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DOE OSTI2025

Experimental study of energy-dependent angular broadening of MeV electron beams for high-resolution imaging in thick samples

In scanning transmission electron microscopy (STEM), spatial resolution is primarily influenced by the projected size of the electron probe within the specimen. In thin samples, a large semi-convergence angle enables a tightly focused beam and sub-nanometer resolution. However, in thick specimens, resolution is fundamentally limited by transverse beam broadening from multiple large-angle scattering events—for example, a probe with 10 mrad angular divergence can broaden by ∼100 nm over a 10 μm path. Since this broadening scales inversely with beam energy, MeV-STEM offers a promising route for high-resolution imaging in thick materials. To quantitatively assess this effect, we performed high-precision measurements at UCLA’s PEGASUS beamline, characterizing beam divergence and intensity profiles for 3–8 MeV electrons transmitted through a wedged-silicon sample of varying thickness. Our results reconcile discrepancies among analytical models and validate Monte Carlo simulations. Here, we find that increasing beam energy from 3.0 to 5.8 MeV reduces angular broadening by a factor of 2.6, with diminishing returns observed at 7.6 MeV. These findings provide a quantitative framework for optimizing MeV-STEM parameters in high-resolution imaging of thick biological and microelectronic specimens, and for guiding beam energy selection in other advanced imaging modes beyond STEM.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Neutral Mode Dominates the Forced Global and Regional Surface Temperature Response in the Past and Future

Abstract Using a large suite of Green's function perturbation experiments, we construct the emergent dynamical operator for surface temperature and extract the most excitable mode. The leading mode turns out to be the most dominant mode excited by the climate forcing of doubling CO 2 , alone capturing 56% of the total spatial variances of the surface temperature response. The pattern of the leading mode is partly organized by the atmospheric annular modes in both hemispheres, and further modulated by the feedbacks from ocean circulations. Though derived from a single model, the leading mode can capture the spatio‐temporal variabilities of the global mean surface temperature over the past century in both the CMIP6 model simulations and observations. Moreover, the leading mode is most efficiently excited by radiative forcings from the midlatitude bands centered around 45°N and 25°S, where most human activities take place, suggesting the potency of human influence on the climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Inpainting radar missing data regions with deep learning

Abstract. Missing and low-quality data regions are a frequent problem for weather radars. They stem from a variety of sources: beam blockage, instrument failure, near-ground blind zones, and many others. Filling in missing data regions is often useful for estimating local atmospheric properties and the application of high-level data processing schemes without the need for preprocessing and error-handling steps – feature detection and tracking, for instance. Interpolation schemes are typically used for this task, though they tend to produce unrealistically spatially smoothed results that are not representative of the atmospheric turbulence and variability that are usually resolved by weather radars. Recently, generative adversarial networks (GANs) have achieved impressive results in the area of photo inpainting. Here, they are demonstrated as a tool for infilling radar missing data regions. These neural networks are capable of extending large-scale cloud and precipitation features that border missing data regions into the regions while hallucinating plausible small-scale variability. In other words, they can inpaint missing data with accurate large-scale features and plausible local small-scale features. This method is demonstrated on a scanning C-band and vertically pointing Ka-band radar that were deployed as part of the Cloud Aerosol and Complex Terrain Interactions (CACTI) field campaign. Three missing data scenarios are explored: infilling low-level blind zones and short outage periods for the Ka-band radar and infilling beam blockage areas for the C-band radar. Two deep-learning-based approaches are tested, a convolutional neural network (CNN) and a GAN that optimize pixel-level error or combined pixel-level error and adversarial loss respectively. Both deep-learning approaches significantly outperform traditional inpainting schemes under several pixel-level and perceptual quality metrics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A sensitivity analysis to predict the neutronics behavior of samples irradiated in the VTR rabbit system

