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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dissipation-assisted steady-state entanglement engineering based on electron transfer models

Here, we propose a series of dissipation-assisted entanglement generation protocols that can be implemented on a trapped-ion quantum simulator. Our approach builds on the single-site molecular electron transfer (ET) model recently realized in experiment [So et al., Sci. Adv. 10, eads8011 (2024)]. This model leverages spin-dependent boson displacement and dissipation controlled by sympathetic cooling. We show that, when coupled to external degrees of freedom, the ET model can be used as a dissipative quantum control mechanism, enabling the precise tailoring of both spin and boson steady states of a target subsystem. We derive simplified analytical formalisms that offer intuitive insights into the dissipative dynamics. Using realistic interactions in a trapped-ion system, we develop a protocol for generating 𝑁-qubit and 𝑁-boson 𝑊 states. Additionally, we generalize this protocol to realize generic 𝑁-qubit Dicke states with tunable excitation numbers. Finally, we outline a realistic experimental setup to implement our schemes in the presence of noise sources.

chemical charge transfer↗
NASA NTRS1997

Advanced Free Flight Planner and Dispatcher's Workstation: Preliminary Design Specification

The National Aeronautics and Space Administration (NASA) has implemented the Advanced Air Transportation Technology (AATT) program to investigate future improvements to the national and international air traffic management systems. This research, as part of the AATT program, developed preliminary design requirements for an advanced Airline Operations Control (AOC) dispatcher's workstation, with emphasis on flight planning. This design will support the implementation of an experimental workstation in NASA laboratories that would emulate AOC dispatch operations. The work developed an airline flight plan data base and specified requirements for: a computer tool for generation and evaluation of free flight, user preferred trajectories (UPT); the kernel of an advanced flight planning system to be incorporated into the UPT-generation tool; and an AOC workstation to house the UPT-generation tool and to provide a real-time testing environment. A prototype for the advanced flight plan optimization kernel was developed and demonstrated. The flight planner uses dynamic programming to search a four-dimensional wind and temperature grid to identify the optimal route, altitude and speed for successive segments of a flight. An iterative process is employed in which a series of trajectories are successively refined until the LTPT is identified. The flight planner is designed to function in the current operational environment as well as in free flight. The free flight environment would enable greater flexibility in UPT selection based on alleviation of current procedural constraints. The prototype also takes advantage of advanced computer processing capabilities to implement more powerful optimization routines than would be possible with older computer systems.

Wilson, J.↗
DOE OSTI2025

AI‐Accelerated Optimization of Self‐Assembled Organic Mixed Ionic‐Electronic Conductors (OMIEC) (Final Report)

This document describes research activities, products and outcomes of a DOE-funded program to help accelerate development of Organic Mixed Ionic Electronic Conductors (OMIECs) using neutron scattering and high-throughput experimentation. OMIECs are organic materials that conduct both ionic and electronic charge carriers. For these new OMIEC materials, self-assembling ion-conducting block copolymers (BCPs) are used as a structural template for electronic conducting polymers. This forms OMIECs with long-range structural order that can help facilitate long-range electronic transport. The conductive properties of OMIECs are closely associated with their structure, which is affected by solution conditions and polymer macromolecular designs. Thus high-throughput experimentation has been implemented to explore this large design space effectively. The BCP-CP OMIEC systems explored are composed of ion-conducting diblock or triblock copolymers containing polyethylene oxide (PEO), di(ethylene glycol) ethyl ether acrylate (DEGEEA) or poly(ethylene glycol) methyl ether acrylate) (PEGMEA) hydrophilic blocks. These blocks will be coupled with either polypropylene oxide (PPO) or polyheptafluorobutyl acrylate (PHFBA) hydrophobic blocks to drive assembly. The electronic conducting component consists of several different types of conjugated polymers. Small angle scattering of neutrons and X-rays (SANS/SAXS) as well as electrochemical analysis have been coupled with modern algorithms for autonomous research using artificial intelligence (AI).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Improving bamboo’s fuel and storage properties with a net energy export through torrefaction paired with catalytic oxidation

