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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nondegenerate Noise-Resilient Superconducting Qubit

We propose a superconducting qubit based on engineering the first and second harmonics of the Josephson energy and phase relation 𝐸 𝐽⁢1 ⁢cos 𝜑 and 𝐸 𝐽⁢2 ⁢cos 2⁢𝜑. By constructing a circuit such that 𝐸 𝐽⁢2 is negative and |𝐸 𝐽⁢1 | ≪ |𝐸 𝐽⁢2 |, we create a periodic potential with two nondegenerate minima. The qubit, which we dub “harmonium,” is formed from the lowest-energy states of each minimum. Bit-flip protection of the qubit arises due to the localization of each qubit state to their respective minima, while phase-flip protection can be understood by considering the circuit within the Born-Oppenheimer approximation. We demonstrate with time-domain simulations that single- and two-qubit gates can be performed in approximately 100 ns. Finally, we compute the qubit coherence times using numerical diagonalization of the complete circuit in conjunction with state-of-the-art noise models. We estimate out-of-manifold heating times on the order of milliseconds, which can be treated as erasure errors using conventional dispersive readout. We estimate pure-dephasing times on the order of many tens of milliseconds, and bit-flip times on the order of seconds.

quantum error correction↗
DOE OSTI2026

Accurate energies for ππ* excited states via exchange scaling: the XS-CASSCF method

The state-averaged complete-active space self-consistent field method (SA-CASSCF) is a widely employed electronic structure method used for studying photochemistry and dynamics owing to its ability to provide a reliable description even of complicated cases while still retaining computational efficiency. However, SA-CASSCF suffers from one Achilles heel, related to the description of ionic ππ* excited states, whose energy is often overestimated by 1–2 eV. In light of this challenge, we present the XS-CASSCF method, a new approach based on the idea of exchange scaling (XS) that screens the involved energy terms to improve the excitation energies of singlet ionic ππ* states. First, we illustrate the power of the XS-CASSCF method using hexatriene and para-quinodimethane as examples, showing that it corrects the targeted ionic states while leaving the other states largely unaffected, giving root-mean-square errors (RMSE) below 0.2 eV for the four lowest states in both cases. Subsequently, XS-CASSCF vertical excitation energies are tested against theoretical best estimates for a set of 11 molecules and 56 excited states. XS-CASSCF performs exceptionally well for the ππ* states of hydrocarbons, reducing the RMSE over 21 excitation energies from 0.96 to 0.27 eV. In the challenging subset of molecules with heteroatoms and a larger number of ππ* and nπ* states, we find that improvements can also be obtained, albeit not as pronounced. We conclude with an outlook into more realistic molecular materials focusing on their singlet–triplet (S 1 /T 1 ) gaps, finding that significant improvements can be obtained along the whole range of S 1 /T 1 gaps studied, going from 0.1 eV to more than 1.5 eV. Owing to notable improvements across significant classes of molecules combined with its conceptual simplicity, we believe that XS-CASSCF is a promising addition to the electronic structure toolbox, serving both as a standalone electronic structure method and as a starting point for further correlated treatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Study of the ${{^{25}}}$Mg(d,p)${{^{26}}}$Mg reaction to constrain the ${{^{25}}}$Al(p,${\gamma }$)${{^{26}}}$Si resonant reaction rates in nova burning conditions

The rate of the 25 Al( p,γ) 26 Si reaction is one of the few key remaining nuclear uncertainties required for predicting the production of the cosmic γ-ray emitter 26 Al in explosive burning in novae. This reaction rate is dominated by three key resonances (J π = 0 + , 1 + and 3 + ) in 26 Si. Only the 3 + resonance strength has been directly constrained by experiment. A high resolution measurement of the 25 Mg(d, p) reaction was used to determine spectroscopic factors for analog states in the mirror nucleus, 26 Mg. A first spectroscopic factor value is reported for the 0 + state at 6.256 MeV, and a strict upper limit is set on the value for the 1 + state at 5.691 MeV, that is incompatible with an earlier ( 4 He, 3 He) study. These results are used to estimate proton partial widths, and resonance strengths of analog states in 26 Si contributing to the 25 Al( p,γ) 26 Si reaction rate in nova burning conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

