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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Mirostructure Characterization of Friction Consolidated Copper-Nickel using a Machine Learning Approach: Developing Process to Microstructure Associations

Friction consolidation (FC) is a solid phase processing approach where discrete material forms such as powders, chips, nuggets, etc. are densified via shear deformation. The precursors are placed in a billet container and brought in contact with a rotating tool that applying the desirable amount of normal force. Under the combined action of the rotation and normal pressure, the discrete precursor is consolidated through porosity reduction and shear deformation. FC is increasingly being studied as an attractive approach to manufacturing fully dense parts from powder forms owing to its ability to mix, alloy and consolidate difficult-to-process precursors in minimal number of process steps. Material consolidation and deformation in shear consolidation processes have been studied extensively previously for different material combinations previously. However, despite the extensive research in this area, understanding of the mechanistic processes in pore consolidation, deformation-induced mixing and material solubility during FC is still evolving. Material development using solid phase processing approaches such as FC is often performed based on research experience/education, which can be biased. Conventional analysis and simulation tools in this area tend to be successful only when material thermodynamic pathways and microstructural evolution sequences resulting from processing are clearly defined or known. They are not as effective for emerging advanced manufacturing technologies where material evolution pathways are not well established. The ability to predict optimal process parameters based on material chemistry and bulk properties is essential to accelerate materials design and processing, as are an understanding of the relevant structure-processing-property relationships. These structure-processing-property-performance relationships are at the core of materials science research. Microstructure characterization provides the link to these four core areas, often through visualizing material microstructure using imaging techniques. However, linking microstructure image data (i.e., micrographs) to variables of interest (e.g., processing parameters, material chemistry) in a reproducible, generalizable, and quantitative manner is a significant challenge. Typically, quantitatively linking image data to processing history relies on significant domain knowledge and manual or subject matter expert (SME)-heuristic based image analysis. Such an approach to image analysis has the potential to be biased, inefficient, and difficult to replicate.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Study of Cathode and Cavity Geometries in Electropolishing (EP): Electric Field Simulation

Niobium (Nb) superconducting radio frequency (SRF) cavities require electropolishing surface treatment to reach high acceleration gradient. EP performed in the plateau region of an I-V curve [1] leads to a smooth finish of the cavity surface including the equator surface. The equator surface is vital because a rough equator surface leads to premature quench and limit the cavity performance. Previous experimental work was performed to optimize EP parameters for low-beta (LB) 650 MHz Nb cavity [1]. It was found that different cathode surface areas vary the plateau onset voltage. A lower onset voltage was achieved for HB650 cavity with an enlarged cathode surface area. The aim of the study is to perform electrostatic and electrochemical simulation to understand the impact of both cavity geometry and cathodes. This report covers an initial result on electric field simulation performed with HB and LB 650 MHz cavities.

Loer, Bereket↗
DOE OSTI2023

First Principles Modeling of Cluster-Based Solid Electrolytes (Final Technical Report)

Given the trend of global warming and the urgent need to transition from fossil fuels to green energy, lithium-ion batteries continue to be an integral part of our lives. Design, development, and understanding of novel solid-state electrolyte materials play the key role for achieving next-generation all-solid-state batteries with high energy and great safety. The current modeling schemes to develop advanced solid electrolytes are focusing on materials in which the building blocks are individual atoms. Our theoretical approach is a paradigm shift in solid-state electrolyte design. Instead of atoms, we focus on clusters as the building blocks and model these solid electrolytes and their interfaces with electrodes, especially Li-metal anode, for their successful implementation in solid-state batteries. The advantage of using the cluster ions to replace elemental ions is that the size, composition, and shape of the former can be tailored to achieve higher ionic conductivity at room temperature, electrochemical stability, and charge transfer across solid-solid interfaces than conventional materials. Specifically, the project includes: (1) Developing cluster-based solid electrolytes, where the halogen components are replaced by cluster ions that mimic the chemistry of halogens but are characterized by additional degrees of freedom, including the size, shape, composition, and motional dynamics under excitation. (2) Providing a fundamental understanding of the ion conduction mechanism in the developed cluster-based solid electrolytes; (3) Modeling the interfacial properties (i.e., structural, chemical, and transport properties) between the cluster-based solid electrolytes and electrodes at the atomic level. For the cluster-based solid electrolytes incompatible with the Li-metal anode or cathode materials, potential candidates for interfacial coatings are identified and studied. (4) Providing a theoretical framework towards optimizing critical parameters of the solid-state electrolytes that guides experimentalists to attain desired cathode-electrode interface for cluster-based solid-state electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2023

