SEARCH · Engineering Papers

Results for “Machine learning, neutron imaging, source reconstruction”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

3 records

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Source shape estimation for neutron imaging systems using convolutional neural networks

Neutron imaging systems are important diagnostic tools for characterizing the physics of inertial confinement fusion reactions at the National Ignition Facility (NIF). In particular, neutron images give diagnostic information on the size, symmetry, and shape of the fusion hot spot and surrounding cold fuel. Images are formed via collection of neutron flux from the source using a system of aperture arrays and scintillator-based detectors. Currently, reconstruction of fusion source geometry from the collected neutron images is accomplished by solving a computationally intensive maximum likelihood estimation problem via expectation maximization. In contrast, it is often useful to have simple representations of the overall source geometry that can be computed quickly. In this work, we develop convolutional neural networks (CNNs) to reconstruct the outer contours of simple source geometries. We compare the performance of the CNN for penumbral and pinhole data and provide experimental demonstrations of our methods on both non-noisy and noisy data.

Machine learning, neutron imaging, source reconstr↗

DOE OSTI2025

Physics-guided dual implicit neural representations for source separation

Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions, such as background and signal distortions, that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated, as well as experimental, momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.

47 OTHER INSTRUMENTATION↗

NASA NTRS2014

Neutron Imaging for Selective Laser Melting Inconel Hardware with Internal Passages

Additive Manufacturing is showing great promise for the development of new innovative designs and large potential life cycle cost reduction for the Aerospace Industry. However, more development work is required to move this technology into space flight hardware production. With selective laser melting (SLM), hardware that once consisted of multiple, carefully machined and inspected pieces, joined together can be made in one part. However standard inspection techniques cannot be used to verify that the internal passages are within dimensional tolerances or surface finish requirements. NASA/MSFC traveled to Oak Ridge National Lab's (ORNL) Spallation Neutron Source to perform some non-destructive, proof of concept imaging measurements to assess the capabilities to understand internal dimensional tolerances and internal passages surface roughness. This presentation will describe 1) the goals of this proof of concept testing, 2) the lessons learned when designing and building these Inconel 718 test specimens to minimize beam time, 3) the neutron imaging test setup and test procedure to get the images, 4) the initial results in images, volume and a video, 4) the assessment of using this imaging technique to gather real data for designing internal flow passages in SLM manufacturing aerospace hardware, and lastly 5) how proper cleaning of the internal passages is critically important. In summary, the initial results are very promising and continued development of a technique to assist in SLM development for aerospace components is desired by both NASA and ORNL. A plan forward that benefits both ORNL and NASA will also be presented, based on the promising initial results. The initial images and volume reconstruction showed that clean, clear images of the internal passages geometry are obtainable. These clear images of the internal passages of simple geometries will be compared to the build model to determine any differences. One surprising result was that a new cleaning process was used on these simply geometric specimens that resulted in what appears to be very smooth internal surfaces, when compared to other aerospace hardware cleaning methods.

Tramel, Terri L.↗