SEARCH · Search NASA

Results for “condition based monitoring”

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 1,531 records · Page 85

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2026

Generalizing synthetic data-trained acoustic predictive models to real-world measurements

Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Extended Sleeve Products Allow Control and Monitoring of Process Fluid Flows Inside Shielding, Behind Walls and Beneath Floors - 13041

Throughout power generation, delivery and waste remediation, the ability to control process streams in difficult or impossible locations becomes increasingly necessary as the complexity of processes increases. Example applications include radioactive environments, inside concrete installations, buried in dirt, or inside a shielded or insulated pipe. In these situations, it is necessary to implement innovative solutions to tackle such issues as valve maintenance, valve control from remote locations, equipment cleaning in hazardous environments, and flow stream analysis. The Extended Sleeve family of products provides a scalable solution to tackle some of the most challenging applications in hazardous environments which require flow stream control and monitoring. The Extended Sleeve family of products is defined in three groups: Extended Sleeve (ESV), Extended Bonnet (EBV) and Instrument Enclosure (IE). Each of the products provides a variation on the same requirements: to provide access to the internals of a valve, or to monitor the fluid passing through the pipeline through shielding around the process pipe. The shielding can be as simple as a grout filled pipe covering a process pipe or as complex as a concrete deck protecting a room in which the valves and pipes pass through at varying elevations. Extended Sleeves are available between roughly 30 inches and 18 feet of distance between the pipeline centerline and the top of the surface to which it mounts. The Extended Sleeve provides features such as ± 1.5 inches of adjustment between the pipeline and deck location, internal flush capabilities, automatic alignment of the internal components during assembly and integrated actuator mounting pads. The Extended Bonnet is a shorter fixed height version of the Extended Sleeve which has a removable deck flange to facilitate installation through walls, and is delivered fully assembled. The Instrument Enclosure utilizes many of the same components as an Extended Sleeve, yet allows the installation of process monitoring instruments, such as a turbidity meter to be placed in the flow stream. The basis of the design is a valve body, which, rather than having a directly mounted bonnet has lengths of concentric pipe added, which move the bonnet away from the valve body. The pipe is conceptually similar to an oil field well, with the various strings of casing, and tubing installed. Each concentric pipe provides a required function, such as the outermost pipes, the valve sleeve and penetration sleeve, which provide structural support to the deck flange. For plug valve based designs, the next inner pipe provides compression on the environmental seals at the top of the body to bonnet joint, followed by the innermost pipe which provides rotation of the plug, in the same manner as an extended stem. Ball valve ESVs have an additional pipe to provide compressive loading on the stem packing. Due to the availability of standard pipe grades and weights, the product can be configured to fit a wide array of valve sizes, and application lengths, with current designs as short as seven inches and as tall as 18 feet. Central to the design is the requirement for no special tools or downhole tools to remove parts or configure the product. Off the shelf wrenches, sockets or other hand tools are all that is required. Compared to other products historically available, this design offers a lightweight option, which, while not as rigidly stiff, can deflect compliantly under extreme seismic loading, rather than break. Application conditions vary widely, as the base product is 316 and 304 stainless steel, but utilizes 17-4PH, and other allows as needed based on the temperature range and mechanical requirements. Existing designs are installed in applications as hot as 1400 deg. F, at low pressure, and separately in highly radioactive environments. The selection of plug versus ball valve, metal versus soft seats, and the material of the seals and seats is all dependent on the application requirements. The design of the Extended Sleeve family of products provides a platform which solves a variety of accessibility problems associated with controlling process flow streams in remote, hard to reach locations in harsh environments. Installation of the equipment described has shown to allow access to flow streams that otherwise would require exceptional means to access and control. The Extended Sleeve family of products provides a scalable solution to both control and monitor process fluid flow through shielding, walls or floors when direct connection is advantageous. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Association of Cortical ß-Amyloid Protein in the Absence of Insoluble Deposits With Alzheimer Disease.

