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DOE OSTI2025

Gluonic Structure from Instantons

The instanton vacuum provides an effective description of chiral symmetry breaking by local topological fluctuations of the gauge fields, as observed in lattice QCD simulations. The resulting effective dynamics at momenta below 1/$\bar{ρ}$ ≈ 0.6 GeV explains the basic features of light-quark correlation functions and is used extensively in studies of hadron structure. The instanton fields also make definite contributions to the gluonic structure of light hadrons, as expressed in the matrix elements of composite quark– gluon or gluon operators. The article reviews the gluonic structure of light hadrons (nucleon, pion) induced by instantons. This includes: (i) twist-2 parton distributions and momentum sum rule; (ii) twist-3 angular momentum and spin-orbit interactions; (iii) twist-3 and twist-4 quark–gluon correlations and power corrections; (iv) trace anomaly and hadron mass decomposition; (v) scalar gluon form factors and mechanical properties; (vi) axial anomaly and pseudoscalar gluon form factors. It also discusses possible further applications of the methods and recent developments including gauge field configurations beyond instantons.

Weiss, C. [Thomas Jefferson National Accelerator F↗

DOE OSTI2023

Code modernization strategies for short-range non-bonded molecular dynamics simulations

Modern HPC systems are increasingly relying on greater core counts and wider vector registers. Thus, applications need to be adapted to fully utilize these hardware capabilities. One class of applications that can benefit from this increase in parallelism are molecular dynamics simulations. In this paper, we describe our efforts at modernizing the ESPResSo++ simulation package for molecular dynamics by restructuring its particle data layout for efficient memory accesses and applying vectorization techniques to benefit the calculation of short-range non-bonded forces, which results in an overall three times speedup and serves as a baseline for further optimizations. We also implement fine-grained parallelism for multi-core CPUs through HPX, a C++ runtime system which uses lightweight threads and an asynchronous many-task approach to maximize concurrency. Our goal is to evaluate the performance of an HPX-based approach compared to the bulk-synchronous MPI-based implementation. This requires the introduction of an additional layer to the domain decomposition scheme that defines the task granularity. On spatially inhomogeneous systems, which impose a corresponding load-imbalance in traditional MPI-based approaches, we demonstrate that by choosing an optimal task size, the efficient work-stealing mechanisms of HPX can overcome the overhead of communication resulting in an overall 1.4 times speedup compared to the baseline MPI version.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

A review of low-rank methods for time-dependent kinetic simulations

Time-dependent kinetic models are ubiquitous in computational science and engineering. The underlying integro-differential equations in these models are high-dimensional, comprised of a six–dimensional phase space, making simulations of such phenomena extremely expensive. In this article we demonstrate that in many situations, the solution to kinetics problems lives on a low dimensional manifold that can be described by a low-rank matrix or tensor approximation. We then review the recent development of so-called low-rank methods that evolve the solution on this manifold. The two classes of methods we review are the dynamical low-rank (DLR) method, which derives differential equations for the low-rank factors, and a Step-and-Truncate (SAT) approach, which projects the solution onto the low-rank representation after each time step. Thorough discussions of time integrators, tensor decompositions, and method properties such as structure preservation and computational efficiency are included. We further show examples of low-rank methods as applied to particle transport and plasma dynamics.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1993

Ignition propagation and heat effects of propellant chips embedded in castable inhibitor using a laser flux test bomb