Advanced Compact Generation Module with Fish Safe Runner Technology

Hydropower is an important contributor of stable, load-leveling renewable energy to our national grid. Increasingly, the development of new hydropower facilities or the retrofit of existing ones hinges not only on minimizing costs but also environmental impact. In this report, the analysis and testing of a modular and scalable low-head hydropower generation design using Natel Energy’s Restoration Hydro Turbine is described. This design leverages many established industry approaches for compactness and efficiency while simultaneously allowing for safe downstream fish passage through the turbines themselves. This unique approach reduces overall hydropower facility costs and enables a simpler inclusive method of project design and operation. To assess this design, mechanical and fluid computational analyses were used to study and optimize key parameters. Passage tests of important migratory species (salmonids, American eel) were conducted through representative turbines. The unique propeller geometry of the fish-safe Restoration Hydro Turbine was subjected to detailed design and testing using advanced manufacturing composite techniques. Comprehensive module cost models were developed and assessed alongside hydraulic efficiency. The results of this project show promising and economical applications for downstream passage offish through Restoration Hydro Turbine modules.

13 HYDRO ENERGY↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

42 ENGINEERING↗
DOE OSTI2024

Fractal Nanostructured Solar Selective Surfaces for Next Gen Concentrating Solar Power (Final Report)

This project reports a novel coating with enhanced solar absorptance and reduced thermal emittance with high efficiency at elevated temperature for next-generation concentrated solar power (CSP) plants, with targeted operating temperatures around 750°C. Highly textured single and multimetallic oxide coatings were electrodeposited onto Inconel substrate by systematically varying the composition and process parameters. The optimized coating exhibited micro-to-nano structures designed to match the wavelengths in the visible region of the solar spectrum. These structures facilitate resonant absorption of solar radiation, significantly boosting solar absorption and accommodating thermal stress during high temperature exposure. A high solar absorptance exceeding 0.985 and a low thermal emittance below 0.5, yielding a thermal efficiency near 95%, was achieved for the optimized coatings without any anti-reflective overcoat, that remained robust after 750 h of isothermal exposure to 750°C in air. The coatings are also robust to severe mechanical and environmental stressors. The innovative approach presented in this study demonstrates the potential for tailoring air-stable solar absorber coatings to achieve high absorption, low emittance, and excellent high-temperature endurance, meeting the rigorous demands of next-generation CSP systems. A technoeconomic analysis reveals the economic advantage of the coatings for Gen3 CSP installations in different geographical zones globally.

14 SOLAR ENERGY↗
DOE OSTI2024

Using interfaces to: create strongly-coupled magnetic-ferroelectrics

Starting at the level of electrons and atoms our long-term goal is to rationally design complex oxide heterostructures and interface-materials with targeted emergent behaviors. This is not a matter of simply optimizing material parameters, but rather begins with understanding a mechanism to control the interplay between the diverse microscopic degrees of freedom prevalent in complex oxides in order to create targeted macroscopic phenomena and ends with the design of new material realizations. These realizations are in turn created with atomic-layer precision, structurally assessed to see that they are the intended realization, and finally their relevant properties are measured. During this program we have developed the scientific ideas necessary to apply this materials-by-design paradigm to the creation of materials offering electrical control of magnetism. At the heart of our approach lies a partnership between theory, synthesis, and characterization. These are the areas of expertise of the three co-PIs, who have worked closely together since their arrival at Cornell. During this project bold ideas on where to put the atoms to provide electrical control of magnetism were met with the latest methods of synthesis science and electron microscopy in an attempt create targeted new multiferroics. Our work applying this materials-by-design methodology to multiferroics has enjoyed multiple successes. The Co-PIs have invented new mechanisms for multiferroics, which provide strong coupling between polarization and magnetism, and reduced them to practice. Specifically, we pioneered multiferroics based on spin-phonon coupling, rotation-driven multiferroicity, and novel geometric multiferroics. Importantly, the latter two of these mechanisms enable the deterministic switching of magnetism by an applied electric field, which is key to the application of multiferroics to future device technologies. Most recently, we performed detailed theoretical analysis on a system involving the combination of magnetoelectric and colossal magnetoresistance behaviors and predict that electrical control of a metal-insulator transition is possible in appropriately strained SmBaMn2O6. The research enabled by this grant led to 63 publications in leading refereed journals including Nature, Nature Materials, Physical Review Letters, and Advanced Materials demonstrating a materials-by-design paradigm to the creation of materials offering electrical control of magnetism.

