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At least 397 records · Page 22

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DOE OSTI2024

Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?

Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-term quantum advantage. However, recent studies have shown that noise can severely limit the trainability of VQAs, e.g., by exponentially flattening the cost landscape and suppressing the magnitudes of cost gradients. Error Mitigation (EM) shows promise in reducing the impact of noise on near-term devices. Thus, it is natural to ask whether EM can improve the trainability of VQAs. In this work, we first show that, for a broad class of EM strategies, exponential cost concentration cannot be resolved without committing exponential resources elsewhere. This class of strategies includes as special cases Zero Noise Extrapolation, Virtual Distillation, Probabilistic Error Cancellation, and Clifford Data Regression. Second, we perform analytical and numerical analysis of these EM protocols, and we find that some of them (e.g., Virtual Distillation) can make it harder to resolve cost function values compared to running no EM at all. As a positive result, we do find numerical evidence that Clifford Data Regression (CDR) can aid the training process in certain settings where cost concentration is not too severe. Our results show that care should be taken in applying EM protocols as they can either worsen or not improve trainability. On the other hand, our positive results for CDR highlight the possibility of engineering error mitigation methods to improve trainability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Enhancing Lattice Kinetic Schemes for Fluid Dynamics with Lattice-Equivariant Neural Networks

A new class of equivariant neural networks is presented, hereby dubbed lattice-equivariant neural networks (LENNs), designed to satisfy local symmetries of a lattice structure. The approach develops within a recently introduced framework aimed at learning neural network-based surrogate models’ lattice Boltzmann collision operators. Whenever neural networks are employed to model physical systems, respecting symmetries and equivariance properties has been shown to be key for accuracy, numerical stability, and performance. Here, hinging on ideas from group representation theory, trainable layers are defined whose algebraic structure is equivariant with respect to the symmetries of the lattice cell. In this work, the presented method naturally allows for efficient implementations, in terms of both memory usage and computational costs, supporting scalable training/testing for lattices in two spatial dimensions and higher (in which the size of symmetry group grows). The approach is validated and tested considering 2D and 3D flowing dynamics, both in laminar and turbulent regimes. It is compared with group-averaged-based symmetric networks and with plain, nonsymmetric, networks, showing how the presented approach unlocks the (a posteriori) accuracy and training stability of the former models and the train/inference speed of the latter networks. (LENNs are about one order of magnitude faster than group-averaged networks in 3D.) The work in this paper opens toward practical use of machine learning-augmented lattice Boltzmann CFD in real-world simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Impact of Dust-Cloud-Radiation-Precipitation Dynamical Feedback on Subseasonal-to-Seasonal Variability of the Asian Summer Monsoon in Global Variable-Resolution Simulations With MPAS-CAM5

In this study, we investigate the effects of increased dust emission from the Middle East deserts on subseasonal-to-seasonal (S₂S) variability of the Asian summer monsoon (ASM). Numerical experiments are performed using the Model for Prediction Across Scales (MPAS) coupled with the Community Atmosphere Model (CAM5) physics, with regional refinement at 30 km grid resolution over South Asia and the surrounding regions. Result shows that increased dust emission and transport from the Middle East/West Asia region induces a strong dust-cloud-radiation-precipitation-circulation feedback, resulting in a colder surface over the desert regions and the western Tibetan Plateau, but warmer and moister troposphere with enhanced cloudiness and precipitation over the Pakistan/Northwest India (PNWI) region. The latter changes are amplified by the dust aerosol induced Elevated Heat Pump (EHP) mechanism along the West Himalayas/Iranian Plateau foothill regions, most pronounced during May-June. During July-August, cloud radiation feedback further enhances the warming of the upper troposphere, and cooling of the land surface over the PNWI and adjacent regions over West Asia. The upper tropospheric heating and increased precipitation over PNWI spur a large-scale anomalous Rossby wavetrain and a northward displacement of the subtropical jetstream, manifesting in a contraction of the South Asian High and westward shift of the Western Pacific Subtropical High. As a result, the entire ASM precipitation-cloud system is displaced westward. Precipitation and cloudiness are intensified over northwest and western India and west Asia, but suppressed over southern and central East Asia. Analyses of the S₂S variability of the upper level vorticity balance suggests that heating by Middle East dust plays an important role in exciting, and anchoring a teleconnection pattern through interactions among dust-cloud radiation, precipitation heating over the PNWI, the development of an upper level Rossby wavetrain, and the northward shift of the boreal summer jetstream over Eurasia.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow

This document presents a master–slave methodology for solving the problem of optimal operation of photovoltaic (PV) distributed generators (DGs) in direct current (DC) networks. This problem was modeled using a nonlinear programming model (NLP) that considers the minimization of three different objective functions in a daily operation of the system. The first one corresponds to the minimization of the total operational cost of the system, including the energy purchasing cost to the conventional generators and maintenance costs of the PV sources; the second objective function corresponds to the reduction of the energy losses associated with the transport of energy in the network, and the third objective function is related to the minimization of the total emissions of CO2 by the conventional generators installed on the DC grid. The minimization of these objective functions is achieved by using a master–slave optimization approach through the application of the Vortex Search algorithm combined with a matrix hourly power flow. To evaluate the effectiveness and robustness of the proposed approach, two test scenarios were used, which correspond to a grid-connected and a standalone network located in two different regions of Colombia. The grid-connected system emulates the behavior of the solar resource and power demand of the city of Medellín-Antioquia, and the standalone network corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá-Choco. A numerical comparison was performed with four optimization methodologies reported in the literature: particle swarm optimization, multiverse optimizer, crow search algorithm, and salp swarm algorithm. The results obtained demonstrate that the proposed optimization approach achieved excellent solutions in terms of response quality, repeatability, and processing times.

14 SOLAR ENERGY↗

DOE OSTI2024

Convergence Analysis for an Online Data-Driven Feedback Control Algorithm

This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The algorithm comprises a particle filter-enabled state estimation component, estimating the controlled system’s state via indirect observations, alongside an efficient stochastic maximum principle-type optimal control solver. By integrating weak convergence techniques for the particle filter with convergence analysis for the stochastic maximum principle control solver, we derive a weak convergence result for the optimization procedure in search of optimal data-driven feedback control. Numerical experiments are performed to validate the theoretical findings.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Impact of Magnetic-field-driven Anisotropies on the Equation of State Probed in Neutron Star Mergers

Binary neutron star mergers can produce extreme magnetic fields, some of which can lead to strong magnetar-like remnants. While strong magnetic fields have been shown to affect the dynamics of outflows and angular momentum transport in the remnant, they can also crucially alter the properties of nuclear matter probed in the merger. In this work, we provide a first assessment of the latter, determining the strength of the pressure anisotropy caused by Landau-level quantization and the anomalous magnetic moment. To this end, we perform the first numerical relativity simulation with a magnetic polarization tensor and a magnetic-field-dependent equation of state using a new algorithm we present here, which also incorporates a mean-field dynamo model to control the magnetic field strength present in the merger remnant. Our results show that—in the most optimistic case—corrections to the anisotropy can be in excess of 10% and are potentially largest in the outer layers of the remnant. This work paves the way for a systematic investigation of these effects.

General relativity↗

DOE OSTI2022

Instanton Effects in Euclidean vacuum, Real Time Production, and in the Light-front Wave Functions

Nontrivial topological structures of non-Abelian gauge fields were discovered in the 1970s. Instanton solutions, describing vacuum tunneling through topological barriers, have fermionic zero modes which are at the origin of ‘t Hooft effective Lagrangian. In the 1980s, instanton ensembles have been used to explain chiral symmetry breaking. In the 1990s, a large set of numerical simulations were performed deriving Euclidean correlation functions. The special role of scalar diquarks in nucleons and color superconductivity in dense quark matter has been elucidated. In these lectures, we discuss further developments of physics related to gauge topology. We show that the instanton–anti-instanton “streamline” configurations describe “sphaleron transitions” in high-energy collisions, which result in production of hadronic clusters with nontrivial topological/chiral charges. (They are not yet observed, but discussions of dedicated experiments at the LHC and RHIC are ongoing.) Another new direction is instanton effects in hadronic spectroscopy, both in the rest frame and on the light front. We will discuss their role in central and spin-dependent potentials, form factors and antiquark nuclear “sea”. Finally, we summarize the advances in the semiclassical theory of deconfinement, and chiral phase transitions at finite temperature, in QCD, and in some of its “deformed” versions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Theoretical Development of Controller Transfer applied to Dynamical Systems

Data-driven control strategies suffer from the necessity of training and its associated computational time and cost. This motivates the continuous increase of popularity of transfer learning based methods. In this paper, we have investigated transfer learning in the context of controller for dynamical systems. First, we have derived conditions under which controller can be transferred between two linear dynamical systems. Furthermore, we have introduced a novel algorithm to design the transferable and non-transferable control components for linear systems. Secondly, we have derived conditions under which a stabilizing controller can be transferred between two different nonlinear dynamical systems. Furthermore, we have used the notion of zero dynamics to transform the nonlinear dynamics into global normal form, to derive the transferability conditions on the controller. Finally, we have numerically evaluated the performance of our transfer learning based methods for two sets of example linear and nonlinear dynamical systems.

