SEARCH · Engineering Papers

Results for “degradation diagnostics”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Huge ensembles – Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators

Abstract. Simulating low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1000–10 000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part 1, we construct an ensemble weather forecasting system based on spherical Fourier neural operators (SFNOs), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest-growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. With large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states during the rollout, and the ML ensemble passes a crucial spectral test in the literature. The IFS and ML ensembles have similar extreme forecast indices, and we show that the ML extreme weather forecasts are reliable and discriminating. These diagnostics ensure that the ensemble can reliably simulate the time evolution of the atmosphere, including low-likelihood high-impact extremes. In Part 2, we generate a huge ensemble initialized each day in summer 2023, and we characterize the simulations of extremes.

Mahesh, Ankur↗

NASA NTRS2021

High Energy Particle Excitations as a Measure of Electronic Crosstalk in MODIS and VIIRS VIS/NIR Bands

The MODIS instruments aboard the Terra and Aqua satellites and the VIIRS instruments aboard the SNPP and NOAA-20 satellites each contain several arrays of Si detectors that measure Earth-reflected radiance in the visible and near-infrared spectral range. Even in the absence of incident light, the Si detectors are occasionally excited by high energy charged particles that pass through the spacecraft. These particle radiation events are, fortunately, infrequent enough that they do not lead to significant degradation of the detectors and they do not have a significant impact on the Earth scene radiance images. On the other hand, they are frequent enough that the cumulative data from many years on orbit may provide valuable diagnostic information about the sensors. In this paper, we provide some basic statistics on the frequency and magnitude of the particle excitation events for MODIS and VIIRS and explore the usefulness of this data as a measure of electronic crosstalk. Large amounts of crosstalk can degrade the quality of the Earth images, so it is crucial to have methods to characterize and correct for it on-orbit, which has previously been done for MODIS using lunar image analysis. The particle excitations can manifest as single-pixel spikes in the otherwise dark space view background, which may be an ideal source for evaluating crosstalk. We derive crosstalk coefficients between the NIR band detectors of Terra MODIS, and compare them to coefficients previously derived from lunar observations. The same approach is applied to SNPP VIIRS, which does not show any significant electronic crosstalk. While the HgCdTe detectors used in the MODIS and VIIRS infrared bands can also be excited by particle radiation, the magnitudes of the excitations are much smaller compared to the Si detectors and in general are not large enough to be useful for examining crosstalk.

MODIS↗

DOE OSTI2025

Optimizing Simulation Fidelity in Direct-Drive Inertial Confinement Fusion with Cassio

Recently, the National Ignition Facility (NIF) demonstrated that inertial confinement fusion (ICF) is capable to achieve thermonuclear (TN) ignition in the laboratory, making it a crucial method on the path to replicate the Sun’s power production mechanism on Earth. However, the physics governing the high-energy density environments is very complex and remains a challenge to fully understand and model. For example, dopants in the TN fuel are important diagnostic tools to extract the thermodynamic conditions of the plasma. However, if their concentration is chosen too high, they can significantly degrade the performance of an ICF capsule. In this study, we use the Los Alamos National Laboratory radiation-hydrodynamics code Cassio to model ICF implosions of capsules which contain deuterium fuel with high-Z dopants like Krypton and Argon from the high-Z campaign conducted 15 years ago. We focus on how chosen computational and physics parameters influence the implosion outcomes. By systematically changing the resolution of the computational mesh and the photon energies as well as modifying settings for the laser drive and TN fuel pre-heat effects, we assess the impact on experimentally measured performance metrics like neutron production from TN burn and x-ray emission during the implosion. Our results will help to improve the fidelity of simulations and guide future numerical studies and experimental designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1986

A study of Kapton degradation under simulated shuttle environment

A system was developed which employs a source of low energy oxygen ion to simulate the shuttle low Earth orbit environment. This source, together with diagnostic tools including surface analysis ans mass spectroscopic capability, was used to measure the dependence of ion energy of the oxygen induced CO signals from pyrolytic graphite and Kapton. For graphite the CO signal was examined at energies ranging form 4.5 to 465 eV and for Kapton from 4.5 to 188 eV. While the relative quantum yields inferred from the data are reasonably precise, there are large uncertainties in the absolute yields because of the assumptions necessary to covert the measured signal strengths to quantum yields. These assumptions are discussed in detail.

