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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Laser Interferometer Space Antenna (LISA) Far Field Phase Patterns

The Laser Interferometer Space Antenna (LISA) consists of three spacecraft in orbit about the sun. The orbits are chosen such that the three spacecraft are always at (roughly) the vertices of a equilateral triangle with 5 million kilometer leg lengths. Even though the distances between the three spacecraft are 5 million kilometers, the expected phase shifts between any two beams, due to a gravitational wave, only correspond to a distance change of about 10 pico meters, which is about 10(exp -5) waves for a laser wavelength of 1064 nm. To obtain the best signal-to-noise ratio, noise sources such as changes in the apparent distances due to pointing jitter must be controlled carefully. This is the main reason for determining the far-field phase patterns of a LISA type telescope. Because of torque on the LISA spacecraft and other disturbances, continuous adjustments to the pointing of the telescopes are required. These pointing adjustments will be a "jitter" source. If the transmitted wave is perfectly spherical then rotations (Jitter) about its geometric center will not produce any effect at the receiving spacecraft. However, if the outgoing wave is not perfectly spherical, then pointing jitter will produce a phase variation at the receiving spacecraft. The following sections describe the "brute force" computational approach used to determine the scalar wave front as a function of exit pupil (Zernike) aberrations and to show the results (mostly graphically) of the computations. This approach is straightforward and produces believable phase variations to sub-pico meter accuracy over distances on the order of 5 million kilometers. As such this analyzes the far field phase sensitivity to exit pupil aberrations.

Waluschka, Eugene↗
NASA NTRS2013

Surface Freshwater Storage and Variability in the Amazon Basin from Multi-Satellite Observations, 1993-2007

The amount of water stored and moving through the surface water bodies of large river basins (river, floodplains, wetlands) plays a major role in the global water and biochemical cycles and is a critical parameter for water resources management. However, the spatio-temporal variations of these freshwater reservoirs are still widely unknown at the global scale. Here, we propose a hypsographic curve approach to estimate surface freshwater storage variations over the Amazon basin combining surface water extent from a multi-satellite-technique with topographic data from the Global Digital Elevation Model (GDEM) from Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Monthly surface water storage variations for 1993-2007 are presented, showing a strong seasonal and interannual variability, and are evaluated against in situ river discharge and precipitation. The basin-scale mean annual amplitude of approx. 1200 cu km is in the range of previous estimates and contributes to about half of the Gravity Recovery And Climate Experiment (GRACE) total water storage variations. For the first time, we map the surface water volume anomaly during the extreme droughts of 1997 (October-November) and 2005 (September-October) and found that during these dry events the water stored in the river and flood-plains of the Amazon basin was, respectively, approx. 230 (approx. 40%) and 210 (approx. 50%) cu km below the 1993-2007 average. This new 15year data set of surface water volume represents an unprecedented source of information for future hydrological or climate modeling of the Amazon. It is also a first step toward the development of such database at the global scale.

Storage↗
NASA NTRS2014

Theoretical Foundations of Remote Sensing for Glacier Assessment and Mapping

The international scientific community is actively engaged in assessing ice sheet and alpine glacier fluctuations at a variety of scales. The availability of stereoscopic, multitemporal, and multispectral satellite imagery from the optical wavelength regions of the electromagnetic spectrum has greatly increased our ability to assess glaciological conditions and map the cryosphere. There are, however, important issues and limitations associated with accurate satellite information extraction and mapping, as well as new opportunities for assessment and mapping that are all rooted in understanding the fundamentals of the radiation transfer cascade. We address the primary radiation transfer components, relate them to glacier dynamics and mapping, and summarize the analytical approaches that permit transformation of spectral variation into thematic and quantitative parameters. We also discuss the integration of satellite-derived information into numerical modeling approaches to facilitate understandings of glacier dynamics and causal mechanisms.

Remote Sensing↗
DOE OSTI2020

Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services

Flexible loads have great potential to improve the electric grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. This paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environmental impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services: Preprint

Flexible loads have great potential to improve the power grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. The paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environment impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.

control↗
NASA NTRS2010

Assessment of Aerodynamic Challenges of a Variable-Speed Power Turbine for Large Civil Tilt-Rotor Application

The main rotors of the NASA Large Civil Tilt-Rotor notional vehicle operate over a wide speed-range (100% at take-off to 54% at cruise). The variable-speed power turbine, when coupled to a fixed-gear-ratio transmission, offers one approach to accomplish this speed variation. The key aero-challenges of the variable-speed power turbine are related to high work factors at cruise, where the power turbine operates at 54% of take-off speed, wide incidence variations into the vane, blade, and exit-guide-vane rows associated with the power-turbine speed change, and the impact of low aft-stage Reynolds number (transitional flow) at 28 kft cruise. Meanline and 2-D Reynolds-Averaged Navier- Stokes analyses are used to characterize the variable-speed power-turbine aerodynamic challenges and to outline a conceptual design approach that accounts for multi-point operation. Identified technical challenges associated with the aerodynamics of high work factor, incidence-tolerant blading, and low Reynolds numbers pose research needs outlined in the paper

