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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Application of Machine Learning to Monitor Metal Powder-Bed Fusion Additive Manufacturing Processes

The use of additive manufacturing (AM) is increasing for high-value, critical applications across a range of disparate industries. This article presents a discussion of high-valued engineering components predominantly used in the aerospace and medical industries. Applications involving metal AM, including methods to identify pores and voids in AM materials, are the focus. The article reviews flaw formation in laser-based powder-bed fusion, summarizes sensors used for in situ process monitoring, and outlines advances made with in situ process-monitoring data to detect AM process flaws. It reviews investigations of ML-based strategies, identifies challenges and research opportunities, and presents strategies for assessing anomaly detection performance.

Reutzel, Edward W.↗
DOE OSTI2024

Aluminum/SmCo 5 composites for structural and magnetic applications

Metal-bonded magnetic composites (MBMCs) present a promising alternative to dense sintered magnets, particularly for intricate components. Compared to polymer-based bonded magnets, MBMCs have wider applicability in harsh environments. In this paper, we demonstrate a solid-state shear-based manufacturing technique to introduce localized magnetization into a paramagnetic aluminum matrix by embedding SmCo5 permanent magnet particles. Our magnetic composites display hard magnetic behavior with a coercivity of 13 kOe and a remanent magnetization of 4.32 emu/g. In addition to magnetization, we also report a 9% improvement in Young’s modulus. Despite the local temperature rise during processing, the magnetic phases didn’t decompose into unwanted phases, preserving the composite's hard magnetic properties. Creation of an interfacial metallurgical bond with the matrix ensured the suitability of the composites for structural applications. Our study investigates the mechanical, and functional properties of composites, paving the way for lightweight structural magnetic composites with a transformative potential in the aerospace, nuclear, and automotive applications. This work underscores the potential for further optimization and development to drive innovations in magnet and equipment design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

In situ electric field X-ray total scattering reveals composition-dependent electromechanical strain mechanisms in (1 − x )BiFe 2/8 Ti 3/8 Mg 3/8 O 3 – x PbTiO 3 ceramics

Ferroelectric materials find many applications in energy and aerospace industries. A major challenge for ferroelectric materials is maintaining their properties at elevated temperatures. The (1 − x )BiFe 2/8 Ti 3/8 Mg 3/8 O 3 – x PbTiO 3 (BFTM–xPT) ferroelectric solid solution is a promising candidate for high-temperature applications as it has a high piezoelectric response while also maintaining its ferroelectric phase until a high Curie temperature (T C ) of 650 °C. In this work, we investigate the piezoelectric mechanism in BFTM–xPT, a novel high-T C ferroelectric ceramic. To elucidate the origin of its enhanced piezoelectric performance, relative to other piezoceramics of similar T C , in situ electric field X-ray total scattering experiments were performed. Total scattering combines Bragg scattering (diffraction) and diffuse scattering (pair distribution function), providing insight on various length scales. With information spanning different length scales, extrinsic contributions (i.e., domain wall motion and interphase boundary motion) can be separated from intrinsic contributions (i.e., piezoelectric lattice strain). We show that, for compositions within the morphotropic phase boundary (MPB, x = 0.325), the piezoelectric response is dominated by intrinsic piezoelectric lattice strain, whereas outside the MPB (x = 0.375) the major component of the piezoelectric response is from extrinsic mechanisms including an electric-field-induced phase transition and tetragonal domain wall motion. This article reveals that the origin of piezoelectric response in BFTM–xPT is composition-dependent and shows that high-performance materials may also be located outside the MPB. These findings can help guide material design to optimize properties for specific applications.

