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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Development of dual phase soft magnetic laminate for advanced electric machines

The development of dual phase soft magnetic laminate for advanced electric machines at General Electric is summarized, including alloy development, properties, manufacturing trials, prototype experience, potential applications, and future material development. Dual phase soft magnetic laminates provide the flexibility to tune magnetic/non-magnetic state at the desired locations, using solution nitriding treatment with protective mask/coating. This enabling material technology benefits electric machine performance by reducing flux leakage in the laminates. It also decouples the electromagnetic and mechanical design, which opens up the design space for advanced electric machines. Magnetic and mechanical properties of dual phase laminates were compared to commercial FeSi and FeCo soft magnetic laminates. Driven by the market needs for high power density and high-speed electric machines, a synchronous reluctance (SynRel) machine without PMs using dual phase soft magnetic laminates was assessed in machine electromagnetic performance as well as rotor stress analysis by finite element simulation. Successful prototype build and testing further retired manufacturing risks and advanced the technology maturity level. Future material development with higher mechanical strength, higher saturation magnetization, higher permeability, and lower loss are highly desired for ground and air transportation electrification.

Rallabandi, Vandana↗
DOE OSTI2025

Visualization of Noisy and Less Noisy Computational Basis States in Quantum Computing

Quantum computing technology holds substantial promise as a reliable computational paradigm. However, current noisy intermediate scale quantum (NISQ) systems, are significantly impacted by noise originating from hardware inconsistencies. This noise causes errors and lowers output fidelity. So we must find which basis states cause errors. However, there are two main challenges in analyzing noise corresponding to basis states. First, the noise distribution data is high dimensional in nature, thereby making its analysis challenging. Second, although functional box plots have been used in the state of the art research to understand such a high dimensional data, they suffer from clutter and occlusion issues because of overplotting. In this study, we introduce an innovative visualization pipeline to address the aforementioned challenges to provide a clear depiction of noisy and less-noisy basis states. Specifically, our proposed visualization pipeline comprises three stages namely, low dimensional embedding, clustering, and violin plot visualization, to reduce visual clutter and effectively analyze high-dimensional noise distribution data. Our analysis uses quantum machine learning (QML) circuits as case study for drawing a distinction between noisy and less noisy basis states.

Senapati, Priyabrata [Kent State University]↗
DOE OSTI2024

Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. Furthermore, the approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.

14 SOLAR ENERGY↗
DOE OSTI2025

Inferring the Isotropic-Nematic Phase Transition with Generative Machine Learning

Generative machine learning models are capable of learning the phase behavior in condensed matter systems such as the Ising model. We utilize a score-based modeling procedure called thermodynamic maps to describe the isotropic-nematic phase transition in a melt of Gay-Berne ellipsoids. When trained on samples from a single temperature on either side of the phase transition, this generative machine learning approach infers effectively the nematic order parameter at intermediate temperatures. Furthermore, these results demonstrate score-based models’ ability to learn the physics of a nontrivial liquid crystal phase transition.

Critical exponents↗
NASA NTRS2023

Leveraging Automated Fiber Placement Computer Aided Process Planning Framework for Defect Validation and Dynamic Layup Strategies

Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. The current state of process planning requires a high degree of interaction from the process planner and could greatly benefit from increased automation. Therefore, a list of key steps and functions are created to identify the more difficult and time-consuming phases of process planning. Additionally, a set of metrics must exist by which to evaluate the effectiveness of the manufactured laminate from the machine code created during the Process Planning stage. Layup strategies, in addition to dog ears, stagger shifts, steering constraints, and starting points, represented the group of functions labeled as process optimization and ranked the highest in terms of priority for automation. The laminates resulting from the selected parameters are evaluated through the occurrences of principal defect metrics such as fiber gaps, overlaps, angle deviation and steering violations. This document presents an automated software solution to the layup strategy and starting point selection phase of process planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.

HiCAM↗
DOE OSTI2024

Machine learning for the redox potential prediction of molecules in organic redox flow battery

Here, organic redox flow batteries (ORFB) are recognized as an innovative technology for the large-scale storage of renewable energy. The redox potential of organic redox-active molecules plays a vital role in their performance. Advanced screening techniques like high-throughput experiment and machine learning (ML) have significantly enhanced organic material performance and transformed the field of ORFB. However, the scarcity of experimental data poses a considerable challenge for ML model development in this domain. In our study, we developed lightweight graph-based Gaussian process regression (GPR) models with GPU-accelerated marginalized graph kernel and hybrid kernel to predict the redox potentials of organic redox-active molecules for ORFBs, specifically focusing on small datasets. To evaluate model accuracy, we created a new experimental database of organic redox-active molecules by the data from hundreds of published papers and assembled previous computational datasets. We also considered some key parameters, such as pH conditions and solvent type, to assess their impact on redox potential prediction. Our GPR model predicted redox potentials with high accuracy across all datasets using minimal training data. The study provides powerful tools for molecule screening and design and delivers valuable guidance on designing training datasets for costly experiments.

