidth.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1973

Optimal space communications techniques

The design, development, and analysis are reported of a digital phase-locked loop (DPLL) for FM demodulation and threshold extension. One of the features of the developed DPLL is its synchronous, real time operation. The sampling frequency is constant and all the required arithmetic and logic operations are performed within one sampling period, generating an output sequence which is converted to analog form and filtered. An equation relating the sampling frequency to the carrier frequency must be satisfied to guarantee proper DPLL operation. The synchronous operation enables a time-shared operation of one DPLL to demodulate several FM signals simultaneously. In order to obtain information about the DPLL performance at low input signal-to-noise ratios, a model of an input noise spike was introduced, and the DPLL equation was solved using a digital computer. The spike model was successful in finding a second order DPLL which yielded a five db threshold extension beyond that of a first order DPLL.

Schilling, D. L.↗
DOE OSTI2020

Cooperative Model Predictive Control Scheme for Dispersed Smart Inverters at the Grid Edge

Growing number of distributed energy resources (DERs) requires cutting-edge control schemes to ensure uniform but not identical involvement of DERs to address rapid fluctuations on voltage of the grid clusters. Designing such control schemes for a grid with high penetration of DERs connected various clusters is complex and depends on physical configuration of the clusters themselves. This paper proposes a two-layered cooperative model predictive control scheme for fleet of dispersed smart inverters connected to a three-phase grid cluster located at the edge of a grid cluster. The upper layer of the controller ensures cooperative operation of fleet of smart inverters to perform voltage regulation and address voltage fluctuations in the grid cluster; while the lower level controller guarantees each individual inverter obeys the dictated references for active/reactive powers by the upper layer controller. In addition, the proposed twolayered controller has low voltage ride through (LVRT) capability to support the grid clusters during voltage sags. By employing the proposed control approach, the smart inverters will inject desired active/reactive powers based on circumstance of the entire cluster while guaranteeing proper power curtailment among the generation units. The control scheme is verified by simulation.

Fard, Amin Y.↗
DOE OSTI2020

BigData Express: Toward Predictable, Schedulable, and High-Performance Data Transfer [Slides]

BigData Express: a schedulable, predictable, and high-performance data transfer service. A peer-to-peer, scalable, and extensible data transfer model; A visually appealing, easy-to-use web portal; A high-performance data transfer engine; On-demand provisioning of end-to-end network paths with guaranteed QoS; Robust and flexible error handling; CILogon-based security (certificate version); A rich set of REST APIs to support scientific workflows

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1993

An application of the IMC software to controller design for the JPL LSCL Experiment Facility

A software package which Integrates Model reduction and Controller design (The IMC software) is applied to design controllers for the JPL Large Spacecraft Control Laboratory Experiment Facility. Modal Cost Analysis is used for the model reduction, and various Output Covariance Constraints are guaranteed by the controller design. The main motivation is to find the controller with the 'best' performance with respect to output variances. Indeed it is shown that by iterating on the reduced order design model, the controller designed does have better performance than that obtained with the first model reduction.

Zhu, Guoming↗
NASA NTRS2022

TBIRD 200-Gbps CubeSat Downlink: System Architecture and Mission Plan

The Terabyte Infrared Delivery (TBIRD) program will establish an optical communication link from a 6U CubeSat in low-Earth orbit (LEO) to a ground station at burst rates up to 200 Gbps, resulting in data volumes that can exceed 1 Terabyte in a single pass. The space and ground terminals utilize commercially available 1550-nm coherent transceivers in conjunction with an automatic repeat request (ARQ) system to guarantee robust communication in the presence of an atmospheric fading channel. This allows the system to perform a reliable end-toend transfer of data from the payload’s 2-TB storage buffer to the ground terminal. In this paper, we describe the system architecture, link analysis, and concept of operations for the upcoming TBIRD flight demonstration in 2022. We also provide an update on the development of the 3U terminal payload and the ground terminal at JPL’s Optical Communications Telescope Laboratory (OCTL).

