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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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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Impact of fused silica debris shields and enhanced mitigation techniques on large-aperture beam-sampling optics for the National Ignition Facility

Here, we show that the large-scale routine use of the fused silica debris shield (FSDS) maintains the ∼100× reduction in damage initiation rate and 70% increase in the install lifetime of a new grating debris shield (GDS) observed during pilot operations. Furthermore, we show that the install lifetimes of recycled GDS optics are nearly tripled using additional mitigation strategies such as expanding mitigation processing to include all damage sites larger than 10 μm (LT10) rather than just larger than 50 μm (LT50) and FSDS. We note that there is still a 50% difference between new and recycled optic installation lifetimes. We show that recycled optics have a 3.5× higher apparent initiation rate than new optics when exposed to nominally identical laser conditions.

FSDS↗
DOE OSTI2020

Vibro-acoustic modulation and data fusion for localizing alkali–silica reaction–induced damage in concrete

This article investigates the application of vibro-acoustic modulation testing for diagnosing damage in concrete structures. The vibro-acoustic modulation technique employs two excitation frequencies on a structure. The interaction of these excitations in the measured response indicates damage through the presence of sidebands in the frequency spectra. Past studies using this technique have mostly focused on metals and composites (thin plates or laminates). Our research focuses on concrete, which is a highly heterogeneous material susceptible to a variety of chemical, physical, and mechanical damage processes. In particular, this article investigates diagnosing cracking in concrete from an expansive gel produced by an alkali–silica reaction in the presence of moisture. Past studies have been limited to damage detection using vibro-acoustic modulation testing, whereas this article extends the technique to damage localization. A cement slab with pockets of reactive aggregate is used to investigate the diagnosis technique. The effects of different testing parameters, such as locations, magnitudes, and frequencies of the two excitations, are analyzed and incorporated in the damage localization methodology. A Bayesian probabilistic methodology is developed to fuse the information from multiple test configurations in order to construct damage probability maps for the test specimen. The results of vibro-acoustic modulation–based damage localization are validated by petrographic study of cores taken from the slab.

Karve, Pranav↗
DOE OSTI2020

Mechanisms influencing and prediction of tool influence function spots during hemispherical sub-aperture tool polishing on fused silica

Sub-aperture tool polishing of precision optics requires a detailed understanding of the local material removal [tool influence function (TIF)] at the contact spot between the workpiece and tool to achieve high removal determinism and hence precision of the optic relative to the desired/design surface figure. In this study, the mechanisms influencing and the quantitative prediction of the removal rate and shape of TIF spots during polishing of fused silica glass with cerium oxide slurry using a rotating hemispherical pad-foam tool for a wide variety of process conditions (including tool properties, kinematics, and applied displacements) are investigated. The TIF volumetric removal rate can be estimated utilizing the average relative velocity and contact area using a simple analytical model. In addition, stability of the volumetric removal rate for fixed process conditions is shown to be greatly dependent on the pad preparation and amount of tool use (affecting both pad topography and slurry buildup), whose general behavior shows an increase in removal rate followed by stabilization with polishing time. The determination of the TIF removal shape is more complex. An extended version of the Preston removal model is developed to explain a comprehensive set of measured TIF removal shapes to within ∼ <#comment/> 22 % <#comment/> . This model incorporates a number of phenomena impacting the TIF removal shape including: (a) temporal and spatial dependent relative velocity between the workpiece and tool; (b) an elastic mechanics based, as well as hydrodynamic, pressure distribution; (c) a spatially dependent friction coefficient possibly caused by both reduced slurry replenishment in low velocity regions and pad slurry islands (100 µm scale) and porosity (millimeter scale); and (d) a shear-based removal mechanism on the periphery of the contact spot.

Suratwala, T. (ORCID:0000000190861039)↗
DOE OSTI2023

Thermally robust and highly stable method for splicing silica glass fiber to crystalline sapphire fiber

This research reports an advancement in splicing silica glass fiber to sapphire single-crystal optical fiber (SCF) using a specialized glass processing device, including data that demonstrate the thermal stability of the splice to 1000°C. A filament heating process was used to produce a robust splice between the dissimilar fibers. A femtosecond laser is used to inscribe a fiber Bragg gratings sensor into the SCF to measure the high-temperature capabilities and signal attenuation characteristics of the splice joint. The experimental results demonstrate that the proposed splicing method produces a splice joint that is robust, stable, repeatable, and withstands temperatures up to 1000°C with a low attenuation of 0.5 dB. Here, the proposed method allows placement of SCF-based sensors in the extreme environments encountered in various engineering fields, such as nuclear, chemical, aviation, and metals manufacturing, to enable improvements in process monitoring, product quality, and production efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Defect passivation in nanostructured silica for stable birefringence in polarization optics

Nanostructured materials with anisotropic microstructure exhibit direction-dependent refractive index, known as form birefringence. One of the main advantages of these nanostructured materials is that the ease of fabrication significantly reduces the cost of production. However, their birefringence is affected by humidity as well as time. The hypothesis is that defects, particularly silanols, are responsible. Here, in this work, we present the use of hexamethyldisilazane (HMDS) vapor as a passivation strategy. Using spectroscopic ellipsometry and Raman scattering, the data show that HMDS successfully passivates silanol groups and promotes O-Si-O bond formation, resulting in improved stability. The results demonstrate a simple method to enhance the reliability of all-silica polarization optics offering potential advancements for high-power laser components.

