Search NASA⌕ Search

SEARCH · Search NASA

Results for “consistency checking”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dark energy survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions

Beyond ΛCDM, physics or systematic errors may cause subsets of a cosmological data set to appear inconsistent when analysed assuming ΛCDM. Here, we present an application of internal consistency tests to measurements from the Dark Energy Survey Year 1 (DES Y1) joint probes analysis. Our analysis relies on computing the posterior predictive distribution (PPD) for these data under the assumption of ΛCDM. We find that the DES Y1 data have an acceptable goodness of fit to ΛCDM, with a probability of finding a worse fit by random chance of p = 0.046. Using numerical PPD tests, supplemented by graphical checks, we show that most of the data vector appears completely consistent with expectations, although we observe a small tension between large- and small-scale measurements. A small part (roughly 1.5 per cent) of the data vector shows an unusually large departure from expectations; excluding this part of the data has negligible impact on cosmological constraints, but does significantly improve the p-value to 0.10. The methodology developed here will be applied to test the consistency of DES Year 3 joint probes data sets.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics

Abstract Background The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. Purpose The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain‐relevant assessments via the analysis of relevant image statistics. Methods As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis. To create the training dataset, an established 3D virtual breast phantom was adapted. The resulting dataset comprised about 108 000 images of size 512 512. For the evaluation of submissions to the Challenge, an ensemble of 10 000 DGM‐generated images from each submission was employed. The evaluation procedure consisted of two stages. In the first stage, a preliminary check for memorization and image quality (via the Fréchet Inception Distance [FID]) was performed. Submissions that passed the first stage were then evaluated for the reproducibility of image statistics corresponding to several feature families including texture, morphology, image moments, fractal statistics, and skeleton statistics. A summary measure in this feature space was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, the four classes in the training data, and also to identify various artifacts. Results Fifty‐eight submissions from 12 unique users were received for this Challenge. Out of these 12 submissions, 9 submissions passed the first stage of evaluation and were eligible for ranking. The top‐ranked submission employed a conditional latent diffusion model, whereas the joint runners‐up employed a generative adversarial network, followed by another network for image superresolution. In general, we observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID‐based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts. Conclusions This Grand Challenge highlighted the need for domain‐specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.

Radiology, Nuclear Medicine & Medical Imaging↗
DOE OSTI2020

Cloud based tool for analysis of chemical kinetic mechanisms

The size and complexity of chemical kinetic models used to simulate combustion of hydrocarbon fuels continue to increase with our improved understanding of the underlying physical mechanisms, along with the improved ability to utilize such models as the result of increased computational power and efficiency. As mechanisms grow beyond thousands of species and tens of thousands of reactions, it becomes increasingly difficult to manually check for errors and physical inconsistencies. In this work, we present several automated methods to check for such issues in the specification of chemical reaction models. First, we demonstrate how discontinuities in thermodynamic data can cause simulation difficulties manifested as long wall-clock times and convergence failures. To correct this type of problem, we describe an automated method for refitting thermodynamic parameters. We also outline several methods to check the timescales of reaction rate coefficients to ensure physical consistency. All the methods are made available through a web-based tool (https://combustiontools.llnl.gov) intended to aid mechanism developers and users to improve the accuracy and performance of fuel models used by the combustion community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Quashing a suspected selection bias in galaxy samples having dynamically measured supermassive black holes

Local early-type galaxies (ETGs) with directly measured black hole masses, M bh , have been reported to represent a biased sample relative to the ETG population at large. Such galaxies with Spitzer Space Telescope imaging have been purported to possess velocity dispersions, σ, at least ∼0.1 dex larger for a given galaxy stellar mass, M * , gal , than is typically observed among thousands of ETGs imaged by the Sloan Digital Sky Survey (SDSS). This apparent offset led Shankar et al. to reduce the normalisation of the observed M bh ∝ σ 5 relation by at least ∼0.5 dex to give their ‘intrinsic relations’, including σ-based modifications to the observed M bh –M * , gal relation. These modifications were based on the untested assumption that the stellar masses had been derived consistently between the two samples. Here, we provide the necessary check using galaxies common to the Spitzer Survey of Stellar Structure in Galaxies (S 4 G) and the SDSS. We find that the stellar masses of galaxies with and without directly measured black hole masses had appeared offset from each other due to the use of inconsistent stellar mass-to-light ratios, Υ * , for the optical and infrared data. We briefly discuss the ‘intrinsic relations’ and why some of these will at times appear to have had partial success when applied to data based on similarly inconsistent values ofΥ * . Finally, we reiterate the importance of the υ (lower-case Υ) term, which we previously introduced into the M bh –M * relations to help avoid Υ * -related mismatches.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

