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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Guest Editorial Special Issue on Wireless Charging Systems

Wireless power transfer is a safe and convenient method for electric vehicle (EV) charging. And, if designed properly, it can be highly efficient. Applications cover a wide range of power transfer levels from less than a kilowatt to a few hundreds of kilowatts with transfer distances from a few millimeters to a few hundreds of millimeters. Applications also cover stationary, dynamic (in-motion), or quasi dynamic wireless charging systems. Safety is inherent in wireless power transfer systems due to the large airgap separation between the source and load sides, and users do not have to touch any cables, connectors, or plugs at all. With the recent advancements in automation, parking aid systems can be integrated and the wireless charging process can be a simple “park and go” approach without requiring any input from the driver.When applied in motion, wireless charging can be the ultimate end of game in transportation electrification, providing reduced battery size and cost with unlimited range for EVs. This approach is also known as ”charge-on-the-go” or “roadway powered vehicle” concept. While the connected and automated vehicles can self-drive and self-park, they currently cannot self-charge. Furthermore, wireless charging is a key enabling technology for the widespread development and adoption of connected and automated vehicle technology for the future of smart mobility.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Secure mmWave Spectrum Sharing with Autonomous Beam Scheduling for 5G and Beyond

Spectrum Sharing (SS) has seen a renewed set of initiatives in 5G with the availability of shared and unlicensed spectrum bands that can be used by multiple cellular service providers and private cellular networks. Beam based transmission, instead of the traditional sector based transmission in conjunction with the spectrum agility of the 5G New Radio (NR) has brought new opportunities to optimized sharing of spectrum. Currently in the U.S., a centralized Spectrum Access Server (SAS) is used to co-ordinate spectrum sharing among networks sharing the same spectrum band. However, SAS becomes a focal point for security attacks and a performance bottleneck. In addition, SAS relies on an Environmental Sensor Network (ESN), separate from the 5G network. Without trusted spectral occupancy information, false reporting of spectrum sensing data can create sub-optimal and unfair spectrum usage. This paper summarizes our recent research findings in using a decentralized scheme for multiple networks to securely share spectrum with autonomous beam scheduling : 1) A new stochastic network framework based on Lyapunov Optimization approach is developed to optimize scheduling at the base stations; 2) Game theoretic (GT) approach is used to formulate the distributed scheduler; 3) Another distributed scheduler with Q-learning is presented that utilizes the Reinforcement Learning (RL) approach; 4) The performance and convergence rate of these distributed solutions to use shared and unlicensed spectrum are compared with existing solutions. Conditions under which the performance of these schedulers approach the theoretical upper bound, which is the performance possible with no interference among the operators sharing the spectrum, are presented; 5) The ability of a base station to use its own user equipment as sensors, for optimal spectrum sharing with base stations in other operator networks, is demonstrated to be an effective approach.

5G↗
DOE OSTI2020

Effects of social structure and management on risk of disease establishment in wild pigs

Contact heterogeneity among hosts determines invasion and spreading dynamics of infectious disease, thus its characterization is essential for identifying effective disease control strategies. Yet, little is known about the factors shaping contact networks in many 28 wildlife species and how wildlife management actions might affect contact networks. Wild pigs in North America are an invasive, socially-structured species that pose a health concern for domestic swine given their ability to transmit numerous devastating diseases 2 such as African swine fever (ASF). Using proximity loggers and GPS data from 48 wild pigs in Florida and South Carolina, USA, we employed a probabilistic framework to estimate weighted contact networks. We determined the effects of sex, social group, and spatial distribution (monthly home range overlap and distance) on wild pig contact. We also estimated the impacts of management-induced perturbations on contact and inferred their effects on ASF establishment in wild pigs with simulation. Social group membership was the primary factor influencing contacts. Between-group contacts depended primarily on space use characteristics, with fewer contacts among groups separated by >2 km and no contacts among groups >4 km apart within a month. Modeling ASF dynamics on the contact network demonstrated that indirect contacts resulting from baiting (a typical method of attracting wild pigs or game species to a site to enhance recreational hunting) increased the risk of disease establishment by ~33% relative to direct contact. Low-intensity population reduction (<5.9% of the population) had no detectable impact on contact structure but reduced predicted ASF establishment risk relative to no population reduction. Here, we demonstrate an approach for understanding the relative role of spatial, social, and individual-level characteristics in shaping contact networks and predicting their effects on disease establishment risk, thus providing insight for optimizing disease control in spatially- and socially-structured wildlife species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Age and growth of early‐life‐stage Atlantic tarpon ( Megalops atlanticus) from the northcentral Gulf of Mexico

