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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 1,549 records · Page 86

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DOE OSTI2020

Toward ultrafast, ultra-stable imaging arrays: Superlattice doping to enhance the performance of backside-illuminated 3D-hybridized silicon photodetectors

In this paper, the authors report the latest results on their development of superlattice-doped, thinned, backside-illuminated (BSI), 3D-integrated photodiode detectors—a step toward their ultimate goal of demonstrating ultrafast, ultrastable CMOS imaging arrays. As with most silicon-based photodetectors, backside-illumination and backside surface passivation are keys to achieve the highest performance capability. The two-dimensional (2D) doping technique developed at the Jet Propulsion Laboratory (JPL) has proved to result in a highly efficient, highly stable detector response when combined with a variety of detectors. Here, JPL's 2D-doping has been combined with Sandia's BSI photodetectors hybridized with custom fanout wafer via copper Direct Bond Interconnect (DBI®), a technology that is rapidly becoming industry standard for BSI CMOS imaging arrays. The prototype detectors were packaged and evaluated with respect to their response to low energy electrons. The authors find that the responsivity of 2D-doped BSI detectors is higher than devices prepared using other surface passivation techniques (i.e., ion implantation). The success of the work described herein verifies that the 2D-doping processes previously developed for Sandia's frontside-illuminated photodetectors are generally applicable to BSI detectors and demonstrates for the first time that JPL's 2D-doping process is compatible with the Cu-DBI® technology.

Jewell, April D.↗

DOE OSTI2022

Monolithic Kerr and electro-optic hybrid microcombs

Microresonator-based soliton generation promises chip-scale integration of optical frequency combs for applications spanning from time keeping to frequency synthesis. Access to the soliton repetition rate is a prerequisite for those applications. While miniaturized cavities harness Kerr nonlinearity and enable terahertz soliton repetition rates, such high rates are not amenable to direct electronic detection. Here, we demonstrate hybrid Kerr and electro-optic microcombs using a lithium niobate thin film that exhibits both Kerr and Pockels nonlinearities. By interleaving the high-repetition-rate Kerr soliton comb with the low-repetition-rate electro-optic comb on the same waveguide, wide Kerr soliton mode spacing is divided within a single chip, allowing for direct electronic detection and feedback control of the soliton repetition rate. Our work establishes an integrated approach to electronically access terahertz solitons, paving the way for building chip-scale referenced comb sources.

42 ENGINEERING↗

NASA NTRS1991

Turbomachinery noise

Summarized here are key advances in experimental techniques and theoretical applications which point the way to a broad understanding and control of turbomachinery noise. On the experimental side, the development of effective inflow control techniques makes it possible to conduct, in ground based facilities, definitive experiments in internally controlled blade row interactions. Results can now be valid indicators of flight behavior and can provide a firm base for comparison with analytical results. Inflow control coupled with detailed diagnostic tools such as blade pressure measurements can be used to uncover the more subtle mechanisms such as rotor strut interaction, which can set tone levels for some engine configurations. Initial mappings of rotor wake-vortex flow fields have provided a data base for a first generation semiempirical flow disturbance model. Laser velocimetry offers a nonintrusive method for validating and improving the model. Digital data systems and signal processing algorithms are bringing mode measurement closer to a working tool that can be frequently applied to a real machine such as a turbofan engine. On the analytical side, models of most of the links in the chain from turbomachine blade source to far field observation point have been formulated. Three dimensional lifting surface theory for blade rows, including source noncompactness and cascade effects, blade row transmission models incorporating mode and frequency scattering, and modal radiation calculations, including hybrid numerical-analytical approaches, are tools which await further application.

Groeneweg, John F.↗

NASA NTRS2014

4D-Var Developement at GMAO

The Global Modeling and Assimilation Offce (GMAO) is currently using an IAU-based 3D-Var data assimilation system. GMAO has been experimenting with a 3D-Var-hybrid version of its data assimilation system (DAS) for over a year now, which will soon become operational and it will rapidly progress toward a 4D-EnVar. Concurrently, the machinery to exercise traditional 4DVar is in place and it is desirable to have a comparison of the traditional 4D approach with the other available options, and evaluate their performance in the Goddard Earth Observing System (GEOS) DAS. This work will also explore the possibility for constructing a reduced order model (ROM) to make traditional 4D-Var computationally attractive for increasing model resolutions. Part of the research on ROM will be to search for a suitably acceptable space to carry on the corresponding reduction. This poster illustrates how the IAU-based 4D-Var assimilation compares with our currently used IAU-based 3D-Var.

