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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Data Driven Fault Detection of Premixer Centerbody Degradation in a Swirl Combustor

This paper introduces a data-driven framework for combustor-focused, performance-based condition monitoring of gas turbines. Commercial condition monitoring systems typically generate huge amounts of data that make efficient onboard monitoring challenging. This paper focuses on quantifying combustor component degradation, using premixer centerbody degradation in a swirl stabilized combustor as a case study. The input for these analyses is acoustic pressure measurements acquired at various locations on the combustor. The diagnosis methodology is based on a classification framework and consists of 3 steps: 1) Data curation, 2) Feature Engineering, and 3) Diagnosis. Data curation ensures good quality of the data that is passed through the algorithm. Feature engineering deals with the extraction of the most informative features, from the most informative sensors, that can accurately capture the introduced fault. To perform diagnosis, the classification model is trained using experimentally acquired data and is then tested on a separate data set. The framework was able to achieve high classification accuracy (>99%) for training size as low as 30% of the total recorded observations. The low number of features required to achieve this accuracy suggests high potential for integration into existing onboard condition monitoring systems.

data driven methods, fault detection, swirl flames↗
DOE OSTI2023

A Physics-Based Data-Driven Approach for Modeling of Environmental Degradation in Elastomers

Abstract Elastomers are now commonly used in a number of industries, including aerospace, structure, transportation, shipbuilding, and automotive, due to their excellent workability, formability, and flexibility. During their activity, elastomers are subjected to harsh environmental conditions, which decreases their resilience. False predictions made early in their lives can have major financial and environmental implications. Elastomers’ performance and properties, such as strength, durability, and density, are influenced by chemical changes in these materials, known as degradation, which occurs over time. This process can alter the morphology of a polymer matrix as well as cause chain scission and cross-linking, resulting in different behaviors than that of the unaged material. To demonstrate the effect of thermaloxidative aging on the mechanical behavior of elastomers, several experimental and theoretical models have been proposed. In view of the large volume of experimental data available on micro-structural evolution in the course of aging, we propose a physics-based data-driven approach to overcome the shortcomings of both phenomenological and micro-mechanical models. This work presents a novel thermodynamically consistent, multiagent machine-learned model for predicting the constitutive behavior of cross-linked elastomers during environmental aging, such as thermo-oxidative and hydrolytic aging for various states of deformation. Single mechanism degradation changes the polymer matrix over time where it is causing chain scission, reduction of cross-links, and morphology change. To capture the idealized Mullins effect and permanent set due to the effect of single aging mechanisms on nonlinear mechanical responses of elastomers, we propose a data-driven model for simulating inelastic elements in a polymer matrix. By using a sequential order reduction, we were able to reduce the 3D stress-strain tensor mapping problem to a small number of super-constrained 1D mapping problems. To systematically classify such mapping problems into a few categories, an assembly of multiple replicated conditional neural network learning agents (L-agents) is used based on our recent work. Each category is represented by a different type of agent. The effect of deformation history, aging time, and aging temperature is captured by this model. The model is validated using a broad collection of data, ranging from our experimental results to data from the literature. In addition, thermodynamic consistency and frame independence are investigated. The most significant achievements of this model are its precision, simplicity, and prediction of inelasticity under various states of deformation. The model’s accuracy and simplicity make it a good option for commercial and industrial applications. Conveniently, due to the model modular nature, it can be expanded in the future to include viscoelasticity and non-isotropic formation for better precision.

Ghaderi, Aref↗
DOE OSTI2020

Bacteria Contribute to Plant Secondary Compound Degradation in a Generalist Herbivore System

Leaf-cutter ants are dominant neotropical herbivores capable of deriving energy from a wide range of plant substrates. The success of leaf-cutter ants is largely due to their external gut, composed of key microbial symbionts, specifically, the fungal mutualist L. gongylophorus and a consistent bacterial community. Both symbionts are known to have critical roles in extracting energy from plant material, yet comparatively little is known about their roles in the detoxification of plant secondary compounds. In this study, we assessed if the bacterial communities associated with leaf-cutter ant fungus gardens can degrade harmful plant chemicals. We identify plant secondary compound detoxification in leaf-cutter ant gardens as a process that depends on the degradative potential of both the bacterial community and L. gongylophorus . Our findings suggest that the fungus garden and its associated microbial community influence the generalist foraging abilities of the ants, underscoring the importance of microbial symbionts in plant substrate suitability for herbivores.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Whole genome sequence of the syntrophic benzoate-degrading bacterium, Syntrophus buswellii DSM 102354 T

