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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Spatial arrangement of dynamic surface species from solid-state NMR and machine learning-accelerated MD simulations

Here, the surface arrangement of motional organic functionalities is explored by experimental dipolar coupling measurements and the prediction of motionally-averaged coupling constant from molecular dynamics simulations. The use of machine learning potentials was key to reaching the timescale required. The distance between dynamic surface species are important in cooperative heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Neural network model of the multi-mode anomalous transport module for accelerated transport simulations

Here, a neural network version of the Multi-Mode Anomalous Transport Module, known as MMMnet, has been developed to calculate plasma turbulent diffusivities in DIII-D with a calculation time suitable for control applications. MMMnet uses a simple artificial neural network structure to predict the ion thermal, electron thermal, and toroidal momentum diffusivities while reproducing Multi-Mode Model (MMM) data with good accuracy and keeping the calculation time as a fraction of that associated with MMM. Model-based control techniques require models with fast calculation times, making many existing physics-oriented predictive codes unsuitable. The control-oriented predictive code COTSIM (Control Oriented Transport SIMulator) calculates the most significant plasma dynamics in response to the different actuators while running at a speed useful for control design. In order to achieve this calculation speed, COTSIM often relies on scaling laws and control-level models. Replacing some of these scaling laws and control-level models with neural network versions of more complex physics-level models has the potential of increasing the range of validity and the level of accuracy of COTSIM without compromising its computational speed. In this work, MMMnet is integrated into COTSIM to improve the turbulent diffusivity predictions, which will in turn improve the prediction accuracy associated with the dynamics of many plasma properties.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Accelerating accurate simulations of calorimeter showers with normalizing flows and probability density distillation

Recently, we introduced caloflow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows. Here, we present caloflow v2, an improvement on our original framework that speeds up shower generation by a further factor of 500 relative to the original. The improvement is based on a technique called probability density distillation, originally developed for speech synthesis in the machine learning literature, and which we develop further by introducing a set of powerful new loss terms. We demonstrate that caloflow v2 preserves the same high fidelity as the original using qualitative (average images, histograms of high-level features) and quantitative (classifier metric between geant4 and generated samples) measures. The result is a generative model for calorimeter showers that matches the state of the art in speed (a factor of 10 4 faster than GEANT4) and greatly surpasses the previous state of the art in fidelity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Accelerating Scientific Simulations with Bi-Fidelity Weighted Transfer Learning

High-fidelity modeling is an essential design tool for many engineering applications. However, for complex systems, computational cost can be a limiting factor. Analyzing parameter sensitivity, uncertainty quantification, and design optimization require many model evaluations. Surrogate models are often used to develop the relationship between model parameters and quantities of interest. However, in the case of complex systems, surrogate models require several degrees of freedom and, thus, a large number of data points to determine the correct dependencies. For many applications, this may be prohibitively expensive. The reduction of computational requirements can be achieved by leveraging low-fidelity models. Low-fidelity models represent the system at a coarser resolution with the advantage of computational efficiency. Therefore, a bi-fidelity modeling paradigm, which augments the accuracy of a low-fidelity model in a computationally efficient manner by invoking limited runs of a high-fidelity model, can be leveraged to sufficiently balance the accuracy and computational requirements. In this work, a bi-fidelity weighted transfer learning method using neural networks was applied to a computational fluid dynamics heat transfer modeling problem. The transfer learning advantage was investigated as a function of hyperparameters. Our main finding is that the use of a bi-fidelity modeling paradigm achieves accuracy close to that of a high-fidelity Gaussian process model while significantly reducing computational cost. The bi-fidelity model achieves comparable performance with 90 high-fidelity samples-that is, 60% less than the samples needed to achieve similar accuracy without the use of bi-fidelity modeling,

Borowiec, Katarzyna↗
DOE OSTI2020

Union: An Automatic Workload Manager for Accelerating Network Simulation

With the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machine learning applications. Simulation is a great research vehicle to understand the performance implications of co-running scientific applications with big data and machine learning workloads on large-scale systems. In this paper, we present Union, a workload manager that provides an automatic framework to facilitate hybrid workload simulation in CODES. Furthermore, we use Union, along with CODES, to investigate various hybrid workloads composed of traditional simulation applications and emerging learning applications on two dragonfly systems. The experiment results show that both message latency and communication time are important performance metrics to evaluate network interference. Network interference on HPC applications is more reflected by the message latency variation, whereas ML application performance depends more on the communication time.

