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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Nucleation, Growth, and Aggregation of Todorokite Nanoparticles from Both Geochemical and Materials Science Perspectives (Final Technical Report)

A vast amount of manganese (Mn) nodules exists in ocean sediments, covering about 30% of the ocean floor. Tunneled and layered Mn(IV) oxides, hereafter called TMOs and LMOs, are the primary mineral phases hosting Mn. Owing to their unique structure and properties, these Mn oxides are highly enriched in transition metals, platinum group metals, and rare earth elements, up to several percentages in weight. Thus, many metals are stored in these nodules and the nodules have thus been increasingly considered for their potential as a source of strategic metals for national security applications and the energy industry. The nodules also play an important role in trace metal biogeochemical cycling in the ocean because they control the partition and distribution of trace metals in oceanic sediments. Layered and tunneled structures differ in their mineralogical and chemical properties. For example, TMOs contain fewer metals compared to LMOs in ocean nodules. Therefore, the relative proportion of the two types of minerals in ocean nodules determines the metal sequestration capacities of the nodules, hence the amount of metals stored in the nodules and metal cycling in oceans. The overall objective of this study is to investigate the mechanisms of layered to tunneled structural transform driven by redox reactions. Specifically, we focus on the redox reactions between Mn(IV) and Mn(II) because they often coexist during Mn redox cycling.

58 GEOSCIENCES↗
DOE OSTI2024

Aggregated Distributed Energy Resources in 2024: The Fundamentals

This report builds upon existing literature and leading examples of ADER pricing and programs in practice to equip commissioners and staff at Public Utilities Commissions (PUCs) and State Energy Offices with the fundamentals of ADER grid services, valuation options, and approaches to compensation. Examples, active debates, and opportunities for further reading are highlighted throughout the document for readers to explore. This report does not seek to outline a specific objective for ADERs in the electricity system nor does it recommend any specific policies for ADERs—it seeks to provide helpful information to inform policymakers’ own ADER strategies and priorities.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