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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Extracting and Interpreting Electrochemical Impedance Spectra (EIS) from Physics-Based Models of Lithium-Ion Batteries

This paper implements a highly efficient algorithm to extract electrochemical impedance spectra (EIS) from physics-based battery models (e.g., a P2D model). The mathematical approach is different from how EIS is practiced experimentally. Experimentally, the voltage (current) is harmonically perturbed over a wide range of frequencies and the amplitude and phase shift of the corresponding current (voltage) is measured. The experimental approach can be implemented in simulation software, but is computationally expensive. The approach here is to determine locally linear state-space models from the full physical model. The four Jacobian matrices that are the basis of the state-space models can be derived by numerical differentiation of the physical model. The EIS is then extracted from the state-space model using computationally efficient matrix-manipulation techniques. The algorithm can evaluate the full EIS at an instant in time during a transient, independent of whether the battery is in a stationary state. The approach is also able to separate the full-cell impedance to evaluate partial EIS, such as for a battery anode alone. Although such partial EIS is difficult to measure experimentally, the partial EIS provides valuable insights in interpreting the full-cell EIS.

25 ENERGY STORAGE↗
DOE OSTI2022

Circuit Quantum Electrodynamics in Hyperbolic Space: From Photon Bound States to Frustrated Spin Models

Circuit quantum electrodynamics is one of the most promising platforms for efficient quantum simulation and computation. In recent groundbreaking experiments, the immense flexibility of superconducting microwave resonators was utilized to realize hyperbolic lattices that emulate quantum physics in negatively curved space. Here we investigate experimentally feasible settings in which a few superconducting qubits are coupled to a bath of photons evolving on the hyperbolic lattice. We compare our numerical results for finite lattices with analytical results for continuous hyperbolic space on the Poincaré disk. We find good agreement between the two descriptions in the long-wavelength regime. We show that photon-qubit bound states have a curvature-limited size. We propose to use a qubit as a local probe of the hyperbolic bath, for example, by measuring the relaxation dynamics of the qubit. We find that, although the boundary effects strongly impact the photonic density of states, the spectral density is well described by the continuum theory. We show that interactions between qubits are mediated by photons propagating along geodesics. We demonstrate that the photonic bath can give rise to geometrically frustrated hyperbolic quantum spin models with finite-range or exponentially decaying interaction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2009

KSC Tech Transfer News, Volume 2, No. 2

This issue contains articles about: (1) the Innovative Partnerships Program (IPP) and the manager of the program, Alexis Hongamen, (2) New Technology Report (NTR) on a Monte Carlo Simulation to Estimate the Likelihood of Direct Lightning Strikes, (3) Kennedy Space Center's Applied Physics Lab, (4) a virtual ruler that is used for many applications, (5) a portable device that finds low-level leaks, (6) a sun-shield, that supports in-space cryogenic propellant storage, (7) lunar dust modeling software, (8) space based monitoring of radiation damage to DNA, (9) the use of light-emitting diode (LED) arrays vegetable production system, (10) Dust Tolerant Intelligent Electrical Connection Systems, (11) Ice Detection Camera System Upgrade, (12) Repair Techniques for Composite Structures, (13) Cryogenic Orbital Testbed, and (14) copyright protection.

Makufka, David↗
NASA NTRS2007

An Astrosocial Observation: The Nobel Connection to the Space Program

The 2006 Nobel Prize in Physics was heralded by some in the press as the 'First Nobel Prize for Space Exploration.' Indeed the Nobel Foundation's announcement specifically cited the Cosmic Background Explorer (COBE) satellite launched by NASA in 1989 as the prime-enabling instrument It elaborated further, 'The COBE results provided increased support for the Big Bang scenario for the origin of the Universe... These measurements also marked the inception of cosmology as a precise science.' NASA also seized this unique moment of fame to honor its favorite son, the first Nobel scientist of the agency, John Mather, of the Goddard Space Flight Center, who shared the honor with Professor G. Smoot of the University of California, the Principal Investigator of the COBE measurement. It is without any dispute that the Nobel Prize is the highest scientific honor and best-known award of admiration and inspiration to the public and educational sectors. Unfortunately in the American culture, youths are mostly exposed to success icons in the sports, entertainment, and business domains. Science icons (of either gender) are largely unknown to them. We sincerely hope that success stories of Nobel scientists will become part of the learning curriculum in the K-16 educational experience. In this paper, we examine the pedigree of a number of Nobel Prizes over the years, and discuss their interactions with, and connections to, the space program. It is advantageous for the context of educational and public outreach to see such connections, because in a number of public surveys, one important customer expectation for the space program is the search for new knowledge, to which the Nobel Prize is a prominent benchmark.