Richtik, Brooke N. [University of Calgary, AB (Can↗
DOE OSTI2021

Utilizing a Dynamic Segmentation Convolutional Neural Network for Microstructure Analysis of Additively Manufactured Superalloy 718

Additive manufacturing (AM) is revolutionizing almost all industries through the production of intricate geometries previously prohibited by cost or machinability. Ni-based superalloys form a primary alloy class for high temperature applications in the petrochemical, aerospace, and nuclear industries because of their intrinsic resistance to creep and the ability to heat treat the superalloy for high strength. Despite these attractive properties, the extreme work hardening of Ni-based superalloys makes traditional manufacturing of complex shapes difficult and thus, these alloys are an attractive target for AM. Superalloy 718 was chosen as an example superalloy because of the wide variety of precipitates that can form within its composition space from the repetitive heating and cooling cycles of the AM process. The precipitates and other microstructure features, such as grain boundaries and dislocations, will dictate the mechanical properties and thus, there is an extensive challenge to characterize the size, number density, composition, and volume fraction of each microstructural feature from AM fabrication using analytical electron microscopy. This work focused on the application of a pixel-wise classification machine learning (ML) model called a dynamic segmentation convolutional neural network (DSCNN) to identify the microstructural features of an as-fabricated additively manufactured superalloy 718.

Taller, Stephen↗
DOE OSTI2025

Estimating the State of Charge in Lithium Primary Batteries: Recent Advances and Critical Insights

Lithium primary batteries (LPBs) remain essential in critical applications such as military, aerospace, medical and emergency devices, and portable electronics. Their superior energy density over lithium-ion batteries offers a significant advantage for long-duration use. Therefore, accurate estimation of the state of charge (SoC) is essential for ensuring the reliable and safe operation of these batteries. While extensive research has been conducted on SoC estimation techniques for lithium-ion secondary batteries, LPBs present unique challenges that complicate accurate SoC estimation. Moreover, research on nondestructive testing techniques for SoC estimation in LPBs is significantly lacking. In this review article, it is aimed to provide a comprehensive overview of recent advancements in SoC estimation for LPBs and generates new insights and directions for future research. Herein, existing methods are discussed and their effectiveness and mechanisms are identified, and areas for further optimization are outlined. More theoretical/experimental efforts to advance SoC detection in LPBs is recommended due to challenges identified with existing techniques.

25 ENERGY STORAGE↗
DOE OSTI2021

Tool-workpiece stick-slip conditions and their effects on torque and heat generation rate in the friction stir welding

Friction stir welding (FSW) has found increased applications in automotive and aerospace industries due to its advantages of solid-state bonding, no fusion and melting, and versatility in various working conditions and material combinations. However, the relationship among processing parameters, material properties, and bonding extent and fidelity remains largely empirical, primarily because of the lack of the mechanistic understanding of the tool-workpiece frictional behavior that affects our subsequent understanding of microstructural evolution and interface bonding formation. While the tool-workpiece stick-slip condition is believed to dictate the resulting torque and heat generation rate during the welding process, it remains rare and elusive to conduct a quantitative experimental measurement of such interfacial field. On the other hand, numerical simulations based on Computational Fluid Dynamics (CFD) rely on ad hoc assumptions of interfacial pressure and shear-stress conditions, but predictions can only be validated via the medium- and far-range temperature field which is known to be insensitive to the interfacial frictional behavior. This work first presents a comparison among two CFD-based simulation methodologies and the Coupled Eulerian Lagrangian (CEL) model in finite element method, the last of which uses the Coulomb friction so that the stick-slip is naturally developed. Based on the Hill-Bower similarity relationship in the contact analysis, an analytical model is developed here to prove why a constant stick-slip fraction will be developed in the steady state, to correlate the stick-slip fraction to processing parameters such as the tool spin rate, and further to derive dimensionless functions for torque and heat-generation-rate predictions. Pros and cons of various numerical approaches in predicting stick-slip are discussed, and our analytical model has been found to agree well with our numerical simulation and literature experimental results. These analyses provide the critical strain-rate and temperature fields that are needed for the bonding analysis in our future work.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nonvolatile electrochemical memory at 600°C enabled by composition phase separation

Silicon-based microelectronics are limited to ~150°C and therefore not suitable for the extremely high temperatures in aerospace, energy, and space applications. While wide-band-gap semiconductors can provide high-temperature logic, nonvolatile memory devices at high temperatures have been challenging. In this work, we develop a nonvolatile electrochemical memory cell that stores and retains analog and digital information at temperatures as high as 600°C. Through correlative scanning transmission electron microscopy, we show that this high-temperature information retention is a result of composition phase separation between the oxidized and reduced forms of amorphous tantalum oxide. This result demonstrates a memory concept that is resilient at extreme temperatures and reveals phase separation as the principal mechanism that enables nonvolatile information storage in these electrochemical memory cells.

