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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Phonon-informed Neural Thermal Scattering (NeTS) Optimization for Crystalline Graphite and Beryllium Metal

Fast neutrons born from fission lose energy through scattering interactions in the process of slowing-down. As neutrons thermalize to the order of $k$ $b$ $T$ (where $k$ $b$ is the Boltzmann constant, and $T$ is the temperature of the medium), their de Broglie wavelength and energy approaches the order of inter-atomic spacing and quantized lattice vibrations, i.e., phonons. At thermal energies, the thermal scattering law (TSL), i.e., $S$($α, β$), captures crystal binding contributions to the total reaction rate, or cross section. This dimensionless material property describes the energy ($β$) and momentum ($α$) exchanges available in a medium. Currently, $S$($α, β$) is evaluated in the Full Law Analysis Scattering System Hub (FLASSH) code for discrete inputs and stored as ENDF/B File 7 for 0-phonon elastic (MT 2) and n-phonon inelastic (MT 4) processes. Further processing recasts $S$($α, β$) into cumulative distribution functions for sampling post-collision scattering kinematics. In practice, interpolation schemes are employed to access data between tabulated values. An improvement to this juncture of the nuclear data pipeline is supplying cross sections on-the-fly (OTF), as has been developed for the un-resolved resonance region to minimize non-physical interpolation errors. This capability may improve simulation accuracy for accident and transient analyses, where rapidly varying changes in temperature and pressure are difficult to predict beforehand. To do so, deep artificial neural networks (ANNs) can be employed which collapse non-linear, complex data into a lightweight dictionary of neural weights and biases. This has been successfully demonstrated for the hydrogen in light water $S$($α, β$) dataset in the form of a Neural Thermal Scattering (NeTS) module. In this work, the NeTS framework is extended to consider the impact of material-dependent dynamical features on optimal neural pre-processing and architecture design decisions, such as number of neurons per hidden layer, residual skip connections and neural depth. New NeTS modules for crystalline graphite and beryllium metal illuminate a novel correlation between dynamical nonlinearity and optimal neural parametrization when deploying $S$($α, β$) on-the-fly.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Excited-State and Nonadiabatic Molecular Dynamics Methods with Broad Applicability

Electronically excited states play a pivotal role in the capture, transformation, storage, and emission of radiative energy. Despite much recent progress for small model systems, excited state properties and dynamics in many larger molecular devices and materials have remained elusive for experiment and theory. Nonadiabatic transitions between electronic states turn radiative energy into heat and are particularly difficult to control. This project aims to develop new electronic structure and nonadiabatic molecular dynamics (NAMD) methods to simulate, predict, and ultimately control the flow and transformation of excitation energy in a wide-range of light-driven molecular devices such as dyes, photocatalysts, and fluorescence emitters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Predicting Safety Rod Reactivity Insertion in the Advanced Test Reactor

The Advanced Test Reactor (ATR), and complimentary zero-power ATR Critical (ATRC) reactor, located at Idaho National Labs (INL), are undergoing conversion from Highly Enriched Uranium (HEU) to Low Enriched Uranium (LEU). Both have a variety of testing locations that can receive large variations in flux due to its unique serpentine design, consisting of five lobes surrounding nine flux traps (see Figure 1). Initial criticality and power distribution throughout the core are controlled by core-external outer shim control cylinders (OSCCs). Distinct test loops allow for testing at specific temperatures, pressures, and irradiation conditions such as flux and fission density. The ATR is one of the key nuclear engineering research and testing facilities within the DOE National Laboratory Complex, and the ATRC supports its operation [1]. Currently, the Office of Material Management and Minimization (M3) within the National Nuclear Security Administration of the DOE is working to convert the remaining research reactors, including the ATR, from 93% HEU fuel to 19.75% LEU fuel (LEU) to support non-proliferation [2]. Extensive materials testing at INL and internationally has demonstrated that a high-density uranium molybdenum (U 10Mo) alloy can meet the performance requirements of the remaining high powered research reactors. The current LEU fuel element design is named the LOWE element. However, there are many technical challenges to address before the conversion to LEU can be successful, including the accurate characterization of the reactor core physics with LEU fuel. To ensure safe operation of the ATR, reactor engineers prepare a CSAP (Core Safety Assurance Package) before each cycle. The purpose of the CSAP is to verify the reactor performance calculation used to determine if the selected fuel loading meets operational, experimental, and safety criteria. Many of the criteria in the CSAP are limits on reactivity insertion in various accident scenarios.

42 ENGINEERING↗
DOE OSTI2022

Recent MOOSE Update Slides for a DOE CI visit

This is an updated slide deck for which much of the content has been previously cleared. This slide deck incorporates recent NRIC Virtual Test Bed work with several updates on modeling and simulation of benchmark advanced reactor problems. Note that none of the models or results contained within these slides is proprietary. Many of these have been openly published on the Virtual Test Bed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Validation and Verification of Python based Neutron Spectrum Unfolding Software

To validate and verify the python-based code (PySL), designed to replicate the programs used by STAYSL for Beam Correction Factor (BCF) and Self-Shielding Factor (SHIELD), a series of tests were performed. To test BCF a python script was written to generate a random flux history file and both versions of the code processed the data. The test verified matching values up to at least one decimal place, approximately 10,000 tests where run and each one passed. Isotopes began to fail the tests once neutron saturation was reached. To verify this the total time of exposure was varied the isotopes that failed were compared to a list of their half-lives. The test process for SHIELD was very similar but, in this case, the code began by producing an input file with varying thickness and device type/environment for the SHIELD input. The failure condition for this test was if any of the data points for an isotope had a difference above 3%. Approximately 40 of these tests were run and there were only 3 isotopes that had reoccurring failures but only 2% of their points were above the 3% difference. A visual comparison was conducted by plotting the results from both programs. Although the test failed, the differences between their values were minuscule, and the self-shielding factor’s shape was preserved when plotted. Next steps for this project will be validating and verifying the python-based SigPhi code and then reproducing and testing the least squares unfolding performed by STAYSL.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1976

Wave refraction diagrams for the Baltimore Canyon region of the mid-Atlantic continental shelf computed by using three bottom topography approximation techniques

The Langley Research Center and Virginia Institute of Marine Science wave refraction computer model was applied to the Baltimore Canyon region of the mid-Atlantic continental shelf. Wave refraction diagrams for a wide range of normally expected wave periods and directions were computed by using three bottom topography approximation techniques: quadratic least squares, cubic least squares, and constrained bicubic interpolation. Mathematical or physical interpretation of certain features appearing in the computed diagrams is discussed.

