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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Rapid Memory Footprint Access Diagnostics

Footprint and reuse distance measure temporal locality and therefore do not capture the significance of access patterns (spacial locality). A strided access pattern has the largest possible footprint but usually has the best performance. To highlight exposed memory latency, we separate footprint into strided (prefetchable) and irregular (non-prefetchable) access components and calculate the growth rate of each. To rapidly compute these footprint access diagnostics, we present two methods, whole-program and precise. Current footprint analyses can cause 200× or more slowdown with realistic inputs and are therefore impractical. Our whole-program method reduces the overhead to 10% by computing upper bounds, but still yields inter-procedural insight through a call path profile. Our precise method uses additional static analysis and profiling to refine the upper bounds for intra-procedural loop nests. We evaluate our approaches using benchmarks that vary access patterns (strided \vs unpredictable), sparsity (all words in a cache line \vs some), and reuse (varying and repeated accesses per element). Notably, for loop nests with unpredictable accesses, the precise method's accuracy is within 10% of ideal. The whole-program method has sufficient accuracy to diagnose bottlenecks.

data locality, memory footprint, footprint access ↗
DOE OSTI2023

Evidence for the charge asymmetry in $pp$ → $t\bar{t}$ production at $\sqrt{s}$ = 13 TeV with the ATLAS detector

Inclusive and differential measurements of the top–antitop ($t\bar{t}$) charge asymmetry ${A}_{C}^{t\bar{t}}$ and the leptonic asymmetry ${A}_{C}^{\ell\bar{\ell}}$ are presented in proton–proton collisions at $\sqrt{s}$ = 13 TeV recorded by the ATLAS experiment at the CERN Large Hadron Collider. The measurement uses the complete Run 2 dataset, corresponding to an integrated luminosity of 139 fb –1 , combines data in the single-lepton and dilepton channels, and employs reconstruction techniques adapted to both the resolved and boosted topologies. A Bayesian unfolding procedure is performed to correct for detector resolution and acceptance effects. The combined inclusive $t\bar{t}$ charge asymmetry is measured to be ${A}_{C}^{t\bar{t}}$ = 0.0068 ± 0.0015, which differs from zero by 4.7 standard deviations. Differential measurements are performed as a function of the invariant mass, transverse momentum and longitudinal boost of the $t\bar{t}$ system. Both the inclusive and differential measurements are found to be compatible with the Standard Model predictions, at next-to-next-to-leading order in quantum chromodynamics perturbation theory with next-to-leading-order electroweak corrections. The measurements are interpreted in the framework of the Standard Model effective field theory, placing competitive bounds on several Wilson coefficients.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Learning to classify quantum phases of matter with a few measurements

We study the identification of quantum phases of matter, at zero temperature, when only part of the phase diagram is known in advance. Following a supervised learning approach, we show how to use our previous knowledge to construct an observable capable of classifying the phase even in the unknown region. By using a combination of classical and quantum techniques, such as tensor networks, kernel methods, generalization bounds, quantum algorithms, and shadow estimators, we show that, in some cases, the certification of new ground states can be obtained with a polynomial number of measurements. An important application of our findings is the classification of the phases of matter obtained in quantum simulators, e.g. cold atom experiments, capable of efficiently preparing ground states of complex many-particle systems and applying simple measurements, e.g. single qubit measurements, but unable to perform a universal set of gates.

quantum machine learning↗
DOE OSTI2022

Flowsheet for the Neutralization of Accelerated Basin De-inventory (ABD) Material

Under the Accelerated Basin De-inventory (ABD) program H-Canyon will be dissolving aluminum spent nuclear fuel (ASNF) and then neutralizing that solution without performing head-end strike or uranium recovery operations. After dissolution in H-Canyon the material will be neutralized to a free hydroxide concentration of either 0.6 M or 1.2 M to meet the waste acceptance criteria for transfer to the SRS Concentration, Storage and Transfer Facility (CSTF). In order to ensure the material could be successfully neutralized and transferred, SRNL completed experiments with simulated H-Canyon dissolver solutions. Two bounding simulants were developed based on the expected compositions of ASNF to be dissolved, one containing only Gd as the neutron poison and one containing Fe as an additional poison.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500

