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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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DOE OSTI2021

Irradiation-Driven Restructuring of UO 2 Thin Films: Amorphization and Crystallization

Combustion synthesis in uranyl nitrate–acetylacetone–2-methoxyethanol solutions was used to deposit thin UO 2 films on aluminum substrates to investigate the irradiation-induced restructuring processes. Thermal analysis revealed that the combustion reactions in these solutions are initiated at ~160 °C. The heat released during the process and the subsequent brief annealing at 400 °C allow the deposition of polycrystalline films with 5–10 nm UO 2 grains. The use of multiple deposition cycles enables tuning of the film thicknesses in the 35–260 nm range. Irradiation with Ar 2+ ions (1.7 MeV energy and a fluence of up to 1 × 10 17 ions/cm 2 ) is utilized to generate a uniform distribution of atomic displacements within the films. X-ray fluorescence (XRF) and alpha-particle emission spectroscopy showed that the films were stable under irradiation and did not undergo sputtering degradation. X-ray photoelectron spectroscopy (XPS) showed that the stoichiometry and uranium ionic concentrations remain stable during irradiation. The high-resolution electron microscopy imaging and electron diffraction analysis demonstrated that at the early stages of irradiation (below 1 × 10 16 ion/cm 2 ) UO 2 films show complete amorphization and beam-induced densification (sintering), resulting in a pore-free disordered film. Prolonged irradiation (5 × 10 16 ion/cm 2 ) is shown to trigger a crystallization process at the surface of the films that moves toward the UO 2 /Al interface, converting the entire amorphous material into a highly crystalline film. This work reports on an entirely different radiation-induced restructuring of the nanoscale UO 2 compared to the coarse-grained counterpart. The preparation of thin UO 2 films deposited on Al substrates fills an area of national need within the stockpile stewardship program of the National Nuclear Security Administration and fundamental research with actinides. Here, the method reported in this work produces pure, robust, and uniform thin-film actinide targets for nuclear science measurements

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

Multi-model Hydroclimate Projections for the Alabama-Coosa-Tallapoosa River Basin in the Southeastern United States

Abstract This study uses a high-resolution, process-based modeling framework to assess the impacts of changing climate on water resources for the Alabama-Coosa-Tallapoosa River Basin in the southeastern United States. A 33-member ensemble of hydrologic projections was generated using 3 distributed hydrologic models (Precipitation-Runoff Modeling System, Variable Infiltration Capacity, and Distributed Hydrology Soil Vegetation Model) of different complexity. These hydrologic models were driven by dynamically downscaled and bias-corrected future climate simulations from 11 Coupled Model Intercomparison Project Phase 5 global climate models under Representative Concentration Pathway 8.5 emission scenario, with 40 years each in baseline (1966–2005) and future (2011–2050) periods. The hydroclimate response, in general, projects an increase in mean seasonal precipitation, runoff, and streamflow. The high and low flows are projected to increase and decrease, respectively, in general, suggesting increased likelihood of extreme rainfall events and intensification of the hydrologic cycle. The uncertainty associated with the ensemble hydroclimate response, analyzed through an analysis of variance technique, suggests that the choice of climate model is more critical than the choice of hydrologic model for the studied region. This study provides in-depth insights of hydroclimate response and associated uncertainties to support informed decisions by water resource managers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evidence and attribution of the enhanced land carbon sink

Climate change has been partially mitigated by an increasing net land carbon sink in the terrestrial biosphere; understanding the processes that drive this sink is thus essential for protecting, managing and projecting this important ecosystem service. In this Review, we examine evidence for an enhanced land carbon sink and attribute the observed response to drivers and processes. This sink has doubled from 1.2 ± 0.5 PgC yr -1 in the 1960s to 3.1 ± 0.6 PgC yr -1 in the 2010s. This trend results largely from carbon dioxide fertilization increasing photosynthesis (driving an increase in the annual land carbon sink of >2 PgC globally since 1900), mainly in tropical forest regions, and elevated temperatures reducing cold limitation, mainly at higher latitudes. Continued long-term land carbon sequestration is possible through the end of this century under multiple emissions scenarios, especially if nature-based climate solutions and appropriate ecosystem management are used. In conclusion, a new generation of globally distributed field experiments is needed to improve understanding of future carbon sink potential by measuring belowground carbon release, the response to carbon dioxide enrichment, and long-term shifts in carbon allocation and turnover.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Data-Consistent Inversion for Stochastic Input-to-Output Maps