heterogeneous workloads↗
DOE OSTI2019

Accelerated Quantum Simulations of Polymer Aging and Degradation

We have conducted extensive quantum simulations to predict the susceptibility of polydimethylsiloxane (PDMS) under tensile loads (i.e., stretching) to hydrolytic reactions. Initial results under uniform strain revealed that the PDMS electronic state is highly sensitive to deformations of the Si-O backbone (Figure 1). Predictions for the energy gap between the highest-occupied and lowestunoccupied molecular orbitals (i.e., the HOMO-LUMO gap, E Gap ) and visualization of these orbitals reveals the distinct possibility that strained PDMS chains have an electronically driven anisotropy in their susceptibility to hydrolysis. First, we observe that EGap begins to drop precipitously as the PDMS chain is stretched beyond 10% (labeled 1.1 in the figure). The increased accessibility of these unoccupied states generally will promote chemical degradation, as excited electronic states due to bond breaking will form more easily. Second, we observe that the geometric character of the LUMO state changes under stretching of 20% (strains of 1.20) and larger, whereby the orbital density localizes to the interior of the O-Si-O bond angles along the backbone. This LUMO localization coupled with a substantial reduction in EGap indicates that the interior O-Si-O bond angles may be potential sites for chemistry due to environmental water or by roaming acids or bases. This is in contrast to a chemical attack on the backside of the O-Si-O angle, where there is practically no density of low-energy unoccupied states. The enhanced electronic susceptibility likely couples with steric shielding by the two methyl groups that also protect the backside of the Si-O chain. These detailed results can be used as inputs for coarse-grained simulation approaches, by helping us to define the susceptibility of specific polymer chain location to scission and/or backbiting reactions, based on the local geometry of the Si-O backbone. Definition of this type of order parameter with application to larger scale models is the subject of our future work.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Denoising Convolutional Networks to Accelerate Detector Simulation

The high accuracy of detector simulation is crucial for modern particle physics experiments. However, this accuracy comes with a high computational cost, which will be exacerbated by the large datasets and complex detector upgrades associated with next-generation facilities such as the High Luminosity LHC. We explore the viability of regression-based machine learning (ML) approaches using convolutional neural networks (CNNs) to "denoise" faster, lower-quality detector simulations, augmenting them to produce a higher-quality final result with a reduced computational burden. The denoising CNN works in concert with classical detector simulation software rather than replacing it entirely, increasing its reliability compared to other ML approaches to simulation. We obtain promising results from a prototype based on photon showers in the CMS electromagnetic calorimeter. Future directions are also discussed.

Banerjee, Sunanda↗
NASA NTRS1979

Reactor for simulation and acceleration of solar ultraviolet damage

An environmental test chamber providing acceleration of UV radiation and precise temperature control (+ or -)1 C was designed, constructed and tested. This chamber allows acceleration of solar ultraviolet up to 30 suns while maintaining temperature of the absorbing surface at 30 C - 60 C. This test chamber utilizes a filtered medium pressure mercury arc as the source of radiation, and a combination of selenium radiometer and silicon radiometer to monitor solar ultraviolet (295-340 nm) and total radiant power output, respectively. Details of design and construction and operational procedures are presented along with typical test data.

Laue, E.↗
NASA NTRS1990

Heart rate and pulmonary function while wearing the launch-entry crew escape suit (LES) during + Gx acceleration and simulated Shuttle launch

Space shuttle crewmembers have been equipped with a launch-entry crew escape system (LES) since the Challenger accident in 1986. Some crewmembers, wearing the new pressure suit, have reported breathing difficulties and increased effort to achieve the desired range of motion. This study was conducted to quantify the reported increased physical workloads and breathing difficulty associated with wearing the LES. Both veteran astronauts and centrifuge panel members were exposed to various + Gx profiles (including simulated shuttle launch) + Gx on the USAF School of Aerospace Medicine (USAFSAM) human-use centrifuge. Maximum heart rate data showed no increased workload associated with arm and head movement in the LES when compared to the flight suit/helmet ensemble (LEH). However, the LES did impose a significant increase in breathing difficulty beginning at +2.5 Gx which was demonstrated by a decrease in forced vital capacity and subjected questionnaries.

Krutz, Robert W., Jr.↗