Abstract Age and growth of early‐life‐stage Atlantic tarpon Megalops atlanticus collected from Mississippi coastal waters in the northcentral Gulf of Mexico (GOM) are described using otolith microstructure analysis. Tarpon leptocephali ( n = 95, 16.0—27.8 mm standard length, L S ) collected from June throughOctober 2013—2018, ranged in age from 22 to 43 days (mean = 30.9 ± 0.5 days). Leptocephalus somatic growth rates ranged 0.46—1.24 mm day −1 (mean = 0.76 ± 0.02 mm day −1 ), and leptocephalus otolith growth rates ranged 1.78—3.97 μm day −1 (mean = 2.58 ± 0.04 μm day −1 ). Growth rates were inversely correlated to leptocephalus age, indicating the shrinkage phase associated with leptocephalus metamorphosis. Juvenile tarpon ( n = 358, 50—359 mm fork length, L F ) were collected from August through December 2007—2018. Juveniles exhibited a positive allometric relationship (adjusted R 2 = 0.99, P < 0.001) between length and mass. The age of 100 juveniles (71—277 mm L F ) ranged from 76 to 174 days. Juvenile growth rate was estimated as 1.56 ± 0.11 mm day −1 . Significant ( P < 0.001) linear relationships were found between juvenile age and otolith metrics, including otolith mass ( R 2 = 0.81) and radius ( R 2 = 0.68). Evaluation of the backcalculated hatch dates suggests that specimens in the collection hatched from late May through mid‐September with slight peaks during July and August. A Rao's Spacing Test of Uniformity indicates the presence of significant lunar periodicity in leptocephalus hatch dates ( n = 95, U = 250.1, P < 0.05), with 50% of the leptocephali hatched within 5 days (before or after) of the full moon. This study fills critical gaps in the scientific knowledge of tarpon and provides estimates of early‐life‐history metrics for an iconic game fish at the northernmost extent of its GOM range.

Graham, Patrick M.↗
DOE OSTI2024

Effects of human presence on African mammal waterhole attendance and temporal activity patterns

Abstract Human impacts on the environment and wildlife populations are increasing globally, threatening thousands of species with extinction. While wildlife‐based tourism is beneficial for educating tourists, generating income for conservation efforts, and providing local employment, more information is needed to understand how this industry may impact wildlife. In this study, we used motion‐activated cameras at 12 waterholes on a private game reserve in northern Namibia to determine if the presence of humans and permanent infrastructure affected mammal visits by examining their (1) number of visits, (2) time spent, and (3) diel activity patterns. Our results revealed no differences in the number of visits based on human presence for any of the 17 mammal species studied. However, giraffes ( Giraffe camelopardalis ) spent more time at waterholes before observer presence compared to during. Additionally, several species changed diel activity patterns when human observers were present. Notably, several carnivore and ungulate species increased overlap in their activity patterns during periods while humans were present relative to when humans were absent. These modifications of mammal temporal activity patterns due to human presence could eventually lead to changes in community structure and trophic dynamics because of altered predator–prey interactions. As humans continue to expand into wildlife habitats, and wildlife‐based tourism increases globally, it is imperative that we fully understand the effects of anthropogenic pressures on mammal behavior. Monitoring of wildlife behavioral changes in response to human activity is crucial to further develop wildlife tourism opportunities in a way that optimizes the impact of conservation goals.