science icons↗
NASA NTRS2000

Computer Modeling of Real-Time Dynamic Lighting

Space Station tasks involve procedures that are very complex and highly dependent on the availability of visual information. In many situations, cameras are used as tools to help overcome the visual and physical restrictions associated with space flight. However, these cameras are effected by the dynamic lighting conditions of space. Training for these is conditions is necessary. The current project builds on the findings of an earlier NRA funded project, which revealed improved performance by humans when trained with computer graphics and lighting effects such as shadows and glare.

Maida, James C.↗
NASA NTRS2010

In the Beginning...

Having just emerged from the warmest decade on record and watching as the oceans acidify and sea level rises, global resources peak, the world's population continues to climb, and nearly half of all known species face extinction by the end of the century. We stand on the threshold of one of the most important transition in human history-the transition from hunting-and-gathering our energy to cultivating sustainable, carbon-neutral, environmentally-friendly energy supplies. Can we develop an alternative to fossil fuels in time to make a difference for our children and the "children" of all species? NASA puts people into outer space, where all resources (food, water, air, pressure, gravity, energy) are limited and far away and where conditions (temperature, radiation, vacuum) are problematic and dangerous-the life expectancy of an unprotected astronaut (physically exposed to the space environment) is 15 seconds. Therefore, by necessity, NASA has explored and developed "life-support systems" that optimize the use of resources, minimize the use of energy, and recycle, refurbish, re-use everything that on earth would be considered a waste material. Emerging from the legacy of life-support systems, the NASA OMEGA project uses microalgae, municipal wastewater, and the encroaching oceans to address our global needs for a sustainable, carbon-neutral, environmentally friendly energy supply that does not compete with agriculture. The OMEGA project is focused on producing aviation fuel, treating municipal wastewater, and sequestering carbon dioxide. More generally, however, OMEGA is an example of an "ecology of technologies" in which all processes are integrated and inter-dependent and wastes become resources. From a NASA perspective, the OMEGA project is also a reminder that "we are not passengers on Spaceship Earth, we are the crew."

Trent, Jonathan↗
NASA NTRS2019

Annual Highlights of Results from the International Space Station October 1, 2017 - October 1, 2018

The International Space Station (ISS) is a unique place – a convergence of science, technology and human innovation that demonstrates new technologies and makes research breakthroughs that cannot be accomplished on Earth. As an international laboratory for scientific research in microgravity, the space station’s international crew lives and works while traveling at a speed of about five miles per second as they make new discoveries in the disciplines of biology and biotechnology, Earth and space science, human research, physical science, educational activities, and technology development and demonstrations.

ISS research highlight↗
NASA NTRS1979

Platforms in space: Evolutionary trends

The problem of physical crowding and the proliferation of separate communication links and ground support systems for multiple free-flying satellites can be overcome by using space platforms and multiplexing the data streams. Pertinent features of the space shuttle orbiter payloads, the solar power satellite, and geostationary and geosynchronous platforms are discussed. Typical payload requirements data which are needed to allow meaningful study of payloads as candidates for platform implementation are cited and factors affecting the compatibility/grouping of payloads are outlined.