Using torrefied char, or biocoal, as solid fuel provides an opportunity to introduce a sustainable feedstock into the energy market. The goals of this study were to investigate how torrefaction improves the energy content and the grindability of a Malaysian bamboo along with understanding the potential for integrated energy recovery from torrefaction gases. The feedstock was torrefied at 250–290 °C for 1 h and the combustion characteristics and grindability of the solid products along with the composition of torrefied gas species were measured. The results showed a beneficial increase in elemental carbon increased from 47 to 63 wt% at 290 °C torrefaction, reflecting an increase in higher heating value from 17.8 to 25.6 MJ/kg. The combustion behavior of all the products appeared to show three distinct combustion zones; however, torrefaction severity leading to higher combustion temperature and time as the fixed carbon augmented with the increase of torrefaction temperature. This increase in severity also lead to more friable and grindable material, and the 290 °C condition required a factor of 2.7 less hold-up time in the mill compared to the raw bamboo, and a factor of 8.5 less energy (938 and 111 kWh/tonne respectively). Through analysis of the gas and volatile formation, a case study showed that catalytic oxidation can convert nearly 100% of the embodied chemical energy into usable thermal energy. These experimental findings were scaled to a 100,000 tonne/y capacity torrefaction plant and in the moderate case of 270 °C operating temperature, the plant has 1.25 GJ/tonne excess energy beyond what the process needs.

09 BIOMASS FUELS↗
DOE OSTI2022

Complex Oxides for Brain–Inspired Computing: A Review

The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Entanglement Enabled Intensity Interferometry of different wavelengths of light

Highlights: • We use quantum entanglement to erase the color of photons for a new imaging method. • Can achieve higher resolution imaging by accessing hidden phase information. • We detail practical experimental designs to implement our theoretical protocols. We propose methods to perform intensity interferometry of photons having two different wavelengths. Distinguishable particles typically cannot interfere with each other, but we overcome that obstacle by processing the particles via entanglement and projection so that they lead to the same final state at the detection apparatus. Specifically, we discuss how quasi-phase-matched nonlinear crystals can be used to convert a quantum superposition of light of different wavelengths onto a common wavelength, while preserving the phase information essential for their meaningful interference. We thereby gain access to a host of new observables, which can probe subtle frequency correlations and entanglement. Further, we generalize the van Cittert–Zernike formula for the intensity interferometry of extended sources, demonstrate how our proposal supports enhanced resolution of sources with different spectral character, and suggest potential applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Real-time estimation and control of divertor surface heat flux on the DⅢ-D tokamak

We report Future tokamaks will require robust technologies for the mitigation of heat exhaust onto the plasma-facing components. As a first step towards this development, a system has been developed at DIII-D that estimates and controls in real-time the heat flux to the PFCs. Real-time estimation of the peak power flux from this model-based approach is validated with off-line infra-red measurements for various DIII-D plasma discharges. A nonlinear free-boundary simulation code (GSevolve) is used for simulating the closed loop response and for the off-line determination of the control parameters. The implementation and first experimental results of the application during the DIII-D plasma campaign are reported.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Perspectives on Microfluidics for the Study of Asphaltenes in Upstream Hydrocarbon Production: A Minireview

