Search NASA⌕ Search

SEARCH · Search NASA

Results for “unreliability”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Robust Decentralized Learning Using ADMM With Unreliable Agents

Many signal processing and machine learning problems can be formulated as consensus optimization problems which can be solved efficiently via a cooperative multi-agent system. However, the agents in the system can be unreliable due to a variety of reasons: noise, faults and attacks. Providing erroneous updates leads the optimization process in a wrong direction, and degrades the performance of distributed machine learning algorithms. This paper considers the problem of decentralized learning using ADMM in the presence of unreliable agents. First, we rigorously analyze the effect of erroneous updates (in ADMM learning iterations) on the convergence behavior of the multi-agent system. We show that the algorithm linearly converges to a neighborhood of the optimal solution under certain conditions and characterize the neighborhood size analytically. Next, we provide guidelines for network design to achieve a faster convergence to the neighborhood. Here, we also provide conditions on the erroneous updates for exact convergence to the optimal solution. Finally, to mitigate the influence of unreliable agents, we propose ROAD , a robust variant of ADMM, and show its resilience to unreliable agents with an exact convergence to the optimum.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Stochastic correlation analysis to rank the impact of intermittent wind generation on unreliability margins of power systems

Power systems are vulnerable to extreme contingencies (like an outage of a major generating substation or transmission line) that can cause significant generation and load loss and can lead to further cascading outages of other transmission facilities and generators in the system. Power systems operators take time-ahead actions (real time to day ahead) actions based on forecasted data as well as telemetry. However, there is a probability of extreme swings in wind generation that can push the power system that is already close to system boundaries into unreliable states. Increasing penetration on wind generation and uncertainties in short term forecast can move the system into operational states that can violate N-1 reliability criteria. While the power system planning accounts for worst case analyses to mitigate such emergencies, it is necessary to understand the spatial and temporal correlation of load buses with intermittent wind generation. The ranking of wind generators based on stochastic correlation to unreliability margins of load buses provides an insight of system vulnerabilities that can be corrected by appropriate emergency controls. A Monte-Carlo simulation of wind uncertainty forecast is performed to identify the probability distributions of unreliability margins. Ranking of wind generators is performed based on stochastic correlation analysis of the wind power generated and the load losses at the buses. This helps in not only identifying weak buses in the power system but also in identifying the vulnerable wind locations to plan for system reinforcements. Power system modeling and hybrid simulation to calculate unreliability margins are performed using DCAT and demonstrated on the 32-bus Nordic test system.

generators, wind forecasting, wind power generatio↗
DOE OSTI2017

Unreliable Sustainable Infrastructure: Three Transformations to Guide Cities towards Becoming Healthy 'Smart Cities'

The term 'leapfrogging' has been applied to cities and nations that have adopted a new form of infrastructure by bypassing the traditional progression of development, e.g., from no phones to cell phones - bypassing landlines all together. However, leapfrogging from unreliable infrastructure systems to 'smart' cities is too large a jump resulting in unsustainable and unhealthy infrastructure systems. In the Global South, a baseline of unreliable infrastructure is a prevalent problem. The push for sustainable and 'smart' [re]development tends to ignore many of those already living with failing, unreliable infrastructure. Without awareness of baseline conditions, uninformed projects run the risk of returning conditions to the status quo, keeping many urban populations below targets of the United Nations' Sustainable Development Goals. A key part of understanding the baseline is to identify how citizens have long learned to adjust their expectations of basic services. To compensate for poor infrastructure, most residents in the Global South invest in remedial secondary infrastructure (RSI) at the household and business levels. The authors explore three key 'smart' city transformations that address RSI within a hierarchical planning pyramid known as the comprehensive resilient and reliable infrastructure systems (CRISP) planning framework.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Verifying quantitative reliability for programs that execute on unreliable hardware