A castable inhibitor is applied to the aft face of the Space Shuttle Redesigned Solid Rocket Motor (RSRM) forward segment propellant grain to control propellant surface burn area. During fabrication, the propellant surface is trimmed prior to the inhibitor application. This produces a potential for small propellant chips to remain undetected on the propellant surface and contaminate the inhibitor during application. The concern was that undetected propellant chips in the inhibitor might provide a fuse path for premature propellant ignition underneath the inhibitor. To evaluate the fuse path potential, testing was performed on inhibitor samples with embedded propellant. The internal motor environment was simulated with a calibrated CO2 laser beam directed onto a sample which was placed in a 4100 kPa (600 psi) nitrogen pressurized bomb (laser bomb). The testing showed definitive results pertaining to fuse path formation. Embedded propellant chips did not autoignite until the receding heat affected inhibitor surface reached, or passed, the propellant chip. Samples with embedded propellant chips in alignment did not propagate ignition from one chip to another with separation distances as small as 0.010 cm(0.004 inc) and some as little as 0.0051 cm (0.002 in). Propellant chips with volumes approximately less than 0.025 cu cm (0.0015 cu in) (which did not propagate ignition) did not increase the inhibitor material decomposition depth more than the resulting void cavity of the burned out propellant chip. In addition, the depth of this void cavity did not increase until it was overtaken by the surrounding material decomposition depth. This was due, in part, to the retention of the protective inhibitor char layer. Samples with embedded propellant strings, whose thicknesses were below 0.023 cm (0.009 in), did not propagate ignition. Propellant string thicknesses above 0.038 cm (0.015 in) did propagate ignition. Test sample char and heat affected layer measurements and observations compared well with those from the Space Shuttle Solid Rocket Motor (SRM) Technical Evaluation Motor no. 9(TEM-9).

Bolton, Douglas E., Jr.↗

DOE OSTI2026

Data-driven Mori–Zwanzig modeling of Lagrangian particle dynamics in turbulent flows

The dynamics of Lagrangian particles in turbulence play a crucial role in mixing, transport, and dispersion in complex flows. Their trajectories exhibit highly nontrivial statistical behavior, motivating the development of surrogate models that can reproduce these trajectories without incurring the high computational cost of direct numerical simulations of the full Eulerian field. This task is particularly challenging because reduced-order models typically lack access to the full set of interactions with the underlying turbulent field. Novel data-driven machine learning techniques can be powerful in capturing and reproducing complex statistics of the reduced-order/surrogate dynamics. In this work, we show how one can learn a surrogate dynamical system that is able to evolve a turbulent Lagrangian trajectory in a way that is point-wise accurate for short-time predictions (with respect to Kolmogorov time) and stable and statistically accurate at long times. This approach is based on the Mori–Zwanzig formalism, which prescribes a mathematical decomposition of the full dynamical system into resolved dynamics that depend on the current state and the past history of a reduced set of observables, and the unresolved orthogonal dynamics due to unresolved degrees of freedom of the initial state. We show how by training this reduced order model on a point-wise error metric on short time-prediction, we are able to correctly learn the dynamics of Lagrangian turbulence, such that also the long-time statistical behavior is stably recovered at test time. This opens up a range of applications, for example, for the control of active Lagrangian agents in turbulence.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2023

Numerical Study on the Aerodynamics of an Iced Airfoil With Scale-Resolving Simulations

Scale-resolving simulations of the NACA 23012 airfoil with horn ice accretion on the leading edge are conducted using the hybrid Reynolds-averaged Navier–Stokes/large-eddy simulation (hybrid RANS/LES) and wall-modeled large-eddy simulation (WMLES) approaches implemented in the Launch, Ascent, and Vehicle Aerodynamics (LAVA) framework. Aerodynamic results at the Reynolds number of 1.8 million show good comparison with the experimental measurements at different angles of attack from pre-stall to post-stall regimes. The pressure plateaus caused by the flow separation and the recovery of pressure inside the separation bubble around the iced leading edge are well predicted with the scale-resolving simulations when sufficient grid resolution is used around the accreted ice. The unsteadiness of the turbulent flows around the iced airfoil is also examined through the turbulent kinetic energy with the Reynolds normal stress anisotropy. Kelvin–Helmholtz instability (KHI) arises at the shear layer triggered by the upper ice horn and leads to rapid laminar-to-turbulent transition over a large range of angle of attack. With the increase of the angle of attack, the region with high turbulence intensity induced by the unstable shear layer spreads quickly over the entire upper surface of the airfoil. The coherent KHI modes from the upper and lower ice horns are extracted using the spectral proper orthogonal decomposition (SPOD) technique. The SPOD modes extracted from the upper shear layer have large-scale variations in the spanwise direction and low-rank behavior where the energy of the leading SPOD mode at each Strouhal number of the KHI largely represents the total energy when the mode number in the spanwise direction is small.