The syntrophic obligate proton-reducing bacterium Syntrophus buswellii DSM 102354 T , isolated from anaerobic digester sludge, degrades benzoate and possibly hydrocinnamate (phenyl-3-propionate) when grown with a suitable hydrogenotrophic partner. The complete genome sequence data provide insight regarding aromatic compound degradation under thermodynamically limiting conditions when exogenous electron acceptors are absent.

Syntrophus↗
DOE OSTI2020

Degradation of Commercial Lithium-Ion Cells as a Function of Chemistry and Cycling Conditions

Energy storage systems with Li-ion batteries are increasingly deployed to maintain a robust and resilient grid and facilitate the integration of renewable energy resources. However, appropriate selection of cells for different applications is difficult due to limited public data comparing the most commonly used off-the-shelf Li-ion chemistries under the same operating conditions. This article details a multi-year cycling study of commercial LiFePO 4 (LFP), LiNi x Co y Al 1–x–y O 2 (NCA), and LiNi x Mn y Co 1–x–y O 2 (NMC) cells, varying the discharge rate, depth of discharge (DOD), and environment temperature. The capacity and discharge energy retention, as well as the round-trip efficiency, were compared. Even when operated within manufacturer specifications, the range of cycling conditions had a profound effect on cell degradation, with time to reach 80% capacity varying by thousands of hours and cycle counts among cells of each chemistry. The degradation of cells in this study was compared to that of similar cells in previous studies to identify universal trends and to provide a standard deviation for performance. All cycling files have been made publicly available at batteryarchive.org, a recently developed repository for visualization and comparison of battery data, to facilitate future experimental and modeling efforts.

25 ENERGY STORAGE↗
DOE OSTI2022

Elucidating Cathode Degradation Mechanisms in LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811)/Graphite Cells Under Fast Charge Rates Using Operando Synchrotron Characterization

Li-ion batteries capable of extreme fast charging (XFC) are in demand to facilitate widespread electric vehicle (EV) adoption. While the impact of fast charge on the negative electrode has been studied, degradation of state-of-the-art NMC811 under XFC conditions has not been studied in detail. Herein, cathode degradation is probed in NMC811/graphite batteries by analysis of structural and chemical changes for recovered samples previously cycled under XFC conditions and during typical cycling. NMC surface reconstruction, as determined by soft X-ray absorption, was not detected for recovered electrodes. Furthermore, bulk redox activity from X-ray absorption near edge structure measurements showed more change in the oxidation state of Ni and Co under the 1C charge rate compared to the 4C rate consistent with the electrochemistry. Increased unit cell volume contraction under the 1C rate as determined by operando X-ray diffraction suggests that higher charge rates may provide a protective effect on the cathode by reducing structural distortion due to less delithiation.

25 ENERGY STORAGE↗
DOE OSTI2022

Degradation Mechanisms in Advanced MEAs for PEM Water Electrolyzers Fabricated by Reactive Spray Deposition Technology

Proton exchange membrane water electrolyzers (PEMWEs) have demonstrated enormous potential as the next generation hydrogen production technology. The main challenges that the state-of-the-art PEMWEs are currently facing are excessive cost and poor durability. Understanding the failure modes in PEMWEs is a key factor for improving their durability, lowering the precious metal loading, and hence cost reduction. Here, reactive spray deposition technology (RSDT) has been used to fabricate a membrane electrode assembly (MEA) with one order of magnitude lower Pt and Ir catalyst loadings (0.2–0.3 mg PGM cm –2 ) in comparison to the precious metal loadings in the stat-of-the-art commercial MEAs (2–3 mg PGM cm –2 ). As fabricated MEA with an active area of 86 cm 2 , has been tested for over 5000 h at steady-state conditions that are typical for an industrial hydrogen production system. Herein, we present and discuss the results from a comprehensive post-test analysis of the MEA of interest. The main degradation mechanisms, governing the performance loss in the RSDT fabricated MEA with ultra-low precious metal loadings, have been identified and discussed in detail. All failure modes are critically compared and the main degradation mechanism with the highest impact on the MEA performance loss among the others is identified.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Degradation Effects at the Porous Transport Layer/Catalyst Layer Interface in Polymer Electrolyte Membrane Water Electrolyzer