Emerging high-performance architectures are expected to contain unreliable components that may exhibit soft errors, which silently corrupt the results of computations. Full detection and masking of soft errors is challenging, expensive, and, for some applications, unnecessary. For example, approximate computing applications (such as multimedia processing, machine learning, and big data analytics) can often naturally tolerate soft errors. We introduce Rely, a programming language that enables developers to reason about the quantitative reliability of an application—namely, the probability that it produces the correct result when executed on unreliable hardware. Rely allows developers to specify the reliability requirements for each value that a function produces. We perform a static quantitative reliability analysis that verifies quantitative requirements on the reliability of an application, enabling a developer to perform sound and verified reliability engineering. The analysis takes a Rely program with a reliability specification and a hardware specification that characterizes the reliability of the underlying hardware components and verifies that the program satisfies its reliability specification when executed on the underlying unreliable hardware platform. We demonstrate the application of quantitative reliability analysis on six computations implemented in Rely.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Development of an Encoding Method on an Co-simulation Platform for Mitigating the Impact of Unreliable Communication

This report presents a hardware-in-the-loop (HIL) based modeling approach for simulating impacts of unreliable communication on the performance of centralized volt-var control and for developing an encoding method to mitigate the impacts. First, an asynchronous real-time HIL simulation platform is introduced to enable multi-rate co-simulation of a distribution system with many inverter-based distributed energy resources (DERs). The distribution system is modeled by milliseconds phasor-based models and the DERs are modeled by micro-seconds power electronic models. Communication connections between a centralized volt-var controller (modeled externally to the HIL testbed) and smart inverters are built by implementing Modbus links and the Long Term Evolution network. On this co-simulation platform, an enhanced, augmented Lagrangian multiplier based encoded data recovery (EALM-EDR) algorithm for mitigating the impact of unreliable communication is developed and validated. Simulation results demonstrate the efficacy of using the HIL-based co-simulation platform as a power grid digital twin for developing algorithms that coordinate a large number of heterogeneous control systems through wired and wireless communication links.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Unreliability of two-band model analysis of magnetoresistivities in unveiling temperature-driven Lifshitz transition

Recently, anomalies in the temperature dependences of the carrier density and/or mobility derived from analysis of the magnetoresistivities using the conventional two-band model have been used to unveil intriguing temperature-induced Lifshitz transitions in various materials. For instance, two temperature-driven Lifshitz transitions were inferred to exist in the Dirac nodal-line semimetal ZrSiSe, based on two-band model analysis of the Hall magnetoconductivities where the second band exhibits a change in the carrier type from holes to electrons when the temperature decreases below T=106K and a dip is observed in the mobility vs temperature curve at T=80K. Here, in this study, we revisit the experiments and two-band model analysis on ZrSiSe. We show that the anomalies in the second band may be spurious because the first band dominates the Hall magnetoconductivities at T>80K, making the carrier type and mobility obtained for the second band from the two-band model analysis unreliable. That is, care must be taken in interpreting these anomalies as evidence for temperature-driven Lifshitz transitions. Our skepticism on the existence of such phase transitions in ZrSiSe is further supported by the validation of Kohler's rule for magnetoresistances for T≤180K. In this paper, we showcase potential issues in interpreting anomalies in the temperature dependence of the carrier density and mobility derived from the analysis of magnetoconductivities or magnetoresistivities using the conventional two-band model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Reliability Analysis for Unreliable FSM Computations

Finite State Machines (FSMs) are fundamental in both hardware design and software development. However, the reliability of FSM computations remains poorly understood. This becomes increasingly critical as the technology scaling becomes more aggressive. Existing reliability analyses are mainly designed for generic computations and are unaware of the special error tolerance characteristics in FSM computations. In this work, we introduce RelyFSM – a state-level reliability analysis framework for FSM computations. By qualitatively reasoning about the transition structures in FSM computations, RelyFSM can precisely capture their inherent error tolerance. Basically, RelyFSM takes an FSM specification as input and analyzes its reliability for a specific execution. At the core of the analysis is a set of mathematical models that accurately define the behaviors of unreliable FSM executions. Our evaluation with a spectrum of FSM benchmarks confirms the accuracy and efficiency of RelyFSM in analyzing the reliability of real-world FSM computations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

Semi-Markov Unreliability-Range Evaluator

Reconfigurable, fault-tolerant systems modeled. Semi-Markov unreliability-range evaluator (SURE) computer program is software tool for analysis of reliability of reconfigurable, fault-tolerant systems. Based on new method for computing death-state probabilities of semi-Markov model. Computes accurate upper and lower bounds on probability of failure of system. Written in PASCAL.

