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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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72 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

PruningAMR

PruningAMR is an algorithm that reads in data stored as a neural network and outputs an adaptive mesh with values of the neural network stored at its vertices. Examples of input data include implicit neural representations (INRs) and physics-informed neural networks (PINNs). The output mesh is a grid-based adaptive mesh with larger elements for regions in which the neural network has coarse-scale variation and smaller elements for regions with fine scale variation. The goal of the software is to discretize the neural network to a mesh that faithfully captures the details encoded in the data without resorting a fine scale mesh.

Zvonek, JenniferE [Lawrence Livermore National Lab↗
DOE OSTI2025

Two-Scale Neural Networks for Partial Differential Equations with Small Parameters

We propose a two-scale neural network method for solving partial differential equations (PDEs) with small parameters using physics-informed neural networks (PINNs). We directly incorporate the small parameters into the architecture of neural networks. The proposed method enables solving PDEs with small parameters in a simple fashion, without adding Fourier features or other computationally taxing searches of truncation parameters. Various numerical examples demonstrate reasonable accuracy in capturing features of large derivatives in the solutions caused by small parameters.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
DOE OSTI2025

Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks

Physics-informed deep learning has emerged as a promising alternative for solving partial differential equations. However, for complex problems, training these networks can still be challenging, often resulting in unsatisfactory accuracy and efficiency. In this work, we demonstrate that the failure of plain physics-informed neural networks arises from the significant discrepancy in the convergence rate of residuals at different training points, where the slowest convergence rate dominates the overall solution convergence. Based on these observations, we propose a pointwise adaptive weighting method that balances the residual decay rate across different training points. The performance of our proposed adaptive weighting method is compared with current state-of-the-art adaptive weighting methods on benchmark problems for both physics-informed neural networks and physics-informed deep operator networks. In conclusion, through extensive numerical results we demonstrate that our proposed approach of balanced residual decay rates offers several advantages, including bounded weights, high prediction accuracy, fast convergence rate, low training uncertainty, low computational cost, and ease of hyperparameter tuning.

Balanced convergence rate↗
DOE OSTI2025

Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks

Uncertainty quantification (UQ) plays a pivotal role in scientific machine learning, especially when surrogate models are used to approximate complex systems. Although multilayer perceptions (MLPs) are commonly employed as surrogates, they often suffer from overfitting due to their large number of parameters. Kolmogorov-Arnold networks (KANs) offer an alternative solution with fewer parameters. However, gradient-based inference methods, such as Hamiltonian Monte Carlo (HMC), may result in computational inefficiency when applied to KANs, especially for large-scale datasets, due to the high cost of back-propagation. To address these challenges, we propose a novel approach, combining the dropout Tikhonov ensemble Kalman inversion (DTEKI) with Chebyshev KANs. This gradient-free method effectively mitigates overfitting and enhances numerical stability. In addition, we incorporate the active subspace method to reduce the parameter-space dimensionality, allowing us to improve the accuracy of predictions and obtain more reliable uncertainty estimates. Extensive experiments demonstrate the efficacy of our approach in various test cases, including scenarios with large datasets and high noise levels. Our results show that the new method achieves comparable or better accuracy, much higher efficiency as well as stability compared to HMC, in addition to scalability. Moreover, by leveraging the low-dimensional parameter subspace, our method preserves prediction accuracy while substantially reducing further the computational cost.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1964

The X-Irradiation of Hydrazine and 1,1- Dimethylhydrazine

Hydrazine and 1,1-dimethylhydrazine were radiolyzed at room temperature by using about 1 gram of liquid or 1/2 to 2 liters of vapor at room-temperature vapor pressure. A tungsten-target X-ray source operating at 300 kilovolts and 6 to 8 milliamperes (0.1 to 1.0 A) was used at intensities of 1 times 10 (sup 2) to 1 times 10 (sup 3) roentgens per minute to give total doses of 4.1 rads to 2.3 times 10 (sup 6) rads. The amounts of decomposition were dependent on total dose and independent of dose rate. In the case of hydrazine vapor, the decomposition was also directly related to surface area. There was a lesser surface effect with liquid hydrazine, but this variable was not studied for 1,1-dimethylhydrazine. The order of decreasing sensitivity to X-radiation is as follows: hydrazine vapor, 1,1-dimethylhydrazine vapor, 1,1-dimethylhydrazine liquid, and hydrazine liquid. The decomposition products of hydrazine were nitrogen, hydrogen, ammonia, and unidentified products. The same products plus methylamine, dimethylamine, and hydrazine were found from the decomposition of 1,1-dimethylhydrazine. In addition, the 1,1-dimethylhydrazine underwent post-irradiation changes. Values for G (number of molecules of sample decomposed per 100 electronvolts of absorbed energy) of the order of 10 (sup 6) suggest that the vapor-phase decompositions are chain reactions.

X-RAY IRRADIATION↗
NASA NTRS1954

Study of the physical properties of petrolatum-stabilized magnesium-hydrocarbon slurry fuels

Magnesium-hydrocarbon slurries containing a moderate proportion of petrolatum have physical properties such that they offer promise as experimental aircraft fuels. The settling of the magnesium is greatly retarded by the petrolatum, and the slurries can easily be remixed to their original condition after storage. Successive batches which have closely similar properties can be prepared readily. The apparent viscosity of these slurries increased rapidly with increasing magnesium concentration, with increasing petrolatum concentration, and with decreasing temperature. As the apparent viscosity increased, the extent of settling and the ease of remixing both decreased. Although no quantitative correlation was found between the properties of the slurry and those of the petrolatum, and no one petrolatum gave slurries which were best in all respects, one of the five petrolatum used was judged to be superior to the others.

FUELS - PROPERTIES, PHYSICAL AND CHEMICAL↗
NASA NTRS1955

Effect of Surface-active Additives on Physical Properties of Slurries of Vapor-process Magnesium

The presence of 3 to 5 percent surface-active additive gave the lowest Brookfield apparent viscosity, plastic viscosity, and yield value that were obtained for slurry fuels containing approximately 50 percent vapor-process magnesium in JP-1 fuel. The slurries settled little and were easily remixed. A polyoxyethylene dodecyl alcohol was the most effective of 13 additives tested in reducing the Brookfield apparent viscosity and the yield value of the slurry. The seven most effective additives all had a hydroxyl group plus an ester or polyoxethylene group in the molecule. The densities of some of the slurries were measured.

Pinns, Murray L↗
NASA NTRS1958

NACA Research on Slurry Fuels

An extensive program was conducted to investigate the use of concentrated slurries of boron and magnesium in liquid hydrocarbon as fuels for afterburners and ramjet engines. Analytical calculations indicated that magnesium fuel would give greater thrust and that boron fuel would give greater range than are obtainable from jet hydrocarbon fuel alone. It was hoped that the use of these solid elements in slurry form would permit the improvement to be obtained without requiring unconventional fuel systems or combustors. Small ramjet vehicles fueled with magnesium slurry were flown successfully, but the test flights indicated that further improvement of combustors and fuel systems was needed.

Pinns, M L↗
NASA NTRSDate not supplied

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗
NASA NTRSDate not supplied

Hybrid Modeling for Complex Systems Health Management

The research work presents application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. The powertrain model consists of Li-ion batteries, electronic speed controller with pulse-width modulation, and brush-less DC motor with connected propeller. Results obtained from combination of laboratory and simulation tests are discussed in this work.

PINNS↗