K 2 CoS 2 : A two-dimensional in-plane antiferromagnetic insulator

Recent discovery of two-dimensional (2D) magnetic materials has brought magnetism to the flatland and opened up exciting opportunities for the exploration of fundamental physics as well as novel device applications. Here, we predict a thermodynamically stable 2D magnetic material, ${\mathrm{K}}_{2}{\mathrm{CoS}}_{2}$, which retains its in-plane bulk antiferromagnetic (AFM) order down to the monolayer and bilayer limits. Magnetic moments $(2.5{{\mu}}_{B}/\mathrm{Co})$ are found to form a quasi-one-dimensional antiferromagnetically ordered chain of Co atoms. The nonmagnetic electronic spectrum of the monolayer film is found to host flatbands and van Hove singularities, which play a key role in stabilizing the magnetic ground state. Based on classical Monte Carlo simulations, we estimate the Neel temperature for the AFM monolayer to be ${\approx}15\mathrm{K}$. Our study demonstrates that ${\mathrm{K}}_{2}{\mathrm{CoS}}_{2}$ hosts a robust AFM state which persists from the monolayer limit to the bulk material.

2-dimensional systems↗
DOE OSTI2020

Network-Scale Ubiquitous Volume Estimation Using Tree-Based Ensemble Learning Methods

Currently ubiquitous volume data for roadway networks remains the key missing dimension in traffic operations. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper/poster explores the use of state-of-art machine learning techniques to estimate accurate volume measures that span the highway network providing ubiquitous coverage in space, and point-in-time measures for a specific date and time. Three tree-based ensemble learning models, random forest (RF), gradient boost machine (GBM), and extreme gradient boost (XGBoost), were tested for volume estimation by learning from combined dataset of commercial probe data provided by TomTom, the FHWA's Travel Monitoring Analysis System (TMAS) data, and other infrastructure attributes such as number of lanes, speed limit, and weather. The methods were tested on major corridors and freeways in the metropolitan area of Denver. All three machine learning methods were able to provide hourly volume estimates 24 hours a day, 7 days a week, and 365 days a year with around 18% mean absolute error to true volume and about 5% of error with respect to roadway capacity. The low error measures allow the potential application by transportation agencies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Data-driven reduced-order models for port-Hamiltonian systems with operator inference

Hamiltonian operator inference has been developed in Sharma et al. (2022) to learn structure-preserving reduced-order models (ROMs) for Hamiltonian systems. The method constructs a low-dimensional model using only data and knowledge of the functional form of the Hamiltonian. The resulting ROMs preserve the intrinsic structure of the system, ensuring that the mechanical and physical properties of the system are maintained. In this work, we extend this approach to port-Hamiltonian systems, which generalize Hamiltonian systems by including energy dissipation, external input, and output. Based on snapshots of the system’s state and output, together with the information about the functional form of the Hamiltonian, reduced operators are inferred through optimization and are then used to construct data-driven ROMs. To further alleviate the complexity of evaluating nonlinear terms in the ROMs, a hyper-reduction method via discrete empirical interpolation is applied. Accordingly, we derive error estimates for the ROM approximations of the state and output. Lastly, we demonstrate the structure preservation, as well as the accuracy of the proposed port-Hamiltonian operator inference framework, through numerical experiments on a linear mass–spring-damper problem and a nonlinear Toda lattice problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Quantum Metrology of Noisy Spreading Channels

We provide the optimal measurement strategy for a class of noisy channels that reduce to the identity channel for a specific value of a parameter (spreading channels). We provide an example that is physically relevant: the estimation of the absolute value of the displacement in the presence of phase randomizing noise. Surprisingly, this noise does not affect the effectiveness of the optimal measurement. We show that, for small displacement, a squeezed vacuum probe field is optimal among strategies with same average energy. A squeezer followed by photodetection is the optimal detection strategy that attains the quantum Fisher information, whereas the customarily used homodyne detection becomes useless in the limit of small displacements, due to the same effect that gives Rayleigh’s curse in optical superresolution. There is a quantum advantage: a squeezed or a Fock state with N average photons allow to asymptotically estimate the parameter with a N better precision than classical states with same energy.

