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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Analysis of buffer-IPyC separation in TRISO fuel particles

During High Temperature Gas-cooled Reactor (HTGR) operation, and due to the neutron irradiation in the reactor core, damage of the nuclear fuel coating layers occurs. The mechanism of damage formation in the TRISO fuel is explored by the Advanced Gas Reactor (AGR) Fuel Development and Qualification Program, in which the debonding process between coating layers was also investigated. The purpose of this paper is to report simulation results for two models. Firstly, the debonding restricted model, where no gap formation between buffer and IPyC layers is permitted. Secondly, a debonding enabled model, where the gap between those layers is created. The simulations were performed with the Bison code. The inputs of the simulated models are based on data from the AGR-1 experiment. Further, the research included simulations on spherical and aspherical fuel types. The simulations match results obtained by the AGR-1 experiment, which as such shows that the Bison code is a good computational method for simulating the behavior of the gap between buffer and IPyC layers in TRISO fuel. Based on the irradiation experiments, and the Bison simulations, it was concluded that the most common scenario is a gap formation along the buffer-IPyC interface, while the least possible scenario is the situation where there is no gap formation at the buffer-IPyC junction. The computational results confirmed that the sphericity of the fuel influences the thickness of the gap that occurs at the buffer-IPyC junction, in a way that with increasing aspect ratio the gap thickness increases. In addition, the fuel sphericity does not influence the Weibull failure probability. The results obtained for spherical and aspherical fuel are nearly identical.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Generalizing Reversible Triplet–Triplet Energy Transfer as a Strategy to Increase Excited-State Lifetime in Copper(I) Charge-Transfer Chromophores

In this work, we generalize reversible triplet–triplet energy transfer (TTET) as a strategy to achieve long (μs range) excited-state lifetimes in copper(I) photosensitizers of the general formula Cu(RNacNac)(CN-Ar) (RNacNac is a substituted β-diketiminate and CN-Ar is an aryl isocyanide). Six new complexes are presented where the isocyanide is substituted with a triplet–acceptor moiety─anthracene, naphthalene, or phenanthrene. In-depth photophysical analysis reveals that energetic matching of the charge-transfer ( 3 CT) and acceptor-localized 3 (π→π*) states is key to achieving long triplet excited-state lifetimes via reversible TTET. With 2-isocyanoanthracene as the acceptor ligand, 3 CT and 3(π→π*) are not energetically proximate, and irreversible quenching of 3 CT occurs. With a small 0.1–0.2 eV gap between the two triplet states, luminescence originates from the 3 CT state with a moderate excited-state lifetime of 250 or 283 ns observed. As key to the success of this approach, we demonstrate that altering the N-substituent on the RNacNac ligand modulates the energy of the 3 CT state, giving three complexes where the 3 CT and 3 (π→π*) energies are perfectly matched and the excited-state lifetimes are much longer, spanning 1.9–2.7 μs. As such, the key lesson of this work is that long triplet-state lifetimes in these compounds can be achieved by proper “mixing and matching” of the RNacNac ligand, which influences the 3 CT energy, and the triplet acceptor group which dictates the energy of the 3 (π→π*) state.

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

Theoretical study of the Alfven eigenmode stability in CFETR steady state discharges

The aim of this study is to analyze the stability of Alfven eigenmodes (AE) in the China Fusion Engineering Test Reactor (CFETR) plasma for steady state operations. The analysis is done using the gyro-fluid code FAR3d including the effect of the acoustic modes, EP finite Larmor radius damping effects and multiple energetic particle populations. Two high poloidal β scenarios are studied with respect to the location of the internal transport barrier (ITB) at r/a ≈ 0.45 (case A) and r/a ≈ 0.6 (case B). Both operation scenarios show a narrow TAE gap between the inner-middle plasma region and a wide EAE gap all along the plasma radius. In this work, the AE stability of CFETR plasmas improves if the ITB is located inwards, case A, showing AEs with lower growth rates with respect to the case B. The AEs growth rate is smaller in the case A because the modes are located in the inner-middle plasma region where the stabilizing effect of the magnetic shear is stronger with respect to the case B. Multiple EP populations effects (NBI driven EP + alpha articles) are negligible for the case A, although the simulations for the case B show a stabilizing effect of the NBI EP on the n = 1 BAE caused by α particles during the thermalization process. If the FLR damping effects are included in the simulations, the growth rate of the EAE/NAE decreases up to 70%, particularly for n > 3 toroidal families. Low n AEs (n < 6) show the largest growth rates. On the other hand, high n modes (n = 6 to 15) are triggered in the frequency range of the NAE, strongly damped by the FLR effects.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Topological band and superconductivity in UTe 2

