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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-Shot Transfer the Dynamic Response of Networked Systems

This article develops a deep graph operator network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e.g., the power grid or traffic) with an underlying subgraph structure. Here, we build our DeepGraphONet by fusing the ability of graph neural networks to exploit spatially correlated graph information and deep operator networks to approximate the solution operator of dynamical systems. The resulting DeepGraphONet can then predict the dynamics within a given short/medium-term time horizon by observing a finite history of the graph state information. Furthermore, we design our DeepGraphONet to be resolution independent. That is, we do not require the finite history to be collected at the exact/same resolution. In addition, to disseminate the results from a trained DeepGraphONet, we design a zero-shot learning strategy that enables using it on a different subgraph. Finally, empirical results on the transient stability prediction problem of power grids and traffic flow forecasting problem of a vehicular system illustrate the effectiveness of the proposed DeepGraphONet.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Application of nitrogen plasma immersion ion implantation to titanium nasal implants with nanonetwork surface structure

In nasal reconstruction, the response of cells to titanium (Ti) implants is mainly determined by surface features of the implant. In a pilot study, the authors applied electrochemical anodization to Ti surfaces in an alkaline solution to create a network of nanoscale surface structures. This nanonetwork was intended to enhance the responses of primary human nasal epithelial cell (HNEpC) to the Ti surface. In this study, the authors then treated the anodized, nanonetwork-structured Ti surface using nitrogen plasma immersion ion implantation (NPIII) in order to further improve the HNEpC response to the Ti surface. Subsequently, surface characterization was performed to elucidate morphology, roughness, wettability, and chemistry of specimens. Cytotoxicity, blood, and HNEpC responses were also evaluated. Our results demonstrate that NPIII treatment led to the formation of a noncytotoxic TiN-containing thin film (thickness <100 nm) on the electrochemically anodized Ti surface with a nanonetwork-structure. NPIII treatment was shown to improve blood clotting and the adhesion of platelets to the anodized Ti surface as well as the adhesion and proliferation of hNEpC. This research spreads our understanding of the fact that a TiN-containing thin film, produced using NPIII treatment, could be used to improve blood and HNEpC responses to anodized, nanonetwork-structured Ti surfaces in nasal implant applications.

42 ENGINEERING↗
DOE OSTI2020

Physics-informed neural networks for inverse problems in nano-optics and metamaterials

In this paper, we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials and nano-optics technologies. In particular, we successfully apply mesh-free PINNs to the difficult task of retrieving the effective permittivity parameters of a number of finite-size scattering systems that involve many interacting nanostructures as well as multi-component nanoparticles. Our methodology is fully validated by numerical simulations based on the finite element method (FEM). The development of physics-informed deep learning techniques for inverse scattering can enable the design of novel functional nanostructures and significantly broaden the design space of metamaterials by naturally accounting for radiation and finite-size effects beyond the limitations of traditional effective medium theories.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Quantum Graph Analysis

In recent years, advanced network analytics have become increasingly important to na- tional security with applications ranging from cyber security to detection and disruption of ter- rorist networks. While classical computing solutions have received considerable investment, the development of quantum algorithms to address problems, such as data mining of attributed relational graphs, is a largely unexplored space. Recent theoretical work has shown that quan- tum algorithms for graph analysis can be more efficient than their classical counterparts. Here, we have implemented a trapped-ion-based two-qubit quantum information proces- sor to address these goals. Building on Sandia's microfabricated silicon surface ion traps, we have designed, realized and characterized a quantum information processor using the hyperfine qubits encoded in two 171 Yb + ions. We have implemented single qubit gates using resonant microwave radiation and have employed Gate set tomography (GST) to characterize the quan- tum process. For the first time, we were able to prove that the quantum process surpasses the fault tolerance thresholds of some quantum codes by demonstrating a diamond norm distance of less than 1 . 9 x 10 [?] 4 . We used Raman transitions in order to manipulate the trapped ions' motion and realize two-qubit gates. We characterized the implemented motion sensitive and insensitive single qubit processes and achieved a maximal process infidelity of 6 . 5 x 10 [?] 5 . We implemented the two-qubit gate proposed by Molmer and Sorensen and achieved a fidelity of more than 97 . 7%.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Resilient Grid Operational Strategies