We report a low-order neutronics model is developed to carry out hundreds of simulations efficiently and investigate the neutronics behavior of samples being irradiated in a test reactor setting under different geometrical constraints. The low-order model allowed for simulations that yield the expected neutronics behavior of any irradiated sample in any environment and allows for the calculation of highly accurate spatially averaged statistics and idealized spatial distributions in the neutron flux. Several benchmarks are performed to evaluate the performance and limitations of the low-order model revealing many important findings. The low-order model predicted the LHGR in the EBR-II driver fuel to within 2.34% by only simulating the fuel rod by itself, which served as a validation for the model. Sensitivity studies investigated 3% enriched UO 2 and U-10Zr being irradiated in the Versatile Test Reactor rabbit system. The analyses investigated a range of combinations of 15 radii and 5 heights for each sample in the rabbit system. Similar data sets are also provided for irradiations in the Advanced Test Reactor’s B-10 irradiation position, which is a thermal neutron spectrum environment. Generalized fits and fit coefficients are obtained for sample heating, reaction rate densities, and local multiplication rate characteristics, allowing the predictions of the neutronics behavior of the samples based on their geometrical constraints. The analyses and fits laid the groundwork for developing a user-end Multiphysics analysis framework to assist and accelerate irradiation experiment design and optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Independent Review of Groundwater Remediation Strategy for Hexavalent Chromium and RDX Groundwater Plumes at Los Alamos National Laboratory (Rev. 1)

Site operations at the Los Alamos National Laboratory (LANL) resulted in the release of oxidized chromium, Cr(VI), into Sandia Canyon from cooling tower effluent from 1956 until 1972. The chromium traveled with the surface water approximately 3 miles downstream before migrating below ground surface. Chromium concentrations exceed 50 μg/L in the upper portion of the aquifer. Another LANL groundwater plume of concern is associated with RDX (Royal Demolition Explosives, 1,3,5-trinitro-1,3,5-triazine). Between 1951 and 1996, RDX was released to the mesa-top facilities' process water outfall, adjacent and underlying soils, and alluvial sediments, along with surface water in Cañon de Valle. Between 2000 and 2010, two remedial actions were deployed, removing much of the near-surface RDX, however, recharge due to precipitation has transported RDX into the perched-intermediate zone and into the regional aquifer. The report documents an independent technical review by scientists from the Department of Energy (DOE) Network of National Laboratories for Environmental Management and Stewardship (NNLEMS) to provide recommendations for potential near term actions to address and optimize remediation for both the Cr(VI) and RDX plumes. The proposed near-term remedial actions include design of pump and treat systems for Cr(VI) and monitoring and study for natural attenuation for RDX. The review assesses existing data, conceptual and numerical modeling, and it recommends a technical integration process to support identifying and implementing strategic, effective and efficient remedies. The DOE Environmental Management Los Alamos Field Office (EM-LA) and their cleanup contractor Newport News Nuclear-BWTX, LLC Los Alamos (N3B) provided the information required for the review. Interviews were also conducted with regulators to obtain the full spectrum of technical, regulatory and scientific perspectives. The independent review team was impressed by the capabilities, experiences, innovativeness, and insightfulness of the technical representatives from both the regulator, the New Mexico Environment Department (NMED) and N3B. Incorporation of vadose zone flow pathways in the conceptual site model (CSM) and configuring the numerical modeling for the site was generally state-of-the-practice (or better). This could be considered state-of-the-art by addressing uncertainties related to spatial extent of hydraulic windows. The reviews from the regulators were thorough and often provided useful concepts for consideration and future/study resolutions. The overarching consensus recommendation of independent review team is that the LANL groundwater plumes should be addressed in context of the emerging "management of complex sites" paradigm.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications

Physics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integro-differential equations with sparse, noisy, unstructured, and multifidelity data. PINNs incorporate all available information, including governing equations (reflecting physical laws), initial-boundary conditions, and observations of quantities of interest, into a loss function to be minimized, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal Laplace operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius δ goes to zero, and to the fractional Laplacian as δ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We provide theoretical convergence rates with respect to δ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, δ and α, characterizing the kernel of the unified operator. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon, that is, different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order α(γ), which is more suitable for modeling turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as δ. More importantly, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

nPINNs: Nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications

Physics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integro-differential equations with sparse, noisy, unstructured, and multifidelity data. PINNs incorporate all available information, including governing equations (reflecting physical laws), initial-boundary conditions, and observations of quantities of interest, into a loss function to be minimized, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal Laplace operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius $\delta$ goes to zero, and to the fractional Laplacian as $\delta$ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We also provide theoretical convergence rates with respect to $\delta$ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, $\delta$ and $\alpha$, characterizing the kernel of the unified operator. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon, that is, different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order $\alpha(y)$, which is more suitable for modeling turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as $\delta$. More importantly, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A Stereo Camera Simulator for Large-Eddy Simulations of Continental Shallow Cumulus Clouds Based on Three-Dimensional Path-Tracing