Butler, Ricky W.↗
NASA NTRS1990

The cost of unreliability in the avionics suite of a launch vehicle

The main objective of the Advanced Launch System (ALS) program is to realize a substantial reduction in recurring launch costs over present launch systems. A methodology is presented for assessing the impact of the reliability of the avionics suite on the recurring launch cost of the ALS. The methodology is illustrated by focusing on two architectures. The first is a distillation of a typical architecture for this type of application. The second is a simplified implementation of the Advanced Information Processing System (AIPS) technology developed at the Charles Stark Draper Laboratory, Inc. Both architectures utilize redundancy of hardware to tolerate faults. the key contributors to the cost of unreliability are identified and modeled utilizing Markov modeling techniques for each of the two architectures. The results are presented along with the more traditional costs for these avionics suites.

Rosch, Gene↗
NASA NTRS1993

Semi-Markov Unreliability Range Evaluator

Semi-Markov Unreliability Range Evaluator, SURE, computer program is software tool for analysis of reconfigurable, fault-tolerant systems. Traditional reliability analyses based on aggregates of fault-handling and fault-occurrence models. SURE provides efficient means for calculating accurate upper and lower bounds for probabilities of death states for large class of semi-Markov mathematical models, and not merely those reduced to critical-pair architectures.

Butler, Ricky W.↗
NASA NTRS1997

Algorithms for Multiple Fault Diagnosis With Unreliable Tests

In this paper, we consider the problem of constructing optimal and near-optimal multiple fault diagnosis (MFD) in bipartite systems with unreliable (imperfect) tests. It is known that exact computation of conditional probabilities for multiple fault diagnosis is NP-hard. The novel feature of our diagnostic algorithms is the use of Lagrangian relaxation and subgradient optimization methods to provide: (1) near optimal solutions for the MFD problem, and (2) upper bounds for an optimal branch-and-bound algorithm. The proposed method is illustrated using several examples. Computational results indicate that: (1) our algorithm has superior computational performance to the existing algorithms (approximately three orders of magnitude improvement), (2) the near optimal algorithm generates the most likely candidates with a very high accuracy, and (3) our algorithm can find the most likely candidates in systems with as many as 1000 faults.

Shakeri, Mojdeh↗
DOE OSTI2015

Communication: CDFT-CI couplings can be unreliable when there is fractional charge transfer

Constrained density functional theory with configuration interaction (CDFT-CI) is a useful, low-cost tool for the computational prediction of electronic couplings between pseudo-diabatic constrained electronic states. Such couplings are of paramount importance in electron transfer theory and transition state theory, among other areas of chemistry. Unfortunately, CDFT-CI occasionally fails significantly, predicting a coupling that does not decay exponentially with distance and/or overestimating the expected coupling by an order of magnitude or more. In this communication, we show that the eigenvalues of the difference density matrix between the two constrained states can be used as an a priori metric to determine when CDFT-CI are likely to be reliable: when the eigenvalues are near 0 or ±1, transfer of a whole electron is occurring, and CDFT-CI can be trusted. We demonstrate the utility of this metric with several illustrative examples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Preliminary Results for Using Uncertainty and Out-of-distribution Detection to Identify Unreliable Predictions.

As machine learning (ML) models are deployed into an ever-diversifying set of application spaces, ranging from self-driving cars to cybersecurity to climate modeling, the need to carefully evaluate model credibility becomes increasingly important. Uncertainty quantification (UQ) provides important information about the ability of a learned model to make sound predictions, often with respect to individual test cases. However, most UQ methods for ML are themselves data-driven and therefore susceptible to the same knowledge gaps as the models themselves. Specifically, UQ helps to identify points near decision boundaries where the models fit the data poorly, yet predictions can score as certain for points that are under-represented by the training data and thus out-of-distribution (OOD). One method for evaluating the quality of both ML models and their associated uncertainty estimates is out-of-distribution detection (OODD). We combine OODD with UQ to provide insights into the reliability of the individual predictions made by an ML model.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

The semi-Markov unreliability range evaluator program

The SURE program is a design/validation tool for ultrareliable computer system architectures. The system uses simple algebraic formulas to compute accurate upper and lower bounds for the death state probabilities of a large class of semi-Markov models. The mathematical formulas used in the program were derived from a mathematical theorem proven by Allan White under contract to NASA Langley Research Center. This mathematical theorem is discussed along with the user interface to the SURE program.

Butler, R. W.↗