Search NASA⌕ Search

SEARCH · Search NASA

Results for “degradation predictions”

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 OSTI2024

Functional diversification within the heme-binding split-barrel family

Due to neofunctionalization, a single fold can be identified in multiple proteins that have distinct molecular functions. Depending on the time that has passed since gene duplication and the number of mutations, the sequence similarity between functionally divergent proteins can be relatively high, eroding the value of sequence similarity as the sole tool for accurately annotating the function of uncharacterized homologs. Here, we combine bioinformatic approaches with targeted experimentation to reveal a large multifunctional family of putative enzymatic and nonenzymatic proteins involved in heme metabolism. This family (homolog of HugZ (HOZ)) is embedded in the “FMN-binding split barrel” superfamily and contains separate groups of proteins from prokaryotes, plants, and algae, which bind heme and either catalyze its degradation or function as nonenzymatic heme sensors. In prokaryotes these proteins are often involved in iron assimilation, whereas several plant and algal homologs are predicted to degrade heme in the plastid or regulate heme biosynthesis. In the plant Arabidopsis thaliana, which contains two HOZ subfamilies that can degrade heme in vitro (HOZ1 and HOZ2), disruption of AtHOZ1 (AT3G03890) or AtHOZ2A (AT1G51560) causes developmental delays, pointing to important biological roles in the plastid. In the tree Populus trichocarpa, a recent duplication event of a HOZ1 ancestor has resulted in localization of a paralog to the cytosol. Structural characterization of this cytosolic paralog and comparison to published homologous structures suggests conservation of heme-binding sites. This study unifies our understanding of the sequence-structure-function relationships within this multilineage family of heme-binding proteins and presents new molecular players in plant and bacterial heme metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1984

Radiation effects in silicon and gallium arsenide solar cells using isotropic and normally incident radiation

Several types of silicon and gallium arsenide solar cells were irradiated with protons with energies between 50 keV and 10 MeV at both normal and isotropic incidence. Damage coefficients for maximum power relative to 10 MeV were derived for these cells for both cases of omni-directional and normal incidence. The damage coefficients for the silicon cells were found to be somewhat lower than those quoted in the Solar Cell Radiation Handbook. These values were used to compute omni-directional damage coefficients suitable for solar cells protected by coverglasses of practical thickness, which in turn were used to compute solar cell degradation in two proton-dominated orbits. In spite of the difference in the low energy proton damage coefficients, the difference between the handbook prediction and the prediction using the newly derived values was negligible. Damage coefficients for GaAs solar cells for short circuit current, open circuit voltage, and maximum power were also computed relative to 10 MeV protons. They were used to predict cell degradation in the same two orbits and in a 5600 nmi orbit. Results show the performance of the GaAs solar cells in these orbits to be superior to that of the Si cells.

Anspaugh, B. E.↗
NASA NTRS1984

Radiation effects in silicon and gallium arsenide solar cells using isotropic and normally incident radiation

Several types of silicon and gallium arsenide solar cells were irradiated with protons with energies between 50 keV and 10 MeV at both normal and isotropic incidence. Damage coefficients for maximum power relative to 10 MeV were derived for these cells for both cases of omni-directional and normal incidence. The damage coefficients for the silicon cells were found to be somewhat lower than those quoted in the Solar Cell Radiation Handbook. These values were used to compute omni-directional damage coefficients suitable for solar cells protected by coverglasses of practical thickness, which in turn were used to compute solar cell degradation in two proton-dominated orbits. In spite of the difference in the low energy proton damage coefficients, the difference between the handbook prediction and the prediction using the newly derived values was negligible. Damage coefficients for GaAs solar cells for short circuit current, open circuit voltage, and maximum power were also computed relative to 10 MeV protons. They were used to predict cell degradation in the same two orbits and in a 5600 nmi orbit. Results show the performance of the GaAs solar cells in these orbits to be superior to that of the Si cells.

Anspaugh, B. E.↗
DOE OSTI2024

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Abstract Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

Degradation of Silica and Sapphire Windows at Elevated Temperatures

Silica and sapphire are very stable oxide materials. However, at high temperatures they are both susceptible to chemical degradation by three major mechanisms: (1) Deposition of fluxing oxides (2) Non-reactive vaporization (3) Reactive vaporization, particularly by water vapor. In order to model and predict each of these, accurate thermodynamic data and information on the environment are needed. Experimental and theoretical methods used at NASA for identifying the generated gas species and obtaining the relevant thermochemical data are discussed. Modeling and prediction of degradation are discussed for different environments and flow conditions. In addition, surface devitrification or etch pitting is likely under some conditions.