36 MATERIALS SCIENCE↗
DOE OSTI2024

“Production of High Specific Activity 155 Tb, 161 Tb and 203 Pb for Research and Clinical Applications: Effective Target Design, Target Material Recycling and Radioisotope Separation”

Radioisotopes are essential for the development and application of radiopharmaceuticals that target specific diseases, such as cancer, offering unique potential for precision medicine. The growing demand for theranostic radioisotopes underscores their critical role in personalized medicine, where they enhance diagnostic imaging, minimize patient radiation exposure, and improve targeted tissue uptake, particularly in receptor- and antigen-directed therapies. The theranostic pair terbium-155 (diagnostic) and terbium-161 (therapeutic) holds significant promise for advancing individualized, targeted, and dosimetry-driven radiotherapies. However, the United States currently lacks routine and reliable production of these isotopes. This project made significant progress toward addressing this supply issue by developing production and separation methods for terbium-155 and terbium-161 while also training the next generation of the nuclear and radiochemistry workforce. This grant also strengthened collaboration between scientists at the University of Washington, the University of Missouri and Brookhaven National Laboratory. The research effort focused on evaluating target preparation methods, optimizing irradiation parameters, and refining isolation processes. In addition, the project provided extensive hands-on training to graduate students and postdoctoral fellows, equipping them with expertise in radioisotope production technologies and fostering the growth of the nuclear science workforce.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Design and Development of a Fixtureless, Pass-through Machine Tool for Extrusion Machining

The aerospace, construction/architecture, and transportation manufacturing industries rely heavily on the mass production of near-net shape metallic and composite extrusions. While the production of raw extrusions is a relatively fast process, adding functional features such as holes and slots require additional time, cost, and energy to produce. To compensate for the inherent flexibility of extrusions, conventional machining requires rigid purpose-built fixtures for operations such as trimming, drilling and thinning. This approach requires that the machine tools be as large or larger than the parts themselves. This results in the need for excess shop floor space, energy for auxiliary equipment and motion systems, and significant capital expenditure. Considerable engineering expense and time involved in the designing, building, and proving out of part-specific fixtures for holding the components in specific configurations while machining add to the overall manufacturing cost. The primary objective of the technical collaboration between Oak Ridge National Laboratory and Fairmount Technologies (FT) is to improve the XM-3, a fixtureless CNC milling machine designed by FT. The machine was developed to trim, drill, and thin extrusions without part specific fixturing to make the manufacturing process more efficient and flexible. Dynamic measurements of the existing structure were collected, and modeling efforts were made to evaluate optimal machining parameters for the current system. Areas of improvement to increase the system stiffness, manufacturability, and machining efficiency were evaluated and highlighted for the next generation design. The impact of this effort may enable agile manufacturing across the commercial and defense aerospace industries, and other industries where extrusions are utilized like in the construction, architecture, and transportation industries.

42 ENGINEERING↗
DOE OSTI2023

Multiscale Porous High-temperature Heat Exchanger Using Ceramic Co-extrusion

In this project, our MIT, Purdue, and GE team aims to design, model, fabricate, and test a novel high temperature, compact, and durable ceramic heat exchanger to be operated under high temperature and pressure conditions for aerospace applications. Our approach is grounded in introducing multiscale porosity, i.e., centimeter-scale channels embedded with micrometer-scale channels, into the ceramic heat exchanger to significantly improve its heat transfer performance and mechanical strength while maintaining minimal pressure losses. We first developed high-fidelity thermal-fluid-mechanical model capable of precisely capturing the heat transfer rate, temperature profile, pressure drop, and mechanical stress throughout the entire heat exchanger design. Guided by our model, we identified the optimal design parameters for the SiC heat exchanger body and manifolds. Then, we established a completed fabrication procedure to create multiscale features in the ceramic heat exchanger, including co-extrusion, lamination, burnout, and sintering. We fabricated multiple heat exchanger bodies consisting of 6 × 6 and 3 × 3 centimeter-scale channels where each individual centimeter-scale channel comprises 625 crack-free microchannels with 90 μm × 90 μm opening. Owing to the multiscale features, our fabricated heat exchanger bodies exhibited desirable mechanical strength with 156 MPa flexural strength under 1300 Celsius degree. Despite the demonstrated highly tailorable microscopic features and superior mechanical strength, we identified delamination due to the complex interaction among ceramic, polymer, and gas species can be a critical challenge to create fully defect-free heat exchanger, which requires further fundamental investigations in future works. To test the heat exchanger performance, we constructed a high-temperature and high-pressure experimental apparatus that can be safely operated under 400 Celsius degree and 4 bar. With insights gained from mechanistic modeling, material development, and detailed characterization, a cost model was finally developed to understand the market potential of the developed technology, where a cost of $43,000 Celsius degree/kW was envisioned. This project developed a transformative approach to high-performance heat exchanger design. The thermal-fluid-mechanical design approach developed in this project can serve as a generic tool to guide the design of various heat-exchangers operated under high-temperature and high-pressure conditions. The material fabrication approach established in this project can be a useful guide for ceramic processing at extreme length scales.

