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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Er : Li Nb O 3 with High Optical Coherence Enabling Optical Thickness Control

Integrated photonics capable of incorporating rare-earth ions with high optical coherence is desirable for realizing efficient quantum transducers, compact quantum memories, and hybrid quantum systems. Here we describe a photonic platform based on the SmartCut erbium-doped lithium niobate thin film, and explore its stable optical transitions at telecom wavelength in a dilution refrigerator. Optical coherence time of up to 180μs, rivaling the value of bulk crystals, is achieved in optical ridge waveguides and ring resonators. With this integrated platform, we demonstrate tunable light-ion interaction and flexible control of optical thickness by exploiting long waveguides, whose lengths are in principle variable. This unique ability to obtain high optical density using low-concentration ions further leads to the observation of multiecho pulse trains in centimeter-long waveguides. Our results establish a promising photonic platform for quantum information processing with rare-earth ions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Heralded Multiplexed High-Efficiency Cascaded Source of Dual-Rail Entangled Photon Pairs Using Spontaneous Parametric Down-Conversion

Deterministic sources of high-fidelity entangled qubit pairs encoded in the dual-rail photonic basis, i.e., presence of a single photon in one of two orthogonal modes, are a key enabling technology of many applications of quantum information processing, including high-rate, high-fidelity quantum communications over long distances. The most popular and mature sources of such photonic entanglement, e.g., those that leverage spontaneous parametric down-conversion (SPDC) or spontaneous four-wave mixing, generate an entangled (so-called continuous-variable) quantum state that contains contributions from high-order photon terms that lie outside the span of the dual-rail basis, which is detrimental to most applications. One often uses low pump power to mitigate the effects of those high-order terms. However, that reduces the pair generation rate, and the source becomes inherently probabilistic. We investigate a cascaded source that performs a linear-optical entanglement swap between two SPDC sources, to generate a heralded photonic entangled state that has a higher fidelity (to the ideal Bell state) compared to a free-running SPDC source. Furthermore, with the Bell swap providing a heralding trigger, we show how to build a multiplexed source, which despite reasonable switching losses and detector loss and noise, yields a fidelity versus success probability trade-off of a high-efficiency source of high-fidelity dual-rail photonic entanglement. We find, however, that there is a threshold of 1.5 dB of loss per switch, beyond which multiplexing hurts the fidelity versus success probability trade-off.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

On-chip pulse shaping of entangled photons

The miniaturization of optical systems via integrated photonics is critical to the ultimate scalability and performance of photonic quantum processors, yet many longstanding optical signal processing capabilities—such as Fourier-transform pulse shaping—remain unrealized on chip. In this work, we demonstrate on-chip spectral shaping of entangled photons using a multichannel microring-resonator-based silicon photonic pulse shaper. Achieving line-by-line phase control on a 3 GHz grid for two frequency-bin-entangled qudits, the pulse shaper's fine spectral resolution enables control of nanosecond-scale temporal features, which are observed by direct coincidence detection of biphoton correlation functions that show excellent agreement with theory. This work marks a demonstration of biphoton pulse shaping using an integrated spectral shaper and holds significant promise for applications in photonic quantum information processing.

Entanglement manipulation↗
DOE OSTI2024

Transition-Metal-Related Quantum Emitters in Wurtzite AlN and GaN

Transition-metal centers exhibit a paramagnetic ground state in wide-bandgap semiconductors and are promising for nanophotonics and quantum information processing. Specifically, there is a growing interest in discovering prominent paramagnetic spin defects that can be manipulated using optical methods. Here, we investigate the electronic structure and magneto-optical properties of Cr and Mn substitutional centers in wurtzite AlN and GaN. We use state-of-the-art hybrid density functional theory calculations to determine level structure, stability, optical signatures, and magnetic properties of these centers. The excitation energies are calculated using the constrained occupation approach and rigorously verified with the complete active space configuration interaction approach. Our simulations of the photoluminescence spectra indicate that $Cr$$^{1+}_{Al}$ in AlN and $Cr$$^{1+}_{Ga}$ in GaN are responsible for the observed narrow quantum emission near 1.2 eV. We compute the zero-field splitting (ZFS) parameters and outline an optical spin polarization protocol for $Cr$$^{1+}_{Al}$ and $Cr$$^{1+}_{Ga}$. Our results demonstrate that these centers are promising candidates for spin qubits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A holistic platform for accelerating sorbent-based carbon capture

