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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Experimental Fabrication of Porous Additive Manufactured Material

Nuclear industries can benefit from materials that can perform well mechanically and thermally in high temperature and corrosion environments. Functionally graded materials (FGM) are materials manufactured with complex spatial, structural, and chemical compositions, creating components predesigned with tailored microstructural and mechanical properties. Porosity-graded FGMs are designed to introduce pores into a component’s structure as a mechanism to increase bulk or surface material performance, such as stress accommodation and negative thermal conductivity. Additive manufacturing (AM) techniques such as laser energy net shape (LENS) and wire arc additive manufacturing (WAAM), excel in high energy density, point-to-point metal deposition, and present in combination with other combinatorial approaches, exciting methods to manufacture porous FGM components. This project is also examining plasma jet printing as another means for method evaluations for smaller scale graded components. This study explores AM FGM process parameters and combinatorial fabrication methodologies for stainless steel components functionally graded in porosity levels. Preliminary characterization results are provided to attest the feasibility of the combinational fabrication techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2023

Irradiation and corrosion testing of laser powder bed fusion-manufactured materials in the AMMT program

The Advanced Materials and Manufacturing Technologies (AMMT) program operating under the Department of Energy Office of Nuclear Energy is accelerating the qualification and deployment of additively manufactured materials in advanced reactor environments. Laser powder bed fusion (LPBF) has emerged as a promising additive manufacturing technique to fabricate complex components with the potential of tailored material properties. In the nuclear industry, metal additive manufacturing can offer numerous advantages, such as reduced lead times, streamlined quality assurance, and cost-effective low-volume production of new and replacement components with conventional or novel materials and geometries. The rapid and effective qualification of the effect of processing variability on the performance and degradation of additively manufactured materials is essential for the deployment of these components into advanced reactor environments. As part of this effort, AMMT is performing irradiation and corrosion testing of several materials built by LPBF, including 316L and 316H stainless steels. Our efforts encompass neutron irradiation testing, ion irradiation testing, and advanced modeling to provide a robust technical basis for understanding the effect of process variability on materials degradation and developing a sound methodology for accelerated ion irradiation testing. We develop focused irradiation test plans that align well with risk-informed and technologically inclusive approaches to licensing. We will also discuss our plans and specific concerns for corrosion testing additively manufactured material, with a primary focus on molten salt and liquid sodium environments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Survey Corrosion Testing Methodologies for Additively Manufactured Materials – PNNL

The Advanced Materials and Manufacturing Technology (AMMT) Program intends to develop cross-cutting technologies in support of a broad range of nuclear reactor technologies, and to maintain U.S. leadership in materials and manufacturing technologies for nuclear energy applications. The overarching vision of the AMMT Program is to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Corrosion Testing Needs and Considerations for Additively Manufactured Materials in Nuclear Reactors

The Advanced Materials and Manufacturing Technologies (AMMT) program within the Department of Energy, Office of Nuclear Energy (DOE-NE) has developed its current recommendation for its corrosion testing strategy to deploy additively manufactured (AM) materials in advanced nuclear reactors. Additive manufacturing technologies have developed rapidly in recent years, creating new opportunities and challenges for the nuclear industry. To adopt AM technologies, the corrosion performance of AM materials needs to be adequately evaluated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2018

Electrostatic Levitation for Studies of Additive Manufacturing Materials for Extreme Environments

The electrostatic levitation (ESL) laboratory at NASA's Marshall Space Flight Center (MSFC) is a national resource for researchers developing advanced materials for new technologies. Researchers have used MSFC's ESL Laboratory to develop advanced high-temperature materials for aerospace applications, coatings and structural materials for rocket nozzles, improved medical and industrial optics, metallic glasses, ablatives for reentry vehicles, and materials with memory. Modeling of additive manufacturing materials for extreme environments is necessary for the control of their resulting materials properties. Unfortunately, there is very little materials properties data for many additive manufacturing materials, especially of the materials in the liquid state. The MSFC ESL lab is ideal for the study of additive manufacturing materials to be used in extreme environments. The lab can provide density, surface tension, and viscosity of molten materials, emissivity measurements, and even creep strength measurements.

SanSoucie, Michael↗
DOE OSTI2021

Methods and apparatus for additively manufacturing structures using in situ formed additive manufacturing materials

A method of additively manufacturing a structure comprises nuclear reactor comprises disposing a feed material on a surface of a substrate in a reaction vessel, disposing at least one material formulated and configured to react with the feed material in the reaction vessel, and exposing the feed material and the at least one material to energy from an energy source to react the feed material and the at least one material to form an additive manufacturing material and reaction by-products. The additive manufacturing material is separated from the reaction by-products and exposed to energy from the energy source to form inter-granular bonds between particles of the additive manufacturing material and form a layer of a structure comprising the additive manufacturing material. Related apparatuses and methods are disclosed.

van Rooyen, Isabella J.↗
NASA NTRS2011

Space Shuttle Propulsion Materials, Manufacturing, and Operational Challenges

Presentations in this session include: (1) External Tank (ET) Materials, Manufacturing, and Operational Challenges; (2) Space Shuttle Main Engine (SSME) Materials, Manufacturing, and Operational Challenges,(3) Reusable Solid Rocket Motor (RSRM) Materials, Manufacturing, and Operational Challenges and (4) Solid Rocket Booster (SRB) Materials, Manufacturing, and Operational Challenges.

