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All sources NASA NTRS DOE OSTI

DOE OSTI2018

Fuel Property Characterization and Prediction

This presentation describes the progress made over the last year on the Fuel Property Characterization and Prediction tasks as part of the CoOptima program under the Vehicle Technologies Office. The tasks reviewed are: Fuel Property Blending Model (F.1.2.2 and F.1.2.3), the Heat of Vaporization Measurement for Full Boiling Range Fuels (F.1.3.2), and the Flow Reactor Autoignition Kinetic Mechanism Development and Validation (F.2.1.4) tasks.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2019

Progress and Challenges in Quantifying Wildfire Smoke Emissions, Their Properties, Transport, and Atmospheric Impacts

Wildfire is a natural and integral ecosystem process that is necessary to maintain species composition, structure, and ecosystem function. Extreme fires have been increasing over the last decades, which have a substantial impact on air quality, human health, the environment, and climate systems. Smoke aerosols can be transported over large distances, acting as pollutants that affect adjacent and distant downwind communities and environments. Fire emissions are a complicated mixture of trace gases and aerosols, many of which are short‐lived and chemically reactive, and this mixture affects atmospheric composition in complex ways that are not completely understood. We present a review of the current state of knowledge of smoke aerosol emissions originating from wildfires. Satellite observations, from both passive and active instruments, are critical to providing the ability to view the large‐scale influence of fire, smoke, and their impacts. Progress in the development of fire emission estimates to regional and global chemical transport models has advanced, although significant challenges remain, such as connecting ecosystems and fuels burned with dependent atmospheric chemistry. Knowledge of the impact of smoke on radiation, clouds, and precipitation has progressed and is an essential topical research area. However, current measurements and parameterizations are not adequate to describe the impacts on clouds of smoke particles (e.g., CNN, INP) from fire emissions in the range of representative environmental conditions necessary to advance science or modeling. We conclude by providing recommendations to the community that we believe will advance the science and understanding of the impact of fire smoke emissions on human and environmental health, as well as feedback with climate system.

I N Sokolik↗

NASA NTRS1994

TEXCAD: Textile Composite Analysis for Design. Version 1.0: User's manual

The Textile Composite Analysis for Design (TEXCAD) code provides the materials/design engineer with a user-friendly desktop computer (IBM PC compatible or Apple Macintosh) tool for the analysis of a wide variety of fabric reinforced woven and braided composites. It can be used to calculate overall thermal and mechanical properties along with engineering estimates of damage progression and strength. TEXCAD also calculates laminate properties for stacked, oriented fabric constructions. It discretely models the yarn centerline paths within the textile repeating unit cell (RUC) by assuming sinusoidal undulations at yarn cross-over points and uses a yarn discretization scheme (which subdivides each yarn not smaller, piecewise straight yarn slices) together with a 3-D stress averaging procedure to compute overall stiffness properties. In the calculations for strength, it uses a curved beam-on-elastic foundation model for yarn undulating regions together with an incremental approach in which stiffness properties for the failed yarn slices are reduced based on the predicted yarn slice failure mode. Nonlinear shear effects and nonlinear geometric effects can be simulated. Input to TEXCAD consists of: (1) materials parameters like impregnated yarn and resin properties such moduli, Poisson's ratios, coefficients of thermal expansion, nonlinear parameters, axial failure strains and in-plane failure stresses; and (2) fabric parameters like yarn sizes, braid angle, yarn packing density, filament diameter and overall fiber volume fraction. Output consists of overall thermoelastic constants, yarn slice strains/stresses, yarn slice failure history, in-plane stress-strain response and ultimate failure strength. Strength can be computed under the combined action of thermal and mechanical loading (tension, compression and shear).