Abstract Reducing carbon dioxide (CO 2 ) emissions urgently requires the large-scale deployment of carbon-capture technologies. These technologies must separate CO 2 from various sources and deliver it to different sinks 1,2 . The quest for optimal solutions for specific source–sink pairs is a complex, multi-objective challenge involving multiple stakeholders and depends on social, economic and regional contexts. Currently, research follows a sequential approach: chemists focus on materials design 3 and engineers on optimizing processes 4,5 , which are then operated at a scale that impacts the economy and the environment. Assessing these impacts, such as the greenhouse gas emissions over the plant’s lifetime, is typically one of the final steps 6 . Here we introduce the PrISMa (Process-Informed design of tailor-made Sorbent Materials) platform, which integrates materials, process design, techno-economics and life-cycle assessment. We compare more than 60 case studies capturing CO 2 from various sources in 5 global regions using different technologies. The platform simultaneously informs various stakeholders about the cost-effectiveness of technologies, process configurations and locations, reveals the molecular characteristics of the top-performing sorbents, and provides insights on environmental impacts, co-benefits and trade-offs. By uniting stakeholders at an early research stage, PrISMa accelerates carbon-capture technology development during this critical period as we aim for a net-zero world.

Science & Technology - Other Topics↗
DOE OSTI2020

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. Here, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. ML offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine Learning-Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variable limit extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools, and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a research octane number (RON) of 80. The ML-GGA approach yielded >2% improvements in the merit function, compared with the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared with traditional approaches.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Vibroacoustic Process Monitoring of 5-Stage Centrifugal Contactors

Vibroacoustic monitoring was investigated to aid in informing process operators with parameters regarding aqueous separation techniques. These techniques have a wide range of applications including nuclear fuel reprocessing. A small-scale solvent extraction system was set up with five magnetic drive contactors and two piston pumps to collect realistic operation. Vibration sensors were placed on three of the contactors and both pumps, and a microphone was placed near the system. Contactor rotational speed and pump flow rate were varied with known values to compare against measured vibroacoustic signatures. Spectral analysis allowed for the determination of contactor rotational speed and the ratio of organic to aqueous flow rate, an important parameter for ensuring optimal extraction. Vibration metrics such as kurtosis and crest factor provided additional details regarding individual rotational speeds and have potential in examination of faults for predictive maintenance. Overall, results indicate the capability of vibroacoustic monitoring to improve operator awareness and process outcomes.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

River Delta Morphotypes Emerge From Multiscale Characterization of Shorelines

Abstract Delta shoreline structure has long been hypothesized to encode information on the relative influence of fluvial, wave, and tidal processes on delta formation and evolution. We introduce here a novel multiscale characterization of shorelines by defining three process‐informed morphological metrics. We show that this characterization yields self‐emerging classes of morphologically similar deltas, that is, delta morphotypes, and also predicts the dominant forcing of each morphotype. Then we show that the dominant forcings inferred from shoreline structure generally align with those estimated via relative sediment fluxes, while positing that misalignments arise from spatiotemporal heterogeneity in deltaic sediment fluxes not captured in their estimates. The proposed framework for shoreline characterization advances our quantitative understanding of how shoreline features reflect delta forcings, and may aid in deciphering paleoclimate from images of ancient deposits and projecting delta morphologic response to changes in sediment fluxes.

Vulis, L.↗
DOE OSTI2022

Writable spin wave nanochannels in an artificial-spin-ice-mediated ferromagnetic thin film

We report magnonics, which employs spin-waves to transmit and process information, is a promising venue for low-power data processing. One of the major challenges is the local control of the spin-wave propagation path. Here, we introduce the concept of writable magnonics by taking advantage of the highly flexible reconfigurability and rewritability of artificial spin ice systems. Using micromagnetic simulations, we show that globally switchable spin-wave propagation and locally writable spin-wave nanochannels can be realized in a ferromagnetic thin film underlying an artificial pinwheel spin ice. The rewritable magnonics enabled by reconfigurable spin wave nanochannels provides a unique setting to design programmable magnonic circuits and logic devices for ultra-low power applications.

42 ENGINEERING↗
DOE OSTI2021

Coupling of Spark Plasma Sintering with Advanced Modeling to Enable Process Scale-Up: LDRD Final Project Poster

Electric-field assisted sintering (EFAS) manufacturing techniques reduce energy usage requirements, fabricate near-net-shape parts, and produce bulk nanostructured materials through rapid heating and cooling rates achievable through Joule heating. Process parameters of pulsed DC voltage and constant pressure are applied through stainless steel rams and graphite tooling to the ceramic yttrium oxide powder to be sintered. Our high-fidelity modeling code application advances manufacturing for extreme environments by enabling process-informed design and improving microstructure consistency through multiphysics multiscale process-structure-property-performance correlation simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Schema Elements for Granta Annual Report: FY2022