ARMD↗

NASA NTRS2023

Numerical Study on the Aerodynamics of an Iced Airfoil With Scale-Resolving Simulations

ARMD↗

DOE OSTI2021

Learning the structure of wind: A data-driven nonlocal turbulence model for the atmospheric boundary layer

In this work, we develop a novel data-driven approach to modeling the atmospheric boundary layer. This approach leads to a nonlocal, anisotropic synthetic turbulence model which we refer to as the deep rapid distortion (DRD) model. Our approach relies on an operator regression problem that characterizes the best fitting candidate in a general family of nonlocal covariance kernels parameterized in part by a neural network. This family of covariance kernels is expressed in Fourier space and is obtained from approximate solutions to the Navier–Stokes equations at very high Reynolds numbers. Each member of the family incorporates important physical properties such as mass conservation and a realistic energy cascade. The DRD model can be calibrated with noisy data from field experiments. After calibration, the model can be used to generate synthetic turbulent velocity fields. To this end, we provide a new numerical method based on domain decomposition which delivers scalable, memory-efficient turbulence generation with the DRD model as well as others. We demonstrate the robustness of our approach with both filtered and noisy data coming from the 1968 Air Force Cambridge Research Laboratory Kansas experiments. Using these data, we witness exceptional accuracy with the DRD model, especially when compared to the International Electrotechnical Commission standard.

17 WIND ENERGY↗

DOE OSTI2026

Secure Wireless Communication Using Distributed Coherent Transmission and Spatial Signal Decomposition

We present a new approach to secure wireless communications using coherent distributed transmission of signals that are spatially decomposed between a two-element distributed antenna array. High-accuracy distributed coordination of microwave wireless systems supports the ability to transmit different parts of a signal from separate transmitters such that they combine coherently at a designated destination. In this article, we explore this concept using a two-element coherent distributed phased array where each of the two transmitters sends a separate component of a communication signal, where each symbol is decomposed into a sum of two pseudorandom signal vectors, the coherent summation of which yields the intended symbol. By directing the transmission to an intended receiver using distributed beamforming, the summation of the two vector components is largely confined to a spatial region at the destination receiver. We implement the technique in a $50−λ$ array operating at 3 GHz. We evaluate the symbol error rate (SER) in 2-D space through simulation and measurement, showing that the approach yields a spatially confined secure region where the information is recoverable (i.e., the received signal has low SER), and outside of which the information is unrecoverable (high SER). The proposed system is also compared against a traditional beamforming system where each node sends the same data. We validate experimentally that our approach achieves a low SER of 0.0082 at broadside and an SER above 0.25 at all other locations compared to a traditional beamforming approach that achieves a SER of 0 at all locations measured.

Engineering - Electronic and electrical engineerin↗

NASA NTRS2021

Non-Causal Controller Approximation Methodology Augmented with Model Reference Control – Robust Design

This paper describes the development of a new control design methodology based on a non-causal controller structure and its approximation. The non-causal controller design is based on a loop shaping approach. Partial fraction decomposition is used, along with derivative approximations, to derive a proper transfer function design for a causal control structure. The premise for this non-causal control methodology is that the phase delay as a function of frequency ideally remains within approximately -90o, which tends to make the system robustly stable to uncertainties in the plant dynamics. To have a predictable control system response in lieu of the tolerance of this methodology to relatively large uncertainty in the plant dynamics, the approach is augmented with model reference control. The addition of model reference control can potentially further enhance the robustness of the design. A control system design example is presented in this paper, along with simulation results, to demonstrate the high degree of robustness of this non-causal methodology and its augmentation.