Patterson, J. R. [Savannah River Ecology Lab, Warn↗
DOE OSTI2021

The effects of allelochemical transfer on the dynamics of hosts, parasitoids, and competing hyperparasitoids

Allelochemicals produced by plants may be ingested by herbivorous insects and transferred to higher trophic levels with potentially deleterious effects. We develop a system of differential equations to investigate the effect of the transfer of allelochemicals, such as nicotine, on the population dynamics of a system of hosts, parasitoids, and two competing hyperparasitoids that attack different life stages of the parasitoids. We find both somewhat deleterious effects of nicotine on the larvae-attacking hyperparasitoids and increased attack rates for the pupae-attacking hyperparasitoids can promote coexistence. We also use an evolutionary game-theoretic approach to determine the optimal distribution of hyperparasitoid attacks among nicotine-producing and nicotine-free plants. With strong deleterious effects of nicotine and increased attack rates for the pupae-attacking hyperparasitoid, we find both species attack parasitoids on the nicotine-free plant but only pupae-attacking hyperparasitoids attack parasitoids on the nicotine-producing plant.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

BI-LEVEL OPTIMIZATION FOR ELECTRICITY TRANSACTION IN SMART COMMUNITY WITH MODULAR PUMP HYDRO STORAGE

Grid integration of the increasing distributed energy resources could be challenging in terms of new infrastructure investment, power grid stability, etc. To resolve more renewables locally and reduce the need for extensive electricity transmission, a community energy transaction market is assumed with market operator as the leader whose responsibility is to generate local energy prices and clear the energy transaction payment among the prosumers (followers). The leader and multi-followers have competitive objectives of revenue maximization and operational cost minimization. This non-cooperative leader-follower (Stackelberg) game is formulated using a bi-level optimization framework, where a novel modular pump hydro storage technology (GLIDES system) is set as an upper level market operator, and the lower level prosumers are nearby commercial buildings. The best responses of the lower level model could be derived by necessary optimality conditions, and thus the bi-level model could be transformed into single level optimization model via replacing the lower level model by its Karush-Kuhn-Tucker (KKT) necessary conditions. Several experiments have been designed to compare the local energy transaction behavior and profit distribution with the different demand response levels and different local price structures. The experimental results indicate that the lower level prosumers could benefit the most when local buying and selling prices are equal, while maximum revenue potential for the upper level agent could be reached with non-equal trading prices.

Chen, Yang↗
DOE OSTI2014

Virtual reality 3D headset based on DMD light modulators

We present the design of an immersion-type 3D headset suitable for virtual reality applications based upon digital micro-mirror devices (DMD). Our approach leverages silicon micro mirrors offering 720p resolution displays in a small form-factor. Supporting chip sets allow rapid integration of these devices into wearable displays with high resolution and low power consumption. Applications include night driving, piloting of UAVs, fusion of multiple sensors for pilots, training, vision diagnostics and consumer gaming. Our design is described in which light from the DMD is imaged to infinity and the user’s own eye lens forms a real image on the user’s retina.

digital micromirror device↗
DOE OSTI2014

SU-E-J-61: Electrodynamics and Nano-Scale Fluid Dynamics in Protein Localization of Nuclear Pore Complexes

Purpose: To develop a simulation to catalyze a reevaluation of common assumptions about 3 dimensional diffusive processes and help cell biologists gain a more nuanced, intuitive understanding of the true physical hurdles of protein signaling cascades. Furthermore, to discuss the possibility of intracellular electrodynamics as a critical, unrecognized component of cellular biology and protein dynamics that is necessary for optimal information flow from the cell membrane to the nucleus. Methods: The Unity 3D gaming physics engine was used to build an accurate virtual scale model of the cytoplasm within a few hundred nanometers of the nuclear membrane. A cloud of simulated pERK proteins is controlled by the physics simulation, where diffusion is based on experimentally measured values and the electrodynamics are based on theoretical nano-fluid dynamics. The trajectories of pERK within the cytoplasm and through the 1250 nuclear pores on the nuclear surface is recorded and analyzed. Results: The simulation quickly demonstrates that pERKs moving solely by diffusion will rarely locate and come within capture distance of a nuclear pore. The addition of intracellular electrodynamics between charges on the nuclear pore complexes and on pERKs increases the number of successful translocations by allowing the electro-physical attractive effects to draw in pERKs from the cytoplasm. The effects of changes in intracellular shielding ion concentrations allowed for estimation of the “capture radius” under varying conditions. Conclusion: The simulation allows a shift in perspective that is paramount in attempting to communicate the scale and dynamics of intracellular protein cascade mechanics. This work has allowed researchers to more fully understand the parameters involved in intracellular electrodynamics, such as shielding anion concentration and protein charge. As these effects are still far below the spatial resolution of currently available measurement technology this simulation is invaluable to researchers to visualize and study current hypotheses concerning protein dynamics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-F-BRB-05: Collision Avoidance Mapping Using Consumer 3D Camera