Eck, T. G.↗

NASA NTRS1996

Fault Diagnosis of Power Systems Using Intelligent Systems

The power system operator's need for a reliable power delivery system calls for a real-time or near-real-time Al-based fault diagnosis tool. Such a tool will allow NASA ground controllers to re-establish a normal or near-normal degraded operating state of the EPS (a DC power system) for Space Station Alpha by isolating the faulted branches and loads of the system. And after isolation, re-energizing those branches and loads that have been found not to have any faults in them. A proposed solution involves using the Fault Diagnosis Intelligent System (FDIS) to perform near-real time fault diagnosis of Alpha's EPS by downloading power transient telemetry at fault-time from onboard data loggers. The FDIS uses an ANN clustering algorithm augmented with a wavelet transform feature extractor. This combination enables this system to perform pattern recognition of the power transient signatures to diagnose the fault type and its location down to the orbital replaceable unit. FDIS has been tested using a simulation of the LeRC Testbed Space Station Freedom configuration including the topology from the DDCU's to the electrical loads attached to the TPDU's. FDIS will work in conjunction with the Power Management Load Scheduler to determine what the state of the system was at the time of the fault condition. This information is used to activate the appropriate diagnostic section, and to refine if necessary the solution obtained. In the latter case, if the FDIS reports back that it is equally likely that the faulty device as 'start tracker #1' and 'time generation unit,' then based on a priori knowledge of the system's state, the refined solution would be 'star tracker #1' located in cabinet ITAS2. It is concluded from the present studies that artificial intelligence diagnostic abilities are improved with the addition of the wavelet transform, and that when such a system such as FDIS is coupled to the Power Management Load Scheduler, a faulty device can be located and isolated from the rest of the system. The benefit of these studies provides NASA with the ability to quickly restore the operating status of a space station from a critical state to a safe degraded mode, thereby saving costs in experimentation rescheduling, fault diagnostics, and prevention of loss-of-life.

Momoh, James A.↗

NASA NTRS1975

Processes of lunar crater degradation - Changes in style with geologic time

Relative age schemes of crater degradation are calibrated to radiometric dates obtained from lunar samples, changes in morphologic features are analyzed, and the style and rate of lunar surface degradation processes are modeled in relation to lunar geologic time. A comparison of radiometric age scales and the relative degradation of morphologic features for craters larger than about 5 km in diameter shows that crater degradation can be divided into two periods: Period I, prior to about 3.9 billion years ago and characterized by a high meteoritic influx rate and the formation of large multiringed basins, and Period II, from about 3.9 billion years ago to the present and characterized by a much lower influx rate and a lack of large multiringed basins. Diagnostic features for determining the relative ages of craters are described, and crater modification processes are considered, including primary impacts, lateral sedimentation, proximity weathering, landslides, and tectonism. It is suggested that the fundamental degradation of early Martian craters may be associated with erosional and depositional processes related to the intense bombardment characteristics of Period I.

Head, J. W.↗

DOE OSTI2025

In-flight performance of the XRISM/Resolve detector system

The Resolve instrument was launched on board the XRISM observatory in early September 2023. The Resolve spectrometer is based on a high-sensitivity X-ray calorimeter detector system (DS) that has been successfully deployed in many ground and sub-orbital spectrometers. However, the Resolve instrument is the first long-term implementation in space. The instrument will provide essential diagnostics for nearly every class of X-ray emitting objects, from galactic supernova remnants to the outskirts of galaxy clusters, without degradation for spatially extended objects. The Resolve DS consists of a 36-pixel microcalorimeter array operated at a heat sink temperature of 50 mK. In pre-flight testing, the DS demonstrated a resolving power of better than 1300 at 6 keV with a simultaneous bandpass from below 0.3 keV to above 12 keV and a timing precision better than 100μs. An anti-coincidence detector placed directly behind the microcalorimeter array effectively suppresses background. The detector energy-resolution budget included terms for interference from the Resolve cooling system and the spacecraft. Additional terms for energy-scale stability, on-orbit effects, and use of mid-grade events were also included, predicting an end-of-life, on-orbit performance for high- and mid-resolution grade events that meet the requirement of 7 eV FWHM at 6 keV. Here, we discuss the actual on-orbit performance of the Resolve DS and compare this with the performance in pre-flight testing, on-orbit predictions, and the almost identical Hitomi/SXS instrument. We will also discuss the on-orbit gain stability, an assessment of on-orbit interference, and measurements of the on-orbit background.