Control Systems↗

DOE OSTI2026

Machine learning-based bias-corrected future projections of ozone concentrations from a chemistry-climate model

Reliable projection of future near-surface ozone is crucial for air quality management and health risk assessment. However, potential biases in spatial distribution, magnitude and trends in ozone concentrations simulated by global chemistry-climate models limit their applicability in regional-scale evaluations. In this study, LightGBM, a machine learning (ML) algorithm is applied to correct biases in CESM2-simulated ozone concentrations over China, the United States and Europe and calibrate future ozone projections under two diverse Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) scenarios from 2020 to 2060. The ML-based correction significantly improves the spatial distribution and reduces the model bias by 40%–60%. It also reverses the potentially incorrect trend of ozone change under SSP1-2.6 in eastern China. When applying ML-based bias correction to CESM2 future projections, warm season mean ozone concentrations decrease across China, the United States, and Europe by –13.5, –17.9, and –13.7 µg/m³, respectively, between 2020 and 2060 in SSP1-2.6, while they increase by 9.4, 2.0, and 5.2 µg/m³ in SSP5-8.5. Decomposition analysis show that changes in anthropogenic emissions dominate future ozone changes in both scenarios, while strong climate penalty from ozone changes occurs in polluted eastern China and climate benefit is found in western China, the United States and Europe under SSP5-8.5. These findings demonstrate the value of combining ML with chemistry-climate models to produce more accurate air quality projections, thereby informing more effective and region-specific environmental protection strategies.

Chemistry Model↗

DOE OSTI2024

Transferable Water Potentials Using Equivariant Neural Networks

Machine learning interatomic potentials (MLIPs) have emerged as a technique that promises quantum theory accuracy for reduced cost. It has been proposed [J. Chem. Phys. 2023, 158, 084111] that MLIPs trained on solely liquid water data cannot accurately transfer to the vapor–liquid equilibrium while recovering the many-body decomposition (MBD) analysis of gas-phase water clusters. This suggests that MLIPs do not directly learn the physically correct interactions of water molecules, limiting transferability. In this work, we show that MLIPs using equivariant architecture and trained on 3200 liquid water structures reproduces liquid-phase water properties (e.g., density within 0.003 g/cm 3 between 230 and 365 K), vapor–liquid equilibrium properties up to 550 K, the MBD analysis of gas-phase water cluster up to six-body interactions, and the relative energy and the vibrational density of states of ice phases. We show that potentials developed using equivariant MLIPs allow transferability for arbitrary phases of water that remain stable in nanosecond long simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1987

Energy yields for hydrogen cyanide and formaldehyde syntheses - The HCN and amino acid concentrations in the primitive ocean

Simulated prebiotic atmospheres containing either CH4, CO, or CO2, in addition to N2, H2O, and variable amounts of H2, were subjected to the spark from a high-frequency Tesla coil, and the energy yields for the syntheses of HCN and H2CO were estimated from periodic (every two days) measurements of the compound concentrations. The mixtures with CH4 were found to yield the highest amounts of HCN, whereas the CO mixtures produced the highest yields of H2CO. These results model atmospheric corona discharges. From the yearly energy yields calculated and the corona discharge available on the earth, the yearly production rate of HCN was estimated; using data on the HCN production rates and the experimental rates of decomposition of amino acids through the submarine vents, the steady state amino acid production rate in the primitive ocean was calculated to be about 10 nmoles/sq cm per year.