LYNM-PE1 Seismic Parameters from Borehole Log, Laboratory, and Tabletop Measurements

The goal of this work is to provide a database of quality-checked seismic parameters that can be integrated with the Geologic Framework Model (GFM) for the LYNM-PE1 (Low Yield Nuclear Monitoring – Physical Experiment 1) testbed. We integrated data from geophysical borehole logs, tabletop measurements on collected core, and laboratory measurements. We reviewed for internal consistency among each measurement type, documented the caveats of measurement conditions, and integrated lithologic logs to check the validity of outlier values. The resulting consolidated parameter tables can be used as inputs for modeling and analysis codes and are designed to interface with the GFM, which is being actively developed.

58 GEOSCIENCES↗
DOE OSTI2021

Speculative instruction wakeup to tolerate draining delay of memory ordering violation check buffers

A technique for speculatively executing load-dependent instructions includes detecting that a memory ordering consistency queue is full for a completed load instruction. The technique also includes storing data loaded by the completed load instruction into a storage location for storing data when the memory ordering consistency queue is full. The technique further includes speculatively executing instructions that are dependent on the completed load instruction. The technique also includes in response to a slot becoming available in the memory ordering consistency queue, replaying the load instruction. The technique further includes in response to receiving loaded data for the replayed load instruction, testing for a data mis-speculation by comparing the loaded data for the replayed load instruction with the data loaded by the completed load instruction that is stored in the storage location.

Kalamatianos, John↗
DOE OSTI2020

Heterotic instantons for monad and extension bundles

We consider non-perturbative superpotentials from world-sheet instantons wrapped on holomorphic genus zero curves in heterotic string theory. These superpotential contributions feature prominently in moduli stabilization and large field axion inflation, which makes their presence or absence, as well as their functional dependence on moduli, an important issue. We develop geometric methods to compute the instanton superpotentials for heterotic string theory with monad and extension bundles. Using our methods, we find a variety of examples with a non-vanishing superpotential. In view of standard vanishing theorems, we speculate that these results are likely to be attributed to the non-compactness of the instanton moduli space. We test this proposal, for the case of monad bundles, by considering gauged linear sigma models where compactness of the instanton moduli space can be explicitly checked. In all such cases, we find that the geometric results are consistent with the vanishing theorems. Surprisingly, linearly dependent Pfaffians even arise for cases with a non-compact instanton moduli space. This suggests some gauged linear sigma models with a non-compact instanton moduli space may still have a vanishing instanton superpotential.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Dark sink enhances the direct detection of freeze-in dark matter

We describe a simple dark sector structure which, if present, has implications for the direct detection of dark matter (DM); the dark sink. A dark sink transports energy density from the DM into light dark-sector states that do not appreciably contribute to the DM density. As an example, we consider a light, neutral fermion ψ which interacts solely with DM Χ via the exchange of a heavy scalar Φ. We illustrate the impact of a dark sink by adding one to a DM freeze-in model in which Χ couples to a light dark photon γ' which kinetically mixes with the Standard Model (SM) photon. This freeze-in model (absent the sink) is itself a benchmark for ongoing experiments. In some cases, the literature for this benchmark has contained errors; we correct the predictions and provide them as a public code. We then analyze how the dark sink modifies this benchmark, solving coupled Boltzmann equations for the dark-sector energy density and DM yield. We check the contribution of the dark sink ψ’s to dark radiation; consistency with existing data limits the maximum attainable cross section. For DM with a mass between MeV –Ο⁡(10 GeV), adding the dark sink can increase predictions for the direct detection cross section all the way up to the current limits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

