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At least 1,207 records · Page 67
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NASA NTRSDate not supplied

Designing for Advanced Aerial Mobility: Human-Autonomy Teaming and In-Time System-Wide Safety Assurance

The continued growth of aviation shall require new innovative technologies and operational concepts to meet the ever-increasing demands on air transportation. The NASA Advanced Air Mobility (AAM) project focuses on emerging aviation markets, such as Urban Air Mobility (UAM). UAM is defined as “...a safe and efficient system for air passenger and cargo transportation within an urban area. It is inclusive of small package delivery and other urban unmanned aerial system services and supports a mix of onboard/ground-piloted and increasingly autonomous operations” ([1]). The AAM project emphasizes technology development and validating system-level concepts and solutions in coordination with other NASA Aeronautics Research Mission Directorate (ARMD) projects to enable UAM metro- and micro-plex vertiport and airspace concepts of operations. The NASA AAM research portfolio includes the concepts of Remote Supervisor-in-Command (RSC) and Fleet and Airspace Manager (FAM) as possible human roles for consumer fleet providers. NASA research in RSC is focused on development of guidelines and standards for remote pilots/operators passively and actively controlling a large fleet of autonomous aircraft. For FAM, flight and ground system concepts and technologies to enable high density homogeneous operations at increased scale from vertiport(s), and coordination with other humans in the systems (e.g., UAM urban airspace manager, Air Traffic Control) are key research areas. The envisioned UAM operations are posited to require autonomous systems to enable functions ranging from fleet and resource management to vehicle control. Although automation has become increasingly sophisticated and ubiquitous in civil aviation, autonomy represents a significant evolution in automation, which has generally been limited in functional scope and capability. As autonomy takes on increasing responsibilities, humans and machines will be required to work together in new and different ways [2], rather than traditional design approaches focused on how machines (i.e., autonomy) can do the work of people. The emerging field of human-autonomy teaming (HAT) represents a comprehensive and prioritized research-driven approach to enable the success of future emerging aviation market applications through capabilities and principles that facilitate humans and machine working and thinking better together. The NASA Transformational Tools and Technologies (TTT) Autonomous System (AS) Sub-project was created to assist with the transition into higher levels of autonomy to enable new modes of air transportation, such as UAM. TTT-AS has identified HAT as a key research need to enable UAM while maintaining today’s ultra-safe aviation system safety levels. The latter challenge has been taken up by the NASA System-Wide Safety (SWS) Project, which recognizes that aviation safety, as it evolves, shall require new ways of thinking about safety to include integration of a wide-range of existing and new safety systems and practices, enhanced tools and technologies, increased access to data and data fusion, improved data analysis capabilities, enhanced in-time risk monitoring and detection, hazard prioritization and mitigation, safety assurance decision-support, and in-time integrated system analytics [3].The operational concept of UAM represents a variety of work that has been termed, “work-as-imagined” to characterize the idea that how people think that work is done and how work is actually done are often not the same [4]. To ensure design success and system safety, looking at “work-as-done” provides a comparative approach toward UAM concept and technology design through examination of corresponding analogs found today in aviation (e.g., on-demand operations) and other transportation domains (e.g., port operations). The paper shall discuss various alternative applications with specific focus on airline operation center (AOC) operations, and unmanned aerial system (UAS) command-and-control to inform scaled-versions of FAM and RSC, respectively, and with consideration of the national airspace system contextual environment. The tenets and principles of the HAT field and current NASA research efforts under the TTT-AS sub-project shall also be described. Finally, the SWS sub-project efforts to develop In-Time System-Wide Safety Assurance (ISSA) and In-Time Safety Management Systems (IASMS) are discussed in terms of how “in-time” safety assurance may be conceptualized for the on-demand mobility air taxi “work-as-imagined” operational concept [5]. As part of this effort, concepts from the emerging field of resilience engineering, are being studied. Traditional approaches to aviation safety have focused on what can go wrong and how to prevent it. Another approach to thinking about system safety should reflect not only “avoiding things that go wrong” (protective safety) but also “ensuring that things go right” (productive safety), that enables a system to exhibit the resilient performance [6] necessary for the success of the future aviation system emerging concepts of operations. The paper shall describe efforts focused on how productive safety and resilience may enable a more complete approach to system safety thinking and design of ISSA and IASMS for UAM. Future directions and research needs shall also be discussed.