42 ENGINEERING↗
DOE OSTI2022

In-Situ Powder-Directed Energy Process Control for Additively Manufactured Multi-Layer, Functionally Graded Components

Coatings and cladding can experience damage in harsh environments: Erosion Oxidation Delamination External mechanical damage Radiation induced damage Functionally Graded Materials (FGMs) were primarily designed to reduce thermal stress in thermal barrier coatings (TBCs), but can produce coatings which resist a variety of material damage (corrosion, surface damage, diffusion) Powder-fed DED with multiple independent feeders allows for AM FGMs, and provides robust control over process parameters to optimize microstructure and performance. FGMs have a great opportunity for applications in energy applications as a strategy to increase plant efficiency and lifetime.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Design and Development of a Fixtureless, Pass-through Machine Tool for Extrusion Machining

The aerospace, construction/architecture, and transportation manufacturing industries rely heavily on the mass production of near-net shape metallic and composite extrusions. While the production of raw extrusions is a relatively fast process, adding functional features such as holes and slots require additional time, cost, and energy to produce. To compensate for the inherent flexibility of extrusions, conventional machining requires rigid purpose-built fixtures for operations such as trimming, drilling and thinning. This approach requires that the machine tools be as large or larger than the parts themselves. This results in the need for excess shop floor space, energy for auxiliary equipment and motion systems, and significant capital expenditure. Considerable engineering expense and time involved in the designing, building, and proving out of part-specific fixtures for holding the components in specific configurations while machining add to the overall manufacturing cost. The primary objective of the technical collaboration between Oak Ridge National Laboratory and Fairmount Technologies (FT) is to improve the XM-3, a fixtureless CNC milling machine designed by FT. The machine was developed to trim, drill, and thin extrusions without part specific fixturing to make the manufacturing process more efficient and flexible. Dynamic measurements of the existing structure were collected, and modeling efforts were made to evaluate optimal machining parameters for the current system. Areas of improvement to increase the system stiffness, manufacturability, and machining efficiency were evaluated and highlighted for the next generation design. The impact of this effort may enable agile manufacturing across the commercial and defense aerospace industries, and other industries where extrusions are utilized like in the construction, architecture, and transportation industries.

42 ENGINEERING↗
DOE OSTI2024

Design of Novel Hot Gas Path Component for Gas Turbine Engines Enabled by Materials and Additive Manufacturing Process Development

This report covers the activities associated with the development and evaluation of two high-γ’ superalloys that were designed by external partners on this project, namely Carpenter Technologies Corporation and the University of California-Santa Barbra. One alloy was GammaPrint-700, a cobalt-base superalloy, and the other a nickel-base (Ni-base) superalloy GammaPrint-1100. Both were found to be printable through laser powder bed fusion (LPBF) additive manufacturing, with optimal process parameter sets being identified for each alloy. Further, high temperature mechanical testing was conducted on each alloy that showed both materials performed better than the comparative baseline (LPBF Hastelloy X), with the Ni-base superalloy being down-selected for scaling and printing of the tip shoe components.

36 MATERIALS SCIENCE↗
DOE OSTI2025

LLRF System Analysis for the Fermilab PIP-II LINAC

Developing long-lived quantum processing units (QPUs) capable of supporting high-fidelity quantum operations is a crucial challenge on the path toward fault-tolerant quantum computing. TESLA-shaped superconducting RF (SRF) cavities, known for photon relaxation times on the order of seconds, provide an excellent foundation for 3D QPUs and quantum memory. This talk presents a novel design that leverages TESLA cavity modes coupled to ancillary transmon qubits, optimized to preserve coherence and control. By carefully engineering the package geometry, optimizing Hamiltonian parameters, and minimizing lossy participation ratios, we achieve photon relaxation times of over 16 ms and 20 ms for the two cavity modes, representing a significant improvement over previous multimode quantum memories. Despite the reduced coupling between the qubit and cavity modes, which is necessary to preserve long lifetimes, the platform supports robust and universal control schemes that are not limited by low coupling strength. We will also discuss how this architecture can lead to scalable, modular quantum computing systems.