Data-consistent inversion is a recently developed measure-theoretic framework for solving a stochastic inverse problem involving models of physical systems. The goal is to construct a probability measure on model inputs (i.e., parameters of interest) whose associated push-forward measure matches (i.e., is consistent with) a probability measure on the observable outputs of the model (i.e., quantities of interest). Previous implementations required the map from parameters of interest to quantities of interest to be deterministic. This work generalizes this framework for maps that are stochastic, i.e., contain uncertainties and variation not explainable by variations in uncertain parameters of interest. Generalizations of previous theorems of existence, uniqueness, and stability of the data-consistent solution are provided while new theoretical results address the stability of marginals on parameters of interest. A notable aspect of the algorithmic generalization is the ability to query the solution to generate independent identically distributed samples of the parameters of interest without requiring knowledge of the so-called stochastic parameters. This work therefore extends the applicability of the data-consistent inversion framework to a much wider class of problems. This includes those based on purely experimental and field data where only a subset of conditions are either controllable or can be documented between experiments while the underlying physics, measurement errors, and any additional covariates are either uncertain or not accounted for by the researcher. Finally, numerical examples demonstrate application of this approach to systems with stochastic sources of uncertainties embedded within the modeling of a system and a numerical diagnostic is summarized that is useful for determining if a key assumption is verified among competing choices of stochastic maps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: N -body mock challenge for the quasar sample

ABSTRACT The growth rate and expansion history of the Universe can be measured from large galaxy redshift surveys using the Alcock–Paczynski effect. We validate the Redshift Space Distortion models used in the final analysis of the Sloan Digital Sky Survey (SDSS) extended Baryon Oscillation Spectroscopic Survey (eBOSS) Data Release 16 quasar clustering sample, in configuration and Fourier space, using a series of halo occupation distribution mock catalogues generated using the OuterRim N-body simulation. We test three models on a series of non-blind mocks, in the OuterRim cosmology, and blind mocks, which have been rescaled to new cosmologies, and investigate the effects of redshift smearing and catastrophic redshifts. We find that for the non-blind mocks, the models are able to recover fσ8 to within 3 per cent and α∥ and α⊥ to within 1 per cent. The scatter in the measurements is larger for the blind mocks, due to the assumption of an incorrect fiducial cosmology. From this mock challenge, we find that all three models perform well, with similar systematic errors on fσ8, α∥, and α⊥ at the level of $\sigma _{f\sigma _8}=0.013$, $\sigma _{\alpha _\parallel }=0.012$, and $\sigma _{\alpha _\bot }=0.008$. The systematic error on the combined consensus is $\sigma _{f\sigma _8}=0.011$, $\sigma _{\alpha _\parallel }=0.008$, and $\sigma _{\alpha _\bot }=0.005$, which is used in the final DR16 analysis. For baryon acoustic oscillation fits in configuration and Fourier space, we take conservative systematic errors of $\sigma _{\alpha _\parallel }=0.010$ and $\sigma _{\alpha _\bot }=0.007$.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Demonstration of a Novel Phase-Space Painting Method in a Coupled Lattice to Mitigate Space Charge in High-Intensity Hadron Beams

Multiturn charge-exchange injection is the primary method of creating high-intensity hadron beams in circular accelerators, and phase space painting during injection enables tailoring of the accumulated phase space distribution. A technique we call eigenpainting allows injection of particles into a single mode of a coupled ring, providing full four-dimensional control of the phase space distribution. Under ideal conditions, uniform eigenpainting generates a linear-force equilibrium distribution in the transverse plane, with zero volume in four-dimensional transverse phase space, even including space charge. Here, we have implemented eigenpainting for the first time in the spallation neutron source accumulator ring. Injecting 8.8 μ⁢C of an 800 MeV beam, we obtain a final ratio of intrinsic transverse emittances of ≈2.4. We analyze the effect of space charge on the final distribution through comparison of the reconstructed phase space to particle-in-cell simulations.

