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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

The Way to the Future Has Already Started: ICAO Aeronautical Telecommunication Network (ATN) Using Internet Protocol Suite (IPS) Standards and Protocol Evolution Update

Millions of people are able to exchange information over phones, computers, tablets and an array of new interconnected devices such as sensors, cameras, home appliances and others are increasingly becoming networked. At the core of this sizeable interconnection is a set of underlying protocols that enable millions of devices to seamlessly talk to each other. Aviation, as many other industries have, utilizes the internet protocol for ground-to-ground network communications and efforts are underway to bring it to the cockpit.In 2015, the International Civil Aviation Organization (ICAO) published the second edition of Document 9896 (Doc 9896) titled: Manual on the Aeronautical Telecommunication Network (ATN) using Internet Protocol Suite (IPS) Standards and Protocol. This manual adopted the Internet protocol version 6 (IPv6) for Internet layer interoperability. Document 9896 defined data communications protocols and services to be used for implementing aeronautical telecommunication network (ATN) using the Internet protocol suite (IPS). The document provided technical specifications that addressed security, network and transport protocols, described applications, supported by ATN/IPS and provided communications guidance. The international aviation community working under the guidance of ICAO initiated work on updates to Doc 9896. Under the ICAO Communication Panel, Working Group Internetworking (WG-I) got this challenging task. Key areas of work for WG-I are related to ATN/IPS Security and ANT/IPS Mobility. Two sub-groups have been assembled to work on these elements. The ICAO anticipates the release of updated Doc 9896 in 2020. Additional ATN/IPS standardization efforts include Radio Technical Commission for Aeronautics Special Committee 223 (RTCA SC-223) IPS profile development, Airlines Electronic Engineering Committee (AEEC) A658 Roadmap of IPS future activities and European Organization for Civil Aviation Equipment (EUROCAE) WG-108 development of guidance documentation.This paper describes ICAO WG-I approach to update key areas of the document in closed coordination with AEEC, RTCA and EUROCAE committees supporting the development of ATN/IPS. Additionally, the paper will explore WG-I mobility solutions under investigation, security developments, IPv6 addressing challenges and other proposed updates. Finally, as new operational expectations such as Free Route and Remote Piloted Aircraft System integration are currently pushing the boundaries set by old operational concepts, the paper identifies the emerging new challenges that will drive the ATN/IPS development beyond 2020. They are related to future voice utilization, System Wide Information Management and Remotely Piloted Aircraft System deployment. IPS systems are seen as the only way forward.

Apaza, Rafael↗
NASA NTRS2000

Weather Support to the NASA Deep Space Network

This paper outlines the evolution and development of SMG weather support and describes the Goldstone forecast production currently being disseminated as well as the next generation forecast product planned for future implementation.

weather↗
NASA NTRS1989

Dynamical evolution of domain walls in an expanding universe

Whenever the potential of a scalar field has two or more separated, degenerate minima, domain walls form as the universe cools. The evolution of the resulting network of domain walls is calculated for the case of two potential minima in two and three dimensions, including wall annihilation, crossing, and reconnection effects. The nature of the evolution is found to be largely independent of the rate at which the universe expands. Wall annihilation and reconnection occur almost as fast as causality allows, so that the horizon volume is 'swept clean' and contains, at any time, only about one, fairly smooth, wall. Quantitative statistics are given. The total area of wall per volume decreases as the first power of time. The relative slowness of the decrease and the smoothness of the wall on the horizon scale make it impossible for walls to both generate large-scale structure and be consistent with quadrupole microwave background anisotropy limits.

Press, William H.↗
DOE OSTI2022

Photometric redshifts from SDSS images with an interpretable deep capsule network

ABSTRACT Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on ugriz images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of ∼400 000 Sloan Digital Sky Survey galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets (r ≤ 17.8 and zspec ≤ 0.4) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a two-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g. size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g. magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Gravitational Waves and Proton Decay: Complementary Windows into Grand Unified Theories

