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Results for “Intrinsic computation”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Nonlinear optical properties of composite materials

The optical properties of a new class of composite nonlinear materials composed of coated grains, such as cadmium sulfide with a silver coating, are examined. These materials exhibit intrinsic optical bistability and resonantly enhanced conjugate reflectivity. The threshold for intrinsic optical bistability is low enough for practical applications in optical communications and optical computing. Some problems associated with the fabrication of these materials are addressed. Based on preliminary results, switching times are expected to be in the subpicosecond range.

Haus, Joseph W.↗
DOE OSTI2026

Multigroup Thermal Radiation Transport with Tensor Trains

We investigate the application of tensor-train (TT) algorithms to multigroup thermal radiation transport (i.e., photon radiation transport). The TT framework enables simulations at discretizations that might otherwise be computationally infeasible on conventional hardware. We show that solutions to certain multigroup problems possess an intrinsic low-rank structure, which the TT representation leverages effectively. This enables us to solve problems where the discretized solution size exceeds a trillion parameters on a single node. The solver is evaluated on a range of test problems with varying levels of complexity, consistently achieving compression factors greater than 100× and speedups exceeding 2×. We also investigate alternative TT topologies by analyzing the low-rank structure of the merged spatio-spectral core to assess the potential for greater compression. This analysis suggests that compression gains could increase by factors as large as 7. Our results indicate that the low-rank structure of the merged spatio-spectral core captures the spatio-spectral complexity of the solution, largely driven by the opacity structure of the medium. Beyond identifying opportunities for improved compression, this analysis highlights the types of errors that may arise in angle-integrated quantities when exploiting this low-rank structure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Quantifying transport and electrocatalytic reaction processes in a gastight rotating cylinder electrode reactor via integration of Computational Fluid Dynamics modeling and experiments

Understanding the complexity of the multiple processes of mass, momentum, charge, and heat transport, and how these affect reaction kinetics at the electrode/electrolyte interface is one of the major challenges in the field of energy and catalysis. The rapid and rational scale-up of electrocatalytic systems to industrial scales require a detailed understanding of nonlinear transport-reaction processes, accessible only through the building of multi-physics models that capture with high fidelity the complexity of real-world devices. The gastight rotating cylinder electrode (RCE) reactor is a promising lab-scale tool that can decouple transport from intrinsic kinetics to generate data for first-principle models useful in the design of industrial, electrochemical reactors. Computational Fluid Dynamics (CFD) studies have previously been used to investigate the bulk flow in RCE reactors for simple corrosion and electroplating processes. However, the quantification of changes in local concentration within the viscous layer where catalysis takes place requires capturing the correct flow conditions inside the hydrodynamic boundary layer near the surface of the electrode. Further, this requires simulations with spatial resolution in the nm and μm scale and temporal resolutions between ms and s scales that are similar to the timescales for reactions on the electrode surface. In this study, experimental electrocatalysis is combined with CFD modeling to elucidate and parameterize the hydrodynamics in a gastight RCE reactor. CFD simulations of the electrochemical ferricyanide reduction reaction under mass transport limited conditions are used to evaluate the validity of the CFD model parameters by comparing calculated dimensionless mass transport descriptors to dimensionless correlations obtained experimentally. Justifications for assumptions and details of the simulation methods used in this study are presented to provide a detailed understanding of the effect that each model parameter has on the ability to accurately simulate electrocatalysis in RCE systems. The simulation methodology reported here is a first step towards the development of multi-scale models for the study of transport dependent electrocatalytic processes, such as the electrochemical transformation of CO 2 to fuels and chemicals.

42 ENGINEERING↗
DOE OSTI2020

Probabilistic learning and updating of a digital twin for composite material systems

This paper presents an approach for characterizing and estimating statistical dependence between a large number of observables in a composite material system. Conditional regression is carried out using the estimated joint density function, permitting a systematic exploration of interdependence between fine scale and coarse observables that can be used for both prognosis and design of complex material systems. An example demonstrates the integration of experimental data with a computational database. The statistical approach is based on the probabilistic learning on manifolds recently developed by the authors. This approach leverages intrinsic structure detected through diffusion on graphs with projected stochastic differential equations to generate samples constrained to that structure.

