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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Proposed Material Properties, Allowable Stresses, and Design Curves of Diffusion Bonded Alloy 800H for the ASME Code Section III Division 5

Abstract Increased interest in compact heat exchangers (CHXs) to serve as intermediate heat exchangers of very high temperature reactors resulted in significant research and development on their design, analysis, and construction. Printed circuit heat exchangers are a type of CHXs with high thermal efficiency and compactness achieved through diffusion bonding a stack of etched plates with millimeter scaled channels. The diffusion bonding process changes the microstructural and mechanical properties of the wrought metal plates. The current nonnuclear design code ASME section VIII, division 1 captures the material property change through a “joint efficiency factor.” However, the current nuclear design code ASME section III, division 5 does not address or support the diffusion bonded material properties. Hence, there is a need to develop allowable stresses, isochronous curves, and fatigue life curves for various diffusion bonded alloys. In this study, Alloy 800H material was selected to establish the diffusion bonded material properties under tension, creep, fatigue, and creep-fatigue loads at elevated temperatures in the range 550–760 °C. A set of tests on diffusion bonded Alloy 800H (DB 800H) were performed and the acquired data are used in developing allowable stresses Sy, Su, Sr, Sm, St, Smt, So, isochronous curves and fatigue life curves according to the ASME section III, division 5 requirements. This paper also presents detailed procedures used in developing the ASME code section III division 5 design provisions for diffusion bonded Alloy 800H.

Engineering↗

DOE OSTI2018

Contributions of Material Properties and Structure to Increased Bone Fragility for a Given Bone Mass in the UCD-T2DM Rat Model of Type 2 Diabetes

Adults with type 2 diabetes (T2D) have a higher fracture risk for a given bone quantity, but the mechanisms remain unclear. Using a rat model of polygenic obese T2D, we demonstrate that diabetes significantly reduces whole-bone strength for a given bone mass (μCT-derived BMC), and we quantify the roles of T2D-induced deficits in material properties versus bone structure; ie, geometry and microarchitecture. Lumbar vertebrae and ulnae were harvested from 6-month-old lean Sprague-Dawley rats, obese Sprague-Dawley rats, and diabetic obese UCD-T2DM rats (diabetic for 69 ± 7 days; blood glucose >200 mg/dL). Both obese rats and those with diabetes had reduced whole-bone strength for a given BMC. In obese rats, this was attributable to structural deficits, whereas in UCD-T2DM rats, this was attributable to structural deficits and to deficits in tissue material properties. For the vertebra, deficits in bone structure included thinner and more rod-like trabeculae; for the ulnae, these deficits included inefficient distribution of bone mass to resist bending. Deficits in ulnar material properties in UCD-T2DM rats were associated with increased non-enzymatic crosslinking and impaired collagen fibril deformation. Specifically, small-angle X-ray scattering revealed that diabetes reduced collagen fibril ultimate strain by 40%, and those changes coincided with significant reductions in the elastic, yield, and ultimate tensile properties of the bone tissue. Importantly, the biomechanical effects of these material property deficits were substantial. Prescribing diabetes-specific tissue yield strains in high-resolution finite element models reduced whole-bone strength by a similar amount (and in some cases a 3.4-fold greater amount) as the structural deficits. These findings provide insight into factors that increase bone fragility for a given bone mass in T2D; not only does diabetes associate with less biomechanically efficient bone structure, but diabetes also reduces tissue ductility by limiting collagen fibril deformation, and in doing so, reduces the maximum load capacity of the bone.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

