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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Kernel learning backward SDE filter for data assimilation

In this paper, we develop a kernel learning backward SDE filter method to estimate the state of a stochastic dynamical system based on its partial noisy observations. A system of forward backward stochastic differential equations is used to propagate the state of the target dynamical model, and Bayesian inference is applied to incorporate the observational information. Further, to characterize the dynamical model in the entire state space, we introduce a kernel learning method to learn a continuous global approximation for the conditional probability density function of the target state by using discrete approximated density values as training data. Numerical experiments demonstrate that the kernel learning backward SDE is highly effective.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

Quicker Q-Learning in Multi-Agent Systems

Multi-agent learning in Markov Decisions Problems is challenging because of the presence ot two credit assignment problems: 1) How to credit an action taken at time step t for rewards received at t' greater than t; and 2) How to credit an action taken by agent i considering the system reward is a function of the actions of all the agents. The first credit assignment problem is typically addressed with temporal difference methods such as Q-learning OK TD(lambda) The second credit assi,onment problem is typically addressed either by hand-crafting reward functions that assign proper credit to an agent, or by making certain independence assumptions about an agent's state-space and reward function. To address both credit assignment problems simultaneously, we propose the Q Updates with Immediate Counterfactual Rewards-learning (QUICR-learning) designed to improve both the convergence properties and performance of Q-learning in large multi-agent problems. Instead of assuming that an agent s value function can be made independent of other agents, this method suppresses the impact of other agents using counterfactual rewards. Results on multi-agent grid-world problems over multiple topologies show that QUICR-learning can achieve up to thirty fold improvements in performance over both conventional and local Q-learning in the largest tested systems.

Agogino, Adrian K.↗
NASA NTRS2015

Real-Time Prognostics of a Rotary Valve Actuator

Valves are used in many domains and often have system-critical functions. As such, it is important to monitor the health of valves and their actuators and predict remaining useful life. In this work, we develop a model-based prognostics approach for a rotary valve actuator. Due to limited observability of the component with multiple failure modes, a lumped damage approach is proposed for estimation and prediction of damage progression. In order to support the goal of real-time prognostics, an approach to prediction is developed that does not require online simulation to compute remaining life, rather, a function mapping the damage state to remaining useful life is found offline so that predictions can be made quickly online with a single function evaluation. Simulation results demonstrate the overall methodology, validating the lumped damage approach and demonstrating real-time prognostics.

actuator↗
DOE OSTI2022

A robust solver for wavefunction-based density functional theory calculations

A new iterative solver is proposed to efficiently calculate the ground state electronic structure in density functional theory calculations. This algorithm is particularly useful for simulating physical systems considered difficult to converge by standard solvers, in particular metallic systems. Here, the effectiveness of the proposed algorithm is demonstrated on various applications.

42 ENGINEERING↗
DOE OSTI2025

Encoding hierarchical 3D architecture through inverse design of programmable bonds

The ability to fabricate materials and devices at small scales by design has resulted in tremendous technological progress. However, the need for engineered three-dimensional (3D) nanoscale materials requires new strategies for organizing nanocomponents. Here we demonstrate an inverse design approach for the assembly of nanoparticles into hierarchically ordered 3D organizations using DNA voxels with directional, addressable bonds. By identifying intrinsic symmetries in repeating mesoscale structural motifs, we prescribe a set of voxels, termed a mesovoxel, that are assembled into target 3D crystals. The relationship between different degrees of encoded information used for voxel bonds and the fidelity of assembly is investigated using experimental and computational methods. We apply this assembly strategy to create periodic 3D nanoparticle ordered organizations, including structures with low-dimensional elements, helical motifs, a nanoscale analogue of a face-centred perovskite crystal and a distributed Bragg reflector based on a crystal with plasmonic and photonic length-scale regimes.

36 MATERIALS SCIENCE↗
NASA NTRS1972

Excitation of the metastable E(3 Sigma g plus) state of N2 by electron impact

The contribution of the N2(E(3 Sigma g plus)) state to the total metastable excitation function of N2 assessed on the basis of time-of-flight studies of metastable nitrogen molecules. The cross section for electron impact excitation state was determined in the domain of the resonance form threshold (11.87 eV) to an energy of about 13 eV. The maximum value of the cross section was found to be (7.0 + or - 4.0) x 10 to the -18th power sq cm at an energy of 12.2 eV. The measurement was made absolute by using the previously determined yield of the metastable detector, the lifetime of the E state, and by eliminating the energy spread in the electron beam from the raw data. The half-width (FWHM) of the resonance-like excitation function near threshold was found to be about 0.4 eV. No substantial evidence was obtained from the present data for the presence of the nonresonant part of the excitation function for the state studied.