Granta: Materials Intelligence, also known as Granta:MI or Granta, is a commercial database software by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store relevant materials data. For a complete discussion of the use of Granta:MI at NSE sites, see the FY21 annual report. Granta:MI is used by five NSE sites locally, and all NSE sites have access to an enterprise instance on the Enterprise Secure Network (ESN) as well as an unclassified development instance. It has been recognized by NNSA management that a shared repository for additive manufacturing (AM) data would not only ensure data and knowledge is not lost but would provide a pool of information relating AM inputs (build parameters, raw materials properties, post-processing information) to the as-built properties of AM parts. Such a pool of data would enable optimization of AM build design and help NNSA achieve the goals of shortening fielding times for new components.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Influence of disorder on antidot vortex Majorana states in three-dimensional topological insulators

Topological insulator/superconductor two-dimensional heterostructures are promising candidates for realizing topological superconductivity and Majorana modes. In these systems, a vortex pinned by a prefabricated antidot in the superconductor can host Majorana zero-energy modes (MZMs), which are exotic quasiparticles that may enable quantum information processing. However, a major challenge is to design devices that can manipulate the information encoded in these MZMs. One of the key factors is to create small and clean antidots, so the MZMs, localized in the vortex core, have a large gap to other excitations. If the antidot is too large or too disordered, the level spacing for the subgap vortex states may become smaller than temperature. In this paper, we numerically investigate the effects of disorder, chemical potential, and antidot size on the subgap vortex spectrum, using a two-dimensional effective model of the topological insulator surface. Our model allows us to simulate large system sizes with vortices up to 1.8 µ⁢m in diameter (with a 6 nm lattice constant). We also compare our disorder model with the transport data from existing experiments. As a result, we find that the spectral gap can exhibit a nonmonotonic behavior as a function of disorder strength, and that it can be tuned by applying a gate voltage.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Model quantification of the effect of coproducts and refinery co-hydrotreating on the economics and greenhouse gas emissions of a conceptual biomass catalytic fast pyrolysis process

Here we present model results for a scaled-up conceptual process informed by bench scale biomass catalytic fast pyrolysis (CFP) and hydrotreating experimental data. This process uses a Pt/TiO 2 catalyst during CFP, which produces a partially deoxygenated organic biocrude intermediate that is then hydroprocessed to a hydrocarbon fuel blendstock; the catalyst also enables high yields of acetone and methyl-ethyl-ketone (MEK) coproducts. Two options for hydroprocessing were modeled: (A) co-hydrotreating at a petroleum refinery using hydrogen sourced from steam reforming of natural gas and (B) standalone hydrotreating at a biorefinery using hydrogen sourced from CFP off gases. The results revealed that Case A was economically advantageous with a modeled minimum fuel selling price (MFSP) of $\$$2.83/GGE or gallon gasoline equivalent (in 2016 US dollars), while the additional cost of standalone hydrotreating facilities in Case B increased the MFSP to $3.13/GGE. Conversely, greenhouse gas (GHG) emissions were lower for Case B (3.9 g CO 2 e/MJ) compared to Case A (21.5 g CO 2 e/MJ) due to the use of biogenic (Case B) and fossil-derived (Case A) hydrogen. In a third option (Case C), the requirements for separation and purification of acetone and MEK were removed from the refinery co-processing scenario (Case A) to evaluate the impacts of this process simplification. Elimination of these coproducts increased the MFSP to $3.21/GGE and GHG emissions to 35 g CO 2 e/MJ. These comparisons based on our detailed conceptual models provide economic and sustainability guidance regarding processing choices for future biorefineries. While refinery coprocessing using existing equipment and the production of relatively valuable coproducts can benefit the economics, the hydrogen-source and biogenic coproducts can have significant impacts on the sustainability of the process, and feasibility to use CFP off-gases or other renewable sources for hydrogen production can help lower GHG emissions.

09 BIOMASS FUELS↗
DOE OSTI2023

Quantum networks with neutral atom processing nodes

Abstract Quantum networks providing shared entanglement over a mesh of quantum nodes will revolutionize the field of quantum information science by offering novel applications in quantum computation, enhanced precision in networks of sensors and clocks, and efficient quantum communication over large distances. Recent experimental progress with individual neutral atoms demonstrates a high potential for implementing the crucial components of such networks. We highlight latest developments and near-term prospects on how arrays of individually controlled neutral atoms are suited for both efficient remote entanglement generation and large-scale quantum information processing, thereby providing the necessary features for sharing high-fidelity and error-corrected multi-qubit entangled states between the nodes. We describe both the functionality requirements and several examples for advanced, large-scale quantum networks composed of neutral atom processing nodes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Cryogenic hybrid magnonic circuits based on spalled YIG thin films