Astronomy and AstroPhysics↗

DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2013

Strain - The Heart of the Matter

Objective: Identify how much exercise is needed to maintain pre-bedrest / pre-spaceflight strength, minimize any flight complications and reduce time required to reacclimate to Earth's gravity. Subjects volunteered to participate in a 70 day 6deg head down tilt (HDT) study at UTMB hospital in Galveston. HDT are used to study microgravity for several reasons: 1. Allows subjects to experience atrophy of lower extremities from disuse. 2. Allows scientist to study the fluid shifts and the subject's cardiovascular and physiological effects. 3. Allows several studies to be measured and conducted simultaneously in a safe and monitored environment. Throughout those 10 weeks, subjects were either in an exercise or control group. Never before has cardiac strain been monitored at set time points throughout spaceflight or bed rest study. Once we clearly understand this technique, protocols can be formulated for astronauts to use on the ISS as both a diagnostic and monitoring tool. Echoes could have the possibility to predict & monitor heart health, cardiac strength, endurance, and overall rate of muscle degradation. Also, echoes can act as an additional factor in determining exercise prescriptions and effectiveness.

Gapinski, Wendy R.↗

DOE OSTI2023

Health Assessment and Performance Monitoring of Large Machine Diagnostics

Diagnostic machines are a crucial component of the Stockpile Stewardship Program for data collection, toward the ultimate goal of improving our understanding of nuclear physics. These systems consist of interactions between many complex components, and require regular maintenance for acceptable performance. The Cygnus X-ray diagnostic located at the Nevada National Security Site's U1a underground facility which provides radiographic data for subcritical experiments, is a prime example of these systems. Component degradation and failures within Cygnus can result in system downtime and data loss, affecting schedules and increasing experiment costs. To evade such failures, years worth of data on machine performance has been collected on the two Cygnus axes in the form of voltage and current measurements of Cygnus' various components. Using these as input, we have developed machine learning techniques for assessing the health of Cygnus, with the ultimate goal of predicting declining performance and machine failure.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2021

Aircraft Engine Run-to-Failure Dataset Under Real Flight Conditions for Prognostics and Diagnostics

A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications because failures are rare in many safety-critical systems. To foster the development of prognostics methods, we develop a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) model developed at NASA. The damage propagation modelling used in this dataset builds on the modelling strategy from previous work and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet. Second, it extends the degradation modelling by relating the degradation process to its operation history. This dataset also provides the health respectively fault class. Therefore, besides its applicability to prognostics problems, the dataset can be used for fault diagnostics.

CMAPPS↗

DOE OSTI2020

Cherenkov Gamma Ray Detectors on High-Energy-Density Systems

Cherenkov gamma ray detectors are a valuable diagnostic technique for HED systems. Inertial confinement fusion is working towards high gain ignition. Gamma rays from ICF systems can help inform the degradation mechanisms preventing ignition: Carbon ablator is not being degraded, unlike the DT fuel; carbon γ timing can estimate shell energy during bangtime and may suggest hot spot cooling rate; and Cherenkov detectors are also used for high spatial and temporal resolution ICF mix studies on OMEGA. Cherenkov systems also inform energy spectrum of radiographic sources: ARC laser and Mercury pulsed power machine.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2009

Optical Diagnostics for High-Temperature Thermal Barrier Coatings

Thermal barrier coatings (TBCs) are typically composed of translucent ceramic oxides that provide thermal protection for metallic components exposed to high-temperature environments, such as in jet turbine engines. Taking advantage of the translucent nature of TBCs, optical diagnostics have been developed that can provide an informed assessment of TBC health that will allow mitigating action to be taken before TBC degradation threatens performance or safety. In particular, rare-earth-doped luminescent sublayers have been integrated into the TBC structure to produce luminescence that monitors TBC erosion, delamination, and temperature gradients. Erosion monitoring of TBC-coated specimens is demonstrated by utilizing visible luminescence that is excited from a sublayer that is exposed by erosion. TBC delamination monitoring is achieved in TBCs with a base rare-earth-doped luminescent sublayer by the reflectance-enhanced increase in luminescence produced in regions containing buried delamination cracks. TBC temperature monitoring is demonstrated using the temperature-dependent decay time for luminescence originating from the specific coating depth associated with a rare-earth-doped luminescent sublayer. The design and implementation of these TBCs with integrated luminescent sublayers is discussed, including co-doping strategies to produce more penetrating near-infrared luminescence. It is demonstrated that integration of the rare-earth-doped sublayers is achieved with no reduction in TBC life. In addition, results for multilayer TBCs designed to also perform as radiation barriers are also presented.

