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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine-Learning-aided Approach for Predicting the Thermal Expansion Behaviors in Advanced Test Reactor Capsules (NURETH-20 full paper)

Instrumented experiments at test reactors are essential to deploying new advanced reactor systems. Designing new experiments and generating data on specific conditions require both time and cost investment. A high-fidelity model of the experiment environment can be created using finite element analysis software to support the actual experiments, but computation time is still a concern in applying outcomes to real-time usage (e.g., a digital twin). This research proposes a machine-learning-aided approach to temperature and displacement predictions, based on the thickness of the outer gas gap on the experimental capsule used for the in-pile demonstration of a novel thermal conductivity probe in the Advanced Test Reactor. The capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. There were gas gaps between the fuel and rodlet and between the inner and outer capsule. The learning data consisted of an experimental capsule’s radial distributions of temperature and displacement, as obtained from Abaqus and the physical features. For the first step, temperature was predicted using three positional parameters. Then the displacement was predicted using six different positional parameters. Each physical feature was normalized to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement in all cases involving interpolation and extrapolation. Also, data similarity enhancement increased the similarity between training and target data increasing the predictive accuracy of machine-learning models. In some cases of extrapolation, the accuracy of the machine-learning model showed limited performance, but still data similarity enhancement improved the accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Method for simultaneous characterization and expansion of reference libraries for small molecule identification

A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.

Colby, Sean M.↗
DOE OSTI2023

Direct Expansion Heat Pump Using High Glide Low GWP Refrigerant

Next generation of refrigerants require drastically lower global warming potential (GWP). The refrigerant mixtures, with a GWP < 150, e.g., R-457A, etc., tend to have significant temperature glide, which prefer multi-row, cross counter flow configuration. Typical heat pump operation reverses the refrigerant flow direction, compared to the air flow, from cooling mode to heating mode. It is difficult to configurate cross counter-flow heat exchangers in heat pumps. We invented an innovative configuration, having check valve assembly to maintain uniform cross counter flow configuration in both cooling and heat modes, which optimizes the performance of a high temperature slide refrigerant mixture, i.e. R-457A. Based on it, we developed a laboratory prototype, using a 2-stage compressor, achieved a cooling SEER (cooling seasonal energy efficiency rating) of 16.0 (cooling seasonal COP of 4.69) and a heating HSPF (heating seasonal performance factor) > 9.5 (heating seasonal COP of 2.78). The unit was tested in an extensive ambient condition, heating operation from 5˚F to 62˚F (-15˚C to 16.7˚C); cooling operation from 82˚F to 95˚F (27.8˚C to 35˚C). The paper will introduce the system configuration, heat exchanger dimensions and laboratory investigations.

Shen, Bo↗