The porous transport layer (PTL)/catalyst layer (CL) interface plays a crucial role in the achievement of high performance and efficiency in polymer electrolyte membrane water electrolyzers (PEMWEs). This study investigated the effects of the PTL/CL interface on the degradation of membrane electrode assemblies (MEAs) during a 4000 h test, comparing the MEAs assembled with uncoated and Ir-coated Ti PTLs. Our results show that compared to an uncoated PTL/CL interface, an optimized interface formed when using a platinum group metal (PGM) coating, i.e., an iridium layer at the PTL/CL interface, and reduced the degradation of the MEA. The agglomeration and formation of voids and cracks could be found for both MEAs after the long-term test, but the incorporation of an Ir coating on the PTL did not affect the morphology change or oxidation of IrO x in the catalyst layer. In addition, our studies suggest that the ionomer loss and restructuring of the anodic MEA can also be reduced by Ir coating of the PTL/CL interface. Optimization of the PTL/CL interface improves the performance and durability of a PEMWE.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Predicting the Rate of Degradation Related to Oxygen Electrode Delamination in Solid Oxide-Ion Electrolyzers

One of the leading causes for the performance degradation in H 2 -producing solid oxide electrolytic cells (SOECs) is the gradual delamination of oxygen electrode (OE) from the electrolyte under a strong anodic polarization. Identification of the key factor that controls the rate of OE delamination is of paramount importance to achieve long-term stable operation of SOECs. Here we show from thousands of hours of testing data that the exchange current density (i°) of OE can be leveraged as a predictor for the rate of delamination. To obtain i°, we apply DC-biased electrochemical impedance spectroscopy on a three-electrode symmetrical cell to measure polarization resistance (R p ) of OE as a function of current density (i) and time (t). The collected R p -i-t raw data are then converted to overpotential ( η )-i-t, from which i° is extracted from the “low-field” approximation. An analytical relationship between i° and time-to-delamination (TTD) is further established from the established i°-i-t relationship. We show that under a constant anodic polarization current density i, the greater the ratio i/i°, the faster the delamination. Therefore, we conclude that the exchange current density of an OE, i°, can be used to predict the rate of OE degradation in solid oxide-ion electrolyzers.

25 ENERGY STORAGE↗
DOE OSTI2026

The Impact of Operational Strategies and Load Fluctuations on Anion Exchange Membrane Water Electrolyzer Degradation

This study reports on the durability of anion exchange membrane water electrolyzers (AEMWEs) at the cell level in a supporting electrolyte (1 M) and the impact of operational profiles on cell degradation. While constant load shows significant voltage loss, including a current hold at 1 A cm −2 and a potential hold at 2 V, it generally occurs in the first 70 h of testing. These losses are likely due to passivation altering site reactivity and cell kinetics, and are largely recoverable upon load cycling and diagnostics at the end of test. Furthermore, square and triangular-wave cycling do not show cell degradation and indicate that AEMWEs are not vulnerable to load cycling. When operating outside of this potential window (1.45–2 V), cell voltage decay rates markedly increase and the losses are not recoverable. This includes a current density hold at 4.0 A cm −2 with high cell voltages (>2.4 V for test duration) and cycling between 0 and 2 V to simulate start–stop, where more than a 50% loss in current density occurs over 50 h. The findings presented here demonstrate the high durability of AEMWEs, particularly when operating with load fluctuations, and may allow for durability advantages compared to iridium and proton exchange membrane systems.