Naik, Rajiv A.↗

NASA NTRS1995

TEXCAD: TEXile Composite Analysis for Design

The Textile Composite Analysis for Design (TEXCAD) code provides the materials/design engineer with a user-friendly, desktop computer based tool for the analysis of a wide variety of fabric reinforced woven and braided composites. It can be used to calculate overall thermal and mechanical properties along with engineering estimates of damage progression and strength. TEXCAD also calculates laminate properties for stacked, oriented fabric constructions. It discretely models the yarn centerline paths within the textile repeating unit cell (RUC) by assuming sinusoidal undulations at yarn cross-over points and uses a year discretization scheme (which subdivides each yarn into smaller, piecewise straight yarn slices) together with a 3-D stress averaging procedure to compute overall stiffness properties. In the calculations for strength, it uses a curved beam-on-elastic foundation model for yarn undulating regions together with incremental approach in which stiffness properties for the failed yarn slices are reduced based on the predicted yarn slice failure mode. Nonlinear shear effects and nonlinear geometric effects can be stimulated. Input to TEXCAD consists of: (1) material parameters like impregnated yarn and resin properties such as moduli, Poisson's ratios, coefficients of thermal expansion, nonlinear shear parameters, axial failure strains, and in-plane failure stresses; and (2) fabric parameters like yarn sizes, braid angle, yarn packing density, filament diameter, and overall fiber volume fraction. Output consists of overall thermoelastic constants, yarn slice strains/stresses, yarn slice failure history, in-plane strain response, and ultimate failure strength. Strength can be computed under the combined action of thermal and mechanical loading (tension, compression, and shear). A brief overview of the analytical capabilities, program organization, and modules, input and output parameters, computer platforms, distribution, and modifications/extensions of the TEXCAD code are presented here.

Naik, Rajiv A.↗

NASA NTRS2017

CMC Research at NASA Glenn in 2017: Recent Progress and Plans

As part of NASA's Aeronautics research mission, Glenn Research Center has developed advanced constituents for 2700F CMC turbine engine applications. In this presentation, fiber and matrix development and characterization for SiCSiC composites will be reviewed and resulting improvements in CMC durability and mechanical properties will be summarized. Progress toward the development and validation of models predicting the effects of the engine environment on durability of CMC and Environmental Barrier Coatings will be summarized and plans for research and collaborations in 2017 will be summarized.

propulsion↗

DOE OSTI2021

Quantifying the hierarchy of structural and mechanical length scales in granular systems

Continuum modeling of granular media is made possible by the existence of a length scale at and above which grain-resolved properties can be meaningfully homogenized. Progress has been made in identifying such length scales relevant to local structural properties such as porosity. However, a systematic analysis of scales above which different mechanical properties can be homogenized has yet to emerge. Here, X-ray tomography and 3D X-ray diffraction data are examined to identify such length scales. The data was obtained in-situ in compressed granular materials with rigid and flexible confinement. The experimental data are supplemented with validated discrete element simulations which examine different system sizes and different boundary conditions. Overall, our study reveals a hierarchy in the length scales of granular solids, with lengths governing structural variables being the shortest, lengths of stress variables being intermediate, and lengths of energy dissipation being the longest. All structural and mechanical length scales obey a power law based on the theory of Geostatistics, implying that the length scales can be found by analyzing samples significantly smaller than the length scales themselves. The length scales are also found to be sensitive to boundary conditions, implying that they are extrinsic features of granular media.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Controlling the corrosion resistance of multi-principal element alloys

Multi-principal element alloys (MPEAs) offer the possibility of many degrees of freedom in the choice of alloying elements to produce either single phase solid solutions or more complex multiphase microstructures. Large ranges of material properties have been observed for MPEAs and mastery of the selection of elements and their compositions can enable novel combinations of properties not possible in traditional alloys. From the aqueous corrosion perspective, optimization of phase stability, control of heterogeneities, passive film identity and its protectiveness, as well as substrate properties such as metal-metal bond strength and activation energy associated with dissolution, can all be controlling factors governed by alloy composition and structure. These factors can mediate the electrochemical reactions controlling spontaneous corrosion. The quest for superior properties based on well-informed element choice is suggested as a path forward guiding MPEA formulations for corrosion performance. However, gaps in fundamental knowledge exist regarding (a) the specific functions of each element, (b) the behavior of elements in unusual combinations, and (c) the formation of complex protective oxides. These issues currently hold back progress in optimization of corrosion properties.