Yttrium iron garnet (YIG) magnonics has garnered significant research interest because of the unique properties of magnons (quasiparticles of collective spin excitation) for signal processing. In particular, hybrid systems based on YIG magnonics show great promise for quantum information science due to their broad frequency tunability and strong compatibility with other platforms. However, their broad applications have been severely constrained by substantial microwave loss in the gadolinium gallium garnet (GGG) substrate at cryogenic temperatures. In this study, we demonstrate that YIG thin films can be spalled from YIG/GGG samples. Our approach is validated by measuring hybrid devices comprising superconducting resonators and spalled YIG films, which exhibit anti-crossing features that indicate strong coupling between magnons and microwave photons. Such new capability of separating YIG thin films from GGG substrates via spalling and the integrated superconductor-YIG devices represent a significant advancement for integrated magnonic devices, paving the way for advanced magnon-based coherent information processing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Design-Technology Co-Optimization for NVM-based Neuromorphic Processing Elements

An emerging use-case of machine learning (ML) is to train a model on a high-performance system and deploy the trained model on energy-constrained embedded systems. Neuromorphic hardware platforms, which operate on principles of the biological brain, can significantly lower the energy overhead of a machine learning inference task, making these platforms an attractive solution for embedded ML systems. In this paper, we present a design-technology tradeoff analysis to implement such inference tasks on the processing elements (PEs) of a Non-Volatile Memory (NVM)-based neuromorphic hardware. Through detailed circuit-level simulations at scaled process technology nodes, we show the negative impact of technology scaling on the information-processing latency, which impacts the quality-of-service (QoS) of an embedded ML system. At a finer granularity, the latency inside a PE depends on 1) the delay introduced by parasitic components on its current paths, and 2) the varying delay to sense different resistance states of its NVM cells. Based on these two observations, we make the following three contributions. First, on the technology front, we propose an optimization scheme where the NVM resistance state that takes the longest time to sense is set on current paths having the least delay, and vice versa, reducing the average PE latency, which improves the QoS. Second, on the architecture front, we introduce isolation transistors within each PE to partition it into regions that can be individually power-gated, reducing both latency and energy. Finally, on the system-software front, we propose a mechanism to leverage the proposed technological and architectural enhancements when implementing a machine-learning inference task on neuromorphic PEs of the hardware. Evaluations with a recent neuromorphic hardware architecture show that our proposed design-technology co-optimization approach improves both performance and energy efficiency of machine-learning inference tasks without incurring high cost-per-bit.

42 ENGINEERING↗
DOE OSTI2023

Accelerating Additive Manufacturing Process Design for Energy Conversion Materials using In-situ Sensing and Machine Learning

One promising candidate for manufacturing of the bismuth Telluride thermoelectric legs is laser powder bed fusion (LPBF) additive Manufacturing (AM). AM processing parameters highly influence the material properties, however current processing parameter development methods in AM are costly and time consuming. In-situ sensors allow for the capture of physically relevant process information on a layer-by-layer basis and will be used to aide process development. To optimize the AM process for the best thermoelectric performance, process variables, in-situ process sensor data and ex-situ material characterization data are collected. Several different interpretable machine learning (ML) approaches are used, and the performance of each method are assessed. Significant input process variables include laser focus, hatch spacing and laser power. The best performing models are used to determine the manufacturing parameters that maximize the power factor. AM of bismuth telluride material provides the ability to create complex geometries enabling more efficient energy conversion.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

BETO 2021 Peer Review - Catalytic Upgrading of Pyrolysis Vapors 2.3.1.314

Catalytic fast pyrolysis (CFP) is a versatile pathway for the direct liquefaction of biomass and waste carbon sources to generate a stabilized bio-oil intermediate that can be further processed into renewable fuels, chemicals, and materials. The objective of this project is advance the CFP state-of-technology through integrated catalyst and process development, expand market responsiveness by creating routes to novel co-products, and provide experimental data to inform process modelling and scale-up activities. Research advancements over the past two years include reducing analytical uncertainty by achieving 100 ± 1% carbon balances during reaction testing with woody biomass, improving process efficiency by achieving a 4x increase in catalyst cycle length, demonstrating compatibility with waste feedstocks, and confirming process durability for 100+ reaction cycles. Additionally, this project supported a comprehensive pathway review to evaluate scale-up needs. This review resulted in the early identification of technical risks and informed proactive planning for the BETO 2022 Verification. Other impacts from this project include generation of broadly enabling scientific knowledge (14 publications/18 presentations since 2019), engagement with industry partners (e.g., Johnson Matthey and ExxonMobil), and identification of a promising pathway to market that addresses emerging demands for biogenic refinery feedstocks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