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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 523 records · Page 29
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DOE OSTI2025

Multiphase Species Transport Modeling for Molten Salt Reactors in the System Analysis Module: Generation, Decay, Deposition, and Extraction of Insoluble Fission Products

With the increase of interests in the design and deployment of advanced reactor systems, a desire for simulation tools supporting system analysis of reactor operation and safety is rising. Molten salt reactors (MSRs), one of the advanced reactor systems, utilize liquid fused salt fuel as both coolant and fuel. During operation, MSR generates insoluble fission products, including noble metals and gases. The buildup of these species in fuel salt presents safety concerns as they may deposit on surfaces of critical components and produce excessive decay heat, causing the failure of system components. Timely removal of these noble metals and gases would ensure the safe operation of the reactor system. The dynamic nature of salt fuel system, involving the generation, decay, deposition, and extraction of noble metals and gases, calls for robust species transport models to facilitate system analysis and monitoring, and design of efficient species removal components. This paper concentrates on the development of a computational framework for species transport, consisting of multiphase transport model formulation, mass transfer between phases, numerical implementation in MOOSE environment, verification through Method of Manufacture Solutions (MMS) and validation against experimental data from the Molten Salt Reactor Experiment (MSRE). Integrating this framework into the System Analysis Module (SAM) code further enhances SAM’s capabilities for advanced reactor analysis in the future.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Analysis of Second Target Station Target Removal Dose Rates

The Second Target Station (STS) project at Oak Ridge National Laboratory’s spallation neutron source is a crucial initiative for maintaining U.S. leadership in neutron sciences. The STS aims to create the world’s brightest pulsed cold neutron source, enabling cutting-edge research across various scientific disciplines. To ensure safe and efficient maintenance operations, understanding the effects of shutdown dose rates from activated components within the STS target systems is essential. This study establishes a computational framework for calculating decay gamma sources and subsequent shutdown dose rates utilizing advanced methods to account for all activation channels, including high-energy interactions down to thermal neutron capture. This study describes a novel integration of multiple tools and provides an effective means of analyzing activation and shutdown dose rates at spallation neutron facilities. A custom-developed script automates the decay gamma source generation process, ensuring proper sampling during the variance reduction phase, which is critical for accurate predictions of shutdown dose rates.

Transmutation↗
DOE OSTI2015

A UNIFIED COMPUTATIONAL MODEL FOR SOLAR AND STELLAR FLARES

We present a unified computational framework that can be used to describe impulsive flares on the Sun and on dMe stars. The models assume that the flare impulsive phase is caused by a beam of charged particles that is accelerated in the corona and propagates downward depositing energy and momentum along the way. This rapidly heats the lower stellar atmosphere causing it to explosively expand and dramatically brighten. Our models consist of flux tubes that extend from the sub-photosphere into the corona. We simulate how flare-accelerated charged particles propagate down one-dimensional flux tubes and heat the stellar atmosphere using the Fokker–Planck kinetic theory. Detailed radiative transfer is included so that model predictions can be directly compared with observations. The flux of flare-accelerated particles drives return currents which additionally heat the stellar atmosphere. These effects are also included in our models. We examine the impact of the flare-accelerated particle beams on model solar and dMe stellar atmospheres and perform parameter studies varying the injected particle energy spectra. We find the atmospheric response is strongly dependent on the accelerated particle cutoff energy and spectral index.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Nanotechnology for catalysis and solar energy conversion

