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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Dynamic Density: An Air Traffic Management Metric

The definition of a metric of air traffic controller workload based on air traffic characteristics is essential to the development of both air traffic management automation and air traffic procedures. Dynamic density is a proposed concept for a metric that includes both traffic density (a count of aircraft in a volume of airspace) and traffic complexity (a measure of the complexity of the air traffic in a volume of airspace). It was hypothesized that a metric that includes terms that capture air traffic complexity will be a better measure of air traffic controller workload than current measures based only on traffic density. A weighted linear dynamic density function was developed and validated operationally. The proposed dynamic density function includes a traffic density term and eight traffic complexity terms. A unit-weighted dynamic density function was able to account for an average of 22% of the variance in observed controller activity not accounted for by traffic density alone. A comparative analysis of unit weights, subjective weights, and regression weights for the terms in the dynamic density equation was conducted. The best predictor of controller activity was the dynamic density equation with regression-weighted complexity terms.

Laudeman, I. V.↗
DOE OSTI2024

Dynamic density functional theory of polymers with salt in electric fields

Here we present a dynamic density functional theory for modeling the effects of applied electric fields on the local structure of polymers with added salt (polymer electrolytes). Time-dependent equations for the local electrostatic potential and volume fractions of polymer, cation, and anion of added salt are developed using the principles of linear irreversible thermodynamics. For such a development, a field theoretic description of the free energy of polymer melts doped with salts is used, which captures the effects of local variations in the dielectric function. Connections of the dynamic density functional theory with experiments are established by relating the three phenomenological Onsager’s transport coefficients of the theory to the mutual diffusion of electrolyte, ionic conductivity, and transference number of one of the ions. The theory is connected with a statistical mechanical model developed by Bearman and Kirkwood [J. Chem. Phys. 28, 136 (1958)] after relating the three transport coefficients to friction coefficients. The steady-state limit of the dynamic density functional theory is used to understand the effects of dielectric inhomogeneity on the phase separation in polymer electrolytes. The theory developed here provides not only a way to connect with experiments but also to develop multi-scale models for studying connections between local structure and ion transport in polymer electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Urban Air Mobility Airspace Dynamic Density

Airspace safety must be assured for Urban Air Mobility (UAM) to become reality. Emerging operations need to safely integrate with existing and future air traffic. UAM is anticipated to evolve in stages. Early stages, characterized by low-density operations, may be accommodated by traditional air traffic management approaches. However, to enable the scale of UAM operations necessary to reach ubiquitous integration into daily life, new paradigms that utilize collaborative and automated systems are being considered. Correspondingly, traditional approaches for determining a safe number of simultaneous operations in an airspace must be adapted to suit evolving traffic management paradigms. In this paper, we examine dynamic density as an approach to determine when an airspace is excessively populated, and safety may become compromised. We review previous work on dynamic density as it relates to air traffic controller workload and propose factors that may be more fitting for UAMs. Rather than considering controller workload, we propose factors that suggest increased likelihood of loss of separation and may lead to conflicts that require tactical avoidance maneuvers. In addition to monitoring dynamic density for tactical decision making, we endeavor to predict airspace dynamic density with adequate look-ahead to allow for strategic route selection.

UAM↗
NASA NTRSDate not supplied

Urban Air Mobility Airspace Dynamic Density

Effective flight planning requires information about a variety of potential threats, such as adverse weather or airspace restrictions, and about alternatives available if unforeseen events occur. Expected traffic along the route of flight is also essential to a safe outcome so that, for example, adequate fuel/energy supply can be loaded prior to flight. A dynamic density (DD) metric is introduced for the emerging urban air mobility (UAM) concept to predict airspace congestion that may lead to loss of separation between aircraft or less efficient operations. Using inspiration from dynamic density metric research for traditional air traffic management and a two-way highway analogy, we develop a dynamic density metric for a portion of airspace (aUAM corridor) that aggregates the impact from five factors: aircraft density, density of populous clusters, mean number of aircraft in populous clusters, mean distance between aircraft, and minimum distance between aircraft. This works describes our methodology, rationale, use cases, and visualization techniques to efficiently present the DD metric to an operator for informed decision making. We also present an approach for validating the metric. However, validation remains part of future work.