Evans, Nicholas J. [Oak Ridge National Laboratory ↗
DOE OSTI2020

Constraining physical models at gigabar pressures

High-energy-density (HED) experiments in convergent geometry are able to test physical models at pressures beyond hundreds of millions of atmospheres. The measurements from these experiments are generally highly integrated and require unique analysis techniques to procure quantitative infor mation. This work describes a methodology to constrain the physics in convergent HED experiments by adapting the methods common to many other fields of physics. As an example, a mechanical model of an imploding shell is constrained by data from a thin-shelled direct-drive exploding-pusher experiment on the OMEGA Laser System using Bayesian inference, resulting in the reconstruction of the shell dynamics and energy transfer during the implosion. The model is tested by analyzing synthetic data from a 1-D hydrodynamics code and is sampled using a Markov chain Monte Carlo to generate the posterior distributions of the model parameters. As a result, the goal of this work is to demonstrate a general methodology that can be used to draw conclusions from a wide variety of HED experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Modular Quantum Processor with an All-to-All Reconfigurable Router

Superconducting qubits provide a promising approach to large-scale fault-tolerant quantum computing. However, qubit connectivity on a planar surface is typically restricted to only a few neighboring qubits. Achieving longer-range and more flexible connectivity, which is particularly appealing in light of recent developments in error-correcting codes, however, usually involves complex multilayer packaging and external cabling, which is resource intensive and can impose fidelity limitations. Here, we propose and realize a high-speed on-chip quantum processor that supports reconfigurable all-to-all coupling with a large on-off ratio. We implement the design in a four-node quantum processor, built with a modular design comprising a wiring substrate coupled to two separate qubit-bearing substrates, each including two single-qubit nodes. We use this device to demonstrate reconfigurable controlled-𝑍 gates across all qubit pairs, with a benchmarked average fidelity of 96.00% ± 0.08% and best fidelity of 97.14% ± 0.07%, limited mainly by dephasing in the qubits. We also generate multiqubit entanglement, distributed across the separate modules, demonstrating GHZ-3 and GHZ-4 states with fidelities of 88.15% ± 0.24% and 75.18% ± 0.11%, respectively. This approach promises efficient scaling to larger-scale quantum circuits and offers a pathway for implementing quantum algorithms and error-correction schemes that benefit from enhanced qubit connectivity.

Quantum circuits↗
DOE OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Assessing Machine Learning as a Tool to Explain Variance in Deployed Photovoltaic (PV) System Degradation

Degradation remains a large uncertainty in forecasting production for PV plants, creating significant risk for developers and financiers. This study aims to quantify the distribution and drivers of degradation across 10,000 PV systems deployed for distributed or utility generation by training a machine learning model to predict year-over-year degradation rates from metadata characteristics. A combination of K-Means clustering and random forest regressor were found to associate multiple metadata features as potential drivers of degradation, including module characteristics, system design, and climate features. From this, it is inferred that if machine learning is able to find complex patterns between metadata features and system performance loss, such methods can be employed to help developers and financiers make data-informed decisions when estimating long-term energy production forecasts in financial models.

Dunn, Jimmy C.↗
DOE OSTI2021

Tractable Learning in Underexcited Power Grids

Estimating the structure of physical flow networks, such as power grids, is critical to secure delivery of energy. This article discusses statistical structure estimation in power grids in the “underexcited” regime, where a subset of internal nodes has zero injection fluctuations. Prior estimation algorithms based on nodal voltages fail for such grids as the voltage covariance matrix is not invertible. Here, we propose a novel topology learning algorithm for learning underexcited general networks. Our algorithm uses physics-informed conservation laws to first identify the zero-injection buses and their neighbors, and then estimates the remaining edges in the grid. We prove the asymptotic correctness of our algorithm for grids with nonadjacent internal zero-injection nodes. More important, we theoretically analyze our algorithm’s efficacy under noisy measurements, and determine bounds on maximum noise under which asymptotically correct recovery is guaranteed. Our approach is validated through simulations with voltage samples generated on test distribution grids with real injection data and nonlinear power flow models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Peer-to-Peer Energy Trading under Network Constraints Based on Generalized Fast Dual Ascent