Eldridge, Jeffrey I.↗

NASA NTRS2016

In-Flight Performance of the Soft X-Ray Spectrometer Detector System on ASTRO-H

The SXS instrument was launched aboard the Astro-H observatory on February 17, 2016. The SXS spectrometer is based on a high sensitivity x-ray calorimeter detector system that has been successfully deployed in many ground and sub-orbital spectrometers. The instrument was to provide essential diagnostics for nearly every class of x-ray emitting objects from the atmosphere of Jupiter to the outskirts of galaxy clusters, without degradation for spatially extended objects. The SXS detector system consisted of a 36-pixel cryogenic microcalorimeter array operated at a heat sink temperature of 50 mK. In pre-flight testing, the detector system demonstrated a resolving power of better than 1300 at 6 keV with a simultaneous band-pass from below 0.3 keV to above 12 keV with a timing precision better than 100 microsecond. In addition, a solid-state anti-coincidence detector was placed directly behind the detector array for background suppression. The detector error budget included the measured interference from the SXS cooling system and the spacecraft. Additional margin for on-orbit gain-stability, and on-orbit spacecraft interference were also included predicting an on-orbit performance that meets or exceeds the 7 eV FWHM at 6 keV requirement. The actual on-orbit spectral resolution was better than 5 eV FWHM at 6 keV, easily satisfying the instrument requirement. Here we discuss the actual on-orbit performance of the SXS detector system and compare this to performance in pre-flight testing and the on-orbit predictions. We will also discuss the on-orbit gain stability, additional on-orbit interference, and measurements of the on-orbit background.

X-ray astrophysics↗

DOE OSTI2025

Risk-informed Graded Approach for Reliability and Performance Assessment of Machine Learning and Artificial Intelligence for Advanced Condition Monitoring Techniques

With the shift away from time-based maintenance and toward condition-based maintenance, and to reduce overall maintenance costs, there has been an upsurge in the usage and development of advanced condition monitoring (ACM) techniques for real-time monitoring of nuclear power plant (NPP) components. ACM is particularly useful in the development of digital twins, which are designed to predict the failure or degradation of plant components. Successful implementation of ACM requires an assessment to inform the development of a risk-informed approach to evaluate the use of ACM to meet Nuclear Regulatory Committee (NRC) regulations for in-service testing (IST) programs. This includes the monitoring and diagnostics of reactor components and systems in current, new, and advanced reactors. A key component in ACM is the usage of machine learning (ML) and artificial intelligence (AI) algorithms that can employ real-time data from instrumentation and sensors to detect and predict reactor component degradations. Such predictive capabilities enable early detection of component degradation so as to help plant personnel plan and execute necessary maintenance. For successful implementation of ML/AI in ACM such that regulatory requirements are met, a risk-informed graded approach is needed to assess the reliability and performance of ML/AI for ACM. The American Society for Mechanical Engineers (ASME) developed their Operations and Maintenance (O&M) Code to provide guidance on safe, reliable O&M of NPPs. The IST section of the O&M Code specifically establishes requirements for IST and examination to gauge operational readiness of components in water-cooled NPPs. This paper presents a state-of-the-art review of how reliability and risk assessment can be integrated with ACM to assess component performance by non-nuclear industries. This is followed by different methodologies and approaches for conducting performance and reliability assessments so as to meet IST requirements for NPP components.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Risk-informed Graded Approach for Reliability and Performance Assessment for Advanced Condition Monitoring Techniques

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Calibration improvements expand filterscope diagnostic use

The filterscope diagnostic on DIII-D utilizes photomultiplier tubes to measure visible light emission from the plasma. The system has undergone a substantial upgrade since previous attempts to cross-calibrate the filterscope with other spectroscopic diagnostics were unsuccessful. The optics now utilize a dichroic mirror to initially split the light at nearly 99% transmission or reflectance for light below or above 550 nm. This allows the system to measure D α emission without degrading visible light emission from the plasma for wavelengths below 550 nm (to measure D β , D γ , W–I, C-III, etc.). Additional optimization of the optical components and calibration techniques reduce the error in the signal up to 10% in some channels compared to previous methods. Cross-calibration measurements with two other high resolution spectroscopic diagnostics now show excellent agreement for the first time. This expands the capabilities of the filterscope system allowing measurement of divertor detachment, emission profiles, edge-localized mode behavior, and plasma–wall interactions. Additionally, it enables direct comparisons against calculations from boundary plasma simulations. These were not possible before.

47 OTHER INSTRUMENTATION↗