08 HYDROGEN↗
DOE OSTI2025

Statistical Design of Experiments Enables Rapid Exploration of Perfluorobutane Sulfonate Degradation

The phase-out of long-chain PFAS has led to the proliferation of highly recalcitrant short-chain analogs, and mineralization technologies for these short-chain PFAS are needed to mitigate their deleterious effects on environmental and human health. In this study, we utilize a statistical design of experiments, specifically, response surface methodology, to rapidly evaluate the electrochemical degradation of the short-chain PFAS, perfluorobutane sulfonate (PFBS). We evaluate the impacts of the three primary electrochemical parameters (concentration of PFBS, concentration of supporting electrolyte, and applied current) over multiple orders of magnitude on the three primary reaction outcomes of electrochemical PFBS degradation (incomplete PFBS decomposition, complete PFBS mineralization as fluorine, and anodic energy consumption). Our results correspond with literature and clearly identify the well-known tradeoff between energy consumption and complete mineralization. Intriguingly, partial PFBS decomposition and energy consumption demonstrate nonlinear dependencies in the current/supporting electrolyte concentration space and the current/PFBS concentration space, respectively. These findings highlight the utility of the response surface methodology model to efficiently interrogate a large parameter space, identifying both common results and less-obvious interactions between electrochemical parameters and their influences on reaction outcomes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

PV Degradation Tools [SWR-20-71]

This open-source tool functions for calculating degradation of photovoltaic modules. For example, functions to calculate front and rear relative Humidity, as well as Acceleration Factors. A degradation calculation function is also being developed, considering humidity and spectral irradiances models.

Holsapple, Derek↗
DOE OSTI2021

mTORC1 Promotes ARID1A Degradation and Oncogenic Chromatin Remodeling in Hepatocellular Carcinoma

Abstract The SWI/SNF chromatin remodeling complexes control accessibility of chromatin to transcriptional and coregulatory machineries. Chromatin remodeling plays important roles in normal physiology and diseases, particularly cancer. The ARID1A-containing SWI/SNF complex is commonly mutated and thought to be a key tumor suppressor in hepatocellular carcinoma (HCC), but its regulation in response to oncogenic signals remains poorly understood. mTOR is a conserved central controller of cell growth and an oncogenic driver of HCC. Remarkably, cancer mutations in mTOR and SWI/SNF complex are mutually exclusive in human HCC tumors, suggesting that they share a common oncogenic function. Here, we report that mTOR complex 1 (mTORC1) interact with ARID1A and regulates ubiquitination and proteasomal degradation of ARID1A protein. The mTORC1–ARID1A axis promoted oncogenic chromatin remodeling and YAP-dependent transcription, thereby enhancing liver cancer cell growth in vitro and tumor development in vivo. Conversely, excessive ARID1A expression counteracted AKT-driven liver tumorigenesis in vivo. Moreover, dysregulation of this axis conferred resistance to mTOR-targeted therapies. These findings demonstrate that the ARID1A–SWI/SNF complex is a regulatory target for oncogenic mTOR signaling, which is important for mTORC1-driven hepatocarcinogenesis, with implications for therapeutic interventions in HCC. Significance: mTOR promotes oncogenic chromatin remodeling by controlling ARID1A degradation, which is important for liver tumorigenesis and response to mTOR- and YAP-targeted therapies in hepatocellular carcinoma. See related commentary by Pease and Fernandez-Zapico, p. 5608

Oncology↗
DOE OSTI2025

Defining the Antitumor Mechanism of Action of a Clinical-stage Compound as a Selective Degrader of the Nuclear Pore Complex

Cancer cells are acutely dependent on nuclear transport due to elevated transcriptional activity, suggesting an unrealized opportunity for selective therapeutic inhibition of the nuclear pore complex (NPC). Through large-scale phenotypic profiling of cancer cell lines, genome-scale functional genomic modifier screens, and mass spectrometry–based proteomics, we discovered that the clinical drug PRLX-93936 is a molecular glue that binds and reprograms the TRIM21 ubiquitin ligase to degrade the NPC. Upon compound-induced TRIM21 recruitment, the nuclear pore is ubiquitylated and degraded, resulting in the loss of short-lived cytoplasmic mRNA transcripts and the induction of cancer cell apoptosis. Direct compound binding to TRIM21 was confirmed via surface plasmon resonance and X-ray crystallography, whereas compound-induced TRIM21–nucleoporin complex formation was demonstrated through multiple orthogonal approaches in cells and in vitro. Phenotype-guided optimization yielded compounds with 10-fold greater potency and drug-like properties, along with robust pharmacokinetics and efficacy against pancreatic cancer xenografts and patient-derived organoids.