Poole, L. R.↗
NASA NTRS2005

Visualization Methods for Viability Studies of Inspection Modules for the Space Shuttle

An effective simulation of an object, process, or task must be similar to that object, process, or task. A simulation could consist of a physical device, a set of mathematical equations, a computer program, a person, or some combination of these. There are many reasons for the use of simulators. Although some of the reasons are unique to a specific situation, there are many general reasons and purposes for using simulators. Some are listed but not limited to (1) Safety, (2) Scarce resources, (3) Teaching/education, (4) Additional capabilities, (5) Flexibility and (6) Cost. Robot simulators are in use for all of these reasons. Virtual environments such as simulators will eliminate physical contact with humans and hence will increase the safety of work environment. Corporations with limited funding and resources may utilize simulators to accomplish their goals while saving manpower and money. A computer simulation is safer than working with a real robot. Robots are typically a scarce resource. Schools typically don t have a large number of robots, if any. Factories don t want the robots not performing useful work unless absolutely necessary. Robot simulators are useful in teaching robotics. A simulator gives a student hands-on experience, if only with a simulator. The simulator is more flexible. A user can quickly change the robot configuration, workcell, or even replace the robot with a different one altogether. In order to be useful, a robot simulator must create a model that accurately performs like the real robot. A powerful simulator is usually thought of as a combination of a CAD package with simulation capabilities. Computer Aided Design (CAD) techniques are used extensively by engineers in virtually all areas of engineering. Parts are designed interactively aided by the graphical display of both wireframe and more realistic shaded renderings. Once a part s dimensions have been specified to the CAD package, designers can view the part from any direction to examine how it will look and perform in relation to other parts. If changes are deemed necessary, the designer can easily make the changes and view the results graphically. However, a complex process of moving parts intended for operation in a complex environment can only be fully understood through the process of animated graphical simulation. A CAD package with simulation capabilities allows the designer to develop geometrical models of the process being designed, as well as the environment in which the process will be used, and then test the process in graphical animation much as the actual physical system would be run . By being able to operate the system of moving and stationary parts, the designer is able to see in simulation how the system will perform under a wide variety of conditions. If, for example, undesired collisions occur between parts of the system, design changes can be easily made without the expense or potential danger of testing the physical system.

Mobasher, Amir A.↗
DOE OSTI2021

B-PINNs: Bayesian Physics-informal Neural Networks for Forward and Inverse PDE Problems with Noisy Data

We propose a Bayesian physics-informed neural network (B-PINN) to solve both forward and inverse nonlinear problems described by partial differential equations (PDEs) and noisy data. In this Bayesian framework, the Bayesian neural network (BNN) combined with a PINN for PDEs serves as the prior while the Hamiltonian Monte Carlo (HMC) or the variational inference (VI) could serve as an estimator of the posterior. B-PINNs make use of both physical laws and scattered noisy measurements to provide predictions and quantify the aleatoric uncertainty arising from the noisy data in the Bayesian framework. Compared with PINNs, in addition to uncertainty quantification, B-PINNs obtain more accurate predictions in scenarios with large noise due to their capability of avoiding overfitting. We conduct a systematic comparison between the two different approaches for the B-PINNs posterior estimation (i.e., HMC or VI), along with dropout used for quantifying uncertainty in deep neural networks. Our experiments show that HMC is more suitable than VI with mean field Gaussian approximation for the B-PINNs posterior estimation, while dropout employed in PINNs can hardly provide accurate predictions with reasonable uncertainty. Finally, we replace the BNN in the prior with a truncated Karhunen-Loève (KL) expansion combined with HMC or a deep normalizing flow (DNF) model as posterior estimators. The KL is as accurate as BNN and much faster but this framework cannot be easily extended to high-dimensional problems unlike the BNN based framework.

Non linear PDEs, Noisy data, Bysian physics inform↗
DOE OSTI2021

An adaptive approach to machine learning for compact particle accelerators

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1997

The Equivalence of Precession Phenomena in Metric Theories of Gravity

The requirement of general covariance imparts to metric theories of gravity, such as general relavity, important structural features. A precise mathematical form results, ensuring that computation of observable physical effects in the theory gives the same answers independently of the chosen system of coordinates.

metric theories gravitation relativity↗
NASA NTRS1996

The Equivalence of Precession Phenomena in Metric Theories of Gravity

The requirement of general covariance imparts to metric theories of gravity, such as general relativity, important structural features. A precise mathematical form results, ensuring that computation of observable physical effects in the theory gives the same answers independently of the chosen system of coordinates. This coordinate independence property, in turn, can lead to an equivalence of apparently different physical effects.

gravity theories of gravity metric theories covari↗
NASA NTRS1986

NASA Tech Briefs, July/August 1986

Topic include: NASA TU Services; New Product Ideas; Electronic Components and Circuits; Electronic Systems; Materials; Computer Programs; Mechanics; Physical Sciences; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences. 3

National Aeronautics and Space Administration↗