Purpose: To develop a fast and economical method of scanning a patient’s full body contour for use in collision avoidance mapping without the use of ionizing radiation. Methods: Two consumer level 3D cameras used in electronic gaming were placed in a CT simulator room to scan a phantom patient set up in a high collision probability position. A registration pattern and computer vision algorithms were used to transform the scan into the appropriate coordinate systems. The cameras were then used to scan the surface of a gantry in the treatment vault. Each scan was converted into a polygon mesh for collision testing in a general purpose polygon interference algorithm. All clinically relevant transforms were applied to the gantry and patient support to create a map of all possible collisions. The map was then tested for accuracy by physically testing the collisions with the phantom in the vault. Results: The scanning fidelity of both the gantry and patient was sufficient to produce a collision prediction accuracy of 97.1% with 64620 geometry states tested in 11.5 s. The total scanning time including computation, transformation, and generation was 22.3 seconds. Conclusion: Our results demonstrate an economical system to generate collision avoidance maps. Future work includes testing the speed of the framework in real-time collision avoidance scenarios. Research partially supported by a grant from Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Giant perpendicular magnetic anisotropy in Fe/III-V nitride thin films

Large perpendicular magnetic anisotropy (PMA) in transition metal thin films provides a pathway for enabling the intriguing physics of nanomagnetism and developing broad spintronics applications. After decades of searches for promising materials, the energy scale of PMA of transition metal thin films, unfortunately, remains only about 1 meV. This limitation has become a major bottleneck in the development of ultradense storage and memory devices. We discovered unprecedented PMA in Fe thin-film growth on the ($000\overline{1}$) N-terminated surface of III-V nitrides from first-principles calculations. PMA ranges from 24.1 meV/u.c. in Fe/BN to 53.7 meV/u.c. in Fe/InN. Symmetry-protected degeneracy between x 2 - y 2 and xy orbitals and its lift by the spin-orbit coupling play a dominant role. As a consequence, PMA in Fe/III-V nitride thin films is dominated by first-order perturbation of the spin-orbit coupling, instead of second-order in conventional transition metal/oxide thin films. This game-changing scenario would also open a new field of magnetism on transition metal/nitride interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Neuromorphic ionic computing in droplet interface synapses

Ionic devices with memory capabilities can emulate neural functionality, enabling neuromorphic computing and biomedical applications. In this study, we report an ionic spiking synapse based on aqueous droplet interface bilayer assembly. Under stepwise triangular voltages, the device displays coupled memcapacitive-memristive behavior, showing noncrossing pinched hysteretic I-V loops. This hysteretic ion dynamics can be regulated by modifying bilayer components, reconstituting protein channels, or adjusting droplet assembly configuration. Droplet interface synapses (DIS) exhibit fundamental neuromorphic behaviors such as paired-pulse facilitation/depression, spike rate–dependent plasticity, Hebbian learning, and short-term associative learning under classical conditioning. We also used reservoir computing with DIS to implement two learning algorithms: a classification algorithm that recognizes handwritten digits and a reinforcement learning algorithm that learns to play a board game of tic-tac-toe.

