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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Branched tube network and temperature regulating garment with branched tube network

In an embodiment of the present disclosure, a garment for regulating a temperature of a wearer is provided. The garment includes a fabric configured to be worn by the wearer. The fabric includes channels parallel to a primary surface of the fabric. The garment includes a branched tube network for circulating a working fluid. The branched tube network is disposed in the channels of the fabric. The branched tube network includes a plurality of tubes wherein at least one end of each tube of the plurality of tubes is branched and connected to two daughter tubes, or more, the connection having a branch angle of between 1 and 359 degrees, inclusive. The tube network has at least two levels of branches.

Fan, Jintu↗
DOE OSTI2017

Analytical transport network theory to guide the design of 3-D microstructural networks in energy materials: Part 2. Flow with reactions

Here we extend the fully analytical, heuristic “Analytical Transport Network Model” for steady-state, diffusive flow in a 3-D network to account for surface reactions. In the limit of negligible reactions, the model reduces to the conserved flow solution. The extension does not increase the time required to run the model, which in Part 1 was shown to be 0.5–1.5 and 5–6 orders of magnitude faster than electrochemical fin (ECF) theory and finite element analysis (FEA) for conserved flow, respectively. The model is compared to reacting-flow ECF and FEA as well as to experiments and is demonstrated as a potentially useful heuristic for understanding the influence of morphology and topology on reactive-diffusive flow through a 3-D microstructural network.

25 ENERGY STORAGE↗
DOE OSTI2022

Stability Analysis of Substituted Cobaltocenium [Bis(cyclopentadienyl)cobalt(III)] Employing Chemistry-Informed Neural Networks

Cobaltocenium derivatives are promising components of the anion exchange membranes due to their excellent thermal and alkaline stability under the operating conditions of a fuel cell. Here we present an efficient modeling approach of assessing the chemical stability of substituted cobaltocenium CoCp 2 + based on the computed electronic structure enhanced by machine learning techniques. Within the aqueous environment, the positive charge of the metal cation is balanced by the hydroxide anion through formation of the CoCp 2 + OH¯ complexes, whose dissociation is studied within the implicit solvent employing density functional theory. The data set of about 118 species based on 42 substituent groups characterized by a range of electron- donating (ED) and electron-withdrawing (EW) properties is constructed and analyzed. Given 12 carefully chosen chemistry-informed descriptors of the complexes and relevant fragments, the stability of the complexes is found to strongly correlate with the energies of the highest occupied and lowest unoccupied molecular orbitals, modulated by a switching function of the Hirshfeld charge. The latter is used as a measure of the electron withdrawing-donating character of the substituents. Based on this observation from the conventional regression analysis, two fully connected, feed-forward neural network (FNN) models with different unit structures, called the chemistry-informed (CINN) and the quadratic (QNN) neural networks, are developed. Both models predict the bond dissociation energies of the cobaltocenium complexes with mean relative errors less than 5.40% and average absolute errors less than 0.94 kcal/mol. The results show the potential of QNN to efficiently capture more complex relationships. Here, the concept of incorporating the domain (chemical) knowledge/insight into the neural network structure paves the way to applications of machine learning techniques with small data sets, ultimately leading to better predictive models compared to the conventional regression analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

On the closedness and geometry of tensor network state sets

Tensor network states (TNS) are a powerful approach for the study of strongly correlated quantum matter. The curse of dimensionality is addressed by parametrizing the many-body state in terms of a network of partially contracted tensors. These tensors form a substantially reduced set of effective degrees of freedom. In practical algorithms, functionals like energy expectation values or overlaps are optimized over certain sets of TNS. Concerning algorithmic stability, it is important whether the considered sets are closed because, otherwise, the algorithms may approach a boundary point that is outside the TNS set and tensor elements diverge. Here we discuss the closedness and geometries of TNS sets, and we propose regularizations for optimization problems on non-closed TNS sets. We show that sets of matrix product states (MPS) with open boundary conditions, tree tensor network states, and the multiscale entanglement renormalization ansatz are always closed, whereas sets of translation-invariant MPS with periodic boundary conditions (PBC), heterogeneous MPS with PBC, and projected entangled pair states are generally not closed. The latter is done using explicit examples like the W state, states that we call two-domain states, and fine-grained versions thereof.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Neural Network Water Model Based on the MB-Pol Many-Body Potential

The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor–liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. Here, in order to address this limitation, we developed a “deep potential” neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor–liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.