Scully, John R.↗

NASA NTRS1974

Response-produced timeouts under a progressive-ratio schedule with a punished reset option

An attempt is made to extend the generality of timeouts from positive reinforcement to more complex performances. Pigeons were provided with three continuously available options. Responses on one key resulted in food reinforcement under a progressive-ratio schedule, which requires a larger number of responses for successive reinforcements; responses on a second key, which were shocked, reset the progressive-ratio schedule to its first step; and responses on a third key produced a 3-min timeout. Some of these conditions have been studied previously (Dardano, 1968, 1973; Findley, 1958). Questions of general interest included the frequency and stability of response-produced timeouts, changes in the properties of the progressive-ratio performance resulting from the occurrence of timeouts, and the similarity of the relationships between timeouts and the reinforced behavior to relationships found under simpler conditions of reinforcement.

Dardano, J. F.↗

NASA NTRS1999

Progressive Failure Analysis Methodology for Laminated Composite Structures

A progressive failure analysis method has been developed for predicting the failure of laminated composite structures under geometrically nonlinear deformations. The progressive failure analysis uses C(exp 1) shell elements based on classical lamination theory to calculate the in-plane stresses. Several failure criteria, including the maximum strain criterion, Hashin's criterion, and Christensen's criterion, are used to predict the failure mechanisms and several options are available to degrade the material properties after failures. The progressive failure analysis method is implemented in the COMET finite element analysis code and can predict the damage and response of laminated composite structures from initial loading to final failure. The different failure criteria and material degradation methods are compared and assessed by performing analyses of several laminated composite structures. Results from the progressive failure method indicate good correlation with the existing test data except in structural applications where interlaminar stresses are important which may cause failure mechanisms such as debonding or delaminations.

Sleight, David W.↗

NASA NTRS2010

Physics of Life: A Model for Non-Newtonian Properties of Living Systems

This innovation proposes the reconciliation of the evolution of life with the second law of thermodynamics via the introduction of the First Principle for modeling behavior of living systems. The structure of the model is quantum-inspired: it acquires the topology of the Madelung equation in which the quantum potential is replaced with the information potential. As a result, the model captures the most fundamental property of life: the progressive evolution; i.e. the ability to evolve from disorder to order without any external interference. The mathematical structure of the model can be obtained from the Newtonian equations of motion (representing the motor dynamics) coupled with the corresponding Liouville equation (representing the mental dynamics) via information forces. All these specific non-Newtonian properties equip the model with the levels of complexity that matches the complexity of life, and that makes the model applicable for description of behaviors of ecological, social, and economical systems. Rather than addressing the six aspects of life (organization, metabolism, growth, adaptation, response to stimuli, and reproduction), this work focuses only on biosignature ; i.e. the mechanical invariants of life, and in particular, the geometry and kinematics of behavior of living things. Living things obey the First Principles of Newtonian mechanics. One main objective of this model is to extend the First Principles of classical physics to include phenomenological behavior on living systems; to develop a new mathematical formalism within the framework of classical dynamics that would allow one to capture the specific properties of natural or artificial living systems such as formation of the collective mind based upon abstract images of the selves and non-selves; exploitation of this collective mind for communications and predictions of future expected characteristics of evolution; and for making decisions and implementing the corresponding corrections if the expected scenario is different from the originally planned one. This approach postulates that even a primitive living species possesses additional, non-Newtonian properties that are not included in the laws of Newtonian or statistical mechanics. These properties follow from a privileged ability of living systems to possess a self-image (a concept introduced in psychology) and to interact with it. The proposed mathematical system is based on the coupling of the classical dynamical system representing the motor dynamics with the corresponding Liouville equation describing the evolution of initial uncertainties in terms of the probability density and representing the mental dynamics. The coupling is implemented by the information-based supervising forces that can be associated with self-awareness. These forces fundamentally change the pattern of the probability evolution, and therefore, lead to a major departure of the behavior of living systems from the patterns of both Newtonian and statistical mechanics. This innovation is meant to capture the signature of life based only on observable behavior, not on any biochemistry. This will not prevent the use of this model for developing artificial living systems, as well as for studying some general properties of behavior of natural, living systems.

