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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 OSTI2026

Isospin dependence of the nuclear EMC effect from a global QCD analysis

We perform a new global QCD analysis of unpolarized parton distribution functions (PDFs) in the nucleon from proton, deuteron, and A = 3 data, including recent measurements of He 3 / D and H 3 / D cross section ratios from the MARATHON experiment at Jefferson Lab. Simultaneously inferring the PDFs and nucleon off-shell corrections allows both to be determined consistently, without theoretical assumptions about the isospin dependence of nuclear effects. The analysis provides strong evidence for the need of nucleon off-shell corrections to describe the A = 3 data, with large isoscalar and a suggestion of nonzero isovector contributions in A ≤ 3 nuclei. We find that the extracted EMC ratios of nuclear to nucleon structure functions for A = 2 and 3 differ from those naively extrapolated from heavy nuclei down to low A .

Cocuzza, C. [William & Mary] (ORCID:00000003492292↗

DOE OSTI2025

Dark Matter Velocity Distributions for Direct Detection: Astrophysical Uncertainties Are Smaller Than They Appear

The sensitivity of direct detection experiments depends on the phase-space distribution of dark matter near the Sun, which can be modeled theoretically using cosmological hydrodynamical simulations of Milky Way–like galaxies. However, capturing the halo-to-halo variation in the local dark matter speeds—a necessary step for quantifying the astrophysical uncertainties that feed into experimental results—requires a sufficiently large sample of simulated galaxies, which has been a challenge. In this Letter, we quantify this variation with nearly 100 Milky Way–like galaxies from the tng50 simulation, the largest sample to date at this resolution. Moreover, we introduce a novel phase-space scaling procedure that endows every system with a reference frame that accurately reproduces the local standard-of-rest speed of our Galaxy, providing a principled way of extrapolating the simulation results to real-world data. The ensemble of predicted speed distributions is well characterized by the standard halo model, a Maxwell-Boltzmann distribution truncated at the escape speed, though the individual distributions can deviate from it, especially at high speeds. The dark matter–nucleon cross section limits placed by these speed distributions vary by ∼ 60% about the median. This places the 1⁢𝜎 astrophysical uncertainty at or below the level of the systematic uncertainty of current ton-scale detectors, even down to the energy threshold. The predicted uncertainty remains unchanged when subselecting on those TNG 50 galaxies with merger histories similar to the Milky Way. Tabulated speed distributions, as well as Maxwell-Boltzmann fits, are provided for use in computing direct detection bounds or projecting sensitivities.

Milky Way↗

DOE OSTI2026

Large-scale white noise and cosmology

The generation of white noise on large scales is a generic property of the dynamics of physical systems described by local nonlinear partial differential equations. Nonlinearities prevent the small-scale dynamics from being erased by smoothing. Unresolved small-scale dynamics act as an uncorrelated (white or Poissonian) noise (seemingly stochastic but actually deterministic) contribution to large-scale dynamics. This white noise exists even when the dynamics is very nearly linear. In cases where the power spectrum is sub-Poissonian on large scales, this noise will dominate on the largest scale power no matter the amplitude of the inhomogeneities. Such is the case in the standard model of cosmology, where the primordial density power spectrum is expected to have an almost Harrison-Zel’dovich, P [ k ] ∼ k , spectrum on a much broader range of scales than can be observed. Even though linear gravitational evolution dominates nonlinear corrections by a factor of ∼ 10 5 , the nonobservation of white noise on the Hubble scale precludes the extrapolation of this power law below the comoving 1 pc scale. More generally, observation or nonobservation of large-scale white noise provides a powerful probe of the Universe on very small scales in the early Universe. Gravitational radiation, phase transitions, vorticity, and running of the spectral index are all phenomena that can be probed with large-scale white noise. Large-scale white noise is a nonoptional feature of all cosmological models but one which has not heretofore been appreciated.