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

A Mixed integer linear programming‐based distributed energy management for networked microgrids considering network operational objectives and constraints

Abstract Mixed integer linear programming (MILP)–based distributed energy management for networked microgrids embedded modern distribution systems is proposed. Considering the diverse ownership of microgrids, distributed energy resources (DERs) that interface directly with utilities and responsive loads, an alternating direction method of multipliers–based distributed framework was formulated for the scheduling of networked microgrids embedded modern distribution systems by adjusting nodal price signals iteratively. In addition, to make the formulated optimization problems resolvable through more accessible and popular MILP solvers, different linearisation techniques were employed to transform the nonlinear terms into linear or mixed integer linear formats. The proposed MILP‐based distributed method preserves all participants' autonomy (e.g., microgrids, DERs that interface directly with utilities and responsive loads), while incentivising them to actively participate in the distribution system operation with price signals. The proposed method is validated with results of numerical simulation using a modern distribution system consisting of multiple networked microgrids, DERs that interface directly with utilities, as well as responsive loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Thread/OpenThread: A Compromise in Low-Power Wireless Multihop Network Architecture for the Internet of Things

Extending an Internet subnet by connecting resource-constrained nodes (e.g., embedded sensors and actuators) over multiple wireless hops is necessary to support the future Internet of Things (IoT). RPL, the IPv6 routing standard for low-power and lossy networks, tried to achieve this goal but has not seen wide adoption in practice. As an alternative, Thread is a recently standardized low-power network protocol for IoT, driven by the Thread group, an industry consortium led by Google/Nest. Here, we provide a comparative analysis of the technical aspects of RPL and Thread based on their specifications, explaining why using Thread, as opposed to RPL, may make sense for the future Internet. Specifically, the fundamental differences between RPL and Thread are their respective scopes and multihop network architectures, which result in Thread's unique design and advantages over RPL. Lastly, we evaluate Thread in an indoor multihop wireless testbed using OpenThread, an official open source implementation of Thread. This work serves as the first analysis of the Thread protocol in academia.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2017

A symmetric address translation approach for a network layer moving target defense to secure power grid networks

This paper will suggest a robust method for a network layer Moving Target Defense (MTD) using symmetric packet scheduling rules. The MTD is implemented and tested on a Supervisory Control and Data Acquisition (SCADA) network testbed. This method is shown to be efficient while providing security benefits to the issues faced by the static nature of SCADA networks. The proposed method is an automated tool that may provide defense in depth when be used in conjunction with other MTDs and traditional security devices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2016

NASA Near Earth Network (NEN) and Space Network (SN) Support of CubeSat Communications

There has been a historical trend to increase capability and drive down the Size, Weight and Power (SWAP) of satellites and that trend continues today. NASA scientists and engineers across many of NASAs Mission Directorates and Centers are developing exciting CubeSat concepts and welcome potential partnerships for CubeSat endeavors. From a Telemetry, Tracking and Command (TTC) Systems and Flight Operations for Small Satellites point of view, small satellites including CubeSats are a challenge to coordinate because of existing small spacecraft constraints, such as limited SWAP and attitude control, and the potential for high numbers of operational spacecraft. The NASA Space Communications and Navigation (SCaN) Programs Near Earth Network (NEN) and Space Network (SN) are customer driven organizations that provide comprehensive communications services for space assets including data transport between a missions orbiting satellite and its Mission Operations Center (MOC). This paper presents how well the SCaN networks, SN and NEN, are currently positioned to support the emerging small small satellite and CubeSat market as well as planned enhancements for future support.

