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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dynamical Large Deviations of Two-Dimensional Kinetically Constrained Models Using a Neural-Network State Ansatz

We use a neural-network ansatz originally designed for the variational optimization of quantum systems to study dynamical large deviations in classical ones. We use recurrent neural networks to describe the large deviations of the dynamical activity of model glasses, kinetically constrained models in two dimensions. We present the first finite size-scaling analysis of the large-deviation functions of the two-dimensional Fredrickson-Andersen model, and explore the spatial structure of the high-activity sector of the South-or-East model. These results provide a new route to the study of dynamical large-deviation functions, and highlight the broad applicability of the neural-network state ansatz across domains in physics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Machine-learning-assisted high-temperature reservoir thermal energy storage optimization

High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving the goal of the net-zero carbon economy, given its capability to balance the intermittent nature of renewable energy generation. In this study, a machine-learning-assisted computational framework is presented to co-optimize the performance metrics of HT-RTES by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Further, neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

tLaSDI: Thermodynamics-informed latent space dynamics identification

Here we propose a latent space dynamics identification method, namely tLaSDI, that embeds the first and second principles of thermodynamics. The latent variables are learned through an autoencoder as a nonlinear dimension reduction model. The latent dynamics are constructed by a neural network-based model that precisely preserves certain structures for the thermodynamic laws through the GENERIC formalism. An abstract error estimate is established, which provides a new loss formulation involving the Jacobian computation of autoencoder. The autoencoder and the latent dynamics are simultaneously trained to minimize the new loss. Computational examples demonstrate the effectiveness of tLaSDI, which exhibits robust generalization ability, even in extrapolation. In addition, an intriguing correlation is empirically observed between a quantity from tLaSDI in the latent space and the behaviors of the full-state solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Simultaneous control of the electron temperature and safety factor profiles in DIII-D using model-based optimal control techniques

Future tokamak power plants will likely operate using a single, well-defined plasma scenario, either in steady state or for very long pulse lengths. In order to enhance the robustness of the scenario, feedback controllers for a variety of plasma properties will be necessary to counteract any disturbances and ensure safe operation. However, only a limited set of actuators will be available to control many different quantities. Because of this, it is necessary to develop controllers that are able to regulate multiple plasma properties using a limited set of actuators. To this end, a controller has been developed for the simultaneous regulation of both the electron temperature and safety factor profiles in DIII-D. This algorithm uses a linear quadratic integral control synthesis approach based on a linearized model of the dynamics of the two profiles. Two neural network surrogate models, NubeamNet and MMMnet, are included to improve the fidelity of the model. Furthermore, the controller has been tested in simulation using COTSIM, and has demonstrated the ability to simultaneously track changes in both the electron temperature and safety factor targets, including changes in both the magnitude and the shape of the profiles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Nonlinear elasticity and short-range mechanical coupling govern the rate and symmetry of mouth opening in Hydra

Hydra has a tubular bilayered epithelial body column with a dome-shaped head on one end and a foot on the other. Hydra lacks a permanent mouth: its head epithelium is sealed. Upon neuronal activation, a mouth opens at the apex of the head which can exceed the body column diameter in seconds, allowing Hydra to ingest prey larger than itself. While the kinematics of mouth opening are well characterized, the underlying mechanism is unknown. We show that Hydra mouth opening is generated by independent local contractions that require tissue-level coordination. We model the head epithelium as an active viscoelastic nonlinear spring network. The model reproduces the size, timescale and symmetry of mouth opening. It shows that radial contractions, travelling inwards from the outer boundary of the head, pull the mouth open. Nonlinear elasticity makes mouth opening larger and faster, contrary to expectations. Here, the model correctly predicts changes in mouth shape in response to external forces. By generating innervated : nerve-free chimera in experiments and simulations, we show that nearest-neighbor mechanical signaling suffices to coordinate mouth opening. Hydra mouth opening shows that in the absence of long-range chemical or neuronal signals, short-range mechanical coupling is sufficient to produce long-range order in tissue deformations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Thermodynamics-informed latent space dynamics identification