resilience↗
NASA NTRS2019

VIIRS DNB time-dependent stray light correction

The stray light contamination of the VIIRS Day-Night-Band (DNB) on-board the S-NPP satellite has been studied intensively. To alleviate its impact, a stray light correction look-up-table (LUT), which represents the stray light contamination, is derived from the new moon night dataset by subtracting the non stray light signal from the stray light affected signal. The derived LUT can be used to remove the majority of the contamination. However, the LUT remains static until the next update to the Level-1B data processing, usually one month later. Between these two updates, changes in the actual stray light are not captured. We present a method to derive a dynamic stray light correction LUT that covers the time period between updates. By analyzing the patterns in the annual stray light variation, a consistent trend was found in the LUT’s characteristic features which can be quantitatively expressed as time factors. These factors are then applied to the monthly LUT to produce a dynamic stray light LUT for any time of interest. The L1B software can use this algorithm to calculate the LUTs at the time of observation. The results show significant improvement in the DNB product compared to using the monthly static LUT. Furthermore, this time-dependent algorithm provides a basis for deriving a universal stray light correction LUT for VIIRS.

VIIRS↗
NASA NTRS2021

A Machine Learning Approach to Predict Aircraft Landing Times using Mediated Predictions from Existing Systems

We developed a novel approach for predicting the landing time of airborne flights in real-time operations. The first step predicts a landing time by using mediation rules to select from among physics-based predictions (relying on the expected flight trajectory) already available in real time in the Federal Aviation Administration System Wide Information Management system data feeds. The second step uses a machine learning model built upon the mediated predictions. The model is trained to predict the error in the mediated prediction, using features describing the current state of an airborne flight. These features are calculated in real time from a relatively small number of data elements that are readily available for airborne flights. Initial results based on five months of data at six large airports demonstrate that incorporating a machine learning model on top of the mediated physics-based prediction can lead to substantial additional improvements in prediction quality.

Machine learning↗
NASA NTRS2021

A Machine Learning Approach to Predict Aircraft Landing Times using Mediated Predictions from Existing Systems

We developed a novel approach for predicting the landing time of airborne flights in real-time operations. The first step predicts a landing time by using mediation rules to select from among physics-based predictions (relying on the expected flight trajectory) already available in real time in the Federal Aviation Administration System Wide Information Management system data feeds. The second step uses a machine learning model built upon the mediated predictions. The model is trained to predict the error in the mediated prediction, using features describing the current state of an airborne flight. These features are calculated in real time from a relatively small number of data elements that are readily available for airborne flights. Initial results based on five months of data at six large airports demonstrate that incorporating a machine learning model on top of the mediated physics-based prediction can lead to substantial additional improvements in prediction quality.

Machine learning↗
NASA NTRS2021

Discovery and Timing of Three Millisecond Pulsars in Radio and Gamma-Rays with the Giant Metrewave Radio Telescope and Fermi Large Area Telescope

We performed deep observations to search for radio pulsations in the directions of 375 unassociated Fermi Large Area Telescope γ-ray sources using the Giant Metrewave Radio Telescope (GMRT) at 322 and 607 MHz. In this paper we report the discovery of three millisecond pulsars (MSPs), PSR J0248+4230, PSR J1207–5050, and PSR J1536–4948. We conducted follow-up timing observations for ∼5 yr with the GMRT and derived phase-coherent timing models for these MSPs. PSR J0248+4230 and J1207–5050 are isolated MSPs having periodicities of 2.60 ms and 4.84 ms. PSR J1536–4948 is a 3.07 ms pulsar in a binary system with an orbital period of ∼62 days about a companion of a minimum mass of 0.32 M⊙. We also present multifrequency pulse profiles of these MSPs from the GMRT observations. PSR J1536–4948 is an MSP with an extremely wide pulse profile having multiple components. Using the radio timing ephemeris we subsequently detected γ-ray pulsations from these three MSPs, confirming them as the sources powering the γ-ray emission. For PSR J1536–4948 we performed combined radio–γ-ray timing using ∼11.6 yr of γ-ray pulse times of arrival (TOAs) along with the radio TOAs. PSR J1536–4948 also shows evidence for pulsed γ-ray emission out to above 25 GeV, confirming earlier associations of this MSP with a ≥10 GeV point source. The multiwavelength pulse profiles of all three MSPs offer challenges to models of radio and γ-ray emission in pulsar magnetospheres.

