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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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Analysis of map-based models for reciprocating compressors and optimum selection of rating points
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Cryogenic 4D-STEM analysis of an amorphous-crystalline polymer blend: Combined nanocrystalline and amorphous phase mapping
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Choosing pasture maps: An assessment of pasture land classification definitions and a case study of Brazil
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Automated high-resolution satellite-derived coastal bathymetry mapping
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Generative AI models for learning flow maps of stochastic dynamical systems in bounded domains
Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeling of interior stochastic dynamics and boundary interactions. Despite the success of machine learning-based methods in learning SDEs, existing learning methods are not applicable to SDEs in bounded domains because they cannot accurately capture the particle exit dynamics. We present a unified hybrid data-driven approach that combines a conditional diffusion model with an exit prediction neural network to capture both interior stochastic dynamics and boundary exit phenomena. Our ML model consists of two major components: a neural network that learns exit probabilities using binary cross-entropy loss with rigorous convergence guarantees, and a training-free diffusion model that generates state transitions for non-exiting particles using closed-form score functions. The two components are integrated through a probabilistic sampling algorithm that determines particle exit at each time step and generates appropriate state transitions. Here, the performance of the proposed approach is demonstrated via three test cases: a one-dimensional simplified problem for theoretical verification, a two-dimensional advection-diffusion problem in a bounded domain, and a three-dimensional problem of interest to magnetically confined fusion plasmas.
Additive manufacturing of ceramic materials for energy applications: Road map and opportunities
Among engineering materials, ceramics are indispensable in energy applications such as batteries, capacitors, solar cells, smart glass, fuel cells and electrolyzers, nuclear power plants, thermoelectrics, thermoionics, carbon capture and storage, control of harmful emission from combustion engines, piezoelectrics, turbines and heat exchangers, among others. Advances in additive manufacturing (AM) offer new opportunities to fabricate these devices in geometries unachievable previously and may provide higher efficiencies and performance, all at lower costs. This article reviews the state of the art in ceramic materials for various energy applications. The focus of the review is on material selections, processing, and opportunities for AM technologies in energy related ceramic materials manufacturing. The aim of the article is to provide a roadmap for stakeholders such as industry, academia and funding agencies on research and development in additive manufacturing of ceramic materials toward more efficient, cost-effective, and reliable energy systems.
Mapping Molecular Interaction Interface Between Diaphanous Formin-2 and Neuron-Specific Drebrin A
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Multidimensional molecular high-harmonic spectroscopy: A road map for charge migration studies
This work discusses recent experimental and theoretical analyses of high-harmonic spectroscopy in small molecules, with the aim of characterizing charge migration. We discuss the formulation of molecular high-harmonic spectra followed by methods to extract molecular-target-specific information, both in experiments and ab initio simulations. We present measured and simulated high-harmonic spectra from carbon dioxide and carbonyl sulfide to illustrate the necessity for multidimensional analyses for high-harmonic spectroscopies that include both the spectral amplitude and phase. Leveraging these results, we examine how such multidimensional analyses pave the way for the study of charge migration with high-harmonic spectroscopy and illustrate the beneficial role a static molecular feature can play when probing dynamics. Finally, we briefly expand our scope with an outlook on the critical role of integrating theoretical and experimental approaches, beyond just high-harmonic spectroscopy, for the development of versatile harmonic spectroscopic probes of charge migration.
Elemental mapping and iron oxidation state measurement of synthetic low-activity waste feeds
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Acoustic mapping by picosecond ultrasonics for elastic property measurement: Experimental demonstration on a TRISO fuel compact
Picosecond ultrasonics has been demonstrated on a tristructural isotropic (TRISO) fuel compact to measure the elastic properties of each compact layer. This technique utilizes an ultrashort pump laser pulse to excite vibrations in a gold transducer film covering the surface of each component and a second probe laser pulse to record the resulting acoustic strain induced change in optical reflectance. From the damping of this film vibration, the acoustic reflection coefficient, which couples the elastic properties of the transducer film and the sample, can be obtained, enabling a calculation of the sample’s acoustic velocity and elastic modulus. Overall, results obtained from this method are consistent with known values of elastic moduli, namely that the SiC coating is the stiffest component of the compact while the carbonaceous matrix is the most compliant. Nanoindentation was conducted as a benchmark technique on the same sample and shows satisfactory agreement with the results of picosecond ultrasonics. Compared to other methods like nanoindentation, picosecond ultrasonics is multimodal with a capability of measuring several key properties simultaneously and has potentials to be coupled into optical fibers for remote sensing. Thus, these demonstration measurements reveal the methodology to be a promising candidate for in-situ and high-throughput optical characterizations of nuclear materials.