IMPORTANCE: ß-Amyloid deposits are a pathologic hallmark of Alzheimer disease (AD). However, the extent to which cortical ß-amyloid protein in the absence of insoluble deposits is associated with classic features of AD appear to be unknown. OBJECTIVE: To examine the associations of cortical ß-amyloid protein in the absence of insoluble deposits with cognitive decline, neurofibrillary tangles, other age-associated neuropathologic conditions, and APOE. DESIGN, SETTING, AND PARTICIPANTS: This analysis combines data from 2 community-based clinicopathologic cohort studies of aging. The Religious Orders Study started in 1994, and the Rush Memory and Aging Project started in 1997. Both studies are ongoing. Participants without known dementia were enrolled and agreed to annual clinical evaluations and brain donation after death. Primary analyses focused on individuals without ß-amyloid deposits. Data analyses occurred in mid-September 2018. MAIN OUTCOMES AND MEASURES: ß-Amyloid protein abundance was measured by targeted proteomics using selected reaction monitoring. ß-Amyloid deposits were detected using immunohistochemistry. Other neuropathologic indices were quantified via uniform structured evaluation. Linear mixed models were used to examine the association of ß-amyloid protein with cognitive decline. Regression models examined the protein associations with neuropathologic outcomes and the APOE genotype. RESULTS: By mid-September 2018, 3575 older persons were enrolled, and 1559 had died and undergone brain autopsy. Proteomic data were collected in 1208 individuals, and 5 with missing cognitive scores were excluded. Of the remaining 1203, primary analyses focused on 148 individuals (12.3%) without ß-amyloid deposits. In this group, the mean (SD) age at death was 87.0 (7.0) years, and 84 individuals (56.8%) were women. In the absence of ß-amyloid deposits, we did not observe an association of ß-amyloid protein with decline in episodic memory, but the protein was associated with faster rates of decline in processing speed (mean [SE] change, -0.014 [0.005]; P?=?.008) and visuospatial abilities (mean [SE] change, -0.013 [0.005]; P?=?.006). We did not observe protein association with paired helical filament tau tangle density. The protein was associated with amyloid angiopathy (odds ratio, 1.38 [95% CI, 1.15-1.67]; P?<?.001) but no other brain pathology. The associations with cognitive decline were unchanged after controlling for amyloid angiopathy. Neither APOE e4 nor a polygenic Alzheimer risk score was associated with ß-amyloid protein. CONCLUSIONS AND RELEVANCE: Cortical ß-amyloid protein was associated with faster cognitive decline in the absence of ß-amyloid deposits, which supports the role of cortical soluble ß-amyloid as a neurotoxic agent in aging. The lack of protein association with paired helical filament tau tangles, episodic memory decline, or strong genetic drivers of deposited ß-amyloid suggests an underlying neuropathologic change that may differ from that of AD.

Alzheimer’s disease, Insoluble deposits↗

DOE OSTI2022

Laser Doppler vibrometry for piezoelectric coefficient ($d_{33}$) measurements in irradiated aluminum nitride