High energy astrophysics↗
NASA NTRS2022

Spatial Characterization of PACE OCI ETU Using Time-Delay Mode

The OCI (Ocean Color Instrument) is the main sensor on the upcoming PACE (Plankton Aerosol Cloud ocean Ecosystem) mission. OCI has two hyperspectral CCD sensors covering 340nm to 885nm and 9 SWIR (Short Wave IR) bands from 940nm to 2260nm. SWIR bands have nominal 1km ground pixel size and CCD bands have native 1/8 km ground pixel size in diagnostic mode that will be aggregated into 1km pixels to improve SNR and meet the data rate constraints. OCI has a rotating telescope that is synchronized to the readout of the CCD and SWIR detectors. Full pre-launch system level testing for the OCI ETU (Engineering Test Unit) was completed in June 2021.With time-delayed scan mode, a sub-pixel level time-delay step is applied to the detector readout. This sub-pixel level time-delay step causes a sub-pixel level shift in the start of the data collection. After collecting time-delay step scans with different step sizes, a scan profile with sub-pixel resolution can be constructed. 1/8 and 1/4 of CCD pixel resolutions were achieved using this mode. In this paper, the OCI time-delayed scan mode will be described as well as how it was used to calculate OCI’s high spatial resolution PSF (Point Spread Function), IFOV (instantaneous Field of View), MTF (Modulation Transfer Function), and BBR (Band to Band Registration).

PACE↗
DOE OSTI2023

Survey of Time Shift Detection Algorithms for Measured PV Data

In this research, three variations of time shift detection algorithms were tested for their ability to detect time shift issues (including daylight savings time and random time shifts) in measured PV data sets. Two algorithms from the Python PVAnalytics package were assessed, and one algorithm from the Solar-Data-Tools package was assessed. Each algorithm's ability to accurately detect and measure time shifts was assessed.

automated preprocessing↗
DOE OSTI2020

Predicting Saturated Hydraulic Conductivity over Time for Degrading Saltstone Vault Concrete - 20376

At the Savannah River Site (SRS), low-level liquid tank waste is mixed with cementitious materials to produce a solid, monolithic waste form referred to as saltstone. The liquid saltstone mixture is pumped into massive concrete structures referred to as saltstone disposal units (SDUs) for curing and long-term storage. SDUs are vaults made of high performance concrete forming a barrier between saltstone and the surrounding environment. Predicting the long-term performance of SDU vault concrete, as well as the saltstone material itself, is critical to understanding the potential releases of radionuclides to the surrounding environment as described in the Saltstone Disposal Facility (SDF) performance assessment (PA). The hydraulic conductivity profile for SDU vault concrete was conservatively assumed to degrade linearly from the initial saturated hydraulic conductivity of the intact concrete over a long period of time. The initial hydraulic properties of saturated SDU vault concrete have been measured, and the final hydraulic properties of degraded vault concrete are assumed to be the same as the porous backfill material that will surround the vault after closure. The assumed final condition of the vault concrete is also conservative in that it provides a greater estimate of water penetration through the vault than would generally be expected. Therefore, insights that provide more realistic but conservative estimates as to how degradation processes may change the hydraulic properties of the SDU vault concrete, the time dependent relationship of the vault concrete hydraulic properties to degradation (and thus time), and the final hydraulic properties of the vault concrete provides useful additions to the SDF PA. This paper focuses on the assumption that the saltstone vault concrete saturated hydraulic conductivity would decrease linearly as a function of degradation time; this assumption resulted from a worst-case, weighted (by relative layer thickness), arithmetic average of intact and backfill material hydraulic conductivities representing flow parallel to a layered system. This research indicates that the effective saturated hydraulic conductivity of the degraded SDU vault concrete should be based on the relative thickness-weighted geometric mean of the intact and degraded layers hydraulic conductivities. This alternative, which is supported extensively in the relevant literature and evaluated in this paper, leads to a more realistic and defensible estimate of the effective saturated hydraulic conductivity in degraded vault concrete while remaining conservative. The impact of using the more realistic estimate of the effective saturated hydraulic conductivity for the degraded concrete decreases the effective saturated hydraulic conductivity by up to several orders of magnitude for early times (i.e., when the SDU vault concrete is assumed degraded via sulfate attack and carbonation). This revised estimate of the effective saturated hydraulic conductivity has been implemented in the recently revised SDF PA. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Pulsar-Calibrated Timing Source for Synchronized Sampling

