Search NASA⌕ Search

SEARCH · Search NASA

Results for “Physics of Failure”

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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Oxygen-Partial-Pressure Sensor for Aircraft Oxygen Mask

A device that generates an alarm when the partial pressure of oxygen decreases to less than a preset level has been developed to help prevent hypoxia in a pilot or other crewmember of a military or other high-performance aircraft. Loss of oxygen partial pressure can be caused by poor fit of the mask or failure of a hose or other component of an oxygen distribution system. The deleterious physical and mental effects of hypoxia cause the loss of a military aircraft and crew every few years. The device is installed in the crewmember s oxygen mask and is powered via communication wiring already present in all such oxygen masks. The device (see figure) includes an electrochemical sensor, the output potential of which is proportional to the partial pressure of oxygen. The output of the sensor is amplified and fed to the input of a comparator circuit. A reference potential that corresponds to the amplified sensor output at the alarm oxygen-partial-pressure level is fed to the second input of the comparator. When the sensed partial pressure of oxygen falls below the minimum acceptable level, the output of the comparator goes from the low state (a few millivolts) to the high state (near the supply potential, which is typically 6.8 V for microphone power). The switching of the comparator output to the high state triggers a tactile alarm in the form of a vibration in the mask, generated by a small 1.3-Vdc pager motor spinning an eccentric mass at a rate between 8,000 and 10,000 rpm. The sensation of the mask vibrating against the crewmember s nose is very effective at alerting the crewmember, who may already be groggy from hypoxia and is immersed in an environment that is saturated with visual cues and sounds. Indeed, the sensation is one of rudeness, but such rudeness could be what is needed to stimulate the crewmember to take corrective action in a life-threatening situation.

Kelly, Mark↗
NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗
NASA NTRS2014

An Integrated Reliability and Physics-Based Risk Modeling Approach for Assessing Human Spaceflight Systems

This paper presents an integrated reliability and physics-based risk modeling approach for assessing human spaceflight systems. The approach is demonstrated using an example, end-to-end risk assessment of a generic-crewed space transportation system during a reference mission to the International Space Station. The behavior of the system is modeled using analysis techniques from multiple disciplines in order to properly capture the dynamic time- and state- dependent consequences of failures encountered in different mission phases. We discuss how to combine traditional reliability analyses with Monte Carlo simulation methods and physics-based engineering models to produce loss-of- mission and loss-of-crew risk estimates supporting risk-based decision-making and requirement verification. This approach facilitates risk-informed design by providing more realistic representation of system failures and interactions; identifying key risk-driving sensitivities, dependencies, and assumptions; and tracking multiple figures of merit within a single, responsive assessment framework that can readily incorporate evolving design information throughout system development.

Risk assessment↗
DOE OSTI2024

A hardware-in-the-loop (HIL) testbed for cyber-physical energy systems in smart commercial buildings

In recent years, there has been a growing trend toward the development of smart buildings that rely on cyber-physical systems (CPS) to optimize occupant comfort, safety, and energy efficiency. To ensure the reliable and efficient operation of CPS with designed control strategies, it is important to evaluate their performance under various scenarios before deploying them in the real world. This is where a Hardware-in-the-loop (HIL) testbed designed for studying sensor and control-related studies in smart buildings can be highly valuable. With the growing threat of cyber-attacks and physical faults targeting smart buildings, it is essential to ensure the security of building operations. A HIL testbed can emulate cyber-attack and physical fault scenarios, allowing researchers to develop and test threat detection and mitigation algorithms. This enables researchers to identify potential issues and optimize the algorithms in a safe and controlled environment before they are deployed in real-world settings, reducing the risk of failures that can negatively impact occupant comfort, safety, and energy efficiency. Therefore, this paper developed a HIL testbed designed for cyber-physical energy systems (e.g. buildings automation system (BAS)) in smart commercial buildings. The HIL testbed is comprised of a real-time building and Heating, Ventilation, and Air-Conditioning (HVAC) emulator using Modelica-based dynamic models, a set of BAS controllers, and a BAS computer server. The data generation capability of the HIL testbed is demonstrated by tracking normal and faulty operating data in the BAS, as well as monitoring detailed network traffic in the local BAS network. Here, this study further demonstrates the HIL testbed’s capability by conducting case studies on real-time physical fault and cyber-attack experiments using a Department of Energy (DOE) prototype commercial building. It is anticipated that the fully functional HIL testbed will be utilized for a variety of sensor and control-related studies, including but not limited to testing, developing, validating of different HVAC control strategies, fault detection & diagnosis, energy monitoring and analysis, cyber security study, etc.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2008