This software showcases a latent space dynamics identification method, namely tLaSDI, that embeds the first and second principles of thermodynamics. The latent variables are learned through an autoencoder as a nonlinear dimension reduction model. The latent dynamics are constructed by a neural network-based model that precisely preserves certain structures for the thermodynamic laws through the GENERIC formalism. An abstract error estimate is established, which provides a new loss formulation involving the Jacobian computation of autoencoder. The autoencoder and the latent dynamics are simultaneously trained to minimize the new loss. Computational examples demonstrate the effectiveness of tLaSDI, which exhibits robust generalization ability, even in extrapolation. In addition, an intriguing correlation is empirically observed between a quantity from tLaSDI in the latent space and the behaviors of the full-state solution.

Cheung, Siu Wun↗
DOE OSTI2017

Crystalline and Crystalline International Disposal Activities

This report presents the results of work conducted between September 2015 and July 2016 at Los Alamos National Laboratory in the crystalline disposal and crystalline international disposal work packages of the Used Fuel Disposition Campaign (UFDC) for DOE-NE’s Fuel Cycle Research and Development program. Los Alamos focused on two main activities during this period: Discrete fracture network (DFN) modeling to describe flow and radionuclide transport in complex fracture networks that are typical of crystalline rock environments, and a comprehensive interpretation of three different colloid-facilitated radionuclide transport experiments conducted in a fractured granodiorite at the Grimsel Test Site in Switzerland between 2002 and 2013. Chapter 1 presents the results of the DFN work and is divided into three main sections: (1) we show results of our recent study on the correlation between fracture size and fracture transmissivity (2) we present an analysis and visualization prototype using the concept of a flow topology graph for characterization of discrete fracture networks, and (3) we describe the Crystalline International work in support of the Swedish Task Force. Chapter 2 presents interpretation of the colloidfacilitated radionuclide transport experiments in the crystalline rock at the Grimsel Test Site.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1995

Visualization of unsteady computational fluid dynamics

The current computing environment that most researchers are using for the calculation of 3D unsteady Computational Fluid Dynamic (CFD) results is a super-computer class machine. The Massively Parallel Processors (MPP's) such as the 160 node IBM SP2 at NAS and clusters of workstations acting as a single MPP (like NAS's SGI Power-Challenge array) provide the required computation bandwidth for CFD calculations of transient problems. Work is in progress on a set of software tools designed specifically to address visualizing 3D unsteady CFD results in these super-computer-like environments. The visualization is concurrently executed with the CFD solver. The parallel version of Visual3, pV3 required splitting up the unsteady visualization task to allow execution across a network of workstation(s) and compute servers. In this computing model, the network is almost always the bottleneck so much of the effort involved techniques to reduce the size of the data transferred between machines.

Haimes, Robert↗
DOE OSTI2018

Advancing graph-based algorithms for predicting flow and transport in fractured rock

Discrete fracture network (DFN) models are a powerful alternative to continuum models for subsurface flow and transport simulations because they explicitly include fracture geometry and network topology, thereby allowing for better characterization of the latter's influence on flow and transport through fractured media. Recent advances in high performance computing have opened the door for flow and transport simulations in large explicit three-dimensional DFN, but this increase in model fidelity and system size comes at a huge computational cost because of the large number of mesh elements required to represent thousands of fractures (with sizes that can range several orders of magnitude, from mm to km). In most subsurface applications, fracture characteristics are only known statistically and numerous realizations are needed to bound uncertainty in flow and transport in the system, thereby exacerbating the computational burden. blackGraphs provide a simple and elegant way to characterize, query, and interrogate fracture network connectivity. We propose a DFN model reduction framework where various graph-representations are used in conjunction with high-fidelity DFN simulations to increase computational efficiency while retaining accuracy of key quantities of interest. The appropriate choice of a graph-representation, namely, which attributes of the DFN are to be represented as nodes and which ones as edges connecting those nodes, depends on the relevant scientific questions. We demonstrate that the proposed DFN model reduction framework provides an efficient means for DFN modeling through both system reduction of the DFN using graph-based properties and combining DFN and graph-based flow and transport simulations.