The Global Positioning System (GPS) is critical to the real-time synchronized sampling of phasor measurement units (PMUs). Unfortunately, GPS signals are occasionally unstable due to several factors such as weak satellite signal and GPS spoofing, thereby leaving the PMU with a degraded sampling performance. In this letter, a novel Pulsar-calibrated Timing Source (PTS) is proposed as the alternative timing source for synchronized sampling. Further, the PTS can generate the timing signal with a 1 μs drift error within 91 holdover minutes to ensure continuity of sampling accuracy. Experimental tests are conducted and the results reveal the reliability and accuracy of the PTS for PMU synchronization.

42 ENGINEERING↗
DOE OSTI2020

Time-Resolved Structural Kinetics of an Organic Mixed Ionic–Electronic Conductor

The structure and packing of organic mixed ionic–electronic conductors have an especially significant effect on transport properties. In operating devices, this structure is not fixed but is responsive to changes in electrochemical potential, ion intercalation, and solvent swelling. Toward this end, the steady-state and transient structure of the model organic mixed conductor, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), is characterized using multimodal time-resolved operando techniques. Steady-state operando X-ray scattering reveals a doping-induced lamellar expansion of 1.6 Å followed by 0.4 Å relaxation at high doping levels. Time-resolved operando X-ray scattering reveals asymmetric rates of lamellar structural change during doping and dedoping that do not directly depend on potential or charging transients. Further, time-resolved spectroscopy establishes a link between structural transients and the complex kinetics of electronic charge carrier subpopulations, in particular the polaron–bipolaron equilibrium. These findings provide insight into the factors limiting the response time of organic mixed-conductor-based devices, and present the first real-time observation of the structural changes during doping and dedoping of a conjugated polymer system via X-ray scattering.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Time‐Lapse Image Classification Using a Diffractive Neural Network

Diffractive deep neural networks (D 2 NNs), comprised of spatially engineered passive surfaces, collectively process optical input information at the speed of light propagation through a thin diffractive volume, without any external computing power. Diffractive networks were demonstrated to achieve all‐optical object classification and perform universal linear transformations. Herein, a “time‐lapse” image classification scheme using a diffractive network is demonstrated for the first time, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other. In a different context, such relative movements of the objects and/or the camera are routinely being used for image super‐resolution applications; inspired by their success, a time‐lapse diffractive network is designed to benefit from the complementary information content created by controlled or random lateral shifts. The design space and performance limits of time‐lapse diffractive networks are numerically explored, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR‐10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive network on the CIFAR‐10 dataset. Time‐lapse diffractive networks will be broadly useful for the spatiotemporal analysis of input signals using all‐optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantum mechanical out-of-time-ordered-correlators for the anharmonic (quartic) oscillator