Mireles, Marcela [Univ. of Rochester, NY (United S↗
DOE OSTI2026

Subsurface mechanical damage of fused silica glass during grinding by various sub-aperture tools with and without ultrasonics

The subsurface mechanical damage (SSD) depth after grinding fused silica glass with a comprehensive set of sub-aperture fixed abrasive grinding tools [cup, wheel, belt, pad, and rotary face mill (with and without ultrasonics)] and process parameters has been statistically measured using the taper wedge technique and evaluated. Consistent with a previously reported grinding model [J. Non-Cryst. Solids 352, 5601–5617 (2006) Crossref , Materials Science and Technology of Optical Fabrication (Wiley & Sons, 2018)], based on the sliding indentation fracture by sliding particles or asperities where the normal load per particle determines the depth of the fracture (and ultimately the overall SSD depth), the dominant factor controlling SSD depth was found to be the abrasive size regardless of the tool type and process conditions. Compared to full aperture grinding methods, the overall SSD depth was higher using the sub-aperture tools, likely due to the higher effective pressure and higher load per particle distribution. Here, in addition to abrasive size, a significant reduction in SSD depth was achieved by: (1) reducing the load distribution on the abrasive particles via increase in contact area and/or decrease in mechanical loading; (2) using a more compliant host tool medium; and (3) in what we believe is a more novel way, using ultrasonics. Combining low abrasive size, larger contact area, and a compliant host, the 6 µm diamond in a resin matrix (Trizact) on a foam pad led to very low SSD depth (~ 4.6 µm), relatively fast grinding rate (186 mm 3 /h), and little or no figure degradation. This grinding tool/process is an attractive choice for final grind, resulting in significantly reduced polish out (i.e., “grey out”) times. With the rotary face mill tool, the use of ultrasonics consistently led to a SSD depth reduction (ranging from 17%–34%). A new fracture mechanics-based model, to the best of our knowledge, where the relevant normal load is parallel to the feed direction, has been developed to explain how ultrasonics leads to lower SSD depth. The key factors, supported by finite element stress analysis and load measurements, are (1) the initiation of fractures at higher z heights during the tool’s ultrasonic vertical oscillations, thus propagating less deep into workpiece; (2) reduction in load (and therefore reduction in fracture propagation distance) due to smaller tool-workpiece feed direction contact area (again caused by higher heights relative to depth of cut); (3) upward movement of the tool during oscillation leads to fracturing toward the surface instead into the depth; and finally (4) at tool’s lowest point of oscillation cycle, there may not be enough time for the fracture to propagate to its full length.

Optics and optical instruments↗
DOE OSTI2022

Real-time measurement of parametric influences on the refractive index and length changes in silica optical fibers

In this paper, we present a simple cascaded Fabry-Perot interferometer (FPI) that can be used to measure in real-time the refractive index (RI) and length variation in silica optical fibers caused due to external physical parameters, such as temperature, strain, and radiation. As a proof-of-concept, we experimentally demonstrate real-time monitoring of temperature effects on the RI and length and measure the thermo-optic coefficient (TOC) and thermal expansion coefficient (TEC) by using the cascaded FPI within a temperature range of 21–486°C. The experimental results provide a TEC of 5.53 × 10 −7 /°C and TOC of 4.28 × 10 −6 /°C within the specified temperature range. Such a simple cascaded FPI structure will enable the design of optical sensors to correct for measurement errors by understanding the change in RI and length of optical fiber caused by environment parameters.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Literary review and modeling outlook for radiation damage in fused-silica optical fibers

The purpose of this document is to distill, from literature, the effects of radiation damage in fused-silica optical fibers. This information will be used to design a theoretical framework capable of predictive modeling that will be published elsewhere. By inspection of the relevant literature, the devised method is calculation of defect amorphous structures by molecular dynamic techniques, and computation of the macroscopic dielectric function using density functional theory. The dielectric function is then used to directly compute the transmittance of the core material before and after irradiation via solution of Maxwell’s equations in a homogeneous medium.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Functionalized Mesoporous Carbon and Silica for Effective Recovery of Rare Earth Elements from Magnet Scrap