Zak, Michail↗

DOE OSTI2017

Recent Progress in the Understanding of UK Highly Active Liquor Chemistry and Properties - 17144

In the UK Highly Active Liquor (HAL) waste is stored in stainless steel tanks prior to encapsulation in glass in one of three lines in the Waste Vitrification Plant (WVP). The chemistry and physical properties of HAL from the historic and ongoing UK reprocessing operations is well understood, following years of operations and associated research and development programmes using primarily non-active simulants. However the imminent progression to POCO of the Highly Active Storage Tanks (HASTs), where the HAL is stored, requires a more in-depth understanding of the properties of the HAL, in particular the solid phases that are known to exist. Changes in operations are expected, including the addition of new chemical wash reagents, which will result in some fundamental changes to the chemistry in the HASTs. This paper will highlight some of the key findings from recent studies into areas such as solids formation and morphology, chemical dissolution and associated re-precipitation of solids, rheology modification and chemical conversion mechanisms. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

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 NTRS2007

Nondestructive Methods and Special Test Instrumentation Supporting NASA Composite Overwrapped Pressure Vessel Assessments

Many aging composite overwrapped pressure vessels (COPVs), being used by the National Aeronautics and Space Administration (NASA) are currently under evaluation to better quantify their reliability and clarify their likelihood of failure due to stress rupture and age-dependent issues. As a result, some test and analysis programs have been successfully accomplished and other related programs are still in progress at the NASA Johnson Space Center (JSC) White Sands Test Facility (WSTF) and other NASA centers, with assistance from the commercial sector. To support this effort, a group of Nondestructive Evaluation (NDE) experts was assembled to provide NDE competence for pretest evaluation of test articles and for application of NDE technology to real-time testing. Techniques were required to provide assurance that the test article had adequate structural integrity and manufacturing consistency to be considered acceptable for testing and these techniques were successfully applied. Destructive testing is also being accomplished to better understand the physical and chemical property changes associated with progression toward "stress rupture" (SR) failure, and it is being associated with NDE response, so it can potentially be used to help with life prediction. Destructive work also includes the evaluation of residual stresses during dissection of the overwrap, laboratory evaluation of specimens extracted from the overwrap to evaluate physical property changes, and quantitative microscopy to inform the theoretical micromechanics.

Saulsberry, Regor↗

NASA NTRS1994

Failure analysis of woven and braided fabric reinforced composites

A general purpose micromechanics analysis that discretely models the yarn architecture within the textile repeating unit cell was developed to predict overall, three dimensional, thermal and mechanical properties, damage initiation and progression, and strength. This analytical technique was implemented in a user-friendly, personal computer-based, menu-driven code called Textile Composite Analysis for Design (TEXCAD). TEXCAD was used to analyze plain weave and 2x2, 2-D triaxial braided composites. The calculated tension, compression, and shear strengths correlated well with available test data for both woven and braided composites. Parametric studies were performed on both woven and braided architectures to investigate the effects of parameters such as yarn size, yarn spacing, yarn crimp, braid angle, and overall fiber volume fraction on the strength properties of the textile composite.