Algorithms for Spectral Decomposition with Applications to Optical Plume Anomaly Detection

The analysis of spectral signals for features that represent physical phenomenon is ubiquitous in the science and engineering communities. There are two main approaches that can be taken to extract relevant features from these high-dimensional data streams. The first set of approaches relies on extracting features using a physics-based paradigm where the underlying physical mechanism that generates the spectra is used to infer the most important features in the data stream. We focus on a complementary methodology that uses a data-driven technique that is informed by the underlying physics but also has the ability to adapt to unmodeled system attributes and dynamics. We discuss the following four algorithms: Spectral Decomposition Algorithm (SDA), Non-Negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Principal Components Analysis (PCA) and compare their performance on a spectral emulator which we use to generate artificial data with known statistical properties. This spectral emulator mimics the real-world phenomena arising from the plume of the space shuttle main engine and can be used to validate the results that arise from various spectral decomposition algorithms and is very useful for situations where real-world systems have very low probabilities of fault or failure. Our results indicate that methods like SDA and NMF provide a straightforward way of incorporating prior physical knowledge while NMF with a tuning mechanism can give superior performance on some tests. We demonstrate these algorithms to detect potential system-health issues on data from a spectral emulator with tunable health parameters.

Srivastava, Askok N.↗
NASA NTRS2012

Systematic Destruction of Electronic Parts for Aid in Electronic Failure Analysis

NASA analyzes electrical, electronic, and electromechanical (EEE) parts used in space vehicles to understand failure modes of these components. Operational amplifiers and transistors are two examples of EEE parts critical to NASA missions that can fail due to electrical overstress (EOS). EOS is the result of voltage or current over time conditions that exceeds a component s specification limit. The objective of this study was to provide known voltage pulses over well-defined time intervals to determine the type and extent of damage imparted to the device. The amount of current was not controlled but measured so that pulse energy was determined. The damage was ascertained electrically using curve trace plots and optically using various metallographic techniques. The resulting data can be used to build a database of physical evidence to compare to damaged components removed from flight avionics. The comparison will provide the avionics failure analyst necessary information about voltage and times that caused flight or test failures when no other electrical data is available.

Decker, S. E.↗
DOE OSTI2026

Effect of Oil Viscosity and Impact of Ionic Liquid Additive on Electrically Induced Pitting in Rolling Contact

Electrically induced bearing damage (EIBD) is a growing concern in electric drivetrains, where electrical discharge currents generate localized pitting that accelerates wear and premature failure. While ionic liquids (ILs) have shown promise as lubricant additives due to their natural physical adsorption, strong capabilities of tribofilm formation, and high molecular tunability, their role in mitigating EIBD is little known. This gap is especially evident because lubricant viscosity and film thickness affect discharge behavior, and additive performance in these regimes has not been well explored. In this study, tribological tests were performed using a ball-on-disk pure rolling-contact system under an applied voltage on polyalphaolefin oils of three viscosities (4, 10, and 150 cSt measured at 100 °C), with and without the addition of a phosphonium-phosphate IL. Surface damage was analyzed using scanning electron microscopy (SEM) and stylus profilometry. Damage quantification was based on pit morphology analysis of SEM images. The results provide new insights into how oil viscosity governs the discharge behavior and how an IL additive could influence the pit formation. While viscosity seems to play a significant role, an IL additive shows potential to reduce EIBD and deserves further study.

electrically induced bearing damage (EIBD)↗
NASA NTRS1999

Nickel-Hydrogen Cell Testing Experience, NASA/Goddard Space Flight Center

The objectives of the project were to test the Nickel-Hydrogen Cell to: (1) verify the Aerospace Cell Flight Worthiness, (2) Elucidate the Aerospace Cell Thermal Behavior, (3) Develop the Aerospace Battery Assembly Design(s) and In-orbit Battery Management plan(s) and (4) Understand the Aerospace Cell Failure Mechanism. The tests included the LEO and GEO Life cycle tests, Calorimetric Analysis, Destructive Physical analysis, and special tests. Charts show the Mission Profile Cycling Data, Stress Cycling Data. The test data complies with the mission requirements, validating the flight worthiness of batteries. The nominal stress and mission profile cycling performance test shows the charge voltage as high as 1.60V and recharge ratio greater than 1.05. It is apparent that the electrochemical signatures alone do not provide conclusive proof for Nickel precharge. The researchers recommend a gas and positive plate analyses for further confirmation.

