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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Rearrangement collision theory of phonon-driven exciton dissociation

Understanding the processes governing the dissociation of excitons to free charge carriers in semiconductors and insulators is of central importance for photovoltaic applications. Dyson's S-matrix formalism provides a framework for computing scattering rates between quasiparticle states derived from the same underlying Hamiltonian, often reducing to familiar Fermi's “golden rule” like expressions at first order. By presenting a rigorous formalism for multichannel scattering, we extend this approach to describe scattering between composite quasiparticles and, in particular, the process of exciton dissociation mediated by the electron-phonon interaction. Subsequently, we derive rigorous expressions for the exciton dissociation rate, a key quantity of interest in optoelectronic materials, which enforce correct energy conservation and may be readily used in ab initio calculations. We apply our formalism to a three-dimensional model system to compare temperature-dependent exciton rates obtained for different scattering channels.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Basis light-front quantization for the Λ b and Σ b baryons

Within the basis light-front quantization framework, we compute the masses and light-front wave functions of the Λ b baryon and its isospin triplet counterparts Σ b + , Σ b 0 , and Σ b − using a light-front effective Hamiltonian in the leading Fock sector. These wave functions are obtained as eigenstates of the effective Hamiltonian, which incorporates the one-gluon exchange interaction with fixed coupling and a three-dimensional confinement potential. With the quark masses and the couplings as adjustable parameters, the computed masses are set within the experimental range. The resulting predictions for their electromagnetic properties align well with other theoretical calculations. Additionally, the parton distribution functions (PDFs) of these baryons are obtained for the first time, with gluon and sea quark distributions dynamically generated through QCD evolution of the valence quark PDFs. Published by the American Physical Society 2025

Meng, Lingdi (ORCID:0009000096439904)↗
DOE OSTI2026

Accurate Electron-Phonon Interactions from Advanced Density Functional Theory

Electron-phonon coupling (EPC) is key to understanding charge transport, band renormalization, and superconductivity in energy materials, including correlated transition-metal oxides, ferroelectric perovskites, optoelectronic semiconductors, and phonon-mediated superconductors. Although first-principles density functional theory (DFT)-based EPC calculations are used widely, their predictive power is limited by the accuracy, transferability, and efficiency of the underlying exchange-correlation functionals. These limitations become exacerbated in complex 𝑑- and 𝑓-electron materials, where beyond-DFT approaches and additional corrections, such as the Hubbard 𝑈, are commonly invoked. Here, using the examples of Co⁢O and Ni⁢O, we show how the 𝑟 2 SCAN density functional correctly captures strong EPC effects in transition-metal oxides without requiring the introduction of Hubbard 𝑈 parameters. We also find that 𝑟 2 SCAN successfully describes the subtle interplay between ionic and covalent bonding, and strong EPC effects in the low-temperature rhombohedral phase of the prototypical ferroelectric Ba⁢Ti⁢O 3 , without requiring Hubbard 𝑈 and intersite 𝑉 corrections. We further demonstrate the ability of 𝑟 2 SCAN to accurately model the EPC of the main-group semiconductor Ga⁢As and the phonon-mediated superconducting properties of Mg⁢B 2 , with reliable electronic bands and phonons. Our study establishes 𝑟 2 SCAN-based EPC as a transferable, parameter-free, and computationally efficient framework for predictive material-specific modeling of EPC in energy materials, and opens a practical route toward high-throughput screening of superconductors, thermoelectrics, optoelectronic semiconductors, and oxide electrodes and catalysts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Tensor renormalization group approach to critical phenomena via symmetry-twisted partition functions

The locality of field theories strongly constrains the possible behaviors of symmetry-twisted partition functions, and thus they serve as order parameters to detect low-energy realizations of global symmetries, such as spontaneous symmetry breaking (SSB). We demonstrate that the tensor renormalization group (TRG) offers an efficient framework to compute the symmetry-twisted partition functions, which enables us to detect the symmetry-breaking transition and also to study associated critical phenomena. As concrete examples of SSB, we investigate the two-dimensional (2D) classical Ising model and the three-dimensional (3D) classical 𝑂⁡(2) nonlinear sigma model, and we identify their critical points solely from the twisted partition function. By employing the finite-size scaling argument, we find the critical temperature 𝑇 𝑐 = 2.2017⁢(2) with the critical exponent 𝜈 = 0.663⁢(33) for the 3D 𝑂⁡(2) model. In addition, we also study the Berezinskii–Kosterlitz–Thouless (BKT) criticality of the 2D classical 𝑂⁡(2) model by extracting the helicity modulus from the twisted partition functions, and we obtain the BKT transition temperature, 𝑇 BKT = 0.8928⁢(2).