Górecki, Wojciech↗
DOE OSTI2022

Impacts of phase state and water content on secondary organic aerosol formation and partitioning (Final Technical Report)

Secondary organic aerosols (SOA) account for a major fraction of particulate matter in the atmosphere, affecting climate, air quality, and public health. SOA formation and evolution are highly complex processes involving both chemical reactions and transport of molecules in air, at particle surfaces, and within particles. The state of the matter, or “phase state,” comprising SOA can vary from a liquid, over an amorphous semi-solid, to a glassy solid, depending on chemical composition, water content, relative humidity (RH), and temperature. The occurrence of glassy and amorphous semi-solid states can pose limitations on the rate of transport of molecules, affecting gas-particle interactions and challenging the treatment of SOA in atmospheric models. The objective of this project was to improve fundamental understanding of the interplay of the phase state of particles and water content on the evolution of SOA formation. We have developed a method to estimate the glass transition temperature (T g ) of organic compounds to predict the phase state and viscosity of SOA. This method has been applied to high resolution mass spectrometry data of various types of SOA, achieving a good agreement with viscosity measurements. In addition, the viscosity estimation method was implemented into a regional model CMAQ to simulate diel and seasonal variations of SOA phase state over the U.S. Applying kinetic multilayer modeling, we analyzed laboratory experiments and ARM field measurements (e.g., HI-SCALE) on kinetic limitations of amine uptake. We have also conducted numerical simulations to estimate equilibration timescales of SOA partitioning. Overall, we have evaluated the impacts of phase state and water content on SOA lifecycle that should contribute to reducing the uncertainty of SOA representation in regional climate and air quality predictions. The project has yielded 17 publications, in which DE-SC0018349 is explicitly acknowledged. Please see the list of publications as below, followed by summary of research activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Transition-state correlations for predicting thermochemistry of adsorbates and surface reactions

Estimating thermochemical properties from linear correlations may provide a pathway to circumvent expensive density functional theory (DFT) calculations for quantities such as pre-exponentials and temperature corrections to DFT energies. Here, in this work, we construct thermochemical scaling relations between C 1 –C 6 n-alkanes in the gas phase and adsorbed alkyl chains extending from several transition metal surfaces, and examine changes in the slope and fit between metals and adsorption sites. We subsequently add –OH, –NH 2 , C$=$O, and C$=$C functional groups to the C 1 –C 6 molecules and demonstrate strong linear correlations for thermochemistry across all species. We broaden the correlations to incorporate transition states of C 1 –C 6 n-alkane dehydrogenation reactions, where thermochemistry for computationally prohibitive transition-state calculations can be quickly assessed. Additionally, we rationalize the linearity of thermochemical correlations based on the composition of the homologous series and theoretical assessments. As an application of the correlations, we estimate pre-exponentials for elementary surface reactions of ethane and propane hydrogenolysis on Ru(0001), which is of relevance to plastic hydrogenolysis. Depending on kinetically important steps, entropic contributions may be necessary to include in certain reaction mechanisms; in contrasting examples, entropies are found to be relatively insignificant for ethane hydrogenolysis but pertinent for propane hydrogenolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Experimental study of the Si 30 ( He 3 , d ) P 31 reaction and thermonuclear reaction rate of Si 30 ( p , γ ) P 31

Background: Abundance anomalies in some globular clusters, such as the enhancement of potassium and the depletion of magnesium, can be explained in terms of an earlier generation of stars polluting the presently observed ones. It was shown that the potential range of temperatures and densities of the polluting sites depends on the strength of a few critical reaction rates. The 30 Si(p,γ) 31 P reaction has been identified as one of these important reactions. Purpose: The key ingredient for evaluating the thermonuclear 30 Si(p,γ) 31 P reaction rate is the strength of the resonances which, at low energy, are proportional to their proton width. Therefore, the goal of this work is to determine the proton widths of unbound 31 P states. Method: States in 31 P were studied at the Maier-Leibnitz-Laboratorium using the one-proton 30 Si( 3 He,d) 31 P transfer reaction. Deuterons were detected with the high resolution one quadrupole three dipoles (Q3D) magnetic spectrometer. Angular distribution and spectroscopic factors were extracted for 27 states, and proton widths and resonance strengths were calculated for the unbound states. Results: Several 31 P unbound states have been observed for the first time in a one-proton transfer reaction. Above 20 MK, the 30 Si(p,γ) 31 P reaction rate is now entirely estimated from the observed properties of 31 P states. The reaction rate uncertainty from all resonances other than the E$^{c.m.}_{r}$ = 149 keV resonance has been reduced down to less than a factor of 2 above that temperature. The unknown spin and parity of the E$^{c.m.}_{r}$ = 149 keV resonance dominates the uncertainty in the rate in the relevant temperature range. Conclusion: Here, the remaining source of uncertainty on the 30 Si(p,γ) 31 P reaction rate comes from the unknown spin and parity of the E$^{c.m.}_{r}$ = 149 keV resonance which can change the reaction rate by a factor of 10 in the temperature range of interest.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Estimation of sensor measurement errors in reactor coolant systems using multi-sensor fusion