Borst, W. L.↗
DOE OSTI2021

Control of plasmons in doped topological insulators via basis atoms

Collective excitations in topologically nontrivial systems have attracted considerable attention in recent years. Here we study plasmons in the Su-Schrieffer-Heeger model whose low-energy electronic band is only partially filled, such that the system is metallic. Using the random phase approximation, we calculate the intra- and interband polarization functions and determine the bulk plasmonic dispersion from the dielectric function. In this work, we find that the sublattice basis states strongly affect the polarization functions and therefore control the system’s plasmonic excitations. By varying the real-space separation of these local orbitals, one can thus selectively enhance or suppress the plasmonic energies via a tunable trade-off between intraband and interband screening processes. Specifically, this mechanism can be used to stabilize undamped high energy plasmons that have already been reported in related models. We propose scenarios on how to control and observe these effects in experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1992

Monitoring environmental state of Alaskan forests with AIRSAR

During March 1988 and May 1991, the JPL airborne synthetic aperture radar, AIRSAR, collected sets of multi-temporal imagery of the Bonanza Creek Experimental Forest near Fairbanks, Alaska. These data sets consist of series of multi-polarized images collected at P-, L-, and C-bands each over a period of a few days. The AIRSAR campaigns were complemented with extensive ground measurements that included observations of both static canopy characteristics such as forest architecture as well as properties that vary on short term time scales such as canopy dielectric conditions. Observations exist for several stands of deciduous and coniferous species including white spruce (Picea glauca), black spruce (Picea mariana), and balsam poplar (Populus balsamifera). Although the duration of each campaign was fairly short, significant changes in environmental conditions caused notable variations in the physiological state of the canopies. During the 1988 campaign, environmental conditions ranged from unseasonably warm to more normal subfreezing temperatures. This permitted AIRSAR observations of frozen and thawed canopy states. During May 1991, ice jams that occurred along the river caused many stands to flood while the subsequent clearing of the river then allowed the waters to recede, leaving a snow covered ground surface. This allowed observations of several stands during both flooded and nonflooded conditions. Furthermore, the local weather varied from clear sunny days to heavy overcast days with some occurrence of rain. Measurements of leaf water potential indicated that this caused significant variations in canopy water status, allowing SAR observations of water stressed and unstressed trees. Mean backscatter from several stands is examined for the various canopy physiological states. The changes in canopy backscatter that occur as a function of environmental and physiological state are analyzed. Preliminary results of a backscatter signature modeling analysis are presented. The implications of using SAR to monitor canopy phenological state are addressed.

Mcdonald, Kyle C.↗
NASA NTRS1996

Quantum Probability Cancellation Due to a Single-Photon State

When an N-photon state enters a lossless symmetric beamsplitter from one input port, the photon distribution for the two output ports has the form of Bernouli Binormial, with highest probability at equal partition (N/2 at one outport and N/2 at the other). However, injection of a single photon state at the other input port can dramatically change the photon distribution at the outputs, resulting in zero probability at equal partition. Such a strong deviation from classical particle theory stems from quantum probability amplitude cancellation. The effect persists even if the N-photon state is replaced by an arbitrary state of light. A special case is the coherent state which corresponds to homodyne detection of a single photon state and can lead to the measurement of the wave function of a single photon state.