MatLib-1.2: Nuclear Material Properties Library

The U.S. Nuclear Regulatory Commission (NRC) uses the computer code Fuel Analysis under Steady-state and Transients (FAST) to model steady-state and transient fuel behavior to support regulatory decisions. FAST relies on a material properties library (MatLib) that contains the thermal and mechanical properties of the nuclear materials and coolants of interest to support the US commercial nuclear industry. MatLib contains properties for a variety of nuclear fuels, cladding and other structural materials, gases, and coolants. In this document, material property correlations for the materials contained within MatLib are presented and discussed. When available, comparisons are made between the material property correlations and available data. Additionally, uncertainties are quantified on the material proper- ties, which is then used by the NRC to support uncertainty quantification for best-estimate plus uncertainty safety evaluation reviews. This document describes MatLib-1.2, which was updated from MatLib-1.1 to include cleanup of the code for streamlined calls to MatLib from the main FAST source code, and to enable easier readability of the code by developers that maintain and enhance the FAST code. The results of MatLib did not change between MatLib-1.1 and MatLib-1.2; however, the source code changes warranted incrementing the minor numbering of the software version number. The changes may result in faster execution times of FAST-1.2 as compared to FAST-1.1. This document is one of a series of documents on FAST; the other documents detail the models used by FAST as well as its integral assessment to experiments and commercial data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Micro-structural features and material properties impact on adhesive metal joints via computational modeling and machine learning

The quality of structural bonding in practical applications depends on various factors arising from materials, pre-processing conditions, and manufacturing. Understanding how these factors influence bonding performance and determining their relative importance are of significant interest. Thus, this study evaluates the effects of microstructural features and material properties on the structural strength of adhesively-bonded metal joints at the submillimeter scale, utilizing a combination of Finite Element Modeling (FEM) and Machine Learning (ML) with Gradient Boosting Regression (GBR). The microstructural features include adhesive thickness, internal voids within the adhesive, adherend-adhesive interfacial voids, void size and volume fraction, and surface roughness. The material properties include the constitutive behavior of the adhesive, as well as the adherend-adhesive interfacial strength and fracture energy. The changes in structural strength and morphologies of the bonded metal structures with respect to different microstructural features and material properties were clarified by FEM. By further leveraging ML-GBR, the sequence of importance of these factors affecting bonding performance across various scenarios was summarized. This work provides valuable insights into the development of improved structural bonding for adhesive joints in industries such as automotive , aerospace, and beyond.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Investigation of the Effect of Etch Pits on the Material Properties of Carbon Fiber Structures

It is well known that etch pits are formed on carbon surfaces during their consumption in reactive gas environments such as during ablation of carbon-based thermal protection system (TPS) materials. These pits start out from atomic defects on the carbon surface, grow in size and then ultimately lead to the complete removal of the solid carbon material. However, the effect of such etch pits on the material properties of carbon ablators such as FiberForm are poorly understood, as the maximum size of pits are on the order of microns in size, which makes them hard to observe experimentally. Hence, we have developed a module within the Porous Microstructure Analysis code PuMA that can generate etch pits on an arbitrary material surface, with a prescribed distribution of pit size and density. We use this newly developed module to calculate material properties of FiberForm, which is the base material for one of the most commonly used TPS materials - Phenolic Impregnated Carbon Ablator (PICA). The detailed microstructure of FiberForm in PuMA will either be generated synthetically or obtained from X-ray microtomography. Our simulations show that material properties such as the thermal conductivity and tortuosity decrease with increasing degree of pitting. This will be helpful to more accurately predict the degradation of carbon-based TPS during ablation, and furthermore may allow us to identify previously unknown TPS failure modes due to pitting.

PuMA↗

NASA NTRSDate not supplied

Investigation of the effect of etch pits on the material properties of carbon fiber structures

