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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Space Transportation System Availability Relationships to Life Cycle Cost

Future space transportation architectures and designs must be affordable. Consequently, their Life Cycle Cost (LCC) must be controlled. For the LCC to be controlled, it is necessary to identify all the requirements and elements of the architecture at the beginning of the concept phase. Controlling LCC requires the establishment of the major operational cost drivers. Two of these major cost drivers are reliability and maintainability, in other words, the system's availability (responsiveness). Potential reasons that may drive the inherent availability requirement are the need to control the number of unique parts and the spare parts required to support the transportation system's operation. For more typical space transportation systems used to place satellites in space, the productivity of the system will drive the launch cost. This system productivity is the resultant output of the system availability. Availability is equal to the mean uptime divided by the sum of the mean uptime plus the mean downtime. Since many operational factors cannot be projected early in the definition phase, the focus will be on inherent availability which is equal to the mean time between a failure (MTBF) divided by the MTBF plus the mean time to repair (MTTR) the system. The MTBF is a function of reliability or the expected frequency of failures. When the system experiences failures the result is added operational flow time, parts consumption, and increased labor with an impact to responsiveness resulting in increased LCC. The other function of availability is the MTTR, or maintainability. In other words, how accessible is the failed hardware that requires replacement and what operational functions are required before and after change-out to make the system operable. This paper will describe how the MTTR can be equated to additional labor, additional operational flow time, and additional structural access capability, all of which drive up the LCC. A methodology will be presented that provides the decision makers with the understanding necessary to place constraints on the design definition. This methodology for the major drivers will determine the inherent availability, safety, reliability, maintainability, and the life cycle cost of the fielded system. This methodology will focus on the achievement of an affordable, responsive space transportation system. It is the intent of this paper to not only provide the visibility of the relationships of these major attribute drivers (variables) to each other and the resultant system inherent availability, but also to provide the capability to bound the variables, thus providing the insight required to control the system's engineering solution. An example of this visibility is the need to provide integration of similar discipline functions to allow control of the total parts count of the space transportation system. Also, selecting a reliability requirement will place a constraint on parts count to achieve a given inherent availability requirement, or require accepting a larger parts count with the resulting higher individual part reliability requirements. This paper will provide an understanding of the relationship of mean repair time (mean downtime) to maintainability (accessibility for repair), and both mean time between failure (reliability of hardware) and the system inherent availability.

Rhodes, Russel E.↗
NASA NTRS2013

Determining Component Probability using Problem Report Data for Ground Systems used in Manned Space Flight

During the shuttle era NASA utilized a failure reporting system called the Problem Reporting and Corrective Action (PRACA) it purpose was to identify and track system non-conformance. The PRACA system over the years evolved from a relatively nominal way to identify system problems to a very complex tracking and report generating data base. The PRACA system became the primary method to categorize any and all anomalies from corrosion to catastrophic failure. The systems documented in the PRACA system range from flight hardware to ground or facility support equipment. While the PRACA system is complex, it does possess all the failure modes, times of occurrence, length of system delay, parts repaired or replaced, and corrective action performed. The difficulty is mining the data then to utilize that data in order to estimate component, Line Replaceable Unit (LRU), and system reliability analysis metrics. In this paper, we identify a methodology to categorize qualitative data from the ground system PRACA data base for common ground or facility support equipment. Then utilizing a heuristic developed for review of the PRACA data determine what reports identify a credible failure. These data are the used to determine inter-arrival times to perform an estimation of a metric for repairable component-or LRU reliability. This analysis is used to determine failure modes of the equipment, determine the probability of the component failure mode, and support various quantitative differing techniques for performing repairable system analysis. The result is that an effective and concise estimate of components used in manned space flight operations. The advantage is the components or LRU's are evaluated in the same environment and condition that occurs during the launch process.