Ou, Z. Y.↗
NASA NTRS2018

Human-Automation Allocations for Current Robotic Space Operations

Within the Human Research Program, one risk delineates the uncertainty surrounding crew working with automation and robotics in spaceflight. The Risk of Inadequate Design of Human and Automation/Robotic Integration (HARI) is concerned with the detrimental effects on crew performance due to ineffective user interfaces, system designs and/or functional task allocation, potentially compromising mission success and safety. Risk arises because we have limited experience with complex automation and robotics. One key gap within HARI, is the gap related to functional allocation. The gap states: We need to evaluate, develop, and validate methods and guidelines for identifying human-automation/robot task information needs, function allocation, and team composition for future long duration, long distance space missions. Allocations determine the human-system performance as it identifies the functions and performance levels required by the automation/robotic system, and in turn, what work the crew is expected to perform and the necessary human performance requirements. Allocations must take into account each of the human, automation, and robotic systems capabilities and limitations. Some functions may be intuitively assigned to the human versus the robot, but to optimize efficiency and effectiveness, purposeful role assignments will be required. The role of automation and robotics will significantly change in future exploration missions, particularly as crew becomes more autonomous from ground controllers. Thus, we must understand the suitability of existing function allocation methods within NASA as well as the existing allocations established by the few robotic systems that are operational in spaceflight. In order to evaluate future methods of robotic allocations, we must first benchmark the allocations and allocation methods that have been used. We will present 1) documentation of human-automation-robotic allocations in existing, operational spaceflight systems; and 2) To gather existing lessons learned and best practices in these role assignments, from spaceflight operational experience of crew and ground teams that may be used to guide development for future systems. NASA and other space agencies have operational spaceflight experience with two key Human-Automation-Robotic (HAR) systems: heavy lift robotic arms and planetary robotic explorers. Additionally, NASA has invested in high-fidelity rover systems that can carry crew, building beyond Apollo's lunar rover. The heavy lift robotic arms reviewed are: Space Station Remote Manipulator System (SSRMS), Japanese Remote Manipulator System (JEMRMS), and the European Robotic Arm (ERA, designed but not deployed in space). The robotic rover systems reviewed are: Mars Exploration Rovers, Mars Science Laboratory rover, and the high-fidelity K10 rovers. Much of the design and operational feedback for these systems have been communicated to flight controllers and robotic design teams. As part of the mitigating the HARI risk for future human spaceflight operations, we must document function allocations between robots and humans that have worked well in practice.

robotic allocation↗
DOE OSTI2022

Model predictive control for matrix converter operating in current control mode with load current estimation

A matrix converter system operating in current control mode is provided. The matrix converter includes a switching matrix coupled between a low voltage side and a high voltage side, wherein the matrix converter is coupled at its low voltage side to a generator for receiving an input power including an input current and transforming the input power into an output power at its high voltage side, wherein a load is coupled to the high voltage side. The control system includes a load observer and a model predictive controller (MPC). The load observer is configured to estimate a load current that flows to the load from the high voltage side of the matrix converter as a function of a switching state of the switching matrix, an output voltage output at the high voltage side of the matrix converter, and the input current. The MPC is configured to select the switching state of the switching matrix to meet one or more control objectives defined by minimization of a multi-objective function that tracks the output voltage and the input current using the estimated load current.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Range-separated thermal hybrid exchange-correlation density functional for accurate band-gap calculations of warm dense matter

Here, in this study, we present a range-separated thermal hybrid exchange-correlation (XC) functional, termed RS-KDT0, which provides highly accurate band-gap predictions at elevated temperature while reducing the computational cost relative to thermal global hybrid functionals. At zero temperature, RS-KDT0 recovers the widely used ground-state range-separated hybrid functional HSE06. To assess its performance across temperature regimes, we conducted static calculations of electronic pressure, band gaps, and band structures. RS-KDT0 exhibits the same level of accuracy as HSE06 in the low-temperature regime and demonstrates improved agreement with finite-temperature GW calculations at elevated temperatures when compared to other thermal XC functionals such as the generalized gradient approximation (GGA)-level KDT16 and the global hybrid KDT0.

density functional theory↗
DOE OSTI2025

Discovering CO Adsorption and Desorption Pathways from Chemical Reaction Neural Network Modeling of Transient Kinetics Spectroscopy

Here, we demonstrate a data-driven approach to interpreting surface reactions by combining time-resolved gas pulsing infrared spectroscopy with chemical reaction neural networks (CRNNs). Using CO adsorption and desorption on Pd(111) at 460–490 K as a model system, we show how transient kinetic data can reveal detailed reaction mechanisms. Starting with a simple one-species model, we systematically evaluate increasingly complex mechanisms involving hollow and bridge site adsorption. Despite the similar goodness of fit to the same experimental absorbance data, our models predict distinct coverage dynamics for different adsorption sites. Through analysis of spectral peak stability and predicted dynamics, we identify a mechanism in which CO primarily adsorbs on bridge sites followed by rapid conversion to hollow sites as being the most physically consistent with experimental observations. This work provides a framework for extracting mechanistic insights from limited experimental data, demonstrating how machine learning can bridge the gap between transient kinetic measurements and a molecular-level understanding of surface reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Three-State Electrochiroptical Switches Derived from Chiral Stable Carbenes

Chiral redox switches have been used to develop stimuli-responsive materials and organic electronics wherein small molecule chirality produces new functionality. Despite the widespread use of stable carbenes in redox-active materials and asymmetric synthesis, their integration into chiral redox switches remains largely unexplored. Herein, we show that chiral stable carbenes can be used to construct helically chiral overcrowded alkenes which function as three-state electrochiroptical switches. Redox-driven (de)aromatization triggers the reversible exchange of helical and axial chirality via a helically chiral π-radical cation. Due to dramatic changes in both electronic and geometric structure, including the inversion of helical chirality, each state exhibits distinct chiroptical properties. As a proof of concept, we demonstrate multiple cycles of electrochemical ON–OFF switching and sign inversion of the electronic circular dichroism response. Overall, this work establishes chiral stable carbenes as promising building blocks for chiral and redox-switchable materials.