Chakraborty, Indrasis↗

DOE OSTI2020

Real-Time Modal Analysis of Electric Power Grids - The Need for Dynamic State Estimation: Preprint

We articulate the reason why dynamic state estimation is needed to push the boundaries of the modal analysis of electric power grids in real-time operation. Then, we demonstrate how to unravel linear and nonlinear modes by using the extended dynamic mode decomposition along with estimates of the synchronous generators' rotor angles and rotor speed deviations from nominal speed. The estimated modes are associated with electromechanical oscillations that take place continuously in electric power grids because of imbalances between power generation and demand. The numerical simulations are performed on a synthetic, albeit realistic, 2,000-bus network that was designed to resemble the electric power grid of Texas.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2022

Calculation of the Heat Transfer Coefficient in the Outer Body for a Rotational Detonation

Unsteady heat transfer characterization on the combustion surfaces of Rotational Detonation Engines (RDE) is not well understood. It is generally thought that the complex nature of the unsteady, reacting, compressible fluid flow inside the combustion anulus of the RDE causes the convective heat transfer coefficient to be significantly higher than it is in other applications. Empirical models that have been used to analyze this strictly apply to steady flows where dimensionless groups can be employed. The reacting flow fields that are characteristic of RDEs are inherently compressible, three dimensional, unsteady, and turbulent, having properties that change by orders of magnitude throughout the flow field. They will therefore contain multiple length scales and time scales operating everywhere in the flow during all times. Because of this it is not likely that the RDE flow fields will lend themselves to explanation using simple dimensionless parameters. The dimensionless groups have meaning only in situations where length scales and time scales are singular and well defined. In spite of this it may be possible to get a relatively good idea of what the convection heat transfer coefficient is. In this work a numerical study is performed where the inside wall surface temperature distribution in the RDE outer body is systematically changed over a given range that would be characteristic of the start-up flows inside an RDE. For each case, temperature distributions inside the outer containment wall of the RDE was calculated and compared with experimental data. The closet match can then be used to directly calculate the convection heat transfer coefficient on the inside surface of the RDE.

VanOsdol, John↗

DOE OSTI2023

Instability of Rock-Salt Cubic NbN in Density Functional Calculations

All-nitride semiconductor/superconductor heterojunctions utilizing cubic niobium nitride (NbN) are a promising approach to superconducting quantum circuits for next-generation quantum-information systems. However, there are fundamental open questions on the atomic structure of NbN. In calculations with several levels of density functional theory (DFT), we find that the cubic rocksalt structure NbN (Fm-3m, 225) is energetically very unstable against the ground state hexagonal NbN in a tungsten carbide (P-6m2, 187) type lattice. To better understand the appearance of a cubic phase in numerous experiments, we perform a DFT study on possible NbN structures, determining the energy ordering between different polymorphs from databases and structure prediction. We perform supercell calculations of disordered NbN, finding that the rocksalt structure is dynamically unstable and relaxes to a lower energy monoclinic phase (C2/m, 12), which retains an approximate average cubic symmetry. However, the associated energy gain is not substantial enough for a plausible explanation for cubic NbN. We further investigate the role of external factors such as in-plane strain during epitaxial growth as well as the presence of off-stoichiometry and impurity doping on the energy ordering.

CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SU↗

DOE OSTI2023

NOTCH EFFECT ON CREEP-FATIGUE BEHAVIOR OF ALLOY 617 AT ELEVATED TEMPERATURE

High-temperature reactor structural components are often under the complex multiaxial creep-fatigue (CF) loading conditions throughout the lifetime because of geometric and/or metallurgical discontinuities and complex loading paths. To assess the multiaxial CF deformation behavior and to evaluate the CF design rules in the ASME BPVC Section III, Division 5, Subsection HB, Subpart B, experimental and numerical studies are performed on Alloy 617 at 950°C using notch specimen geometries under CF loading in this study.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Performance Investigation of High-temperature Latent Heat Storage for Integration with Advanced Nuclear Reactors

Integrating thermal energy storage (TES) with advanced nuclear reactors enhances the flexible use of nuclear energy, facilitating its expansion beyond conventional electricity generation. High-temperature TES, capable of storing heat above 400 degree C, emerges as a vital carbon-free energy solution for decarbonizing industrial sector, especially when combined with low-emission energy sources like advanced nuclear reactors. Idaho National Laboratory (INL) has recently developed a novel design for high-temperature latent heat storage system, called Heat pipe Integrated Thermal Battery (HITB), and efforts are underway to experimentally demonstrate the concept. HITB employs liquid-metal heat pipes to establish thermal linkage between TES and nuclear systems without direct fluid exchange, minimizing the potential risk of the integrated systems such as cross-contamination. HITB is designed to achieve high charging and discharging efficiency as well as high energy storage density by employing metal alloys as Phase Change Material (PCM). Initial proof-of-concept experiments are being undertaken using an aluminum alloy (Al59%-Mg35%-Zn6%). This paper discusses current progress of the HITB project at INL, seeking to develop high-temperature TES for versatile integration with advanced nuclear reactors, and shares insights from the performance evaluations via numerical modeling and analysis.