The utilization of microfluidics has generated deep insights into asphaltene precipitation mechanisms and oil–water emulsion stabilization. Agglomeration and precipitation of asphaltenes can cause flow assurance problems during the extraction and transportation of crude oil. Change in temperature, pressure, reservoir conditions, and solvents can change the local environment, leading to asphaltene precipitation. Understanding asphaltene properties and precipitation pathways becomes critical in devising mitigation methods, demulsifiers, and suitable conditions during hydrocarbon processing. Microfluidics has helped in high throughput measurement studies, understanding critical processing conditions, fast demulsifier screening, and the effect of solvent concentration on deposition, generating useful information for utilization at the point of resource extraction facilitating improved resource management. It has become possible to capture the porous, complex nature of reservoir formations and the interaction of chemicals during precipitation through integrated analytics and visualization studies available only through microfluidics. The use of droplet microfluidics, with optical microscopy and high-speed imaging to study the oil–water interface, has resulted in greater understanding of the role of asphaltenes in interfacial properties and emulsion stabilization. Here, this minireview highlights the crucial aspects of microfluidics that have been used to understand physicochemical behavior and dynamics of asphaltene deposition. Some of the unique devices have been presented focusing on the key elements of microfluidics design, fabrication, and analysis, as the insight obtained from microfluidics strongly depends on the device design and the controllability of the experimental parameters. Successful implementation of microfluidics for efficient and controlled experiments, short analysis time scales and rapid screening, and generation of high-quality, reliable data that convey asphaltene deposition issues and interface behavior in emulsions shows the importance of microsystems for advancing knowledge in hydrocarbon production and processing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Autonomous quantum error correction and fault-tolerant quantum computation with squeezed cat qubits

We propose an autonomous quantum error correction scheme using squeezed cat (SC) code against excitation loss in continuous-variable systems. Through reservoir engineering, we show that a structured dissipation can stabilize a two-component SC while autonomously correcting the errors. The implementation of such dissipation only requires low-order nonlinear couplings among three bosonic modes or between a bosonic mode and a qutrit. While our proposed scheme is device independent, it is readily implementable with current experimental platforms such as superconducting circuits and trapped-ion systems. Compared to the stabilized cat, the stabilized SC has a much lower dominant error rate and a significantly enhanced noise bias. Furthermore, the bias-preserving operations for the SC have much lower error rates. In combination, the stabilized SC leads to substantially better logical performance when concatenating with an outer discrete-variable code. The surface-SC scheme achieves more than one order of magnitude increase in the threshold ratio between the loss rate κ 1 and the engineered dissipation rate κ 2 . Under a practical noise ratio κ 1 /κ 2 = 10 -3 , the repetition-SC scheme can reach a 10 -15 logical error rate even with a small mean excitation number of 4, which already suffices for practically useful quantum algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Heisenberg-limited Hamiltonian learning for interacting bosons

We develop a protocol for learning a class of interacting bosonic Hamiltonians from dynamics with Heisenberg-limited scaling. For Hamiltonians with an underlying bounded-degree graph structure, we can learn all parameters with root mean square error ϵ using ${\mathcal{O}}(1/\epsilon )$ total evolution time, which is independent of the system size, in a way that is robust against state-preparation and measurement error. In the protocol, we only use bosonic coherent states, beam splitters, phase shifters, and homodyne measurements, which are easy to implement on many experimental platforms. A key technique we develop is to apply random unitaries to enforce symmetry in the effective Hamiltonian, which may be of independent interest.

computer science↗
DOE OSTI2025

An AI-accelerated pathway for reproducible and stable halide perovskites

Halide perovskites (HPs) have remarkable optoelectronic properties, and in the last decade their photovoltaic power conversion efficiency and light-emitting diode efficiency have skyrocketed. Despite the surge in research on these burgeoning materials, two key challenges in the field remain: material irreproducibility and instability. Their behavior is especially dynamic in response to environmental stressors, due to complex interactions with the perovskite crystal lattice. Here, in this review, we survey the latest achievements in HP materials research accomplished with the assistance of artificial intelligence (AI), through the implementation of automated experimentation and machine learning (ML) data analysis. Automated synthesis and characterization tackle problems with material irreproducibility by systematically controlling parameters with very high precision, creating massive datasets, and allowing methodical comparisons from which unbiased conclusions can be drawn. AI can reveal otherwise unnoticed trends, inform future experiments with the highest potential information gain, and forecast future performance. The review concludes with a forward viewpoint of how human-assisted closed-loop laboratories and shared databases allow halide perovskite materials’ processing, properties, and performance to be potentially optimized with AI, accelerating the development of highly reproducible and stable optoelectronic devices.