A nuclear power plant is typically instrumented with a variety of sensors to continually monitor its variables, and their sensor’s measurements may be used to assess the plant state and initiate safety actions, if needed. Errors in sensor measurements, due to factors such as calibration drifts, critically affect such state assessments. Here, we address a problem of estimating sensor errors using physics-informed machine learning methods that use measurements collected under known plant conditions. For a given sensor, we propose an information fusion method that uses measurements from other sensors to estimate its output assuming it is error-free and provides its difference from an actual measurement as an error estimate. We present the ensemble of trees and support vector machine fusers, and evaluate their performance using measurements collected over an emulated test loop of a pressurized water reactor. The plant variables are related to each other through the underlying physical laws under inertial constraints that place bounds on their derivatives, which analytically justify the applicability of machine learning methods for computing these fusers. Under twenty scenarios, we assess their sensor error estimates for pressure sensors of the heat exchanger of a reactor’s primary coolant system. Multiple types of errors are captured by both fusers under externally induced calibration drifts, blockages, minor leaks and air gaps in sensing lines, and electromagnetic interference; the root mean square error of the estimation of error is under 2.2% percent of the maximum measurement. We present generalization equations, in the framework of statistical learning theory, for these methods that characterize the confidence probability that the estimation error is bounded by a specified parameter in future test scenarios.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Wood Heater Design Challenge Workshop Report

To support the United States Department of Energy (DOE) Bioenergy Technologies Office’s (BETO) goal of advancing residential wood heating technologies, Brookhaven National Laboratory (BNL), Lawrence Berkeley National Laboratory (LBNL), and the Alliance for Green Heat (AGH) hosted the fifth Wood Heater Design Challenge (WHDC). The Challenge consists of three virtual workshops and a Technology Slam aimed at inspiring and advancing the next generation of residential wood heaters. With an estimated 12.5 million homes in the United States using wood or pellets for space heating, this challenge stands to create a significant impact on the environmental footprint of domestic heating. This report provides a summary of the activities and outcomes from the three virtual workshops held in January, March, and April of 2022. The goal of the workshops was to bring together stakeholders from across the world to discuss research and development (R&D) challenges and opportunities for advancing residential wood heaters in the United States. The workshops focused on three topics: 1. Advances in wood heater design and technology; 2. Advances in instrumentation used for wood heater testing and field data collection; and 3. Adoption of new wood heater technology and integration with other renewables. Each workshop had more than 100 participants each day which included wood heater manufacturers, researchers, academics, government agencies, and policy makers. Overall, a clear message was presented that collaborative input from experts at air quality agencies, industry, academic institutions, and non-profits is necessary to ensure that wood heating technology continues as a mainstream renewable energy option in our changing energy landscape and to facilitate innovative technology solutions. Participants also provided insights and recommendations that may help guide future R&D investments.

09 BIOMASS FUELS↗
DOE OSTI2020

An experimental system and procedure of unsteady-state relative permeability test for gas hydrate-bearing sediments

Reliable estimations of the relative permeability of gas and water in hydrate-bearing sediments (HBS) and the dependency of the relative permeability on hydrate saturation are critical to predict the productivity of a hydrate reservoir. Yet, this remains poorly estimated owing to lack of experimental data associated with difficulties in conducting multiphase flow experiments in HBS. Recognizing the experimental challenges, this study intends to develop and validate a new experimental system and procedure of unsteady-state relative permeability test that can generate reliable and reproducible flow measurements in HBS. Furthermore, gas hydrate is considered as a part of solid matrix in the sediment, so one of the challenges is to maintain a constant hydrate saturation, which is achieved in this experimental study using tight pressure-temperature (P-T) control near the hydrate stability boundary. The measured differential pressure across the specimen, methane injection flow rate, and volume of displaced brine are used to calculate the relative permeability by adopting a conventional Buckley-Leverett theory-based interpretation method. Residual brine saturation calculated for the hydrate-bearing specimen is higher than that of hydrate-free specimen, presumably due to decrease in pore size, increase in heterogeneity of solid matrix, and increase in size distribution of solid matrix and pore in the presence of hydrates. Further studies are necessary to represent the results of the unsteady-state flow experiment in HBS with a gas hydrate-dependent relative permeability model.