The complex spatial and temporal structure of cumulus clouds complicates their representation in weather and climate models. Classic meteorological instrumentation struggles to fully capture these features. Networks of multiple high-resolution hemispheric cameras are increasingly used to fill this data gap, and provide information on this missing multi-dimensional spatial information. In this study, a path-tracing algorithm is used to generate virtual camera images of resolved clouds in large-eddy simulations (LES). These images are then used as a camera network simulator, allowing reconstructions of three-dimensional cloud edges from the model output. Because the actual LES cloud field is fully known, the combined path-tracing and reconstruction method can be statistically analyzed. The method is applied to LES realizations of summertime shallow cumulus at the Jülich Observatory for Cloud Evolution (JOYCE), Germany, which also routinely operates a camera network. We find that the path-tracing method allows accurate reconstruction of up to 70% of the visible cloud edges. Additional sensitivity tests show that the method is robust for changes in its hyperparameters. The sensitivity to cloud optical thickness is also investigated, finding a cloud boundary placement error of approximately 182 m. This error can be considered typical for cloud boundary reconstruction using real stereo camera imagery. The results provide proof of principle for future use of the method for evaluating LES clouds against camera network imagery, and for further optimizing the configuration of such camera networks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Assessing the Influence of Climate on the Spatial Pattern of West Nile Virus Incidence in the United States

West Nile virus (WNV) is the leading cause of mosquito-borne disease in humans in the United States. Since the introduction of the disease in 1999, incidence levels have stabilized in many regions, allowing for analysis of climate conditions that shape the spatial structure of disease incidence. Our goal was to identify the seasonal climate variables that influence the spatial extent and magnitude of WNV incidence in humans. We developed a predictive model of contemporary mean annual WNV incidence using U.S. county-level case reports from 2005 to 2019 and seasonally averaged climate variables. We used a random forest model that had an out-of-sample model performance of R 2 =0.61. Our model accurately captured the V-shaped area of higher WNV incidence that extends from states on the Canadian border south through the middle of the Great Plains. It also captured a region of moderate WNV incidence in the southern Mississippi Valley. The highest levels of WNV incidence were in regions with dry and cold winters and wet and mild summers. The random forest model classified counties with average winter precipitation levels <23.3 mm/month as having incidence levels over 11 times greater than those of counties that are wetter. Among the climate predictors, winter precipitation, fall precipitation, and winter temperature were the three most important predictive variables. We consider which aspects of the WNV transmission cycle climate conditions may benefit the most and argued that dry and cold winters are climate conditions optimal for the mosquito species key to amplifying WNV transmission. Our statistical model may be useful in projecting shifts in WNV risk in response to climate change.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Magneto-optical study of Nb thin films for superconducting qubits

Among the recognized sources of decoherence in superconducting qubits, the spatial inhomogeneity of the superconducting state and the possible presence of magnetic-flux vortices remain comparatively underexplored. Niobium is commonly used as a structural material in transmon qubits that host Josephson junctions, and excess dissipation anywhere in the transmon can become a bottleneck that limits overall quantum performance. The metal/substrate interfacial layer may simultaneously host pair-breaking loss channels (e.g., two-level systems, TLS) and control thermal transport, thereby affecting dissipation and temperature stability. Here, we use quantitative magneto-optical imaging of the magnetic-flux distribution to characterize the homogeneity of the superconducting state and the critical current density, $j_{c}$, in niobium films fabricated under different sputtering conditions. The imaging reveals distinct flux-penetration regimes, ranging from a nearly ideal Bean critical state to strongly nonuniform thermo-magnetic dendritic avalanches. By fitting the measured magnetic-induction profiles, we extract $j_{c}$ and correlate it with film physical properties and with measured qubit internal quality factors. Our results indicate that the Nb/Si interlayer can be a significant contributor to decoherence and should be considered an important factor that must be optimized.

Datta, Amlan [Ames Lab; Iowa State U.]↗

DOE OSTI2026

Magneto-optical study of Nb thin films for superconducting qubits

Abstract Among the recognized sources of decoherence in superconducting qubits, the spatial inhomogeneity of the superconducting state and the possible presence of magnetic-flux vortices remain comparatively underexplored. Niobium is commonly used as a structural material in transmon qubits that host Josephson junctions, and excess dissipation anywhere in the transmon can become a bottleneck that limits overall quantum performance. The metal/substrate interfacial layer may simultaneously host pair-breaking loss channels (e.g. two-level systems) and control thermal transport, thereby affecting dissipation and temperature stability. Here, we use quantitative magneto-optical imaging of the magnetic-flux distribution to characterize the homogeneity of the superconducting state and the critical current density, j c , in niobium films fabricated under different sputtering conditions. The imaging reveals distinct flux-penetration regimes, ranging from a nearly ideal Bean critical state to strongly nonuniform thermo-magnetic dendritic avalanches. By fitting the measured magnetic-induction profiles, we extract j c and try to correlate it with film physical properties and with measured qubit internal quality factors. Our results indicate that the Nb/Si interlayer can be a significant contributor to decoherence and should be considered an important factor that must be optimized.