42 ENGINEERING↗
DOE OSTI2022

Surface-Driven Evolution of the Anomalous Hall Effect in Magnetic Topological Insulator MnBi 2 Te 4 Thin Films

We report understanding the effects of the interfacial modification to the functional properties of magnetic topological insulator thin films is crucial for developing novel technological applications from spintronics to quantum computing. Here, a large electronic and magnetic response is reported to be induced in the intrinsic magnetic topological insulator MnBi 2 Te 4 by controlling the propagation of surface oxidation. It is shown that the formation of the surface oxide layer is confined to the top 1–2 unit cells but drives large changes in the overall magnetic response. Specifically, a dramatic reversal of the sign of the anomalous Hall effect is observed to be driven by finite thickness magnetism, which indicates that the film splits into distinct magnetic layers each with a unique electronic signature. These data reveal a delicate dependence of the overall magnetic and electronic response of MnBi 2 Te 4 on the stoichiometry of the top layers. This study suggests that perturbations resulting from surface oxidation may play a non-trivial role in the stabilization of the quantum anomalous Hall effect in this system and that understanding targeted modifications to the surface may open new routes for engineering novel topological and magnetic responses in this fascinating material.

36 MATERIALS SCIENCE↗
NASA NTRS1992

A stochastic damage model for the rupture prediction of a multi-phase solid. I - Parametric studies. II - Statistical approach

A stochastic damage model for predicting the rupture of a brittle multiphase material is developed, based on the microcrack-macrocrack interaction. The model, which incorporates uncertainties in locations, orientations, and numbers of microcracks, characterizes damage by microcracking and fracture by macrocracking. A parametric study is carried out to investigate the change of the stress intensity at the macrocrack tip by the configuration of microcracks. The inherent statistical distribution of the fracture toughness arising from the intrinsic random nature of microcracks is explored using a statistical approach. For this purpose, a computer simulation model is introduced, which incorporates a statistical characterization of geometrical parameters of a random microcrack array.

Lua, Yuan J.↗
DOE OSTI2026

Oxide-nitride heteroepitaxy for low-loss dielectrics in superconducting quantum circuits

Superconducting qubits show great promise for the realization of fault-tolerant quantum computing, but lossy, amorphous dielectrics limit current technology. Identifying highly crystalline and stoichiometric dielectrics with intrinsically low microwave loss is therefore a central materials challenge, yet experimentally validated platforms remain scarce. In this work, we integrate a crystalline dielectric into a heteroepitaxial TiN/$γ$-Al$_2$O$_3$/TiN trilayer grown via pulsed laser deposition. Correlative high-resolution imaging, diffraction, and spectroscopy measurements confirm the single-crystal quality and chemical integrity of all layers, with minimal defects and limited anion interdiffusion across the oxide-nitride interfaces. Using microwave lumped-element resonators with parallel-plate capacitors, we report the first direct measurement of the dielectric loss of epitaxial $γ$-Al$_2$O$_3$, for which we find a low intrinsic two-level system loss, $δ_{\text{TLS}}^0 = (2.8 \pm 0.1) \times 10^{-5}$. These results establish heteroepitaxial oxides on transition metal nitrides as an attractive materials platform for superconducting quantum circuits, particularly for integration into compact device architectures such as merged-element transmons and microwave kinetic inductance detectors.