Hering, Abigail R. [Univ. of California, Davis, CA↗
DOE OSTI2020

A novel method to measure ion density in ICF experiments using x-ray spectroscopy of cylindrical tracers

The indirect drive approach to inertial confinement fusion has undergone important advances in the past few years. Improvements in temperature and density diagnostic methods are leading to more accurate measurements of the plasma conditions inside the Hohlraum and therefore to more efficient experimental designs. The implementation of dot spectroscopy has proven to be a versatile approach to extracting space- and time-dependent electron temperatures. In this method, a microdot of a mid-Z material is placed inside the Hohlraum and its K-shell emission spectrum is used to determine the plasma temperature. However, radiation transport of optically thick lines acting within the cylindrical dot geometry influences the outgoing spectral distribution in a manner that depends on the viewing angle. This angular dependence has recently been studied in the high energy density regime at the OMEGA laser facility, which allowed us to design and benchmark appropriate radiative transfer models that can replicate these geometric effects. By combining these models with the measurements from the dot spectroscopy experiments at the National Ignition Facility, we demonstrate here a novel technique that exploits the transport effects to obtain time-resolved measurements of the ion density of the tracer dots, without the need for additional diagnostics. Furthermore, we find excellent agreement between experiment and simulation, opening the possibility of using these geometric effects as a density diagnostic in future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Global linear and nonlinear gyrokinetic simulations of tearing modes

Abstract To better understand multi-scale interactions between global tearing modes and microturbulence in the Madison Symmetric Torus (MST) reversed-field pinch (RFP), the global gyrokinetic code Gene is modified to describe global tearing mode instability via a shifted Maxwellian (SM) distribution consistent with experimental equilibria. The implementation of the SM is tested and benchmarked by comparisons with different codes and models. Good agreement is obtained in code-code and code-theory comparisons. Linear stability of tearing modes of a non-reversed MST discharge is studied. A collisionality scan is performed to the lowest order unstable modes ( n = 5, n = 6) and shown to behave consistently with theoretical scaling. The nonlinear evolution is simulated, and saturation is found to arise from mode coupling and transfer of energy from the most unstable tearing mode to small-scale stable modes mediated by the m = 2 tearing mode. The work described herein lays the foundation for nonlinear simulation and analysis of the interaction of tearing modes and gyroradius-scale instabilities in RFP plasmas.

Physics↗
DOE OSTI2020

Assessing three closed-loop learning algorithms by searching for high-quality quantum control pulses

Designing a high-quality control is crucial for reliable quantum computation. Among the existing approaches, closed-loop leaning control is an effective choice. Its efficiency depends on the learning algorithm employed, thus deserving algorithmic comparisons for its practical applications. Here we assess three representative learning algorithms, including GRadient Ascent Pulse Engineering (GRAPE), improved Nelder-Mead (NMplus), and Differential Evolution (DE), by searching for high-quality control pulses to prepare the Bell state. We first implement each algorithm experimentally in a nuclear magnetic resonance system and then conduct a numerical study considering the impact of some possible significant experimental uncertainties. The experiments report the successful preparation of the high-fidelity target state by the three algorithms, while NMplus converges fastest, and these results coincide with the numerical simulations when potential uncertainties are negligible. However, under certain significant uncertainties, these algorithms possess distinct performance with respect to their resulting precision and efficiency, and DE shows the best robustness. Finally, this study provides insight to aid in the practical application of different closed-loop learning algorithms in realistic physical scenarios.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Gate-Controlled Anyon Generation and Detection in Kitaev Spin Liquids