Chirality↗
DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermally driven surface phase separation in intermetallic alloys

Intermetallic compounds are widely recognized for their high-temperature phase stability and resistance to composition and structural changes. However, we reveal athermally activated bulk-to-surface mass exchange mechanism that drives surface phase separation, resulting in the formation of surface precipitates with distinct composition andstructure from the bulk matrix. Using the archetypal β-NiAl system, we show that asymmetries in vacancy formation energies between Ni and Al atoms induce preferential Ni segregation to the surface, forming Ni-rich γ'-Ni 3 Al precipitates. By integrating in-situ electron microscopy, synchrotron X-ray absorption spectroscopy and first-principles computational modeling, we establish a direct mechanistic connection between bulk thermal defect dynamics, surface compositional evolution, and phase segregation behavior. This bulk-surface coupling mechanism can be a driver of surface phase separation in multicomponent alloys under thermal stress. In conclusion, these results refine the thermodynamic boundaries of intermetallic stability and provide insights into managingthe performance and durability of intermetallic alloys for demanding high-temperature applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

GdWN 3 is a nitride perovskite

Nitride perovskites ABN3 are an emerging and highly underexplored class of materials that are of interest due to their intriguing calculated ferroelectric, optoelectronic, and other functional properties. Incorporating novel A-site cations is one strategy to tune and expand such properties; for example, Gd 3+ is compelling due to its large magnetic moment, potentially leading to multiferroic behavior. However, the theoretically predicted ground state of GdWN 3 was a non-perovskite monoclinic structure. Here, we experimentally show that GdWN 3-y crystallizes in a perovskite structure. High-throughput combinatorial sputtering with activated nitrogen is employed to synthesize thin films of Gd 2-x W x N 3-y O y with oxygen content y < 0.05. Ex situ annealing crystallizes a polycrystalline perovskite phase in a narrow composition window near x = 1. LeBail fits of synchrotron grazing incidence wide angle x-ray scattering data are consistent with a perovskite ground-state structure. Refined density functional theory calculations that included antiferromagnetic configurations confirm that the ground-state structure of GdWN 3 is a distorted Pnma perovskite with antiferromagnetic ordering, in contrast to prior predictions. Initial property measurements find that GdWN 3-y is paramagnetic down to T = 2 K with antiferromagnetic correlations and that the absorption onset depends on cation stoichiometry. This work provides an important path toward both the rapid expansion of the emerging family of nitride perovskites and understanding their potential multiferroic properties.

14 SOLAR ENERGY↗
DOE OSTI2024

Investigating the composition of the $K$$^{*}_{0}$(700) state with π ± $K$$^{0}_{S}$ correlations at the LHC

The first measurements of femtoscopic correlations with the particle pair combinations π ± $K$$^{0}_{S}$ in pp collisions at $\sqrt{s}$ = 13 TeV at the Large Hadron Collider (LHC) are reported by the ALICE experiment. Using the femtoscopic approach, it is shown that it is possible to study the elusive $K$$^{*}_{0}$(700) particle that has been considered a tetraquark candidate for over forty years. Source and final-state interaction parameters are extracted by fitting a model assuming a Gaussian source to the experimentally measured two-particle correlation functions. The final-state interaction in the π ± $K$$^{0}_{S}$ system is modeled through a resonant scattering amplitude, defined in terms of a mass and a coupling parameter, The extracted mass and Breit–Wigner width, derived from the coupling parameter, of the final-state interaction are found to be consistent with previous measurements of the $K$$^{*}_{0}$ (700). The small value and increase of the correlation strength with increasing source size support the hypothesis that the $K$$^{*}_{0}$(700) is a four-quark state, i.e. a tetraquark state of the form (q 1 , $\bar{q}$ 2 , q 3 , $\bar{q}$ 3 ) in which q 1 , q 2 and q 3 indicate the flavor of the valence quarks of the π and $K$$^{0}_{S}$. This latter trend is also confirmed via a simple geometric model that assumes a tetraquark structure of the $K$$^{*}_{0}$(700) resonance.

13 TeV pp collisions↗