Anthropogenic increases in atmospheric greenhouse gas concentrations are the main driver of current and future climate change. The integrated assessment community has quantified anthropogenic emissions for the shared socio-economic pathway (SSP) scenarios, each of which represents a different future socio-economic projection and political environment. Here, we provide the greenhouse gas concentrations for these SSP scenarios using the reduced-complexity climate–carbon-cycle model MAGICC7.0. We extend historical, observationally based concentration data with SSP concentration projections from 2015 to 2500 for 43 greenhouse gases with monthly and latitudinal resolution. CO 2 concentrations by 2100 range from 393 to 1135 ppm for the lowest (SSP1-1.9) and highest (SSP5-8.5) emission scenarios, respectively. We also provide the concentration extensions beyond 2100 based on assumptions regarding the trajectories of fossil fuels and land use change emissions, net negative emissions, and the fraction of non-CO 2 emissions. By 2150, CO 2 concentrations in the lowest emission scenario are approximately 350 ppm and approximately plateau at that level until 2500, whereas the highest fossil-fuel-driven scenario projects CO 2 concentrations of 1737 ppm and reaches concentrations beyond 2000 ppm by 2250. We estimate that the share of CO 2 in the total radiative forcing contribution of all considered 43 long-lived greenhouse gases increases from 66% for the present day to roughly 68% to 85% by the time of maximum forcing in the 21st century. For this estimation, we updated simple radiative forcing parameterizations that reflect the Oslo Line-By-Line model results. In comparison to the representative concentration pathways (RCPs), the five main SSPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are more evenly spaced and extend to lower 2100 radiative forcing and temperatures. Performing two pairs of six-member historical ensembles with CESM1.2.2, we estimate the effect on surface air temperatures of applying latitudinally and seasonally resolved GHG concentrations. We find that the ensemble differences in the March–April–May (MAM) season provide a regional warming in higher northern latitudes of up to 0.4 K over the historical period, latitudinally averaged of about 0.1 K, which we estimate to be comparable to the upper bound (~5 % level) of natural variability. In comparison to the comparatively straight line of the last 2000 years, the greenhouse gas concentrations since the onset of the industrial period and this studies' projections over the next 100 to 500 years unequivocally depict a “hockey-stick” upwards shape. The SSP concentration time series derived in this study provide a harmonized set of input assumptions for long-term climate science analysis; they also provide an indication of the wide set of futures that societal developments and policy implementations can lead to – ranging from multiple degrees of future warming on the one side to approximately 1.5°C warming on the other.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗
DOE OSTI2025

Revealing Robust Room Temperature Ferromagnetism in Gd‐Doped Few‐Layered MoS 2 Thin Films

2D MoS 2 holds great promise for spintronics, yet is limited by intrinsic diamagnetism. This study demonstrates inducing ferromagnetic behavior in MoS 2 films doped with 0.47% Gd, achieving an ultrahigh saturation magnetization of 454 emu/cm 3 in a few‐layered film over 11‐times higher than bulk films (40 nm). Raman spectroscopy, X‐ray photoelectron spectroscopy, X‐ray magnetic circular dichroism, and density functional theory (DFT) calculations reveal an interplay between Gd dopants and Mo, S vacancies (V 1Mo+2S ), leading to the formation of bound magnetic polarons (BMPs) that drive ferromagnetic ordering. H 2 S annealing and DFT calculations reveal that defect healing reduces the saturation magnetization by 83%. High sulfur migration barrier in few‐layered films helps preserve BMPs, thereby sustaining ferromagnetism, whereas lower migration barriers in bulk films lead to suppression. These findings highlight the synergy between Gd doping and defect engineering in achieving ultrahigh room‐temperature ferromagnetism, offering a scalable strategy for developing high‐performance 2D magnetic materials for spintronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Impact of dynamical chiral symmetry breaking and dynamical diquark correlations on proton generalized parton distributions