GMAO↗

NASA NTRSDate not supplied

Phase transition energetics-based mass loss modeling of chondritic Near Earth Objects

Mitigating potentially hazardous asteroids relies on accurate knowledge of their composition and critical physical characteristics such as shape and mass. Ground-based observatories and sample return missions in recent years have focused on such characterization to track Near Earth Objects. Historically, entry speeds of asteroids or mass change due to break-up/airburst have been considered in risk assessment studies as critical parameters. To this effect, most hydrocode simulations that consider energy deposition techniques rely on gross meteor modeling with simplistic spherical shapes while neglecting heat of ablation considerations. This is the general approach for fragment-cloud, pancake and hybrid fragmentation models. Alternatively, aerothermal fragmentation modeling that accounts for meteoritic shape change have typically focused on environmental modeling to understand break-up points primarily due to radiative heating. A physics-based risk assessment could benefit by modeling the environment and material response of typical meteoroidal materials as precursors to fragmentation. A greater focus on the material response to applied environmental heating will be emphasized here. This work presents a forward analysis of thermal and material response of stony meteorites by ac-counting for mass loss and shape change effects through a boundary layer formalism. This allows for simulations starting from simplistic shapes that could eventually lead to alterations in contours due to ablation. These effects would be characterized through phase transitions of the surface, such as melting and vaporization, in response to aerodynamic heating. Coupled ablation and radiative heating effects have been found to lead to reduced heat transfer coefficients in the computation of meteor mass loss rates. This has the potential to increase ground damage footprints for hazardous impacts. The effort here quantifies the heat transfer coefficient by accounting for ablation assisted by phase transition. The melt and vaporization mediated ablation of chondritic meteors provides essential recession estimates based upon surface environments. The altered mass due to such phenomena would be presented for a H-chondrite meteoritic sample.

Pratibha Raghunandan↗

DOE OSTI2025

Interfacial Strong Coupling and Negative Dispersion of Propagating Polaritons in Freestanding Oxide Membranes

Membranes of complex oxides like perovskite SrTiO3 extend the multi-functional promise of oxide electronics into the nanoscale regime of 2D materials. Here, it is demonstrated that freestanding oxide membranes supply a reconfigurable platform for nano-photonics based on propagating surface phonon polaritons. Infrared near-field imaging and spectroscopy enabled by a tunable ultrafast laser are applied to study pristine nano-thick SrTiO3 membranes prepared by hybrid molecular beam epitaxy. As predicted by coupled mode theory, it is found that strong coupling of interfacial polaritons realizes symmetric and antisymmetric hybridized modes with simultaneously tunable negative and positive group velocities. By resolving reflection of these propagating modes from membrane edges, defects, and substrate structures, their dispersion is quantified with position-resolved nano-spectroscopy. Remarkably, polariton negative dispersion is found to be both robust and tunable through choice of membrane dielectric environment and thickness, and proposes a novel design for in-plane Veselago lensing harnessing this control. This work lays the foundation for tunable transformation optics at the nanoscale using polaritons in a wide range of freestanding complex oxide membranes.

freestanding oxide membranes↗

DOE OSTI2021

Quantum-assisted associative adversarial network: applying quantum annealing in deep learning

Abstract Generative models have the capacity to model and generate new examples from a dataset and have an increasingly diverse set of applications driven by commercial and academic interest. In this work, we present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model. This graphical model is learned by a Boltzmann machine which learns low-dimensional feature representation of data extracted by the discriminator. A quantum processor can be used to sample from the model to train the Boltzmann machine. This novel hybrid quantum-classical algorithm joins a growing family of algorithms that use a quantum processor sampling subroutine in deep learning, and provides a scalable framework to test the advantages of quantum-assisted learning. For the latent space model, fully connected, symmetric bipartite and Chimera graph topologies are compared on a reduced stochastically binarized MNIST dataset, for both classical and quantum sampling methods. The quantum-assisted associative adversarial network successfully learns a generative model of the MNIST dataset for all topologies. Evaluated using the Fréchet inception distance and inception score, the quantum and classical versions of the algorithm are found to have equivalent performance for learning an implicit generative model of the MNIST dataset. Classical sampling is used to demonstrate the algorithm on the LSUN bedrooms dataset, indicating scalability to larger and color datasets. Though the quantum processor used here is a quantum annealer, the algorithm is general enough such that any quantum processor, such as gate model quantum computers, may be substituted as a sampler.

