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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Diverse Redundant Systems for Reliable Space Life Support

Reliable life support systems are required for deep space missions. The probability of a fatal life support failure should be less than one in a thousand in a multi-year mission. It is far too expensive to develop a single system with such high reliability. Using three redundant units would require only that each have a failure probability of one in ten over the mission. Since the system development cost is inverse to the failure probability, this would cut cost by a factor of one hundred. Using replaceable subsystems instead of full systems would further cut cost. Using full sets of replaceable components improves reliability more than using complete systems as spares, since a set of components could repair many different failures instead of just one. Replaceable components would require more tools, space, and planning than full systems or replaceable subsystems. However, identical system redundancy cannot be relied on in practice. Common cause failures can disable all the identical redundant systems. Typical levels of common cause failures will defeat redundancy greater than two. Diverse redundant systems are required for reliable space life support. Three, four, or five diverse redundant systems could be needed for sufficient reliability. One system with lower level repair could be substituted for two diverse systems to save cost.

life support↗
NASA NTRS2010

Constellation Ground Systems Launch Availability Analysis: Enhancing Highly Reliable Launch Systems Design

Success of the Constellation Program's lunar architecture requires successfully launching two vehicles, Ares I/Orion and Ares V/Altair, in a very limited time period. The reliability and maintainability of flight vehicles and ground systems must deliver a high probability of successfully launching the second vehicle in order to avoid wasting the on-orbit asset launched by the first vehicle. The Ground Operations Project determined which ground subsystems had the potential to affect the probability of the second launch and allocated quantitative availability requirements to these subsystems. The Ground Operations Project also developed a methodology to estimate subsystem reliability, availability and maintainability to ensure that ground subsystems complied with allocated launch availability and maintainability requirements. The verification analysis developed quantitative estimates of subsystem availability based on design documentation; testing results, and other information. Where appropriate, actual performance history was used for legacy subsystems or comparative components that will support Constellation. The results of the verification analysis will be used to verify compliance with requirements and to highlight design or performance shortcomings for further decision-making. This case study will discuss the subsystem requirements allocation process, describe the ground systems methodology for completing quantitative reliability, availability and maintainability analysis, and present findings and observation based on analysis leading to the Ground Systems Preliminary Design Review milestone.

Gernand, Jeffrey L.↗
NASA NTRS2010

Constellation Ground Systems Launch Availability Analysis: Enhancing Highly Reliable Launch Systems Design

Success of the Constellation Program's lunar architecture requires successfully launching two vehicles, Ares I/Orion and Ares V/Altair, within a very limited time period. The reliability and maintainability of flight vehicles and ground systems must deliver a high probability of successfully launching the second vehicle in order to avoid wasting the on-orbit asset launched by the first vehicle. The Ground Operations Project determined which ground subsystems had the potential to affect the probability of the second launch and allocated quantitative availability requirements to these subsystems. The Ground Operations Project also developed a methodology to estimate subsystem reliability, availability, and maintainability to ensure that ground subsystems complied with allocated launch availability and maintainability requirements. The verification analysis developed quantitative estimates of subsystem availability based on design documentation, testing results, and other information. Where appropriate, actual performance history was used to calculate failure rates for legacy subsystems or comparative components that will support Constellation. The results of the verification analysis will be used to assess compliance with requirements and to highlight design or performance shortcomings for further decision making. This case study will discuss the subsystem requirements allocation process, describe the ground systems methodology for completing quantitative reliability, availability, and maintainability analysis, and present findings and observation based on analysis leading to the Ground Operations Project Preliminary Design Review milestone.

Gernand, Jeffrey L.↗
DOE OSTI2022

Recent advances and challenges in optimization models for expansion planning of power systems and reliability optimization

Optimization models for expansion planning of power systems aim to determine capacities, investment timing, and location of power systems to satisfy the power demands while minimizing the total cost. The models have become complex in recent years to reflect both regulations on conventional energy sources and the increasing penetration of renewable energy sources (RES). This paper reviews the basic concepts and optimization models for expansion planning of power systems. We first explain the definition and features of generation expansion planning (GEP), transmission expansion planning (TEP), and generation and transmission expansion planning (GTEP). To address the computational challenges of large-scale expansion planning problems, we review several simplifications including temporal and spatial aggregation, and decomposition methods. This paper also addresses power system reliability defined as the probability of satisfying the load demand while withstanding failures of components. Finally, the goal of this paper is to provide a research overview, discuss trends in expansion planning of power systems, and suggest directions for future research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

Reliability analysis techniques for engine-out failures in main propulsion systems

Reliability formulas are derived for a liquid main propulsion system consisting of n engines, which allows for mission success in the event a single-engine controlled shutdown occurs afer some given time. The formulas include terms to reflect different reliabilities at different engine power levels, startup and throttling reliabilities, benign and catastrophic failures, and failure-distribution scale and shape parameters. These formulas can be used for reliability analyses and sensitivity studies. A parametric launch-vehicle reliability-comparison technique for conceptual preliminary design studies is also described.

Lee, Robert A.↗
NASA NTRS1973

A reliability and comparative analysis of two standby system configurations.

Equations are derived which enable one to calculate the system reliability for parallel or triple modular redundant systems with standby spares. Software error detection is introduced into the TMR/Spares system configuration in order to utilize fully all of the units. An indication of the sensitivity of the system reliability to an increase in the number of spares, partitioning, switching, variations in the powered and unpowered failures rates, and time is presented. A comparison of the parallel and the TMR/Spares system configurations, under similar conditions, is given.