Satellite↗
DOE OSTI2025

Quantum Wrapper Networking for Entanglement Management and Control in Transparent Optical Quantum Networks

The overarching goal of this three-year project was to design and experimentally demonstrate a quantum wrapper networking (QWN) protocol and architecture for entanglement management and control in coexisted classical-quantum networks, facilitating a global quantum Internet. In QWN, the quantum payload (qubits) is wrapped with classical bits as a header. The classical bits facilitate end-to-end transport of the quantum payload without having to read or alter the quantum data payload at intermediate switches and routers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Eulerian network modeling of longitudinal dispersion: EULERIAN NETWORK MODELING OF DISPERSION

A novel Eulerian network model that incorporates “shear dispersion,” the stretching of solute due to nonuniform velocity profiles within pore throats is developed. The superposing transport method (STM) is nonlocal in time (i.e., uses information from several previous time steps) and is equivalent to performing network-wide time convolutions of elementary throat response functions. Predicted macroscopic longitudinal dispersion coefficients for disordered sphere packs are in good agreement with published experimental data. We further investigate the impact of mixing assumptions within pores on macroscopic longitudinal dispersion and find the dependence to be weak for disordered sphere packs. Limitations of Eulerian network models as a whole are also discussed, and their inappropriateness for ordered porous media concluded.

58 GEOSCIENCES↗
DOE OSTI2025

Advancing the understanding of coastal disturbances with a network-of-networks approach

Coastal ecosystems are at the nexus of many high priority challenges in environmental sciences, including predicting the influences of compounding disturbances exacerbated by climate change on biogeochemical cycling. Extreme events such as hurricanes, flooding, landslides, and wildfires influence biogeochemical cycling in these systems. However, while research in coastal science is fundamentally transdisciplinary – as drivers of biogeochemical and ecological processes often span scientific and environmental domains – traditional place-based approaches are still often employed to understand coastal ecosystems. In this perspective, we argue that integration among distributed research sites from a macrosystem perspective is crucial to understand how compounding disturbances affect coastal ecosystems. We identify a roadmap for the implementation of an integrated network-of-networks framework that leverages existing research network sites in coastal ecosystems to advance continental-scale process understanding for studying extreme events and global change. We also identify specific ways that existing research efforts can maximize mutual benefit, and where additional infrastructure investments might increase return-on-investment along the coast, using the coastal continental US as a case study.

Myers-Pigg, Allison N. [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2016

The Role of National Research and Education Networks Providing Connectivity and Advanced Network Services to Virtual Communities in Collaborative R&E Projects. CUDI: The Mexican Case Part 1, , In: ISUM 2015: High Performance Computer Applications

This paper describes the role of National Research and Education Networks (NRENs) and their aim to provide a seamless, large scale, high-speed, low-latency network to accelerate scientific and education discovery for national, regional or global collaborative projects that are dependent on the use of modern high performance distributed computing systems (HPDCS) to process mathematical models, produce new knowledge and drive innovation when resolving complex problems. NRENs have become the mainstream of communication and collaboration to integrate the efforts of large geographically distributed teams of Virtual Communities (VC) that - train, teach and learn-, -create, use and share- knowledge, data, information and communications technology resources (e.g. computer and information systems, middleware, tools, instruments, data sets, repositories, applications and services) building e-infrastructures necessary to meet the collaborative projects’ goals. The paper focuses on the current status of the Mexican NREN: CUDI, its future organization and political support, necessary to provide seamless, large scale, high-speed, low latency and secure national backbone and international links providing connectivity, advanced network services and technical support to VCs and their e-infrastructures in collaborative R&E projects within the country, the LAC region and worldwide.

Computer Science↗