The dependence on international supplies of rare earth elements (REEs) has prompted the US to explore alternative sources and sustainable technologies for domestic REE production. One potential source is the secondary electronic and industrial wastes across the US. These hazardous wastes require high costs for the maintenance and remediation, while their high-REE contents offer unique opportunities for REE recovery. However, current hydrometallurgical technologies for REE extraction are energy intensive and costly, with low REE recovery efficiency and the generation of hazardous secondary wastes. Thus, it is beneficial to develop environmentally friendly technologies for REE extraction from the secondary wastes. This project aims to develop novel functionalized mesoporous carbon fiber (MCF) and silica materials for effective recovery of REEs from NdFeB magnet scrap.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Machine Learning for Processing Ultrasonic Data from Long-Term Monitoring of Concrete with Alkali-Silica Reaction (ASR)

The alkali–silica reaction (ASR) is a phenomenon that leads to material degradation in concrete, resulting in the formation of microcracks and cracks. This deterioration causes a loss of mechanical properties, concrete damage, and even corrosion. To address this issue, ultrasonic nondestructive evaluation can be employed as a technique for long-term monitoring of ASR development and condition assessment of concrete subjected to ASR. However, traditional approaches typically utilize only a few wave parameters, such as wave velocity or amplitude, to characterize ASR-induced concrete damage while disregarding the majority of information present in the ultrasonic signals.

36 MATERIALS SCIENCE↗
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↗
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↗
DOE OSTI2025

High Performance (R-10/inch) Clay-Cellulose-Silica Nanopore Insulation Board For New and Retrofit Buildings (Final Scientific Report)

The objective of Liatris’s 3-year DOE BENEFIT project was to develop and validate a high-performance, lightweight, aerogel-based insulation board of ≥R-10/inch at a cost of ≤$\$$1.50 per board foot using non-flammable inorganic nanocomposites. We have summarized each yearly goal below: Year 1 – Ambient dried inorganic/organic nanocomposite, R-6/inch Year 2 – Processing efficiency and cost down, R-8/inch at $\$$2.0/board-ft Year 3 – Scale up to continuous, pilot production of R-10 at ≤ $\$$1.50/board-ft Over the course of the project period, Liatris has successfully met or exceeded all of the goals outlined above. The project scope over 3 years was extensive, covering method development of ambient dried silica aerogels in Budget Period 1, process formulation and R-value improvement in Budget Period 2, and composite scale up with corporate partners in Budget Period 3. Over the course of the project Liatris has been able to progressively improve the thermal resistance properties from about R-6/in to > R-11/in, 2-3X the performance of conventional insulation products.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modeling moisture outgassing from silica-filled silicones with the generalized outgassing kinetics

Even though a generalized moisture outgassing modeling has been established and published in a peer-reviewed venue for as-received silica-filled silicones, it is still not yet intuitive for use in engineering/scientific applications. A simplification of the mathematics down to a practical level and illustration of how to do it would be beneficial to the practitioners in the field. In this report, simplified and more intuitive demonstrations of how to use the generalized outgassing kinetics for the prediction of H 2 O release from as-received and previously vacuum-baked silicones will be illustrated.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Impact of Laboratory-Accelerated Aging Methods to Study Alkali–Silica Reaction and Reinforcement Corrosion on the Properties of Concrete

This study focuses on two separate investigations of the main aging mechanisms: alkali–silica reactivity (ASR) and the corrosion of reinforcing steel (rebar) concrete, both of which may result in a premature failure to meet the serviceability or strength requirements of a concrete structure. However, these processes occur very slowly, spanning decades. The impact of direct chemical additives to fresh concrete to accelerate ASR and the corrosion of reinforcing steel on the fresh and hardened properties of the ensuing material are investigated to inform the potential use of chemicals in large-scale studies. The deterioration of reinforced concrete (RC) is determined by means of expansion, cracking, bulk diffusivity and surface resistivity measurements, and compressive, split tensile and flexural strength tests. The results indicate that the addition of sodium hydroxide and calcium chloride can effectively accelerate the crack formation and propagation in concrete due to ASR and the corrosion of rebar, respectively. The ASR-induced cracks maintained a constant crack width from 0.05 mm to 0.1 mm over the measurement period regardless of the intensity of aging acceleration. Adding 4% chloride by weight of cement for accelerating rebar corrosion resulted in an average crack that was 82% larger than in the case of ASR accelerated with the addition of sodium hydroxide. The addition of alkali resulted in an increase in early-age (7-day) strength. At a total alkali loading of 2.98 kg/m3, 3.84 kg/m3 and 5.57 kg/m3, the 28-day compressive strength of concrete decreased by 3%, 10% and 24%, respectively. Similarly, a higher early-age strength and a lower later-age strength was observed for the concrete in the presence of corrosive calcium chloride. The results from this research are expected to inform future studies on the long-term performance of RC structures under accelerated ASR and corrosion.

36 MATERIALS SCIENCE↗