Sensors used for experiments in advanced reactors must survive in harsh environments. Few material systems can be used to construct sensors viable for extreme conditions. Aluminum nitride (AlN) is one such material because it has high thermal stability and radiation resistance and sustains good piezoelectric and dielectric properties at high temperatures. In this work, the piezoelectric coefficient $d_{33}$ of the AlN single-crystal with thermal and irradiation damage was investigated using an indirect method with a laser Doppler vibrometer (LDV). Surface electrodes were deposited on the AlN samples, and the vibration response of the samples to an applied voltage was monitored using the LDV as a function of the excitation frequency. The $d_{33}$ estimation was based on the excitation voltage and the thickness-mode displacement extracted from the LDV measurements. Further, six AlN substrates were irradiated with 8 MeV Al 2+ at three fluences (10 15 , 10 16 , and 10 17 ions/cm 2 ) and two temperatures (300 °C and 500 °C). The $d_{33}$ for the six irradiated samples and one pristine sample were measured, and the measurement uncertainty was estimated based on five repeated tests. All samples were also measured by a commercial piezometer for comparison. The experimental results demonstrate that the piezoelectric coefficients obtained by LDV were about 0.8–1.16 pm/V lower than those obtained by the piezometer. With the compensation of the clamping effect, the corrected LDV values are similar to the piezometer results. Both show similar trends in all samples, which validates the feasibility of the proposed method for $d_{33}$ measurement. Based on the LDV results, the irradiated samples show a 12%–22% decrease in $d_{33}$ compared with the pristine samples. The samples irradiated under the same fluence at a higher temperature (500 °C) demonstrated a lower $d_{33}$ than those at 300 °C. The effect of the retro-reflective tape, sample temperature, and sample size on the $d_{33}$ measurement were also studied.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A dedicated mirror-magnet experiment to study quench characteristics and dependencies in $Nb_{3}Sn$ coils and explore improvements of diagnostics capabilities

A single Nb3Sn short quadrupole coil in a mirror-magnet configuration was employed to investigate a wide range of phenomena and to serve as test-bed for diagnostics developments. A configurable array of spot-heaters was installed on the inner coil surface for control of induced quench conditions. Several different quench antenna arrays were positioned along the coil inner surface, and included significant sensor overlap for assessment of their relative efficiency and operation. Multiple acoustic sensors were placed on the pole and at coil ends. Optical fibers in grid configurations were put in different places on the coil and magnet for strain assessment, along with standard resistive strain gauges. Current spikes in the magnet circuit were monitored. This plethora of instrumentation aimed to support investigations on induced and spontaneous quenches, among other goals, and was supplemented by standard voltage-tap-based measurements. Voltage tap data of induced quenches from various spot-heater configurations was investigated for similarities to voltage development in spontaneous quenches. Quench antennas provided insights about current redistribution in the quenching coil and coil splices. The Quench Current-boosting Device was applied and the effect on coil training examined. This paper describes motivations behind the research, the overall test setup and main results.

Stoynev, Stoyan [Fermilab]↗

NASA NTRS1987

Stability of bromine, iodine monochloride, copper (II) chloride, and nickel (II) chloride intercalated pitch-based graphite fibers

Four different grades of pitch-based graphite fibers (Amoco P-55, P-75, P-100. and P-120) were intercalated with each of four different intercalates: bromine (Br2), iodine monochloride (ICl), copper (II) chloride (CuCl2), and nickel (II) chloride (NiCl2). The P-55 fibers did not react with Br2 or NiCl2, and the P-75 did not react with NiCl2. The stability of the electrical resistance of the intercalated fibers was monitored over long periods of time in ambient, high humidity (100 percent at 60 C), vacuum (10 to the -6 torr), and high temperature (up to 400 C) conditions. Fibers with lower graphitization form graphite intercalation compounds (GIC's) which are more stable than those with higher graphitization (i.e., P-55 (most stable) greater than P-75 greater than P-100 greater than P-120 (least stable). Br2 formed the most stable GIC's followed in order of decreasing stability by ICl, CuCl2, and NiCl2. While Br2 GIC's had the most stability, ICl had the advantages of forming GIC's with slightly greater reduction in resistance (by about 10%) than Br2, and the ability to intercalate P-55 fiber. Transition metal chlorides are susceptible to water vapor and high temperature. The stability of fibers in composites differs.

