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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter (β) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Two-in-one: construction of hydroxyl and imidazolium-bifunctionalized ionic networks in one-pot toward synergistic catalytic CO 2 fixation

Here, a succinct strategy was demonstrated for constructing a hydroxyl group and imidazolium-bifunctionalized ionic network via a onepot quaternization. Key to success lies in the rational design of multi-imidazole precursor and hydroxyl-containing counterpart. Unique properties of the resultant ionic network render its high catalytic efficiency toward CO 2 fixation under ambient conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

Fiber-optic interconnection networks for spacecraft

The overall goal of this effort was to perform the detailed design, development, and construction of a prototype 8x8 all-optical fiber optic crossbar switch using low power liquid crystal shutters capable of operation in a network with suitable fiber optic transmitters and receivers at a data rate of 1 Gb/s. During the earlier Phase 1 feasibility study, it was determined that the all-optical crossbar system had significant advantages compared to electronic crossbars in terms of power consumption, weight, size, and reliability. The result is primarily due to the fact that no optical transmitters and receivers are required for electro-optic conversion within the crossbar switch itself.

Powers, Robert S.↗
DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
NASA NTRS2011

High-Efficiency Ka-Band Waveguide Two-Way Asymmetric Power Combiner

NASA is planning a number of Space Exploration, Earth Observation and Space Science missions where Ka-band solid-state power amplifiers (SSPAs) could have a role. Monolithic microwave integrated circuit (MMIC) based SSPAs with output powers on the order of 10 W at Ka-band frequencies would be adequate to satisfy the data transmission rate requirements at the distances involved. MMICs are a type of integrated circuit fabricated on a GaAs wafer, which operates at micro wave frequencies and performs the function of signal amplification. The highest power Ka-band (31.8 to 32.3 GHz) SSPA to have flown in space had an output power of 2.6 W with an overall efficiency of 14.3 percent. This SSPA was built around discrete GaAs pHEMT (high electron mobility transistor) devices and flew aboard the Deep Space One spacecraft. State-of-the-art GaAs pHEMT-based MMIC power amplifiers (PAs) can deliver RF power at Ka-band frequencies anywhere from 3 W with a power added efficiency (PAE) of 32 percent to 6 W with a PAE of 26 percent. However, to achieve power levels higher than 6 W, the output of several MMIC PAs would need to be combined using a high-efficiency power combiner. Conventional binary waveguide power combiners, based on short-slot and magic-T circuits, require MMIC PAs with identical amplitude and phase characteristics for high combining efficiency. However, due to manufacturing process variations, the output powers of the MMIC PAs tend to be unequal, and hence the need to develop unequal power combiners. A two-way asymmetric magic-T based power combiner for MMIC power amplifiers, which can take in unequal inputs, has been successfully designed, fabricated, and characterized over NASA s Deep Space Network (DSN) frequency range of 31.8 to 32.3 GHz. The figure is a transparent view of the a sym - metric combiner that shows the 4-port configuration and the internal structure. The rod, post, and iris are positioned by design to achieve the desired asymmetric power ratio, phase equality, and port isolation. Although the combiner was designed for an input power ratio of 2:1, it can be custom-designed for any arbitrary power ratio and frequency range. The manufactured prototype combiner was precision machined from aluminum and is less than 2 in.3 (32.8 cm3). Previously investigated rectangular waveguide unequal power combiners were based on shunt/series coupling slots, E-plane septums, or H-plane T-junctions. All the prior art unequal power combiners operated at or below X-band (10 GHz) frequencies and were primarily used in the feed network of antenna arrays. The only reported asymmetric magic-T was developed as a 2:1 power divider for operation at a much lower frequency, around 500 MHz. The measured power ratio when tested as a power divider was very close to 2 and the phase balance was within 2.6, resulting in near ideal performance. When tested as a combiner using two MMIC SSPAs with a 2:1 power output ratio, an efficiency greater than 90 percent was demonstrated over the 500 MHz DSN frequency range. The return loss at the combiner output port (1) was greater than 18 dB and the input port (2 and 3) isolation was greater than 22 dB. The results show the asymmetric combiner to be a good candidate for high-efficiency power combining of two or more SSPAs needed to achieve the 6 to 10 W required by space communications systems of future NASA missions.