Monaghan, Mark W.↗
NASA NTRS2016

Limitations of Reliability for Long-Endurance Human Spaceflight

Long-endurance human spaceflight - such as missions to Mars or its moons - will present a never-before-seen maintenance logistics challenge. Crews will be in space for longer and be farther way from Earth than ever before. Resupply and abort options will be heavily constrained, and will have timescales much longer than current and past experience. Spare parts and/or redundant systems will have to be included to reduce risk. However, the high cost of transportation means that this risk reduction must be achieved while also minimizing mass. The concept of increasing system and component reliability is commonly discussed as a means to reduce risk and mass by reducing the probability that components will fail during a mission. While increased reliability can reduce maintenance logistics mass requirements, the rate of mass reduction decreases over time. In addition, reliability growth requires increased test time and cost. This paper assesses trends in test time requirements, cost, and maintenance logistics mass savings as a function of increase in Mean Time Between Failures (MTBF) for some or all of the components in a system. In general, reliability growth results in superlinear growth in test time requirements, exponential growth in cost, and sublinear benefits (in terms of logistics mass saved). These trends indicate that it is unlikely that reliability growth alone will be a cost-effective approach to maintenance logistics mass reduction and risk mitigation for long-endurance missions. This paper discusses these trends as well as other options to reduce logistics mass such as direct reduction of part mass, commonality, or In-Space Manufacturing (ISM). Overall, it is likely that some combination of all available options - including reliability growth - will be required to reduce mass and mitigate risk for future deep space missions.

Owens, Andrew C.↗
NASA NTRS1983

Reliability considerations in the placement of control system components

This paper presents a methodology, along with applications to a grid type structure, for incorporating reliability considerations in the decision for actuator placement on large space structures. The method involves the minimization of a criterion that considers mission life and the reliability of the system components. It is assumed that the actuator gains are to be readjusted following failures, but their locations cannot be changed. The goal of the design is to suppress vibrations of the grid and the integral square of the grid modal amplitudes is used as a measure of performance of the control system. When reliability of the actuators is considered, a more pertinent measure is the expected value of the integral; that is, the sum of the squares of the modal amplitudes for each possible failure state considered, multiplied by the probability that the failure state will occur. For a given set of actuator locations, the optimal criterion may be graphed as a function of the ratio of the mean time to failure of the components and the design mission life or reservicing interval. The best location of the actuators is typically different for a short mission life than for a long one.

Montgomery, R. C.↗
NASA NTRS1989

A non-homogeneous Markov model for phased-mission reliability analysis

Three assumptions of Markov modeling for reliability of phased-mission systems that limit flexibility of representation are identified. The proposed generalization has the ability to represent state-dependent behavior, handle phases of random duration using globally time-dependent distributions of phase change time, and model globally time-dependent failure and repair rates. The approach is based on a single nonhomogeneous Markov model in which the concept of state transition is extended to include globally time-dependent phase changes. Phase change times are specified using nonoverlapping distributions with probability distribution functions that are zero outside assigned time intervals; the time intervals are ordered according to the phases. A comparison between a numerical solution of the model and simulation demonstrates that the numerical solution can be several times faster than simulation.