Wilson, Max (ORCID:0000000207983391)↗

DOE OSTI2023

Control of Photoswitching Kinetics with Strong Light–Matter Coupling in a Cavity

Most photochemistry occurs in the regime of weak light–matter coupling, in which a molecule absorbs a photon and then performs photochemistry from its excited state. In the strong coupling regime, enhanced light–matter interactions between an optical field and multiple molecules lead to collective hybrid light–matter states called polaritons. This strong coupling leads to fundamental changes in the nature of the excited states including multi-molecule delocalized excitations, modified potential energy surfaces, and dramatically altered energy levels relative to non-coupled molecules. Here, the effect of strong light–matter coupling on covalent photochemistry has not been well explored. Photoswitches undergo reversible intramolecular photoreactions that can be readily monitored spectroscopically. In this work, we study the effect of strong light–matter coupling on the kinetics of photoswitching within optical cavities. Reproducing prior experiments, photoswitching of spiropyran/merocyanine photoswitches is decelerated in a cavity. Fulgide photoswitches, however, show the opposite effect, with strong coupling accelerating photoswitching. While modified merocyanine switching can be explained by changes in radiative decay rates or the amount of light in the cavity, modified fulgide switching kinetics suggest direct changes to excited-state reaction kinetics.

Cavities↗

DOE OSTI2020

A higher-order finite element reactive transport model for unstructured and fractured grids

Abstract This work presents a new reactive transport framework that combines a powerful geochemistry engine with advanced numerical methods for flow and transport in subsurface fractured porous media. Specifically, the PhreeqcRM interface (developed by the USGS) is used to take advantage of a large library of equilibrium and kinetic aqueous and fluid-rock reactions, which has been validated by numerous experiments and benchmark studies. Fluid flow is modeled by the Mixed Hybrid Finite Element (FE) method, which provides smooth velocity fields even in highly heterogenous formations with discrete fractures. A multilinear Discontinuous Galerkin FE method is used to solve the multicomponent transport problem. This method is locally mass conserving and its second order convergence significantly reduces numerical dispersion. In terms of thermodynamics, the aqueous phase is considered as a compressible fluid and its properties are derived from a Cubic Plus Association (CPA) equation of state. The new simulator is validated against several benchmark problems (involving, e.g., Fickian and Nernst-Planck diffusion, isotope fractionation, advection-dispersion transport, and rock-fluid reactions) before demonstrating the expanded capabilities offered by the underlying FE foundation, such as high computational efficiency, parallelizability, low numerical dispersion, unstructured 3D gridding, and discrete fraction modeling.

58 GEOSCIENCES↗

DOE OSTI2021

Observation of spatially resolved Rashba states on the surface of CH 3 NH 3 PbBr 3 single crystals

Hybrid organic-inorganic perovskites (HOIPs) are prime candidates for studying Rashba effects due to the heavy metal and halogen atoms in their crystal structure coupled with predicted inversion symmetry breaking. Nevertheless, observation of the Rashba effect in cubic CH 3 NH 3 PbBr 3 single crystals that possess bulk inversion symmetry is the subject of extensive debate due to the lack of conclusive experiments and theoretical explanations. In this work, we provide experimental evidence that Rashba state in cubic CH 3 NH 3 PbBr 3 single crystals at room temperature occurs exclusively on the crystal surface and depends on specific surface termination that results in local symmetry breaking. We demonstrate this using a suite of spatially resolved and depth-sensitive techniques, including circular photogalvanic effect, inverse spin Hall effect, and multiphoton microscopy, that are supported by first principle calculations. Our work suggests using surface Rashba states in these materials for spintronic applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Dilute magnetic impurity-induced effective phonon magnetic moment in Fe-doped monolayer MoS 2

Realization of large effective phonon magnetic moment in monolayer MoS 2 has established an important route for exploring intriguing magnetic phenomena in a nonmagnetic material. The sizable coupling between the orbital transition and the circularly polarized phonon results in the large effective phonon magnetic moment. In this work, using magneto-Raman spectroscopy, we investigate substitutional doping of magnetic atoms as a tuning knob of the electronic and phononic properties of MoS 2 . We show that Fe-doping polarizes the spin of the conduction bands and introduces a localized Fe band underneath the conduction band. As a result, an additional orbital transition between the Mo 4d and Fe 3d states emerges, producing an orbital-phonon hybridized mode at 283 cm −1 . Our magnetic field dependent measurements demonstrate that this new mode carries 2.8 $\mu_{\mathrm{B}}$ effective phonon magnetic moment, which is comparable to that of the undoped MoS 2 . Moreover, even though a long-range magnetic order is absent in Fe-doped MoS 2 , the local magnetic moment of Fe modifies the nature of the spin fluctuation, producing monotonically increasing quasielastic scattering spectral weight as temperature decreases. Our results highlight two-dimensional dilute magnetic semiconductors synthesized by substitutional doping as a promising material platform to manipulate the phonon magnetic moment through orbital-phonon coupling.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Dark-exciton energy splitting in monolayer W Se 2 : Insights from time-dependent density functional theory