We calculate the leading-twist, helicity-independent generalized parton distributions (GPDs) of the proton, at finite skewness, in the Nambu–Jona-Lasinio (NJL) model of quantum chomodynamics (QCD). The NJL model reproduces low-energy characteristics of QCD, including dynamical chiral symmetry breaking (DCSB). The proton bound-state amplitude is solved for using the Faddeev equation in a quark-diquark approximation, including both dynamical scalar and axial vector diquarks. GPDs are calculated using a dressed non-local correlator, consistent with DCSB, which is obtained by solving a Bethe-Salpeter equation. The model and approximations used observe Lorentz covariance, and as a consequence the GPDs obey polynomiality sum rules. Extractions of electromagnetic and gravitational form factors are performed. We find a D-term of -1.08 when the non-local correlator is properly dressed, and 0.85 when the bare correlator is used instead, suggesting that within this framework proton stability requires the constituent quarks to be dressed consistently with DCSB. In conclusion, we also find that the anomalous gravitomagnetic moment vanishes, as required by Poincare symmetry.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Spectroscopic study of a possible $Λnn$ resonance and a pair of $ΣNN$ states using the $(e,e'K^+)$ reaction with a tritium target

Here, a mass spectroscopy experiment with a pair of nearly identical high resolution spectrometers and a tritium target was performed in Hall A at Jefferson Lab. Utilizing the (e,e'K + ) reaction, enhancements, which may correspond to a possible $\Lambda$nn resonance and a pair of ΣNN states, were observed with an energy resolution of about 1.21 MeV (σ), although greater statistics are needed to make definitive identifications. An experimentally measured Λnn state may provide a unique constraint in determining the Λn interaction, for which no scattering data exist. In addition, although bound A = 3 and 4 Σ hypernuclei have been predicted, only an A = 4 Σ hypernucleus ($^4_Σ$He) was found, utilizing the (K - ,π - ) reaction on a 4 He target. The possible bound ΣNN state is likely a Σ 0 nn state, although this has to be confirmed by future experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ssDNA is an allosteric regulator of the C. crescentus SOS-independent DNA damage response transcription activator, DriD

DNA damage repair systems are critical for genomic integrity. However, they must be coordinated with DNA replication and cell division to ensure accurate genomic transmission. In most bacteria, this coordination is mediated by the SOS response through LexA, which triggers a halt in cell division until repair is completed. Recently, an SOS-independent damage response system was revealed in Caulobacter crescentus. This pathway is controlled by the transcription activator, DriD, but how DriD senses and signals DNA damage is unknown. To address this question, we performed biochemical, cellular, and structural studies. We show that DriD binds a specific promoter DNA site via its N-terminal HTH domain to activate transcription of genes, including the cell division inhibitor didA. A structure of the C-terminal portion of DriD revealed a WYL motif domain linked to a WCX dimerization domain. Strikingly, we found that DriD binds ssDNA between the WYL and WCX domains. Comparison of apo and ssDNA-bound DriD structures reveals that ssDNA binding orders and orients the DriD domains, indicating a mechanism for ssDNA-mediated operator DNA binding activation. Biochemical and in vivo studies support the structural model. Our data thus reveal the molecular mechanism underpinning an SOS-independent DNA damage repair pathway.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

High-Energy Coherent X-Ray Dffraction Microscopy of Polycrystal Grains: First Steps Towards a Multi-Scale Approach