Out-of-time-ordered correlators (OTOCs) have been suggested as a means to study quantum chaotic behavior in various systems. In this work, I calculate OTOCs for the quantum mechanical anharmonic oscillator with quartic potential, which is classically integrable and has a Poisson-like energy-level distribution. For low temperature, OTOCs are periodic in time, similar to results for the harmonic oscillator and the particle in a box. For high temperature, OTOCs exhibit a rapid (but power-like) rise at early times, followed by saturation consistent with 2 < x 2 >T < p 2 > T at late times. At high temperature, the spectral form factor decreases at early times, bounces back and then reaches a plateau with strong fluctuations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Null Raychaudhuri: canonical structure and the dressing time

Abstract We initiate a study of gravity focusing on generic null hypersurfaces, non-perturbatively in the Newton coupling. We present an off-shell account of the extended phase space of the theory, which includes the expected spin-2 data as well as spin-0, spin-1 and arbitrary matter degrees of freedom. We construct the charges and the corresponding kinematic Poisson brackets, employing a Beltrami parameterization of the spin-2 modes. We explicitly show that the constraint algebra closes, the details of which depend on the non-perturbative mixing between spin-0 and spin-2 modes. Finally we show that the spin zero sector encodes a notion of a clock, called dressing time, which is dynamical and conjugate to the constraint. It is well-known that the null Raychaudhuri equation describes how the geometric data of a null hypersurface evolve in null time in response to gravitational radiation and external matter. Our analysis leads to three complementary viewpoints on this equation. First, it can be understood as a Carrollian stress tensor conservation equation. Second, we construct spin-0, spin-2 and matter stress tensors that act as generators of null time reparametrizations for each sector. This leads to the perspective that the null Raychaudhuri equation can be understood as imposing that the sum of CFT-like stress tensors vanishes. Third, we solve the Raychaudhuri constraint non-perturbatively. The solution relates the dressing time to the spin-2 and matter boost charge operators. Finally we establish that the corner charge corresponding to the boost operator in the dressing time frame is monotonic. These results show that the notion of an observer can be thought of as emerging from the gravitational degrees of freedom themselves. We briefly mention that the construction offers new insights into focusing conjectures.

Physics↗
DOE OSTI2024

Real-time chiral dynamics at finite temperature from quantum simulation

In this study, we explore the real-time dynamics of the chiral magnetic effect (CME) at a finite temperature in the (1+1)-dimensional QED, the massive Schwinger model. By introducing a chiral chemical potential μ 5 through a quench process, we drive the system out of equilibrium and analyze the induced vector currents and their evolution over time. The Hamiltonian is modified to include the time-dependent chiral chemical potential, thus allowing the investigation of the CME within a quantum computing framework. We employ the quantum imaginary time evolution (QITE) algorithm to study the thermal states, and utilize the Suzuki-Trotter decomposition for the real-time evolution. This study provides insights into the quantum simulation capabilities for modeling the CME and offers a pathway for studying chiral dynamics in low-dimensional quantum field theories.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

A Time-Dependent Directional Damage Theory for Brittle Rocks Considering the Kinetics of Microcrack Growth

In this work, a novel microcrack damage theory for describing the time-dependent behavior of brittle rocks is proposed. Instead of using scalar or tensorial variables to approximate the effect of microcracks, the concept of directional damage is introduced by upscaling the directional distribution density of microcracks through the noninteracting homogenization scheme. The contribution of an individual crack on global strain is first derived from the crack-opening displacements including sliding, closure, and dilation. The model is then cast in the free energy form, and the range of parameters ensuring compliance with the second law of thermodynamics is derived rigorously. Considering that the subcritical propagation of microcracks is the dominant mechanism behind the time-dependent deformation and failure of brittle rocks, the driving force for directional damage is defined as the corresponding energy release rate, and the effect of time is introduced through a damage evolution law inspired from the kinetics of subcritical crack growth. The model is applied to simulate the behavior of basalt rock under various loading conditions. In addition to predicting the stress–strain–time response, the model offers enhanced resolution in representing the anisotropic characteristics of microcrack-induced damage in brittle rocks and their time-dependent evolutions.