25 ENERGY STORAGE↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
NASA NTRS2001

Computation of Coupled Thermal-Fluid Problems in Distributed Memory Environment

The thermal-fluid coupling problems are very important to aerospace and engineering applications. Instead of analyzing heat transfer and fluid flow separately, this study merged two well-accepted engineering solution methods, SINDA for thermal analysis and FDNS for fluid flow simulation, into a unified multi-disciplinary thermal fluid prediction method. A fully conservative patched grid interface algorithm for arbitrary two-dimensional and three-dimensional geometry has been developed. The state-of-the-art parallel computing concept was used to couple SINDA and FDNS for the communication of boundary conditions through PVM (Parallel Virtual Machine) libraries. Therefore, the thermal analysis performed by SINDA and the fluid flow calculated by FDNS are fully coupled to obtain steady state or transient solutions. The natural convection between two thick-walled eccentric tubes was calculated and the predicted results match the experiment data perfectly. A 3-D rocket engine model and a real 3-D SSME geometry were used to test the current model, and the reasonable temperature field was obtained.

Wei, H.↗
NASA NTRS1978

Experiments on the dynamic behavior of cavitating pumps

This paper describes experiments performed to measure the dynamic transfer matrices for cavitating (and noncavitating) pumps. These transfer matrices describe the relationship between small linear oscillatory perturbations in the pressures and mass flow rates at inlet and discharge from the hydraulic machine. The matrices were deduced from direct measurements of these fluctuating quantities for different modes of excitation of the machine. Results for a cavitating inducer are presented as functions of frequency and mean operating state. Though some of the trends in the data are consistent with existing theoretical models of inducer dynamics, others are not, indicating a need for further theoretical investigation of the dynamic characteristics of such flows. The results exhibit increasingly complex dynamics with increasing cavitation; it appears that the hydraulic machine deviates from an essentially passive response without cavitation to an increasingly active response as the cavitation number is reduced.

Ng, S. L.↗
DOE OSTI2026

A tunable autonomous RNA-fueled micro-engine

Autonomous molecular machines capable of converting chemical energy into mechanical motion are foundational components for synthetic nanoscale systems. Inspired by biological motors, we report the construction of a tunable, RNA-fueled DNA origami engine that drives the cyclic movement of a 500 nm-diameter particle at the microscale. The engine operates via sequential RNA–DNA hybridization and enzymatic cleavage by RNase H, enabling reversible switching between folded and unfolded conformations without external intervention. By modulating RNA and enzyme concentrations and controlling temperature, we achieve tunable switching kinetics, with transition periods as short as ~10 s. Kinetic modeling reveals that the folding pathway is governed by both productive RNA binding and the enzymatic clearance of misfolded intermediates, while unfolding is primarily controlled by RNase H activity. Since the RNA fuel binds specifically to the DNA strands, each engine is addressable simply by changing the sequences. This work demonstrates a programmable, self-resetting molecular actuator and offers a blueprint for building more complex nanomechanical systems with forces and energies comparable to molecular motors.

DNA nanomachines↗
DOE OSTI2026

Comparison of CNN-Based Image Classification Approaches for Implementation of Low-Cost Multispectral Arcing Detection

Camera-based sensing has benefited in recent years from developments in machine learning data processing methods, as well as improved data collection options such as Unmanned Aerial Vehicles (UAV) mounted sensors. However, cost considerations, both for the initial purchase of sensors as well as updates, maintenance, or potential replacement if damaged, can limit adoption of more expensive sensing options for some applications. To evaluate more affordable options with less expensive, more available, and more easily replaceable hardware, we examine the use of machine learning-based image classification with custom datasets, utilizing deep learning based-image classification and the use of ensemble models for sensor fusion. Utilizing the same models for each camera to reduce technical overhead, we showed that for a very representative training dataset, camera-based detection can be successful for detection of electrical arcing. We also use multiple validation datasets, based on conditions expected to be of varying difficulty, to evaluate custom data. These results show that ensemble models of different data sources can mitigate risks from gaps in training data, though the system will be less redundant for those cases unless other precautions are taken. We found that with good quality custom datasets, data fusion models can be utilized without specialization in design to the specific cameras utilized, allowing for less specialized, more accessible equipment to be utilized as multispectral camera components. This approach can provide an alternative to expensive sensing equipment for applications in which lower-cost or more easily replaceable sensing equipment is desirable.

convolutional neural networks↗
DOE OSTI2025

Design, Optimization, and Control of a 100 kW Electric Traction Motor Meeting or Exceeding DOE 2025 Targets

The overall objective of the electric motor portion of the Electric Drives Technology consortium is to research, develop, and test electric motors for use in electric vehicle applications capable of a peak power greater than 100 kW, power density greater than or equal to 50 kW/l, and a cost less than 3.3 $/kW. To meet the electric traction motor power density and cost targets a number of approaches were pursued simultaneously throughout the course of this project which address all of the major volumetric power density variables. The specific research thrusts at the Illinois Institute of Technology (IIT) are the following: multiphysics design for increased power density through maximum utilization of active materials, synthesis of electric machine windings and PM flux barriers with controlled space harmonics, high slot fill windings for increased current loadings or efficiency, aggressive cooling strategies, and design studies and prototype construction of candidate electric machines.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Solid-state NMR and theoretical studies illuminate lanthanum borohydride C–H borylation catalysts confined within a zeolite