UAM↗
NASA NTRSDate not supplied

Urban Air Mobility Airspace Dynamic Density Safety Metric

NASA’s Advanced Air Mobility (AAM) project focuses on enabling emerging aviation markets by accelerating development of safe, high-volume flight operations.[1] It involves development and validation of vehicles, airspace, and automation changes required to support concepts such as Urban Air Mobility, a vision for electric or hybrid electric, vertical or short take-off and landing vehicles that can transport passengers and cargo over an urban environment.[2] NASA’s System Wide Safety (SWS) project is coordinating with AAM by understanding how safety could be affected by these emerging operations. We approach the assessment of airspace safety by identifying threats to operations and then monitoring and predicting the evolution of those threats encoded in a set of safety metrics.[3] Toward this end, we have been developing a dynamic density metric to predict the likelihood of vehicle conflicts in the airspace.

Dynamic Density↗
NASA NTRS2000

Rayleigh Scattering Diagnostic for Simultaneous Measurements of Dynamic Density and Velocity

A flow diagnostic technique based on the molecular Rayleigh scattering of laser light is used to obtain dynamic density and velocity data in turbulent flows. The technique is based on analyzing the Rayleigh scattered light with a Fabry-Perot interferometer and recording information about the interference pattern with a multiple anode photomultiplier tube (PMT). An artificial neural network is used to process the signals from the PMT to recover the velocity time history, which is then used to calculate the velocity power spectrum. The technique is illustrated using simulated data. The results of an experiment to measure the velocity power spectrum in a low speed (100 rn/sec) flow are also presented.

Seasholtz, Richard G.↗
NASA NTRS2023

Vertiport Dynamic Density

Advanced Air Mobility (AAM) is envisioned to be another spoke in a region’s transportation system, supplementing ground-based travel with air-based travel. Eventually, air taxis will be pervasive, convenient, and affordable. As demand and operations tempo increase, congestion may arise. Several characteristics of AAM reduce the applicability of techniques used in today’s National Airspace System (NAS) to manage congestion. AAM will include both scheduled and non-scheduled, on-demand operations, challenging strategic deconfliction algorithms. Flights will be fairly short, traversing an urban area, not hundreds or thousands of miles, with corresponding low energy reserves, prohibiting excessive delays. Flight operators will need flexibility in operations, scheduling a flight only minutes before departure, or diverting to an alternate vertiport if it becomes advantageous, further adding to trajectory uncertainty that would challenge strategic deconfliction. Finally, operators will desire privacy to protect sensitive information or preserve competitive advantage, limiting early access to intent information. In this paper, we present an approach for managing congestion at vertiports by providing insight into the traffic situation to support operationally-advantageous and safe land or divert decisions. We propose a metric designed with usefulness and usability in AAM operations in mind. The metric uses the sociology concept of dynamic density (DD) that takes into consideration not only number of flights, but also the interaction of those flights with the vertiport’s limited resources, namely the landing pads and the parking spots. DD supports a Pilot in Command (PIC) with decisions about whether to proceed, expedite, delay, or divert; and supports air traffic control (ATC) and vertiport operators in airspace management and vertiport usage. We demonstrate the metric on a notional vertiport scenario. We also show that DD provides better insight into congestion and resulting flight delays than an aircraft count metric used in traditional air traffic management.

Lilly Spirkovska↗
NASA NTRS2021

Refining the Transit Timing and Photometric Analysis of TRAPPIST-1: Masses, Radii, Densities, Dynamics, and Ephemerides

We have collected transit times for the TRAPPIST-1 system with the Spitzer Space Telescope over four years. We add to these ground-based, HST, and K2 transit-time measurements, and revisit an N-body dynamical analysis of the seven-planet system using our complete set of times from which we refine the mass ratios of the planets to the star. We next carry out a photodynamical analysis of the Spitzer light curves to derive the density of the host starand the planet densities. We find that all seven planets’ densities may be described with a single rocky mass–radius relation which is depleted in iron relative to Earth, with Fe 21 wt% versus 32 wt% for Earth, and otherwise Earth-like in composition. Alternatively, the planets may have an Earth-like composition but enhanced in light elements,such as a surface water layer or a core-free structure with oxidized iron in the mantle. We measure planet masses toa precision of 3%–5%, equivalent to a radial-velocity(RV)precision of 2.5 cm s−1, or two orders of magnitude more precise than current RV capabilities. We find the eccentricities of the planets are very small, the orbits are extremely coplanar, and the system is stable on 10 Myr timescales. We find evidence of infrequent timing outliers, which we cannot explain with an eighth planet; we instead account for the outliers using a robust likelihood function. We forecast JWST timing observations and speculate on possible implications of the planet densities forthe formation, migration, and evolution of the planet system.