Mass spectrometry↗
NASA NTRS1993

Ground-based simulation of LEO environment: Investigations of a select LDEF material: FEP Teflon (trademark)

The Long Duration Exposure Facility (LDEF) has produced a wealth of data on materials degradation in the low earth orbit (LEO) space environment and has conclusively shown that surface chemistry (as opposed to surface physics-sputtering) is the key to understanding and predicting the degradation of materials in the LEO environment. It is also clear that materials degradation and spacecraft contamination are closely linked and that the fundamental mechanisms responsible for this linking are in general not well understood especially in the area of synergistic effects. The study of the fundamental mechanisms underlying materials degradation in LEO is hampered by the fact that the degradation process itself is not observed during the actual exposure to the environment. Rather the aftermath of the degradation process is studied, i.e., the material that remains after exposure is observed and mechanisms are proposed to explain the observed results. The EOIM-3 flight experiment is an attempt to bring sophisticated diagnostic equipment into the space environment and monitor the degradation process in real time through the use of mass spectrometry. More experiments of this nature which would include surface sensitive diagnostics (Auger and photoelectron spectroscopes) are needed to truly unravel the basic chemical mechanisms involved in the materials degradation process. Since these in-space capabilities will most likely not be available in the near future, ground-based LEO simulation facilities employing sophisticated diagnostics are needed to further advance the basic understanding of the materials degradation mechanisms. The LEO simulation facility developed at Los Alamos National Laboratory has been used to investigate the atomic oxygen/vacuum ultraviolet (AO/VUV) enhanced degradation of FEP Teflon. The results show that photo-ejection of polymer fragments occur at elevated temperature (200 C), that VUV synergistic rare gas sputtering of polymer fragments occur even at 25 C, and that combined OA/VUV interaction produces a wide variety of gas phase reaction products.

Cross, Jon B.↗
DOE OSTI2022

3D Frequency Domain Reflectometry Digital Twin of an Electrical Cable: A First Glance

Electrical cables within nuclear power plants (NPPs) are critical components required for power, control, and instrumentation systems which may be exposed to stressors, such as elevated temperatures and gamma radiation. Such stressors can lead to a reduction in the remaining useful life of electrical cables, jeopardizing the safety of NPP systems. To evaluate the effect of stressors on the degradation of electrical cables, electrical reflectometry methods are commonly employed. Frequency domain reflectometry (FDR) is a non-destructive electrical reflectometry method that uses transmission line theory to detect degradation or impedance changes within electrical cables. However, in most cases FDR is only applied to de-energized cables, limiting the application in NPPs as the cable system must be taken offline. In this work, we explore the development of an FDR digital twin to predict the degradation of an electrical cable exposed to elevated temperature, which is expected to reduce the need for offline FDR. A 3-conductor low-voltage electrical cable was selected for evaluation of the digital twin. The fully three-dimensional digital twin was developed in COMSOL using the RF module. A cable length of 30-m and frequency bandwidth of 400 MHz was selected to mimic real-world application of FDR. Over a 1-m region, the permittivity of the insulation was varied by up to 20% to model thermal degradation. The results demonstrate accurate detection of the insulation damage region, supporting further investigation of the FDR digital twin using real-world data and machine learning for predictive damage estimation or remaining lifetime.

Spencer, Mychal P.↗
NASA NTRS1984

Helicopter rotor performance degradation in natural icing encounter

The analytical model described by Korkan et al. (1982) for predicting the performance degradation of propellers in a natural icing encounter is used to determine the feasibility of predicting helicopter performance degradation in hover during natural icing. The flight condition selected for analysis involves an altitude of 3000 ft and a free-air temperature of 1 F. The values of degradation yielded by the model for rime ice accretion are representative of those experienced in actual flight.