Here, we present proof-of-concept imaging measurements of a polycrystalline material that integrate the elements of conventional high-energy x-ray diffraction microscopy with coherent diffraction-imaging techniques, and that can enable in situ strain-sensitive imaging of lattice structure in ensembles of deeply embedded crystals over five decades of length scale upon full realization. We demonstrate that the combination of these two characterization methods enables a more complete picture of the crystal-lattice strain than either method is capable of providing individually. Such complementary imaging capabilities are critical to addressing questions in a variety of research areas such as materials science and engineering, chemistry, and solid-state physics. Towards this eventual goal, the following key aspects are demonstrated: (1) high-energy Bragg coherent diffraction imaging (HE BCDI) of submicron-scale crystallites at 52 keV at current third-generation synchrotron light sources, (2) HE BCDI performed in conjunction with far-field high-energy diffraction microscopy (FF HEDM) on the grains of a polycrystalline sample in a smoothly integrated manner, and (3) the orientation information of an ensemble of grains obtained via FF HEDM used to perform complementary HE BCDI on multiple Bragg reflections of a single targeted grain. The imaged structures are seen to be 477 ± 50 nm or smaller in size, with an estimated strain resolution of 2.5 x 10 -4 . These steps lay the foundation for integration of HE BCDI, which typically provides a spatial resolution tens of nanometers, into a broad suite of well-established HEDM methods, extending HEDM beyond the few-micrometer resolution bound and into the nanoscale, and positioning the approach to take full advantage of the orders-of-magnitude improvement of x-ray coherence expected at fourth-generation light sources presently being built and commissioned worldwide.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Binding of Formic Acid on Anatase TiO2(101)

The adsorption of formic acid (FA) and the formation of formate species on metal oxide surfaces are of great interest in catalysis. Formic acid is used to probe the adsorption properties of surface sites and formates are common intermediates in many catalytic reactions. Here we focus on the interaction of FA with a prototypical anatase TiO2(101) surface by using a combination of scanning tunneling microscopy (STM), infrared reflection absorption spectroscopy (IRAS), electron stimulated desorption (ESD), and density functional theory (DFT) to assess the coverage dependent evolution of different FA-derived surface species at low temperatures (80–240 K). We find that isolated FA adsorbs at 80 K in both monodentate (MD) and bidentate (BD) configurations on top of undercoordinated Ti sites (Ti5c). The MD form is likely deprotonated and readily converts upon annealing to bridging BD on two neighboring Ti5c sites. DFT calculations show that molecularly-bound MD FA is metastable and readily converts into a deprotonated state in the proximity of subsurface oxygen vacancy. The stability of the MD species increases as the availability of the paired Ti5c sites for BD formation diminishes at high coverages. Upon surface saturation, a mixed configuration of alternating bidentate and monodentate species with coverage of 2/3 FA/Ti5c represents the most favorable configuration. This is in contrast with the adsorption of FA on rutile TiO2(110) and many other oxides, where bidentate formate species dominate. Y.W., A.D., G.A.K., R.R., N.G.P., and Z.D. were supported by the U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences (BES), Division of Chemical Sciences, Geosciences and Biosciences (CSGB) under FWP 47319. B.W. and A.S. acknowledge the support of DOE BES, CSGB Division under Award DESC0007347. The experimental studies were performed in EMSL, a national scientific user facility sponsored by the Department of Energy’s Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL). PNNL is a multiprogram national laboratory operated for DOE by Battelle. The authors also acknowledge computational resources from the TIGRESS high-performance computer center at Princeton University.