Piazzolla, Sabino↗
NASA NTRS2015

TTEthernet for Integrated Spacecraft Networks

Aerospace projects have traditionally employed federated avionics architectures, in which each computer system is designed to perform one specific function (e.g. navigation). There are obvious downsides to this approach, including excessive weight (from so much computing hardware), and inefficient processor utilization (since modern processors are capable of performing multiple tasks). There has therefore been a push for integrated modular avionics (IMA), in which common computing platforms can be leveraged for different purposes. This consolidation of multiple vehicle functions to shared computing platforms can significantly reduce spacecraft cost, weight, and design complexity. However, the application of IMA principles introduces significant challenges, as the data network must accommodate traffic of mixed criticality and performance levels - potentially all related to the same shared computer hardware. Because individual network technologies are rarely so competent, the development of truly integrated network architectures often proves unreasonable. Several different types of networks are utilized - each suited to support a specific vehicle function. Critical functions are typically driven by precise timing loops, requiring networks with strict guarantees regarding message latency (i.e. determinism) and fault-tolerance. Alternatively, non-critical systems generally employ data networks prioritizing flexibility and high performance over reliable operation. Switched Ethernet has seen widespread success filling this role in terrestrial applications. Its high speed, flexibility, and the availability of inexpensive commercial off-the-shelf (COTS) components make it desirable for inclusion in spacecraft platforms. Basic Ethernet configurations have been incorporated into several preexisting aerospace projects, including both the Space Shuttle and International Space Station (ISS). However, classical switched Ethernet cannot provide the high level of network determinism required by real-time spacecraft applications. Even with modern advancements, the uncoordinated (i.e. event-driven) nature of Ethernet communication unavoidably leads to message contention within network switches. The arbitration process used to resolve such conflicts introduces variation in the time it takes for messages to be forwarded. TTEthernet1 introduces decentralized clock synchronization to switched Ethernet, enabling message transmission according to a time-triggered (TT) paradigm. A network planning tool is used to allocate each device a finite amount of time in which it may transmit a frame. Each time slot is repeated sequentially to form a periodic communication schedule that is then loaded onto each TTEthernet device (e.g. switches and end systems). Each network participant references the synchronized time in order to dispatch messages at predetermined instances. This schedule guarantees that no contention exists between time-triggered Ethernet frames in the network switches, therefore eliminating the need for arbitration (and the timing variation it causes). Besides time-triggered messaging, TTEthernet networks may provide two additional traffic classes to support communication of different criticality levels. In the rate-constrained (RC) traffic class, the frame payload size and rate of transmission along each communication channel are limited to predetermined maximums. The network switches can therefore be configured to accommodate the known worst-case traffic pattern, and buffer overflows can be eliminated. The best-effort (BE) traffic class behaves akin to classical Ethernet. No guarantees are provided regarding transmission latency or successful message delivery. TTEthernet coordinates transmission of all three traffic classes over the same physical connections, therefore accommodating the full spectrum of traffic criticality levels required in IMA architectures. Common computing platforms (e.g. LRUs) can share networking resources in such a way that failures in non-critical systems (using BE or RC communication modes) cannot impact flight-critical functions (using TT communication). Furthermore, TTEthernet hardware (e.g. switches, cabling) can be shared by both TTEthernet and classical Ethernet traffic.

Loveless, Andrew↗
NASA NTRS1990

High-order neural models for error correcting code

The decoding and error-correction of data transmitted over a noisy channel is in principle equivalent to the operation of a neural network performing as a content-addressable memory. For a successful application, however, the neural network has to be capable of storing arbitrary words, and it has to be guaranteed that the stored words represent the only stable attractors of the memory. This paper presents a novel high-order neural network architecture that has these characteristics. The analog nature of the network can be used to perform soft-decision decoding with any block code. The performance in terms of postdecoding bit error rate versus SNR is demonstrated for two exemplary block codes. The comparison with a conventional decoding algorithm for a (15,5) cyclic redundancy code shows, for example, that the bit error rate at 7dB SNR can be decreased by two orders of magnitude.

Jeffries, Clark↗