Korkan, K. D.↗
NASA NTRS2013

Prognostics Applied to Electric Propulsion UAV

Health management plays an important role in operations of UAV. If there is equipment malfunction on critical components, safe operation of the UAV might possibly be compromised. A technology with particular promise in this arena is equipment prognostics. This technology provides a state assessment of the health of components of interest and, if a degraded state has been found, it estimates how long it will take before the equipment will reach a failure threshold, conditional on assumptions about future operating conditions and future environmental conditions. This chapter explores the technical underpinnings of how to perform prognostics and shows an implementation on the propulsion of an electric UAV. A particle filter is shown as the method of choice in performing state assessment and predicting future degradation. The method is then applied to the batteries that provide power to the propeller motors. An accurate run-time battery life prediction algorithm is of critical importance to ensure the safe operation of the vehicle if one wants to maximize in-air time. Current reliability based techniques turn out to be insufficient to manage the use of such batteries where loads vary frequently in uncertain environments.

Batteries↗
DOE OSTI2021

A control oriented comprehensive degradation model for battery energy storage system life prediction

In order to ensure that stationary battery energy storage systems (BESSs) provide reliable energy buffering, both for power quality and economic purposes, the degradation must be considered. Cell degradation involves various side reactions and is highly dependent on its operating conditions. To accurately track cell degradation and predict its impact on battery behavior, a comprehensive physics-based degradation model based on an electrolyte phase-enhanced single particle (SP) model is developed. Key degradation physics, namely solid electrolyte interphase (SEI) layer formation and growth, Li plating on the graphite anode, and Mn dissolution on cathode of nickel-cobalt-manganese oxide (NMC622) are considered. The model is validated against experimental capacity data. The results reveal that the deposition rate of both SEI layer and Li metal increase as the charge voltage increases. At the cathode side, the solvent oxidation rate determines the Mn dissolution rate. As a result, the volume fraction of NMC622 in the cathode continually decreases at a gradually increasing rate.

25 ENERGY STORAGE↗
NASA NTRS1988

Predictions of airfoil aerodynamic performance degradation due to icing

An overview of NASA's ongoing efforts to develop an airfoil icing analysis capability is developed. An indication is given to the approaches being followed to calculate the water droplet trajectories past the airfoil, the buildup of ice on the airfoil, and the resultant changes in aerodynamic performance due to the leading edge ice accretion. Examples are given of current code capabilities/limitations through comparisons of predictions with experimental data gathered in various calibration/validation experiments. A brief discussion of future efforts to extend the analysis to handle three dimensional components is included.

Shaw, Robert J.↗
NASA NTRS1989

Predictions of airfoil aerodynamic performance degradation due to icing

An overview of NASA's ongoing efforts to develop an airfoil icing analysis capability is developed. An indication is given to the approaches being followed to calculate the water droplet trajectories past the airfoil, the buildup of ice on the airfoil, and the resultant changes in aerodynamic performance due to the leading edge ice accretion. Examples are given of current code capabilities/limitations through comparisons of predictions with experimental data gathered in various calibration/validation experiments. A brief discussion of future efforts to extend the analysis to handle three-dimensional components is included.

Shaw, Robert J.↗
DOE OSTI2021

Adaptive Machine Learning for Robust Diagnostics and Control of Time-Varying Particle Accelerator Components and Beams

Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multivariate prognosis of battery advanced state of health via transformers

De-risking energy storage investments necessary to meet CO 2 reduction targets requires a deep understanding of the connections between battery health, design, and use. The historical definition of the battery state of health (SOH) as the percentage of current versus initial capacity is inadequate for this purpose, motivating an expanded SOH consisting of an interrelated set of descriptors including capacity, energy, ionic and electronic impedances, open-circuit voltages, and microstructure metrics. In this work, we introduce deep transformer networks for the simultaneous prognosis of 28 battery SOH descriptors using two cycling datasets representing six lithium-ion cathode chemistries, multiple electrolyte/anode compositions, and different charge-discharge scenarios. The accuracy of these predictions for battery life (with an unprecedented mean absolute error of 19 cycles in predicting end of life for a lithium-iron-phosphate fast-charging dataset) illustrates the promise of deep learning toward providing enhanced understanding and control of battery health.