Here, we present here a formalism based on time-dependent density functional theory to describe characteristics of both intra- and intervalley excitons in semiconductors, the latter of which had remained a challenge. Through the usage of an appropriate exchange-correlation kernel (nanoquanta), we trace the energy difference between the intra- and intervalley dark excitons in monolayer (1L) W⁢ Se 2 to the domination of the exchange part in the exchange-correlation energies of these states. Furthermore, our calculated transition contribution maps establish the momentum resolved weights of the electron-hole excitations in both bright and dark excitons thereby providing a comprehensive understanding of excitonic properties of 1L W⁢ Se 2 . We find that the states consist of hybridized excitations around the corresponding valleys which leads to brightening of the dark excitons, i.e., significantly decreasing their lifetime which is reflected in the PL spectrum. Using many-body perturbation theory, we calculate the phonon contribution to the energy band gap and the linewidths of the excited electrons, holes, and (bright) exciton to find that as the temperature increases the band gap significantly decreases, while the linewidths increase. Our work paves for describing the ultrafast charge dynamics of transition metal dichalcogenide within an ab initio framework.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Persistent Room-Temperature Photodarkening in Cu-Doped β - Ga 2 O 3

β–Ga 2 O 3 is an ultrawide band gap semiconductor with emerging applications in power electronics. Here, the introduction of acceptor dopants yields semi-insulating substrates necessary for thin-film devices. In the present work, exposure of Cu-doped β–Ga 2 O 3 to UV light > 4 eV is shown to cause large, persistent photo-induced darkening at room temperature. Electron paramagnetic resonance spectroscopy indicates that light exposure converts Cu 2+ to Cu 3+ , a rare oxidation state that is responsible for the optical absorption. The photodarkening is accompanied by the appearance of O–H vibrational modes in the infrared spectrum. Hybrid function calculations show that Cu acceptors can favorably complex with hydrogen donors incorporated as interstitial (Hi) or substitutional (HO) defects. When Cu Ga –HO complexes absorb light, hydrogen is released, contributing to the observed Cu 3+ species and O–H modes.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model

Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. Furthermore, uncertain weather conditions pose a great challenge to the estimation accuracy of the estimation models. With the enhanced integration of intelligent electronic devices and the realization of associated automation in the power grid, renewable energy data is becoming more accessible, which can be utilized by deep learning models and improve the PV power generation estimation accuracy. In this paper, a hybrid deep learning model driven by external weather data is proposed to do day-ahead PV output forecasting at 15-minute-interval. The proposed model is motivated by the recent advancement of Long-Short-Term-Memory (LSTM) networks and AutoEncoder (AE), which estimates uncertainties in sequence while making the prediction for complex weather conditions. Meanwhile, the persistence model (PM) is used to predict continuous sunny weather conditions. The forecasting result is validated with data from multiple locations

42 ENGINEERING↗

DOE OSTI2025

Comprehensive Diagnostics for Bond-Selective Nanocarbon Growth in Plasmas

This award was continuation funding for our previously supported efforts into carbon nanoparticle synthesis with tunable bond hybridization, including for luminescent carbon nanoparticles, amorphous carbon nanoparticles, and eventually nanodiamond. Beginning in early 2021 we supported preliminary experiments with in-house funds. In February 2021 our user proposal to the PCRF to investigate this topic was accepted and our experiments at PCRF took place in July 2022. DOE awarded this proposal to support data analysis based on those experiments, and supported the continued efforts towards finalizing that work. With those results and further efforts, we will be able to finalize our early research into nucleation and growth of carbon nanoparticles for a long-term objective of selective bonding in carbon and other nanomaterials grown in low-temperature plasmas. We made several important discoveries.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2016

An Application of CFD to Guide Forced Boundary-Layer Transition for Low-Speed Tests of a Hybrid Wing-Body Configuration

A hybrid transition trip-dot sizing and placement test technique was developed in support of recent experimental research on a hybrid wing-body configuration under study for the NASA Environmentally Responsible Aviation project. The approach combines traditional methods with Computational Fluid Dynamics. The application had three-dimensional boundary layers that were simulated with either fully turbulent or transitional flow models using established Reynolds-Averaged Navier-Stokes methods. Trip strip effectiveness was verified experimentally using infrared thermography during a low-speed wind tunnel test. Although the work was performed on one specific configuration, the process was based on fundamental flow physics and could be applicable to other configurations.

