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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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154 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Selecting an Encapsulant for an Aerospace Superconducting Machine

Achieving larger benefits from electrified propulsion in single aisle aircraft (the largest commercial aircraft market segment) necessitates that a significant fraction of their 20 MW or greater propulsion system be electrified. 5 to possibly 20 of the MW-scale electric machines investigated by NASA would be required to meet this need. Approximately 10-20 of NASA’s MW-scale electric machines would be required to reach the necessary power levels. Therefore, electric machines of 5 MW or greater are of interest. However, conventionally cooled high power density MW-scale electric machines still produce a significant amount of waste heat, which makes obtaining these power levels problematic. Highly efficient fully superconducting electric machines (or those with a cryogenically cooled, high purity copper or aluminum stator) are a possible solution but have multiple barriers to be overcome. One such barrier is the durability of superconducting windings. Superconductors are typically brittle, and encapsulants used to hold windings in place are a significant source of stress because of the mismatches in coefficient of thermal expansion at temperatures between room temperature and operational temperatures (20 to 77 K). These encapsulants also form a thermal barrier that can be detrimental to cooling the superconductors. This paper describes an initial study of commercially available encapsulants as it relates to superconducting machine applications along with initial modeling of thermal stress in superconducting windings for a superconducting or cryogenically cooled stator in a 5 MW electric machine.

encapsulant↗
DOE OSTI2026

Active learning using hybrid surrogate tool life modeling for machining process optimization

Here, this paper describes an active learning approach for part-to-part iterative machining process optimization using a hybrid surrogate tool life model. A probabilistic interpolating tool life model is developed by combining the empirical Taylor-type tool life equation and the model fit error. The probabilistic tool life model is then used to calculate the machining cost per part distribution. The optimal machining parameters are selected using an expected improvement in machining cost per part criterion. The method is validated numerically using experimental results; the results show a median convergence error of 2.2% after three tests over 400 simulations. The method is validated experimentally on two industrial applications for Ti-6Al-4V roughing resulting in a cost per part reduction greater than 23% after two tests. The described method is a robust solution for rapid convergence to optimal machining parameters in an industrial production environment.

Active learning↗
NASA NTRSDate not supplied

Selecting an Encapsulant for an Aerospace Superconducting Machine

Achieving larger benefits from electrified propulsion in single aisle aircraft (the largest commercial aircraft market segment) necessitates that a significant fraction of their 20 MW or greater propulsion system be electrified. These aircraft would require up to 10-20 of today’s state-of-the art machines, which have been demonstrated up to 2.5 MW for aerospace use, and could benefit further from 5 MW+ designs. However, when conventionally cooled, high power density machines still produce a significant amount of waste heat, translating into heavy thermal management and detracting from system benefits. Highly efficient fully superconducting electric machines (or those with a cryogenically cooled, high purity copper or aluminum stator) are a possible solution but have multiple barriers to overcome. One such barrier is the durability of superconducting windings. Superconductors are typically brittle, and encapsulants used to hold windings in place are a significant source of stress due to mismatches in coefficient of thermally induced contraction between ambient and operational temperatures (20 to 77 K). Lack of compliance between the materials at operational temperatures also adds significant stress. These encapsulants also form a thermal barrier that can be detrimental to cooling the superconductors. This work describes an initial study of commercially available encapsulants as it relates to superconducting machine applications, along with initial modeling of thermal stress in superconducting windings for a superconducting or cryogenically cooled stator in a 5 MW electric machine.

encapsulant↗
DOE OSTI2025

Detecting thermodynamic phase transition via explainable machine learning of photoemission spectroscopy

Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk phases, but their main measurable quantity—electronic energy gaps—is no longer an effective order parameter in low-dimensional and fluctuating systems. Combining angle-resolved photoemission with a domain-adversarial neural network, we report a data-driven method to identify thermodynamic phase transitions solely based on single-particle spectra. We demonstrate 97.6% accuracy in cuprate superconductor Bi 2 Sr 2 CaCu 2 O 8+δ with strong superconducting fluctuations. This model notably compensates for the scarcity of experimental data by leveraging virtually inexhaustible simulated data. Further, its explainability reveals the crucial role of in-gap spectral weight in detecting phase fluctuations and thermodynamic transitions. Our work pinpoints the spectroscopic signatures of fluctuating orders and enables using spectroscopy for machine-learning-assisted material discovery for low-dimensional and strong coupling systems.