HarDWR - Cumulative Water Rights Curves

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on Harmonized Water Rights Records v2.0 sourced from WestDAAT - Added "Unspecified" was a water source category, and files associated with this category v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description This product an updated version of the database used as input to the WBM model (Grogan et al. in review; Grogan et al. 2022), and is the result of the step creating cumulative water rights curves described in Lisk et al. (2024). This database contains 2,667 individual .csv files, three for each Water Management Area (WMA) in the 11-state region. File Naming WMA_[###]_[X]W.csv, where [###] is the unique identifier for each WMA, and [X] is either "S" for surface water rights, "G" for groundwater rights, or "U" for unspecified. Column headers in each file: Year: the priority date year CUML: the total cumulative water rights allocated up to this priority date year (ft3s-1) Irrigation: The percent of cumulative water rights allocated to the Irrigation category up to this priority date year (%) Domestic: The percent of cumulative water rights allocated to the Domestic category up to this priority date year (%) Livestock: The percent of cumulative water rights allocated to the Livestock category up to this priority date year (%) Fish: The percent of cumulative water rights allocated to the Fish category up to this priority date year (%) Industrial: The percent of cumulative water rights allocated to the Industrial category up to this priority date year (%) Environmental: The percent of cumulative water rights allocated to the Environmental category up to this priority date year (%) Other: The percent of cumulative water rights allocated to the Other category up to this priority date year (%) In addition to the database files, there is a companion .csv file, called stateWMAs_ID.csv. The main purpose of this file is to provide the means of translating between the [###] unique identifier and the various other id of the WMA the file is attached to. This translation file has five columns, which are: basinNum: The official state given alpha-numeric identifier of the WMA basinName: the state provided English name of the area, where applicable state: the state name uniID: a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID ID: a unique numeric identifier we created as a requirement for the files to be used within WBM (Grogan et al. in review) For those whom may be interested in exploring our code more in depth, we are also making available an internal data file for convenience. The file is in .RData format and contains everything described above as well as some minor additional objects used within the code calculating the cumulative curves. For completeness, here is a detailed description of the various objects which can be found within the .RData file: states: A character vector containing the state names for those states in the study region. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the second index in this object, the data for California will also be in the second index for each accompanying list. wmasRightsPersGrd: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for groundwater. wmasRightsPersSur: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for surface water. wmasRightsPersUnsp: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for water from an unspecified source. wmasRightsTotsGrd: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for groundwater. This object is provided for convenience to check out original total values, if desired. wmasRightsTotsSur: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for surface water. This object is provided for convenience to check out original total values, if desired. wmasRightsTotsUnsp: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for water from an unspecified source. This object is provided for convenience to check out original total values, if desired. wmaIDByState: A list of data frames which contain the by state allocation by WMA matrix. There is a matrix for each state in a different data frame within the object.

Economics↗
DOE OSTI2025

Improved Weld Residual Stress Modeling System in BlackBear

This report presents enhancements to the MOOSE-based BlackBear application aimed at improving its capability to simulate welding and other thermo-mechanical manufacturing processes. Two primary avenues of improvement are pursued. First, to enhance user accessibility, we introduce a centralized default block restriction mechanism that ensures coverage checks are performed within user-specified default blocks. This default setting is applied consistently to all block-describable objects, such as variables, kernels, and more. In addition, we develop a modular action for moving heat source simulations, which integrates path file parsing, subdomain modification, and heat source kernel enforcement into a single, streamlined configuration. Second, to improve solver robustness, we implement an alternative method for assigning initial conditions to the updated active domain during the simulation, thereby enhancing convergence behavior. To validate the framework, we design and conduct several benchmark simulations, including heat conduction with progressive material addition, linear elasticity with time-dependent material deposition, and viscoplasticity model with isotropic hardening under similar conditions. Finally, we demonstrate the effectiveness of the proposed framework through large-scale thermo-mechanical welding simulations in both two and three dimensions.