Barenboim, Gabriela [Valencia U., IFIC; Valencia U↗

DOE OSTI2026

Carbon-nitrogen complex as an alternative to the 𝑇 center in Si

A first-principles study of a carbon-nitrogen (CN) impurity complex in silicon is presented as an isoelectronic alternative to the T center [(CCH) Si ]. The latter has been pursued for applications in quantum information science, yet its sensitivity to the presence of hydrogen is still problematic. Our proposed complex has no hydrogen, thereby eliminating this issue. First, we show that the CN complex is stable against decomposition into substitutional and interstitial defects. Next, we show that due to being isoelectronic to the T center, the CN complex has a similar electronic structure, and therefore could be used in similar applications. We assess several low-energy configurations of the CN complex, finding (CN) Si to be stable and have the largest Debye-Waller factor. We predict a zero-phonon line (ZPL) of 828 meV (in the telecom S-band) and a radiative lifetime of 4.2 μs, comparable to the T center. Due to the presence of a bound exciton, choice of the exchange-correlation functional and also supercell-size scaling of the ZPL and transition dipole moment require special scrutiny; we rigorously justify our extrapolation schemes that allow computing values in the dilute limit.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Resonant neutron scattering lengths

Unlike most of the periodic table, many rare-earth elements display considerable resonant scattering for thermal neutrons. Although this property is accompanied by strong neutron absorption, modern high-intensity neutron sources make diffraction experiments possible with these elements. Computation of scattering intensities is accomplished by fitting the variation in resonant scattering lengths ( b 0 , b ′ and b ′′) to a semi-empirical Breit–Wigner formalism, which can be evaluated over the range of neutron energies useful for diffraction, typically E = 10–600 meV; λ = 0.4–2.8 Å (with good extrapolation to longer wavelengths).

Von Dreele, Robert B. (ORCID:0000000253390634)↗

DOE OSTI2026

Analytical desmearing of Bonse–Hart ultra-small-angle neutron scattering data via truncated Abel inversion

A non-iterative analytical framework based on the truncated Abel inversion is developed for desmearing Bonse–Hart ultra-small-angle neutron scattering (USANS) data. The method directly inverts the slit-averaged intensity without empirical extrapolation or iterative regularization, establishing a closed-form relationship between the measured and intrinsic scattering profiles. Numerical benchmarks on representative models, including a rigid-line form factor, a Lorentzian function and a fractal structural model, demonstrate quantitative recovery of the ground-truth intensity across the full Q range. Application to a deuterated polystyrene/poly(2-vinylpyridine) blend further confirms that the approach yields smooth continuous profiles consistent with companion small-angle neutron scattering data. The truncated Abel inversion thus provides a stable, model-independent and physically transparent route for accurate desmearing of Bonse–Hart USANS measurements.

Huang, Guan-Rong [National Tsing Hua University, T↗

DOE OSTI2021

Intelligent Prediction of States in Multi-port Autonomous Reconfigurable Solar power plant (MARS)

In power electronics, prediction of states may be used for identification of faults, determination of aging of components, identification of bad data measurements, among others. Prediction of states in power electronics have broadly been based on: (a) physics-based models, (b) data-driven models, and (c) hybrid models. In this paper, data-driven approaches are presented for intelligent prediction of states in multi-port autonomous reconfigurable solar power plant (MARS) and compared. The data-set needed to train the data-driven models based on artificial intelligence (AI) algorithms has been identified and the trained models are evaluated under different extrapolated normal and abnormal operating conditions. The AI algorithms include nonlinear auto-regressive exogenous model (NARX), spiking neural networks (SNN), and decision tree. The models are compared and contrasted. The best model (NARX) is evaluated under different normal and abnormal operating conditions that have indicated accurate prediction.