UTe 2 is a likely spin-triplet superconductor that also exhibits evidence for chiral Majorana edge states. A characteristic structural feature of UTe 2 is inversion-symmetry related pairs of U atoms, forming rungs of ladders. Here we show how each rung's two sublattice degrees of freedom play a key role in understanding the electronic structure and the origin of superconductivity. In particular, DFT + U calculations generically reveal a topological band near the chemical potential originating from a band inversion associated with 5f electrons residing on these rungs, necessitating a microscopic description that includes these rung degrees of freedom. Furthermore, we show that a previously identified strong ferromagnetic interaction within a U-U rung leads to a pseudospin-triplet superconducting state that accounts for a nonzero polar Kerr angle, the observed magnetic field-temperature phase diagrams, and nodal Weyl fermions. Finally, our analysis may also be relevant for other U-based superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Missing-Data Nonparametric Coherency Estimation

Chave recently proposed an estimator for multitaper spectral density where the time series contains missing values. In this article we generalize this technique to a multitaper estimator of coherence and phase and show that one can also obtain bootstrapped confidence intervals. Additionally, we give two examples. The first is a toy example in which the true coherence is known. In the second example we show that the multitaper missing-data coherence estimator computed on real data with a single gap comprising 11% of the data outperforms the Daniell-smoothed coherence estimator where there are no gaps. The case where the two time series have different missing indices is also discussed.

42 ENGINEERING↗
DOE OSTI2024

Quantifying Uncertainty in HPC Job Queue Time Predictions

High Performance Computing (HPC) has developed at an unprecedented pace in recent decades. This growth has demanded corresponding development in the area of HPC Operational Data Analytics (ODA), which encompasses a wide range of data analysis techniques, ML/AI efforts, tools, and visualizations. Published studies in ODA offer a variety of practical ways to inform HPC users, administrators, procurement managers, and other stakeholders. Uncertainty analysis, however, is rare in the related published literature. For instance, we identify only 1 out of 14 existing studies focused on job queue time prediction that investigates the uncertainty aspect of their proposed predictions. We recognize the utmost importance uncertainty quantification can have in such predictive analytics solutions, with consequences in how users interpret information they receive, and attempt to bridge this gap. With the goal of improving access to such insights, we develop a process for determining upper and lower bounds of the predicted queue times of a regression model at a specified confidence level. Our current research is focused on the uncertainty in predicting job queue times, yet our approach may be employed in predicting other metrics.

HPC↗
DOE OSTI2025

RectifHydPlus: Forty Year Hydropower Generation Reanalysis for Conterminous United States, Version 1.1.

This dataset contains monthly hydropower net-generation totals for 590 plants (each >10 MW) across the conterminous United States (CONUS) from 1980 to 2019. RectifHydPlus v1.1 includes one harmonized table of historical monthly generation—backfilled with observed monthly values where available—and two companion tables: (i) an estimates-only version with no backfill and (ii) a hydrological-control version that removes the effects of capacity and operational change. Each table comprises 23,600 records (590 plants × 40 years). The dataset was developed to address temporal gaps and inconsistencies in publicly available hydropower generation data as available through EIA-923 survey reports. Each record includes a quality label denoting the underlying proxy—from best (direct reservoir releases) to weakest (pattern copied from similar years). By combining the agency-reported survey records with observed and simulated hydrologic releases, RectifHydPlus offers complete, quality-labeled monthly estimates suitable for trend analysis and generation of hydropower generation inputs for energy-water modeling.