Smotherman, Mark↗
NASA NTRS2003

Advanced Data Acquisition Systems

Current and future requirements of the aerospace sensors and transducers field make it necessary for the design and development of new data acquisition devices and instrumentation systems. New designs are sought to incorporate self-health, self-calibrating, self-repair capabilities, allowing greater measurement reliability and extended calibration cycles. With the addition of power management schemes, state-of-the-art data acquisition systems allow data to be processed and presented to the users with increased efficiency and accuracy. The design architecture presented in this paper displays an innovative approach to data acquisition systems. The design incorporates: electronic health self-check, device/system self-calibration, electronics and function self-repair, failure detection and prediction, and power management (reduced power consumption). These requirements are driven by the aerospace industry need to reduce operations and maintenance costs, to accelerate processing time and to provide reliable hardware with minimum costs. The project's design architecture incorporates some commercially available components identified during the market research investigation like: Field Programmable Gate Arrays (FPGA) Programmable Analog Integrated Circuits (PAC IC) and Field Programmable Analog Arrays (FPAA); Digital Signal Processing (DSP) electronic/system control and investigation of specific characteristics found in technologies like: Electronic Component Mean Time Between Failure (MTBF); and Radiation Hardened Component Availability. There are three main sections discussed in the design architecture presented in this document. They are the following: (a) Analog Signal Module Section, (b) Digital Signal/Control Module Section and (c) Power Management Module Section. These sections are discussed in detail in the following pages. This approach to data acquisition systems has resulted in the assignment of patent rights to Kennedy Space Center under U.S. patent # 6,462,684. Furthermore, NASA KSC commercialization office has issued licensing rights to Circuit Avenue Netrepreneurs, LLC , a minority-owned business founded in 1999 located in Camden, NJ.

Perotti, J.↗
NASA NTRS2009

Lunar Base Life Support Failures

Dynamic simulation of the lunar outpost habitat life support was undertaken to investigate the impact of life support failures and to investigate responses. Some preparatory static analysis for the Lunar Outpost life support model, an earlier version of the model, and an investigation into the impact of Extravehicular Activity (EVA) were reported previously. (Jones, 2008-01-2184, 2008-01-2017) The earlier model was modified to include possible resupply delays, power failures, recycling system failures, and atmosphere and other material storage failures. Most failures impact the lunar outpost water balance and can be mitigated by reducing water usage. Food solids, nitrogen can be obtained only by resupply from Earth. The most time urgent failure is a lass of carbon dioxide removal capability. Life support failures might be survivable if effective operational solutions are provided in the system design.

Jones, Harry W.↗
NASA NTRSDate not supplied

In-Time Safety Assessment & Risk Prediction for Unmanned Aerial Systems

One of the critical challenges in emerging autonomous systems is timely mitigation of hazards encountered during operation which may not be known or accounted for at the time of design. Efficient execution of unmanned systems therefore demands a paradigm shift from scheduled periodic maintenance to predictive risk analysis that includes condition-based-monitoring, real-time reliability assessment and hazard mitigation. Particularly, the state-of-health parameters needs to be computed at the component level, unit level as well as the integrated system level. While in the former two levels, the physics of health propagation may be based on underlying electro-mechanical properties, system level prognostics often relies on data-driven models. Further, uncertainty from model, measurements and input sources should be accurately quantified to generate meaningful prediction results that can be fed into reliable decision making processes. Finally, the expected risk and time to failure has to be computed based on the current state-of-health of the overall system. This talk presents a conceptual design of such an in-time safety assurance approach for unmanned aerial vehicles (UAV) operating at low altitudes near and over populated areas. Typical in-flight hazard incidents include unplanned detour, proximity to obstacles, mid-flight component faults, limited battery life and poor quality of GPS measurements. Safety assessment therefore comprises trajectory generation and re-plan, battery RUL computation, distributed fault diagnostics and uncertainty management of predicted trajectory based on GPS measurement noise. The entire monitoring framework will be demonstrated on simulated as well as real UAV flight experiments conducted at the NASA Langley Research Center. This tutorial will therefore guide the audience through a step-by-step tracking of an autonomous system with focus on in-time risk prediction in the presence of unforeseen hazards and uncertain environment.

diagnostics↗
NASA NTRS1979

Vulnerability of Quick Disconnect Connectors to Carbon Fibers

Unprotected quick disconnect electrical connectors were exposed to fibers of 3mm, 7mm, and 12mm lengths. The average exposure to failure was determined. Results indicate that when the fiber length divided by the airgap is less than one, multiple fibers are required to bridge the gap, and the exposure time to failure rises rapidly.