Rao, Gopalakrishna M.↗
NASA NTRS2002

Probabilistic Design of a Mars Sample Return Earth Entry Vehicle Thermal Protection System

The driving requirement for design of a Mars Sample Return mission is to assure containment of the returned samples. Designing to, and demonstrating compliance with, such a requirement requires physics based tools that establish the relationship between engineer's sizing margins and probabilities of failure. The traditional method of determining margins on ablative thermal protection systems, while conservative, provides little insight into the actual probability of an over-temperature during flight. The objective of this paper is to describe a new methodology for establishing margins on sizing the thermal protection system (TPS). Results of this Monte Carlo approach are compared with traditional methods.

Dec, John A.↗
NASA NTRS2006

Importance Of Quality Control in Reducing System Risk, a Lesson Learned From The Shuttle and a Recommendation for Future Launch Vehicles

This paper presents lessons learned from the Space Shuttle return to flight experience and the importance of these lessons learned in the development of new the NASA Crew Launch Vehicle (CLV). Specifically, the paper discusses the relationship between process control and system risk, and the importance of process control in improving space vehicle flight safety. It uses the External Tank (ET) Thermal Protection System (TPS) experience and lessons learned from the redesign and process enhancement activities performed in preparation for Return to Flight after the Columbia accident. The paper also, discusses in some details, the Probabilistic engineering physics based risk assessment performed by the Shuttle program to evaluate the impact of TPS failure on system risk and the application of the methodology to the CLV.

Safie, Fayssal M.↗
NASA NTRS2014

Physics-based Entry, Descent and Landing Risk Model

A physics-based risk model was developed to assess the risk associated with thermal protection system failures during the entry, descent and landing phase of a manned spacecraft mission. In the model, entry trajectories were computed using a three-degree-of-freedom trajectory tool, the aerothermodynamic heating environment was computed using an engineering-level computational tool and the thermal response of the TPS material was modeled using a one-dimensional thermal response tool. The model was capable of modeling the effect of micrometeoroid and orbital debris impact damage on the TPS thermal response. A Monte Carlo analysis was used to determine the effects of uncertainties in the vehicle state at Entry Interface, aerothermodynamic heating and material properties on the performance of the TPS design. The failure criterion was set as a temperature limit at the bondline between the TPS and the underlying structure. Both direct computation and response surface approaches were used to compute the risk. The model was applied to a generic manned space capsule design. The effect of material property uncertainty and MMOD damage on risk of failure were analyzed. A comparison of the direct computation and response surface approach was undertaken.

descent and landing↗
NASA NTRS2017

Simulated Space Environment Effects on a Candidate Solar Sail Material

For long duration missions of solar sails, the sail material needs to survive harsh space environments and the degradation of the sail material controls operational lifetime. Therefore, understanding the effects of the space environment on the sail membrane is essential for mission success. In this study, we investigated the effect of simulated space environment effects of ionizing radiation, thermal aging and simulated potential damage on mechanical, thermal and optical properties of a commercial off the shelf (COTS) polyester solar sail membrane to assess the degradation mechanisms on a feasible solar sail. The solar sail membrane was exposed to high energy electrons (about 70 keV and 10 nA/cm2), and the physical properties were characterized. After about 8.3 Grad dose, the tensile modulus, tensile strength and failure strain of the sail membrane decreased by about 20 ~ 95%. The aluminum reflective layer was damaged and partially delaminated but it did not show any significant change in solar absorbance or thermal emittance. The effect on mechanical properties of a pre-cracked sample, simulating potential impact damage of the sail membrane, as well as thermal aging effects on metallized PEN (polyethylene naphthalate) film will be discussed.