Naik, Rajiv A.↗

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↗

NASA NTRS1992

Theoretical study of thermodynamic properties and reaction rates of importance in the high-speed research program

One of the primary goals of NASA's high-speed research program is to determine the feasibility of designing an environmentally safe commercial supersonic transport airplane. The largest environmental concern is focused on the amount of ozone destroying nitrogen oxides (NO(x)) that would be injected into the lower stratosphere during the cruise portion of the flight. The limitations placed on NO(x) emission require more than an order of magnitude reduction over current engine designs. To develop strategies to meet this goal requires first gaining a fundamental understanding of the combustion chemistry. To accurately model the combustor requires a computational fluid dynamics approach that includes both turbulence and chemistry. Since many of the important chemical processes in this regime involve highly reactive radicals, an experimental determination of the required thermodynamic data and rate constants is often very difficult. Unlike experimental approaches, theoretical methods are as applicable to highly reactive species as stable ones. Also our approximation of treating the dynamics classically becomes more accurate with increasing temperature. In this article we review recent progress in generating thermodynamic properties and rate constants that are required to understand NO(x) formation in the combustion process. We also describe our one-dimensional modeling efforts to validate an NH3 combustion reaction mechanism. We have been working in collaboration with researchers at LeRC, to ensure that our theoretical work is focused on the most important thermodynamic quantities and rate constants required in the chemical data base.

Langhoff, Stephen↗

DOE OSTI2023

Progress and outlook of Sn–Pb mixed perovskite solar cells

Abstract Organic–inorganic hybrid perovskites have revolutionized solar cell research owing to their excellent material properties. Most previous research has been done on Pb-based perovskites. Recently, efforts to discover a Pb-free or Pb-less perovskite material with an ideal bandgap ranging 1.1–1.3 eV have led researchers to investigate Sn–Pb mixed perovskites. Sn–Pb mixed perovskites have a bandgap of ~ 1.25 eV, which is suitable for high-efficiency single-junction and perovskite/perovskite tandem solar cells. Moreover, the Pb content of Sn–Pb mixed perovskites is 50–60% lower than that of Pb-based perovskites, partially mitigating the Pb toxicity issue. However, incorporating Sn 2+ into the crystal structure also causes various drawbacks, such as inhomogeneous thin film morphologies, easy oxidation of Sn 2+ , and more vulnerable surface properties. Researchers have made substantial progress in addressing these challenges through improvements in compositional design, structural optimization, precursor design, and surface treatments. In this review, we provide a comprehensive overview of the progress in Sn–Pb mixed perovskite solar cells. Furthermore, we analyze the key variables and trends as well as provide an outlook for future directions in the research on Sn–Pb mixed perovskites. Graphical Abstract

14 SOLAR ENERGY↗

DOE OSTI2018

Carbon-Nanotube-Based Thermoelectric Materials and Devices

Conversion of waste heat to voltage has the potential to significantly reduce the carbon footprint of a number of critical energy sectors, such as the transportation and electricity-generation sectors, and manufacturing processes. Thermal energy is also an abundant low-flux source that can be harnessed to power portable/wearable electronic devices and critical components in remote off-grid locations. As such, a number of different inorganic and organic materials are being explored for their potential in thermoelectric-energy-harvesting devices. Carbon-based thermoelectric materials are particularly attractive due to their use of nontoxic, abundant source-materials, their amenability to high-throughput solution-phase fabrication routes, and the high specific energy (i.e., W g-1) enabled by their low mass. Single-walled carbon nanotubes (SWCNTs) represent a unique 1D carbon allotrope with structural, electrical, and thermal properties that enable efficient thermoelectric-energy conversion. Here, the progress made toward understanding the fundamental thermoelectric properties of SWCNTs, nanotube-based composites, and thermoelectric devices prepared from these materials is reviewed in detail. This progress illuminates the tremendous potential that carbon-nanotube-based materials and composites have for producing high-performance next-generation devices for thermoelectric-energy harvesting.

77 NANOSCIENCE AND NANOTECHNOLOGY↗