Garcia-Wetten, David A. [Northwestern U.]↗
DOE OSTI2023

Magnetoresistance anomaly during the electrical triggering of a metal-insulator transition

Phase separation naturally occurs in a variety of magnetic materials and it often has a major impact on both electric and magnetotransport properties. In resistive switching systems, phase separation can be created on demand by inducing local switching, which provides an opportunity to tune the electronic and magnetic state of the device by applying voltage. Here we explore the magnetotransport properties in the ferromagnetic oxide La 0.7 ⁢Sr 0.3 ⁢MnO 3 (LSMO) during the electrical triggering of an intrinsic metal-insulator transition (MIT), which produces volatile resistive switching. This switching occurs in a characteristic spatial pattern, i.e., the formation of a high-resistance barrier perpendicular to the current flow, enabling an electrically actuated ferromagnetic-paramagnetic-ferromagnetic phase separation. At the threshold voltage of the MIT triggering, both anisotropic and colossal magnetoresistances exhibit anomalies including a large increase in magnitude and a sign flip. Computational analysis revealed that these anomalies originate from the coupling between the switching-induced phase separation state and the intrinsic magnetoresistance of LSMO. Furthermore, this work demonstrates that driving the MIT material into an out-of-equilibrium resistive switching state provides the means for electrical control of the magnetotransport phenomena.

Complex oxides↗
DOE OSTI2021

Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation

Non-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerging from various domain sciences. They provide a low-dimensional emulation framework for systems that may be intrinsically high-dimensional. This is accomplished by utilizing a construction algorithm that is purely data-driven. It is no surprise, therefore, that the algorithmic advances of machine learning have led to non-intrusive ROMs with greater accuracy and computational gains. However, in bypassing the utilization of an equation-based evolution, it is often seen that the interpretability of the ROM framework suffers. This becomes more problematic when black-box deep learning methods are used which are notorious for lacking robustness outside the physical regime of the observed data. In this article, we propose the use of a novel latent-space interpolation algorithm based on Gaussian process regression. Notably, this reduced-order evolution of the system is parameterized by control parameters to allow for interpolation in space. The use of this procedure also allows for a continuous interpretation of time which allows for temporal interpolation. The latter aspect provides information, with quantified uncertainty, about full-state evolution at a finer resolution than that utilized for training the ROMs. This research assesses the viability of this algorithm for an advection-dominated system given by the inviscid shallow water equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification

Focal Area(s): (2)Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models. (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge- guided AI. Science Challenge: The Energy Exascale Earth System Model (E3SM) is a fully coupled, state-of-the-science Earth system model that uses code optimized for DOE's advanced computers to address the most critical scientific questions facing our nation and society (Golaz et al., 2019). The E3SM Land model (ELM) is designed to understand how the changes in terrestrial land surfaces will interact with other Earth system components and has been used to understand hydrologic cycles, biogeophysics, and ecosystem dynamics. In spite of great successes, the ELM has several known issues that restrain rapid improvements. For example, the ELM uses equilibrium models to simulate dynamic land-climate interactions and it requires long model spin-up time to identify suitable initial conditions for transient simulations. The ELM lacks built-in uncertainty mechanisms that can improve the robustness of model predictions. The ELM is a holistic, deterministic model system with a rigid design, and in many situations, it is hard to modify the ELM system to incorporate new theory/hypothesis and new data across scales to address emerging science problems (such as predicting the impacts of water cycle extremes). In addition, The ELM is technically optimized for traditional CPU-centric computers and it cannot fully utilize the current and incoming leadership computers for model simulations and uncertainty quantification (UQ). The success of artificial intelligence (AI) has inspired scientists to use AI models to discover intrinsic features from simulation data (Chattopadhyay et al., 2020) and observational data (Reichstein et al., 2019) to gain further process understanding of Earth science problems. However, autonomous AI model training through deep learning usually requires a huge amount of annotated data. To overcome the limitations from the data and computing resources, knowledge-guided AI models are necessary where human-knowledge is ingested in model construction (Banino et al., 2018) and training process (Silver et al., 2016) for efficient learning. Herein, we present a new way that leverages the process understanding from the ELM to guide AI model development for the ELM enhancement and UQ. We hope this study can inspire further Earth and environmental system model developments and transformations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Extending the computational reach of a superconducting qutrit processor