42 ENGINEERING↗
DOE OSTI2022

Analysis of the laser welding keyhole using inline coherent imaging

Laser beam welding is a widely used fusion welding process in many industrial applications such as automotive, aerospace, energy, defense, and medical products. Industry has a fundamental need to model the laser welding process to minimize experimental testing and improve confidence in production welds. However, computational models attempting to predict weld formation are limited by an incomplete understanding of the beam-material interactions. As a consequence, these models do not accurately predict the mechanisms associated with laser weld formation. To improve the current state of prediction capabilities, it is vital to better detect/measure the physical aspects of the weld pool during high energy density welding. In this work, a novel real-time laser weld monitoring device using inline coherent imaging (ICI) was used to provide a fundamental understanding of laser weld formation via vaporization. The objective of this work was to investigate and quantify the relationship relating laser weld parameters and the vapor capillary (keyhole) through a state-of-the-art measurement technique. Bead-on-plate laser beam welds were produced with partial penetration on 304L stainless steel, 2205 duplex stainless steel, and Ti-6Al-4V. Keyhole monitoring was performed using a commercially available ICI system to collect keyhole penetration data in real time. These measurements were reconstructed to generate the vapor capillary shape at different welding parameters. Process parameters significantly influenced the keyhole shape and the keyhole root position relative to the process beam. Finally, the keyhole geometry showed distinct differences between the stainless steel alloys and Ti-6Al-4V.

2205↗
DOE OSTI2025

A snapshot of high-entropy alloy processing techniques and their effects on resulting mechanical properties

High-entropy alloys (HEAs) exhibit exceptional strength, corrosion resistance, and thermal stability, making them promising candidates for nuclear, aerospace, and other extreme applications. While most prior work has focused on compositional design, manufacturing techniques themselves can alter microstructure and mechanical properties as dramatically as alloy chemistry. This review compiles and compares the effects of processing routes—including arc melting, induction melting, mechanical alloying with spark plasma sintering, and additive manufacturing—on the structure and properties of HEAs. Quantitative comparisons highlight, for example, that SPS-processed alloys can achieve ∼20–45 % higher yield and tensile strength than arc-melted counterparts, while Bridgman solidification produces nearly single-crystal structures with elongation to failure exceeding 80 %. Additive manufacturing routes such as selective laser melting offer fine microstructures but also introduce anisotropy and porosity, leading to yield strengths spanning 100–600 MPa for the same composition. By synthesizing such results, this review provides actionable insight into how processing routes interact with HEA core effects (high entropy, lattice distortion, sluggish diffusion, and cocktail effect) to determine performance, thereby offering a practical guide for optimizing manufacturing strategies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Microbe-Encapsulated Silica Gel Biosorbents for Selective Extraction of Scandium from Coal Byproducts

Establishing an inexpensive and environmentally friendly scandium (Sc) supply is critical for the development of clean energy technologies. However, refining Sc from Sc-bearing sources using current technologies poses economic and environmental challenges due to its low concentration relative to base metals and chemically similar lanthanides. In this work, we developed a biosorption-based flow-through process for extraction of Sc from low-grade feedstocks. A novel biosorbent material was synthesized by encapsulating Arthrobacter nicotianae, a bacterium that selectively adsorbs Sc, within a porous silica matrix. Through the use of several complementary microscopy techniques, we demonstrated a highly porous biosorbent structure with a high cell-loading density and a homogenous cell distribution. Batch adsorption assays revealed selective Sc adsorption over lanthanides and common based metals, with the exception of Fe(III), as evidenced by separation factors greater than 50. The MESG particles were packed into fixed-bed columns to enable adsorption under flow-through conditions, which demonstrated effective Sc extraction at flow rates up to 0.08 cm/s and high stability for reuse; greater than 95% of the adsorption capacity was maintained after 10 consecutive adsorption/desorption cycles. When applied to a lignite coal leachate, whose Fe content was depleted through pH-mediated precipitation, the MESG particles yielded Sc breakthrough at 30 bed volumes, whereas all other metals broke through after only a few bed volumes. Following desorption, an eluate with a 124-fold increase in Sc purity was achieved relative to the lignite leachate with Sc constituting 96.4% of the total REEs. This study established a rapid, facile, and scalable cell immobilization approach that enables the use of microbial biomass for Sc recovery from low-grade sources.