It is well known that etch pits are formed on carbon surfaces during their consumption in reactive gas environments such as during ablation of carbon-based thermal protection system (TPS) materials. These pits start out from atomic defects on the carbon surface, grow in size and then ultimately lead to the complete removal of the solid carbon material [1, 2]. However, the effect of such etch pits on the material properties of carbon ablators such as FiberForm are poorly understood, as the maximum size of pits are on the order of microns in size, which makes them hard to observe experimentally. Hence, we have developed a module within the Porous Microstructure Analysis code PuMA [3] that can generate etch pits on an arbitrary material surface, with a prescribed distribution of pit size and density. Figure 1 shows a FiberForm sample with artificially generated pits of varying radii and pit density. We use this newly developed module to calculate material properties of FiberForm, which is the base material for one of the most commonly used TPS materials - Phenolic Impregnated Carbon Ablator (PICA). The detailed microstructure of FiberForm in PuMA will either be generated synthetically or obtained from X-ray microtomography [4]. Our simulations show that material properties such as the thermal conductivity and tortuosity decrease with increasing degree of pitting. This will be helpful to more accurately predict the degradation of carbon-based TPS during ablation, and furthermore may allow us to identify previously unknown TPS failure modes due to pitting.

PuMA↗

DOE OSTI2017

Influence of physical activity on tibial bone material properties in laying hens

Laying hens develop a type of osteoporosis that arises from a loss of structural bone, resulting in high incidence of fractures. In this study, a comparison of bone material properties was made for lines of hens created by divergent selection to have high and low bone strength and housed in either individual cages, with restricted mobility, or in an aviary system, with opportunity for increased mobility. Improvement of bone biomechanics in the high line hens and in aviary housing was mainly due to increased bone mass, thicker cortical bone and more medullary bone. However, bone material properties such as cortical and medullary bone mineral composition and crystallinity as well as collagen maturity did not differ between lines. However, bone material properties of birds from the different type of housing were markedly different. The cortical bone in aviary birds had a lower degree of mineralization and bone mineral was less mature and less organized than in caged birds. Here, these differences can be explained by increased bone turnover rates due to the higher physical activity of aviary birds that stimulates bone formation and bone remodeling. Multivariate statistical analyses shows that both cortical and medullary bone contribute to breaking strengthThe cortical thickness was the single most important contributor while its degree of mineralization and porosity had a smaller contribution. Lastly, bone properties had poorer correlations with mechanical properties in cage birds than in aviary birds presumably due to the greater number of structural defects of cortical bone in cage birds.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks

Abstract Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2009

Effective Materials Property Information Management for the 21st Century

This paper discusses key principles for the development of materials property information management software systems. There are growing needs for automated materials information management in various organizations. In part these are fueled by the demands for higher efficiency in material testing, product design and engineering analysis. But equally important, organizations are being driven by the need for consistency, quality and traceability of data, as well as control of access to sensitive information such as proprietary data. Further, the use of increasingly sophisticated nonlinear, anisotropic and multi-scale engineering analyses requires both processing of large volumes of test data for development of constitutive models and complex materials data input for Computer-Aided Engineering (CAE) software. And finally, the globalization of economy often generates great needs for sharing a single "gold source" of materials information between members of global engineering teams in extended supply chains. Fortunately, material property management systems have kept pace with the growing user demands and evolved to versatile data management systems that can be customized to specific user needs. The more sophisticated of these provide facilities for: (i) data management functions such as access, version, and quality controls; (ii) a wide range of data import, export and analysis capabilities; (iii) data "pedigree" traceability mechanisms; (iv) data searching, reporting and viewing tools; and (v) access to the information via a wide range of interfaces. In this paper the important requirements for advanced material data management systems, future challenges and opportunities such as automated error checking, data quality characterization, identification of gaps in datasets, as well as functionalities and business models to fuel database growth and maintenance are discussed.

Ren, Weiju↗

NASA NTRS2017

From Tomography to Material Properties of Thermal Protection Systems

A NASA Ames Research Center (ARC) effort, under the Entry Systems Modeling (ESM) project, aims at developing micro-tomography (micro-CT) experiments and simulations for studying materials used in hypersonic entry systems. X-ray micro-tomography allows for non-destructive 3D imaging of a materials micro-structure at the sub-micron scale, providing fiber-scale representations of porous thermal protection systems (TPS) materials. The technique has also allowed for In-situ experiments that can resolve response phenomena under realistic environmental conditions such as high temperature, mechanical loads, and oxidizing atmospheres. Simulation tools have been developed at the NASA Ames Research Center to determine material properties and material response from the high-fidelity tomographic representations of the porous materials with the goal of informing macroscopic TPS response models and guiding future TPS design.