2D materials↗
DOE OSTI2026

A generative machine learning model for designing metal hydrides applied to hydrogen storage

Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized hydrides, which constrains the discovery of optimal candidates. This work presents a framework that integrates causal discovery with a lightweight generative machine learning model to generate novel metal hydride candidates that may not exist in current databases. Using a dataset of 450 samples (270 training, 90 validation, and 90 testing), the model generates 1000 candidates. After ranking and filtering, six previously unreported chemical formulas and crystal structures are identified, four of which are validated by density functional theory simulations and show strong potential for future experimental investigation. Overall, the proposed framework provides a scalable and time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery.

generative model↗
NASA NTRS2026

A Review of Superconducting Electric Machines with On-Board Cryocoolers

This paper reviews the evolution and emerging direction of superconducting electric machines that employ onboard cryocoolers integrated directly into the rotor, eliminating the need for cryogenic fluid coupling and, in some cases, rotary seals. Traditional low-temperature superconducting (LTS) machines relied on external cryogenic systems and liquid helium transfer couplers, which introduced excessive complexity, poor reliability, and significant parasitic energy losses. The advent of high-temperature superconductors (HTS) has enabled compact, closed-cycle cryocoolers that support self-contained, fluid-free refrigeration architectures suitable for rotating applications. This paper examines the key technological challenges associated with on-board cryocooler integration and reviews three representative efforts by KAIST, NASA, and Hinetics, each illustrating distinct strategies and milestones toward practical implementation. KAIST demonstrated early proof-of-concept for rotating machines with on-board cryocoolers, NASA developed a shaft-integrated Stirling-type pulse tube cryocooler for a 1.4 MW hybrid-electric motor, and Hinetics achieved full-scale validation of a self-contained HTS rotor incorporating a commercial Stirling cryocooler and spoke-suspension torque tube. Collectively, these achievements confirm the technical feasibility of on-board cryogenic refrigeration and highlight steady progress toward compact and efficient superconducting rotating systems across various applications. Embedding cryocoolers directly within the rotor enables practical, efficient, and commercially viable superconducting propulsion technologies.

Cryogenics↗
NASA NTRS2025

Improving Adhesive Bondline Time of Flight Predictions During Autoclave Cure Utilizing Machine Learning

Composite materials are increasingly being used in aerospace applications due to their superior strength-to-weight ratio compared to commonly used metals. A current limitation to widespread adoption is the certification of adhesively bonded joints. One approach to improving adhesive bonding in composites is accurately measuring the thickness of adhesive bondlines in composite laminates. Precise bondline thickness control is essential for aerospace applications where adhesive layer thickness directly affects joint fracture properties and structural performance. This study focused on implementing machine learning techniques to determine the ultrasonic time of flight (directly correlated to thickness) in adhesive bondlines throughout autoclave cure cycles. A high-temperature (use up to 180°C) ultrasonic scanning system was deployed in an autoclave to provide time of flight data through composite panels. Three experiments were conducted on the curing of 305 mm × 305 mm unidirectional composite panels. In the first experiment, a piecewise function was fit for the temperature correction factor to account for changing autoclave temperatures. Due to deficiencies in the first calibration experiment, a second experiment was run, and the results were used to train a machine learning model. The revised experiment, in combination with the machine learning model, significantly increased the accuracy of the bondline time of flight predictions (~14% error reduced to <1%). Data was processed using the Regression Learner Application in MATLAB®, with a Support Vector Machine selected for the model. The result was a machine learning algorithm capable of reliably quantifying ultrasonic time of flight through adhesive bondlines. The third experiment provided independent test data for the machine learning model, demonstrating that the model produces accurate predictions from data beyond its training set.

