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At least 757 records · Page 42

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DOE OSTI2016

Collaborative Research: Fundamental studies of plasma control using surface embedded electronic devices

The proposed study will investigate the effect of active electron injection of from electrode surfaces To the best of our knowledge, no such a study has ever been attempted even though it could lead to the formation of whole new classes of plasma based devices and systems. We are motivated by recent articles and simple theory which gives strong reason to believe that embedded electronic devices can be used to exert control over the SEE coefficient of semiconductor surfaces (and maybe other surface types as well). Furthermore, the research will explore how such sub-surface electronic devices can best be used to exert control over an associated plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

How Well Can Quantum Embedding Method Predict the Reaction Profiles for Hydrogenation of Small Li Clusters?

Quantum computing leverages the principles of quantum mechanics in novel ways to tackle complex chemistry problems that cannot be accurately addressed using traditional quantum chemistry methods. However, the high computational cost and available number of physical qubits with high fidelity limit its application to small chemical systems. This work employed a quantum-classical framework which features a quantum active space-embedding approach to perform simulations of chemical reactions that require up to 14 qubits. This framework was applied to prototypical example metal hydrogenation reactions: the coupling between hydrogen and Li 2 , Li 3 , and Li 4 clusters. Particular attention was paid to the computation of barriers and reaction energies. The predicted reaction profiles compare well with advanced classical quantum chemistry methods, demonstrating the potential of the quantum embedding algorithm to map out reaction profiles of realistic gas-phase chemical reactions to ascertain qualitative energetic trends. Additionally, the predicted potential energy curves provide a benchmark to compare against both current and future quantum embedding approaches.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Verifying status of a termination end cap of a microinverter chain

A photovoltaic (PV) system includes a system control module that determines the presence of a microinverter chain termination end cap. The end cap includes an embedded circuit. The embedded circuit includes components having resistive, reactive, or impedance values. A signal source provides a signal through the microinverter chain. A parameter for a sensed signal detected by the system control module is used to determine the presence of the end cap using a change between the parameter for the sense signal and a reference parameter.

14 SOLAR ENERGY↗

DOE OSTI2021

Verifying status of a termination end cap of a microinverter chain

Chapman, Patrick L.↗

DOE OSTI2014

A passively tunable acoustic metamaterial lens for selective ultrasonic excitation

In this paper, we present an approach to ultrasonic beam-forming and beam-steering in structures based on the concept of embedded acoustic metamaterial lenses. The lens design exploits the principle of acoustic drop-channel that enables the dynamic coupling of multiple ultrasonic waveguides at selected frequencies. In contrast with currently available technology, the embedded lens allows exploiting the host structure as a key component of the transducer system therefore enabling directional excitation by means of a single ultrasonic transducer. The design and the performance of the lens are numerically investigated by using Plane Wave Expansion and Finite Difference Time Domain techniques applied to bulk structures. Then, the design is experimentally validated on a thin aluminum plate waveguide where the lens is implemented by through-holes. The dynamic response of the embedded lens is estimated by reconstructing, via Laser Vibrometry, the velocity field induced by a single source located at the center of the lens.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Intelligent energy buildings based on RES and nanotechnology

The paper presents the design features, the energy modelling and optical performance details of two pilot Intelligent Energy Buildings, (IEB). Both are evolution of the Zero Energy Building (ZEB) concept. RES innovations backed up by signal processing, simulation models and ICT tools were embedded into the building structures in order to implement a new predictive energy management concept. In addition, nano-coatings, produced by TiO2 and ITO nano-particles, were deposited on the IEB structural elements and especially on the window panes and the PV glass covers. They exhibited promising SSP values which lowered the cooling loads and increased the PV modules yield. Both pilot IEB units were equipped with an on-line dynamic hourly solar radiation prediction model, implemented by sensors and the related software to manage effectively the energy source, the loads and the storage or the backup system. The IEB energy sources covered the thermal loads via a south façade embedded in the wall and a solar roof which consists of a specially designed solar collector type, while a PV generator is part of the solar roof, like a compact BIPV in hybrid configuration to a small wind turbine.

14 SOLAR ENERGY↗

DOE OSTI2020

Interface learning of multiphysics and multiscale systems

Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws. To address such challenges, we introduce an interface learning paradigm and put forth a data-driven closure approach based on memory embedding to provide physically correct boundary conditions at the interface. To enable the interface learning for hyperbolic systems by considering the domain of influence and wave structures into account, we put forth the concept of upwind learning toward a physics-informed domain decomposition. The promise of the proposed approach is shown for a set of canonical illustrative problems. Here, we highlight that high-performance computing environments can benefit from this methodology to reduce communication costs among processing units in emerging machine-learning-ready heterogeneous platforms toward exascale era.