Gaier, James R.↗

NASA NTRS2012

The Cabauw Intercomparison Campaign for Nitrogen Dioxide Measuring Instruments (CINDI): Design, Execution, and Early Results

From June to July 2009 more than thirty different in-situ and remote sensing instruments from all over the world participated in the Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI). The campaign took place at KNMI's Cabauw Experimental Site for Atmospheric Research (CESAR) in the Netherlands. Its main objectives were to determine the accuracy of state-ofthe- art ground-based measurement techniques for the detection of atmospheric nitrogen dioxide (both in-situ and remote sensing), and to investigate their usability in satellite data validation. The expected outcomes are recommendations regarding the operation and calibration of such instruments, retrieval settings, and observation strategies for the use in ground-based networks for air quality monitoring and satellite data validation. Twenty-four optical spectrometers participated in the campaign, of which twenty-one had the capability to scan different elevation angles consecutively, the so-called Multi-axis DOAS systems, thereby collecting vertical profile information, in particular for nitrogen dioxide and aerosol. Various in-situ samplers and lidar instruments simultaneously characterized the variability of atmospheric trace gases and the physical properties of aerosol particles. A large data set of continuous measurements of these atmospheric constituents has been collected under various meteorological conditions and air pollution levels. Together with the permanent measurement capability at the CESAR site characterizing the meteorological state of the atmosphere, the CINDI campaign provided a comprehensive observational data set of atmospheric constituents in a highly polluted region of the world during summertime. First detailed comparisons performed with the CINDI data show that slant column measurements of NO2, O4 and HCHO with MAX-DOAS agree within 5 to 15%, vertical profiles of NO2 derived from several independent instruments agree within 25% of one another, and MAX-DOAS aerosol optical thickness agrees within 20-30% with AERONET data. For the in-situ NO2 instrument using a molybdenum converter, a bias was found as large as 5 ppbv during day time, when compared to the other in-situ instruments using photolytic converters.

Atmospheric composition↗

DOE OSTI2011

Visual-servoing optical microscopy

The present invention provides methods and devices for the knowledge-based discovery and optimization of differences between cell types. In particular, the present invention provides visual servoing optical microscopy, as well as analysis methods. The present invention provides means for the close monitoring of hundreds of individual, living cells over time; quantification of dynamic physiological responses in multiple channels; real-time digital image segmentation and analysis; intelligent, repetitive computer-applied cell stress and cell stimulation; and the ability to return to the same field of cells for long-term studies and observation. The present invention further provides means to optimize culture conditions for specific subpopulations of cells.

47 OTHER INSTRUMENTATION↗

DOE OSTI2013

Visual-servoing optical microscopy

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Lubricant impacts on piston deposit formation in the Enterprise marine diesel research engine

The impact of lubricant formulation on piston deposits was studied using the Enterprise, a reduced-scale, single-cylinder, two-stroke crosshead marine diesel research engine. The Enterprise engine was specially designed for marine diesel lubricant research, with a custom reduced-scale cylinder lubricant injection system and extensive instrumentation of thermal boundary conditions on both the liner and piston. Lubricant conditions typical of full-scale marine diesel engines are obtained by matching mean piston speed, liner temperature profile, and combustion metrics to realistic values.Piston deposit thicknesses were measured after a set period of operation, with a focus on lubricant-based deposits on the lands and ring grooves, using both optical and contact-based measurement techniques. Engine operation for each lubricant was conducted according to a standardized protocol, with precise control of engine speed and load, cylinder lubricant injection rate, coolant temperatures, and system oil temperature; the liner and piston temperatures are continuously monitored during operation. After operation with each lubricant was completed, the engine was disassembled, and the piston and ring deposits were characterized. The piston was then thoroughly cleaned, and the lubricant system flushed, between each lubricant formulation being evaluated. The impacts on piston deposits of the lubricants being evaluated can thus be accurately quantified and are described herein.

