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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
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 NTRSDate not supplied

Convolution-Based Numerical Solutions of Transient Temperature Fields during Powder Bed Fusion Additive Manufacturing: Theory, Accuracy, and Computational Cost

Powder bed fusion (PBF) additive manufacturing has found numerous applications in the aerospace domain. However, components fabricated via PBF have a complex time-temperature history that significantly impacts subsequent mechanical performance. This study examines convolution-based numerical solutions of transient temperature fields that support simulations involving arbitrary beam shapes and paths during PBF. The convolutional approach is verified through comparisons with analytical solutions of the temperature field. The computational speed and accuracy of the method are assessed through comparisons with other explicit and implicit numerical techniques. In addition, the straightforward translation of the approach from a CPU to a GPU implementation and the resultant performance improvement are presented. The role of the technique in predicting microstructure evolution during PBF (for a greater process-structure-property-performance framework) is also demonstrated. This work supports the development of computational materials methods for understanding and controlling the time-temperature history during PBF.

powder bed fusion↗
NASA NTRSDate not supplied

Modernization of Insulative Reusable Thermal Protection Systems (IRTPS)

Insulative thermal protection systems, such as Flexible Reusable Surface Insulation blankets and High Temperature Reusable Surface Insulation tiles, were developed for the Shuttle Orbiter to enable reuse of the vehicle for low-earth orbit missions. Since reusability is essential to many new industry launch and space vehicles, Shuttle-derived thermal protection materials (TPMs) are being sought for their flight proven performance. Alumina Enhanced Thermal Barrier (AETB) is the state-of-the-art tile material that was developed in the 90’s. AETB along with associated coatings, reaction cured glass (RCG) and Toughened Unipiece Fibrous Insulation (TUFI), are currently made by NASA using heritage raw materials derived from lifetime purchases. Finding viable replacements for raw materials that have changed in nature or are obsolete is important for continuation of these TPMs. In some cases, the use of modern raw materials has been shown to yield tile with reduced performance, most notably in mechanical properties. ​ In this work, the production of AETB will be discussed to better understand the process-structure-property relationships and for allowing use of these modern alternatives. A small-scale tile casting system was developed for rapid and efficient exploration of the manufacturing variables such raw material selection and pre-processing, casting process parameters, and billet firing protocols. An optical transmission defect characterization technique was implemented to correlate process variables to structure. Comparisons between TUFI/RCG coated AETB derived from heritage and modern raw materials will be shown including AHF arc jet test results completed under a collaboration with Stratolaunch.

thermal protection materials↗
NASA NTRS2026

Integrated Computational Materials Engineering (ICME) Capability Maturity Levels for Ecosystems Enabling Digital Transformation

Digital engineering (DE) and integrated computational materials engineering (ICME) are widely recognized as critical enablers of faster, more affordable, and more reliable aerospace systems. However, many organizations have struggled to realize the promised return on investment (ROI) from digital initiatives. A primary reason is the absence of a shared, decision-focused framework that distinguishes simple digitization of existing workflows from true digital transformation that fundamentally changes how engineering decisions are made. This paper introduces an ICME capability maturity framework that fills this gap. The framework defines six cumulative ICME capability maturity levels (CMLs), explicitly tied to decision authority, engineering integration, optimization, and uncertainty management across material, process, structure, and performance scales. It is designed to complement established readiness metrics such as technology readiness levels (TRLs), manufacturing readiness levels (MRLs), and integration readiness levels (IRLs), by addressing a missing dimension: the conditions required for model-informed decision authority across scales. A unifying figure and capability table illustrate the six-level ICME Capability Maturity Framework, showing how organizations progress from digitization—with limited or negative ROI—to true digital transformation, where ICME-enabled workflows deliver measurable improvements in decision quality, cycle time, risk reduction, and reuse. The framework is intended for both technical practitioners and executive leadership, providing a common language to assess current state, guide roadmaps, align software ecosystem investments, and set realistic expectations for digital transformation outcomes. A regulatory-relevant statement clarifying the relationship between ICME capability and existing certification frameworks is provided.