Tomography↗

NASA NTRS2016

Summary Report for the Technical Interchange Meeting on Development of Baseline Material Properties and Design Guidelines for In-Space Manufacturing Activities

NASA Marshall Space Flight Center (MSFC) and the Agency as a whole are currently engaged in a number of in-space manufacturing (ISM) activities that have the potential to reduce launch costs, enhance crew safety, and provide the capabilities needed to undertake long-duration spaceflight. The recent 3D Printing in Zero-G experiment conducted on board the International Space Station (ISS) demonstrated that parts of acrylonitrile butadiene styrene (ABS) plastic can be manufactured in microgravity using fused deposition modeling (FDM). This project represents the beginning of the development of a capability that is critical to future NASA missions. Current and future ISM activities will require the development of baseline material properties to facilitate design, analysis, and certification of materials manufactured using in-space techniques. The purpose of this technical interchange meeting (TIM) was to bring together MSFC practitioners and experts in materials characterization and development of baseline material properties for emerging technologies to advise the ISM team as we progress toward the development of material design values, standards, and acceptance criteria for materials manufactured in space. The overall objective of the TIM was to leverage MSFC's shared experiences and collective knowledge in advanced manufacturing and materials development to construct a path forward for the establishment of baseline material properties, standards development, and certification activities related to ISM. Participants were asked to help identify research and development activities that will (1) accelerate acceptance and adoption of ISM techniques among the aerospace design community; (2) benefit future NASA programs, commercial technology developments, and national needs; and (3) provide opportunities and avenues for further collaboration.

Prater, T. J.↗

DOE OSTI2013

Influence of Material Properties on TiO 2 Nanoparticle Agglomeration

Emerging nanomaterials are being manufactured with varying particle sizes, morphologies, and crystal structures in the pursuit of achieving outstanding functional properties. These variations in these key material properties of nanoparticles may affect their environmental fate and transport. To date, few studies have investigated this important aspect of nanoparticles’ environmental behavior. In this study, the aggregation kinetics of ten different TiO 2 nanoparticles (5 anatase and 5 rutile each with varying size) was systematically evaluated. Our results show that, as particle size increases, the surface charge of both anatase and rutile TiO 2 nanoparticles shifts toward a more negative value, and, accordingly, the point of zero charge shifts toward a lower value. The colloidal stability of anatase sphere samples agreed well with DLVO theoretical predictions, where an increase in particle size led to a higher energy barrier and therefore greater critical coagulation concentration. In contrast, the critical coagulation concentration of rutile rod samples correlated positively with the specific surface area, i.e., samples with higher specific surface area exhibited higher stability. Finally, due to the large innate negative surface charge of all the TiO 2 samples at the pH value (pH = 8) tested, the addition of natural organic matter was observed to have minimal effect on TiO 2 aggregation kinetics, except for the smallest rutile rods that showed decreased stability in the presence of natural organic matter.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Latent Catalysis as a Platform for Accessing Diverse Material Properties in Vat Photopolymerization 3D Printing

Vat photopolymerization (VP) 3D printing is an attractive strategy to manufacture customized polymer parts. The properties of printed materials are limited by the need to employ a low viscosity liquid resin and achieve rapid polymerization kinetics. To circumvent this limitation, dual‐cure methods have been developed using reagents embedded in the liquid resin formulation; however, the reagent‐based approach requires the discovery and optimization of new chemistry for each desired material. Here, in this work, we demonstrate a catalytic, dual‐cure platform that enables access to both Nylon‐6 and polyester interpenetrating networks through VP 3D printing under a universal approach. Structure–reactivity relationships of the latent NHC catalysts led to the identification of a magnesium chloride–NHC adduct as a latent catalyst that is orthogonal to radical polymerization and can be unmasked at elevated temperatures post‐printing to initiate ring‐opening polymerization of lactones and lactams. This strategy results in access to semicrystalline materials, which are a challenging morphology to access via VP 3D printing, that have attractive mechanical properties and can be printed at high resolution. This work represents the first photochemical‐based 3D printing of Nylon‐based materials and demonstrates the value of catalytic approaches to access new material properties in VP 3D printing.

