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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.

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24 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A self-healing liquid metal anode with PEO-Based polymer electrolytes for rechargeable lithium batteries

In this study, a Ga-Sn liquid metal alloy material is demonstrated as a self-healing anode system due to its fluidity via operando synchrotron-based transmission X-ray microscopy and X-ray diffraction experiments. Cracks formed due to volume expansions can be recovered by the fluidity of the liquid metals. By incorporating with a poly(ethylene oxide) (PEO)-based electrolyte at 60°C, the Ga-Sn anode shows a reversible lithium insertion and extraction process with a high initial discharge specific capacity of 682 mAh g -1 , followed by delivering a capacity of 462 mAh g -1 in the second cycle at C/20 rate. Furthermore, compared with its solid counterparts, the Ga-Sn liquid metal anode demonstrates a better capability to maintain its mechanical integrity and better contact with PEO solid electrolytes due to its advantageous features of the liquid. This study suggests a potential strategy to use liquid metal alloys with polymer solid electrolyte to solve the anode challenges in rechargeable lithium batteries.

36 MATERIALS SCIENCE↗
NASA NTRS2013

Damage Detection in Composite Structures with Wavenumber Array Data Processing

Guided ultrasonic waves (GUW) have the potential to be an efficient and cost-effective method for rapid damage detection and quantification of large structures. Attractive features include sensitivity to a variety of damage types and the capability of traveling relatively long distances. They have proven to be an efficient approach for crack detection and localization in isotropic materials. However, techniques must be pushed beyond isotropic materials in order to be valid for composite aircraft components. This paper presents our study on GUW propagation and interaction with delamination damage in composite structures using wavenumber array data processing, together with advanced wave propagation simulations. Parallel elastodynamic finite integration technique (EFIT) is used for the example simulations. Multi-dimensional Fourier transform is used to convert time-space wavefield data into frequency-wavenumber domain. Wave propagation in the wavenumber-frequency domain shows clear distinction among the guided wave modes that are present. This allows for extracting a guided wave mode through filtering and reconstruction techniques. Presence of delamination causes spectral change accordingly. Results from 3D CFRP guided wave simulations with delamination damage in flat-plate specimens are used for wave interaction with structural defect study.

Tian, Zhenhua↗
DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗
NASA NTRS2002

Major Effects of Nonmetallic Inclusions on the Fatigue Life of Disk Superalloy Demonstrated

The fatigue properties of modern powder metallurgy disk alloys can vary because of the different steps of materials and component processing and machining. Among these variables, the effects of nonmetallic inclusions introduced during the powder atomization and handling processes have been shown to significantly degrade low-cycle fatigue life. The levels of inclusion contamination have, therefore, been reduced to less than 1 part per million in state-of-the-art nickel disk powder-processing facilities. Yet the large quantities of compressor and turbine disks weighing from 100 to over 1000 lb have enough total volume and surface area for these rare inclusions to still be present and limit fatigue life. The objective of this study was to investigate the effects on fatigue life of these inclusions, as part of the Crack Resistant Disk Materials task within the Ultra Safe Propulsion Project. Inclusions were carefully introduced at elevated levels in a nickel-base disk superalloy, U720, produced using powder metallurgy processing. Multiple strain-controlled fatigue tests were then performed on extracted test specimens at 650 C. Analyses were performed to compare the low-cycle fatigue lives and failure initiation sites as functions of inclusion content and fatigue conditions. Powder of the nickel-base superalloy U720 was atomized in argon at Special Metals Corporation, Inc., using production-scale high-cleanliness powder-processing facilities and handling practices. The powder was then passed through a 270-mesh screen. One portion of this powder was set aside for subsequent consolidation without introduced inclusions. Two other portions of this powder were seeded with alumina inclusions. Small, polycrystalline soft (Type 2) inclusions of about 50 mm diameter were carefully prepared and blended into one powder lot, and larger hard (Type 1) inclusions of about 150 mm mean diameter were introduced into the other seeded portion of powder. All three portions of powder were then sealed in separate containers, hot isostatically pressurized, extruded, forged into subscale disks, and heat treated. Low-cycle-fatigue specimens were then extracted, machined, and tested. Fatigue tests were performed at 650 C in closed-loop servohydraulic testing machines using induction heating and axial extensometers. All tests were continued to failure, and fractographic evaluations were performed on all specimens to determine the crack initiation sites. A large majority of the failures in specimens with introduced inclusions occurred at cracks initiating from inclusions at the specimen surface, as shown for each type of inclusion in the following bar chart. The inclusions significantly reduced fatigue life from unseeded material levels, as shown in the bar chart. These effects were found to depend on the strain range, strain ratio, and inclusion size. Tests at lower strain ranges and higher strain ratios resulted in larger effects of inclusions on life. Inclusion effects on life were thereby maximized in tests at the lowest strain range of 0.6 percent and the most positive strain ratio of 0.5. Under these conditions, small Type 2 inclusions reduced life substantially-- about 20 times, whereas large Type 1 inclusions dramatically reduced life 100 times. These results clearly demonstrate that it is essential to include the effects of inclusions for realistic predictions of disk fatigue life. Important issues, including temperature dependence, crack initiation versus propagation, surface treatments, realistic disk features and machining, and realistic disk spin testing will be addressed to accurately model inclusion effects on disk fatigue life. Fatigue life varied from well over 105 cycles for no inclusions to a little over 103 cycles for 100-micrometer inclusions. A single crack initiating at a surface-connected seeded inclusion caused failure in each case.