Owen, James↗
NASA NTRS2014

Electrostatic Levitation for Studies of Additive Manufactured Materials

The electrostatic levitation (ESL) laboratory at NASA's Marshall Space Flight Center is a unique facility for investigators studying high temperature materials. The laboratory boasts two levitators in which samples can be levitated, heated, melted, undercooled, and resolidified. Electrostatic levitation minimizes gravitational effects and allows materials to be studied without contact with a container or instrumentation. The lab also has a high temperature emissivity measurement system, which provides normal spectral and normal total emissivity measurements at use temperature. The ESL lab has been instrumental in many pioneering materials investigations of thermophysical properties, e.g., creep measurements, solidification, triggered nucleation, and emissivity at high temperatures. Research in the ESL lab has already led to the development of advanced high temperature materials for aerospace applications, coatings for rocket nozzles, improved medical and industrial optics, metallic glasses, ablatives for reentry vehicles, and materials with memory. Modeling of additive manufacturing materials processing is necessary for the study of their resulting materials properties. In addition, the modeling of the selective laser melting processes and its materials property predictions are also underway. Unfortunately, there is very little data for the properties of these materials, especially of the materials in the liquid state. Some method to measure thermophysical properties of additive manufacturing materials is necessary. The ESL lab is ideal for these studies. The lab can provide surface tension and viscosity of molten materials, density measurements, emissivity measurements, and even creep strength measurements. The ESL lab can also determine melting temperature, surface temperatures, and phase transition temperatures of additive manufactured materials. This presentation will provide background on the ESL lab and its capabilities, provide an approach to using the ESL in supporting the development and modeling of the selective laser melting process for metals, and provide an overview of the results to date.

SanSoucie, Michael P.↗
DOE OSTI2025

Advanced Materials & Manufacturing Technology (AMMT): Process understanding for qualifying LPBF 316H SS

Investigations were conducted in fiscal years (FY) 2023 and 2024 to gather relevant data sets addressing challenges related to qualifying 316H stainless steel (SS) for use in future nuclear reactors. This work was a collaborative effort involving researchers from Idaho National Laboratory (INL), Argonne National Laboratory (ANL), and Oak Ridge National Laboratory (ORNL). Key outcomes included: • Development of process-structure-property data sets to better understand the relationships between manufacturing processes, material structure, and performance characteristics. • Establishment of an in-situ monitoring system to link these various data sets together. • Detailed characterization of the raw material feedstock to further strengthen the understanding of process-structure and process-property relationships. Building on this foundation, in FY25 there was interest in exploring the behavior of additively manufactured 316H SS under different test conditions to support the overall material qualification process. Los Alamos National Laboratory (LANL) was tasked with providing specimen samples to the collaborating labs, who then conducted a round-robin study examining factors like selective heat treatment, low-cycle fatigue (LCF), and tensile-creep (T-C) properties. Additionally, high-temperature differential scanning calorimetry (DSC) was performed by LANL to better understand how the material's thermal characteristics change as it is heated up to the melting point. This provided a more comprehensive understanding of the material's behavior. The combined results from these investigations could potentially be used to support the inclusion of 316H stainless steel in Section III, Division 5 of the relevant codes and standards, allowing its use in future nuclear reactor applications.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Establishing and Maintaining the Digital Thread of Additively Manufactured Materials and Applications

Additive Manufacturing (AM) and Integrated Computational Materials Engineering (ICME) are complementary enabling technologies for design and manufacturing of “fit-for-purpose” materials. Both technologies will impact rapid material design, reduction in cost- and time-to-market for new applications, and discovery and implementation of new materials. An ICME approach to design, however, requires experimentally validated material models at multiple length and time scales, an integrated framework that can connect analysis tools with one another to ensure the digital thread of an application is maintained, and the manufacturing (e.g., AM) capability to leverage processing-structure-property-performance (PSPP) relationships to achieve spatially varying material properties where desired. AM enables the implementation of the design of an optimized, spatially varying microstructure through careful selection of the processing parameters used during an additively manufactured build. In order to establish these PSPP relations, a large amount of data is necessary, and that data must be properly captured, analyzed and maintained in an information management system that can establish the required traceability between various aspects of the design process to ensure an application’s digital thread is maintained (from design to end of life). Such an information management system must be able to capture feedstock material pedigree, resulting microstructure from various build parameters, subsequent mechanical properties derived from testing, developed material models, and enable spatial variations in material assignment in an engineering application. Furthermore, the information management system should be easily integrated with traditionally engineered materials in a single, centralized platform to enable an ICME optimization tool to explore both types of manufacturing processes. At NASA GRC, a robust, 21st century materials information management system has been previously developed with a focus towards enabling ICME. In this work, GRC’s ICME schema is extended to accommodate additively manufactured materials, enabling storage of both traditionally and additively manufactured materials in the same construct. The methodology for properly capturing additively manufactured materials across the entire material lifecycle is presented, following the previously established database best practices, as a potential framework for establishing PSPP relationships for additively manufactured materials and applying them to engineering applications.