Wang, Yang↗
DOE OSTI2020

Imaging viscous flow of the Dirac fluid in graphene

The electron–hole plasma in charge-neutral graphene is predicted to realize a quantum critical system in which electrical transport features a universal hydrodynamic description, even at room temperature. This quantum critical ‘Dirac fluid’ is expected to have a shear viscosity close to a minimum bound, with an interparticle scattering rate saturating1 at the Planckian time, the shortest possible timescale for particles to relax. Although electrical transport measurements at finite carrier density are consistent with hydrodynamic electron flow in graphene a clear demonstration of viscous flow at the charge-neutrality point remains elusive. In this work, we directly image viscous Dirac fluid flow in graphene at room temperature by measuring the associated stray magnetic field. Nanoscale magnetic imaging is performed using quantum spin magnetometers realized with nitrogen vacancy centres in diamond. Scanning single-spin and wide-field magnetometry reveal a parabolic Poiseuille profile for electron flow in a high-mobility graphene channel near the charge-neutrality point, establishing the viscous transport of the Dirac fluid. This measurement is in contrast to the conventional uniform flow profile imaged in a metallic conductor and also in a low-mobility graphene channel. Furthermore, via combined imaging and transport measurements, we obtain viscosity and scattering rates, and observe that these quantities are comparable to the universal values expected at quantum criticality. This finding establishes a nearly ideal electron fluid in charge-neutral, high-mobility graphene at room temperature4. Our results will enable the study of hydrodynamic transport in quantum critical fluids relevant to strongly correlated electrons in high-temperature superconductors9. This work also highlights the capability of quantum spin magnetometers to probe correlated electronic phenomena at the nanoscale.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Automated Classification of Vehicle Movements at Signalized Intersections Using Vehicle Trajectories

Accurate vehicle movement classification through signalized intersections is of paramount importance to the analysis of intersection performance and the optimization of traffic control strategies. Conventional techniques for tracking vehicle turning movements depend on infrastructure-based strategies like human counts, loop detectors, and video analytics, all of which are costly, prone to errors, and spatially constrained. High-frequency trajectory data can be utilized to determine vehicle movement patterns in a scalable and infrastructure-independent method due to the adoption of connected vehicles (CVs). In recent years, several studies have utilized connected vehicle data to generate performance measures. Most of the trajectory-based performance measures approaches, however, require map matching-i.e., extracting geospatial references from maps to identify the movements that individual vehicles make at a signalized intersection. These approaches are often time-consuming and hinder scalability since geographic features need to be provided for an analysis to be conducted. Map matching methods are prone to errors as different map versions change these geographic features. This research presents a novel automatic classification pipeline that uses CV trajectory data to classify vehicle movements at signalized crossings, specifically pass-through left-turn and right-turn maneuvers. The process starts by filtering trips that cross a spatial bounding box that has been defined at the target intersection. Approach and departure headings for each trajectory crossing the boundary are computed and are clustered together to identify dominant movements. The proposed algorithm is used to classify the movement of vehicles at 10 intersections in the state of California, and the results indicate that the algorithm can classify movements at these intersections with varying traffic volumes and road network configurations, all in a map-less framework with no need for conflation of vehicle trajectories to a digital base map.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

How Topological Polymer Loops on the Nanoparticle Surface Control the Mechanical Properties of Nanocomposites

Carbon black (CB) and silica (SiO 2 ) filled elastomers are known to be the most successful polymer nanocomposites (PNCs) in industry, where “bound rubber (BR)” (i.e., polymer chains that are physically or chemically adsorbed on the nanofiller surface) plays a critical role in their reinforcement. Here, we report a molecular-scale mechanism underlying the “BR-induced reinforcement” by integrating neutron scattering experiments and molecular dynamics simulations. Simplified non-cross-linked SiO 2 -filled polybutadiene (PB) and CB-filled PB reveal the critical role of topological polymer loops in the BR for the enhanced mechanical performance. The average loop size on the SiO 2 surface modified with a silane coupling agent is much smaller than that on the CB surface and the loops on the SiO 2 surface are densely formed, preventing interdigitation with the matrix chains. On the other hand, the larger, uncrowded loops formed on the CB surface facilitate the interdigitation with the matrix polymer chains even near the filler surface. In this way, a strong connectivity is established between a matrix and a nanofiller, resulting in an adhesive filler–polymer interface. Furthermore, our findings shed light on rich and complex physics and materials design problems in PNCs, where the topological polymer structure on the nanofiller surface directly controls the macroscopic mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantum simulation of boson-related Hamiltonians: techniques, effective Hamiltonian construction, and error analysis