Debnath, Suman↗

DOE OSTI2022

Reduced Switching Frequency Finite Control Set Model Predictive Control (FCS-MPC) for DFIG

An enhanced Finite Control Set Model Predictive Control (FCS-MPC) strategy with a two-step prediction horizon for a Doubly-Fed Induction Generator (DFIG) is the focus of this paper. The DFIG and RL filter's discrete-time model is used in the proposed control scheme for a two-step prediction horizon of rotor and filter currents for the converter's eight possible switching states. Afterward, the control algorithm selects the ideal switching state, which minimizes currents' objective function. The proposed control scheme does not require a modulation stage for internal controllers. We use Lagrange extrapolations to predict the dynamic changes in currents. The switching frequency reduction is achieved by integrating an optimization constraint in the algorithm's cost function. Furthermore, the Total Harmonic Distortion level of currents is kept below 5%, according to IEEE Std 519-14. Obtained results reveal that the switching frequency of the DFIG converters is considerably reduced without losing control; it is reduced by 13.89KHz for the Grid Side Converter (GSC) and by 1.78KHz for the Rotor Side Converter (RSC).

doubly-fed induction generator↗

DOE OSTI2021

Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients’ recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Adaptive Computing for Scale-Up Problems

Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocation adaptive to some objective such as optimization or model training. The framework enables the characterization and management of uncertainties associated with predictive models of complex systems when scale-up questions lead to significant model extrapolation. A key advancement of this framework is its integration of multi-fidelity surrogate modeling, uncertainty management, and automated orchestration of various computing and experimentation resources into a single integrated software package. This enables efficient multi-fidelity modeling across multiple computing resources by incorporating real-world constraints such as relative queue times and throughput on individual machines into the multi-fidelity sampling decision. We discuss applications of this framework to problems in the renewable energy space, including biofuels production, material synthesis, perovskite crystal growth, and building electrical loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Model-Free Probabilistic Forecasting of Nodal Voltages in Distribution Systems

As the penetration of distributed energy resources (DERs) into distribution systems increases, so does the interest in forecasting relevant system variables to help mitigate the associated challenges. One such challenge is the more frequent occurrence of excessive voltages in distribution systems with higher shares of DERs. Accurate and reliable estimates together with forecasts of system states (i.e., nodal voltages) will therefore play a key role in improving the utilization of these variable and uncertain sources while mitigating potential operational risks. Whilst recent literature has explored machine learning (ML) methods for voltage estimation and their extrapolation for a short-time period into the future, few have taken uncertainty quantification into account, and these methods have not yet been translated into operations. This paper discusses the advantages offered by probabilistic voltage forecasts and proposes a non-parametric Bayesian method suitable for forecasting nodal voltages at short-term time horizons while accounting for uncertainties in load and distributed photovoltaic (PV) generation. We demonstrate the value of the proposed Gaussian process (GP) model for a case study using historical forecasts and observation data.

distribution system↗

DOE OSTI2020

Insights into Na + Diffusion in Silicon Modules under Operating Conditions: Measuring Low Concentrations by D-SIMS

Sodium induced shunting under an electric field is a challenging reliability issue in crystalline Si solar modules. The source of this Potential-Induced Degradation of the Shunting type (PID-s) is well understood and its influence on power loss has been intensively studied based on phenomenological models on cell or module level relating the experimental power-loss to stressing parameters (time, temperature, voltage). However, little is known about the Na ion migration kinetics, responsible for PID on a microscopic level, and its quantitative relation to the efficiency degradation. In this paper we present our investigations of sodium ion migration in Ethylene-Vinyl Acetate (EVA) and silicon through Dynamic Secondary Ion Mass Spectroscopy (D-SIMS). Each sample was annealed at field relevant temperatures from 60-90 °C to address typical migration mechanisms of common PV installations. Analysis of the SIMS migration profiles revealed a diffusivity constant D 0,EVA = 0.09 ± 0.14 cm 2 /s and an activation energy E A,EVA = 0.85 ± .04 eV for Na in EVA and diffusivities higher than extrapolated literature values in silicon (D 0,Si = (3.03 ± 2.42)x10 -5 cm 2 /s, and E A,Si = 0.98 ± 0.02 eV). The new insight will be included in a drift-diffusion based degradation model accounting for the partition coefficient across all relevant interfaces. This model can assist in predicting PID-failure in the field based on the given model stack and the diffusion of Na + through each material. Furthermore, this tool can be used for process optimization as well as material selection significantly reducing the cost and time to validate a technology.