Welch, Gerand E.↗
NASA NTRS2020

Using Flexible Execution, Replanning, and Model Parameter Updates to Address Environmental Uncertainty for a Planetary Lander

Planning in an unknown environment presents a number of challenges: incorrect world models, unpredictable variation in execution, and fixed depletable resources. Productivity in the face of these challenges requires an integrated approach to planning and execution that is capable of accomplishing goals, reacting to variation, and maximizing overall utility. We examine this problem in the context of a Europa Lander concept mission. We model the problem as a hierarchical task network, framing it as a utility maximization problem with a depletable energy resource. Then, we propose a planning and execution framework that responds to feedback using three techniques: flexible execution, periodic replanning, and online parameter estimation and re-goaling. We demonstrate the efficacy of each of these techniques through simulation of a Europa Lander mission, showing higher utility achievement compared to simpler approaches, when presented with variation in energy consumption. We demonstrate that an approach to scheduling and execution that is grounded in decision theory and integrated planning and execution will be an enabling technology for future tightly-constrained planetary surface missions.

Chien, Steve↗
NASA NTRSDate not supplied

Where Lies the Quantumness Behind Detecting Electromagnetic Waves for Frequencies from Infrared and Up?

The synthesis of Newtonian concept of corpuscles during emission with Huygens’ concept of secondary-wavelets during propagation implies that all EM radiations from quantized atoms and molecules are released as discrete amount of energies. However, they propagate out as time-finite Maxwellian light pulses. Huygens also underscored that his secondary wavelets keep propagating as independent pulses in the absence of any interacting medium, or until intercepted by an interface with a medium or a detector. Then we use the Superposition Principle and the coherence theory to derive Einstein’s photoelectric equation by summing innumerable random time-finite pulses. This process driven approach should yield the characteristic statistical variations of photoelectron current pulses, as generated by photodetectors for different kinds of light sources. Lamb & Scully originally proposed this semiclassical approach without assuming that light actually consists of time finite pulses. The quantumness remains confined within the excitation and de-excitations processes in the material particles.

Electromagnetic Waves↗
DOE OSTI2023

Extending Parsimonious Bayesian Inference

Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Analysis of the Approach Direction of HLS to a South Pole Landing Site Subject to Lighting and Communication Constraints

The NRHO used as the staging orbit for crewed missions to the lunar surface by the HLS remains nearly perpendicular to the Earth-Moon line of sight, however, due to the libration of the Moon the location of the South Pole is ever shifting beneath it. DTE communication as well as ground illumination conditions near the South Pole coincide with lunar libration, with approximately 2-week cycles of DTE communication and solar availability. Additionally, the constant shifting of the South Pole region with respect to the NRHO leads to variations in the nominal HLS approach path to the surface in order to maintain near in-plane alignment of the NRHO, LLO, and approach phase. Making adjustments to the nominal approach path to achieve desired communication and lighting conditions quickly raises in-space ΔV. This analysis compares the ΔV impact of several trajectory strategies that could be used to alter the HLS approach path.

HLS↗
NASA NTRSDate not supplied

Analysis of the Approach Direction of the Human Landing System to a South Pole Landing Site Subject to Lighting and Communication Constraints

The NRHO used as the staging orbit for crewed missions to the lunar surface by the HLS remains nearly perpendicular to the Earth-Moon line of sight, however, due to the libration of the Moon the location of the South Pole is ever shifting beneath it. DTE communication as well as ground illumination conditions near the South Pole coincide with lunar libration, with approximately 2-week cycles of DTE communication and solar availability. Additionally, the constant shifting of the South Pole region with respect to the NRHO leads to variations in the nominal HLS approach path to the surface in order to maintain near in-plane alignment of the NRHO, LLO, and approach phase. Making adjustments to the nominal approach path to achieve desired communication and lighting conditions quickly raises in-space ∆V. This analysis compares the ∆V impact of several trajectory strategies that could be used to alter the HLS approach path.

HLS↗
NASA NTRS1973

Analysis of response to wind-shears using the optimal control model of the human operator

The effects of wind-shears on the approach performance of a STOL aircraft are analyzed using the optimal-control model of the human operator. This analysis involves a time-varying situation that is more complex than is traditionally treated by human operator modelling techniques. The extensions to the time-varying case are discussed and results are presented that illustrate the effects of wind shears on category II window-performance and the effects on performance of pilot time-delay and of variations in pilot gain during approach.

Baron, S.↗
NASA NTRS2004

Modeling Studies of the MODIS Solar Diffuser Attenuation Screen and Comparison with On-Orbit Measurements

The MODIS instrument relies on solar calibration to achieve the required radiometric accuracy. This solar calibration occurs as the TERRA spacecraft comes up over the North Pole. The earth underneath the spacecraft is still dark for approximately one minute and the sun is just rising over the earth's north polar regions. During this time the sun moves through about 4 degrees, the scan mirror rotates about 19 times and about 50 frames (exposures) are made of the white solar diffuser. For some of MODIS'S bands the brightness of the screen is reduced, to prevent detector saturation, by means of a pinhole screen, which produces approximately 600 pinhole images of the sun within the field of view of any one detector. Previous attempts at creating a detailed radiometric model of this calibration scenario produced intensity variations on the focal planes with insufficient detail to be useful. The current computational approach produces results, which take into account the motion of the sun and the scan mirror and produces variations, which strongly resemble the observed focal plane intensity variations. The computational approach and results and a comparison with actual observational data are presented.