Wintucky, E. G.↗
NASA NTRS1984

The embedded operating system project

This progress report describes research towards the design and construction of embedded operating systems for real-time advanced aerospace applications. The applications concerned require reliable operating system support that must accommodate networks of computers. The report addresses the problems of constructing such operating systems, the communications media, reconfiguration, consistency and recovery in a distributed system, and the issues of realtime processing. A discussion is included on suitable theoretical foundations for the use of atomic actions to support fault tolerance and data consistency in real-time object-based systems. In particular, this report addresses: atomic actions, fault tolerance, operating system structure, program development, reliability and availability, and networking issues. This document reports the status of various experiments designed and conducted to investigate embedded operating system design issues.

Campbell, R. H.↗
DOE OSTI2024

Virtual node graph neural network for full phonon prediction

Understanding the structure-property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Γ-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Γ-phonon containing over 146,000 materials and phonon band structures of zeolites. Additionally, our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility. In this study, the authors present a virtual node graph neural network to enable the prediction of material properties with variable output dimensions. This method offers fast and accurate predictions of phonon band structures in complex solids.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Feasibility of Hybrid Electro- and Acousto-Dynamical Systems for Quantum Optical Networks

We present results on an exploratory research program that aims to study the feasibility of novel, sustained, dense, and efficient quantum information generation, storage, retrieval, relay and distribution devices and systems towards future advanced quantum networking architectures. The pathfinder program is anchored on original ideas and device conceptual designs that turn the phononic-sourced challenges into an opportunity towards optimized transduction chains over a broad range of length and energy/temperature scales. The proposal involves conceptual design and feasibility towards integration, commissioning and benchmarking of challenging transduction devices in a scaled quantum network setup.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1984

Configuration study for a 30 GHz monolithic receive array, volume 1

Gregorian, Cassegrain, and single reflector systems were analyzed in configuration studies for communications satellite receive antennas. Parametric design and performance curves were generated. A preliminary design of each reflector/feed system was derived including radiating elements, beam-former network, beamsteering system, and MMIC module architecture. Performance estimates and component requirements were developed for each design. A recommended design was selected for both the scanning beam and the fixed beam case. Detailed design and performance analysis results are presented for the selected Cassegrain configurations. The final design point is characterized in detail and performance measures evaluated in terms of gain, sidelobe level, noise figure, carrier-to-interference ratio, prime power, and beamsteering. The effects of mutual coupling and excitation errors (including phase and amplitude quantization errors) are evaluated. Mechanical assembly drawings are given for the final design point. Thermal design requirements are addressed in the mechanical design.

Nester, W. H.↗
DOE OSTI2023

Fast b -tagging at the high-level trigger of the ATLAS experiment in LHC Run 3

The ATLAS experiment relies on real-time hadronic jet reconstruction and b-tagging to record fully hadronic events containing b-jets. These algorithms require track reconstruction, which is computationally expensive and could overwhelm the high-level-trigger farm, even at the reduced event rate that passes the ATLAS first stage hardware-based trigger. In LHC Run 3, ATLAS has mitigated these computational demands by introducing a fast neural-network-based b-tagger, which acts as a low-precision filter using input from hadronic jets and tracks. It runs after a hardware trigger and before the remaining high-level-trigger reconstruction. This design relies on the negligible cost of neural-network inference as compared to track reconstruction, and the cost reduction from limiting tracking to specific regions of the detector. In the case of Standard Model HH → bb̅bb̅, a key signature relying on b-jet triggers, the filter lowers the input rate to the remaining high-level trigger by a factor of five at the small cost of reducing the overall signal efficiency by roughly 2%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A Scalable Compact Additively Manufactured Molten Salt to Supercritical Carbon Dioxide Heat Exchanger for Solar Thermal Application

Design of an additively manufactured molten salt (MS) to supercritical carbon dioxide (sCO 2 ) primary heat exchanger (PHE) for solar thermal power generation is presented. The PHE is designed to handle temperatures up to 720 °C on the MS side and an internal pressure of 200 bar on the sCO 2 side. In the core, MS flows through a three-dimensional periodic lattice network, while sCO 2 flows within pin arrays. The design includes integrated sCO 2 headers located within the MS flow, allowing for a counterflow design of the PHE. The sCO 2 headers are configured to enable uniform flow distribution into each sCO 2 plate while withstanding an internal pressure of 200 bar and minimizing obstruction to the flow of MS around it. The structural integrity of the design is verified on additively manufactured (AM) 316 stainless steel sub-scale specimens. An experimentally validated, correlation-based sectional PHE core thermofluidic model is developed to study the impact of flow and geometrical parameters on the PHE performance, with varied parameters including the mass flowrate, surface roughness, and PHE dimensions. A process-based cost model is used to determine the impact of parameter variation on build cost. The model results show that a heat exchanger with a power density of 18.6 MW/m 3 (including sCO 2 header volume) and effectiveness of 0.88 can be achieved at a heat capacity rate ratio of 0.8. As a result, the impact of design and AM machine parameters on the cost of the PHE are assessed.