lattice field theory↗
DOE OSTI2025

Stochastic Model Predictive Control With Gaussian Wind Direction Preview for Wake Steering

This article addresses the problem of wake steering control for wind farms that explicitly consider the tradeoff between farm-level power generation and yaw duty cycle under variable and uncertain wind conditions. A novel stochastic model predictive control (MPC) algorithm is presented, which utilizes a stochastic model of the freestream wind field components in a receding horizon framework to compute optimal yaw set points that maximize the expected value of the farm power while constraining the yaw actuation. Different configurations of the algorithm are evaluated using a steady-state wind farm simulator. The proposed stochastic MPC algorithm can plan control actions over a future prediction horizon based on probabilistic estimates of the incoming wind magnitude and direction.

17 WIND ENERGY↗
DOE OSTI2024

Electronic descriptors for dislocation deformation behavior and intrinsic ductility in bcc high-entropy alloys

Controlling the balance between strength and damage tolerance in high-entropy alloys (HEAs) is central to their application as structural materials. Materials discovery efforts for HEAs are therefore impeded by an incomplete understanding of the chemical factors governing this balance. Through first-principles calculations, this study explores factors governing intrinsic ductility of a crucial subset of HEAs—those with a body-centered cubic (bcc) crystal structure. Analyses of three sets of bcc HEAs comprising nine different compositions reveal that alloy chemistry profoundly influences screw dislocation core structure, dislocation vibrational properties, and intrinsic ductility parameters derived from unstable stacking fault and surface energies. Key features in the electronic structure are identified that correlate with these properties: the fraction of occupied bonding states and bimodality of the d-orbital density of states. The findings enhance the fundamental understanding of the origins of intrinsic ductility and establish an electronic structure–based framework for computationally accelerated materials discovery and design.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Generalized Canonical Polyadic Tensor Decomposition

Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error. For instance, we can use logistic loss or Kullback--Leibler divergence, enabling tensor decomposition for binary or count data. We present a variety of statistically motivated loss functions for various scenarios. We provide a generalized framework for computing gradients and handling missing data that enables the use of standard optimization methods for fitting the model. Furthermore, we demonstrate the flexibility of the GCP decomposition on several real-world examples including interactions in a social network, neural activity in a mouse, and monthly rainfall measurements in India.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Ab initio calculations of material properties for modeling debris (FY2021 Progress Report)

This report summarizes the work done during the first 6 months of the project “Ab initio calculations of material properties for modeling debris” that is part of the Rapid Response Research (R3) Venture. The main focus for this fiscal year (FY) has been the evaluation of classical interatomic potentials for the description of the condensation of uranium oxides in the regimes relevant to fireball conditions. We also delineate how these functions will be used within the molecular dynamics and Monte Carlo frameworks to compute thermodynamic properties of these materials. These descriptions are to be utilized within continuum modeling formulations to generate a more accurate characterization of the homogeneous nucleation process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Fission Product Evaluation (Quarterly Report)

The LLNL team has developed a theoretical framework to compute the fission fragment charge and mass distributions of odd-mass nuclei, which can be formed in neutron-induced reactions on even-even nuclei. For example, n+ 238 U leads to the compound nucleus 239 U which has 147 neutrons. Odd-mass and odd-odd nuclei are spin-polarized: the spin of the nucleus is not equal to 0 as in even-even nuclei (a consequence of pairing correlations) but is in fact set by a single particle. In practice, that single particle can occupy different quantum-mechanical configurations, each with different spin. This affects the deformation properties of the nucleus and, as a result, the distribution of fission fragments. These distributions require two inputs for the calculation: a potential energy surface (= the total energy of the nucleus as a function of the deformation of the nuclear shape, often parametrized with 2 degrees of freedom) and the collective inertia tensor associated with each point of the PES. In an odd-mass or odd-odd nucleus, each quantity depends on the configuration of the single particle. We computed of the order of 20 to 30 different such configurations to extract the PES for spins K=1/2, 3/2, 5/2, 7/2, and 9/2 of 239 U. We then computed the primary yields for each such PES, as shown in Fig. 1 below. These yields were weighed with the spin distribution associated with the reaction 238 U+n and the resulting distribution used as input to FREYA to compute the independent yields shown in Fig. 2. These yields are then used as inputs to the FIER code to extract the cumulative yields and compare with LLNL-LANL measurements at TUNL.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Quantifying Uncertainty in PV Energy Estimates Final Report