42 ENGINEERING↗

DOE OSTI2019

Vertically-Aligned Carbon-Nanotubes Embedded in Ceramic Matrices for Hot Electrode Applications

The proposed is aimed to develop carbon nanotube-ceramic (CNT-C) composite structures, in which vertically aligned CNTs (VA-CNTs) are embedded in ceramic matrices for hot electrode applications in Magnetohydrodynamics (MHD) power systems, including four objectives: 1) Super growth of VA-CNT carpets; 2) Fabrication of CNT-boron nitride (CNT-BN) composite structures by chemical vapor infiltration; 3) Stability and resistance studies of the CNT-BN Composite structures; and 4) Thermionic emissions of the CNT-BN composite structures. The researchers will deploy water vapor-assisted chemical vapor deposition (CVD) method in the super growth of VA-CNT carpets on copper (Cu) with a maximum length up to 1 cm. Fabrication of the CNT-BN composite structures will be achieved by infiltrating gaseous boron precursors into the VA-CNTs followed by a universal heating, converting the precursors to solid BN crystals. Thermal stability and oxidation resistivity of the CNT-BN composite structures will be investigated using Thermogravimetric Analysis (TGA) and Differential thermal analysis (DTA) under an oxidation environment from room temperature to 3000 K. An oxy-acetylene flame will be employed to simulate the extreme environment in MHD channels for investigating the thermal and electrical conductivity, and corrosion and erosion resistance of the CNT-BN composite structures under the extreme conditions. Thermionic emission performance of the CNT-BN composite structures will be investigated to study the transport properties of the composite structures. If the CNT-BN composite does not perform as anticipated, selected ultra-high-temperature ceramics, such as titanium boride (TiB2), will be investigated as a backup candidate wrapping CNTs. Upon the successful completion of the propose research, CNT-C composite structures, such as CNT-BN and CNT-TiB2, will be developed for “hot” electrode applications. Fundamental understanding on the thermal and electrical transport properties of CNTs at high temperatures up to 3000 K will be established. Wrapping CNTs in ceramic matrices will isolate CNTs from the extreme environments in the MHD channels. The embedded CNTs will serve as conductive channels for heat and electricity transport. It is anticipated that the CNT-C composite structures would withstand the extreme environments in MHD channels and provide sufficient electrical and thermal conductivities for direct power extraction from seeded oxy-fuel combustion fluids. Successful development of the CNT-C composite structures will significant improve the reliability and promote the commercial applications of MHD generators, which are capable of large scale mass energy conversion at an unprecedented efficiency of 60% with reduced overall production of hazardous fossil fuel wastes.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning

Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis–nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Search for giant magnetic anisotropy in transition-metal dimers on defected hexagonal boron nitride sheet

Structural and magnetic properties of many transition-metal dimers embedded in a defected hexagonal boron nitride monolayer are investigated through density functional calculations to search for systems with magnetic anisotropy energies (MAEs) larger than 30meV. In particular, Ir–Ir@Dh–BN is found to have both large MAE (∼126 meV) and high structural stability against dissociation and diffusion, and it hence can serve as magnetic unit in spintronics and quantum computing devices. This giant MAE mainly results from the spin orbit coupling and the magnetization of the upper Ir atom, which is in a rather isolated environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Transfer learning nonlinear plasma dynamic transitions in low dimensional embeddings via deep neural networks

Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel, data-driven model reduction methods, coupled with detection of abnormal modes with plasma physics, opens a unique opportunity to identify plasma instabilities through automated construction of parsimonious models that can be tuned to balance accuracy and cost. Our fusion transfer learning (FTL) model demonstrates success in rapidly reconstructing nonlinear kink mode structures by learning from a limited amount of nonlinear simulation data. The knowledge transfer process leverages a pre-trained neural encoder–decoder network, initially trained on linear simulations, to effectively capture nonlinear dynamics. The low-dimensional embeddings extract the coherent structures of interest, while preserving the inherent dynamics of the complex system. Experimental results highlight FTL’s capacity to capture transitional behaviors and dynamical features in plasma dynamics—a task often challenging for conventional methods. The model developed in this study is generalizable and can be extended broadly through transfer learning to address various magnetohydrodynamics modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