Zeolite-supported single-site lanthanum borohydride catalyzes C–H borylation of hydrocarbons, while the related silica-supported complex is inactive under comparable conditions. The identical composition of support-La(BH 4 ) 2 (THF) 2 sites in the two materials implies that the support bestows underlying structural features onto La that are required for bond activation catalysis, yet the nature of these effects, which could include confinement effects and/or electronic modulation of the site itself, remains to be identified. We used solid-state nuclear magnetic resonance (SSNMR) spectroscopy and molecular dynamics simulations with machine-learning potentials (ML-MD) to analyze the electronic and steric effects imparted by the faujasite support on the precatalyst structure to correlate with catalytic activity. ML-MD simulations show that THF dissociates from La under the influence of confinement, leading to coordinatively unsaturated sites in the zeolite pores. Then, the La complex grafts on Brønsted acid sites (La BAS ) or isolated silanols (La SiO ) or remains physisorbed in the zeolite pores. Catalytic studies comparing compounds supported on faujasite zeolites containing or lacking BAS and/or silanols show that only the former complexes lead to active sites, ruling out confinement as the sole requirement for catalysis. The DFT calculations and ML-MD simulations also reveal that the surface-lanthanum coordination number is two (bidentate) for La BAS , with the metal center forming long, flexible bonds to two oxygen atoms bridging Si and Al, but only one oxygen atom (monodentate) for La SiO . The structure–activity relationship identifies confined, BAS-grafted species as active sites and provides important guidance for the design of enhanced atom-efficient catalysts.

Cui, Jinlei [Ames Laboratory, and Iowa State Univ.↗
DOE OSTI2025

Status of the FCC-ee interaction region design

This paper presents a comprehensive overview of the Machine Detector Interface (MDI) design developed for the FCC-ee Feasibility Study. It highlights novel studies related to the lightweight interaction region, including a mechanical model of the vacuum chambers, integration of the vertex detector, the MDI alignment system, and assessments of machine-induced backgrounds. The small beam pipe radius and thickness, as well as the high power to be dissipated, require state-of-the-art mechanical design. The integration of all mechanical elements and detectors is challenging, necessitating careful studies to allow fulfilling conflicting requirements. The optimisation of the machine detector interface against formidable backgrounds is presented.

Beam-induced backgrounds↗
DOE OSTI2025

ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers

We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mechanical tunneling based threshold-annealing process. The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing dynamics onto a network of integrate-and-fire neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer and the resulting spiking dynamics replicates the optimal escape mechanism and convergence of SA, particularly at low-temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved benchmark combinatorial optimization problems such as MAX-CUT and Max Independent Set. Across multiple runs, NeuroSA consistently generates distribution of solutions that are concentrated around the state-of-the-art results (within 99%) or surpass the current state-of-the-art solutions for Max Independent Set benchmarks. Furthermore, NeuroSA is able to achieve these superior distributions without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform.

42 ENGINEERING↗
DOE OSTI2026

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗
DOE OSTI2026

From microbial diversity to functional potential using dimensionality reduction

The high dimensionality of microbial diversity data from ‘omics observations can be reduced using Machine Learning, with many recent studies showcasing ML utility for exploratory ecological feature finding and process prediction. Here, we compare the Self Organizing Map (SOM) dimensionality reduction method to the well-documented sample-based Principal Coordinate Analysis (PCoA) and taxa-based Weighted Gene Correlation Network Analysis (WGCNA) using near daily 16S rRNA gene amplicon sequencing data from the 2019 to 2020 MOSAiC International Arctic Drift Expedition. We then map k-means clustering outputs from each method to available metagenomes, extracting functionally distinct seasonal microbial ecotypes in the surface Arctic Ocean. Our results indicate the SOM method better represented expected seasonal transitions and identified a greater number of metabolically distinct functional groups than the more traditional PCoA ordination. Ultimately, we identified four community ecotypes with distinct taxonomic and functional cut-offs driven by seasonality, water mass, and substrate turnover, highlighting the importance of succession in functional diversity for the central Arctic Ocean. These results reinforce ML dimensionality reduction as a meaningful translator in the mining of historical amplicon datasets to address modern mechanistic questions and potentially provide ’omics informed ecotype diversity to leverage in mechanistic biogeochemical models.

Arctic Ocean↗
NASA NTRS1988

Lisp Object State Saver (LOSS): A facility used to save partial schedules of the Hubble Space Telescope

Current research in the area of long term scheduling of the Hubble Space Telescope is being done using Common Lisp and Flavors on Lisp Machines. The planning tools manipulate memory-resident data structures which represent the many entities and relationships that represent planning states. The Lisp Object State Saver (LOSS), a general purpose utility, was constructed which allows one to take a snapshot of memory by storing a representation of the structures in a text file. This text file can later be loaded thus restoring the pre-existing and logically equivalent planning state. A LOSS template must be created for each datatype to be stored and a simple grammar governs the creation of such templates.

Sponsler, Jeffrey L.↗