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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine-Learning Microstructure for Inverse Material Design

Metallurgy and material design have thousands of years’ history and have played a critical role in the civilization process of humankind. The traditional trial-and-error method has been unprecedentedly challenged in the modern era when the number of components and phases in novel alloys keeps increasing, with high-entropy alloys as the representative. New opportunities emerge for alloy design in the artificial intelligence era. Here a successful machine-learning (ML) method is developed to identify the microstructure images with eye-challenging morphology for a number of martensitic and ferritic steels. Assisted by it, a new neural-network method is proposed for the inverse design of alloys with 20 components, which can accelerate the design process based on microstructure. The method is also readily applied to other material systems given sufficient microstructure images. This work lays the foundation for inverse alloy design based on microstructure images with extremely similar features.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CommAwareNet: Towards Communication‐Aware Smart Facilities: Designing an Energy‐Efficient High‐Data‐Rate and Reliable Hybrid THz/VLC Comm. Arch. Reinforced with Intelligent Surfaces for Future Network

This project will develop foundations for energy-efficient, high-data-rate, and reliable communication links for next-generation advanced wireless technologies. The Internet of Everything is emerging as a major technology that can establish a massively connected network among both human-type users and machine-type devices. Particularly, massive machine-type communication is becoming the dominant communication paradigm for numerous emerging areas, including data center networking and wireless backhaul, healthcare (e-health), manufacturing (industry 4.0), utilities, transportation (connected cars and public safety), and virtual or augmented reality for human-machine interaction applications. The rising demand for high data rates, especially in indoor scenarios and densely populated outdoor environments, will eventually overload conventional RF-based technologies. This project aims to conduct fundamental research to design, optimize, and validate a communication-aware, energy-efficient, and reliable network (CommAwareNet) architecture for future smart facilities and environments that require unprecedented high throughput needs for massive numbers of the machine- and human-type users. The scientific and technical merit of this work lies in the research required to bring the proposed transformative CommAwareNet solution into fully functional and practical architecture.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Double-sided LCLC-compensated topology for capacitive power transfer

A double-sided LCLC-compensated network is proposed for a capacitive power transfer (CPT) system. In one design, four metal plates are used to form two power transmitting and receiving capacitors and the LCLC network is used to compensate the capacitors. In the second design, two extra metal plates are used to couple with the previous four plates at the transmitting and receiving side, respectively, which forms the capacitor-integrated structure. The circuit parameter values are tuned to achieve zero voltage switching (ZVS) of the input side switches. There is also a CLLC topology proposed, which is a similar variation of LCLC circuit. A 3.3 kW input power capacitive power transfer prototype is designed and built. The experiment results show that the proposed CPT system can transfer 3.1 kW output power through an air gap distance of 70 mm with a dc-to-dc efficiency of 92.1%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

Implementation of Sensor and Control Designs for Bioregenerative Systems

The goal of the Spring 1990 EGM 4001 Design class was to design, fabricate, and test sensors and control systems for a closed loop life support system (CLLSS). The designs investigated were to contribute to the development of NASA's Controlled Ecological Life Support System (CELSS) at Kennedy Space Center (KSC). Designs included a seed moisture content sensor, a porous medium wetness sensor, a plant health sensor, and a neural network control system. The seed group focused on the design and implementation of a sensor that could detect the moisture content of a seed batch. The porous medium wetness group concentrated on the development of a sensor to monitor the amount of nutrient solution within a porous plate incorporating either infrared reflectance or thermal conductance properties. The plant health group examined the possibility of remotely monitoring the health of the plants within the Biomass Production Chamber (BPC) using infrared reflectance properties. Finally, the neural network group concentrated on the ability to use parallel processing in order to control a robot arm and analyze the data from the health sensor to detect regions of a plant.

Rodriguez, Pedro R.↗
DOE OSTI2020

Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks

Abstract We show that deep generative neural networks, based on global optimization networks (GLOnets), can be configured to perform the multiobjective and categorical global optimization of photonic devices. A residual network scheme enables GLOnets to evolve from a deep architecture, which is required to properly search the full design space early in the optimization process, to a shallow network that generates a narrow distribution of globally optimal devices. As a proof-of-concept demonstration, we adapt our method to design thin-film stacks consisting of multiple material types. Benchmarks with known globally optimized antireflection structures indicate that GLOnets can find the global optimum with orders of magnitude faster speeds compared to conventional algorithms. We also demonstrate the utility of our method in complex design tasks with its application to incandescent light filters. These results indicate that advanced concepts in deep learning can push the capabilities of inverse design algorithms for photonics.

