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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Framework and Tool for Artificial Intelligence & Machine Learning (AI/ML) Enabled Automated Non-Destructive Inspection of Composites Aerostructures Manufacturing

Vehicles and systems in the field of aerospace have two major requirements: a high demand for a large quantity and an expectation to perform for their lifetime with little to no failures. Thus, there is a need for a fast production rate of aerospace products with high quality. Improvements to production rate have many benefits, including a reduction in energy consumption per unit manufactured. This would be from factory energy usage, which is required to build and verify a product. Manufacturing process specifications require inspection of parts to determine if any flaws are present. Depending on factory planning and product quality, especially at higher rates, the evaluation process can pose a production rate bottleneck. This project was comprised of using artificial intelligence and machine learning (AI/ML) methods on inspection evaluations with the objective of reducing the required time to produce an aerospace structure or product and without reducing the final quality.

42 ENGINEERING↗
DOE OSTI2026

Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels (Final Scientific/Technical Report)

The project, "Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels," represents a significant advancement in nuclear fuel recycling technology. It integrates cutting-edge multimodal sensor fusion, machine learning (ML), and digital twin (DT) technologies to address challenges in material safeguarding, process optimization, and regulatory compliance for pyroprocessing facilities. This research has significantly enhanced the understanding of pyrochemical fuel recycling processes by developing innovative tools and methodologies. The Multimodal Safeguards Monitoring Unit (MSMU) combines electroanalytical techniques, Raman spectroscopy, and differential thermal analysis (DTA) to enable high-fidelity, near-real-time material accountancy measurements. Machine learning techniques, such as Long Short-Term Memory (LSTM) autoencoders, are utilized to detect anomalies in material balances and sensor data, improving the reliability of safeguards monitoring. Additionally, digital twin technology has been established to provide real-time system-level monitoring and diagnostics, integrating physics-based models with sensor data to optimize process safety and efficiency.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRSDate not supplied

Trend Analysis of AI/ML Tools and Services in NASA

Usage of Machine Learning (ML) algorithms within NASA’s Science Mission Directorates have been increasing over the years. This can be quantitatively observed in the upward trends of ML usage found by analyzing the publications and presentations (in affiliation with NASA) available through NASA Technical Reports Server (NTRS) and PubMed Central(PMC). Identifying the problem types and class of ML algorithms used to tackle them across the divisions can present opportunities for collaborations, interdisciplinary projects and knowledge transfer for sustainable partnerships. In this presentation, we will present the trend analysis of ML algorithms used in different SMD divisions based on the publications and presentations publicly available. We identify these trends by leveraging ML algorithms which are able to search through the publication texts semantically; which are also highly scalable. We will also present an analysis on the available opensource tools and services in NASA leveraging AI/ML algorithms. This work will provide ample avenues for collaborative efforts across different disciplines based on the surfaced trends.

Slesa Adhikari↗
NASA NTRSDate not supplied

Trend Analysis of AI/ML Tools and Services in NASA

Usage of Machine Learning (ML) algorithms within NASA’s Science Mission Directorates have been increasing over theyears. This can be quantitatively observed in the upward trends of ML usage found by analyzing the publications andpresentations (in affiliation with NASA) available through NASA Technical Reports Server (NTRS) and PubMed Central(PMC). Identifying the problem types and class of ML algorithms used to tackle them across the divisions can presentopportunities for collaborations, interdisciplinary projects and knowledge transfer for sustainable partnerships. In thispresentation, we will present the trend analysis of ML algorithms used in different SMD divisions based on the publicationsand presentations publicly available. We identify these trends by leveraging ML algorithms which are able to search throughthe publication texts semantically; which are also highly scalable. We will also present an analysis on the available opensource tools and services in NASA leveraging AI/ML algorithms. This work will provide ample avenues for collaborativeefforts across different disciplines based on the surfaced trends.

Slesa Adhikari↗
NASA NTRSDate not supplied

Standards for AI/ML and Emerging Technologies

Standards development activities require a keen and deep understanding of the problem being solved by the standard as well as the technologies being deployed in any reference implementation of the solution. It is important to understand the mechanisms and limits of the fundamental, underlying science of implementation and verification technologies used to realize and assure systems. We need to understand the limits of what current process and metrics can provide with respect to new technologies. US leadership is important in this endeavor, and it is vital that we have a measured approach that yields sound results. We wish to start with simple, well-defined, non-safety critical applications and then progress to functions which have (1) clearly defined requirements, (2) means of checking the answer/output, and (3) means of intervention and mitigation of incorrect answers/outputs.

Standards Development↗
NASA NTRSDate not supplied

Assurance Issues in Developing AI/ML Components (and their Standards) for Civil Aviation

Standards development activities require a keen and deep understanding of the problem being solved by the standard as well as the technologies being deployed in any reference implementation of the solution. It is important to understand the mechanisms and limits of the fundamental, underlying science of implementation and verification technologies used to realize and assure systems. We need to understand the limits of what current process and metrics can provide with respect to new technologies. US leadership is important in this endeavor, and it is vital that we have a measured approach that yields sound results. We wish to start with simple, well-defined, non-safety critical applications and then progress to functions which have (1) clearly defined requirements, (2) means of checking the answer/output, and (3) means of intervention and mitigation of incorrect answers/outputs.

Aviation Safety↗
DOE OSTI2026

Predicting U 3 O 8 powder processing conditions: An AI/ML approach analyzing deep learning embeddings of SEM micrographs

High-resolution SEM images of uranium-oxide powders encode micro- and nanoscale clues to their synthesis route and calcination temperature. We trained a ResNet-50 model on 11 commercial-scale U₃O₈ classes, ammonium diuranate (ADU) or uranyl peroxide (H₂O₂) precursors calcined at temperatures ranging from 400 to 750 °C and added a 256-D projection head before the classifier to analyze the learned representation. The best of eight seeds reached 92.4 % accuracy on reserved testing data, but our focus is the structure of the embedding space rather than the accuracy and labels. We quantify class relatedness in the original 256-D space using centroid similarity and distributional distances, and we use Uniform Manifold Approximation Projection (UMAP) for visualization. ‘Unknown’ images from different preparation methods, SEM operators, and from the literature localized near the expected classes under a nearest-centroid analysis without retraining, as well as clustered in similar UMAP space. In conclusion, this embedding-centered workflow complements black-box classification by providing quantitative, similarity-based comparisons of U₃O₈ morphologies and reduces storage space by up to 98 % for image data used in millisecond vector search comparisons.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MuSIKAL: Multiphysics Simulations and Knowledge Discovery through AI/ML Technologies

Under the MuSiKAL project, we developed a framework for a coastal digital twin (DT) platform capable of integrating diverse data resources, configuring multiscale model simulations, performing SciML‐accelerated predictions, with applications primarily driven by storm surge and heavily rainfall events impacting the Gulf Coast of the U.S.

54 ENVIRONMENTAL SCIENCES↗