Turner, Sean [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2024

Control, Fault Management, and Grid Support Functionality of an MV AC-DC Solid State Transformer based EV Extreme Fast Charging Station

Electric vehicles (EVs) have become increasingly popular in recent times while revolutionizing the consumer and commercial transportation market. The development of charging infrastructure has become one of the priorities for increasing the adoption of EVs. Extreme fast charging (XFC) technology can reduce the so-called ’range anxiety’ of consumers as they significantly reduce the charging time. With the advent of wide band-gap (WBG) power devices and improvement in power electronic converters, medium voltage (MV) solid state transformer (SST) based XFC system has the potential to replace the traditional XFC stations because of the lower footprint, ease of installation, enhanced control feature, and better system efficiency. The control system design is one of the critical aspects of the SST development process. Careful consideration and detailed analysis are required to find out suitable control method for the SST based on its topology among different centralized and decentralized control architectures. Also, the control parameters selection and potential improvement to the transient response of the controller ought to be investigated. Another major concern of the SST is different types of internal fault which reduces the overall reliability of the XFC system. As a result, designing a robust protection system is essential. Among different fault modes, open circuit switch faults have received significant attention as an active research area because of their likelihood and severe effects on converters. Therefore, the power stages used in the XFC system require functional and accurate open circuit switch fault management methods. An equally significant aspect of this SST based XFC is its compatibility in a microgrid where there is no synchronous generator present. When the grid is not available, the XFC SSTs can provide grid forming capability and continue supplying the critical loads in islanded mode. The transition between grid connected and islanded mode, especially the grid resynchronization process has to be carefully performed for the safety of the microgrid components. The challenges posed by the aforementioned issues have inspired the work done in this dissertation. Here, a 13.2 kV, 1 MVA, AC/DC SST for the XFC system is examined and a comparative analysis is conducted to select the control architecture based on feasibility of implementation and performance. A detailed control parameter design process is demonstrated considering the sensor dynamics and delay. The selected decentralized control method is augmented by introducing a novel sensor-less load current feedforward method to provide better voltage regulation at the DC bus during a change of load. Next, in the fault management section, a hierarchical failure mode effect analysis (FMEA) is proposed to enable a systematic design of the internal fault protection of the XFC SST as there are limited examples in the literature regarding the analysis of the safety and design of the protection of a power electronic converter system. Novel open circuit switch fault management methods for the converters in the system are presented. Finally, XFC SST based MV microgrid operations in grid connected mode and islanded mode are explored. A secondary control method for grid resynchronization is presented and a design process of control parameters is shown to ensure the stability of the secondary voltage and frequency regulation.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Exploring few and single layer CrPS 4 with near-field infrared spectroscopy

Abstract We combine synchrotron-based near-field infrared spectroscopy and first principles lattice dynamics calculations to explore the vibrational response of CrPS 4 in bulk, few-, and single-layer form. Analysis of the mode pattern reveals a C 2 polar + chiral space group, no symmetry crossover as a function of layer number, and a series of non-monotonic frequency shifts in which modes with significant intralayer character harden on approach to the ultra-thin limit whereas those containing interlayer motion or more complicated displacement patterns soften and show inflection points or steps. This is different from MnPS 3 where phonons shift as 1/size 2 and are sensitive to the three-fold rotation about the metal center that drives the symmetry crossover. We discuss these differences as well as implications for properties such as electric polarization in terms of presence or absence of the P–P dimer and other aspects of local structure, sheet density, and size of the van der Waals gap.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impact of battery cell imbalance on electric vehicle range

Due to manufacturing variation, battery cells often possess heterogeneous characteristics, leading to battery state-of-charge variation in real-time. Since the lowest cell state-of-charge determines the useful life of battery pack, such variation can negatively impact the battery performance and electric vehicles range. Existing research has been focused on control design to mitigate cell imbalance. However, it is yet unclear how much impacts the cell imbalance can have on electric vehicle range. This paper closes this knowledge gap by using a simulation environment consisting of real-world driving speed data, vehicle longitudinal control, propulsion and vehicle dynamics, and cell level battery modeling. In particular, each battery cell is modeled as an equivalent circuit model, and variations among cell parameters are introduced to assess their impact on electric vehicles range and to identify the most influential parameter variations. Simulation results and analysis can be used to assist balancing control design and to benchmark control performance.