Kaul, Brian↗

DOE OSTI2026

Reservoir Sediment Management and Monitoring Database

Overview This dataset compiles dam sediment management and monitoring information from surveys, case studies, and journal articles. Additionally, features described by the National Inventory of Dams (i.e., presence of sluice gates) are included to indicate known infrastructure features that may address sediment releases. The location and description of records from downstream monitoring gages are catalogued in order to help with tracking conditions over time (e.g., before and after management actions, as operations change, etc.). The data help address national scale understanding of challenges and solutions related to the accumulation of sediment behind a dam as well as downstream passage. Sediment trapping causes problems as it reduces storage capacity, disrupts dam and reservoir function, impedes access for recreation, alters water quality/habitat conditions, and contributes to riverbank and coastal erosion within the reservoir. Data compilation from a variety of sources is a first step towards assessing system-wide efficacy of management solutions. This dataset was developed under the Water Power Technologies Office funded effort which began as a Seedling on Reservoir Sedimentation Data, and was supported by the Reservoir Sedimentation Modeling Framework and Data Analysis project. These projects have addressed challenges in describing sediment transport, trapping, and management at dams throughout the US. Methodology An outer join on dams/reservoirs with surveys and survey reports (documented in the RESSED database, USBR or USACE databases, project websites, etc.) with the National Inventory of Dams, based on the NIDID to determine dams with documented management and/or sluice gates. Additional dams with documented management activity were identified through review of technical articles from the past 25 years in Journal of Hydrology, Journal of Water Resources Planning and Management, Geomorphology, Journal of Hydraulic Engineering, Water, Journal of Cleaner Production, International Journal of Sediment Research, Nature Scientific Reports, Earth Surface Processes and Landforms, and Environmental Science and Pollution Research. Individual records were created for each survey or management activity documented. To evaluate downstream sediment monitoring records, the nhdPlusTools and dataRetrieval packages in R were used to find gages within 10km of each dam in the management database. Length of record and location of matched gages were retrieved for those parameters relevant to sediment concentration or total sediment discharge.

Hansen, Carly [ORNL] (ORCID:0000000193280838)↗

NASA NTRS2016

Investigation of Asymmetric Thrust Detection with Demonstration in a Real-Time Simulation Testbed

The purpose of this effort is to develop, demonstrate, and evaluate three asymmetric thrust detection approaches to aid in the reduction of asymmetric thrust-induced aviation accidents. This paper presents the results from that effort and their evaluation in simulation studies, including those from a real-time flight simulation testbed. Asymmetric thrust is recognized as a contributing factor in several Propulsion System Malfunction plus Inappropriate Crew Response (PSM+ICR) aviation accidents. As an improvement over the state-of-the-art, providing annunciation of asymmetric thrust to alert the crew may hold safety benefits. For this, the reliable detection and confirmation of asymmetric thrust conditions is required. For this work, three asymmetric thrust detection methods are presented along with their results obtained through simulation studies. Representative asymmetric thrust conditions are modeled in simulation based on failure scenarios similar to those reported in aviation incident and accident descriptions. These simulated asymmetric thrust scenarios, combined with actual aircraft operational flight data, are then used to conduct a sensitivity study regarding the detection capabilities of the three methods. Additional evaluation results are presented based on pilot-in-the-loop simulation studies conducted in the NASA Glenn Research Center (GRC) flight simulation testbed. Data obtained from this flight simulation facility are used to further evaluate the effectiveness and accuracy of the asymmetric thrust detection approaches. Generally, the asymmetric thrust conditions are correctly detected and confirmed.