Jiang, Jiaqi↗
DOE OSTI2015

Spatial design principles for sustainable hydropower development in river basins

How can dams be arranged within a river basin such that they benefit society? Recent interest in this question has grown in response to the worldwide trend toward developing hydropower as a source of renewable energy in Asia and South America, and the movement toward removing unnecessary dams in the US. Environmental and energy sustainability are important practical concerns, and yet river development has rarely been planned with the goal of providing society with a portfolio of ecosystem services into the future. We organized a review and synthesis of the growing research in sustainable river basin design around four spatial decisions: Is it better to build fewer mainstem dams or more tributary dams? Should dams be clustered or distributed among distant subbasins? Where should dams be placed along a river? At what spatial scale should decisions be made? We came up with the following design principles for increasing ecological sustainability: (i) concentrate dams within a subset of tributary watersheds and avoid downstream mainstems of rivers, (ii) disperse freshwater reserves among the remaining tributary catchments, (iii) ensure that habitat provided between dams will support reproduction and retain offspring, and (iv) formulate spatial decision problems at the scale of large river basins. Based on our review, we discuss trade-offs between hydropower and ecological objectives when planning river basin development. We hope that future testing and refinement of principles extracted from our review will define a path toward sustainable river basin design.

13 HYDRO ENERGY↗
DOE OSTI2019

Molecular Junctions Inspired by Nature: Electrical Conduction through Noncovalent Nanobelts

Charge transport has been famous for occuring naturally in a range of bio molecular systems, from enzymes to neurotransmitters whose structures have covalent and non covalent bonds. Understanding from these systems have yet to translate into molecular junction devices. In this work, we design junctions which have hydrogen bonds between the edges of a series of prototype noncovalent nanobelts (NCNs) and vary the number of donor acceptors to study their electrical properties. From frontier molecular orbitals (FMOs) and projected density of states (DOS) calculations, we found these NCN dimer junctions (bridging Au electrodes) to be gapless systems, with eigenvalues of occupied and unoccupied levels between -5.8 and -5.1 eV respectively, and states at the Fermi level suggesting these are metallic like systems. Their conductance properties were studied with non equilibrium Green’s functions density functional theory (NEGF-DFT) and conductance was found to decrease with cooperative H bonding, that is, the conductance decreased as the alternating donors and acceptors around the nanobelts attenuates to a uniform distribution in the H-bonding arrays. The highest conductance was 51.3 × 10 -6 S (τ=0.662 at the Fermi level), with a pair of NCN dimers having only donors on one nanobelt and acceptors on the other in the NCN junction. The Seebeck coefficient demonstrated that these are n type (-36 to 39 µV K -1 ), while the lower conductors with alternating H bonds are p type (49.7 to 204 µV K -1 ). In addition, the NCNs have appreciable binding energies (19.8 to 46.1 kcal/mol -1 ), implying that in practice, they could form self-assembled monolayer (SAM) heterojunctions, and future systems could be designed to yield a noncovalent polymeric network of nanobelts for long range charge transport.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution

Here we introduce an inverse design framework based on artificial neural networks, genetic algorithms, and tight-binding calculations, capable to optimize the very large configuration space of nanoelectronic devices. Our non-linear optimization procedure operates on trial Hamiltonians through superoperators controlling growth policies of regions of distinct doping. We demonstrate that our algorithm optimizes the doping of graphene-based three-terminal devices for valleytronics applications, monotonously converging to synthesizable devices with high merit functions in a few thousand evaluations (out of similar or equal to ≃ 2 3800 possible configurations). The best-performing device allowed for a terminal-specific separation of valley currents with similar or equal to ≃ 96% (≃ 94%) K(K') valley purity. Importantly, the devices found through our non-linear optimization procedure have both a higher merit function and higher robustness to defects than the ones obtained through geometry optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Floodplain Wetland Channel Planform, Cross-Sectional Morphology, and Sediment Characteristics Along an Estuarine to Tidal River Gradient

