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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Theoretical foundations of waste factor and waste figure with applications to fixed wireless access and relay systems

The growing energy demands of next-generation wireless systems call for unified, system-level metrics to evaluate and optimize energy efficiency. This paper advances the concept of the Waste Factor (W), or Waste Figure (WF) in decibel scale, as a general framework for modeling power loss across cascaded communication components. By integrating W into the Consumption Factor (CF)–the ratio of data rate to consumed power–we reveal how component inefficiencies influence the minimum achievable energy per bit. Closed-form expressions are derived for energy-per-bit consumption in both direct and relay-assisted links, along with a decision rule for selecting the more energy-efficient path. While not explicitly modeled, Reflective Intelligent Surfaces (RIS) are shown to fit naturally within this framework. The analysis is further applied to a Fixed Wireless Access (FWA) scenario, where asymmetries in traffic direction and hardware inefficiencies are jointly considered, demonstrating the utility of the Waste Factor in guiding energy-aware system design.

Sevim, Nurullah [Texas A&M University, College Sta↗
DOE OSTI2014

Foundation of Hydrodynamics of Strongly Interacting Systems

Hydrodynamics and quantum mechanics have many elements in common, as the density field and velocity fields are common variables that can be constructed in both descriptions. Starting with the Schroedinger equation and the Klein-Gordon for a single particle in hydrodynamical form, we examine the basic assumptions under which a quantum system of particles interacting through their mean fields can be described by hydrodynamics.

Wong, Cheuk-Yin↗
DOE OSTI2011

Foundations of Quantum Mechanics: recent developments at INRIM

This paper's purpose is to show some experiments performed in the 'Carlo Novero' labs of the Optics Division of the National Institute of Metrological Research (INRIM, Torino, Italy) in the last years, aiming to discriminate between Standard Quantum Mechanics and some specific, restricted class of Hidden Variable Theories (HVTs).The first experiment, realized in two different configurations, will perform the Alicki - Van Ryn non-classicality test on single particles, in our specific case heralded single photons. The second experiment instead will be on the testing of two restricted Local Realistic Theories (LRTs), properly built to describe polarization entangled photons experiments, whose inequalities are not affected by the detection loophole.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Universal scaling of potential energy functions describing intermolecular interactions. I. Foundations and scalable forms of new generalized Mie, Lennard-Jones, Morse, and Buckingham exponential-6 potentials

Based on the formulation of the analytical expression of the potential V(r) describing intermolecular interactions in terms of the dimensionless variables r*=r/rm and !*=V/!, where rm is the separation at the minimum and ! the well depth, we propose more generalized scalable forms for the commonly used Lennard-Jones, Mie, Morse and Buckingham exponential-6 potential energy functions (PEFs). These new generalized forms have an additional parameter from and revert to the original ones for some choice of that parameter. In this respect, the original forms can be considered as special cases of the more general forms that are introduced. We also propose a scalable, but nonrevertible to the original one, 4-parameter extended Morse potential.