25 ENERGY STORAGE↗
DOE OSTI2025

Symmetry progression and band vs Mott character of CdPS 3 under pressure

Complex chalcogenides are renowned for their tunable electronic, magnetic, and optical properties under external stimuli. The MPX 3 family (M = Mn, Ni, Co, V; X = S, Se) is a platform for many exciting discoveries—especially under compression—although CdPS3 is thought to be different because the Cd center possesses a filled 4d shell, which precludes Mottness. Here, we combine synchrotron-based infrared absorbance and Raman scattering spectroscopies with diamond anvil cell techniques, complementary lattice dynamics calculations, and an analysis of the energy landscape to reveal a series of structural phase transitions in CdPS 3 . We find four distinct pressure-driven transitions, with low frequency modes detectable over the full 35 GPa range of our investigation. A group–subgroup analysis along with our first-principles calculations allows us to partially unravel the space group sequence. For instance, the first critical pressure is a monoclinic C2/m to trigonal $\overline{R}$3 transition at 10 GPa. Despite the softness and overall sensitivity to pressure, we do not locate an insulator-to-metal transition in this pressure range, indicating that the energy scale for gap closure is significantly higher than expected. We discuss these findings in terms of force-induced color change and Mott vs band character in this system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Assessment of the Impact of the Single Stream Recycling on Paper Contamination in Recovery Facilities and Paper Mills

Contaminated recyclables pose one of the largest roadblocks to an effective implementation of a circular economy system. Current knowledge surrounding the effects of various recycling methods on contamination rates is limited, thus preventing effective quantification of contamination effects. This study aims to fill this knowledge gap by assessing the impact of single stream recycling on paper contamination in MRFs and their end-products based on different composition studies conducted by facilities nationwide. This project expects to provide the foundational knowledge that could catalyze significant cost savings and energy consumption at MRFs and paper mills by understanding the levels of inbound and outbound contamination rates in SSR systems. Statistical analysis conducted in this project utilizes the available nationwide inbound and outbound samples collected as part of material composition and audit studies to identify and measure the significance of various factors on the resultant contamination levels. The research results provide benefits to both the recycling industry and its customers by addressing the causes and impact of contamination in recycled products, particularly with the wide applicability of single stream recycling within the United States.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Smart-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

The explosion of new ideas for distributed energy resources and advanced distribution grid algorithms faces a critical challenge: how can one effectively develop and evaluate these breakthrough technologies without an adequate set of power systems network, load, and resource data to test against? Today's open test systems, particularly for distribution systems, are tiny and incomplete; and though with enough effort, obtaining utility data under NDA can overcome scale challenges, the results can't be openly shared, preventing fair comparison. The Smart-DS project-led by NREL in partnership with MIT, Universidad Pontificia Comillas, CYME, and EDD-is working to overcome this gap by generating multiple large-scale, open, synthetic, distribution systems that are realistic but not real. These datasets go beyond a single medium voltage feeder (~1000 customers) to cover entire metropolitan areas and their surrounds with up to hundreds of feeders and millions of customers, complete with low voltage customer connections and high voltage sub-transmission. This scale allows adequate testing and analysis of not just local controls and advanced DERs, but also multiple substation interactions, optimized switching, full-scale distribution-OPF, and more. To accompany these datasets, the project is also building a rich set of scenario generation tools that can be used for distribution and transmission systems alike. This includes automated, spatially aware access to world-class, high-resolution solar, wind, and weather data: forward looking generation mixes: and highly configurable DER, load, climate, outage, control-scheme, and other scenarios. We will also highlight DiTTo, an exciting open-source, multi-way distribution dataset transformation tool for many-to-many format translation with scenario, merge/split, and many other manipulation capabilities.