Meyers, J. A.↗
DOE OSTI2021

StressNet - Deep learning to predict stress with fracture propagation in brittle materials

Abstract Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, StressNet, is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 h, with an average MAPE of 2% relative to test data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Microbial electrolysis cell recovery after inducing operational failure conditions

Microbial Electrolysis Cells (MECs) are often documented for their ability to produce hydrogen through new sources and new configurations. However, few studies attempt to document the recovery of the bioanode after being exposed to operational failure conditions. This study attempted to compare the behavior and recovery of MECs after being exposed to four operational failure conditions: acidification, aeration, osmotic shock, and voltage reversal. For each failure condition, three time points were tested: before failure, immediately after failure, and after recovery efforts. Of the modes tested, acidification caused the largest loss in performance and operational efficiency, while osmotic shock caused the second largest loss in performance. Aeration and voltage reversal caused negligible losses in performance immediately after failure conditions and after the recovery period. Aeration and voltage reversal had a minimal effect on the removal of individual compounds. However, acetate accumulated after acidification, and propionic acid accumulated after osmotic shock. Furthermore these findings could be useful for determining if failure will require more significant repair efforts in commercially deployed devices. While acidification created permanent losses to performance, if device failures like aeration, osmotic shock, and voltage reversal are caught early (less than 15 min), full recovery without bioanode replacement is likely.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Development and demonstration of a methodology to evaluate high burnup fuel susceptibility to pulverization under a loss of coolant transient

For economic reasons, the US nuclear industry is renewing efforts to build a technical basis to extend rod average burnup limits above the current regulatory burnup limit of 62 GWd/MTU. The primary driver is to increase pressurized water reactor cycle lengths to 24 months, reducing the number of fresh fuel assemblies and core design constraints, thereby making core energy utilization more efficient. However, fuel pellet fragmentation and pulverization, termed high burnup fuel fragmentation (HBFF), has been observed in the high burnup (>90 GWd/MTU) Halden loss-of-coolant-accident (LOCA) integral test series. The issue gained attention when fuel fragmentation and pulverization were also observed closer to the current US regulatory limit during the US Nuclear Regulatory Commission (NRC) sponsored out-of-core integral test at Studsvik Nuclear in early 2011. This led to NRC concerns with potential changes to fuel and core designs relative to fuel pellet pulverization. In a letter to the NRC Commissioners, the staff specifically identified a need to “…define the boundary of safe operation for key fuel design and operating parameters,” stating that “the staff is challenged to evaluate the acceptability of future fuel design advancements and fuel utilization changes.” As such, it can be concluded that HBFF and potential dispersal into the reactor coolant system introduces additional complications in light-water reactor (LWR) fuel safety evaluations. However, it is not clear how much fuel will be susceptible to HBFF; nor has there been a methodology developed to evaluate fuel susceptibility to HBFF. To that end, this paper proposes an analysis methodology to assess fuel susceptibility to HBFF during LOCA scenarios. The work presented here uses the BISON fuel performance code to evaluate a representative pressurized water reactor fuel rod exposed to a rod average burnup of 75 GWd/MTU. Sensitivity studies investigated the impact of the peak cladding temperature, transient fission gas released, and pre-transient fission gas release on cladding ballooning and burst timing. Subsequently, a methodology to assess fuel susceptibility to HBFF will be developed based on experimental data published in the open literature. The methodology will then be demonstrated by calculating the mass of fuel susceptibility to HBFF. The BISON results conclude that increasing peak cladding temperature drastically decreased time to failure, and decreased balloon size both of which have been confirmed experimentally. Additionally, the effect of pre-transient and transient fission gas release affected cladding balloon size and burst timing. Finally, fuel susceptibility to HBFF significantly decreased as a function of peak cladding temperature.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1995

Estimating Maintenance Demands Of A Space Station

RENEW computer program simulates maintenance events and estimates data pertinent to maintenance demands. Developed in support of Space Station Freedom Program (SSFP) Work Package 4. Uses data on reliability and maintainability (R&M) as well as logistical data to estimate both average and time-dependent maintenance demands. Estimates failure and repair times by use of Monte Carlo simulations. Written in BASIC and Assembly language.