Proton decay is a smoking gun signature of grand unified theories (GUTs). Searches by Super-Kamiokande have resulted in stringent limits on the GUT symmetry-breaking scale. The large-scale multipurpose neutrino experiments DUNE, Hyper-Kamiokande, and JUNO will either discover proton decay or further push the symmetry-breaking scale above 10 16 GeV. Another possible observational consequence of GUTs is the formation of a cosmic string network produced during the breaking of the GUT to the standard model gauge group. The evolution of such a string network in the expanding Universe produces a stochastic background of gravitational waves which will be tested by a number of gravitational wave detectors over a wide frequency range. We demonstrate the nontrivial complementarity between the observation of proton decay and gravitational waves produced from cosmic strings in determining SO(10) GUT-breaking chains. We show that such observations could exclude SO(10) breaking via flipped SU(5) × U(1) or standard SU(5), while breaking via a Pati-Salam intermediate symmetry, or standard SU(5) × U(1), may be favored if a large separation of energy scales associated with proton decay and cosmic strings is indicated. We note that recent results by the NANOGrav experiment have been interpreted as evidence for cosmic strings at a scale of ~10 14 GeV. This would strongly point toward the existence of GUTs, with SO(10) being the prime candidate. We show that the combination with already available constraints from proton decay allows us to identify preferred symmetry-breaking routes to the standard model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

How production networks amplify economic growth

Technological improvement is the most important cause of long-term economic growth. In standard growth models, technology is treated in the aggregate, but an economy can also be viewed as a network in which producers buy goods, convert them to new goods, and sell the production to households or other producers. We develop predictions for how this network amplifies the effects of technological improvements as they propagate along chains of production, showing that longer production chains for an industry bias it toward faster price reduction and that longer production chains for a country bias it toward faster growth. These predictions are in good agreement with data from the World Input Output Database and improve with the passage of time. The results show that production chains play a major role in shaping the long-term evolution of prices, output growth, and structural change.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Structural characterization and computational analysis of PDZ domains in Monosiga brevicollis

Abstract Identification of the molecular networks that facilitated the evolution of multicellular animals from their unicellular ancestors is a fundamental problem in evolutionary cellular biology. Choanoflagellates are recognized as the closest extant nonmetazoan ancestors to animals. These unicellular eukaryotes can adopt a multicellular‐like “rosette” state. Therefore, they are compelling models for the study of early multicellularity. Comparative studies revealed that a number of putative human orthologs are present in choanoflagellate genomes, suggesting that a subset of these genes were necessary for the emergence of multicellularity. However, previous work is largely based on sequence alignments alone, which does not confirm structural nor functional similarity. Here, we focus on the PDZ domain, a peptide‐binding domain which plays critical roles in myriad cellular signaling networks and which underwent a gene family expansion in metazoan lineages. Using a customized sequence similarity search algorithm, we identified 178 PDZ domains in the Monosiga brevicollis proteome. This includes 11 previously unidentified sequences, which we analyzed using Rosetta and homology modeling. To assess conservation of protein structure, we solved high‐resolution crystal structures of representative M. brevicollis PDZ domains that are homologous to human Dlg1 PDZ2, Dlg1 PDZ3, GIPC, and SHANK1 PDZ domains. To assess functional conservation, we calculated binding affinities for mbGIPC, mbSHANK1, mbSNX27, and mbDLG‐3 PDZ domains from M. brevicollis . Overall, we find that peptide selectivity is generally conserved between these two disparate organisms, with one possible exception, mbDLG‐3. Overall, our results provide novel insight into signaling pathways in a choanoflagellate model of primitive multicellularity.

Gao, Melody↗
NASA NTRS1991

The data distribution satellite system

The Data Distributed Satellite (DDS) will be capable of providing the space research community with inexpensive and easy access to space payloads and space data. Furthermore, the DDS is shown to be a natural outgrowth of advances and evolution in both NASA's Space Network and commercial satellite communications. The roadmap and timescale for this evolution is described along with key demonstrations, proof-of-concept models, and required technology development that will support the projected system evolution toward the DDS.