Machine Learning↗
DOE OSTI2026

Feedback, physics, and forecasts: The emerging paradigm of machine learning-driven battery research

Machine learning (ML) is reshaping how we understand, predict, and optimize electrochemical systems. In batteries, ML accelerates discovery across chemistry, design, and operation by transforming massive experimental and simulated datasets into predictive, interpretable models. This review consolidates a decade of progress in ML-driven battery innovation, from early-cycle feature extraction to operando image analysis and physics-informed modeling. We categorize approaches by data domain and physical fidelity, emphasizing interpretable ML for diagnostics, reinforcement learning for control, and multi-objective optimization for lifetime extension strategies. Additionally, we demonstrate how integrated models accelerate discovery, reduce testing time, and guide sustainable design. Economic analyses furthermore illustrate how these advances can lower cost per cycle and improve circularity. Together, these developments chart a path toward self-optimizing, sustainable battery technologies.

artificial intelligence↗
DOE OSTI2026

A Unified Scaling Framework for Comparative Analysis of Major Electrical Machine Topologies

This paper presents a unified framework for comparing major electrical machine topologies under identical output and thermal constraints, with emphasis on supply-chain-aware selection among rare-earth-intensive, reduced-rare-earth, and rare-earth-free solutions. Using power factor and air-gap flux density as the principal descriptors, the framework links topology choice to relative size, copper demand, magnet dependence, cost sensitivity, and inertia. To support robust early-stage screening, the deterministic scaling model is combined with uncertainty representation, Monte Carlo scenario propagation, and hesitationaware ranking. The results show that rare-earth-rich machines remain compact and dense, whereas reduced-rare-earth and rare-earth-free alternatives become more attractive under specific material-risk and cost scenarios.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗
NASA NTRSDate not supplied

Helmholtz Motor: A Novel Electric Machine That Enables High Temperature Superconducting Armatures

Electrified aircraft are being developed to increase the efficiency and reduce the cost of operating subsonic transport aircraft. Achieving a substantial impact necessitates focusing on single- and twin-aisle aircraft which use propulsion systems with >20 MW ratings. For these aircraft, multi-MW superconducting electric machines are being developed due to their high specific power and high efficiency. Cryogenic electric machines are a promising technology for multi megawatt electric aircraft drivetrains. Second generation high temperature superconductors (HTS) are the ideal conductor for the field winding (typically the rotor) of superconducting machines, and they have attractive features for the armature winding (typically the stator) of these machines. However, their use in armature windings has been severely limited due to excessive AC losses in conventional electric machines. This presentation will present the working principle, design, and a performance trade study of a novel electric machine – termed a Helmholtz machine – that enables HTS armatures by significantly reducing AC losses. The working principle of the patent pending Helmholtz motor is to optimize the rotor-produced magnetic field (i.e., the field winding) so that it produces a magnetic field almost purely in the plane of the HTS. This is accomplished using 2 rotors that contain a set of matched Helmholtz coil pairs (or matched permanent magnet Halbach arrays). The armature (stator) HTS coils are positioned between the rotors and oriented to minimize the out of plane magnetic field. The armature HTS coils are also designed to minimize the out of plane component of their self field. The presentation will include the lessons learned in the sizing and design of this motor, including equations for how to match the magnet arrays. A performance trade study will be presented for a partially superconducting, radial flux version of the motor. The trade study involved an optimization of a motor design code based on analytical calculations (electromagnetic, AC loss, thermofluid, mechanical, and motor sizing) that determine the motor’s total efficiency and total specific power. The design code will be described in the presentation, with an emphasis on the calculation of magnetic fields, AC loss, and temperature distribution.

Superconducting electric machines↗
DOE OSTI2026

Machine Learning for Multipactor Susceptibility Prediction in Planar RF Gaps

Multipactor discharge is a nonlinear electron avalanche that limits the performance of high-power radio-frequency (RF) and vacuum electronic devices. Predicting multipactor susceptibility traditionally relies on Monte Carlo or particle-in-cell (PIC) simulations, which become computationally expensive for large parametric studies. In this work, we present a supervised machine-learning (ML) framework for prediction of multipactor susceptibility in a two-surface planar geometry. The models are trained using high-fidelity PIC simulation generated susceptibility data and learn the relationship between operational parameters, geometry, and material-dependent secondary electron emission properties. The proposed approach enables rapid reconstruction of susceptibility charts while preserving the physical structure of multipactor growth regions.

43 PARTICLE ACCELERATORS↗