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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 307 records · Page 17
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Steady Surface Pressure Measurement via the Lifetime Method With High-Speed Cameras in NASA's Unitary Plan Wind Tunnel

High spatial resolution measurement of steady surface pressure via pressure-sensitive paint at NASA Ames has traditionally relied on specialized cameras equipped to accumulate charge over multiple exposures, whereas measurement of the fluctuating component of pressure uses an altogether separate set of high-speed cameras. To reduce complexity of installation, data acquisition, and processing, we have implemented methods to use a single set of commercial off-the-shelf cameras to produce both steady and unsteady pressure measurements. Both imaging systems were installed in the 11-by 11-foot NASA Ames Unitary Plan Wind Tunnel and acquired images of a scaled model of the Space Launch System Block 1B Crew and Cargo configurations over a variety of flow conditions. This work focuses on methods for data acquisition, processing, and calibration to produce steady-state pressure estimates on the surface of the wind tunnel model based on the lifetime method. It will then compare the steady solutions produced by the legacy and high-speed imaging systems.

Pressure Sensitive Paint↗
DOE OSTI2024

Investigating explainable transfer learning for battery lifetime prediction under state transitions

Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development. Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions. However, the internal mechanisms of batteries are sensitive to many factors, such as charging/discharging protocols, manufacturing/storage conditions, and usage patterns. These factors will induce state transitions, thereby decreasing the prediction accuracy of data-driven approaches. Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources. Hence, we develop two transfer learning methods, Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit, to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance. From our results, our transfer learning methods reduce root-mean-squared error by 41% through adapting to the target domain. Furthermore, the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining. These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.

25 ENERGY STORAGE↗
DOE OSTI2025

Phonon lifetimes and mode softening in cubic Cs 2 AgBiBr 6

Metal halide perovskites (MHPs) have emerged as noteworthy candidates for photovoltaic applications in recent years. Their high power-conversion efficiency is largely attributed to the structure-function relationship, which is not well understood. In this study, we examine the low-frequency phonons of the double perovskite Cs 2 AgBiBr 6 using neutron inelastic scattering. We find that the acoustic phonon lifetimes decrease from 16 to 3 ps along Γ to X, which is indicative of significant anharmonicity that contributes to the ultralow thermal conductivity. Additionally, we observe a linear temperature dependence of the square of the zone-center soft optical phonon energy, which is consistent with a weakly first-order displacive cubic-tetragonal structural phase transition. These results provide a deeper understanding of the lattice dynamics and phase transitions in MHPs as well as the effects of anharmonicity by comparison with prototypical hybrid organic-inorganic MHPs, (CH 3 NH 3 ) PbX 3 (X = Br, I).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Low embodied energy and carbon, high lifetime silicon boules via a combined chemical vapor deposition/float zone process

This work evaluates a new process route to making float zone (Fz)-quality silicon wafers using a combination of computational fluid dynamics (CFD) modeling and technoeconomic analysis. Our analysis finds that the new process competes with Czochralski (Cz)-grown wafers on a levelized cost of energy system level. The new process also decreases embodied energy and carbon of silicon photovoltaics (PV) by ~6x circumventing the energy-costly Siemens process used in polycrystalline silicon (poly-Si) production plants to generate feedstock for Fz and Cz boules. Instead of using poly-Si from the Siemens process to feed crystallization, the new process uses the high-purity, trichlorosilane (TCS) precursor gas to grow a poly-Si feed rod in-situ during a modified Fz1,2 boule growth process. The gas-to-boule float zone process enables opportunity to produce high-purity (low metals and oxygen content), uniformly doped single crystal silicon boules and wafers with high bulk lifetimes (τ bulk > 15 ms) to enable higher efficiency cells (>27 %) with fewer known degradation mechanisms than Czochralski (Cz)-grown wafers. These benefits reduce the levelized cost of electricity (LCOE) of PV-produced electricity. Here we show the results of our CFD and chemical modeling of the process to prove feasibility and economic viability.

14 SOLAR ENERGY↗
DOE OSTI2024

Dynamic cycling enhances battery lifetime

Laboratory aging campaigns benchmark and elucidate the complex degradation behavior of lithium-ion batteries, and are critical not only for developing new battery chemistries and cell designs but also for engineering reliable battery management systems. Critically, these laboratory experiments aim to quantify and capture realistic aging mechanisms. In this study, we systematically compare dynamic discharge profiles representative of electric vehicle driving to the well-accepted constant-current profiles. Surprisingly, we discovered that dynamic discharge enhances lifetime substantially compared to constant current discharge. Specifically, for the same average current and voltage window, varying the dynamic discharge profile leads to an increase of up to 38 % in equivalent full cycles at end-of-life. Explainable machine learning reveals the importance of low-frequency current pulses as well as time-induced aging under these realistic discharge conditions. Our work quantifies the importance of evaluating new battery chemistries and designs with realistic load profiles, and highlights the opportunities to revisit our understanding of aging mechanisms at the chemistry, materials, and cell levels.

25 ENERGY STORAGE↗