Waluschka, Eugene↗
NASA NTRS1979

Effect of steady-state temperature distortion and combined distortion on inlet flow to a turbofan engine

Flow angle, static pressure, total temperature and total pressure were measured in the inlet duct upstream of a turbofan engine operating with temperature distortion or combined pressure-temperature distortion. Such measurements are useful in the evaluation of analytical models of inlet distortion. A rotating gaseous-hydrogen burner and a circumferential 180 degrees-extent screen configuration mounted on a rotatable assembly generated the distortions. Reynolds number index was maintained at 0.5 and engine corrected low-rotor speeds were held at 6000 and 8600 rpm. The measurements showed that at the entrance to the engine, flow angle was largest in the hub region. As flow approached the engine, yaw angle (circumferential variation) increased and pitch angle (radial variation) decreased. The magnitude of static-pressure distortion measured along the inlet-duct and extended bullet nose walls increased exponentially as flow approached the engine.

Soeder, R. H.↗
NASA NTRS2009

Mean Element Propagations Using Numerical Averaging

The long-term evolution characteristics (and stability) of an orbit are best characterized using a mean element propagation of the perturbed two body variational equations of motion. The averaging process eliminates short period terms leaving only secular and long period effects. In this study, a non-traditional approach is taken that averages the variational equations using adaptive numerical techniques and then numerically integrating the resulting EOMs. Doing this avoids the Fourier series expansions and truncations required by the traditional analytic methods. The resultant numerical techniques can be easily adapted to propagations at most solar system bodies.

orbit propagations↗
DOE OSTI2025

Towards robust surrogate models: Benchmarking machine learning approaches to expediting phase field simulations of brittle fracture

Data-driven approaches have the potential to make modeling complex, nonlinear physical phenomena significantly more computationally tractable. For example, computational modeling of fracture is a core challenge where machine learning techniques have the potential to provide a much needed speedup that would enable progress in areas such as multi-scale modeling and uncertainty quantification. Currently, phase field modeling (PFM) of fracture is one such approach that offers a convenient variational formulation to model crack nucleation, branching and propagation. To date, machine learning techniques have shown promise in approximating PFM simulations. While standard fracture benchmarks represent realistic scenarios frequently observed in practice, they typically do not provide sufficiently challenging tests for data-driven methods. Here, to address this gap, we introduce a challenging dataset based on PFM simulations designed to benchmark and advance ML methods for fracture modeling. This dataset includes three energy decomposition methods, two boundary conditions, and 1000 random initial crack configurations for a total of 6000 simulations. Each sample contains 100 time steps capturing the temporal evolution of the crack field. Alongside this dataset, we also implement and evaluate Physics Informed Neural Networks (PINN), Fourier Neural Operators (FNO), and UNet models as baselines, and explore the impact of ensembling strategies on prediction accuracy. With this combination of our dataset and baseline models drawn from the literature we aim to provide a standardized and challenging benchmark for evaluating machine learning approaches to solid mechanics. Our results highlight both the promise and limitations of popular current models, and demonstrate the utility of this dataset as a testbed for advancing machine learning in fracture mechanics research.

Benchmark dataset↗
DOE OSTI2021

Gutzwiller hybrid quantum-classical computing approach for correlated materials

Rapid progress in noisy intermediate-scale quantum (NISQ) computing technology has led to the development of novel resource-efficient hybrid quantum-classical algorithms, such as the variational quantum eigensolver (VQE), that can address open challenges in quantum chemistry, physics, and material science. Proof-of-principle quantum chemistry simulations for small molecules have been demonstrated on NISQ devices. While several approaches have been theoretically proposed for correlated materials, NISQ simulations of interacting periodic models on current quantum devices have not yet been demonstrated. Here, we develop a hybrid quantum-classical simulation framework for correlated electron systems based on the Gutzwiller variational embedding approach. We implement this framework on Rigetti quantum processing units (QPUs) and apply it to the periodic Anderson model, which describes a correlated heavy electron band hybridizing with noninteracting conduction electrons. Our simulation results quantitatively reproduce the known ground state quantum phase diagram including metallic, Kondo and Mott insulating phases. This is the first fully self-consistent hybrid quantum-classical simulation of an infinite correlated lattice model executed on QPUs, demonstrating that the Gutzwiller hybrid quantum-classical embedding framework is a powerful approach to simulate correlated materials on NISQ hardware. This benchmark study also puts forth a concrete pathway towards practical quantum advantage on NISQ devices.

36 MATERIALS SCIENCE↗