Elementary quantum mechanics proposes that a closed physical system consistently evolves in a reversible manner. However, control and readout necessitate the coupling of the quantum system to the external environment, subjecting it to relaxation and decoherence. Consequently, system-environment interactions are indispensable for simulating physically significant theories. A broad spectrum of physical systems in condensed-matter and high-energy physics, vibrational spectroscopy, and circuit and cavity QED necessitates the incorporation of bosonic degrees of freedom, such as phonons, photons, and gluons, into optimized fermion algorithms for near-future quantum simulations. In particular, when a quantum system is surrounded by an external environment, its basic physics can usually be simplified to a spin or fermionic system interacting with bosonic modes. Nevertheless, troublesome factors such as the magnitude of the bosonic degrees of freedom typically complicate the direct quantum simulation of these interacting models, necessitating the consideration of a comprehensive plan. This strategy should specifically include a suitable fermion/boson-to-qubit mapping scheme to encode sufficiently large yet manageable bosonic modes, and a method for truncating and/or downfolding the Hamiltonian to the defined subspace for performing an approximate but highly accurate simulation, guided by rigorous error analysis. In this pedagogical tutorial review, we aim to provide such an exhaustive strategy, focusing on encoding and simulating certain bosonic-related model Hamiltonians, inclusive of their static properties and time evolutions. Specifically, we emphasize two aspects: (1) the discussion of recently developed quantum algorithms for these interacting models and the construction of effective Hamiltonians, and (2) a detailed analysis regarding a tightened error bound for truncating the bosonic modes for a class of fermion-boson interacting Hamiltonians.

bosonic Hamiltonian↗
DOE OSTI2024

Evaluation of Minimum NOx Emission From Ammonia Combustion

Abstract Ammonia (NH3) is being explored as a hydrogen carrier with no carbon emissions. However, if burned directly as NH3, rather than being completely decomposed back to N2/H2, the fuel-bound nitrogen comes with a potentially significant NOx emissions penalty. Indeed, several existing studies are showing ammonia combustion NOx emissions that exceed current natural gas fueled, DLN technologies by one to two orders of magnitude. Therefore, it is important to establish the theoretical minimum NOx emissions for an ammonia combustor, to determine how much NOx levels can be reduced via further technology development. In other words, the purpose of this work is not to analyze the performance of a specific combustor but, rather, the fundamental limits of what is achievable. This study quantifies this minimum NOx level for a two-stage combustor system for a given combustor exit temperature and residence time, with a constraint on unburned fuel levels. As expected, the optimum configuration is a rich front end combustor to burn and crack ammonia with significant H2 production, followed by an NO relaxation reactor, followed by a lean stage that consumes the remaining H2. The optimum residence time and stoichiometry of each zone are determined in the fast mixing limit, which essentially balances between NOx production in the primary and secondary zones. These results show minimum NOx levels are in 200–400 ppm range at 1 bar, but drop to levels of ∼25 ppm at 20 bar. These NOx emissions are dominated by NOx production in the primary stage which relaxes to equilibrium levels quite slowly. As processes controlling NOx relaxation to equilibrium in the primary stage dominate overall NO emission levels, combustor NOx sensitivities are essentially opposite that of natural gas fired, DLN systems. Specifically, NOx values drop with increased combustor residence time, increased pressure, and increased combustor exit temperature. These results also suggest that the most important strategy for NOx minimization is to provide sufficient relaxation time after the primary zone for NOx to approach equilibrium—this can be done via kinetic means to accelerate this relaxation rate, such as enhancing pressure or temperature, or increasing residence times. Indeed, this work shows that low pressure combustors specifically optimized for ammonia will have residence times that are one to two orders of magnitude larger than current natural gas systems. By doing so, NOx levels below 10 ppm may be achievable. Finally, we discuss the sensitivity of these values to uncertainties in ammonia kinetics.

Engineering↗