Butler, J. M., Jr.↗
DOE OSTI2024

Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems

Abstract Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by approximating mappings between infinite-dimensional Banach spaces, yet their performance degrades with system size and complexity. We propose an approach for learning neural operators in latent spaces, facilitating real-time predictions for highly nonlinear and multiscale systems on high-dimensional domains. Our method utilizes the deep operator network architecture on a low-dimensional latent space to efficiently approximate underlying operators. Demonstrations on material fracture, fluid flow prediction, and climate modeling highlight superior prediction accuracy and computational efficiency compared to existing methods. Notably, our approach enables approximating large-scale atmospheric flows with millions of degrees, enhancing weather and climate forecasts. Here we show that the proposed approach enables real-time predictions that can facilitate decision-making for a wide range of applications in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Realistic simulations of spin squeezing and cooperative coupling effects in large ensembles of interacting two-level systems

We describe an efficient numerical method for simulating the dynamics of interacting spin ensembles in the presence of dephasing and decay. The method builds on the discrete truncated Wigner approximation for isolated systems, which combines the mean-field dynamics of a spin ensemble with a Monte Carlo sampling of discrete initial spin values to account for quantum correlations. Here we show how this approach can be generalized for dissipative spin systems by replacing the deterministic mean-field evolution by a stochastic process, which describes the decay of coherences and populations while preserving the length of each spin. We demonstrate the application of this technique for simulating nonclassical spin-squeezing effects or the dynamics and steady states of cavity QED models with 10 5 interacting two-level systems. This opens up the possibility to perform accurate real-scale simulations of a diverse range of experiments in quantum optics or with solid-state spin ensembles under realistic laboratory conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Active learning of ternary alloy structures and energies

Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemistry↗
DOE OSTI2022

Synthesis and Physical Properties of NbMnP Single Crystals

Single crystalline NbMnP was grown by the high-temperature solution growth technique and characterized by room temperature X-ray diffraction, temperature- and field-dependent magnetization, temperature-dependent resistivity, and heat capacity measurements. NbMnP is isostructural to TiNiSi with the space group of Pnma. Physical characterizations suggest that NbMnP is metallic and goes through an anti-ferromagnet transition at around 230 K with a weak magnetic anisotropy. A small ferromagnetic component is found to be perpendicular to [010].

Zhao, Jianda↗
NASA NTRS2016

A First Look at the Upcoming SISO Space Reference FOM

Simulation is increasingly used in the space domain for several purposes. One example is analysis and engineering, from the mission level down to individual systems and subsystems. Another example is training of space crew and flight controllers. Several distributed simulations have been developed for example for docking vehicles with the ISS and for mission training, in many cases with participants from several nations. Space based scenarios are also used in the "Simulation Exploration Experience", SISO's university outreach program. We have thus realized that there is a need for a distributed simulation interoperability standard for data exchange within the space domain. Based on these experiences, SISO is developing a Space Reference FOM. Members of the product development group come from several countries and contribute experiences from projects within NASA, ESA and other organizations. Participants represent government, academia and industry. The first version will focus on handling of time and space. The Space Reference FOM will provide the following: (i) a flexible positioning system using reference frames for arbitrary bodies in space, (ii) a naming conventions for well known reference frames, (iii) definitions of common time scales, (iv) federation agreements for common types of time management with focus on time stepped simulation, and (v) support for physical entities, such as space vehicles and astronauts. The Space Reference FOM is expected to make collaboration politically, contractually and technically easier. It is also expected to make collaboration easier to manage and extend.

Crues, Edwin↗
DOE OSTI2021

Cyber-Attack Detection for Photovoltaic Farms Based on Power-Electronics-Enabled Harmonic State Space Modeling

Here in this paper, a physics-data-based detection method is proposed to detect a variety of cyber-attacks in Photovoltaic (PV) farms using the power electronics-enabled harmonic state space (HSS) models, which, to our knowledge, is original. At the device level, HSS-based detection is developed to monitor harmonic vectors of individual PV converter with minimum sensor measurements, thus improving accuracy and robustness compared to Kalman Filter-based detection. At the system level that involves multiple PV converters, a clustering approach is developed to investigate attack propagation and accurately locate attack sources within a PV farm. The proposed approach is one of the first attempts to address PV security through interaction between the device and system, maximizing the accuracy and robustness at different levels. To verify the feasibility, a comprehensive attacks model is built, including single attack, coordinated attacks, and replay attacks. Besides, the impacts of irradiance changes are taken into consideration in the test scenarios. With the real-time data acquisition and hardware-in-the-loop testbed, comprehensive test results are provided to verify the feasibility of the proposed detection methodology.