Bruno, Ronald C.↗
NASA NTRS2002

Molnets: An Artificial Chemistry Based on Neural Networks

The fundamental problem in the evolution of matter is to understand how structure-function relationships are formed and increase in complexity from the molecular level all the way to a genetic system. We have created a system where structure-function relationships arise naturally and without the need of ad hoc function assignments to given structures. The idea was inspired by neural networks, where the structure of the net embodies specific computational properties. In this system networks interact with other networks to create connections between the inputs of one net and the outputs of another. The newly created net then recomputes its own synaptic weights, based on anti-hebbian rules. As a result some connections may be cut, and multiple nets can emerge as products of a 'reaction'. The idea is to study emergent reaction behaviors, based on simple rules that constitute a pseudophysics of the system. These simple rules are parameterized to produce behaviors that emulate chemical reactions. We find that these simple rules show a gradual increase in the size and complexity of molecules. We have been building a virtual artificial chemistry laboratory for discovering interesting reactions and for testing further ideas on the evolution of primitive molecules. Some of these ideas include the potential effect of membranes and selective diffusion according to molecular size.

Colombano, Silvano↗
DOE OSTI2023

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.

43 PARTICLE ACCELERATORS↗
NASA NTRS2001

Porosity and Variations in Microgravity Aerogel Nano-Structures: New Laser Speckle Characterization Methods - 2

The transition from sol to gel is a process that is critical to the properties of engineered nanomaterials, but one with few available techniques for observing the dynamic processes occurring during the evolution of the gel network. Specifically, the observation of various cluster aggregation models, such as diffusion-limited and reaction-limited cluster growth can be quite difficult. This can be rather important as the actual aggregation model can dramatically influence the mechanical properties of gels, and is significantly affected by the presence of convective flows, or their absence in microgravity. We have developed two new non-intrusive optical methods for observing the aggregation processes within gels in real time. These make use of the dynamic behavior of laser speckle patterns produced when an intense laser source is passed through a gelling sol. The first method is a simplified time-correlation measurement, where the speckle pattern is observed using a CCD camera and information on the movement of the scattering objects is readily apparent. This approach is extremely sensitive to minute variations in the flow field as the observed speckle pattern is a diffraction-based image, and is therefore sensitive to motions within the sol on the order of the wavelength of the probing light. Additionally, this method has proven useful in determining a precise time for the gel-point, an event often difficult to measure. Monitoring the evolution of contrast within the speckle field is another method that has proven useful for studying aeration. In this case, speckle contrast is dependent upon the size (correlation length) and number of scattering centers, increasing with increasing size, and decreasing with increasing numbers. The dynamic behavior of cluster growth in gels causes both of these to change simultaneously with time, the exact rate of which is determined by the specific aggregation model involved. Actual growth processes can now be observed, and the effects of varying gravity fields on the growth processes qualitatively described. Results on preliminary ground-based measurements have been obtained.

Hunt, A. J.↗
DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Dispersed Nickel Phthalocyanine Molecules on Carbon Nanotubes as Cathode Catalysts for Li-CO 2 Batteries

The Li-CO 2 battery has great potential for both CO 2 utilization and energy storage, but its practical application is limited by low energy efficiency and short cycle life. Efficient cathode catalysts are needed to address this issue. Herein, this work reports on molecularly dispersed electrocatalysts (MDEs) of nickel phthalocyanine (NiPc) anchored on carbon nanotubes (CNTs) as the cathode catalyst for Li-CO 2 batteries. The dispersed NiPc molecules efficiently catalyze CO 2 reduction, while the conductive and porous CNTs networks facilitate CO 2 evolution reaction, leading to enhanced discharging and charging performance compared to the NiPc and CNTs mixture. Octa-cyano substitution on NiPc (NiPc-CN) further enhances the interaction between the molecule and CNTs, resulting in better cycling stability. The Li-CO 2 battery with the NiPc-CN MDE cathode shows a high discharge voltage of 2.72 V and a small discharging–charging potential gap of 1.4 V, and can work stably for over 120 cycles. The reversibility of the cathode is confirmed by experimental characterizations. Finally, this work lays a foundation for the development of molecular catalysts for Li-CO 2 battery cathodes.