ICME↗
NASA NTRSDate not supplied

NASA BPS Reduced Gravity Integrated Computational Materials Engineering (ICME) Study

The Biological and Physical Sciences (BPS) Division of NASA’s Science Mission Directorate (SMD), and its predecessors, has sponsored extensive flight and ground experiments yielding benchmark datasets in many materials science research areas including thermophysical properties and solidification microstructure formation and evolution. The subject study sought to motivate and focus BPS’s engagement within the broader Integrated ICME community to understand the phenomena underlying material processing, structure, and properties in the microgravity environment of space and to support future space exploration efforts.

Louise Littles↗
DOE OSTI2025

Development and Validation of MALAMUTE model for Electric Field Assisted Sintering of Structural Materials

Fusion power plant designs feature extreme material performance requirements for structural material candidates. In addition to conventional alloys, more advanced composites and oxide dispersion strengthened (ODS) alloys are being explored, however, achieving the desired microstructures to maximize performance using traditional manufacturing methods can be challenging. The advanced manufacturing (AM) electric field-assisted sintering (EFAS) technique offers improved control over the final microstructure through higher heating and cooling rates and moderate pressures. Modeling and simulation tools show promise in elucidating the process-structure-property-performance (PSPP) correlation for AM-produced parts, including the EFAS process. An inherently multiscale process, the EFAS technique aligns well with the multiscale modeling capability of the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE)[cite]. We present here an electro-thermo-mechanical approach to modeling the EFAS process using the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) code. Prediction of the field and gradient distributions across the EFAS tooling is required to accurately describe the conditions for the lower-scale microstructural evolution models. In this work we present the MALAMUTE model developed to predict the electrical potential, temperature, and mechanical stress distribution across the EFAS graphite tooling and part at the larger engineering-scale. Validation of the MALAMUTE engineering-scale model is completed using data from experimental densification and pre-densified runs of iron powder via EFAS at 1000oC. These runs were conducted using a Thermal Technology DCS-5 EFAS system. Data collected during the experiment runs include the direct current (DC) supplied to the graphite tooling, the temperature of the graphite tooling as measured with a pyrometer, and the force applied to the top of the graphite tooling stack, and the data were recorded every 10 seconds. Our validation approach used the current and force data from the EFAS run as boundary condition inputs to the MAMALUTE simulation; the temperature data were used to evaluate the MALAMUTE EFAS model prediction. Results of the MALAMUTE simulations are employed to connect the external pyrometer temperature measurement to the temperature profile across the part undergoing consolidation. We investigate the impact of material property variation and mesh deformation on the temperature profile as predicted by MALAMUTE. We conclude by highlighting projects where the MALAMUTE EFAS modeling and simulation capabilities will be used to assist experimental design.

36 - MATERIALS SCIENCE↗
DOE OSTI2023

Defect-Promoted Ni-Based Layer Double Hydroxides with Enhanced Deprotonation Capability for Efficient Biomass Electrooxidation

Ni-based hydroxides are promising electrocatalysts for biomass oxidation reactions, supplanting the oxygen evolution reaction (OER) due to lower overpotentials while producing value-added chemicals. The identification and subsequent engineering of their catalytically active sites are essential to facilitate these anodic reactions. Herein, the proportional relationship between catalysts’ deprotonation propensity and Faradic efficiency of 5-hydroxymethylfurfural (5-HMF)-to-2,5 furandicarboxylic acid (FDCA, FEFDCA) is revealed by thorough density functional theory (DFT) simulations and atomic-scale characterizations, including in situ synchrotron diffraction and spectroscopy methods. The deprotonation capability of ultrathin layer-double hydroxides (UT-LDHs) is regulated by tuning the covalency of metal (M)-oxygen (O) motifs through defect site engineering and selection of M 3+ co-chemistry. NiMn UT-LDHs show an ultrahigh FE FDCA of 99% at 1.37 V versus reversible hydrogen electrode (RHE) and retain a high FE FDCA of 92.7% in the OER-operating window at 1.52 V, about 2× that of NiFe UT-LDHs (49.5%) at 1.52 V. Ni–O and Mn–O motifs function as dual active sites for HMF electrooxidation, where the continuous deprotonation of Mn–OH sites plays a dominant role in achieving high selectivity while suppressing OER at high potentials. The results showcase a universal concept of modulating competing anodic reactions in aqueous biomass electrolysis by electronically engineering the deprotonation behavior of metal hydroxides, anticipated to be translatable across various biomass substrates.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Double Hydroxide Nanocatalysts for Urea Electrooxidation Engineered toward Environmentally Benign Products