The formation of wetland channel networks on coastal river floodplains is affected by spatial transitions in tidal-fluvial processes. This study evaluated sedimentation patterns in tidal marshes on the 147-km tidal Columbia River and its 87-km estuary and characterized the cross-sectional geometry and planform morphometry of tidal wetlands along the estuarine to riverine gradient. Tidal marshes were predominantly depositional (median 0.7 cm yr-1, primarily silt), consistent with late-Holocene rates. Marsh sediment accretion rate increased with annual maximum 7-day average daily maximum water depth. Elevation was negatively correlated with accretion rate in the tidal river and positively correlated with total organic carbon (TOC%) in the tidal river and estuary. TOC% was greater in the estuary and, like percent fines, greater farther from the main-channel Columbia River. For four single-channel and six channel-network planforms of tidal marsh, shrub-dominated, and forested wetlands: (1) Spatial patterns of cross-sectional morphology conformed to typical coastal wetland morphology in estuarine reaches up to the tidal river. (2) Reach-based and common slope linear models for channel perimeter on wetland area were predictive in the estuary (R2 > 0.81) and differed between island (n = 144) and mainland (n = 164) wetlands. No planform linear models were predictive for the entire study area or the tidal river. No models for channel outlets were acceptable. (3) Channel perimeter, surface area, and outlets were all highly variable within and among estuarine–riverine reaches. Analyses of sediment, cross-section, and planform support the use of a “natural network paradigm” for wetland-restoration design on tidal river floodplains.

channel morphology↗
DOE OSTI2015

The Lusi seismic experiment: An initial study to understand the effect of seismic activity to Lusi

The spectacular Lumpur Sidoarjo (Lusi) eruption started in northeast Java on the 29 of May 2006 following a M6.3 earthquake striking the island [1,2]. Initially, several gas and mud eruption sites appeared along the reactivated strike-slip Watukosek fault system [3] and within weeks several villages were submerged by boiling mud. The most prominent eruption site was named Lusi. The Lusi seismic experiment is a project aims to begin a detailed study of seismicity around the Lusi area. In this initial phase we deploy 30 seismometers strategically distributed in the area around Lusi and along the Watukosek fault zone that stretches between Lusi and the Arjuno Welirang (AW) complex. The purpose of the initial monitoring is to conduct a preliminary seismic campaign aiming to identify the occurrence and the location of local seismic events in east Java particularly beneath Lusi.This network will locate small event that may not be captured by the existing BMKG network. It will be crucial to design the second phase of the seismic experiment that will consist of a local earthquake tomography of the Lusi-AW region and spatial and temporal variations of vp/vs ratios. The goal of this study is to understand how the seismicity occurring along the Sunda subduction zone affects to the behavior of the Lusi eruption. Our study will also provide a large dataset for a qualitative analysis of earthquake triggering studies, earthquake-volcano and earthquake-earthquake interactions. In this study, we will extract Green’s functions from ambient seismic noise data in order to image the shallow subsurface structure beneath LUSI area. The waveform cross-correlation technique will be apply to all of recordings of ambient seismic noise at 30 seismographic stations around the LUSI area. We use the dispersive behaviour of the retrieved Rayleigh waves to infer velocity structures in the shallow subsurface.

58 GEOSCIENCES↗
DOE OSTI2023

Stochastic transition in synchronized spiking nanooscillators

This work reports that synchronization of Mott material-based nanoscale coupled spiking oscillators can be drastically different from that in conventional harmonic oscillators. We investigated the synchronization of spiking nanooscillators mediated by thermal interactions due to the close physical proximity of the devices. Controlling the driving voltage enables in-phase 1:1 and 2:1 integer synchronization modes between neighboring oscillators. Transition between these two integer modes occurs through an unusual stochastic synchronization regime instead of the loss of spiking coherence. In the stochastic synchronization regime, random length spiking sequences belonging to the 1:1 and 2:1 integer modes are intermixed. The occurrence of this stochasticity is an important factor that must be taken into account in the design of large-scale spiking networks for hardware-level implementation of novel computational paradigms such as neuromorphic and stochastic computing.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

The Persistent Challenge of Data Locality in the Post-Exascale Era

The era of exascale computing, exemplified by systems like Frontier achieving exaflop-level performance, marks a milestone. However, the quest for sheer compute power leads to strong imbalance in system design. Hence, scaling advancements in memory, network bandwidth, and storage are also necessary and pose challenges, with a crucial need to address data locality issues. This article underscores the fundamental importance of data locality as a key abstraction for optimizing application performance. Despite notable software solutions, the growing complexity of parallelism and memory hierarchy demands performance-portable data locality solutions across diverse computing platforms. Additionally, the article revisits data locality aspects, covering hardware considerations, application perspectives, software stack abstractions, and tool support. It concludes with insights into data locality challenges and opportunities, emphasizing the ongoing significance of collaborative research for progress in this critical issue.