Quantum computing with qudits is an emerging approach that exploits a larger, more connected computational space, providing advantages for many applications, including quantum simulation and quantum error correction. Nonetheless, qudits are typically afflicted by more complex errors and suffer greater noise sensitivity which renders their scaling difficult. In this work, we introduce techniques to tailor arbitrary qudit Markovian noise to stochastic Weyl–Heisenberg channels and mitigate noise that commutes with our Clifford and universal two-qudit gate in generic qudit circuits. We experimentally demonstrate these methods on a superconducting transmon qutrit processor, and benchmark their effectiveness for multipartite qutrit entanglement and random circuit sampling, obtaining up to 3× improvement in our results. To the best of our knowledge, this constitutes the first-ever error mitigation experiment performed on qutrits. Our work shows that despite the intrinsic complexity of manipulating higher-dimensional quantum systems, noise tailoring and error mitigation can significantly extend the computational reach of today’s qudit processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Controllable Reset Behavior in Domain Wall–Magnetic Tunnel Junction Artificial Neurons for Task-Adaptable Computation

Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall–magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. Edgy-relaxed behavior, where a frequently firing neuron experiences a lower action potential threshold, may provide additional artificial neuronal functionality when executing repeated tasks. In this letter, we demonstrate that this behavior can be implemented in DW-MTJ artificial neurons via three alternative mechanisms: shape anisotropy, magnetic field, and current-driven soft reset. Using micromagnetics and analytical device modeling to classify the Optdigits handwritten digit dataset, we show that edgy-relaxed behavior improves both classification accuracy and classification rate for ordered datasets while sacrificing little to no accuracy for a randomized dataset. This letter establishes methods by which artificial spintronic neurons can be flexibly adapted to datasets.

42 ENGINEERING↗
DOE OSTI2020

Observation of an Unexpected n -Type Semiconducting Behavior in the New Ternary Zintl Phase Eu 3 InAs 3

The ternary arsenides Eu 3 InAs 3 and Sr 3 InAs 3 have been obtained by reactions of the elements in In flux at 1373 K. Structure elucidation by single-crystal X-ray diffraction reveals that Eu 3 InAs 3 and Sr 3 InAs 3 adopt the same orthorhombic structure (space group Pnma, Z = 4, Ca 3 AlAs 3 structure type) with unit cell parameters a = 12.9179(9) Å, b = 4.3990(3) Å, c = 13.9337(10) Å and a = 13.0218(11) Å, b = 4.4364(4) Å, c = 14.1339(12) Å, respectively. The structure consists of linear chains of corner-sharing InAs4 tetrahedra, [InAs 2 As 2/2 ] 6– , and Eu 2+ /Sr 2+ cations. Therefore, both Eu 3 InAs 3 and Sr 3 InAs 3 are valence-precise Zintl phases. As expected from the closed-shell electronic configurations, semiconducting behavior is confirmed by resistivity measurements on single crystals for both and by electronic band structure calculations for Sr 3 InAs 3 . The temperature dependence of resistivity and the computational work are in agreement that Eu 3 InAs 3 and Sr 3 InAs 3 are intrinsic semiconductors with narrow band gaps. Thermopower measurement on single-crystalline samples of Eu 3 InAs 3 shows that in the whole measured temperature range, from 300 to 700 K, the values for the Seebeck coefficient are negative. The observation of a negative Seebeck coefficient with very large absolute value (>400–500 μV K –1 at 700 K) is unexpected among the Zintl phases and suggestive that electrons are the majority charge carriers. Such a rare, n-type charge transport in an undoped compound such as Eu 3 InAs 3 , a material that has not been purposely optimized, could indicate native “defect” chemistry, and not extrinsic doping, as a reason for the unusual behavior. Here, a possible explanation involves a mixed-valent Eu 2+ /Eu 3+ state, which might be inferred from the measured effective paramagnetic moment of 7.2 μB per Eu atom, which is lower than the theoretically predicted value for free-ion moment of 7.9 μ B /Eu.