Abstract Recent advancements in the electrochemical urea oxidation reaction (UOR) present promising avenues for wastewater remediation and energy recovery. Despite progress toward optimized efficiency, hurdles persist in steering oxidation products away from environmentally unfriendly products, mostly due to a lack of understanding of structure‐selectivity relationships. In this study, the UOR performance of Ni and Cu double hydroxides, which show marked differences in their reactivity and selectivity is evaluated. CuCo hydroxides predominantly produce N 2 , reaching a current density of 20 mA cm geo −2 at 1.04 V – 250 mV less than NiCo hydroxides that generate nitrogen oxides. A collection of in‐situ spectroscopies and scattering experiments reveal a unique in situ generated Cu (2‐x)+ ‐OO −• active sites in CuCo, which initiates nucleophilic substitution of NH 2 from the amide, leading to N‐N coupling between * NH on Co and Cu. In contrast, the formation of nitrogen oxides on NiCo is primarily attributed to the presence of high‐valence Ni 3+ and Ni 4+ , which facilitates N‐H activation. This process, in conjunction with the excessive accumulation of OH − ions on Jahn‐Teller (JT) distorted Co sites, leads to the generation of NO 2 − as the primary product. This work underscores the importance of catalyst composition and structural engineering in tailoring innocuous UOR products.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantifying the cooperative evolution of microphase segregation and nanostructural order in annealed polyurethanes of MDI-BDO-PTHF

Here, we quantify the relationship between microphase segregation and local structure development in thermoplastic polyurethanes. Samples were prepared with varying ratios of 4,40 -methylene diphenyl diisocyanate (MDI) and 1,4-butanediol (BDO) to polytetrahydrofuran (PTHF) and annealed at temperatures T a between RT–140 °C for 20 h each. Distinct populations of smaller and larger ordered domains are distinguished by pair distribution func tion analysis. The intermediate-range order of nanoscale hard domains in the paracrystalline state (form I) is directly extracted and characterized. The results suggest that form I of the MDI-BDO:PTHF system presents a conformational and packing order similar to the form III structure, typically obtained from stretching/annealing, but with limited spatial coherence of 3–7 nm. Heating above T g of the hard segments is necessary to achieve a substantial structural response from the annealing treatment. Increasing T a promotes purification of the soft phase via microphase segregation, coalescence of the hard blocks, and growth of paracrystalline phase, while a minority fraction of longer-range ordered domains left over from production remains relatively constant. We observe a composition-dependent decrease in the average nearest-neighbor dis tance that can be correlated with the greater number of C C, C N, and C O bonds with increased hard segment content. A non-linear deviation in the trend is observed to correlate with sample densification. The mechanical and thermal behavior of the samples is intimately tied to the segregation state.

processing-structure-properties↗
DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗
DOE OSTI2023

Flow Strength Measurements of Wrought and AM SS304L via Pressure Shear Plate Impact Experiments

Pressure-shear plate impact experiments were performed to quantify flow strength of wrought, as-built additively manufactured (AM), and heat-treated and recrystallized AM 304 L stainless steel (SS304L) under combined loading. Impact velocities spanned between 0.03 and 0.24 mm/μs, resulting in corresponding pressures of 0.62–5.93 GPa. Flow strength measurements are comparable for the sample variants across the studied loading conditions; however, shear wave structures significantly differ between sample type. Microstructurally aware simulations indicate local strain differences attributed to anisotropic elastic constants of large grains (~1 mm) in the as-built and heat-treated AM may impede the ability to uniformly transmit a shear wave.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interpretation of wide-angle x-ray diffraction patterns from mesophase pitch-based carbon fibers – a simulation and experimental study