Dong, Ziye↗
DOE OSTI2023

Embedding thermocouples in SS316 with laser powder bed fusion

Recent advances in manufacturing technologies have enabled the fabrication of complex geometries for a wide range of applications, including the energy, aerospace, and civil sectors. The ability to integrate sensors at critical locations within these complex components during the manufacturing process could benefit process monitoring and control by reducing reliance on models to relate surface measurements to internal phenomena. This study investigated embedding thermocouples in a SS316 matrix using laser powder bed fusion. Under optimal processing conditions, embedded thermocouples were characterized post-building, finding good bonding to the matrix with no melt pool penetration to the sensing elements. Futher, the embedded thermocouple performed similarly to an identical non-embedded thermocouple during thermal testing to 500 °C with only a slight difference in response time, which was attributed to the differences in mass and the associated thermal time constants.

image analysis↗
DOE OSTI2021

Impact of Thermal Stress on Attachment and Stability of High Temperature Strain Sensors

Static and dynamic strain sensing is required in high-temperature (HT), harsh-environments (HE) for industrial, aerospace, and energy sector applications to ensure equipment and process safety, reduce the cost of operation and maintenance, and increase process efficiency. Challenges that arise in HT HE sensing applications include device mounting, packaging, integrity, and stability to HT HE conditions. In previously reported work, static and dynamic strain surface acoustic wave resonator (SAWR) sensors were fabricated on langasite (LGS) and mounted on Inconel 625 strain beams for wireless testing up to 400°C. In this work, it has been identified that after subjecting the mounted SAWR strain sensor to thermal cycling between 100°C and 425°C, the measured sensitivity to dynamic strain decreased by 73% due to cracking at the adhesive/LGS interface, further deteriorating after additional thermal cycles. Strain modeling of the mounted LGS sensor chip up to 400°C revealed the existence of concentrated strain at the borders of the LGS chip. Microcracks caused by dicing make the chip boarders the most susceptible location for cracks to initiate when the sensor is subjected to thermal stress. In an attempt to mitigate the high strain at the LGS chip borders due to heating, adhesive shaping is proposed in this work. Simulations indicate a strain reduction of 50% at the border is achieved using both circular and triangular adhesive shapes, while also reducing the maximum strain over the entire adhesive/LGS interface by around 30%. Furthermore, the technique is thus promising for improving the integrity, reliability, and stability of static and dynamic strain sensors, particularly while operating under HT HE with several hundreddegree Celsius temperature excursions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

Complex structural systems deployed for aerospace, civil, or mechanical applications must operate reliably under varying operational conditions. Structural health monitoring (SHM) systems help ensure the reliability of these systems by providing continuous monitoring of the state of the structure. SHM relies on synthesizing measured data with a predictive model to make informed decisions about structural states. However, these models—which may be thought of as a form of a digital twin—need to be updated continuously as structural changes (e.g., due to damage) arise. We propose an uncertainty-aware machine learning model that enforces distance preservation of the original input state space and then encodes a distance-aware mechanism via a Gaussian process (GP) kernel. The proposed approach leverages the spectral-normalized neural GP algorithm to combine the flexibility of neural networks with the advantages of GP, subjected to structure-preserving constraints, to produce an uncertainty-aware model. This model is used to detect domain shift due to structural changes that cannot be observed directly because they may be spatially isolated (e.g., inside a joint or localized damage). This work leverages detection theory to detect domain shift systematically given statistical features of the prediction variance produced by the model. The proposed approach is demonstrated on a nonlinear structure being subjected to damage conditions. In conclusion, it is shown that the proposed approach is able to rely on distances of the transformed input state space to predict increased variance in shifted domains while being robust to normative changes.