Stribling, Roscoe↗

DOE OSTI2022

The Schwarz Alternating Method for the Seamless Coupling of Nonlinear Reduced Order Models and Full Order Models

Projection-based model order reduction allows for the parsimonious representation of full order models (FOMs), typically obtained through the discretization of a set of partial differential equations (PDEs) using conventional techniques (e.g., finite element, finite volume, finite difference methods) where the discretization may contain a very large number of degrees of freedom. As a result of this more compact representation, the resulting projection-based reduced order models (ROMs) can achieve considerable computational speedups, which are especially useful in real-time or multi-query analyses. One known deficiency of projection-based ROMs is that they can suffer from a lack of robustness, stability and accuracy, especially in the predictive regime, which ultimately limits their useful application. Another research gap that has prevented the widespread adoption of ROMs within the modeling and simulation community is the lack of theoretical and algorithmic foundations necessary for the “plug-and-play” integration of these models into existing multi-scale and multi-physics frameworks. This paper describes a new methodology that has the potential to address both of the aforementioned deficiencies by coupling projection-based ROMs with each other as well as with conventional FOMs by means of the Schwarz alternating method [41]. Leveraging recent work that adapted the Schwarz alternating method to enable consistent and concurrent multiscale coupling of finite element FOMs in solid mechanics [35, 36], we present a new extension of the Schwarz framework that enables FOM-ROM and ROM-ROM coupling, following a domain decomposition of the physical geometry on which a PDE is posed. In order to maintain efficiency and achieve computation speed-ups, we employ hyper-reduction via the Energy-Conserving Sampling and Weighting (ECSW) approach [13]. We evaluate the proposed coupling approach in the reproductive as well as in the predictive regime on a canonical test case that involves the dynamic propagation of a traveling wave in a nonlinear hyper-elastic material.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Improved reconstruction stability for chemical shift encoded hyperpolarized 13 C magnetic resonance spectroscopic imaging using k‐t spiral acquisitions

Purpose A multiecho, field of view (FOV)‐oversampled k‐t spiral acquisition and direct iterative decomposition of water and fat with echo asymmetry and least‐squares estimation reconstruction is demonstrated to improve the stability of hyperpolarized 13 C magnetic resonance spectroscopic imaging (MRSI) in the presence of signal ambiguities attributed to low‐SNR (signal‐to‐noise‐ratio) species, local uncertainties in metabolite peaks, and echo‐to‐echo signal inconsistencies. Theory k‐t spiral acquisitions redistribute readout points to be more densely spaced radially in k‐space by acquiring an FOV and matrix that are oversampled by η . These more densely spaced spiral turns constitute effective intraspiral echoes and can supplement conventional interspiral echoes to improve spectral separation and reduce spectral cross‐talk to better resolve 13 C‐labeled species for spectroscopic imaging. Methods Digital simulations and imaging phantom experiments were performed for a range of interspiral echo spacings and η using multiecho, k‐t spiral acquisitions. Image spectral cross‐talk artifacts were evaluated both qualitatively and quantitatively as the percent error in measured metabolite ratios. In vivo murine experiments evaluated the feasibility of multiecho, k‐t spiral [1‐ 13 C]pyruvate MRSI to reduce spectral cross‐talk for 3 scenarios of different expected reconstruction stability. Results Digital simulations and imaging phantom experiments both demonstrated reduced or comparable image spectral cross‐talk and percent errors in measured metabolite ratios with increasing η and better choices of echo spacings. In vivo images displayed markedly reduced spectral cross‐talk in lactate images acquired with η = 7 versus η = 1. Conclusion The precision of hyperpolarized 13 C metabolic imaging and quantification in the presence of low‐SNR species, local uncertainties in metabolite resonances, and echo‐to‐echo signal inconsistencies can be improved with the use of FOV‐oversampled k‐t spiral acquisitions.