Taylor, D. S.↗
DOE OSTI2020

Multi-Stage and Multi-Timescale Robust Co-Optimization Planning for Reliable and Sustainable Power Systems. Final Report

In this project, Clarkson University, in collaboration with Southern Methodist University and University of Pittsburgh, conducted the research to model, design, and implement a sophisticated generation and transmission co-optimization planning decision tool, called Multi-stage and Multi-timescale robust Co-Optimization Planning (MMCOP). The MMCOP decision tool intends to facilitate generation and transmission co-optimization planning of emerging power systems, while mitigating risks and uncertainties in both short-term operation dynamics and long-term policy and technology changes. Long-term power system planning aims at optimizing asset utilization by investing in a proper mix of various generation technologies and transmission lines to supply the future load growth. In particular, the Clean Power Plan (CPP), which is designed to combat climate change and reduce carbon emissions by setting a national limit on carbon pollution from power plants, may dramatically change the landscape of the power industry by further promoting clean energy and phasing out emissions-intensive generation technologies. In addition, novel non-wire alternatives (e.g., demand response (DR), distributed generation (DG), energy efficiency (EE), and smart grid technologies) and the computational complexity for large-scale systems significantly complicate the system planning procedure even further. However, existing conventional planning approaches neglect short-term variability and uncertainty of renewable energy, hourly chronological operation details, and physical nonlinear characteristics of the alternating current transmission network. As a result, existing conventional planning approaches may not work properly, and power systems reliability could be in jeopardy. In observing the limitations of existing conventional planning approaches and addressing new challenges of emerging power systems, the main scope of this project to develop co-optimization planning models within a multi-stage and multi-timescale framework. In particular, random contingencies, key uncertainty factors, and AC power flows are included to derive expansion plans while considering both long-term reliability and short-term flexibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Integrating Transactive Energy into Reliability Evaluation for a Self-healing Distribution System with Microgrid

Non-utility owned distributed energy resources (DERs) are mostly untapped currently, but they can provide many grid services such as voltage regulation and service restoration, if properly controlled, and can improve the distribution systems reliability when coordinated with utility-owned assets such as self-healing control and microgrids. This paper integrates transactive energy control into the distribution system reliability evaluation to quantitatively assess the impact of non-utility owned DERs on reliability improvement. Here, a transactive reactive power control strategy is designed to incentivize the DERs to provide reactive power support for improving voltage profiles thus enabling additional customer load restoration during an outage. Also, an operational sequence to coordinate the non-utility owned DERs with the utility owned self-healing control and utility owned microgrids is designed and integrated into the service restoration process with the operational constraints guaranteed by checking the three-phase unbalanced power flow for post-fault network reconfiguration. The reliability indices are then calculated through a Monte Carlo simulation. The transactive reactive power control strategy is tested on a four-feeder distribution system operated by Duke Energy in the U.S. Results demonstrate that the non-utility owned DERs with the transactive control improve the reliability of both the system and critical loads by more than 30%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Reliable statistics-based detection and investigation of anomalies in a SMART valve system

Reliable anomaly detection and diagnosis are critical for the safe operation of complex engineered systems. This study presents a unified framework that integrates statistical, model-based, and data-driven techniques for anomaly detection and investigation, demonstrated on SMART valve systems in hybrid energy applications. Four detection methods—mean deviation, seasonal extreme studentized deviate, ARIMA forecasting, and matrix profiling—were implemented and compared. Matrix profiling was particularly effective in revealing subtle deviations and hidden relationships among variables. Anomaly investigation was performed by analyzing variable-level and grouped signal profiles, with system topology incorporated to distinguish primary faults from propagated effects. Grouping signals by type enhanced interpretability, enabling accurate localization of anomalies across multi-dimensional datasets. Experimental results confirmed the framework's capability to consistently detect and isolate anomalies while providing actionable insights into system interdependencies. The proposed methodology offers a robust, interpretable, and scalable solution for condition monitoring, with potential applications in safety-critical domains such as nuclear energy, aerospace, and process industries.

ARIMA models↗
NASA NTRS1985

Dynamic Considerations for Control of Closed Life Support Systems

Reliability of closed life support systems depend on their ability to continue supplying the crew's needs during perturbations and equipment failures. The dynamic considerations interact with the basic static design through the sizing of storages, the specification of excess capacities in processors, and the choice of system initial state. A very simple system flow model was used to examine the possibilities for system failures even when there is sufficient storage to buffer the immediate effects of the perturbation. Two control schemes are shown which have different dynamic consequences in response to component failures.

Babcock, P. S.↗
DOE OSTI2025

ON THE LANGUAGE OF RELIABILITY: A SYSTEM ENGINEER PERSPECTIVE

In its classical definition, risk is defined by three elements: what can go wrong, what are its consequences and how likely is it to occur. While this definition makes sense in a regulatory based framework to estimate risk associated to power plants (in terms of core damage frequency and large early release frequency), this approach does not provide a useful snapshot of the health of the plant. A possible alternate path can start by redefining the word “risk” to a broader meaning that better reflects the needs of a system health and asset management decision making process. Rather than asking how likely an event can occur (in probabilistic terms), we can ask how far this event is from occurring. We will show how, given the data available from plant equipment reliability and monitoring/diagnostic/prognostic centers, a margin can be described and determined for all type of maintenance approaches (e.g., corrective or predictive maintenance). We will show how to link SSC margin-based reliability models to system reliability models (i.e., fault trees) in order to assess system/plant health and how to perform margin-based system calculations. These calculations are not solved using classical probabilistic calculations applied to sets (as performed by any PRA code) but, instead, through metric spaces operations (i.e., distance/margin based approach).

97 - MATHEMATICS AND COMPUTING↗