14 SOLAR ENERGY↗
DOE OSTI2023

Design, execution, and interpretation of plant RNA-seq analyses

Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1965

Threshold logic - a synthesis approach.

Book on threshold logic for logical design of digital systems including threshold element synthesis by iteration, tabulation and network synthesis

NETWORK SYNTHESIS↗
NASA NTRS2014

Training Knowledge Bots for Physics-Based Simulations Using Artificial Neural Networks

Millions of complex physics-based simulations are required for design of an aerospace vehicle. These simulations are usually performed by highly trained and skilled analysts, who execute, monitor, and steer each simulation. Analysts rely heavily on their broad experience that may have taken 20-30 years to accumulate. In addition, the simulation software is complex in nature, requiring significant computational resources. Simulations of system of systems become even more complex and are beyond human capacity to effectively learn their behavior. IBM has developed machines that can learn and compete successfully with a chess grandmaster and most successful jeopardy contestants. These machines are capable of learning some complex problems much faster than humans can learn. In this paper, we propose using artificial neural network to train knowledge bots to identify the idiosyncrasies of simulation software and recognize patterns that can lead to successful simulations. We examine the use of knowledge bots for applications of computational fluid dynamics (CFD), trajectory analysis, commercial finite-element analysis software, and slosh propellant dynamics. We will show that machine learning algorithms can be used to learn the idiosyncrasies of computational simulations and identify regions of instability without including any additional information about their mathematical form or applied discretization approaches.

Samareh, Jamshid A.↗
NASA NTRS1984

Design optimization of the 34-meter DSN-NCP antennas

The new NASA Deep Space Network (DSN) 34-m-diameter azimuth-elevation (Az-El) antenna structure is an example of an essentially computer-automated design. In addition to pivotal comptuer Lagrange multiplier design optimization software, much of the associated pre- and post-processing was also performed by computer. The construction of one of these antennas at Goldstone, California, is well advanced and will be completed this summer. A second installation is in progress in Australia. Both atennas will be used primarily for spacecraft tracking and will operate in the 8.5-GHz, 3.5-cm (1.4-in.) wavelength microwave frequency.

Levy, R.↗
DOE OSTI2023

Simulation study of the effects of polymer network dynamics and mesh confinement on the diffusion and structural relaxation of penetrants

The diffusion of small molecular penetrants through polymeric materials represents an important fundamental problem, relevant to the design of materials for applications such as coatings and membranes. Polymer networks hold promise in these applications because dramatic differences in molecular diffusion can result from subtle changes in the network structure. In this paper, we use molecular simulation to understand the role that cross-linked network polymers have in governing the molecular motion of penetrants. By considering the local, activated alpha relaxation time of the penetrant and its long-time diffusive dynamics, we can determine the relative importance of activated glassy dynamics on penetrants at the segmental scale vs entropic mesh confinement on penetrant diffusion. We vary several parameters, such as the cross-linking density, temperature, and penetrant size, to show that cross-links primarily affect molecular diffusion through the modification of the matrix glass transition, with local penetrant hopping at least partially coupled to the segmental relaxation of the polymer network. This coupling is very sensitive to the local activated segmental dynamics of the surrounding matrix, and we also show that penetrant transport is affected by dynamic heterogeneity at low temperatures. To contrast, only at high temperatures and for large penetrants or when the dynamic heterogeneity effect is weak, does the effect of mesh confinement become significant, even though penetrant diffusion more broadly empirically follows similar trends as established models of mesh confinement-based transport.

Chemistry↗
DOE OSTI2021

Agent-Supervisor Coordination for Decentralized Event-Triggered Optimization

This letter proposes decentralized resource-aware coordination schemes for solving network optimization problems defined by objective functions that combine locally evaluable costs with network-wide coupling components. These methods are well suited for a group of supervised agents trying to solve an optimization problem under mild coordination requirements. Each agent has information on its local cost and coordinates with the network supervisor for information about the coupling term of the cost. The proposed approach is feedback-based and asynchronous by design, guarantees anytime feasibility, and ensures the asymptotic convergence of the network state to the desired optimizer. Numerical simulations on a power system example illustrate our results.

decentralized algorithms↗