58 GEOSCIENCES↗
DOE OSTI2021

An Integrated Transmission-Distribution Co-Simulation for a Distribution System with High Renewable Penetration

Traditionally, transmission and distribution system dynamic simulations are performed independently. However, dynamic co-simulation of transmission-distribution systems can be beneficial in analyzing distribution systems with high penetration of distributed renewable resources. The authors describe a framework for conducting transmission-distribution dynamic co-simulation (TDDS). A model of a real network including detailed models of solar photovoltaic units in the distribution system is constructed. The initial operating point of the network is validated against field measurements. The TDDS tool is used to simulate a single line-to-ground fault on the distribution system, highlighting the need for conducting transmission-distribution co-simulations.

14 SOLAR ENERGY↗
DOE OSTI2019

Secondary Pore Network Design Optimization Analytical Diffusion Model for Lithium Ion Battery

Fast ion transport along the electrode thickness is required for Lithium ion battery (LIB) to sustain continuous high-rate discharge or charge currents, which is a desired feature for the fast charging of electric vehicle batteries. Advance in LIB electrode manufacturing allows to control, to a certain extent, the electrode microstructure architecture in order to improve the macroscopic performances of these devices. For instance, several groups have experimentally tested architectures tailored specifically to increase the through plane ionic diffusion in regards for fast charge application by adding channels or groove-like large pores oriented along the electrode thickness. These voids do not replace the existing pores of the electrode microstructure but consist in a second, coarser, pore network, called the secondary pore network (SPN). The model quantifies the impact of SPN on ionic diffusion with a composite electrode through a simple analytical approach, considering various pore channel geometries and comparing to standard electrodes with identical gravimetric and identical volumetric specific theoretical capacity. The goal is to find optimal SPN design parameters according to three optimization objectives aiming to balance the improved overall through-plane, thanks to the coarse aligned channels, and the degraded in-plane diffusion, due to the porous matrix densification required to maintain gravimetric and volumetric specific theoretical capacity. The analytical model must be considered as a pre-screening tool to quickly investigate large design space, that can be refined later with more complex electrochemical CPU-expensive numerical models.

Usseglio Viretta, Francois↗
DOE OSTI2024

Deep Image Prior Enabled Full Waveform Inversion (Final Technical Report)

MS Student Naveen Gupta worked on the problem of full waveform inversion (FWI) using neural networks as shown in Figure 1. Our goal was to learn a neural network to represent the subsurface velocity model, which when fed into the FWI module (implemented using a numerical forward model of wave equations) produces amplitude estimates that match with ground-truth observations of amplitude. We used neural networks to solve the inverse problem of estimating velocity distributions for a given seismic amplitude data such that, once trained, our neural network model can generate a distribution of velocity profiles for different random vectors fed as inputs to the neural network model.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

An Acoustic Emission and Acousto-Ultrasonic Analysis of Impact Damaged Composite Pressure Vessels