42 ENGINEERING↗
DOE OSTI2020

Capabilities of multivariate Bayesian inference toward seismic hazard assessment

Multivariate Bayesian analysis can bring significant benefits to seismic hazard analysis: Its multivariate feature enables computing scalar and vector hazard without making any approximations; Correlations between intensity measures are implicitly modeled, permitting direct simulation of ground motion selection tools such as the conditional mean spectrum and the generalized conditioning intensity measure; and Its updating feature enables a seamless integration of new ground motion data into the hazard results. Here, we first develop a multivariate Bayesian ground motion model through the NGA-West2 database. The model functional form considers fault-type, magnitude, and distance dependencies, and also the linear and the rock intensity dependent site response. We use a hybrid Markov Chain Monte Carlo sampling to perform Bayesian inference consisting of Gibbs step and a multilevel Metropolis-Hastings step. We then perform several checks on the model and note that its performance is satisfactory. Finally, we illustrate the merits of this multivariate Bayesian analysis, which include: ground motion model updating with ground motion data recorded in the last four years not part of the NGA-West2 database; computation of scalar and vector seismic hazard using the un-updated and updated ground motion models for Los Angeles, CA; and simulation of the conditional mean spectrum under scalar and vector IM conditioning while accounting for different sources of aleatoric and epistemic uncertainties.

58 GEOSCIENCES↗
DOE OSTI2024

The DEHVILS in the details: Type Ia supernova Hubble residual comparisons and mass step analysis in the near-infrared

Measurements of type Ia supernovae (SNe Ia) in the near-infrared (NIR) have been used both as an alternate path to cosmology compared to optical measurements and as a method of constraining key systematics for the larger optical studies. With the DEHVILS sample, the largest published NIR sample with consistent NIR coverage of maximum light across three NIR bands ( Y, J , and H ), we check three key systematics: (i) the reduction in Hubble residual scatter as compared to the optical, (ii) the measurement of a “mass step” or lack thereof and its implications, and (iii) the ability to distinguish between various dust models by analyzing slopes and correlations between Hubble residuals in the NIR and optical. We produce SN Ia simulations of the DEHVILS sample and find that it is harder to differentiate between various dust models than previously understood. Additionally, we find that fitting with the current SALT3-NIR model does not yield accurate wavelength-dependent stretch-luminosity correlations, and we propose a limited solution for this problem. From the data, we see that (i) the standard deviation of Hubble residual values from NIR bands treated as standard candles are 0.007–0.042 mag smaller than those in the optical, (ii) the NIR mass step is not constrainable with the current sample size of 47 SNe Ia from DEHVILS, and (iii) Hubble residuals in the NIR and optical are correlated in the data. We test a few variations on the number and combinations of filters and data samples, and we observe that none of our findings or conclusions are significantly impacted by these modifications.

Astronomy & Astrophysics↗
DOE OSTI2023

Thermal Hydraulic Modeling of an Advanced Nuclear Reactor using open-source MOOSE tools

The aim of this study is to develop model of a small High Temperature Gas cooled Reactor (HTGR) including the balance of plant. This microreactor produces electricity using the thermal power of the nuclear reaction. This work utilizes the MOOSE Multiphysics simulation tools, which are mainly developed at Idaho National Laboratory (INL, Idaho, United States of America). Its thermal-hydraulics and Heat conduction Modules are used to study the fluids behavior in the primary loop and power conversion system and their interactions with the heating structures. More specifically, one verifies that the temperatures, pressures and mass flow rates of the fluids in both loops are consistent and that, at the same time, all the power transfers occur as expected. Moreover, the various mechanical components characteristics (turbine, compressor, pump) are adapted to the reactor operating conditions. This first analysis is conducted using simplified and one-dimensional model for the core. Ultimately, the obtained results are used to build a higher fidelity core model. This step aims to verify that the initial simplified simulation is consistent with the three-dimensional core modeling. In addition, the calculated material temperatures are checked. This study provides a fairly complete model of the thermal hydraulic phenomena for a particular design of High Temperature Gas cooled Reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Durability Study of a High-Pressure Common Rail Fuel Injection System Using Lubricity Additive-Dosed Gasoline-Like Fuel—Improved Endurance with Upgraded Hardware