propulsion system performance↗

DOE OSTI2011

9975 SHIPPING PACKAGE LIFE EXTENSION SURVEILLANCE PROGRAM RESULTS SUMMARY

Results from the 9975 Surveillance Program at the Savannah River Site (SRS) are summarized for justification to extend the life of the 9975 packages currently stored in the K-Area Materials Storage (KAMS) facility from 10 years to 15 years. This justification is established with the stipulation that surveillance activities will continue throughout this extended time to ensure the continued integrity of the 9975 materials of construction and to further understand the currently identified degradation mechanisms. The current 10 year storage life was developed prior to storage. A subsequent report was later used to extend the qualification of the 9975 shipping packages for 2 years for shipping plus 10 years for storage. However the qualification for the storage period was provided by the monitoring requirements of the Storage and Surveillance Program. This report summarizes efforts to determine a new safe storage limit for the 9975 shipping package based on the surveillance data collected since 2005 when the surveillance program began. KAMS is a zero-release facility that depends upon containment by the 9975 to meet design basis storage requirements. Therefore, to confirm the continued integrity of the 9975 packages while stored in KAMS, a 9975 Storage and Surveillance Program was implemented alongside the DOE required Integrated Surveillance Program (ISP) for 3013 plutonium-bearing containers. The 9975 Storage and Surveillance Program performs field surveillance as well as accelerated aging tests to ensure any degradation due to aging, to the extent that could affect packaging performance, is detected in advance of such degradation occurring in the field. The Program has demonstrated that the 9975 package has a robust design that can perform under a variety of conditions. As such the primary emphasis of the on-going 9975 Surveillance Program is an aging study of the 9975 Viton(reg.sign) GLT containment vessel O-rings and the Celotex(reg.sign) fiberboard thermal insulation at bounding conditions of radiation and elevated temperatures. Other materials of construction, however, are also discussed.

42 ENGINEERING↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Battery Prognostics↗

DOE OSTI2015

The compressed state Kalman filter for nonlinear state estimation: Application to large-scale reservoir monitoring

Reservoir monitoring aims to provide snapshots of reservoir conditions and their uncertainties to assist operation management and risk analysis. These snapshots may contain millions of state variables, e.g., pressures and saturations, which can be estimated by assimilating data in real time using the Kalman filter (KF). However, the KF has a computational cost that scales quadratically with the number of unknowns, m, due to the cost of computing and storing the covariance and Jacobian matrices, along with their prod-ucts. The compressed state Kalman filter (CSKF) adapts the KF for solving large-scale monitoring problems. The CSKF uses N preselected orthogonal bases to compute an accurate rank-N approximation of the covari-ance that is close to the optimal spectral approximation given by SVD. The CSKF has a computational cost that scales linearly in m and uses an efficient matrix-free approach that propagates uncertainties using N + 1 forward model evaluations, where N << m. Here in this paper we present a generalized CSKF algorithm for nonlin-ear state estimation problems such as CO 2 monitoring. For simultaneous estimation of multiple types of state variables, the algorithm allows selecting bases that represent the variability of each state type. Through synthetic numerical experiments of CO 2 monitoring, we show that the CSKF can reproduce the Kal-man gain accurately even for large compression ratios (m/N). For a given computational cost, the CSKF uses a robust and flexible compression scheme that gives more reliable uncertainty estimates than the ensemble Kalman filter, which may display loss of ensemble variability leading to suboptimal uncertainty estimates.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Uncertainty quantification for evaluating impacts of caprock and reservoir properties on pressure buildup and ground surface displacement during geological CO2 sequestration

A series of numerical test cases reflecting broad and realistic ranges of geological formation properties was developed to systematically evaluate and compare the impacts of those properties on geomechanical responses to CO2 injection. A coupled hydro-geomechanical subsurface transport simulator, STOMP (Subsurface Transport over Multiple Phases), was adopted to simulate the CO2 migration process and geomechanical behaviors of the surrounding geological formations. A quasi-Monte Carlo sampling method was applied to efficiently sample a high-dimensional parameter space consisting of injection rate and 14 subsurface formation properties, including porosity, permeability, entry pressure, irreducible gas and aqueous saturation, Young’s modulus, and Poisson’s ratio for both reservoir and caprock. Generalized cross-validation and analysis of variance methods were used to quantitatively measure the significance of the 15 input parameters. Reservoir porosity, permeability, and injection rate were found to be among the most significant factors affecting the geomechanical responses to the CO2 injection. We used a quadrature generalized linear model to build a reduced-order model that can estimate the geomechanical response instantly instead of running computationally expensive numerical simulations.

CO2 geological sequestration↗