Colliver, Cali N. [University of North Carolina, C↗

DOE OSTI2023

A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties

Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.

Biswas, Arpan↗

DOE OSTI2019

Spontaneous driving forces give rise to protein-RNA condensates with coexisting phases and complex material properties

Phase separation of multivalent protein and RNA molecules underlies the biogenesis of biomolecular condensates such as membraneless organelles. In vivo, these condensates encompass hundreds of distinct types of molecules that typically organize into multilayered structures supporting the differential partitioning of molecules into distinct regions with distinct material properties. The interplay between driven (active) versus spontaneous (passive) processes that are required for enabling the formation of condensates with coexisting layers of distinct material properties remains unclear. Here, we deploy systematic experiments and simulations based on coarse-grained models to show that the collective interactions among the simplest, biologically relevant proteins and archetypal RNA molecules are sufficient for driving the spontaneous emergence of multilayered condensates with distinct material properties. These studies yield a set of rules regarding homotypic and heterotypic interactions that are likely to be relevant for understanding the interplay between active and passive processes that control the formation of functional biomolecular condensates.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2013

Material Property Characterization of AS4/VRM-34 Textile Laminates

Several material properties (modulus, strengths, and fracture toughness) of a textile composite have been evaluated to provide input data to analytical models of Pultruded Rod Stiffened Efficient Unitized Structure (PRSEUS). The material system is based on warp-knitted preforms of AS4 carbon fibers and VRM-34 epoxy resin, which have been processed via resin infusion and oven curing. Tensile, compressive, shear, and fracture toughness properties have been measured at ambient and elevated temperatures. All specimens were tested in as-fabricated (dry) condition. Specimens were tested with and without through-thickness stitching.

Grenoble, Ray W.↗

DOE OSTI2012

AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

Empirical databases of crystal structures and thermodynamic properties are fundamental tools for materials research. Recent rapid proliferation of computational data on materials properties presents the possibility to complement and extend the databases where the experimental data is lacking or difficult to obtain. Enhanced repositories that integrate both computational and empirical approaches open novel opportunities for structure discovery and optimization, including uncovering of unsuspected compounds, metastable structures and correlations between various characteristics. The practical realization of these opportunities depends on a systematic compilation and classification of the generated data in addition to an accessible interface for the materials science community. In this paper we present an extensive repository, aflowlib.org, comprising phase-diagrams, electronic structure and magnetic properties, generated by the high-throughput framework AFLOW. This continuously updated compilation currently contains over 150,000 thermodynamic entries for alloys, covering the entire composition range of more than 650 binary systems, 13,000 electronic structure analyses of inorganic compounds, and 50,000 entries for novel potential magnetic and spintronics systems. The repository is available for the scientific community on the website of the materials research consortium, aflowlib.org.

Materials Science↗

NASA NTRS2005

Assessment of Technologies for the Space Shuttle External Tank Thermal Protection System and Recommendations for Technology Improvement - Part III: Material Property Characterization, Analysis, and Test Methods

The objective of this report is to contribute to the independent assessment of the Space Shuttle External Tank Foam Material. This report specifically addresses material modeling, characterization testing, data reduction methods, and data pedigree. A brief description of the External Tank foam materials, locations, and standard failure modes is provided to develop suitable background information. A review of mechanics based analysis methods from the open literature is used to provide an assessment of the state-of-the-art in material modeling of closed cell foams. Further, this report assesses the existing material property database and investigates sources of material property variability. The report presents identified deficiencies in testing methods and procedures, recommendations for additional testing as required, identification of near-term improvements that should be pursued, and long-term capabilities or enhancements that should be developed.

Gates, Thomas S.↗