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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Rapid Inverse Parameter Inference Using Physics-Informed Neural Network

As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration. However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration analysis is discussed for both the single particle model (SPM) and the pseudo two-dimensional (P2D) model. Surrogate models are constructed using physics-informed neural networks (PINNs) that encode the influence of internal properties on observed voltage responses. In practice, a neural network can be trained by: 1) using simulation results of the physics-based model (i.e., a data-loss approach); 2) using the residuals of the governing equations themselves (i.e., a physics-loss approach); or 3) using a combination of simulation results and governing equation residuals. In the present work, PINNs are developed using a variety of training losses and neural network architectures. In this analysis, it is shown that a PINN surrogate model can be reliably trained with only physics-informed loss. However, using a coupled data-informed and physics-loss approach produced the most accurate PINNs.

Bayesian calibration↗
DOE OSTI2024

Benchmarking of Different Inverse Point Kinetics Implementations for an Autocorrected Reactimeter Algorithm

In November 2017, the Transient Reactor Test Facility returned to operation. Since that time, many transient test series have been completed; each has provided valuable data for materials performance, reactor safety that can be applied in future designs. During each experimental series, detector count rates provided important information on the core behavior during transients. However, a limitation of these data is that variations in the neutron distribution during experiments cause errors when attempting to infer reactivity evolution from detector signals. Neutron physics codes can be used to compute the flux shape variations. However, this is a poor solution when the experimental data is used to do verification, validation and uncertainty quantification (VVUQ) on codes. Indeed, if the output of the code is used both as a reference and to correct what the reference is compared to, the circular dependency limits the quality of the VVUQ approach. To overcome this problem, an Autocorrected Reactimeter Algorithm (ACRA) has been developed. This approach infers time-dependent reactivity evolution by testing different spatial corrections and selecting the one that minimizes reactivity variations when the core is in a frozen configuration (i.e. when there is no variation in parameters affecting reactivity). However, the scope of this method was limited to transients where there were negligible thermal feedback. Indeed, the core is never in a frozen configuration when the fuel temperature varies during the whole transient. This is our motivation for the development of an improved version of the ACRA which does not require frozen configurations

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

A Pressure-Induced Inverse Order–Disorder Transition in Double Perovskites

Chemical intuition tells us that pressure increases ordering in most known materials. The discovery of pressure-induced disorder in the double perovskites Y 2 CoIrO 6 and Y 2 CoRuO 6 , which is reported by Z. Deng, C.-J. Kang, C. Jin, M. Greenblatt, and co-workers in their Research Article on page 8240, is in contrast to traditional theories of order–disorder mechanisms and calls for reconsideration of pressure effects in solid state sciences.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Pressure‐Induced Inverse Order–Disorder Transition in Double Perovskites

Abstract Given the consensus that pressure improves cation ordering in most of known materials, a discovery of pressure‐induced disordering could require recognition of an order–disorder transition in solid‐state physics/chemistry and geophysics. Double perovskites Y 2 CoIrO 6 and Y 2 CoRuO 6 polymorphs synthesized at 0, 6, and 15 GPa show B‐site ordering, partial ordering, and disordering, respectively, accompanied by lattice compression and crystal structure alteration from monoclinic to orthorhombic symmetry. Correspondingly, the long‐range ferrimagnetic ordering in the B‐site ordered samples are gradually overwhelmed by B‐site disorder. Theoretical calculations suggest that unusual unit‐cell compressions under external pressures unexpectedly stabilize the disordered phases of Y 2 CoIrO 6 and Y 2 CoRuO 6 .

Deng, Zheng↗
DOE OSTI2023

Rational Design and Reticulation of Infinite qbe Rod Secondary Building Units into Metal–Organic Frameworks through a Global Desymmetrization Approach for Inverse C 3 H 8 /C 3 H 6 Separation

Abstract The development of reticular chemistry has enabled the construction of a large array of metal–organic frameworks (MOFs) with diverse net topologies and functions. However, dominating this class of materials are those built from discrete/finite secondary building units (SBUs), yet the designed synthesis of frameworks involving infinite rod‐shaped SBUs remain underdeveloped. Here, by virtue of a global linker desymmetrization approach, we successfully targeted a novel Cu‐MOF (Cu‐ASY) incorporating infinite Cu‐carboxylate rod SBUs with its structure determined by micro electron diffraction (MicroED) crystallography. Interestingly, the rod SBU can be simplified as a unique cylindric sphere packingqbetubule made of [4 3 .6 2 ] tiles, which further connect the tritopic linkers to give a newly discovered 3,5‐connectedgfcnet. Cu‐ASY is a permanent ultramicroporous material featuring 1D channels with highly inert surfaces and shows a preferential adsorption of propane (C 3 H 8 ) over propene (C 3 H 6 ). The efficiency of C 3 H 8 selective Cu‐ASY is validated by multicycle breakthrough experiments, giving C 3 H 6 productivity of 2.2 L/kg. Density functional theory (DFT) calculations reveal that C 3 H 8 molecules form multiple C−H⋅⋅⋅π and atypical C−H⋅⋅⋅ H−C van der Waals interactions with the inner nonpolar surfaces. This work therefore highlights the linker desymmetrization as an encouraging and intriguing strategy for achieving unique MOF structures and properties.

Chemistry↗
DOE OSTI2024

Forward and inverse predictive transient models of TREAT using surrogate reactivity models

In this work, we present two novel approaches for predicting the power evolution and control rod height of the Transient Reactor Test Facility (TREAT) to support experiment modeling; specifically, transient analysis of the NASA-sponsored Sirius series of experiments. These approaches utilize steady-state Monte Carlo model, point kinetics model, and surrogate models to predict power evolution and control rod axial position during transient experiments. Both approaches were tested and validated against several Sirius experiments that were performed in the TREAT facility at different power levels. The validation test results show very good agreement with the experimental data, and the models were able to accurately predict the power evolution and the axial control rod position with an average error within 3.0%. Finally, this indicates that these approaches will help the reactor engineering team of the TREAT facility in preparing and predicting the power and temperature of the experiment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