Unat, Didem [Koc University, Istanbul (Turkey)] (O↗
DOE OSTI2022

Dynamic Parameter Estimation with Physics-based Neural Ordinary Differential Equations

Accurate estimation of dynamic parameters of gen-erators is crucial to building a reliable model for dynamical studies and reliable operation of the power system. This paper develops a physics-based neural ordinary differential equations (ODE) approach to learn the parameters of generator dynamic model using phasor measurement units (PMU) data. We design a physics-based neural network to represent the swing equations of the power system dynamics. A loss function is defined as the difference between dynamic simulation results from the physics-based neural networks and pseudo PMU measurements. The parameters of generator dynamic model are iteratively updated using the neural ODEs and the adjoint method. By exploiting the mini-batch scheme in neural ODE training, the parameter estimation performance is significantly improved. Numerical study results on a 3-machine 9-bus system show that the proposed algorithm outperforms state-of-the-art baseline method in both computation time and dynamic parameter estimation accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Non-Blind Deblurring for Fluorescence: A Deformable Latent Space Approach with Kernel Parameterization

We report N\non-blind deblurring (NBD) is a modeling method of the image deblurring problem in computer vision, where the blurring kernel is known or can be externally estimated. In this paper, we attempt to solve a parametric NBD problem, inspired by the simultaneous acquisition of ptychography and fluorescent imaging (FI). Ptychography is an imaging method that favors larger probes, i.e. convolutional kernels, while FI relies on a small probe for high resolution. Also, the kernel can be solved during ptychographic reconstruction. With Ptycho-FI using the same larger kernel, we can perform NBD on the blurred fluorescent images to achieve high-resolution FI, and thus speed up the experiments. To this end, we design a deep latent space deformation network that is directly parameterized by the kernel. The network consists of three components: encoder, deformer, and decoder, where the deformer is specifically meant to rectify the latent space representations of blurred images to a standard latent space, regardless of the kernel. The deformation network is trained with a two-stage training scheme. We conduct extensive experiments to confirm that our parametric model can adapt to drastically different blurring kernels and perform robust deblurring.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

A brain-plausible neuromorphic on-the-fly learning system implemented with magnetic domain wall analog memristors

Neuromorphic computing is an approach to efficiently solve complicated learning and cognition problems like the human brain using electronics. To efficiently implement the functionality of biological neurons, nanodevices and their implementations in circuits are exploited. Here, we describe a general-purpose spiking neuromorphic system that can solve on-the-fly learning problems, based on magnetic domain wall analog memristors (MAMs) that exhibit many different states with persistence over the lifetime of the device. The research includes micromagnetic and SPICE modeling of the MAM, CMOS neuromorphic analog circuit design of synapses incorporating the MAM, and the design of hybrid CMOS/MAM spiking neuronal networks in which the MAM provides variable synapse strength with persistence. Using this neuronal neuromorphic system, simulations show that the MAM-boosted neuromorphic system can achieve persistence, can demonstrate deterministic fast on-the-fly learning with the potential for reduced circuitry complexity, and can provide increased capabilities over an all-CMOS implementation.

42 ENGINEERING↗
DOE OSTI2018

Cross-geography scientific data transferring trends and behavior

Wide area data transfers play an important role in many science applications but rely on expensive infrastructure that often delivers disappointing performance in practice. In response, we present a systematic examination of a large set of data transfer log data to characterize transfer characteristics, including the nature of the datasets transferred, achieved throughput, user behavior, and resource usage. This analysis yields new insights that can help design better data transfer tools, optimize networking and edge resources used for transfers, and improve the performance and experience for end users. Our analysis shows that (i) most of the datasets as well as individual files transferred are very small; (ii) data corruption is not negligible for large data transfers; and (iii) the data transfer nodes utilization is low. Insights gained from our analysis suggest directions for further analysis.

Liu, Zhengchun↗