42 ENGINEERING↗
DOE OSTI2026

Topology-Dependent Performance of Free-Space Photonic Quantum Networks Under Noise

Photonic quantum communication enables secure and high-fidelity information transfer beyond classical limits, with direct relevance to emerging quantum networks operating in free-space environments. While physical-layer models of depolarizing noise, Gamma–Gamma turbulence statistics, entanglement swapping, and decoy-state QKD security bounds are individually well established, prior work typically treats these components in isolation or under fixed network assumptions. In this work, we develop a unified topology-aware analytical framework that simultaneously integrates free-space optical link budgets, turbulence-induced visibility degradation, depolarizing qubit noise, multi-hop entanglement cascade dynamics, teleportation fidelity thresholds, CHSH nonlocality certification, and asymptotic decoy-state secret key rate bounds across star, mesh, and ring graph structures. Rather than introducing new physical channel models, we demonstrate that identical physical links exhibit fundamentally different end-to-end performance once embedded within different network topologies. Mesh architectures minimize visibility cascade through hop-count reduction but incur quadratic hardware scaling. Star topologies minimize link count but concentrate noise and synchronization overhead at the hub. Ring configurations offer linear hardware scaling with multiplicative fidelity degradation. The results establish topology as a first-order design parameter in near-term free-space quantum networks operating without full quantum repeater infrastructures. While motivated by distributed multi-agent architectures, the framework applies broadly to terrestrial, airborne, and satellite-assisted photonic quantum communication systems.

QKD↗
DOE OSTI2020

Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

One of the open problems in scientific computing is the long-time integration of nonlinear stochastic partial differential equations (SPDEs), especially with arbitrary initial data. We address this problem by taking advantage of recent advances in scientific machine learning and the spectral dynamically orthogonal (DO) and borthogonal (BO) methods for representing stochastic processes. The recently introduced DO/BO methods reduce the SPDE to solving a system of deterministic PDEs and a system of stochastic ordinary differential equations. Specifically, we propose two new physics-informed neural networks (PINNs) for solving time-dependent SPDEs, namely the neural network (NN)-DO/BO methods. The proposed methods incorporate the DO/BO constraints into the loss function (along with the modal decomposition of the SPDE) with an implicit form instead of generating explicit expressions for the temporal derivatives of the DO/BO modes. Hence, the NN-DO/BO methods can overcome some of the drawbacks of the original DO/BO methods. For example, we do not need the assumption that the covariance matrix of the random coefficients is invertible as in the original DO method, and we can remove the assumption of no eigenvalue crossing as in the original BO method. Moreover, the NN-DO/BO methods can be used to solve time-dependent stochastic inverse problems with the same formulation and same computational complexity as for forward problems. Furthermore, we demonstrate the capability of the proposed methods via several numerical examples, namely: (1) A linear stochastic advection equation with deterministic initial condition: we obtain good results with the proposed methods, while the original DO/BO methods cannot be applied directly in this case. (2) Long-time integration of the stochastic Burgers' equation: we show the good performance of NN-DO/BO methods, especially the effectiveness of the NN-BO approach for such problems with many eigenvalue crossings during the whole time evolution, while the original BO method fails. (3) Nonlinear reaction diffusion equation: we consider both the forward problem and the inverse problems, including very noisy initial point values, to investigate the flexibility of the NN-DO/BO methods in handling inverse and mixed type problems. Taken together, these simulation results demonstrate that the NN-DO/BO methods can be employed to effectively quantify uncertainty propagation in a wide range of physical problems, but future work should address the efficiency issue of PINNs for forward problems.

97 MATHEMATICS AND COMPUTING↗