Carbon fiber is a critical material in a wide range of industries, where it is highly valued for its high specific strength/stiffness, excellent wear resistance, efficient electrical and thermal transport properties, chemical resistance, and low coefficient of thermal expansion. The properties of a specific carbon fiber are closely tied to its structural characteristics at all length scales. Here, in this work, we applied wide-angle x-ray diffraction to a set of heat-treated mesophase pitch-based carbon fibers, with the goal of elucidating the crystalline structures as a function of fiber orientation. To assist with analysis and interpretation of the experimental data, we employed diffraction pattern simulations using the scalar and vector forms of the Debye scattering equation to determine the influence of basal plane orientation, crystalline ordering (turbostratic-graphitic), and basal plane asymmetry on the diffraction patterns. The results presented here suggest that growth of the transverse crystallites in mesophase pitch-based carbon fiber is fixed until graphitization temperatures are reached. The work completed here provides a framework for the analysis of carbon fiber and other oriented carbon-based materials via diffraction.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Additive manufacturing of metal matrix composites

Although Metal matrix composites (MMCs) are superior to most sought-after metallic alloys, their challenging fabricability has limited their widespread use in bulk-form applications. Among the many advanced fabrication techniques, Additive Manufacturing (AM), owing to its unique capabilities to produce near-net shapes, has drawn significant traction in the past two decades, especially for materials that are difficult to process using traditional methods. However, unlike pure metal/alloy systems, MMCs are highly sensitive to the processing conditions prevailing in AM techniques due to factors such as the high melting point of reinforcement particles and the potential for in-situ reactions. Therefore, it may be a while before metal matrix composites are commercially produced via AM. This review will discuss the current state-of-the-art design, fabricability, and performance of various additively manufactured MMCs. A particular focus will be on microstructural evolution and microstructure-property relationships. The most employed AM techniques, such as directed energy deposition, powder bed fusion, binder jetting, sheet lamination, and solid-state friction stir processing, are fundamentally different in terms of thermo-kinetics, forming the perspective for this review. A detailed comparison of microstructural evolution and process parameter optimization, including feedstock preparation methods and the role of machine learning and modeling among the different AM processes, is also presented. Finally, a critical evaluation of emerging AM technologies for MMCs is also provided, highlighting their potential advantages and challenges.

36 - MATERIALS SCIENCE↗
DOE OSTI2023

Orientational Order in Spin-Cast Lead-Iodide Perovskite Nanocrystal Solids

Combined synthetic control over size and composition renders colloidal lead-halide perovskite nanocrystals a tunable platform for high-efficiency optoelectronic applications. However, the properties and operational stability of devices based on nanocrystal solids are often dictated by the method of the evaporation-induced assembly. Ubiquitous slow evaporation techniques can produce highly ordered nanocrystal domains but limit the prospects for scalable fabrication of continuous device layers, calling for investigation of approaches to more rapidly form ordered perovskite nanocrystal solids. Here, we study orientationally ordered lead-iodide perovskite nanocrystal solids prepared by conventional spin coating with molecular additives (excess ligand) to enhance ordering within the arrays. In situ X-ray scattering measurements reveal that orientational ordering occurs rapidly upon solvent removal during spin coating and can be further enhanced by manipulating the spin speed. We vary the additive ligand length and explore trade-offs between ordering and layered perovskite impurity formation. Arrays treated with the intermediate-length octylamine ligand exhibit increased in-plane electronic conductivity, suggesting orientational ordering and internanocrystal electronic coupling can be enhanced by the treatment. In conclusion, these results highlight the prospects of establishing long-range order in lead-halide perovskite nanocrystal solids by using simple and fast coating methods.

14 SOLAR ENERGY↗
DOE OSTI2025

Embedded Sensing in Additive Manufacturing Metal and Polymer Parts: A Comparative Study of Integration Techniques and Structural Health Monitoring Performance

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications.

additive manufacturing↗