Datta, Amlan [Ames National Laboratory; Iowa State↗

DOE OSTI2025

Nanotomography for Quantitative 3D Particle Reconstruction

Particulates are ubiquitous across fuel cycle operations and carry critical information about particle formation, processing, and potential proliferation-related activities. Traditional analytical techniques, including micro-Raman spectroscopy and standard electron microscopy, are often limited in spatial resolution or dimensionality, particularly when used to examine metallic or submicron-scale features. Understanding particle morphology, phase distribution, and internal porosity is essential for constraining formation conditions, thermodynamic environments, and material transport behavior. In this report, we demonstrate the application of plasma focused ion beam nanotomography to reconstruct micron-scale particulates at nanoscale resolution. Using high-resolution backscattered electron imaging and Avizo software, we obtained 3D reconstructions that enabled quantitative analysis of particle morphology, phase composition, and internal voids. Representative examples include a Ta particle with a large central void and a composite particle with embedded tetrahedral crystalline structures. These reconstructions reveal structural and compositional details that are inaccessible through conventional 2D imaging. The results demonstrate that nanotomography provides both qualitative and quantitative insights into particle formation and behavior. Using nanotomography, porosity and phase distributions can be quantified to inform models of particle density, transport, and solidification conditions. Beyond technical insights, the workflow developed here establishes a transferable capability for analyzing heterogeneous particles and has potential applications in bulk materials studies via x-ray computed tomography or other volumetric imaging modalities. Ongoing efforts are focused on optimizing the workflow to process multiple particles simultaneously, increasing throughput and statistical robustness. Overall, this work illustrates the power of nanotomography as a tool for connecting particulate morphology to formation mechanisms, composition, and transport, thereby strengthening analytical capabilities for nuclear forensics, fuel cycle analysis, and related scientific investigations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Cellular fluidics

The natural world provides many examples of multiphase transport and reaction processes that have been optimized by evolution. These phenomena take place at multiple length and time scales and typically include gas–liquid–solid interfaces and capillary phenomena in porous media. Many biological and living systems have evolved to optimize fluidic transport. However, living things are exceptionally complex and very difficult to replicate, and human-made microfluidic devices (which are typically planar and enclosed) are highly limited for multiphase process engineering. In this paper, we introduce the concept of cellular fluidics: a platform of unit-cell-based, three-dimensional structures—enabled by emerging 3D printing methods—for the deterministic control of multiphase flow, transport and reaction processes. We show that flow in these structures can be ‘programmed’ through architected design of cell type, size and relative density. We demonstrate gas–liquid transport processes such as transpiration and absorption, using evaporative cooling and CO 2 capture as examples. We design and demonstrate preferential liquid and gas transport pathways in three-dimensional cellular fluidic devices with capillary-driven and actively pumped liquid flow, and present examples of selective metallization of pre-programmed patterns. Our results show that the design and fabrication of architected cellular materials, coupled with analytical and numerical predictions of steady-state and dynamic behaviour of multiphase interfaces, provide deterministic control of fluidic transport in three dimensions. Cellular fluidics may transform the design space for spatial and temporal control of multiphase transport and reaction processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Robust inference of the Galactic Centre gamma-ray excess spatial properties

ABSTRACT The gamma-ray Fermi-LAT Galactic Centre excess (GCE) has puzzled scientists for over 15 yr. Despite ongoing debates about its properties, and especially its spatial distribution, its nature remains elusive. We scrutinize how the estimated spatial morphology of this excess depends on models for the Galactic diffuse emission, focusing particularly on the extent to which the Galactic plane and point sources are masked. Our main aim is to compare a spherically symmetric morphology – potentially arising from the annihilation of dark matter (DM) particles – with a boxy morphology – expected if faint unresolved sources in the Galactic bulge dominate the excess emission. Recent claims favouring a DM-motivated template for the GCE are shown to rely on a specific Galactic bulge template, which performs worse than other templates for the Galactic bulge. We find that a non-parametric model of the Galactic bulge derived from the VISTA Variables in the Via Lactea survey results in a significantly better fit for the GCE than DM-motivated templates. This result is independent of whether a galprop-based model or a more non-parametric ring-based model is used to describe the diffuse Galactic emission. This conclusion remains true even when additional freedom is added in the background models, allowing for non-parametric modulation of the model components and substantially improving the fit quality. When adopted, optimized background models provide robust results in terms of preference for a boxy bulge morphology for the GCE, regardless of the mask applied to the Galactic plane.

