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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Designing alloys with process-mapping AI pre-trained on empirical knowledge

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Accelerated materials design should match the recent trends in the product development cycles. Materials data analytics can be used to significantly shorten development time of specialized alloys needed for next generation energy applications. However, it faces a challenge of scarce data available for training ML models. Incorporation of the domain knowledge into deep-learning graph structure via fuzzy pre-training and causal process imitation presents a viable approach to developing accurate data-driven models and reliable alloy design tools, with limited datasets. Artificial Intelligence (AI) was used in this study to incorporate such knowledge in the domain-specific computational tool, pyroMind. The tool provides not only novel design ideas but also their interpretation via physics and engineering concepts.</span>

Romanov, Vyacheslav↗
DOE OSTI2022

A Semi-Empirical Correlation for the Onset of Density Wave Oscillations in a Helical Coil Steam Generator

NuScale Power has completed full length helical coil steam generator (HCSG) tests at SIET Laboratories in Piacenza, Italy. The test program included an investigation of the onset and evolution of density wave oscillations (DWO). This paper describes the mechanisms leading to the onset of DWO as inferred from tests performed in the full-height TF-2 counter-flow test facility. This research supports the concept that the onset of DWO is caused by liquid bridging inside the boiling length of the tubes. Using a separate cylinders approximation, a DWO onset correlation is obtained by performing a steady-state momentum balance on the density front created by liquid bridging. This yields a momentum balance equation in terms of a set of dimensionless superficial velocities, a corresponding dimensionless pressure drop term and an average void fraction. Envelope theory is then used to obtain a critical void fraction which is substituted into the dimensionless momentum balance to obtain a DWO onset correlation similar to the Wallis counter-current flooding correlation. A comparison of the DWO onset correlation to the TF-2 DWO experimental data is presented and shows excellent agreement when the average equilibrium vapor quality is greater than 0.2.

Density Wave Oscillations, Helical Coil Steam Gene↗
DOE OSTI2022

Heterogeneous Visualization: A Call for Empirically Driven Technology Advancement and Adoption; Preprint

Our ability to generate data from simulations and experimental platforms is generally outpacing our ability to analyze those data efficiently and effectively. There are a growing number and variety of possible visualization display modalities, from commodity head-mounted displays to large-scale immersive environments, from desktop and touch displays to high-resolution display walls. Many of these visualization technologies and visual environments are rapidly evolving and could revolutionize analysis for certain classes of complex scientific and engineering data. However, the choice of what technology or combination of technologies to use has historically been largely ad hoc and lacks a carefully validated understanding of how to best utilize different visualization technologies to advance the science. To maximize the impact of our data and analyses, it is imperative that we use the most effective visualization environments for the analysis task at hand.

heterogeneous visualization↗
DOE OSTI2024

From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption

Accurate short-term energy consumption forecasting for commercial buildings is crucial for smart grid operations. While smart meters and deep learning models enable forecasting using past data from multiple buildings, data heterogeneity from diverse buildings can reduce model performance. The impact of increasing dataset heterogeneity in time series forecasting, while keeping size and model constant, is understudied. We tackle this issue using the ComStock dataset, which provides synthetic energy consumption data for U.S. commercial buildings. Two curated subsets, identical in size and region but differing in building type diversity, are used to assess the performance of various time series forecasting models, including finetuned open-source foundation models (FMs). The results show that dataset heterogeneity and model architecture have a greater impact on post-training forecasting performance than the parameter count. Moreover, despite the higher computational cost, finetuned FMs demonstrate competitive performance compared to base models trained from scratch.

commercial buildings↗