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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Electrified Aircraft Propulsion Systems: Potential Failure Modes and Failure Mitigation Strategies

Electrified aircraft propulsion (EAP) systems hold great potential for the reduction of aircraft fuel burn, emissions, and noise. Currently, NASA and other organizations are actively working to identify and mature technologies necessary to bring EAP designs to reality. A requirement for the development of any civil aircraft and its systems is to ensure that potential hazards in the design are identified and appropriately mitigated to ensure that the system is safe. During aircraft development, a system safety assessment that consists of a functional hazard assessment is conducted to identify all potential failure conditions of each function, and classify those failures according to the severity of their effects on the aircraft or its occupants. The more severe a function's failure condition classification, the greater the development assurance level required for the function to ensure that the probability of the hazard is acceptably low. Today, aircraft engines and their control systems receive type certificate approval as a stand-alone system to signify their airworthiness. However, the complex coupling and distributed nature of EAP designs are expected to place added challenges on the certification of these systems. This presentation will provide an initial high-level review of the potential failure modes and hazards posed by a generic EAP system along with potential mitigation strategies for those failures. The EAP system is assumed to be a hybrid design consisting of gas turbine engines, mechanical drives, electric machines, power electronics and distribution systems, energy storage devices, and motor driven propulsors. The functionality provided by each of these EAP subsystems will be discussed along with the potential failure modes they may encounter. This will include a discussion of coupled failure effects, where a fault in one EAP subsystem effects the operation of other subsystems in the architecture. Next, potential failure mitigation strategies are discussed including both software-based and hardware-based mitigation strategies. The presentation will conclude with an example evaluation of the potential failure modes and mitigation strategies for a concept EAP system proposed by NASA.

Simon, Donald L.↗
NASA NTRS2002

A Framework for Creating a Function-based Design Tool for Failure Mode Identification

Knowledge of potential failure modes during design is critical for prevention of failures. Currently industries use procedures such as Failure Modes and Effects Analysis (FMEA), Fault Tree analysis, or Failure Modes, Effects and Criticality analysis (FMECA), as well as knowledge and experience, to determine potential failure modes. When new products are being developed there is often a lack of sufficient knowledge of potential failure mode and/or a lack of sufficient experience to identify all failure modes. This gives rise to a situation in which engineers are unable to extract maximum benefits from the above procedures. This work describes a function-based failure identification methodology, which would act as a storehouse of information and experience, providing useful information about the potential failure modes for the design under consideration, as well as enhancing the usefulness of procedures like FMEA. As an example, the method is applied to fifteen products and the benefits are illustrated.

Arunajadai, Srikesh G.↗
NASA NTRS1972

Failure modes, effects and criticality analyses.

Failure mode, effects and criticality analyses were developed by NASA as a means of assuring that hardware built for space applications has the desired reliability characteristics. The failure mode and effects analysis is a qualitative reliability technique for systematically analyzing each possible failure mode within a hardware system, and identifying the resulting effect on that system, the mission and personnel. The criticality analysis is a quantitative procedure which ranks the critical failure modes according to their probability of occurrence. This paper describes the failure modes, effects analysis and the criticality analysis. It employs a simple hardware system, not related to the aerospace field, to illustrate the method. It encourages application of this type of analysis to industrial development programs outside the aerospace and defense complex.

Jordan, W. E.↗
NASA NTRS1979

Mod 1 wind turbine generator failure modes and effects analysis

A failure modes and effects analysis (FMEA) was directed primarily at identifying those critical failure modes that would be hazardous to life or would result in major damage to the system. Each subsystem was approached from the top down, and broken down to successive lower levels where it appeared that the criticality of the failure mode warranted more detail analysis. The results were reviewed by specialists from outside the Mod 1 program, and corrective action taken wherever recommended.

Source record↗
NASA NTRS1972

Failure mode analysis to predict product reliability.

The failure mode analysis (FMA) is described as a design tool to predict and improve product reliability. The objectives of the failure mode analysis are presented as they influence component design, configuration selection, the product test program, the quality assurance plan, and engineering analysis priorities. The detailed mechanics of performing a failure mode analysis are discussed, including one suggested format. Some practical difficulties of implementation are indicated, drawn from experience with preparing FMAs on the nuclear rocket engine program.