Astronomy & Astrophysics↗

DOE OSTI2022

The Concept of a Quantum Edge Simulator: Edge Computing and Sensing in the Quantum Era

Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Multidimensional simulations of Mckenna-driven flow tube configuration: Investigating non-ideality in NO x formation flow tube experiments

Multidimensional simulations have been conducted to simulate atmospheric pressure, flat-flame/McKenna-burner-driven-flow tube experiments targeted to obtain NO x speciation data for predicting/analyzing syngas combustion emissions. In a prior work, we demonstrated the impacts of multidimensional transport on post flame region prediction departures from those assuming unidimensional flow/transport conditions. In this work, we develop and utilize a multidimensional laminar reacting flow solver to simulate the fully coupled flame and post flame regions to further elucidate the impacts of the earlier unidimensional modeling assumptions on interpreting post flame NO x experimental data. The model is used to simulate a lean, premixed syngas/air flame and its associated post flame regions within a cylindrical flow-tube-like arrangement. The combustion process takes place under atmospheric condition with trace amount of NO x seeding fed into the inlet gas stream. The spatial evolution of NO x species (NO and NO 2 ) in the flame and in the post-combustion zone suggests two distinct regions: 1) a region encompassing the flame structure itself; and 2) a post flame region in which the temperature decays due to both axial and radial transport processes. The predictions show that for the conditions studied, a pulsatile flow field exists due to the formation of an expanding and contracting recirculation zone in the outer periphery of the flow tube. By resolving the nature of the flow, the resulting time-averaged temperature and species concentrations show improved agreement with existing experimental measurements. The flow-field interaction results in radial inhomogeneities in the NO 2 profiles with the maximum concentration offset from the flow centerline. The location of the peak in NO 2 is coupled with radial temperature gradients from wall cooling effects and their significant influence on NO/NO 2 interconversion kinetics, producing notable NO 2 accumulation in regions near the wall. Geometrical configurations capable of suppressing/minimizing the pulsatile nature are also investigated and the results are compared. Other experimental configurations could be considered in parametric simulations to determine the optimal configuration that would minimize non-idealities in the observations. The work shows the value in performing such computations in advance of settling on a particular design for flow tube/flow reactor experiments.

42 ENGINEERING↗

DOE OSTI2026

Degenerate coupled-cluster theory

A size-extensive, converging, black-box, ab initio coupled-cluster (ΔCC) ansatz is introduced that computes the energies and wave functions of states from any degenerate or nondegenerate Slater-determinant references with any numbers of α- and β-spin electrons, any patterns of orbital occupancy, any spin multiplicities, and any spatial symmetries. For a nondegenerate reference, it reduces to the single-reference coupled-cluster ansatz. For a degenerate multireference, it is a natural coupled-cluster extension of degenerate Møller–Plesset perturbation (ΔMP) theory. For ionized and electron-attached references, it is a coupled-cluster Green’s function, although the present theory is convergent toward the full-configuration-interaction limits, while the Feynman–Dyson many-body Green’s function (MBGF) theory generally is not. Its single-excitation instance is a projection Hartree–Fock theory as per the Thouless theorem, which may be useful for core ionizations, high-spin states, and possibly electron affinities. Additionally, a new multireference coupled-cluster theory for a general model space is developed. This quasidegenerate coupled-cluster (QCC) theory is exactly converging, but not black-box, and intended for strong correlation. Determinant-based, general-order algorithms of ΔCC and QCC theories are implemented and compared with configuration-interaction (CI) and equation-of-motion coupled-cluster (EOM-CC) theories through octuple excitations and with ΔMP and MBGF theories up to the nineteenth order. An algebraic, optimal-scaling algorithm of the ΔCC theory is computer-synthesized at the levels of single excitations (ΔCCS) and of single and double excitations (ΔCCSD). As a result, the order of performance is QCC ≈ ΔCC > EOM-CC > CI at the same order or QCC ≈ ΔCC > ΔMP > MBGF at the same cost scaling.

Hirata, So [University of Illinois at Urbana-Champ↗