Yuan, Linjie [Stanford School of Medicine, CA (Uni↗
DOE OSTI2024

Temporal contrast degradation from mid-spatial-frequency surface error on stretcher mirrors

Temporal contrast degradation due to mid-spatial-frequency error in chirped-pulse amplification stretcher optics is studied. Third-order cross-correlation measurements reveal a temporal peak that appears when using two different mirrors processed by magnetorheological finishing, despite an improvement in rms roughness compared to a third unprocessed mirror. Simulations based on measured power spectral density show how the actual impact on contrast is different from measurements using a typical bandwidth-limited third-order cross correlator. Furthermore, strategies are proposed to avoid this type of contrast degradation while exploiting computer numerically controlled polishing techniques for enhancement of surface figure and roughness.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Bacteria isolated from the grape phyllosphere capable of degrading guaiacol, a main volatile phenol associated with smoke taint in wine

Recent wildfires near vineyards in the Pacific United States have caused devastating financial losses due to smoke taint in wine. When wine grapes (Vitis vinifera) are exposed to wildfire smoke, their berries absorb volatile phenols derived from the lignin of burning plant material. Volatile phenols are released during the winemaking process giving the finished wine an unpleasant, smokey, and ashy taste known as smoke taint. Bacteria are capable of undergoing a wide variety of metabolic processes and therefore present great potential for bioremediation applications in many industries. In this study, we identify two strains of the same species that colonize the grape phyllosphere and are able to degrade guaiacol, a main volatile phenol responsible for smoke taint in wine. We identify the suite of genes that enable guaiacol degradation in Gordonia alkanivorans via RNAseq of cells growing on guaiacol as a sole carbon source. Additionally, we knockout guaA, a cytochrome P450 gene involved in the conversion of guaiacol to catechol; ΔguaA cells cannot catabolize guaiacol in vitro, providing evidence that GuaA is necessary for this process. Furthermore, we analyze the microbiome of berries and leaves exposed to smoke in the vineyard to investigate the impact of smoke on the grape microbial community. We found smoke has a significant but small effect on the microbial community, leading to an enrichment of several genera belonging to the Bacilli class. Collectively, this research shows that studying microbes and their enzymes has the potential to identify novel tools for alleviating smoke taint.

Castro, Claudia [United States Department of Agric↗
DOE OSTI2023

Reference data, predictors, and probability grids for forest degradation classes in three sites in the Brazilian Amazon

Forest degradation by fires and selective logging is widespread in the Amazon region. We implemented a gradient boosted classification modeling framework to classify intact, logged, and burned forests at three Amazonian sites: Feliz Natal Municipality and Xingu Indigenous Territory in Mato Grosso State, and Saracá-Taquera National Forest in Pará State. We used forest degradation history from Landsat time-series as reference data and textural metrics derived from PlanetScope images as predictors. Textural metrics were computed using the Gray-Level Co-Occurrence Matrix (GLCM) textural technique. Included in the attached zip file are ten files: - a shapefile containing the reference data (fire and selective logging polygons and year of event) for each site; - a multiband tif file containing the 8 GLCM metrics used as predictors (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, Correlation) at the original PlanetScope resolution (3.125m) for each site; - a multiband tif file containing the 72 aggregated GLCM metrics used as predictors (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, and Correlation aggregated using the mean, first quartile, third quartile, maximum, median, minimum, root mean square, standard deviation, and skewness statistics) at 562m resolution for each site; - a multiband tif file containing the 3 probability grids for either intact, logged, or burned forests at the aggregation resolution (562m) for each site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants

The transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, technologies used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants (NPPs) are under evaluation. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval thus reducing the maintenance cost. This paper describes distributed high-temperature stable fiber sensors fabricated in optical fibers through a roll-to-roll laser direct writing process using femtosecond lasers. Using phase-sensitive optical time domain reflectometry, distributed acoustic and vibration sensors can be developed and deployed to critical components and systems in NPPs to perform active measurements with spatial resolution down to 0.5-meter throughout the piping systems. Complex acoustic and vibration signatures harnessed by distributed sensors are registered and analyzed by artificial intelligence algorithms for degradation detection and flaw identification. Piping elbows with machined-in flaws were instrumented with fiber sensors. High-spatial-resolution data were used to develop and validate machine learning algorithms, including both linear and nonlinear regression, and classification. Additionally, classification and sensor analysis were also performed for data analysis. The paper concludes with recommendations and future work on applications of machine learning enabled high-resolution fiber sensors for piping degradation monitoring in current or future NPPs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