Zemanick, P. P.↗
NASA NTRS2018

An Abrupt Transition to an Intergranular Failure Mode in the Near-Threshold FCG Regime in Ni-Based Superalloys

Cyclic near-threshold FCG behavior of two disk superalloys was evaluated, and was shown to exhibit an unexpected sudden failure mode transition from a mostly transgranular failure mode at higher stress intensities to an almost completely intergranular failure mode in the threshold regime. The change in failure modes was associated with a crossover effect in which the conditions that produced higher FCG rates in the Paris regime resulted in lower FCG rates and increased ΔKth values in the threshold region. High resolution scanning and transmission electron microscopy was used to carefully characterize the crack tips at these near-threshold conditions. Formation of stable Al-oxide followed by Cr and Ti oxides was found to occur at the crack tip prior to formation of unstable oxides. To contrast with the threshold failure mode regime, a quantitative assessment of the role that the intergranular failure mode has on cyclic FCG behavior in the Paris regime was also performed. It was demonstrated that the even a very limited intergranular failure content dominates the FCG response under mixed mode failure conditions.

fatigue crack growth↗
NASA NTRS2018

An Abrupt Transition to an Intergranular Failure Mode in the Near-Threshold Fatigue Crack Growth Regime in Ni-Based Superalloys

Cyclic near-threshold fatigue crack growth (FCG) behavior of two disk superalloys was evaluated and was shown to exhibit an unexpected sudden failure mode transition from a mostly transgranular failure mode at higher stress intensity factor ranges to an almost completely intergranular failure mode in the threshold regime. The change in failure modes was associated with a crossover of FCG resistance curves in which the conditions that produced higher FCG rates in the Paris regime resulted in lower FCG rates and increased Kth values in the threshold region. High-resolution scanning and transmission electron microscopy were used to carefully characterize the crack tips at these near-threshold conditions. Formation of stable Al-oxide followed by Cr-oxide and Ti-oxides was found to occur at the crack tip prior to formation of unstable oxides. To contrast with the threshold failure mode regime, a quantitative assessment of the role that the intergranular failure mode has on cyclic FCG behavior in the Paris regime was also performed. It was demonstrated that even a very limited intergranular failure content dominates the FCG response under mixed mode failure conditions.

Telesman, J.↗
NASA NTRS2000

Failure Modes and Effects Analysis (FMEA): A Bibliography

Failure modes and effects analysis (FMEA) is a bottom-up analytical process that identifies process hazards, which helps managers understand vulnerabilities of systems, as well as assess and mitigate risk. It is one of several engineering tools and techniques available to program and project managers aimed at increasing the likelihood of safe and successful NASA programs and missions. This bibliography references 465 documents in the NASA STI Database that contain the major concepts, failure modes or failure analysis, in either the basic index of the major subject terms.

Source record↗
NASA NTRS2002

Decomposition-Based Failure Mode Identification Method for Risk-Free Design of Large Systems

When designing products, it is crucial to assure failure and risk-free operation in the intended operating environment. Failures are typically studied and eliminated as much as possible during the early stages of design. The few failures that go undetected result in unacceptable damage and losses in high-risk applications where public safety is of concern. Published NASA and NTSB accident reports point to a variety of components identified as sources of failures in the reported cases. In previous work, data from these reports were processed and placed in matrix form for all the system components and failure modes encountered, and then manipulated using matrix methods to determine similarities between the different components and failure modes. In this paper, these matrices are represented in the form of a linear combination of failures modes, mathematically formed using Principal Components Analysis (PCA) decomposition. The PCA decomposition results in a low-dimensionality representation of all failure modes and components of interest, represented in a transformed coordinate system. Such a representation opens the way for efficient pattern analysis and prediction of failure modes with highest potential risks on the final product, rather than making decisions based on the large space of component and failure mode data. The mathematics of the proposed method are explained first using a simple example problem. The method is then applied to component failure data gathered from helicopter, accident reports to demonstrate its potential.

Tumer, Irem Y.↗
NASA NTRS2009

Lunar Module ECS (Environmental Control System) - Design Considerations and Failure Modes

Design considerations and failure modes for the Lunar Module (LM) Environmental Control System (ECS) are described. An overview of the the oxygen supply and cabin pressurization, atmosphere revitalization, water management and heat transport systems are provided. Design considerations including reliability, flight instrumentation, modularization and the change to the use of batteries instead of fuel cells are discussed. A summary is provided for the LM ECS general testing regime.

Interbartolo, Michael↗
NASA NTRS2002

Solar Array Arcing Failure Mode and High Voltage Array Testing

In 1998, a new failure mode for space solar arrays was discovered. A flowchart for this failure mode is presented. Since the discovery of this arc failure mode, many tactics have been used to defeat it. The arc thresholds and arc mitigation strategies must be determined in vacuum-plasma tank testing on Earth. Results from these tests must then be extrapolated to the space plasma environment. Thus, the test conditions on Earth must be adequate to reproduce the important aspects of the phenomenon in space. At Glenn Research Center, we have been testing solar arrays for their arc thresholds and sustained arcing thresholds. In this paper, we detail the test conditions for a specific set of tests-those aimed at qualifying the Boeing Solar Tile solar arrays to operate in space at very high voltages (300 V or more).