Gasoline compression ignition (GCI) is a promising combustion technology that can help the commercial transportation sector achieve operational flexibility and meet upcoming criteria pollutant regulations. However, high-pressure fuel injection systems (>1000 bar) are needed to enable GCI and fully realize its benefits compared to conventional diesel combustion. This work is a continuation of previous durability studies that identified three key technical risks after running gasoline-like fuel through a heavy-duty, common rail injection system: (i) cavitation damage to the inlet check valve of the high-pressure pump, (ii) loss of injector fueling capacity, (iii) cavitation erosion of the injector nozzle holes. Upgraded hardware solutions were tested on a consistent 400- to 800-hour NATO durability cycle with the same gasoline-like fuel as previous studies. The upgraded pump showed no signs of abnormal wear or cavitation damage to the inlet check valve. In contrast to previous studies, there were no signs of pump performance degradation observed after 400 hours of testing. Material selection and design upgrades were also made to the injector which, only showed a 6.5% loss in fueling capacity after 800 hours of durability testing compared to 49.3% previously. Finally, geometric nozzle hole features such as higher inlet radius of curvature and higher K-factor were found to correlate with reduced cavitation erosion. However, mitigation of eccentric radial needle motion (i.e., wobble) is likely needed to further suppress cavitation. In general, the results from this study indicate there are viable hardware-based solutions for improving the endurance of high-pressure systems when running with gasoline-like fuel.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Investigation of U content in J-swipe/PIC samples

This final report documents the results of an evaluation of the U content in J-swipes for the International Atomic Energy Agency (IAEA). J-swipes are manufactured with inked numbers (1–30) and U backgrounds that are considered consistent across different lots and manufacturing times. These characteristics make them ideal for use by IAEA inspectors for pre-inspection check samples to ensure inspectors are not cross-contaminating sites. However, recent work has found elevated U concentrations in the inked numbers compared with the J-swipe material. To determine whether the inked numbers correlate with the U backgrounds, triplicate U content measurements were made on Swipes 1, 2, 11, 22, and 28. Triplicates of each swipe were processed through the standard chemistry used by the Oak Ridge National Laboratory Network of Analytical Laboratories team and analyzed for U content by isotope dilution via multicollector inductively coupled plasma mass spectrometry. Finally, samples were made by mass consisting of only ink areas and only cotton swipe material to determine the trace element differences between the cotton and the ink.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

The Atacama Cosmology Telescope: Cosmology from Cross-correlations of unWISE Galaxies and ACT DR6 CMB Lensing

We present tomographic measurements of structure growth using cross-correlations of Atacama Cosmology Telescope (ACT) DR6 and Planck cosmic microwave background (CMB) lensing maps with the unWISE Blue and Green galaxy samples, which span the redshift ranges 0.2 ≲ z ≲ 1.1 and 0.3 ≲ z ≲ 1.8, respectively. We improve on prior unWISE cross-correlations not just by making use of the new, high-precision ACT DR6 lensing maps, but also by including additional spectroscopic data for redshift calibration and by analyzing our measurements with a more flexible theoretical model. We determine the amplitude of matter fluctuations at low redshifts (z ≃ 0.2–1.6), finding S 8 ≡ σ 8 (Ω m /0.3) 0.5 = 0.813 ± 0.021 using the ACT cross-correlation alone and S 8 = 0.810 ± 0.015 with a combination of Planck and ACT cross-correlations; these measurements are fully consistent with the predictions from primary CMB measurements assuming standard structure growth. The addition of baryon acoustic oscillation data breaks the degeneracy between σ 8 and Ω m , allowing us to measure σ 8 = 0.813 ± 0.020 from the cross-correlation of unWISE with ACT and σ 8 = 0.813 ± 0.015 from the combination of cross-correlations with ACT and Planck. These results also agree with the expectations from primary CMB extrapolations in ΛCDM cosmology; the consistency of σ 8 derived from our two redshift samples at z ∼ 0.6 and 1.1 provides a further check of our cosmological model. Our results suggest that structure formation on linear scales is well described by ΛCDM even down to low redshifts z ≲ 1.

79 ASTRONOMY AND ASTROPHYSICS↗
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↗