Uncertainty in PV energy estimates is "one of the most critical areas of lack of understanding" according to independent engineers, financiers, PV model developers, and other industry stakeholders. The primary problem is a lack of rigorous, transparent, widely accepted methods for quantifying uncertainty in energy production estimates. Uncertainty in energy production estimates arises from variability of the solar resource, inexact PV performance models and their parameters, and system reliability considerations. Uncertainty in annual energy production is frequently calculated for larger projects in order to quantify financial risk. Key statistics for energy, such as the P-values "P50" and "P90" (the annual energy values that are exceeded in future years with 50\% and 90\% probability, respectively) are used by financing institutions to calculate the repayment risk for the project. The current methods to estimate these statistics are typically proprietary, specialized, and involve significant post-processing of commercial performance model results. This black-box approach leads to inconsistent P-value estimates from different parties, which reduces investors' confidence in the results. Since the financial community bases its risk assessment on these estimates, reduced confidence increases perceived project risk, and consequently financing costs. The goal of this project was to establish a set of best practices for quantifying uncertainty in energy production estimates, including identifying what sources of uncertainty must be considered with clear definitions and metrics, determining which sources are the biggest drivers of uncertainty, and providing a computationally efficient framework for combining different sources of uncertainty that is flexible enough to accommodate substitutions of data or methods when better information is available. We engaged a wide set of stakeholders to ensure industry endorsement and adoption, and leveraged complementary projects investigating individual sources of uncertainty in great detail, as well as others' work that started down this path.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
DOE OSTI2024

Practical and Optimal Sequential Bayesian Experimental Design for Complex Systems Incorporating Human Experimenter Preferences (Final Scientific/Technical Report)

Experiments are indispensable for developing models of complex systems. Carefully designed experiments can provide substantial savings for these expensive data-acquisition opportunities. However, designs based on heuristics are often suboptimal for systems with multiphysics, nonlinear dynamics, and uncertain and noisy environments. Optimal experimental design, while leveraging predictive models, seeks to systematically quantify and maximize the value of experiments. In this project, we focused on the design of multiple experiments, where current approaches are largely suboptimal: batch-design does not adapt to new data acquired during the experiment campaign (no feedback), and greedy/myopic design ignores future dynamics and consequences (no lookahead). We developed the mathematical framework and computational methods for sequential optimal experimental design (sOED) for complex systems. We enabled tractable model-based sOED in a rigorous manner through novel algorithms based on reinforcement learning, and investigated the effects of human experimenters on the design process. Our methods are fully Bayesian, able to quantify and update uncertainty in a principled manner. The traits aimed by our approach—mathematical rigor and optimality, human effects and uncertainty quantification, computational practicality—are crucial for elevating the standards of artificial intelligence (AI) to support decision-making in scientific domains, and contribute toward trust and realistic adoption of AI in experimental design practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Macroalgal Cultivation Modeling System (MACMODS): Evaluating the Role of Physical-Biological Coupling on Nutrients and Farm Yield