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

New insights into backbending in the symmetry-adapted shell-model framework

Here we provide insights into the backbending phenomenon within the symmetry-adapted framework which naturally describes the intrinsic deformation of atomic nuclei. For 20 Ne, the canonical example of backbending in light nuclei, the ab initio symmetry-adapted no-core shell model shows that while the energy spectrum replicates the backbending from experimental energies under the rotor-model assumption, there is no change in the intrinsic deformation or intrinsic spin of the yrast band around the backbend. For the traditional example of 48 Cr, computed in the valence shell with empirical interactions, we confirm a high-spin nucleus that is effectively near-spherical, in agreement with previous models. However, we find that this spherical distribution results, on average, from an almost equal mixing of deformed prolate shapes with deformed oblate shapes. Microscopic calculations confirm the importance of spin alignment and configuration mixing, but surprisingly unveil no anomalous increase in moment of inertia. Furthermore, this finding opens the path toward further understanding the rotational behavior and moment of inertia of medium-mass nuclei.

20 ≤ A ≤ 38↗
NASA NTRS2005

Camera Image Transformation and Registration for Safe Spacecraft Landing and Hazard Avoidance

Inherent geographical hazards of Martian terrain may impede a safe landing for science exploration spacecraft. Surface visualization software for hazard detection and avoidance may accordingly be applied in vehicles such as the Mars Exploration Rover (MER) to induce an autonomous and intelligent descent upon entering the planetary atmosphere. The focus of this project is to develop an image transformation algorithm for coordinate system matching between consecutive frames of terrain imagery taken throughout descent. The methodology involves integrating computer vision and graphics techniques, including affine transformation and projective geometry of an object, with the intrinsic parameters governing spacecraft dynamic motion and camera calibration.

Jones, Brandon M.↗
NASA NTRS2012

Cloud Computing for Mission Design and Operations

The space mission design and operations community already recognizes the value of cloud computing and virtualization. However, natural and valid concerns, like security, privacy, up-time, and vendor lock-in, have prevented a more widespread and expedited adoption into official workflows. In the interest of alleviating these concerns, we propose a series of guidelines for internally deploying a resource-oriented hub of data and algorithms. These guidelines provide a roadmap for implementing an architecture inspired in the cloud computing model: associative, elastic, semantical, interconnected, and adaptive. The architecture can be summarized as exposing data and algorithms as resource-oriented Web services, coordinated via messaging, and running on virtual machines; it is simple, and based on widely adopted standards, protocols, and tools. The architecture may help reduce common sources of complexity intrinsic to data-driven, collaborative interactions and, most importantly, it may provide the means for teams and agencies to evaluate the cloud computing model in their specific context, with minimal infrastructure changes, and before committing to a specific cloud services provider.

cloud computing↗
DOE OSTI2021

An efficient computational framework for charge density estimation in twisted bilayer graphene

Electronic properties such as band structure and Fermi velocity in low-angle twisted bilayer graphene (TBG) are intrinsically dependent on the atomic structure. Rigid rotation between individual graphene layers provides an approximate description of the bilayer symmetry. Upon relaxation, in-plane displacement of the atoms in low angle TBG causes a change in the symmetry through the enlargement of the AB stacking regions and the reduction in size of AA and SP stacking regions. However, the effect of this in-plane relaxation on the charge density remains unexplored, because the necessary electronic structure calculations of such large supercells of low twist angle TBG are computationally infeasible. Therefore, we develop a computationally efficient framework that enables the exploration of the charge density symmetry of the low twist angle TBG. This framework is based on the Fourier representation of the charge density which presents high intensity Bragg peaks. Here we find that with the decrease of twist angle, low intensity satellite peaks also become apparent. Our framework incorporates these satellite peaks which reveals transformation of symmetry in the charge density distribution from high to low twist angle TBG. One striking outcome is the demonstration of the electron localization in the AA region of low twist angle TBG. Our framework helps to explain the effect of the atomistic relaxation on the charge density distribution and thus, it provides information about exotic electronic properties of low twist angle TBG at a low computational expense.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Regional surrogates for predictive control of digital twins

Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.

Data-driven model↗