Gabb, Timothy P.↗
DOE OSTI2024

Failure Analysis for Molten Salt Thermal Energy Storage Tanks for In-Service CSP Plants

Thermal Energy Storage (TES) is a fundamental component in concentrating solar power (CSP) plants to increase the plant's dispatchability, capacity factor, while reducing the levelized cost of electricity. In central receivers CSP plants, nitrate molten salts have been used for several years for operation temperatures of up to 565 degrees C. Despite many efforts to advance nitrate salt to higher operation temperatures (even considering a replacement with molten chloride salts) to achieve higher energy conversion efficiencies, the 565 degrees C temperature is currently considered the state-of-the art. Although molten salt tanks have been broadly deployed in commercial CSP plants worldwide, several failures have been reported in these tanks after a few months or years of operation, causing significant economic loss and mistrust in CSP technologies. Most of these failures are associated with the infancy of the technology and multiple issues related to tank design, fabrication, commissioning, and aggressive operation. A technical standard dedicated to the design and fabrication of molten nitrate TES tanks does not exist today. Current in-service molten salt tanks have been generally designed based on the American Petroleum Institute's (API) 650 and ASME Section II standards. The API 650 code provides guidelines for dimensions and fabrication for oil storage tanks up to 260 degrees C. The ASME standard provides allowable stress values for various materials at a range of temperatures and conditions. Both standards seem to be limited for molten salt TES tanks where high temperatures, thermal cycling, and transient conditions are expected. In 2020, NREL released the Concentrating Solar Power Best Practices Study (NREL/TP-5500-75763) that summarized multiple issues in CSP plants, along with potential alternatives and recommendations to address those issues based on information collected from participants representing about 80% of operating CSP plants in the world. One of the recommendations from this study was the development of accurate and validated models to evaluate the plant's transient operation, capable of capturing the effect of short-term clouds and operator response, while being flexible in being adapted to various spatial and temporal resource data. The "Failure Analysis for Molten Salt Thermal Energy Tanks for In-Service CSP Plants" project was inspired on this recommendation and was focused on (1) the development and validation of a physics-based model for a representative, commercial-scale molten salt tank, (2) performing simulations to evaluate the behavior of the tank as a function of typical plant operation conditions, (3) understanding tank failures mechanisms, (4) determining the residual stress and distortion in the tank floor after welding fabrication and evaluating their impact in the stresses developed in the tank during operation, (5) assessing the impact of key operation parameters on the temperature and stress distribution, (6) conduct a preliminary evaluation of design features to reduce stress and improve tank's reliability, and (7) estimate the tank's service life based on the stress developed under diverse operation scenarios. From the analysis conducted in the project and presented in this report, it was found that maximum stresses surpassing the yield strength point of the stainless steel (SS) 347H are developed on the tank floor near the perimeter. These large stresses are strongly influenced by the initial residual stresses and distortion of the tank floor after welding fabrication. During operation, large stresses are developed in the tank floor at high operation temperatures with large salt inventory levels during transient operation. High stresses are also related to elevated temperature gradients in the tank floor that could be attributed to insufficient mixing within the salt inflow and the salt inventory. Based on the analysis, creep is the predominant failure mechanism. However, the large stress levels could favor the plastic deformation into buckles, and crack formation due to stress relaxation cracking during cycle operation. A lifetime below 3 years was estimated for the typical plant operation conditions and a specific initial residual stress and deformation distribution of the tank floor. The estimated life agrees with the service time to failure reported in several commercial molten salt tanks. Desing and operation guidelines can be extracted from the analysis presented in this report, which could be adopted by tank manufacturers and CSP operators to advance toward an ultimate solution for tank failures by reducing residual and operational stresses to achieve a tank service life of more than 30 years. Addressing failures in molten salt TES tanks is fundamental for the CSP industry's survivability, but it is also important for other industrial and power generation applications using this technology, including nuclear and concentrating solar thermal.

14 SOLAR ENERGY↗
DOE OSTI2024

Assessment of Machine Learning for Ultrasonic Nondestructive Evaluation of Alkali–Silica Reaction in Concrete

Alkali–silica reaction (ASR) is a type of material degradation in concrete structures that leads to concrete cracking and rebar corrosion, thereby reducing the material’s structural integrity and the overall structure’s lifetime and raising safety concerns. Ultrasonic nondestructive evaluation (NDE) has been proven to be a valuable technique for assessing concrete properties and monitoring ASR progression in concrete. However, the deployment and analysis of ultrasonic NDE and its data requires specialized expertise, often relying on the engineer’s subjective interpretation. With the surge in computational power, artificial intelligence (AI) and machine learning (ML) algorithms have become popular in automating NDE data analysis. Various industrial sectors are increasingly adopting ML algorithms for NDE data analysis with a growing emphasis on AI–assisted automation. Regulatory agencies are also preparing for this technological shift, anticipating corresponding revisions in standards. Thus, there is an urgent need to identify the capabilities and limitations of current ML technologies for the evaluation of concrete material properties and damage status. Furthermore, the effects of various factors on ML model performance must be thoroughly investigated. The study summarized herein evaluated the effectiveness of two ML models (i.e., support vector regression (SVR) and deep neural network (DNN)) in predicting concrete material damage induced by ASR based on the long-term ultrasonic monitoring data. Four distinct concrete specimens were cast with artificially induced ASR, and over a period exceeding 500 days, ultrasonic signals and expansion data were continuously collected. For the SVR model, wave velocity and 12 other wave features were extracted from the ultrasonic signals, with 6 out of 13 features selected as input for the model. Different combinations of training and testing datasets were designed to explore factors influencing prediction performance, including the range of data within training and testing sets, in addition to various signal preprocessing methodologies. These findings suggest the importance of using a training dataset with a broader data range compared with testing datasets for improved model performance alongside consistent signal preprocessing across datasets.

36 MATERIALS SCIENCE↗