25 ENERGY STORAGE↗

DOE OSTI2026

Self-consistent mean-field quantum approximate optimization

We introduce a self-consistent mean-field quantum optimization algorithm that approximates the ground state of classical Ising Hamiltonians. The algorithm decomposes the problem into independent subproblems and treats the interactions between them in a mean-field manner. These interactions are captured by a common environment, constructed self-consistently through a variational quantum circuit, and which modifies the subproblems to account for mutual influence while maintaining computational independence. Consequently, subproblems can be solved individually, avoiding the computational cost of the full problem. We explore the properties of the generated environment and assess the algorithm's performance through extensive numerical simulations on Sherrington-Kirkpatrick spin glasses. Furthermore, we apply it experimentally to a weighted maximum clique problem applied to molecular docking. This framework enables the solution of problems that would otherwise exceed the qubit and gate counts of current quantum hardware.

Dupont, Maxime [Rigetti Computing] (ORCID:00000001↗

DOE OSTI2026

Self-consistent mean-field quantum approximate optimization

Dupont, Maxime [Rigetti Computing] (ORCID:00000001↗

DOE OSTI2026

Self-consistent mean-field quantum approximate optimization

Dupont, Maxime [Rigetti Computing] (ORCID:00000001↗

DOE OSTI2025

Direct Discontinuous Galerkin methods for the reacting multi-component flow equations

The Direct Discontinuous Galerkin (DDG (Liu and Yan, 2008)) method and a counterpart with Interface Correction (DDGIC (Danis and Yan, 2022)) are extended to compute diffusion terms that arise when solving the compressible multi-component flow equations in thermochemical nonequilibrium. Thermodynamic properties, transport properties, chemical reaction rates, and energy exchange terms are computed using Mutation++ (Scoggins et al., 2020). The DG method is applied on unstructured grids, where the accuracy and convergence rates can be sensitive to the numerical method chosen for parabolic terms. A method for determining the homogeneity tensor of the flow equations required for DDGIC is shown. The convergence properties of the DDG methods are studied and compared to the Interior Penalty (IP) method. A number of numerical experiments are conducted to assess the accuracy and performance of the method. The numerical results and convergence studies indicate that DDG and DDGIC provide accurate solutions and perform well for general flows in thermochemical nonequilibrium.

Diffusion↗

DOE OSTI2026

Experimental and simulation study of target biasing effects on plasma transport in linear plasma device MPS-LD

Linear plasma devices (LPDs) are important experimental platforms for investigating plasma–material interactions (PMI). In PMI experiments, it has been found that applying a target bias not only effectively modifies the incident ion energy, but also induces significant changes in the electron density and electron temperature, whereby the evolution of these plasma parameters is primarily governed by plasma transport processes. However, at present, the physical process and mechanism underlying such bias-induced variations remain unclear. In this work, biasing experiments under argon plasma discharge conditions were first carried out on the MPS-LD device. For the corresponding experiments, an electric potential model was newly developed based on the BOUT++ LPD module, enabling self-consistent simulations of plasma transport under biased conditions. Numerical simulations were then performed to reproduce the experimental results and to validate the accuracy of the proposed model. Finally, by combining experimental measurements with numerical simulations, a bias-voltage scan was performed to investigate how the electron density and electron temperature vary with the bias voltage (U bias ). The results show that applying negative bias decreases the target electron density (n e,T ) while increasing the target electron temperature (T e,T ). In contrast, positive bias increases both n e,T and T e,T ; however, at high positive bias, n e,T first reaches a maximum and subsequently decreases with further increases in U bias . The underlying physical mechanisms are analyzed using particle flux, momentum, and energy conservation. It indicates that the applied bias regulates the parallel electric field, thereby changing ion and electron velocities, and consequently affecting the electron density. At high positive bias, the ion velocity is further influenced by ion viscosity, leading to the reversal in n e,T . Meanwhile, the enhanced parallel electric field drives stronger currents, significantly increasing ion–electron frictional work and converting the input bias power into electron energy, which raises the electron temperature. In conclusion, these results contribute to a deeper understanding of the effects and mechanisms of biasing on plasma transport in the MPS-LD device.

BOUT++ simulation↗