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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Stress evolution and creep deformation in solid-oxide electrolysis cell systems – Dynamic modeling and multi-objective optimization to maximize stack life and efficiency

Here, this study develops a thermal stress model of solid-oxide electrolysis cells (SOECs) including a model for creep strain and failure probability that is integrated with a dynamic plant-wide model of a hydrogen production process. Uncertainties in key material properties of the cell are quantified to assess their impact on stress profile variability. The oxygen electrode is found to have about 10 times higher failure probability compared to the fuel electrode. The study shows that if the stack operation is not optimized, cycling operation would lead to stress build-up eventually leading to catastrophic failure. A dynamic optimization problem is set up for obtaining the optimal operational profile considering a variable hydrogen production rate. Due to the tradeoff between the efficiency and stress build-up, the dynamic optimization problem is multi-objective. It is observed that the optimizer can considerably reduce the stress build-up (i.e., can increase the stack life) albeit at the cost of a lower efficiency thus exhibiting strong tradeoffs between capital and operating costs. For example, if the stack would be replaced in 0.5 yr, specific energy requirement would be 48.5 kWh/kg H 2 while for a stack replacement time of about 6 yr, the specific energy requirement rises by about 4.2 %.

SOEC↗
DOE OSTI2024

A Pseudoreversible Normalizing Flow for Stochastic Dynamical Systems with Various Initial Distributions

Here, we present a pseudoreversible normalizing flow method for efficiently generating samples of the state of a stochastic differential equation (SDE) with various initial distributions. The primary objective is to construct an accurate and efficient sampler that can be used as a surrogate model for computationally expensive numerical integration of SDEs, such as those employed in particle simulation. After training, the normalizing flow model can directly generate samples of the SDE’s final state without simulating trajectories. The existing normalizing flow model for SDEs depends on the initial distribution, meaning the model needs to be retrained when the initial distribution changes. The main novelty of our normalizing flow model is that it can learn the conditional distribution of the state, i.e., the distribution of the final state conditional on any initial state, such that the model only needs to be trained once and the trained model can be used to handle various initial distributions. This feature can provide a significant computational saving in studies of how the final state varies with the initial distribution. Additionally, we propose to use a pseudoreversible network architecture to define the normalizing flow model, which has sufficient expressive power and training efficiency for a variety of SDEs in science and engineering, e.g., in particle physics. We provide a rigorous convergence analysis of the pseudoreversible normalizing flow model to the target probability density function in the Kullback–Leibler divergence metric. Numerical experiments are provided to demonstrate the effectiveness of the proposed normalizing flow model.

97 MATHEMATICS AND COMPUTING↗