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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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Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning
Proton exchange membrane fuel cells, consuming hydrogen and oxygen to generate clean electricity and water, suffer acute liquid water challenges. Accurate liquid water modelling is inherently challenging due to the multi-phase, multi-component, reactive dynamics within multi-scale, multi-layered porous media. In addition, currently inadequate imaging and modelling capabilities are limiting simulations to small areas (<1 mm 2 ) or simplified architectures. Herein, an advancement in water modelling is achieved using X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, and direct multi-phase simulation. The resulting image is the most resolved domain (16 mm 2 with 700 nm voxel resolution) and the largest direct multi-phase flow simulation of a fuel cell. This generalisable approach unveils multi-scale water clustering and transport mechanisms over large dry and flooded areas in the gas diffusion layer and flow fields, paving the way for next generation proton exchange membrane fuel cells with optimised structures and wettabilities.
Physics-informed models of domain wall dynamics as a route for autonomous domain wall design via reinforcement learning
Prompted by limited available data, we explore data-aggregation strategies for material datasets, aiming to boost machine learning performance. Our findings suggest that intuitive aggregation schemes are ineffective in enhancing predictive accuracy.
Physics model-informed Gaussian process for online optimization of particle accelerators
Not Available
Detection of False Data Injection Attacks in Battery Stacks Using Physics-Based Modeling and Cumulative Sum Algorithm.
Abstract not provided.
On-Line Prediction of Resistant Force During Soil–Tool Interaction
For off-road vehicles such as excavators and wheel loaders, a large portion of energy is consumed to overcome the soil resistant force in the digging process. For optimal control of the digging tool, a high-fidelity model of the soil–tool interaction force is important to reduce energy consumption. Here, in this paper, an on-line soil resistant force prediction method is proposed. In this method, a hybrid model, which combines a physical model and a data-driven model, is used for the force prediction. In addition, the parameters of the hybrid model can be updated on-line based on real-time data. Comparisons with experimental data demonstrate that the proposed prediction method has an average error of around 12.7%.
Nondestructive Pulse Testing to Estimate a Subset of Physics-Based-Model Parameter Values for Lithium-Ion Cells
Not provided.
Multi-scale Physics-based Modeling of Particle-Impact Erosion of CMCs.
Abstract not provided.
Assembling training datsets for generalizable machine-learned models of physical phenomena.
Abstract not provided.
Phase-Field Modeling of Physical Vapor Deposition of Polycrystalline Thin Films.
Abstract not provided.
Detecting False Data Injection Attacks in Battery Stacks Using Physics-Based Modeling and Cumulative Sum Algorithm.
Abstract not provided.
A physics-based model of cladding wastage layer formation rate
In this report, lower length scale simulations to inform an engineering-scale model of fuel-cladding chemical interaction (FCCI) conducted under the auspices of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in FY22 are described. An overall strategy for the implementation of the BISON model is described, and the past and current lower length scale work on FCCI formation is put into the context of this overall strategy. Density functional theory and kinetic Monte Carlo simulations are used to determine the diffusion coefficient of neodymium in iron. Density functional theory calculations are also used to parameterize a parabolic approximation for the dependence of free energy on composition for the intermetallic compound Fe 17 Nd 2 . A phase-field model of the Fe-Nd system was developed that includes the random solid solution body-centered cubic phase and the intermetallic Fe 17 Nd 2 . The model was used to simulate growth of the intermetallic layer in a diffusion couple and the results were compared to experiment. The model’s prediction of the parabolic growth constant is within 40 % of the experimental value. The model is also used to simulate the formation of a grain boundary attack layer that is observed in experiment. The simulations show enhanced Nd concentration along the grain boundaries, but do not clearly show formation of a Fe 17 Nd 2 layer
Detection of False Data Injection Attacks in Battery Stacks Using Physics-Based Modeling and Cumulative Sum Algorithm.
Abstract not provided.
Detection of False Data Injection Attacks in Battery Stacks Using Physics-Based Modeling and Cumulative Sum Algorithm.
Abstract not provided.
A Validation Study of Hypersonic Aerodynamics with Multiple Physics-Fidelity Models.
Abstract not provided.
Informing Missing Physics with Model Form Error and Model Selection.
Abstract not provided.