Bream, B. L.↗
NASA NTRS2024

Extended Investigation into Fault-Tolerant Integrated Motor Drive for a Quadrotor Urban Air Mobility (UAM) Aircraft

Electric propulsor machines and their associated power electronics have been identified in past studies as low-reliability components in emerging Urban Air Mobility (UAM) Vertical Takeoff and Landing (VTOL) vehicles that raise their predicted catastrophic failure rates. This research effort extends previously presented work investigating the use of fault-tolerant (FT) motor drives in quadrotor aircraft as a promising approach to significantly increase their mean-time-to-failure (MTTF). In particular, fault-tolerant modular motor drives (FT-MMDs) are identified as strong candidates for closing the electric propulsor reliability gap. These FT-MMDs divide a machine drive into multiple redundant modules, each consisting of three phase stator windings and power electronics units, enabling continued machine operation after a module failure. Achieving the highest possible isolation between modules (physical, magnetic, thermal, electrical) is critically important in FT-MMDs to prevent the propagation of any failure between modules. Key additional requirements for achieving the highest possible reliability characteristics with FT-MMDs are high repair rates and aggressive suppression of all single-point failures.

quadrotor↗
NASA NTRS1977

Kinetic studies of stress-corrosion cracking

Use of time-to-failure curves for stress-corrosion cracking processes may lead to incorrect estimates of structural life, if material is strongly dependent upon prestress levels. Technique characterizes kinetics of crackgrowth rates and intermediate arrest times by load-level changes.

Noronha, P. J.↗
NASA NTRS2002

Advanced Self-Calibrating, Self-Repairing Data Acquisition System

An improved self-calibrating and self-repairing Data Acquisition System (DAS) for use in inaccessible areas, such as onboard spacecraft, and capable of autonomously performing required system health checks, failure detection. When required, self-repair is implemented utilizing a "spare parts/tool box" system. The available number of spare components primarily depends upon each component's predicted reliability which may be determined using Mean Time Between Failures (MTBF) analysis. Failing or degrading components are electronically removed and disabled to reduce power consumption, before being electronically replaced with spare components.

Pedro J Medelius↗
DOE OSTI2021

Machine Learning Predicts the Timing and Shear Stress Evolution of Lab Earthquakes Using Active Seismic Monitoring of Fault Zone Processes

Abstract Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab‐based studies have used acoustic emission data to predict time‐to‐failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active‐source seismic data, which probe asperity‐scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P‐wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to “learn” about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics‐informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.

58 GEOSCIENCES↗
DOE OSTI2025

Real-Time Lifetime Prediction of Semiconductor Devices Using Hardware-in-the-Loop

This paper presents a unique approach to enable real-time lifespan prediction of semiconductor power modules using a Hardware-in-the-Loop (HIL) system. By integrating the module's overall loss characteristics-specifically switching and conduction losses-with a thermoelectric model of the thermal management system, this research demonstrates that the model can dynamically estimates the junction temperature profile of the semiconductor devices in response to a changing torque demand profile for the motor drive system. This capability enables continuous monitoring of the module's operational time and cumulative stress induced on the devices to compute accumulated remaining lifetime or time-to-failure (TTF). This study provides an architectural framework for the HIL system with high-fidelity component models of multiple physical domains, allowing simulation of dynamic behaviors of a closely-coupled motor drive system. The advanced real-time computation and measurement functionalities of the HIL system allow for both dynamic lifetime calculations based on simulated data and aggregate lifetime predictions utilizing historical data. Moreover, this paper details an algorithm that not only computes cumulative damage but also synthesizes these data into a comprehensive aggregated lifetime metric. This methodology can enhance the maintenance scheduling strategies and operational reliability of semiconductor devices in critical applications, ultimately extending their service life while optimizing performance.

hardware-in-the-loop (HIL)↗