Extreme weather-related disturbances, such as hurricanes, are a leading cause of grid outages historically. Although physical asset hardening is perhaps the most common way to mitigate the impacts of severe weather, operational strategies may be deployed to limit the extent of societal and economic losses associated with weather-related physical damage.1 The purpose of this study is to examine bulk power-system operational strategies that can be deployed to mitigate the impact of severe weather disruptions caused by hurricanes, thereby increasing grid resilience to maintain continuity of critical infrastructure during extreme weather. To estimate the impacts of resilient grid operational strategies, Los Alamos National Laboratory (LANL) developed a framework for hurricane probabilistic risk analysis (PRA). The probabilistic nature of this framework allows us to estimate the probability distribution of likely impacts, as opposed to the worst-case impacts. The project scope does not include strategies that are not operations related, such as transmission system hardening (e.g., undergrounding, transmission tower reinforcement and substation flood protection) and solutions in the distribution network.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Overlapping Domain Coupling of Multidimensional and System Codes in NEAMS - Pronghorn and SAM

This report describes the implementation and testing of domain-overlapping coupling of the Pronghorn and SAM codes. Both Pronghorn and SAM are codes developed by the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program for analysis of advanced nuclear reactors. Pronghorn focuses on analyzing multi-dimensional core flow conditions, while SAM focuses on analysis of the entire system including, among other components, piping, pumps, and heat exchangers. It is desirable for many advanced reactor thermal-hydraulics simulations to couple Pronghorn and SAM to obtain self-consistent solutions in the core and the system. The domain-overlapping coupling approach provides a robust and numerically efficient candidate for this coupling. We describe the algorithm for obtaining self-consistent solutions between a SAM network of flow channels and an overlapped multi-dimensional Pronghorn domain. The algorithm is tested for a variety of simple canonical test problems and a model of the Molten Salt Reactor Experiment (MSRE). Results demonstrate that the domain overlapping coupling approach provides consistent pressure, mass flow rate, enthalpy, and passive scalar distributions between Pronghorn and SAM and converges reliably and efficiently (at most 15 iterations, usually much less than 10) for the considered test problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Kennedy Space Center network documentation system

The Kennedy Space Center Network Documentation System (KSC NDS) is being designed and implemented by NASA and the KSC contractor organizations to provide a means of network tracking, configuration, and control. Currently, a variety of host and client platforms are in use as a result of each organization having established its own network documentation system. The solution is to incorporate as many existing 'systems' as possible in the effort to consolidate and standardize KSC-wide documentation.

Lohne, William E.↗
DOE OSTI2013

Demand-Side Response from Industrial Loads

Through a research study funded by the Department of Energy, Smart Grid solutions company ENBALA Power Networks along with the Oak Ridge National Laboratory (ORNL) have geospatially quantified the potential flexibility within industrial loads to leverage their inherent process storage to help support the management of the electricity grid. The study found that there is an excess of 12 GW of demand-side load flexibility available in a select list of top industrial facilities in the United States. Future studies will expand on this quantity of flexibility as more in-depth analysis of different industries is conducted and demonstrations are completed.

demand response↗
DOE OSTI2018

NREL + Verizon Wireless

Verizon Wireless and the National Renewable Energy Laboratory (NREL) are working to develop and validate a new design solution for telecommunications facilities. Network equipment is all DC, so rather than relying on conventional AC power, the design will employ photovoltaics (PV) and batteries, and it will require new DC cooling systems. The new design will ease utility interconnection by connecting PV to the DC bus and batteries, increase resiliency by allowing PV to contribute - and cooling to continue - in a power outage, lower costs with day-to-day utility cost savings, and increase efficiency through reduced part count and fewer power conversions from DC to AC.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Thermodynamic Modeling of Complex Solid Solutions in the Lu-H-N System via Graph Neural Network Accelerated Monte Carlo Simulations

Metal hydrides are important across diverse applications, such as hydrogen storage, batteries, gas sensors, nuclear reactions, and high-temperature superconductivity. Previous computational studies of metal hydrides under extreme pressures, e.g., 𝑂⁡(10 2 ) ⁢GPa, usually treat them as stoichiometric compounds without considering interstitial lattice disorder. As pressures become more moderate in the 𝑂⁡(10 0 ) ⁢GPa and below range, hydrogen disorder at interstitial lattice sites becomes prominent, e.g., in the N-doped Lu hydride that was recently claimed superconducting near 1 GPa. Further adding compositional complexity from alloying and/or multielement interstitial occupation makes elucidating pressure- and temperature-dependent observables intractable by first-principles calculations alone. We therefore propose a lattice graph neural-network surrogate modeling approach to predict configuration- and pressure-dependent equation-of-state properties. Their efficiency permits Monte Carlo simulations to calculate Gibbs energies and pressure-dependent phase diagrams, thereby revealing insights into the synthesis conditions required for achieving desired phase equilibria. We demonstrate this concept for the compositionally complex cubic Lu(H,N,Va) 3 system where three constituents (hydrogen, nitrogen and vacancy) have disordered multielement interstitial occupancies and insights into pressure-dependent phase equilibria are critically needed, e.g., N-doping levels can significantly lower dehydrogenation temperatures and provide a new strategy to optimize hydrogen-storage alloys. This work can improve the thermodynamic understanding of the Lu-H-N system and help rational synthesis of N-doped Lu hydrides, but more generally demonstrates an efficient approach to model pressure-dependent thermodynamics of multicomponent solid solutions.