Algorithms↗
DOE OSTI2020

Scalability of OpenFOAM Density-Based Solver with Runge–Kutta Temporal Discretization Scheme

Compressible density-based solvers are widely used in OpenFOAM, and the parallel scalability of these solvers is crucial for large-scale simulations. In this paper, we report our experiences with the scalability of OpenFOAM’s native rhoCentralFoam solver, and by making a small number of modifications to it, we show the degree to which the scalability of the solver can be improved. The main modification made is to replace the first-order accurate Euler scheme in rhoCentralFoam with a third-order accurate, four-stage Runge-Kutta or RK4 scheme for the time integration. The scaling test we used is the transonic flow over the ONERA M6 wing. This is a common validation test for compressible flows solvers in aerospace and other engineering applications. Numerical experiments show that our modified solver, referred to as rhoCentralRK4Foam, for the same spatial discretization, achieves as much as a 123.2% improvement in scalability over the rhoCentralFoam solver. As expected, the better time resolution of the Runge–Kutta scheme makes it more suitable for unsteady problems such as the Taylor–Green vortex decay where the new solver showed a 50% decrease in the overall time-to-solution compared to rhoCentralFoam to get to the final solution with the same numerical accuracy. Finally, the improved scalability can be traced to the improvement of the computation to communication ratio obtained by substituting the RK4 scheme in place of the Euler scheme. All numerical tests were conducted on a Cray XC40 parallel system, Theta, at Argonne National Laboratory.

Li, Sibo↗
DOE OSTI2021

Cooperative Research and Development Agreement between National Energy Technology Laboratory and Ampere Scientific [Abstract]

Electro-Slag-Remelting (ESR) is an industry standard process used to manufacture alloys for mission critical components associated with energy, aerospace, defense, and other applications. For example, ESR is specifically used in the production and refinement of Nickel (Ni)-alloys such as IN 740H used for critical turbine applications. The aim of this CRADA is to develop and validate a sensor technology to monitor the melting of ingots during the ESR. The sensor will allow for feedback on the melting operation, allowing the operator to make adjustment on the fly and produce better ingots. Further, through better control, operators can more efficiently produce ingots by ESR.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Microwave-Assisted Production of Polycarbonate Diols using Carbon Dioxide

Carbon dioxide (CO2) is a cheap and readily available resource that can be converted to value-added chemicals including fuels, polymers, and other products. One lucrative option is polycarbonate diols, which are the precursor to polyurethanes that find wide applications in automotive and aerospace industries. The traditional process for the synthesis of polycarbonate diols involves using hazardous phosgene and alcohols in a strongly basic medium that generates significant amounts of salt. Alternately, CO2 can be reacted with alkanediols over CeO2 catalysts to produce polycarbonate diols. For the current work, a microwave-assisted atmospheric flow system was investigated for converting CO2 into polycarbonate diols and was compared against a thermal system. Various parameters such as type and ratio of solvents, and the addition of dehydrating agents for the microwave system was tested. The different CeO2 catalysts used were characterized using Brunauer–Emmett–Teller (BET) surface area analysis, Raman spectroscopy, X-ray diffraction, and temperature programmed desorption (NH3-TPD and CO2-TPD).

CO2 utilization↗
DOE OSTI2024

Sonic Wafering of III-V substrates for High Efficiency Cells: A path to <$0.50/W

This project developed and demonstrated Sonic Lift-off, a novel technology that enables the reuse of expensive semiconductor substrates used to manufacture high-efficiency solar cells. By using sound waves to precisely separate thin layers of material, the process significantly reduces manufacturing costs while maintaining the performance of advanced III–V solar cells. These solar cells are among the most efficient in the world and are used in space, aerospace, and emerging terrestrial applications. The results of this work show that substrate reuse can be achieved without degrading device performance, offering a pathway to more affordable, high-performance solar technologies. This advancement supports U.S. clean energy goals by enabling broader deployment of renewable energy systems and strengthening domestic manufacturing capabilities in advanced photovoltaics.

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↗