03 NATURAL GAS↗
DOE OSTI2019

Quantum eigenvalue estimation via time series analysis

We present an efficient method for estimating the eigenvalues of a Hamiltonian H from the expectation values of the evolution operator for various times. For a given quantum state ρ, our method outputs a list of eigenvalue estimates and approximate probabilities. Each probability depends on the support of ρ in those eigenstates of H associated with eigenvalues within an arbitrarily small range. The complexity of our method is polynomial in the inverse of a given precision parameter ε, which is the gap between eigenvalue estimates. Unlike the well-known quantum phase estimation algorithm that uses the quantum Fourier transform, our method does not require large ancillary systems, large sequences of controlled operations, or preserving coherence between experiments, and is therefore more attractive for near-term applications. The output of our method can be used to estimate spectral properties of H and other expectation values efficiently, within additive error proportional to ε.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Efficient Trainability of Linear Optical Modules in Quantum Optical Neural Networks

The existence of “barren plateau landscapes” for generic discrete-variable quantum neural networks, which obstructs efficient gradient-based optimization of cost functions defined by global measurements, would be surprising in the case of generic linear optical modules in quantum optical neural networks due to the tunability of the intensity of continuous variable states and the relevant unitary group having exponentially smaller dimension. We demonstrate that coherent light in m modes can be generically compiled efficiently if the total intensity scales sublinearly with m, and extend this result to cost functions based on homodyne, heterodyne, or photon detection measurement statistics, and to noisy cost functions in the presence of attenuation. Here, we further demonstrate efficient trainability of m mode linear optical quantum circuits for variational mean field energy estimation of positive quadratic Hamiltonians for input states that do not have energy exponentially vanishing with m.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Availability Estimates of Low-Cost, Dispatch-Constrained Electricity (LDE)

This dataset accompanies the report "Potential Availability of and Supply Curves for Low-Cost, Dispatch-Constrained Electricity", which provides initial estimates of the quantity and availability of the potential low-cost, dispatch-constrained electricity (LDE) resource in the United States under scenarios with high variable renewable energy (VRE) penetrations. The analysis also estimates supply curves that can be used in subsequent analysis of the opportunity to use the LDE. The estimated LDE resource could be used for various applications that value low-cost electricity and can operate at reduced capacity factors, including electrolytic hydrogen production and carbon capture. The analysis accompanying this dataset was performed in 2018-2019 to support an analysis of "The Technical and Economic Potential of the H2@Scale Concept within the United States" ( https://www.nlr.gov/docs/fy21osti/77610.pdf ). This dataset includes the quantity of estimated LDE resource for each hour of the year in 2050, aggregated to both the national level and the ReEDS balancing area level. As described in the accompanying report, the ReEDS model was used to project the evolution of the power sector over time. Links to the mapping of ReEDS balancing areas to states and ReEDS balancing area shape files are provided. This dataset also includes the report figure data.

08 HYDROGEN↗
DOE OSTI2020

The prospective impacts of 2019 state energy policies on the U.S. electricity system

Over the past several years state energy policies have been evolving rapidly, and are more frequently including higher targets, including 100% clean energy targets. This study assesses the aggregate impacts of state clean energy standards and emissions policies on national electricity generation, power sector carbon dioxide (CO 2 ) emissions, and electricity prices and system costs. To do so, we apply the Regional Energy Deployment System (ReEDS) model, which is a detailed electric sector capacity expansion model, to evaluate scenarios with and without state policies and using a range of renewable energy technology cost projections. Across the scenarios analyzed, we find that the state policies drive 1.9%–10.7% of total nationwide clean energy and reduce cumulative power sector CO 2 emissions by 2.6%–5.4% over the 2020–2050 study period. This incremental generation is predominantly, but not exclusively, from renewable energy technologies. In most cases, the state policies result in increases to electricity prices and electricity system costs, and policy costs are sensitive to the future cost of clean energy technologies. Across all scenarios, the levelized cost of incremental policy-driven clean energy generation is estimated to be $17–38/MWh and the average cost of CO 2 abatement from the state-level policies is estimated to be $29–74/metric ton.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