Macdonald, Erin B.↗

DOE OSTI2026

Efficient Measurement-Driven Eigenenergy Estimation with Classical Shadows

Quantum algorithms exploiting real-time evolution under a target Hamiltonian have demonstrated remarkable efficiency in extracting key spectral information. However, the broader potential of these methods, particularly beyond ground-state calculations, is underexplored. In this work, we introduce the framework of multiobservable dynamic mode decomposition (MODMD), which combines the observable dynamic mode decomposition (DMD), a measurement-driven eigensolver tailored for near-term implementation, with classical shadow tomography. MODMD leverages random scrambling in the classical shadow technique to construct, with exponentially reduced resource requirements, a signal subspace that encodes rich spectral information. Notably, we replace typical Hadamard-test circuits with a protocol designed to predict low-rank observables, thereby broadening the use of classical shadow tomography for predicting many low-rank observables. We establish theoretical guarantees on the spectral approximation from MODMD, taking into account distinct sources of error. In the ideal case, we prove that the spectral error scales as exp (−Δ⁢𝐸⁢𝑡 max ), where Δ⁢𝐸 is the Hamiltonian spectral gap and 𝑡 max is the maximal simulation time. This analysis provides a rigorous justification of the rapid convergence observed across simulations. To demonstrate the utility of our framework, we consider its application to fundamental tasks, such as determining the low-lying, i.e., ground or excited, energies of representative many-body systems. Our work paves the path for efficient designs of measurement-driven algorithms on near-term and early fault-tolerant quantum devices.

quantum algorithms & computation↗

NASA NTRS1994

Vector quantization for efficient coding of upper subbands

This paper examines the application of vector quantization (VQ) to exploit both intra-band and inter-band redundancy in subband coding. The focus here is on the exploitation of inter-band dependency. It is shown that VQ is particularly suitable and effective for coding the upper subbands. Three subband decomposition-based VQ coding schemes are proposed here to exploit the inter-band dependency by making full use of the extra flexibility of VQ approach over scalar quantization. A quadtree-based variable rate VQ (VRVQ) scheme which takes full advantage of the intra-band and inter-band redundancy is first proposed. Then, a more easily implementable alternative based on an efficient block-based edge estimation technique is employed to overcome the implementational barriers of the first scheme. Finally, a predictive VQ scheme formulated in the context of finite state VQ is proposed to further exploit the dependency among different subbands. A VRVQ scheme proposed elsewhere is extended to provide an efficient bit allocation procedure. Simulation results show that these three hybrid techniques have advantages, in terms of peak signal-to-noise ratio (PSNR) and complexity, over other existing subband-VQ approaches.

Zeng, W. J.↗

DOE OSTI2023

Simulating excited-state complex ensembles: Fluorescence and solvatochromism in amine-arene exciplexes

Exciplexes are excited-state complexes formed as a result of partial charge transfer from the donor to the acceptor species when one moiety of the donor–acceptor pair is electronically excited. The arene–amine exciplex formed between oligo-(p-phenylene) (OPP) and triethylamine (TEA) is of interest in the catalytic photoreduction of CO2 because it can compete with complete electron transfer to the OPP catalyst. Therefore, formation of the exciplex can hinder the generation of a radical anion OPP·− necessary for subsequent CO2 reduction. We report an implementation of a workflow automating quantum-chemistry calculations that generate and characterize an ensemble of structures to represent this exciplex state. We use FireWorks, Pymatgen, and Custodian Python packages for high-throughput ensemble generation. The workflow includes time-dependent density functional theory optimization, verification of excited-state minima, and exciplex characterization with natural transition orbitals, exciton analysis, excited-state Mulliken charges, and energy decomposition analysis. Fluorescence spectra computed for these ensembles using Boltzmann-weighted contributions of each structure agree better with experiment than our previous calculations based on a single representative exciplex structure [Kron et al., J. Phys. Chem. A 126, 2319–2329 (2022)]. The ensemble description of the exciplex state also reproduces an experimentally observed red shift of the emission spectrum of [OPP-4–TEA]* relative to [OPP-3–TEA]*. The workflow developed here streamlines otherwise labor-intensive calculations that would require significant user involvement and intervention.

Chemistry↗