Kang, Jin Ho↗
NASA NTRS2017

Simulated Space Environment Effects on a Candidate Solar Sail Material

For long duration missions of solar sail vehicles, the sail material needs to survive the harsh space environment as the degradation of the sail material determines its operational lifetime. Therefore, understanding the effects of the space environment on the sail membrane is essential for mission success. In this study, the effect of simulated space environments of ionizing radiation and thermal aging were investigated. In order to assess some of the potential damage effects on the mechanical, thermal and optical properties of a commercial off the shelf (COTS) polyester solar sail membrane. The solar sail membrane was exposed to high energy electrons [about 70 keV and 10 nA/cm(exp. 2)], and the physical properties were characterized. After about 8.3 Grad dose, the tensile modulus, tensile strength and failure strain of the sail membrane decreased by 20 to 95%. The aluminum reflective layer was damaged and partially delaminated but it did not show any significant change in solar absorbance or thermal emittance. The mechanical properties of a precracked sample, simulating potential impact damage of the sail membrane, as well as thermal aging effects on metallized PEN (polyethylene naphthalate) film, will be discussed.

Kang, Jin Ho↗
NASA NTRSDate not supplied

NASA Systems Engineering and Safety Culture: Aerospace Project Design and Implementation Challenges

Using Systems Engineering principles and the NASA Glenn Safety procedures and protocols, a rigorous method was developed to ensure safe testing and operation of a small, commercial off the shelf turbofan engine at the Aero-Acoustic Propulsion Laboratory (AAPL). The DGEN380 is a small, ~500-lbf thrust class, high-bypass, geared- turbofan engine with a separate flow nozzle. It is a modular, compact, lightweight turbofan with Full-Authority Digital Engine Control. It’s general characteristics make it an ideal candidate for utilization as a testbed for engine aero-acoustic technology maturation in a relevant performance environment. The DGEN380 is the core component of the DGEN Aero-propulsion Research Turbofan (DART) mobile test rig. The full presentation will describe the risk assessment and mitigation process applied to the DART by the project team. The identification and analysis of failure modes led to the development of risk mitigation plans, which include administrative, engineering, and physical controls for the safe operation of the DART test rig at the AAPL facility.

Systems Engineering, Aeronautics, safety↗
DOE OSTI2024

Fuel Performance Evaluation of THOR-C Experiments

The Temperature Heatsink Overpower Response Commissioning (THOR-C) and THOR-Metal (THOR-M) experiments will be performed as part of an ongoing project for testing sodium fast reactor fuels with the Japan Atomic Energy Agency (JAEA). The THOR-C experiments consist of fresh metallic fuel pins and have been analyzed using the ABAQUS, Ansys codes and the BISON fuel performance code. THOR-M-Loss of Flow-1 (THOR-M-LOF-1) is designed to test an EBR-II irradiated fuel pin under LOF conditions. Simulation of the THOR-MLOF-1 experiment required first simulating the base irradiation of the fuel pin in EBR-II. MFUEL module of SAS4A/SASSYS-1 [1] is a physics-based metallic fuel performance model applicable to the normal operation, transient scenarios and fuel failure modeling including scenarios with bulk fuel melting. The model has been validated using EBR-II normal operation, separate effect transient tests as well as TREAT M-Series transient tests [2]. In this study, MFUEL models has been utilized together with a new capsule heat transfer model developed in this project. The new heat transfer model was necessary due to (1) significant amount of heat losses that required 2D heat transfer, (2) the presence of a titanium heat sink, rejecting a significant amount of heat, and (3) stagnant coolant conditions, which are inconsistent with SAS4A/SASSYS-1 (SAS) heat transfer model. Updates to SAS4A/SASSYS-1 and MFUEL has been described below, followed by a preliminary validation effort using the results from THOR-C-2 fresh fuel capsule experiment. A previous study for THOR-C-2 analysis using BISON code is also utilized in this study to model this test [3]. [1] D. O’Grady, A. J. Brunett, L. Ibarra, A. Karahan, T. Kim, T. S. Sumner, R. Thomas, T. H. Fanning, “The SAS4A/SASSYS-2 Version 5.7 Safety Analysis Code System,” Argonne National Laboratory,ANL/NSE-SAS/5.7, (2023). [2] A. Karahan, T. Kim, T. Fanning, D. O’Grady, “Validation of MFUEL Metal Fuel Performance Models of SAS4A/SASSYS-1,” Argonne National Laboratory, ANL/NSE-23/11, (2023). [3] M. Mihelish, A. Zabriskie, K. Paaren, P. Medvedev, C. Jensen, “Fuel Performance Predictions for the TREAT THOR-C Experiments,” Idaho National Laboratory, INL/RPT-23-73397, Revision 0, (2023)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Machine Learning and Data Science to Advance Laboratory Earthquake Prediction and Illuminate the Mechanics of Precursors to Failure