Xantheas, Sotiris S.↗
DOE OSTI2018

The physical foundation of the reconnection electric field

Magnetic reconnection is a key charged particle transport and energy conversion process in environments ranging from astrophysical systems to laboratory plasmas [Yamada et al., Rev. Mod. Phys. 82, 603–664 (2010)]. Magnetic reconnection facilitates plasma transport by establishing new connections of magnetic flux tubes, and it converts, often explosively, energy stored in the magnetic field to kinetic energy of charged particles [J. L. Burch and J. F. Drake, Am. Sci. 97, 392–299 (2009)]. The intensity of the magnetic reconnection process is measured by the reconnection electric field, which regulates the rate of flux tube connectivity changes. The change of magnetic connectivity occurs in the current layer of the diffusion zone, where the plasma transport is decoupled from the transport of magnetic flux. Here we report on computer simulations and analytic theory to provide a self-consistent understanding of the role of the reconnection electric field, which extends substantially beyond the simple change of magnetic connections. Rather, we find that the reconnection electric field is essential to maintain the current density in the diffusion region, which would otherwise be dissipated by a set of processes. Natural candidates for current dissipation are the average convection of current carriers away from the reconnection region by the outflow of accelerated particles, or the average rotation of the current density by the magnetic field reversal in the vicinity. Instead, we show here that the current dissipation is the result of thermal effects, underlying the statistical interaction of current-carrying particles with the adjacent magnetic field. We find that this interaction serves to redirect the directed acceleration of the reconnection electric field to thermal motion. This thermalization manifests itself in form of quasi-viscous terms in the thermal energy balance of the current layer. This collisionless viscosity, found in the pressure evolution equation, dominates near the x-line. These quasi-viscous terms act to increase the average thermal energy. Our predictions regarding current and thermal energy balance are readily amenable to exploration in the laboratory or by satellite missions, in particular, by NASA's Magnetospheric Multiscale mission.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Metriplectic foundations of gyrokinetic Vlasov–Maxwell–Landau theory

Here, this Letter reports on a metriplectic formulation of a collisional, nonlinear full-f electromagnetic gyrokinetic theory compliant with energy conservation and monotonic entropy production. In an axisymmetric background magnetic field, the toroidal angular momentum is also conserved. Notably, a new collisional current, contributing to the gyrokinetic Maxwell–Ampère equation and the gyrokinetic charge conservation law, is discovered.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Foundations of magnetohydrodynamics

In this tutorial, a derivation of magnetohydrodynamics (MHD) valid beyond the usual ideal gas approximation is presented. Non-equilibrium thermodynamics is used to obtain conservation equations and linear constitutive relations. When coupled with Maxwell's equations, this provides closed fluid equations in terms of material properties of the plasma, described by the equation of state and transport coefficients. These properties are connected to microscopic dynamics using the Irving–Kirkwood procedure and Green–Kubo relations. Symmetry arguments and the Onsager–Casimir relations allow one to vastly simplify the number of independent coefficients. Importantly, expressions for current density, heat flux, and stress (conventionally Ohm's law, Fourier's law, and Newton's law) take different forms in systems with a non-ideal equation of state. The traditional form of the MHD equations, which is usually obtained from a Chapman–Enskog solution of the Boltzmann equation, corresponds to the ideal gas limit of the general equations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Nutrient availability and senescence spatially structure the dynamics of a foundation species

Significance Landscapes are spatially heterogeneous, and the measurement of spatial pattern is dependent upon observation scale. Understanding plant populations requires assessing their extrinsic interactions with the environment as well as intrinsic biological processes and has been difficult because of the inability to track both plant abundance and health on appropriate scales. We introduce remote sensing observations that assess the abundance and health of giant kelp, an important ecosystem-structuring species, over regional and local scales. We find that both extrinsic nutrient availability and intrinsic senescence processes regulate population dynamics but on regional and local scales, respectively. This suggests that future satellite missions will be able to assess plant abundance and health and their interactions with the environment on local to global scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Foundations of atmospheric pressure non-equilibrium plasmas

Non-equilibrium plasmas have been intensively studied over the past century in the context of material processing, environmental remediation, ozone generation, excimer lamps and plasma display panels. Research on atmospheric pressure non-equilibrium plasmas intensified over the last two decades leading to a large variety of plasma sources that have been developed for an extended application range including chemical conversion, medicine, chemical analysis and disinfection. The fundamental understanding of these discharges is emerging but there remain a lot of unexplained phenomena in these intrinsically complex plasmas. The properties of non-equilibrium plasmas at atmospheric pressure span over a huge range of electron densities as well as heavy particle and electron temperatures. Thus, this paper provides an overview of the key underlying processes that are important for the generation and stabilization of atmospheric pressure non-equilibrium plasmas. The unique physical and chemical properties of theses discharges are also summarized.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