Monte Carlo methods↗
NASA NTRS1972

On implicit equations in nonlinear network analysis.

Derivation of conditions for existence of unique solutions to implicit equations of the form y = f(x, u) or x = h(x, u) which are frequently encountered in the analysis of nonlinear networks. The existence of such solutions is demonstrated by applying the principle of contraction mapping and by ascertaining the possibility of a functional inversion according to the Palais (1959) theorem.

Sathe, S. T.↗
DOE OSTI2026

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max↗
DOE OSTI2022

Determination of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids and Deep Eutectic Solvents by Factorization-Machine-Based Neural Networks

Widely known as “green solvents,” ionic liquids (ILs) and deep eutectic solvents (DESs) have been used as substitutes for traditional organic solvents in separation science. To achieve better separations using ILs and DESs, this work aimed to predict the infinite dilution activity coefficients (IDACs) of molecular solutes in these solvents with the state-of-the-art factorization-machine-based neural network (DeepFM). DeepFM combines the benefits of factorization machines and deep neural networks to learn both low-order and high-order interactions among features. The IDAC prediction model was established with 52,372 experimental IDAC datapoints including 260 solvents (252 ILs and 8 DESs) and 112 molecular solutes collected at various temperatures from 288.15 to 428.15 K. Chemical information describing the ILs, DESs, and molecular solutes was included in the IDAC prediction model, including chemical functional groups, molecular weights, and Abraham solvation parameters. The IDAC prediction model showed an improved accuracy compared with alternative models; additionally, DESs were included in the IDAC prediction model for the first time. Here, the model will reduce the energy and resources needed to optimize the selection of ILs and DESs for specific separations, which will promote the development of these green solvents for sustainable chemical processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Augmented Topological Descriptors of Pore Networks for Material Science

One potential solution to reduce the concentration of carbon dioxide in the atmosphere is the geologic storage of captured CO 2 in underground rock formations, also known as carbon sequestration. There is ongoing research to guarantee that this process is both efficient and safe. We describe tools that provide measurements of media porosity, and permeability estimates, including visualization of pore structures. Existing standard algorithms make limited use of geometric information in calculating permeability of complex microstructures. This quantity is important for the analysis of biomineralization, a subsurface process that can affect physical properties of porous media. This paper introduces geometric and topological descriptors that enhance the estimation of material permeability. Our analysis framework includes the processing of experimental data, segmentation, and feature extraction and making novel use of multiscale topological analysis to quantify maximum flow through porous networks. We illustrate our results using synchrotron-based X-ray computed microtomography of glass beads during biomineralization. As a result, we also benchmark the proposed algorithms using simulated data sets modeling jammed packed bead beds of a monodispersive material.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Routing-as-a-Service (RaaS): A Framework for Tenant-Directed Route Control in Data Center

In a multi-tenant data center environment, the current paradigm for route control customization involves a labor-intensive ticketing process where tenants submit route control requests to the landlord. This results in tight coupling between tenants and the landlord, extensive human resource deployment, and long ticket resolution time. We propose Routing-as-a-Service (RaaS), a framework for tenant-directed route control in data centers. Here, we show that RaaS-based implementation provides a route control platform where multiple tenants can perform route control independently with little administrative involvement, and the landlord can set the overall network policies. RaaS-based solutions can run on commercial off-the-shelf (COTS) hardware and leverage existing technologies, so it can be implemented in existing networks without major infrastructural overhaul. We present the design of RaaS, introduce its components, and evaluate a prototype based on RaaS.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Application of the Global Positioning System to crustal deformation measurement. II - The influence of errors in orbit determination networks

The errors introduced into GPS measurements of a geodetic network by means of adopting different sets of stations in central and southern California as fixed points (termed fiducial stations) were investigated. Solutions for a crustal deformation network spanning distances up to 350 km were computed with a variety of fiducial networks, using fiducial coordinates determined from VLBI. Results showed that three receivers collocated with VLBI sites provide a stable reference system at a few millimeter level in the horizontal components and at 10 mm in the vertical components, indicating that local survey errors between VLBI and GPS markers at fiducial sites do not severely affect the estimates of interstation vectors.

Larson, Kristine M.↗