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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Co-Located Wave Energy Converter (WEC) and Aquaculture System Annotated Bibliography

This annotated bibliography includes references that could aid in the design of a co-located WEC and aquaculture system off the coast of Guam. The breadth of this work covers multiple co-location archetypes such as: 1. WEC seawater desalination system a) Nearshore and deepwater WEC deployment b) Onshore and offshore aquaculture 2. WEC powering an offshore aquaculture platform a) Nearshore or deepwater WEC deployment 3. WEC powering an onshore aquaculture system a) Nearshore WEC deployment 4. Wave powered seawater pump a) Nearshore WEC deployment b) Onshore aquaculture There are two archetypes that may be of immediate interest to the community in Guam are to service the existing Fadian Hatchery (Mangilao) and to support freshwater aquaculture activities. First, the seawater pump at the hatchery that fills the facility’s seawater storage unit is broken. A nearshore seawater pumping WEC could be a solution to this issue. In addition, due to the high frequency of typhoons/extreme conditions and the island’s bathymetry, the likelihood of community support for an offshore aquaculture platform or WEC deployed in deepwater is low. Proactive and resilient solutions not just for power, but for freshwater are of interest as well to support any freshwater aquaculture activities. Therefore, a nearshore WEC desalination system is another archetype to consider.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Introduction to Digital Image Correlation (DIC) with annotated bibliography

Digital Image Correlation (DIC) is “a non-contact means of measuring motion and deformation using digital images of the object of interest”, capable of full-field measurements over large areas. Getting started in DIC can be difficult, since there are many different techniques, and the body of literature on DIC is extensive. IEEE Xplore alone lists nearly 11,000 references, including magazines, conference proceedings, journal articles, books, etc. A Google search returns around 145 million results. The intent of this annotated bibliography is to provide an entry point for beginners and to collect references that may be useful to more advanced practitioners.

42 ENGINEERING↗
DOE OSTI2024

Transporter annotations are holding up progress in metabolic modeling

Mechanistic, constraint-based models of microbial isolates or communities are a staple in the metabolic analysis toolbox, but predictions about microbe-microbe and microbe-environment interactions are only as good as the accuracy of transporter annotations. A number of hurdles stand in the way of comprehensive functional assignments for membrane transporters. These include general or non-specific substrate assignments, ambiguity in the localization, directionality and reversibility of a transporter, and the many-to-many mapping of substrates, transporters and genes. In this perspective, we summarize progress in both experimental and computational approaches used to determine the function of transporters and consider paths forward that integrate both. Investment in accurate, high-throughput functional characterization is needed to train the next-generation of predictive tools toward genome-scale metabolic network reconstructions that better predict phenotypes and interactions. More reliable predictions in this domain will benefit fields ranging from personalized medicine to metabolic engineering to microbial ecology.

Casey, John↗
DOE OSTI2025

A Preferences Corpus and Annotation Scheme for Human-Guided Alignment of Time-Series GPTs

The process of time-series forecasting such as predicting trajectories of silicon content in blast furnaces is a difficult task. Most time-series approaches today focus on scalar-type MSE loss optimization. This optimization approach, while widely common, could benefit from the use of human expert or process-level preferences. In this paper, we introduce a novel alignment and fine-tuning approach that involves learning from a corpus of preferred and dis-preferred time-series prediction trajectories. Our contributions include (1) a preference annotation pipeline for time-series forecasts, (2) the application of Score-based Preference Optimization (SPO) to train decoder-only transformers from preferences, and (3) results showing improvements in forecast quality. The approach is validated on both proprietary blast furnace data and the UCI Appliances Energy dataset. The proposed preference corpus and training strategy offer a new option for fine-tuning sequence models in industrial settings.

DPO↗