We report the wide deployment of renewable energy resources, combined with a more proactive demand-side management, is inducing a new paradigm in both power system operation and electricity market trading, which especially boosts the emergence of the peer-to-peer (P2P) market. A more flexible local market mechanism is highly desirable in response to fast changes in renewable power generation at the distribution network level. Moreover, large-scale implementation of P2P energy trading inevitably affects the secure and economic operation of the distribution network. This paper presents a new P2P electricity trading framework with distribution network security constraints considered using the generalized fast dual ascent method. First, an event-driven local P2P market framework is presented to facilitate short-term or immediate local energy transactions. Then, the sensitivity analysis of nodal voltage and network loss with respect to nodal power injections is used to evaluate the impacts of P2P transactions on the distribution network, which ensures the secure operation of the distribution system. Thereby, the external operational constraints are internalized, and the cost of P2P energy trading can be appropriately allocated in an endogenous way. Moreover, a generalized fast dual ascent method is employed to implement distributed market-clearing efficiently. Finally, numerical results indicate that the proposed model could guarantee secure operation of the distribution system with P2P energy trading, and the solution method enjoys good convergence performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Guest Editorial: Special Section on Sustainable Energy for Enhancing Grid Resiliency

Extreme weather threatens lives, disables communities, and devastates energy generation, transmission, and distribution systems. These extreme events are likely to become more frequent or more intense due to climate change. Energy networks have shown significant vulnerability during record hurricanes, deadly heat waves, destructive wildfires, and winter storms in the past few years. Because of the energy transition process, modern power grids will feature a high penetration level of the use of renewable resources. The adoption of renewable energy and the rise of omnidirectional power delivery mean that our energy network is more decentralized than ever. In this new environment, grid operation becomes more complex, and achieving resiliency is more difficult than in the past. Many utilities are seeking the latest technologies to improve energy security and responsiveness to severe events. On the one hand, sustainable energy resources can provide emergency power and assist grid restoration in disastrous events. On the other hand, their volatility, susceptibility, interdependency, and other unique features must be carefully considered when being used for grid resilience enhancement. This special section brings together 22 papers that range from innovative research advances tackling fundamental challenges of achieving more resilient grids to real-world demonstrations of leveraging sustainable energy resources to enhance grid resiliency. These papers can be categorized into three groups.

climate change↗
DOE OSTI2020

Evidence for anti-synergism between ion-assisted etching and in-plasma photoassisted etching of silicon in a high-density chlorine plasma

Etching of p-Si in 60 mTorr 10%Cl2/90%Ar Faraday-shielded inductively coupled high density plasmas was investigated under both ionassisted etching (IAE) and photoassisted etching (PAE) conditions. Real-time etching rates and after-etching Si surface chemical compositions were obtained by laser interferometry and vacuum-transfer x-ray photoelectron spectroscopy (XPS), respectively. Precisely controlled ion energy distributions (IEDs) were generated by applying pulsed negative DC bias on the conductive sample stage. Above a 36 eV threshold at a total flow rate of 250 SCCM, the IAE rate increased with the square root of the ion energy. In contrast to the DC bias, etching under RF bias did not exhibit a threshold ion energy because of the wide IED. XPS spectra revealed that the surface layer under PAE conditions had a significantly lower chlorine content, composed of only SiCl. Under IAE conditions, however, silicon dangling bonds (Si•), SiCl 2 , and SiCl 3 were found on the surface, in addition to SiCl, with a relative abundance of SiCl > SiCl 2 , and SiCl 3 . The absence of higher chlorides and Si• under PAE conditions suggested that vacuum ultraviolet photons and above threshold-energy ions interact with the surface very differently. By varying the duty cycle of the pulsed DC bias, it was found that the IAE rate scaled with the energetic ion dose, but only for low duty cycles. For higher duty cycles, the apparent IAE yield fell off with an increasing Cl coverage on the surface, as the duty cycle went up, which pointed to a negative synergy (antisynergism) between PAE and IAE as the explanation. This antisynergism was further supported by the observed decrease of the total etching rate with an increasing period of the pulsed DC bias. A plausible mechanism is that increasing the pulsing period causes more near-surface damage, creating more recombination centers that lead to a higher loss rate of electron-hole pairs through recombination, thereby reducing the PAE rate.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Study of self-assembly behavior and ionic conductivity of conjugated liquid crystals with T-shaped facial-polyphilic structure

The unique self-assembly of liquid crystals (LCs), combined with their potential application as organic semiconductors, has become a focus of recent research. Here, a joint experimental and computational study of the self-assembly and ionic conduction was carried out on a series of T-shaped conjugated LCs consisting of three incompatible components. By extending the EOn side-chain length, several experimental evaluations confirmed a decrease of the order-disorder transition temperature, while coarse-grained simulations revealed a structural evolution from a smectic phase to a columnar phase. Ionic conductivity of these molecules was achieved by adding Li salt, leading to a maximum conductivity of 1.1 × 10 −3 siemens per centimeter observed at 120°C. All-atom simulations were performed to examine the Li-ion solvation environment and to evaluate the intrachain and interchain Li-ion hopping mechanisms. The molecule with a long EOn side chain was found to generate a densely distributed network of Li-ion solvation sites, which can facilitate effective interchain hopping to promote ion transport.