14 SOLAR ENERGY↗

DOE OSTI2023

A New Method for the Evaluation of Majority and Minority Carrier Contact Resistivity of Polysilicon on Oxide Contacts

In this contribution we present a novel technique for characterizing the majority and minority carrier contact resistivity in polysilicon on oxide passivating contacts. Traditionally, contact resistivity is measured through the Transfer Length Method (TLM). This method presents difficulties when measuring the minority carrier contact due to the presence of a blocking diode which prevents current flow between the pads. Our novel method is performed by creating a unipolar device which has the same contact on both sides (either symmetric n-type or symmetric p-type) and metalizing the device with a grid on one side, and a full metal contact on the back (the same pattern that will be used in a final cell). We then measure the resistance across the device at increasing illumination. Since the resistivity of the wafer is a function of carrier density, when increasing illumination, the wafer resistivity decreases. We can then fit modeling equations to the measured resistivity and extrapolate the fit to infinite illumination where only the contact resistivity is measured to accurately measure both the minority and majority carrier contact resistivity.

current measurement↗

DOE OSTI2022

Material Plasma Exposure eXperiment (MPEX) High Heat Flux Bellows for Component Alignment in High Microwave Environment

The Material Plasma Exposure eXperiment (MPEX) device is a linear plasma device developed to perform plasma material interaction experiments under the conditions prototypic of a fusion reactor divertor. MPEX has multiple systems that must be precisely aligned to the plasma axis, including an electron cyclotron heating system that emits up to 400 kW of microwave power into the vacuum vessel. Five distinct systems require precise alignment on the MPEX device, thus requiring four bellows, all of which are adjacent to the plasma at a relatively high heat flux of approximately 47 kW/m2 and microwave power regions. The MPEX high heat flux bellows (HHFB) is designed to deliver 6 degrees of freedom positioning. The HHFB includes titanium–zirconium–molybdenum (TZM) inserts that are brazed into a Glidcop AL-15 body using a high-temperature braze alloy, thus blocking direct line of sight to an edge-welded bellows from the plasma and microwave screen to block microwaves from the bellows. Further, a custom ConFlat knife edge is machined into the Glidcop AL-15, so the vacuum flanges do not need a braze or weld joint on the vacuum interface. Fingerstock or copper mesh is used to restrict microwave power from entering the interstitial space between the water-cooled Glidcop AL-15 body and edge-welded bellows. Glidcop AL-15 was selected as the material of choice for the water-cooled body because it can maintain mechanical integrity at elevated temperatures, and it also allows for a high-temperature braze. TZM was selected for its machinability and compatibility with vacuum and plasma requirements. A test article is also being considered for a similarly shaped component, the MPEX limiter, that will demonstrate the integrity of the braze joint under high thermal load. Results from this testing will be extrapolated to deduce the lifetime and integrity of the HHFB design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Data-Driven Analysis of Multipactor Dynamics via Dynamic Mode Decomposition

Multipactor effect is a performance-limiting kinetic plasma effect that can occur in high-power microwave and radio frequency (RF) devices. Multipactor effect is of special concern in vacuum or near-vacuum conditions such as those in particle accelerators and spaceborne devices. In this work, we present a data-driven reduced-order model (ROM) based on dynamic mode decomposition (DMD) for modeling of multipactor effects. We study multipactor effects and the resulting nonlinear harmonic generation by processing high-fidelity data generated from electromagnetic particle-in-cell (EMPIC) simulations using the DMD algorithm. We also investigate time-delay embedding extensions of DMD with improved generalizability and accuracy for modeling the electron plasma current density behavior. Here, the results show that DMD provides valuable insights into multipactor phenomena by extracting relevant modal spatiotemporal patterns and frequencies. In addition, DMD offers the potential to time extrapolate EMPIC simulations at a minimal cost, thereby reducing overall simulation time.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Pediatric dosing for locally acting drugs in submissions to the U.S. Food and Drug Administration between 2002 and 2020