Luckring, James M.↗

NASA NTRSDate not supplied

Enabling Interoperability in Earth System Digital Twins (ESDT): Integrating Observations, Models, and AI for Actionable Insights Through NASA'S Intelligent Systems Technology Program

NASA’s Intelligent Systems Technology Program (IST) is driving a paradigm shift in Earth science through the development of Earth System Digital Twins (ESDT). These integrated information systems create a dynamic "digital replica" of the Earth by harmonizing continuous, multi-source observations with high-fidelity models and state-of-the-art artificial intelligence (AI) that enable “What now?”, “What next?”, and “What if?” scenario building. These scenarios are reflected in NASA IST’s series of ESDTs, from the Coastal Zone Digital Twin that integrates complex data on the current state of the Chesapeake Bay to the Terrestrial Environmental Rapid-Replication and Assimilation Hydrometeorological (TerraHydro) AI-based ESDT that forecasts water movement across Earth’s surface, to the Agriculture Land Information System (AgLIS) which can be used to assess optimal planting dates and crop yield estimates. By bridging the gap between vast data archives and actionable insights, these projects enable a system-of-systems approach to understanding complex, interacting Earth processes. This poster will highlight recent innovations and future directions from NASA’s ESDT initiatives: Continuous Data Assimilation & Multi-Source Fusion. A core requirement of the ESDT work is the transition from static models to dynamic "living" replicas. This involves creating frameworks for the continual assimilation of near-real-time data from uncoordinated, heterogeneous sources, including satellite observations and airborne assets, and ground-based Internet of Things (IoT) sensors. These systems link design, operational status, and environmental data, ensuring the digital twin accurately reflects the current state of the physical Earth system. High-Fidelity Hybrid Modeling & Computational Acceleration to enable interactive "what-if" explorations, programs are moving beyond traditional, slow physical solvers by developing fast surrogate machine learning models and Deep Generative Models (DGMs). These hybrid approaches use neural networks to emulate complex physics, such as cloud feedback or ocean dynamics, at a fraction of the original computing cost, often leveraging advanced hardware like Graphics Processing Units (GPUs) to achieve the necessary scale. Federated Ecosystems & Interoperable Frameworks rather than building isolated tools, NASA IST is moving toward federated ESDTs and reusable analytic collaborative frameworks. This theme focuses on interoperability standards and common ontologies that allow specialized digital twins to interact and share data. This system-of-systems architecture supports multi-discipline investigations, such as analyzing how upstream watershed changes impact downstream urban flooding or how wildfire emissions affect regional air quality. By leveraging these advancements, ESDTs empower researchers and decision-makers to conduct real-time analysis and run complex hypothetical scenarios, ultimately improving our understanding of Earth’s evolving systems and informing critical real-world applications.

Earth System↗

DOE OSTI2021

Incorporating Valve Switching Losses Into a Static Optimal Control Algorithm for the Hybrid Hydraulic-Electric Architecture (HHEA)

The Hybrid Hydraulic-Electric Architecture (HHEA) has, in recent years, been proposed as an energy efficient alternative to conventional load-sensing architectures in mobile machines such as excavators and wheel-loaders. HHEA leverages the advantages of hydraulic power and electric power to eliminate throttling valves while also improving the energy and control performance of the system. The architecture utilizes a set of common pressure rails to provide a majority of power and and a small electric motor driven pump to modulate this power to meet the exact demand. Previous work has developed a computationally efficient Lagrange Multiplier approach for determining the optimal pressure rail selections that minimizes the energy losses in the system. The static model used considers only the energy use for each pressure rail selection but not the losses associated with the valves during the transition. This paper presents an approach to include the switching losses in the model and in the optimization procedure. To capture the switching losses, switching events between different rails and at various input and output flow rates were simulated with consideration of valve spool dynamics. A parameterized model that summarizes the losses is then obtained, allowing switching losses to be added to the previous energy analysis. The performance of the switching loss model was compared with reference data obtained from a high-fidelity simulation model. To incorporate the switching losses into optimal control algorithm, an efficient dynamic programming approach that prevents frequent switching is adopted in place of the Lagrange multiplier approach. The overall effect of switching losses on the energy consumption and optimal control decisions is presented. In general, switching losses contribute to about 9–10% of input energy.

24 POWER TRANSMISSION AND DISTRIBUTION↗