A model of spatio-temporal regulation within biomaterials using DNA reaction–diffusion waveguides

In multi-cellular organisms, cells and tissues coordinate biochemical signal propagation across length scales spanning micrometres to metres. Designing synthetic materials with similar capacities for coordinated signal propagation could allow these systems to adaptively regulate themselves across space and over time. Here, we combine ideas from cell signalling and electronic circuitry to propose a biochemical waveguide that transmits information in the form of a concentration of a DNA species on a directed path. The waveguide could be seamlessly integrated into a soft material because there is virtually no difference between the chemical or physical properties of the waveguide and the material it is embedded within. We propose the design of DNA strand displacement reactions to construct the system and, using reaction–diffusion models, identify kinetic and diffusive parameters that enable super-diffusive transport of DNA species via autocatalysis. Finally, to support experimental waveguide implementation, we propose a sink reaction and spatially inhomogeneous DNA concentrations that could mitigate the spurious amplification of an autocatalyst within the waveguide, allowing for controlled waveguide triggering. Chemical waveguides could facilitate the design of synthetic biomaterials with distributed sensing machinery integrated throughout their structure and enable coordinated self-regulating programmes triggered by changing environmental conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

SU-E-T-433: Calibration Accuracy in Mailed High-Resolution 3D Dosimetry Service for SRS/SBRT QA

Purpose: SRS/SBRT combines hypofractionation with excellent dose distributions. However, extremely steep gradients across the target along with dose escalation, if not administered accurately, may lead to serious complications, recurrences, or even fatalities. Existing commercial QA products either lack adequate spatial resolution or the 3D aspect. By contrast, the new CrystalBall™ mailed high-resolution 3D dosimetry service removes the above limitations while reducing the overall workload on medical physics staff. The exposed dosimeters, which change optical density in proportion to local dose, are sent back to the manufacturer (MGS Research Inc., Madison, CT) for sub-millimeter-resolution laser-CT scanning and QA data analysis. QA report is returned electronically within 24 hours. The purpose of this study was to evaluate the dose calibration accuracy in this system. Methods: Two spherical CrystalBall™ polymer gel dosimeters from the same batch, 166 mm diameter, with embedded 3D image registration markers, were mounted in a special phantom designed for reproducible positioning. For full end to end testing, the optical guidance array was mounted onto the phantom and a CT was taken. Two separate Rapid Arc SRS plans were designed. Varian Medical Systems optical guidance system was used to position the phantom and the SRS treatment plans were delivered to the two spheres on Varian's Trilogy Accelerator. Exposed dosimeters were mailed back to the manufacturer for laser CT scanning and analysis. Results: For each plan, 3D gamma passing rate was 100% for 2%/2mm distance-to-agreement criteria above 50% isodose level. The two calibration curves, generated using volumetric dose and optical density data, showed excellent mutual agreement (max difference 2.2%, median difference 0.75%). Conclusion: The clinical utility of new CrystalBall™ mailed QA service for SRS/SBRT and high accuracy of dose calibration have been validated. The workflow associated with the use of the CrystalBall™ in clinical setting was found to be minimal. The presenting author is the founder of and has an ownership in MGS Research Inc which manufactures the CrystalBall system for 3D dosimetry.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Large-scale harmonic balance simulations with Krylov subspace and preconditioner recycling

The multi-harmonic balance method combined with numerical continuation provides an efficient framework to compute a family of time-periodic solutions, or response curves, for large-scale, nonlinear mechanical systems. The predictor and corrector steps repeatedly solve a sequence of linear systems that scale by the model size and number of harmonics in the assumed Fourier series approximation. In this paper, a novel Newton–Krylov iterative method is embedded within the multi-harmonic balance and continuation algorithm to efficiently compute the approximate solutions from the sequence of linear systems that arise during the prediction and correction steps. Further, the method recycles, or reuses, both the preconditioner and the Krylov subspace generated by previous linear systems in the solution sequence. A delayed frequency preconditioner refactorizes the preconditioner only when the performance of the iterative solver deteriorates. The GCRO-DR iterative solver recycles a subset of harmonic Ritz vectors to initialize the solution subspace for the next linear system in the sequence. The performance of the iterative solver is demonstrated on two exemplars with contact-type nonlinearities and benchmarked against a direct solver with traditional Newton–Raphson iterations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Towards utility-scale electronic structure with sample-based quantum bootstrap embedding