Eric Agol↗
DOE OSTI2026

High resolution measurements of core density collapse dynamics in LHD

The core density collapse (CDC) is an abrupt instability that degrades the performance of super dense core stellarator plasmas on the Large Helical Device (LHD). In LHD, CDCs have proved to be an operational limit in so-called outward-shifted configurations. New measurements with high time resolution Thomson scattering characterize dynamics of the instability in the core. The density profile is observed to collapse inwards, narrowing the density peak till the core itself collapses. A hot, radially localized feature is also observed in the electron temperature profile during the collapse and disappears within a few hundred microseconds of CDC onset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1986

Low density gas dynamic wall boundary conditions

Low density nozzles or large expansion ratio nozzles used in space experience rarefaction effects near their exit in the form of velocity slip and temperature jump at the walls. In addition, the boundary layers become very thick and there is a very strong viscous/inviscid interaction. For these reasons no existing design technique has been found to accurately predict the nozzle flow properties up to the nozzle exit. The objective of this investigation was to examine the slip boundary conditions and formulate them in a form appropriate for use with a full Navier-Stokes numerical code. The viscous/inviscid interaction would automatically be accounted for by using a compressible Navier-Stokes code. Through examination of the interaction of molecules with solid surfaces, a model for the distribution function of the reflected molecules has been determined and this distribution function has been used to develop a new slip boundary condition that can be shown to yield more realistic surface boundary conditions.

Collins, F. G.↗
NASA NTRS1999

Multiple Point Dynamic Gas Density Measurements Using Molecular Rayleigh Scattering

A nonintrusive technique for measuring dynamic gas density properties is described. Molecular Rayleigh scattering is used to measure the time-history of gas density simultaneously at eight spatial locations at a 50 kHz sampling rate. The data are analyzed using the Welch method of modified periodograms to reduce measurement uncertainty. Cross-correlations, power spectral density functions, cross-spectral density functions, and coherence functions may be obtained from the data. The technique is demonstrated using low speed co-flowing jets with a heated inner jet.

Seasholtz, Richard↗
NASA NTRS2010

Analysis of Different Cost Functions in the Geosect Airspace Partitioning Tool

A new cost function representing air traffic controller workload is implemented in the Geosect airspace partitioning tool. Geosect currently uses a combination of aircraft count and dwell time to select optimal airspace partitions that balance controller workload. This is referred to as the aircraft count/dwell time hybrid cost function. The new cost function is based on Simplified Dynamic Density, a measure of different aspects of air traffic controller workload. Three sectorizations are compared. These are the current sectorization, Geosect's sectorization based on the aircraft count/dwell time hybrid cost function, and Geosect s sectorization based on the Simplified Dynamic Density cost function. Each sectorization is evaluated for maximum and average workload along with workload balance using the Simplified Dynamic Density as the workload measure. In addition, the Airspace Concept Evaluation System, a nationwide air traffic simulator, is used to determine the capacity and delay incurred by each sectorization. The sectorization resulting from the Simplified Dynamic Density cost function had a lower maximum workload measure than the other sectorizations, and the sectorization based on the combination of aircraft count and dwell time did a better job of balancing workload and balancing capacity. However, the current sectorization had the lowest average workload, highest sector capacity, and the least system delay.

Wong, Gregory L.↗
DOE OSTI2022

Physics-driven learning of Wasserstein GAN for density reconstruction in dynamic tomography

Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction and density reconstruction methods may not provide the high accuracy needed in many applications and can break down in the presence of unmodeled or anomalous scatter and other experimental artifacts. Incorporating machine-learning models could prove beneficial for accurate density reconstruction, particularly in dynamic imaging, where the time evolution of the density fields could be captured by partial differential equations or by learning from hydrodynamics simulations. In this work, we demonstrate the ability of learned deep neural networks to perform artifact removal in noisy density reconstructions, where the noise is imperfectly characterized. Here, we use a Wasserstein generative adversarial network (WGAN), where the generator serves as a denoiser that removes artifacts in densities obtained from traditional reconstruction algorithms. We train the networks from large density time-series datasets, with noise simulated according to parametric random distributions that may mimic noise in experiments. The WGAN is trained with noisy density frames as generator inputs, to match the generator outputs to the distribution of clean densities (time series) from simulations. A supervised loss is also included in the training, which leads to an improved density restoration performance. In addition, we employ physics-based constraints such as mass conservation during the network training and application to further enable highly accurate density reconstructions. Our preliminary numerical results show that the models trained in our frameworks can remove significant portions of unknown noise in density time-series data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