Data management↗
NASA NTRS1972

Development of lightweight graphite/polyimide sandwich panels. Phase 2: Thin gage material manufacture

Thin gage materials selected and the rationale for their basic requirements are discussed. The resin used in all prepreg manufacture is Monsanto RS-6234 polyimide. The selected fiber for core manufacture is Hercules HT-S, and the selected fiber for face sheets is Hercules HM-S. The technique for making thin gage prepreg was to wind spread carbon fiber tows into a resin film on a large drum. This technique was found to be superior to others investigated. A total of 22 pounds of 1 to 2 mil/ply prepreg was fabricated for use on the program.

Merlette, J. B.↗
DOE OSTI2020

Kinetic Monte Carlo simulations of structural evolution during anneal of additively manufactured materials

Our experiments indicated that upon a post-processing anneal, an additively manufactured 316L stainless steel exhibits cubic grains rather than the conventional equiaxed grains. In this work, we have used kinetic Monte Carlo simulations to explore the origin of these cubic grains. First, we implemented a new kinetic Monte Carlo model in parallel code SPPARKS to simulate grain growth and recrystallization under a residual energy distribution. Our model incorporates physical properties and real-time, as opposed to generic properties and relative time. We further validated that our SPPARKS simulations reproduced the expected kinetic behavior of single-grain evolution. We then used the validated approach to simulate the anneal of an additively manufactured material under the same conditions used in our experiments. We found that the cubic grains can origin from a periodically varying residual energy that may be present in additively manufactured materials.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Computational investigation on the combined effect of pore attributes on strain concentrators in metal additively manufactured materials

Metal additive manufacturing provides a pathway for creating highly optimized components. However, porosity continues to be a prevalent issue for fatigue performance despite best efforts in optimizing process parameters and postprocessing techniques. This work uses finite element analysis to conduct a parametric study to link various pore attributes (i.e., aspect ratio, orientation, and location) to strain concentration factors (SCF) under elastic/plastic deformation during uniaxial loading. Keyhole and lack-of-fusion pores are idealized by prolate and oblate ellipsoids, respectively. Each simulation of the parametric study assumes a single isolated pore in a Ti-6Al-4V material, modeled with J2-plasticity. A reduced-order model is developed to relate pore attributes to SCF, and then used to quantify the variability in SCF for a given experimentally characterized probability distribution of pore attributes. This reduced-order model can be used in a production environment to provide a first-order evaluation to rapidly screen sub-optimally performing components.

Additive Manufacturing↗
NASA NTRS2018

Space Environmental Effects on Additively Manufactured Materials

Space simulations including thermal vacuum, atomic oxygen, and ultraviolet radiation were performed to study the durability of various additively manufactured materials. In addition to ground simulations, additively manufactured materials were selected for a one-year flight on the Materials on International Space Station Experiment (MISSE) Flight Facility. The space environment is composed of atomic oxygen, ultraviolet radiation, protons, electrons, meteoroid/space debris impacts, thermal cycling, and hard vacuum. An improved UV sensor is also discussed.

Finckenor, M. M.↗
DOE OSTI2023

Investigating Marine Environmental Degradation of Additive Manufacturing Materials for Renewable Energy Applications

Marine renewable energy is a relatively young industry where there is a great need for rapid prototyping in design-build-test campaigns to quickly mature groundbreaking technologies. Additive manufacturing has an important role to play in the industry; however, little information is available to marine energy developers to help inform them on which additive manufacturing materials are appropriate for highly loaded structures in harsh marine environments. This paper presents an initial study on the mechanical characterization of polymeric additive manufacturing materials and the degradation effects due to the marine environment. Ultem 9085, acrylonitrile styrene acrylate, and chopped carbon-filled nylon, as well as continuous carbon and glass fiber-reinforced nylon were chosen for this study. Samples were manufactured to perform a variety of tension, shear, and compression mechanical characterization tests on the materials. Half of the samples were conditioned in Pacific Ocean water for approximately 6 months at the Pacific Northwest National Laboratory's Marine and Coastal Research Laboratory before being returned for mechanical characterization. The mechanical testing results showed that the Ultem 9085 and acrylonitrile styrene acrylate materials experienced little to no degradation in stiffness or strength after exposure to the marine environment. On the other hand, the nylon-based materials suffered significant stiffness and strength degradation (over 50% in some cases) after environmental conditioning. Ultimately, these data sets should serve as starting points to allow marine renewable energy developers to make informed additive manufacturing material choices for their prototype deployments.

additive manufacturing↗