Ferguson, Dale C.↗
NASA NTRS2002

Failure Mode Identification Through Clustering Analysis

Research has shown that nearly 80% of the costs and problems are created in product development and that cost and quality are essentially designed into products in the conceptual stage. Currently, failure identification procedures (such as FMEA (Failure Modes and Effects Analysis), FMECA (Failure Modes, Effects and Criticality Analysis) and FTA (Fault Tree Analysis)) and design of experiments are being used for quality control and for the detection of potential failure modes during the detail design stage or post-product launch. Though all of these methods have their own advantages, they do not give information as to what are the predominant failures that a designer should focus on while designing a product. This work uses a functional approach to identify failure modes, which hypothesizes that similarities exist between different failure modes based on the functionality of the product/component. In this paper, a statistical clustering procedure is proposed to retrieve information on the set of predominant failures that a function experiences. The various stages of the methodology are illustrated using a hypothetical design example.

Arunajadai, Srikesh G.↗
NASA NTRS1979

Maximum likelihood estimation for life distributions with competing failure modes

The general model for the competing failure modes assuming that location parameters for each mode are expressible as linear functions of the stress variables and the failure modes act independently is presented. The general form of the likelihood function and the likelihood equations are derived for the extreme value distributions, and solving these equations using nonlinear least squares techniques provides an estimate of the asymptotic covariance matrix of the estimators. Monte-Carlo results indicate that, under appropriate conditions, the location parameters are nearly unbiased, the scale parameter is slightly biased, and the asymptotic covariances are rapidly approached.

Sidik, S. M.↗
NASA NTRS2020

Continued Discussion of Failure Mode Modeling and Overall Component Reliability: Are the Data Missing or Censored?

This paper is the continuation of a paper presented at the 13th Probabilistic Safety Assessment and Management Conference, in which a methodology of modeling failure modes of complex components was presented; see Paulos and Smith (2016). This methodology is not particularly helpful in the space industry where there is a lack of failure data, but is more helpful in industries that see a lot of component repairs and improvements, such as in the aircraft or automotive industries. The previous paper demonstrated how the typical approach of treating failure modes as being exponential in nature may yield optimistic predictions when estimating how improvements to components will perform in the future. It is more accurate to model the failure modes as a race in time; unfortunately, this does not give a closed-form solution. This paper uses simulation to solve for the model of the world, and the results compared to the standard methodology of treating the failure modes as being exponential random failures. The standard method is shown to have optimistic predictions, which will lead to prediction errors when failure modes are removed or “fixed.” The failure mode methodology presented in the first paper treated the data as being censored when the test stopped. In this paper, we will compare the results from treating the data as both censored and missing.

Smith, Curtis↗
NASA NTRSDate not supplied

Failure Modes and Mitigation Strategies for a Turboelectric Aircraft Concept with Turbine Electrified Energy Management

The electrification of gas turbine engines represents a major step-change in aircraft propulsion systems commonly known as Electrified Aircraft Propulsion (EAP). EAP involves the integration of electric machines potentially functioning as generators for large scale power extraction, as motors providing electrical augmentation of the engine spools, and even as a means of driving propulsors beyond the immediate scope of the engine. It may also include the use of energy storage. These and other characteristics of hybrid electric propulsion systems are relatively new to aircraft propulsion. The expanded propulsion architecture can be leveraged to improve propulsive efficiency and achieve better vehicle aerodynamics and controllability. It can also allow for additional benefits such as those enabled through Turbine Electrified Energy Management (TEEM). As the propulsion system and its functions expand, new failure modes are introduced. Due to the highly coupled nature of the propulsion system, failures could propagate throughout the system in ways that are unique to the new EAP architectures. To build confidence in the safety and practicality of EAP concepts, these failure modes need to be identified, explored, and addressed through mitigation strategies. This paper seeks to evaluate failure modes and failure mitigation strategies for the EAP concept known as the Single aisle Turboelectric AiRCaft with Aft Boundary Layer propulsor (STARC-ABL). The TEEM control concept is applied to improve transient operability. Several failure modes are considered and control based failure mitigation strategies are proposed with the goal of retaining operability and overall thrust. The results demonstrate the ability to maintain operability and a substantial amount of thrust in the event of various types of failures. Some challenges are also identified and discussed.

Turbine Electrified Energy Management↗
NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

reinforcement learning↗
NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