25 ENERGY STORAGE↗
NASA NTRS1995

Correlation of electron and proton irradiation-induced damage in InP solar cells

When determining the best solar cell technology for a particular space flight mission, accurate prediction of solar cell performance in a space radiation environment is essential. The current methodology used to make such predictions requires extensive experimental data measured under both electron and proton irradiation. Due to the rising cost of accelerators and irradiation facilities, such extensive data sets are expensive to obtain. Moreover, with the rapid development of novel cell designs, the necessary data are often not available. Therefore, a method for predicting cell degradation based on limited data is needed. Such a method has been developed at the Naval Research Laboratory based on damage correlation using 'displacement damage dose' which is the product of the non-ionizing energy loss (NIEL) and the particle fluence. Displacement damage dose is a direct analog of the ionization dose used to correlate the effects of ionizing radiations. In this method, the performance of a solar cell in a complex radiation environment can be predicted from data on a single proton energy and two electron energies, or one proton energy, one electron energy, and Co(exp 60) gammas. This method has been used to accurately predict the extensive data set measured by Anspaugh on GaAs/Ge solar cells under a wide range of electron and proton energies. In this paper, the method is applied to InP solar cells using data measured under 1 MeV electron and 3 MeV proton irradiations, and the calculations are shown to agree well with the measured data. In addition to providing accurate damage predictions, this method also provides a basis for quantitative comparisons of the performance of different cell technologies. The performance of the present InP cells is compared to that published for GaAs/Ge cells. The results show InP to be inherently more resistant to displacement energy deposition than GaAs/Ge.

Walters, Robert J.↗
DOE OSTI2023

Exploring the determinants of organic matter bioavailability through substrate-explicit thermodynamic modeling

Microbial decomposition of organic matter (OM) in river corridors is a major driver of nutrient and energy cycles in natural ecosystems. Recent advances in omics technologies enabled high-throughput generation of molecular data that could be used to inform biogeochemical models. With ultrahigh-resolution OM data becoming more readily available, in particular, the substrate-explicit thermodynamic modeling (SXTM) has emerged as a promising approach due to its ability to predict OM degradation and respiration rates from chemical formulae of compounds. This model implicitly assumes that all detected organic compounds are bioavailable, and that aerobic respiration is driven solely by thermodynamics. Despite promising demonstrations in previous studies, these assumptions may not be universally valid because OM degradation is a complex process governed by multiple factors. To identify key drivers of OM respiration, we performed a comprehensive analysis of diverse river systems using Fourier-transform ion cyclotron resonance mass spectrometry OM data and associated respiration measurements collected by the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium. In support of our argument, we found that the incorporation of all compounds detected in the samples into the SXTM resulted in a poor correlation between the predicted and measured respiration rates. The data-model consistency was significantly improved by the selective use of a small subset (i.e., only about 5%) of organic compounds identified using an optimization method. Through a subsequent comparative analysis of the subset of compounds (which we presume as bioavailable) against the full set of compounds, we identified three major traits that potentially determine OM bioavailability, including: (1) thermodynamic favorability of aerobic respiration, (2) the number of C atoms contained in compounds, and (2) carbon/nitrogen (C/N) ratio. We found that all three factors serve as “filters” in that the compounds with undesirable properties in any of these traits are strictly excluded from the bioavailable fraction. This work highlights the importance of accounting for the complex interplay among multiple key traits to increase the predictive power of biogeochemical and ecosystem models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Beyond RMS: Understanding the Evolution of Beam Distributions in High Intensity Linacs

Understanding the evolution of beams with space charge is crucial to design and operation of high intensity linacs. While the community holds a broad understanding of the mechanisms leading to emittance growth and halo formation, there is outstanding discrepancy between measurements and beam evolution models that precludes prediction of halo losses. This may be due in part to insufficient information of the initial beam distribution. This talk will describe work at the SNS Beam Test Facility to directly measure the 6D beam distribution. Full-and-direct 6D measurement has revealed hidden but physically significant dependence between the longitudinal distribution and transverse coordinates. This nonlinear correlation is driven by space charge and reproduced by self-consistent simulation of the RFQ. Omission of this interplane correlation, common when bunches are reconstructed from lower-dimensional measurements, degrades downstream predictions. This talk will also describe the novel diagnostics supporting this work. This includes ongoing improvements to efficiency of the 6D phase space measurement as well as recent achievement of six orders of dynamic range in 2D phase space.

Ruisard, Kiersten↗