Li, Zhongwu [Lawrence Livermore National Laborator↗
DOE OSTI2022

Perovskite solar cells can take the heat

Solar panels must endure extreme conditions during their service lifetime. There are few other products, let alone pristine semiconductor electronics, that are manufactured today and expected to withstand decades of abuse from full exposure to outdoor elements, including harsh ultraviolet sunlight, rain and hail, and daily and seasonal thermal cycles. Over the past decade, a new class of semiconductors, known as metal halide perovskites, has emerged, which constitutes the light-absorbing material in solar panels and has made incredible advancements, potentially taking photovoltaics in new directions. Perovskite materials have been touted as a potential game changer for solar energy production.

14 SOLAR ENERGY↗
DOE OSTI2014

Quantum neural networks: Current status and prospects for development

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Leveraging Multitime Hamilton–Jacobi PDEs for Certain Scientific Machine Learning Problems

Hamilton-Jacobi partial differential equations (HJ PDEs) have deep connections with a wide range of fields, including optimal control, differential games, and imaging sciences. By considering the time variable to be a higher dimensional quantity, HJ PDEs can be extended to the multi-time case. In this paper, we establish a novel theoretical connection between specific optimization problems arising in machine learning and the multi-time Hopf formula, which corresponds to a representation of the solution to certain multi-time HJ PDEs. Through this connection, we increase the interpretability of the training process of certain machine learning applications by showing that when we solve these learning problems, we also solve a multi-time HJ PDE and, by extension, its corresponding optimal control problem. As a first exploration of this connection, we develop the relation between the regularized linear regression problem and the Linear Quadratic Regulator (LQR). We then leverage our theoretical connection to adapt standard LQR solvers (namely, those based on the Riccati ordinary differential equations) to design new training approaches for machine learning. Lastly, we provide some numerical examples that demonstrate the versatility and possible computational advantages of our Riccati-based approach in the context of continual learning, post-training calibration, transfer learning, and sparse dynamics identification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Foundations of automatic feature extraction at LHC–point clouds and graphs

Abstract Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as “replacing the expert” in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.

Bhardwaj, Akanksha↗
DOE OSTI2018

Defense strategies for cloud computing multi-site server infrastructures

We consider cloud computing server infrastructures for big data applications, which consist of multiple server sites connected over a wide-area network. The sites house a number of servers, network elements and local-area connections, and the wide-area network plays a critical, asymmetric role of providing vital connectivity between them. We model this infrastructure as a system of systems, wherein the sites and wide-area network are represented by their cyber and physical components. These components can be disabled by cyber and physical attacks, and also can be protected against them using component reinforcements. The effects of attacks propagate within the systems, and also beyond them via the wide-area network.We characterize these effects using correlations at two levels using: (a) aggregate failure correlation function that specifies the infrastructure failure probability given the failure of an individual site or network, and (b) first-order differential conditions on system survival probabilities that characterize the component-level correlations within individual systems. We formulate a game between an attacker and a provider using utility functions composed of survival probability and cost terms. At Nash Equilibrium, we derive expressions for the expected capacity of the infrastructure given by the number of operational servers connected to the network for sum-form, product-form and composite utility functions.

Rao, Nageswara S.↗
DOE OSTI2019

Defense strategies and expected capacity of high performance computing infrastructures

We consider high performance computing infrastructures consisting of multiple sites connected over a wide-area network. These sites house heterogeneous computing systems, network elements and local-area connections, and the wide-area network plays a critical, asymmetric role of providing the vital connectivity between them. We model this infrastructure as a recursive system of systems, wherein the basic systems at the finest levels are composed of discrete cyber and physical components. These components may be disabled by cyber and physical disruptions, and their reinforcements protect against them albeit at a cost. We utilize results from a game formulation based on (i) characterizing the disruptions between systems at each level of recursion using aggregate failure correlation functions, and within basic systems using the multiplier functions, and (ii) using utility functions expressed in terms of the number of basic system components attacked and reinforced. At Nash Equilibrium, we derive expressions for the expected capacity of the infrastructure given by the number of computing nodes that are operational and connected to the network.

Rao, Nageswara↗