DERs↗
DOE OSTI2024

Verification, Validation, and Calibration Through a Causal Lens

While typical validation and verification approaches focus on identifying the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on identifying causal relationships between data elements. Statistical and machine-learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between data sets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify, and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles, it is known as a directed acyclic graph. A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts can identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

VALIDATION, VERIFICATION, AND CALIBRATION THROUGH A CAUSAL LENS

This paper presents an alternative method based on causal inference to perform validation, verification, and calibration of simulation models. While classical validation and verification approaches focus on the identification of the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on the identification of causal relationships between data elements. Statistical and machine learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between datasets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, then the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles it is known as a directed acyclic graph (DAG). A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and from experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts have a means to identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Supporting U.S. National Security Through Cybersecurity Partnerships

At NLR, we're studying energy evolutions and threats to understand the challenges they pose and uncover ways to leverage grid advancements to achieve more secure, defensible, and reliable systems. Our integrated research approach bridges the gap between cyber threats and real-world consequences to deliver actionable solutions that reduce vulnerabilities and help strengthen U.S. national security.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

The Markov gap for geometric reflected entropy

The reflected entropy S R (A : B) of a density matrix ρ AB is a bipartite correlation measure lower-bounded by the quantum mutual information I(A : B). In holographic states satisfying the quantum extremal surface formula, where the reflected entropy is related to the area of the entanglement wedge cross-section, there is often an order-N 2 gap between S R and I. We provide an information-theoretic interpretation of this gap by observing that S R - I is related to the fidelity of a particular Markov recovery problem that is impossible in any state whose entanglement wedge cross-section has a nonempty boundary; for this reason, we call the quantity S R - I the Markov gap. We then prove that for time-symmetric states in pure AdS 3 gravity, the Markov gap is universally lower bounded by log(2)ℓ AdS /2G N times the number of endpoints of the cross-section. We provide evidence that this lower bound continues to hold in the presence of bulk matter, and comment on how it might generalize above three bulk dimensions. Finally, we explore the Markov recovery problem controlling S R - I using fixed area states. This analysis involves deriving a formula for the quantum fidelity — in fact, for all the sandwiched Rényi relative entropies — between fixed area states with one versus two fixed areas, which may be of independent interest. We discuss, throughout the paper, connections to the general theory of multipartite entanglement in holography.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Molecular Dynamics Investigation of Nanoscale Hydrophobicity of Polymer Surfaces: What Makes Water Wet?

The wettability of a polymer surface–related to its hydrophobicity or tendency to repel water–can be crucial for determining its utility, such as for a coating or a purification membrane. While wettability is commonly associated with the macroscopic measurement of a contact angle between surface, water, and air, the molecular physics that underlie these macroscopic observations are not fully known, and anticipating relative behavior of different polymers is challenging. To address this gap in molecular-level understanding, we use molecular dynamics simulations to investigate and contrast interactions of water with six chemically distinct polymers: polytetrafluoroethylene, polyethylene, polyvinyl chloride, poly(methyl methacrylate), Nylon 66, and polyvinyl alcohol. We show that several prospective quantitative metrics for hydrophobicity agree well with experimental contact angles. Moreover, the behavior of water in proximity to these polymer surfaces can be distinguished with analysis of interfacial water dynamics, extent of hydrogen bonding, and molecular orientation–even when macroscopic measures of hydrophobicity are similar. The predominant factor dictating wettability is found to be the extent of hydrogen bonding between polymer and water, but the precise manifestation of hydrogen bonding and its impact on surface-water structure varies. In the absence of hydrogen bonding, other molecular interactions and polymer mechanics control hydrophobic ordering. Furthermore, these results provide new insights into how polymer chemistry specifically impacts water-polymer interactions and translates to surface hydrophobicity. Such factors may facilitate the design or processing of polymer surfaces to achieve targeted wetting behavior, and presented analyses can be useful in studying the interfacial physics of other systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