The research presented herein summarizes the development of acoustic emission (AE) and acousto-ultrasonic (AU) techniques for the nondestructive evaluation of filament wound composite pressure vessels. Vessels fabricated from both graphite and kevlar fibers with an epoxy matrix were examined prior to hydroburst using AU and during hydroburst using AE. A dead weight drop apparatus featuring both blunt and sharp impactor tips was utilized to produce a single known energy 'damage' level in each of the vessels so that the degree to which the effects of impact damage could be measured. The damage levels ranged from barely visible to obvious fiber breakage and delamination. Independent neural network burst pressure prediction models were developed from a sample of each fiber/resin material system. Here, the cumulative AE amplitude distribution data collected from low level proof test (25% of the expected burst for undamaged vessels) were used to measure the effects of the impact on the residual burst pressure of the vessels. The results of the AE/neural network model for the inert propellant filled graphite/epoxy vessels 'IM7/3501-6, IM7/977-2 and IM7/8553-45' demonstrated that burst pressures can be predicted from low level AE proof test data, yielding an average error of 5.0%. The trained network for the IM7/977-2 class vessels was also able to predict the expected burst pressure of taller vessels (three times longer hoop region length) constructed of the same material and using the same manufacturing technique, with an average error of 4.9%. To a lesser extent, the burst pressure prediction models could also measure the effects of impact damage to the kevlar/epoxy 'Kevlar 49/ DPL862' vessels. Here though, due to the higher attenuation of the material, an insufficient amount of AE amplitude information was collected to generate robust network models. Although, the worst case trial errors were less than 6%, when additional blind predictions were attempted, errors as high as 50% were produced. An acousto-ultrasonic robotic evaluation system (AURES) was developed for mapping the effects of damage on filament wound pressure vessels prior to hydroproof testing. The AURES injects a single broadband ultrasonic pulse into each vessel at preprogrammed positions and records the effects of the interaction of that pulse on the material volume with a broadband receiver. A stress wave factor in the form of the energy associated with the 750 to 1000 kHz and 1000 to 1250 kHz frequency bands were used to map the potential failure sites for each vessel. The energy map associated with the graphite/epoxy vessels was found to decrease in the region of the impact damage. The kevlar vessels showed the opposite trend, with the energy values increasing around the damage/failure sites.

Walker, James L.↗
NASA NTRS1983

Network simulation using the simulation language for alternate modeling (SLAM 2)

The simulation language for alternate modeling (SLAM 2) is a general purpose language that combines network, discrete event, and continuous modeling capabilities in a single language system. The efficacy of the system's network modeling is examined and discussed. Examples are given of the symbolism that is used, and an example problem and model are derived. The results are discussed in terms of the ease of programming, special features, and system limitations. The system offers many features which allow rapid model development and provides an informative standardized output. The system also has limitations which may cause undetected errors and misleading reports unless the user is aware of these programming characteristics.

Shen, S.↗
DOE OSTI2025

Coupled Lake‐Atmosphere‐Land Physics Uncertainties in a Great Lakes Regional Climate Model

Abstract This study develops a surrogate‐based method to assess the uncertainty within a convective permitting integrated modeling system of the Great Lakes region, arising from interacting physics parameterizations across the lake, atmosphere, and land surface. Perturbed physics ensembles of the model during the 2018 summer are used to train a neural network surrogate model to predict lake surface temperature (LST) and near‐surface air temperature (T2m). Average physics uncertainties are determined to be 1.5C for LST and T2m over land, and 1.9C for T2m over lake, but these have significant spatiotemporal variations. We find that atmospheric physics parameterizations alone are the dominant sources of uncertainty (45%–53%), while lake and land parameterizations account for 33% and 38% of the uncertainty of LST and T2m over land respectively. Interactions of atmosphere physics parameterizations with those of the land and lake contribute to an additional 13%–17% of the total variance. LST and T2m over the lake are more uncertain in the deeper northern lakes, particularly during the rapid warming phase that occurs in late spring/early summer. The LST uncertainty increases with sensitivity to the lake model's surface wind stress scheme. T2m over land is more uncertain over forested areas in the north, where it is most sensitive to the land surface model, than the more agricultural land in the south, where it is most sensitive to the atmospheric planetary boundary and surface layer scheme. Uncertainty also increases in the southwest during multiday temperature declines with higher sensitivity to the land surface model.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1980

Video distribution system cost model

A cost model that can be used to systematically identify the costs of procuring and operating satellite linked communications systems is described. The user defines a network configuration by specifying the location of each participating site, the interconnection requirements, and the transmission paths available for the uplink (studio to satellite), downlink (satellite to audience), and voice talkback (between audience and studio) segments of the network. The model uses this information to calculate the least expensive signal distribution path for each participating site. Cost estimates are broken downy by capital, installation, lease, operations and maintenance. The design of the model permits flexibility in specifying network and cost structure.

Gershkoff, I.↗