58 GEOSCIENCES↗
DOE OSTI2023

Effects of volitional emigration timing and smolt size on survival and age-at-return in a Pacific salmon hatchery population

Abstract Many Pacific salmon populations are returning from sea at younger ages and smaller sizes. Hatchery culture, management practices, and environmental factors influence juvenile release size and emigration timing, which in turn affect important demographic characteristics in returning adults. We analyzed data from approximately 345,000 tagged spring Chinook Salmon juveniles exiting Cle Elum Supplementation and Research Facility (Yakima River, Washington, USA) acclimation sites over thirteen brood years (2002–2014), evaluating smolt size, emigration timing, river flow, juvenile survival, and age-at-return. We observed a relationship between size and volitional exit timing of smolts from acclimation sites, with larger fish tending to emigrate earlier than smaller fish. Early emigration was also coincident with lower river flows near acclimation sites. Later emigration timing was correlated with an increase in apparent survival of juveniles to Bonneville Dam (500–530 km downstream of acclimation sites), but also with a lower rate of survival to return from sea. In general, for juveniles successfully emigrating downstream of Bonneville Dam, age-at-return increased with decreasing juvenile fish size and later emigration timing. Our results support a growing body of evidence that hatchery practices may result in larger smolts that tend to return at younger ages. Given the biological and economic consequences of younger age-at-maturation, methods to reverse this trend should be further explored and implemented.

Bosch, William J. (ORCID:0000000300447390)↗
DOE OSTI2026

Parallel-in-Time Solution of Allen-Cahn Equations by Integrating Operator Learning into the Parareal Method

While recent advances in deep learning have shown promising efficiency gains in solving time-dependent partial differential equations (PDEs), matching the accuracy of conventional numerical solvers still remains a challenge. One strategy to improve the accuracy of deep learning-based solutions for time-dependent PDEs is to use the learned model as the coarse propagator in the Parareal method and a traditional numerical method as the fine solver. However, successful integration of deep learning into the Parareal method requires consistency between the coarse and fine solvers, particularly for PDEs exhibiting rapid changes such as sharp transitions. Here, to ensure this consistency, we propose using convolutional neural networks (CNNs) to learn the fully discrete time-stepping operator defined by the same numerical scheme employed as the fine solver. We demonstrate the effectiveness of the proposed method in solving the classical and mass-conservative Allen–Cahn (AC) equations. Through iterative updates in the Parareal algorithm, our approach achieves a significant computational speedup compared to traditional fine solvers while converging to high-accuracy solutions. Our results highlight that the proposed hybrid Parareal algorithm effectively accelerates simulations, particularly when implemented on multiple GPUs, and converges to the desired accuracy in only a few iterations. Another advantage of our method is that the CNN model is trained on trajectory-based data generated from random initial conditions, such that the trained model can be used to solve the AC equations with various initial conditions without retraining. This work demonstrates the potential of integrating neural network methods into parallel-in-time frameworks for efficient and accurate simulations of time-dependent PDEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Modeling the influence of the solid electrolyte interphase on the sand’s time and dendrite formation on lithium metal electrodes

Lithium metal is a sought after battery material for its high energy density due to the low electrochemical potential and density. However, lithium metal is also highly reactive, which results in a strong propensity for dendrite formation. The Sand’s time has previously been used to predict the time of dendrite initiation on metals that do not form a solid-electrolyte interphase (SEI), but it has been shown that the Sand’s time is not accurate for lithium electrodes when using transport parameters associated with the electrolyte. Thus, we built a numerical model to simulate lithium ion transport through a growing SEI to predict the Sand’s time. The numerical model is shown to be more accurate than previous analytical solutions, especially for low current densities. We then analyze the sensitivity of the Sand’s time to different SEI properties and the chemical potential gradients present in the SEI, driving lithium transport. The results showed that high lithium concentration has a greater impact at high current density, while fast diffusivity is more important at low current density. Lastly, we modeled the influence of surface roughness on the plating evolution and chemical potential gradients when an SEI is present in comparison to the electrolyte. As a result, we demonstrate that the SEI plays a critical role in lithium electrode stability, and that improved characterization techniques are needed to better understand transport through the SEI and increase lithium metal utilization in energy storage devices.

Chemistry↗