25 ENERGY STORAGE↗
DOE OSTI2022

Glycerol Binding at the Narrow Channel of Photosystem II Stabilizes the Low-Spin S 2 State of the Oxygen-Evolving Complex

The oxygen-evolving complex (OEC) of photosystem II (PSII) cycles through redox intermediate states S i (i = 0–4) during the photochemical oxidation of water. The S 2 state involves an equilibrium of two isomers including the low-spin S 2 (LS-S 2 ) state with its characteristic electron paramagnetic resonance (EPR) multiline signal centered at g = 2.0, and a high-spin S 2 (HS-S 2 ) state with its g = 4.1 EPR signal. The relative intensities of the two EPR signals change under experimental conditions that shift the HS-S 2 /LS-S 2 state equilibrium. Here, we analyze the effect of glycerol on the relative stability of the LS-S 2 and HS-S 2 states when bound at the narrow channel of PSII, as reported in an X-ray crystal structure of cyanobacterial PSII. Our quantum mechanics/molecular mechanics (QM/MM) hybrid models of cyanobacterial PSII show that the glycerol molecule perturbs the hydrogen-bond network in the narrow channel, increasing the pK a of D1-Asp61 and stabilizing the LS-S 2 state relative to the HS-S 2 state. Here, the reported results are consistent with the absence of the HS-S 2 state EPR signal in native cyanobacterial PSII EPR spectra and suggest that the narrow water channel hydrogen-bond network regulates the relative stability of OEC catalytic intermediates during water oxidation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection

Abstract Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithm DyComPar , which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Optimized Lie–Trotter–Suzuki decompositions for two and three non-commuting terms

Lie–Trotter–Suzuki decompositions are an efficient way to approximate operator exponentials exp ( t H ) when H is a sum of n (non-commuting) terms which, individually, can be exponentiated easily. They are employed in time-evolution algorithms for tensor network states, digital quantum simulation protocols, path integral methods like quantum Monte Carlo, and splitting methods for symplectic integrators in classical Hamiltonian systems. Here, we provide optimized decompositions up to order t 6 . The leading error term is expanded in nested commutators (Hall bases) and we minimize the 1-norm of the coefficients. For n = 2 terms, several of the optima we find are close to those in McLachlan (1995). Generally, our results substantially improve over unoptimized decompositions by Forest, Ruth, Yoshida, and Suzuki. We explain why these decompositions are sufficient to efficiently simulate any one- or two-dimensional lattice model with finite-range interactions. This follows by solving a partitioning problem for the interaction graph.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Challenges in the designing, planning and deployment of hydrogen refueling infrastructure for fuel cell electric vehicles

Hydrogen can power transportation with near zero greenhouse gas emissions. With government support, early market development is now underway in several nations thanks to technological advances in fuel cell vehicles and electricity generation from renewable energy. Deploying a sustainable hydrogen refueling infrastructure faces methodological and practical challenges ranging from the creation of appropriate codes to managing the co-evolution of the refueling network and growth of the stock of hydrogen fuel cell vehicles. Furthermore, this paper presents a comprehensive review of the challenges facing the designing, planning and deployment of hydrogen refueling infrastructure progress to date and outlook for the future. The design and costs of refueling infrastructure as well as the lifecycle environmental effects of hydrogen vehicles depend on how hydrogen is produced and delivered to refueling stations. In recent years, important advances have been made in methods for planning the numbers, sizes and location of hydrogen stations. Institutional barriers are also gradually being overcome. Co-evolving the deployment of stations and the demand for fuel cell vehicles remains a crucial subject for future research.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A Machine‐Learning‐Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection

Abstract A machine‐learning‐assisted stochastic cloud population model is coupled with the Advanced Research Weather Research and Forecasting (WRF) model to represent fluctuations in the cloud‐base mass flux associated with the life cycles and interactions among cumulus convection cells. In this cloud population model, the size distribution and the associated cloud‐base mass flux of the convective cells are related to their previous state and to the change in the total convective area via a transition function. The convective area tendency in turn is assumed to depend on the cloud‐base mass flux that is resolved by the host WRF model. The transition function is represented by a single hidden‐layer neural network trained by the evolution of convective cell size distributions in a 1‐km grid‐spacing WRF simulation run over the Australian Monsoon region. At every grid point of the host model, the cloud population model predicts the cell size and cloud‐base mass flux distributions from which a random sample of cells is fed to an entraining parcel model that calculates precipitation as well as the associated liquid water potential temperature and total moisture tendencies. These tendencies are averaged over the cells and provided to the host model. Several regional simulations are performed over tropical and midlatitude domains to test this as a potential approach to scale‐aware parameterization. It is shown that such an approach could be a new promising path to simulating realistic precipitation statistics and propagation of precipitation associated with the Madden‐Julian Oscillation while maintaining realistic depictions of the diurnal cycle over both land and ocean.

54 ENVIRONMENTAL SCIENCES↗