Liu, Ziwei [Cornell Univ., Ithaca, NY (United Stat↗
DOE OSTI2025

Design of an Out-Of-Pile Experimental Facility to Demonstrate the Feasibility of In Situ Thermal Conductivity Measurements of Nuclear Fuels Under Irradiation

There is substantial merit in quantifying nuclear fuel performance under irradiation. At Oak Ridge National Laboratory (ORNL), the MiniFuel irradiation platform has become the primary test vehicle for conducting separate-effects fuel performance irradiation experiments. The MiniFuel experiment is a passively controlled capsule design deployed in the High Flux Isotope Reactor (HFIR) through which fuel performance data is collected post-irradiation. Separate effects fuels irradiation capabilities are being expanded at ORNL by developing instrumented capsule designs that aim to capture fuel performance phenomena in-situ. One such capsule will specifically target fuel specimen thermal conductivity changes as a function of fuel burnup. Due to the complexity of making this measurement on nuclear fuel in-pile, this paper describes the necessary out-of-pile testing conducted on the thermal conductivity capsule (TCC) design. The measurement is ascertained via a thermopile system with heat transferred unidirectionally through a surrogate fuel specimen sandwiched between two conductive materials. The capsules investigated in this study are representative of the in-pile design, with the primary departure from irradiation conditions being the distribution of heat generation within the capsule. In the out-of-pile experiment, an external heater was used to drive heat through the conductive slug materials and into the specimen. This paper expounds the design of the out-of-pile experimental system and the thermal conductivity measurement technique. Predictive models used to determine the sensitivity of the measurement to variables governing thermal contact conductance between the specimen and slug materials and to predict experimental results are also described. Data from the out-of-pile experiment will be used to validate the readiness of the design for insertion into HFIR for irradiation.

Parker, Trevor [ORNL]↗
DOE OSTI2020

Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks

Abstract We show that deep generative neural networks, based on global optimization networks (GLOnets), can be configured to perform the multiobjective and categorical global optimization of photonic devices. A residual network scheme enables GLOnets to evolve from a deep architecture, which is required to properly search the full design space early in the optimization process, to a shallow network that generates a narrow distribution of globally optimal devices. As a proof-of-concept demonstration, we adapt our method to design thin-film stacks consisting of multiple material types. Benchmarks with known globally optimized antireflection structures indicate that GLOnets can find the global optimum with orders of magnitude faster speeds compared to conventional algorithms. We also demonstrate the utility of our method in complex design tasks with its application to incandescent light filters. These results indicate that advanced concepts in deep learning can push the capabilities of inverse design algorithms for photonics.

Jiang, Jiaqi↗
DOE OSTI2026

The 1990 San Francisco Bay Area Metropolitan Transportation Commission Household Survey

The San Francisco Bay Area's Metropolitan Transportation Commission (MTC) and the regional transportation planning agency for the nine-county Bay Area conducted the survey. The basic purpose for the survey was for the development of travel demand models to simulate the various components of travel behavior: auto ownership, trip frequency (trip generation), and the distribution of trips between origins and destinations by travel mode. Planners at MTC envisioned the 1990 survey as the first wave of a multi-wave household panel survey designed to capture both short- and long-term changes in regional travel behavior. The 1990 survey collected single-weekday travel data from nearly 9,900 Bay Area households and multiple-weekday travel data from nearly 1,500 households. The 1990 survey effort also included a separate sub-project, funded by the Bay Area Rapid Transit (BART) District, to collect multiple-weekday travel data from 1,000 BART-using households. The BART survey and the MTC multiple-weekday survey were completed in the spring of 1990; the MTC single-weekday survey was continued and completed during the autumn of 1990.

1Hz data↗