One of the main applications for which quantum computers are hoped to find utility is in simulating ground state energies and other observables of molecular chemical systems. The recently proposed sample-based diagonalization method is a readily implementable method for this task on current-day hardware using short circuit depths and has been demonstrated on as many as 85 qubits in recent studies. In this work, we combine the recently proposed quantum bootstrap embedding (QBE) method with sampled-based diagonalization (QBE-SQD) and present the first benchmarking study of the QBE method on real quantum hardware, ibm_pittsburgh, a Heron r3 processor with 156 qubits. Our test system is a hydrogen ring with 8 hydrogen atoms in the cc-pVDZ basis. We show that for this system, QBE-SQD using an active space of (8e, 19o) per fragment with a 43 qubit footprint produces a ground state energy accuracy which exceeds that of an SQD calculation with an (8e, 30o) active space with a 67 qubit footprint when using a comparable number of Slater determinants. This demonstrates that the use of quantum bootstrap embedding techniques is a promising path towards extending the capabilities of state-of-the-art quantum eigensolvers on near-term devices.

Bierman, Joel [North Carolina State University, Ra↗

DOE OSTI2014

Local Hamiltonians for quantitative Green's function embedding methods

Embedding calculations that find approximate solutions to the Schrödinger equation for large molecules and realistic solids are performed commonly in a three step procedure involving (i) construction of a model system with effective interactions approximating the low energy physics of the initial realistic system, (ii) mapping the model system onto an impurity Hamiltonian, and (iii) solving the impurity problem. We have developed a novel procedure for parametrizing the impurity Hamiltonian that avoids the mathematically uncontrolled step of constructing the low energy model system. Instead, the impurity Hamiltonian is immediately parametrized to recover the self-energy of the realistic system in the limit of high frequencies or short time. The effective interactions parametrizing the fictitious impurity Hamiltonian are local to the embedded regions, and include all the non-local interactions present in the original realistic Hamiltonian in an implicit way. We show that this impurity Hamiltonian can lead to excellent total energies and self-energies that approximate the quantities of the initial realistic system very well. Moreover, we show that as long as the effective impurity Hamiltonian parametrization is designed to recover the self-energy of the initial realistic system for high frequencies, we can expect a good total energy and self-energy. Lastly, we propose two practical ways of evaluating effective integrals for parametrizing impurity models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Pore Resolved Simulations of Joule Heating in Fibrous Media using an Embedded Boundary Method

Joule heating has been regarded as an energy-efficient and sustainable method for heating materials and gases at large scales. The modeling of local temperature effects at pore-resolved scales for such systems, however, has been difficult to achieve due to challenges in coupling thermo-chemical processes in complex porous media and in large representative volume elements (RVEs). To this end, we developed an electro-thermal model at the pore scale to study Joule heating effects in large heterogeneous systems with different microstructures. This was achieved using the level set method to implicitly delineate distinct regions within the domain, and an embedded boundary method to facilitate heat exchange across the fluid-solid interface. Moreover, we applied this method to investigate unsteady non-linear electro-thermal effects in non-woven fibrous graphite conductors for RVEs with characteristic lengths of 2 mm, with different fiber orientations, porosity (80% – 90%) and fiber diameters (10 – 20µm). The coupled equations were solved numerically and they produced peak temperatures greater than 2000 K resulting in heating rates as high as 80,000 K/s. Moreover, the results depended strongly on the microstructure of the fiber skeleton and current density. Geometries with large fibers (∼ 20µm) had the highest average and peak temperatures with the mean temperature increasing by 3.9 % while the peak temperature increased by 9.9 %. Anisotropic domains on the other hand had the lowest mean and peak temperatures with peak and mean temperatures of 2293 K and 1437.7K respectively representing a corresponding 12.1% and 5.1% drop in the temperatures. An increase in porosity from 80% to 90%, however, led to an increase in the peak temperature by 5.1%.

Joule heating↗

DOE OSTI2022

Use of Event-Time Embeddings via RNN to Discern Novel Event Sequences in EHRs

In highly configurable health information technology (HIT) systems, such as VistA of the Veterans Health Administration, the variations in how the system is used among different healthcare facilities and how the data are recorded can be significant. Despite the successful standardization of care efforts, some of these variations can be indicative of HIT hazards and demand further investigation. In this work, we implemented a recurrent neural network (RNN) architecture to learn clinical provider order sequences and their temporal dynamics while predicting the orders' terminal state. We demonstrate model performance and provide a use case for the model discerning novel event sequences. This model is proposed to find novel event sequences in an operational environment.

Ozmen, Ozgur↗