Varghese, P. [Fermilab]↗
DOE OSTI2025

Clean Water Production in Cooling Towers

This project developed and demonstrated a novel technology that produces clean water from cooling tower recirculating water by using the natural evaporation and condensation cycle inside cooling towers. The system captures the escaping plume and converts blowdown quality water into high purity water suitable for on-site reuse such as boiler feed. The technology uses electric fields to ionize exhaust plumes, charge the entrained droplets, and direct them toward collection electrodes where they coalesce and flow downward. This allows water recovery at a low energy cost while reducing visible plume emissions. In addition, we developed a complementary software platform that improves overall cooling tower performance. The system uses wireless sensors and physics-based machine learning algorithms to optimize key parameters of the cooling process. For power generation facilities, this increases the thermal efficiency of the cooling loop and condenser, resulting in measurable cycle efficiency gains. Improvements of one percent or more can deliver significant increases in electricity production for the same fuel input.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

A case study in contrastive learning information combination: Application to technical forensics of additive manufacturing filament source identification

Combination of information from disparate data sources into a single decision is a core challenge in many fields, including the field of technical forensics. Technical forensics (TF) utilizes technical characterization of questioned samples to determine properties of that sample; these properties are then used to infer information of forensic interest, such as provenance, age, or attribution. TF is utilized in traditional forensic applications, such as the attribution of material fragments from an explosive, and in nuclear forensic applications, such as the attribution of actinides which have been interdicted out of regulatory control. The challenge of combining information from disparate sources, described alternately by many terms including “Data Fusion” and “Data Integration”, is exacerbated in the technical forensics domain due to at least two factors: the challenge of interpreting each information source singularly, and the relatively small data set sizes available. Extensive literature exists attempting to combine technical forensics information sources, both in manual and automated processes. These attempts are often bespoke to the specific information sources (such as the bi-, tri-, or quad-isotope chart (Moody, Grant, and Hutcheon 2005)), with some emerging examples of simple early- and late- fusion (, respectively). Simultaneous to the information combination efforts described in the previous paragraph, the field of natural language processing attempted (and largely succeeded) in combining information from multiple non-technical information sources. The ecosystem of “multi-modal” language models, which can take text and images as input, and generate text and images as output, became large and diverse by 2025 (Khan et al. 2025). In a generalized sense, many of these methods are trained by learning neural networks which can convert raw text or images into a vector of numbers describing the text or image, hereafter called “embeddings” and the neural networks performing the conversion are called “embedders”. By using a separate embedder for text and images, finding coincident text and images (such as images with their captions), and optimizing the parameters of the embedders such that the embeddings for the text and the image are similar, the field has found a bridge between text and images (Girdhar et al. 2023). It is the contention of the authors of this report that this insight is not limited to text and images but instead can be extended to any modality which can be found coincidently. The subject of the rest of this report is the application of this method to example multi-modal technical forensic data. Some details about the data used in this report are not appropriate for this report, and are included in a companion report (PNNL-38669).

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimization of boron carbide density using direct current sintering for use as sputtering sources at the National Ignition Facility

Several experimental trials were conducted to determine optimal sintering parameters and compositions for purchased boron carbide powder using spark plasma sintering/direct current sintering. Sputtering targets used to create ablators for the National Ignition Facility have been unable to survive post processing procedures suggesting that in-house fabrication may yield better results due to extended control.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Integrating Experiments, Simulations, and Artificial Intelligence to Accelerate the Discovery of High-Performance Green Composites

The imperative for incorporating greener materials into the aerospace industry necessitates addressing significant challenges associated with the microstructural variability exhibited by recycled and sustainable feedstocks. In this study, we propose an integrated methodology that combines experimental investigations, finite element analysis, and artificial intelligence to develop sustainable composites with consistent properties. Our approach utilizes a pipeline comprising an automated mechanical tester, a finite element method simulator, and a convolutional neural network predictor to identify and optimize fabrication parameters for achieving desired mechanical characteristics in composites. By employing a nested-loop pipeline, our methodology improves sample efficiency, accuracy, and effectively bridges the gap between simulations and real-world performance. This unique methodology offers a promising avenue for facilitating the adoption of aerospace-appropriate green composites.

Athanasiou, Christos↗