Abstract Deriving pediatric doses for locally acting drugs (LADs) presents a unique challenge because limited systemic exposure hinders commonly used approaches such as pharmacokinetic matching to adults. This study systematically evaluated drug development practices used for pediatric dose selection of LADs approved by the U.S. Food and Drug Administration from 2002 to 2020. The three study objectives were: (1) to determine the dose selection approach for the labeled pediatric dose, (2) to examine the studied pediatric dose(s), and (3) to evaluate the characteristics of the pediatric clinical programs used to support the labeled pediatric dose. A total of 187 pediatric submissions were characterized for the labeled and studied pediatric doses of LADs. The pediatric dose was predominantly labeled as a flat dose (91%) and at a single‐dose level (67%) similar to adults. The majority (68.4%) of the submissions had the same labeled dose for pediatrics and adults. Independent pharmacodynamic/efficacy studies in pediatric patients commonly (64.2%) provided supportive evidence for the labeled pediatric dose. Inhalation, nasal, and injectable submissions had the highest number of clinical trials, lowest usage of an extrapolation of efficacy approach, and utilized diverse approaches in selecting the studied pediatric doses. This article highlights approaches for LAD dosing in pediatric patients and can be used to inform drug development of these products in the pediatric population.

Research & Experimental Medicine↗

DOE OSTI2020

Phosphorus alleviation of nitrogen‐suppressed methane sink in global grasslands

Abstract Grassland ecosystems account for more than 10% of the global CH 4 sink in soils. A 4‐year field experiment found that addition of P alone did not affect CH 4 uptake and experimental addition of N alone significantly suppressed CH 4 uptake, whereas concurrent N and P additions suppressed CH 4 uptake to a lesser degree. A meta‐analysis including 382 data points in global grasslands corroborated these findings. Global extrapolation with an empirical modelling approach estimated that contemporary N addition suppresses CH 4 sink in global grassland by 11.4% and concurrent N and P deposition alleviates this suppression to 5.8%. The P alleviation of N‐suppressed CH 4 sink is primarily attributed to substrate competition, defined as the competition between ammonium and CH 4 for the methane mono‐oxygenase enzyme. The N and P impacts on CH 4 uptake indicate that projected increases in N and P depositions might substantially affect CH 4 uptake and alter the global CH 4 cycle.

Zhang, Lihua↗

DOE OSTI2025

Storms Are an Important Driver of Change in Tropical Forests

Tropical forest dynamics and composition have changed over recent decades, but the proximate drivers of these changes remain unclear. Investigations into these trends have focused on increasing drought stress, CO 2 , temperature, and fires, whereas convective storms are generally overlooked. We argue that existing literature provides clear support for the importance of storms as drivers of forest change. We reanalyze the largest plot-based study of tropical forest carbon dynamics to show that lightning frequency—an indicator of storm activity—strongly predicts forest carbon storage and residence time, and its inclusion improves model fit and weakens evidence for the effects of high temperatures. Convective storm activity has increased 5%–25% per decade over the past half century. Extrapolating from historic trends, we estimate that storms likely contribute ca. 50% of the reported increases in biomass mortality across Amazonia, with all realistic combinations of assumptions indicating a possible range of 12%–118%. Spatial variation in storm activity shows weak relationships with drought, demonstrating that forests can experience high drought stress, high storm activity, or both. Accordingly, we hypothesise that convective storms are among the most important drivers of tropical forest change, and as such, they require significant research investment to avoid misguiding science, policy, and management.

biomass carbon↗