Earthquakes represent one of our greatest natural hazards and in recent years human induced seismicity is adding to the threat. Even a modest improvement in the ability to forecast devastating large earthquakes or smaller shallow events associated with fluid injection could save thousands of lives and billions of dollars. Current efforts to forecast earthquakes are limited by knowledge of earthquake physics and hampered by a lack of reliable lab or field observations. However, recent work has provided a critical opportunity for advancement. We have found: 1) clear and consistent precursors prior to earthquake-like failure in the laboratory and 2) that lab earthquakes can be predicted using machine learning (ML). These works show that stick-slip failure events –the lab equivalent of earthquakes– are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Remarkably, ML predicts the fault zone stress state, the failure time and in some cases the magnitude of lab earthquakes. In addition, the observations include clear precursors to failure in the form of changes in fault zone properties prior to lab earthquakes. Precursors have been observed in previous laboratory studies but their origin is poorly understood and their possible connection to ML based earthquake prediction is unknown. The work conducted under our project has dramatically expanded these efforts. We have developed an integrated data science approach to illuminate the physics of earthquake precursors and lab earthquake prediction. Our work has accelerated the development of ML, artificial intelligence (AI), and related data science approaches by providing massive data sets that are tightly connected to critical scientific problems and by bringing together leading subject matter experts and data scientists. Earthquake physics involves phenomena that are far from equilibrium. Our work has leveraged data science methods to illuminate these phenomena and investigate how they relate to earthquake prediction. In addition to a large database with many types of labeled events that is available to everyone, our work has advanced the fundamental understanding of seismic forecasting, earthquake physics, and fault rheology

58 GEOSCIENCES↗
NASA NTRS1987

A vector-based failure detection and isolation algorithm for a dual fail-operational redundant strapdown inertial measurement unit

A vector-based failure detection and isolation technique for a skewed array of two degree-of-freedom inertial sensors is developed. Failure detection is based on comparison of parity equations with a threshold, and isolation is based on comparison of logic variables which are keyed to pass/fail results of the parity test. A multi-level approach to failure detection is used to ensure adequate coverage for the flight control, display, and navigation avionics functions. Sensor error models are introduced to expose the susceptibility of the parity equations to sensor errors and physical separation effects. The algorithm is evaluated in a simulation of a commercial transport operating in a range of light to severe turbulence environments. A bias-jump failure level of 0.2 deg/hr was detected and isolated properly in the light and moderate turbulence environments, but not detected in the extreme turbulence environment. An accelerometer bias-jump failure level of 1.5 milli-g was detected over all turbulence environments. For both types of inertial sensor, hard-over, and null type failures were detected in all environments without incident. The algorithm functioned without false alarm or isolation over all turbulence environments for the runs tested.

Morrell, Frederick R.↗
NASA NTRS1986

Report of the Presidential Commission on the Space Shuttle Challenger Accident, Volume 1

The findings of the Commission regarding the circumstances surrounding the Challenger accident are reported and recommendations for corrective action are outlined. All available mission data, subsequent tests, and wreckage analyses were reviewed and specific failure scenarios were developed. The Commission concluded that the cause of the Mission 51-L accident was the failure of the pressure seal in the aft field joint of the right solid rocket motor. The failure was due to a faulty design unacceptably sensitive to a number of factors. These factors were the effects of temperature, physical dimensions, the character of materials, the effects of reuse, processing, and the reaction of the joint to dynamic loading. In addition to analyzing the material causes of the accident, the Commission examined the chain of decisions that culminated in approval of the launch. It concluded that the decision making process was flawed in several ways including (1) failure in communication resulting in a launch decision based on incomplete and misleading information, (2) a conflict between engineering data and management judgements, and (3) a NASA management structure that permitted flight safety problems to bypass key Shuttle managers.

Rogers, W. P.↗