Offshore aquaculture has the potential to expand the macroalgal industry. However, moving into deeper waters requires suspended structures that will present novel farm-environment interactions. Here, we present a computational modeling framework, the Macroalgal Cultivation Modeling System (MACMODS), to explore within-farm modifications to light, seawater flow, and nutrient fields across time and space scales relevant to macroalgae. A regional ocean model informs the site-specific setting, the Santa Barbara Channel in the Southern California Bight. A fine-scale hydrodynamic model predicts modified flows and turbulent mixing within the farm. A spatially resolved macroalgal growth model, parameterized for giant kelp, Macrocystis pyrifera , predicts kelp biomass. Key findings from model integration are that regional ocean conditions set overall farm performance, while fine-scale within-farm circulation and nutrient delivery are important to resolve variation in within-farm macroalgal performance. Therefore, we conclude that models resolving within-farm dynamics can provide benefit to farmers with insight on how farm design and regional ocean conditions interact to influence overall yield. Here, the presence of repeating longlines aligned with the mean current generate flow diversions around the farm as well as attached Langmuir circulations and increased turbulence intensity. These flow-induced phenomena lead to less biomass in the interior portion of the farm relative to the edges. We also find that there is an effluent “footprint” that extends as much as 20 km beyond the farm. In this regard, MACMODS can be used to not only evaluate farm design and cultivation practices that maximize yield but also explore interactions between the farm and ecosystem in order to minimize impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Improving the Precision of First-Principles Calculation of Parton Physics from Lattice Quantum Chromodynamics

Large momentum effective theory (LaMET) provides a general framework for computing the multi-dimensional partonic structure of the proton from first principles using lattice quantum chromodynamics (QCD). In this effective field theory approach, LaMET predicts parton distributions through a power expansion and perturbative matching of a class of Euclidean observables—quasi-distributions—evaluated at large proton momenta. Recent advances in lattice renormalization, such as the hybrid scheme with leading renormalon resummation, together with improved matching kernel that incorporates higher-loop corrections and resummations, have enhanced both the perturbative and power accuracy of LaMET, enabling a reliable quantification of theoretical uncertainties. Moreover, the Coulomb-gauge correlator approach further simplifies lattice analyses and improves the precision of transverse-momentum-dependent structures, particularly in the non-perturbative region. State-of-the-art LaMET calculations have already yielded certain parton observables with important phenomenological impact. In addition, the recently proposed kinematically enhanced lattice interpolation operators promise access to unprecedented proton momenta with greatly improved signal-to-noise ratios, which will extend the range of LaMET prediction and further suppress the power corrections. The remaining challenges, such as controlling excited-state contamination in lattice matrix elements and extracting gluonic distributions, are expected to benefit from emerging lattice techniques for ground-state isolation and noise reduction. Thus, lattice QCD studies of parton physics have entered an exciting stage of precision control and systematic improvement, which will have a broader impact for nuclear and particle experiments.

Zhao, Yong [Argonne National Laboratory (ANL), Arg↗
DOE OSTI2022

Calculating Radiation Damage (DPA) from Transmutation Products

This is a poster for an INL poster session. Accurate models for radiation damage are crucial for predicting material performance in radiation environments. The uncertainty of state-of-the-art radiation damage models is large, contributing to excessive safety margins. A major source of this uncertainty is neglecting the effect that transmutation products have on radiation damage. Transmutation products are new nuclides formed by neutron activation during irradiation; they can contribute to radiation damage by additional neutron capture or decay events. Ignoring the contribution of transmutation products leads to a significant underprediction of the radiation damage (e.g., >10% error in 316 stainless steel). This underprediction is accounted for in part by adding larger safety margins to designs. Currently, the state of the art explicitly accounts for only a single transmutation product, namely nickel-59, during the radiation damage calculation. All other transmutation products are assumed to not contribute to the radiation damage, because there is currently no established method to systematically track all or a selection of radiation damage contributions of transmutation products during activation. In the case of nickel-59, the current method is to apply a precalculated correlation that cannot be used for any other nuclide and is largely dependent on all nuclear engineers being experts in this niche topic. This project proposed to methodically find other transmutation products that cause significant radiation damage, and then to develop a general framework for systematically tracking the radiation damage from these nuclides. This was accomplished by combining the radiation damage calculation into the transmutation calculation already performed for irradiated structural materials. The key idea of our framework is to introduce radiation-damage "pseudo-nuclides" to the list of nuclides used in the transmutation analysis. This allows radiation damage to be tracked alongside the creation and destruction of transmutation products. The main deliverable of this project is a general framework for computing radiation damage while the damaged material undergoes transmutation; this capability allows a significantly more accurate estimation of radiation damage, and in turn reduce required safety margins thereby reducing the cost to construct reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