Reliable manipulation of non-Abelian Ising anyons supported by Kitaev spin liquids may enable intrinsically fault-tolerant quantum computation. Here, we introduce a standalone scheme for both generating and detecting individual Ising anyons using tunable gate voltages in a heterostructure containing a non-Abelian Kitaev spin liquid and a monolayer semiconductor. In this study, the key ingredients of our setup are a Kondo coupling to stabilize an Ising anyon in the spin liquid around each electron in the semiconductor, and a large charging energy to allow control over the electron numbers in distinct gate-defined regions of the semiconductor. In particular, a single Ising anyon can be generated at a disk-shaped region by gate tuning its electron number to one, while it can be interferometrically detected by measuring the electrical conductance of a ring-shaped region around it whose electron number is allowed to fluctuate between zero and one. We provide concrete experimental guidelines for implementing our proposal in promising candidate materials like α-RuCl 3 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Efficiency and Output Power Sensitivity to Control Parameter Variations in an LCC-Series Wireless Power Transfer System

In wireless power transfer (WPT) systems, output power (Pout) can be controlled through input parameters such as, input voltage (Vin) , duty cycle (d) , and switching frequency (fsw) . The impact of these control parameters on the overall system efficiency has not been explored in previous studies. This paper focuses on the efficiency (η) and Pout sensitivity to these control parameter variations in an LCC-Series tuned WPT system. There are multiple combinations of Vin, d , and fsw that can achieve a specified target Pout . However, it is important to determine the optimal combination of these control variables that will yield the maximum η for a target Pout . A MATLAB/Simulink model is built to calculate and measure η and Pout . In order to achieve this result, several simulations are run where Vin,d , and fsw are swept across a wide range to cover all possible combinations. MATLAB's Sensitivity Analysis toolbox is used to evaluate how the parameters influence the model's output. Finally, an experimental prototype is implemented. The optimal values acquired using the sensitivity analysis is then plugged into the hardware prototype and compared with the simulation. The results obtained confirm the validity of the proposed method.

Harave, Sudarshan↗
DOE OSTI2023

A Novel LCC-CLCC Resonant Tuning Network for Light-Load Conditions in Wireless Power Transfer Systems

In wireless power transfer (WPT) systems, voltage and current distortions are observed at the vehicle side rectifier when power flow is from grid to vehicle (G2V) under light load conditions. These distortions can increase switching losses and decrease the overall efficiency of the WPT system. To address this issue, this paper proposes adding a higher value inductor in series with the original LCC tuning network at the vehicle side. However, increasing the series inductance causes the input impedance and phase angle to move away from the resonant frequency. To solve this problem, a capacitor is added in series to tune out the difference between the original and modified inductor values. This series tuning capacitor also improves the power factor and brings the input impedance and phase angle back to the resonant frequency. The traditional LCC-LCC and proposed LCC-CLCC WPT systems are compared and analyzed analytically, and simulated in a MATLAB/Simulink environment to verify parameters such as efficiency and power transfer capacity. An experimental prototype is implemented and compared with the simulation. The obtained results confirm the validity of the proposed method.

Harave, Sudarshan↗
DOE OSTI2021

Efficient Parallel Sparse Symmetric Tucker Decomposition for High-Order Tensors

Tensor based methods are receiving renewed attention in recent years due to their prevalence in diverse real-world applications. There is considerable literature on tensor representations and algorithms for tensor decompositions, both for dense and sparse tensors. Many applications in hypergraph analytics, machine learning, psychometry, and signal processing result in tensors that are both sparse and symmetric, making it an important class for further study. Similar to the critical Tensor Times Matrix chain operation (TTMc) in general sparse tensors, the Sparse Symmetric Tensor Times Same Matrix chain (S3TTMc) operation is compute and memory intensive due to high tensor order and the associated factorial explosion in the number of non-zeros. In this work, we present a novel compressed storage format CSS for sparse symmetric tensors, along with an efficient parallel algorithm for the S3TTMc operation. We theoretically establish that S3TTMc on CSS achieves a better memory versus run-time trade-off compared to state-of-the-art implementations. We demonstrate experimental findings that confirm these results and achieve up to 2.9× speedup on synthetic and real datasets.

Shivakumar, Shruti↗