This roadmap on Nanotechnology for Catalysis and Solar Energy Conversion focuses on the application of nanotechnology in addressing the current challenges of energy conversion: 'high efficiency, stability, safety, and the potential for low-cost/scalable manufacturing' to quote from the contributed article by Nathan Lewis. This roadmap focuses on solar-to-fuel conversion, solar water splitting, solar photovoltaics and bio-catalysis. It includes dye-sensitized solar cells (DSSCs), perovskite solar cells, and organic photovoltaics. Smart engineering of colloidal quantum materials and nanostructured electrodes will improve solar-to-fuel conversion efficiency, as described in the articles by Waiskopf and Banin and Meyer. Semiconductor nanoparticles will also improve solar energy conversion efficiency, as discussed by Boschloo et al in their article on DSSCs. Perovskite solar cells have advanced rapidly in recent years, including new ideas on 2D and 3D hybrid halide perovskites, as described by Spanopoulos et al 'Next generation' solar cells using multiple exciton generation (MEG) from hot carriers, described in the article by Nozik and Beard, could lead to remarkable improvement in photovoltaic efficiency by using quantization effects in semiconductor nanostructures (quantum dots, wires or wells). These challenges will not be met without simultaneous improvement in nanoscale characterization methods. Terahertz spectroscopy, discussed in the article by Milot et al is one example of a method that is overcoming the difficulties associated with nanoscale materials characterization by avoiding electrical contacts to nanoparticles, allowing characterization during device operation, and enabling characterization of a single nanoparticle. Besides experimental advances, computational science is also meeting the challenges of nanomaterials synthesis. The article by Kohlstedt and Schatz discusses the computational frameworks being used to predict structure–property relationships in materials and devices, including machine learning methods, with an emphasis on organic photovoltaics. The contribution by Megarity and Armstrong presents the 'electrochemical leaf' for improvements in electrochemistry and beyond. In addition, biohybrid approaches can take advantage of efficient and specific enzyme catalysts. These articles present the nanoscience and technology at the forefront of renewable energy development that will have significant benefits to society.

14 SOLAR ENERGY↗
DOE OSTI2020

Neutrino halo effect on collective neutrino oscillation in iron core-collapse supernova model of a 9.6 M ⊙ star

We extend the multi-angle computational framework and investigate the time evolution of the neutrino halo on collective neutrino oscillation in the core collapse of an iron core progenitor. We find that in the case of the 9.6 M progenitor adopted in this work, there are windows of time when the effects of neutrino halo and collective neutrino oscillation are not simultaneously large. Inside the shock, the impact of the inward-scattered halo neutrino cannot in general be neglected compared to the outward-propagating neutrino flux. However, during early epochs, collective neutrino oscillation is effectively shut down by multi-angle matter suppression. During the intermediate epoch, collective neutrino oscillation is not suppressed, but its onset radius is beyond the still relatively small explosion shock front where the halo is prominent. We also find in the case of the 9.6 M progenitor the halo neutrinos induce a delay in the onset of collective neutrino oscillations. This causes novel flavor conversions which sharpen collective neutrino oscillation spectral features. We predict that the inclusion of neutrino halo effects makes neutrino signals that are more clearly distinct from thermal emission that when halo neutrinos are omitted.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

An Unsteady Actuator Line Solver to Enable Adjoint Sensitivity Studies for Wake Steering

This study demonstrates the sensitivity of wind turbine wake steering performance to blade design. An actuator line model was implemented within an unsteady adjoint solver that enables efficient execution of gradient-based optimization and sensitivity studies. After first confirming the feasibility of wake steering by controlling actuator line chord profiles and formulating a suitable objective function for wake position, a sensitivity study was conducted to determine the relative importance of chord length as a function of spanwise position on the resulting turbine wake deflection. The results presented here support the idea that blade design choices play a role in wake control. In a larger context, this study demonstrates a computational framework in which turbine and blade designs can be studied at the individual and farm-wide level to enhance wind plant controllability and manage power output.

17 WIND ENERGY↗
DOE OSTI2021

A multiscale insight into the growth of h-BN: effect of the enclosure

Abstract There is a lack of knowledge on the fundamental growth mechanisms governing the characteristics of 2D materials synthesized by the chemical vapor deposition (CVD) technique and their correlation with experimentally controllable parameters, which hindered their wafer-scale synthesis. Here, we pursued an analytical and computational approach to access the system states that are not experimentally viable to address these critical needs. We developed a multiscale computational framework correlating the macroscale heat and mass flow with the mesoscale morphology of the as-grown 2D materials by solving the coupled system of heat/mass transfer and phase-field equations. We used hexagonal boron nitride (h-BN) as our model material and investigated the effect of substrate enclosure on its growth kinetics and final morphology. We revealed a lower concentration with a more uniform distribution on the substrate in an enclosed-growth than open-growth. It leads to a more uniform size distribution of the h-BN islands, consistent with existing experimental investigations.

Materials Science↗
DOE OSTI2024

Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution

Abstract Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Latent Twins

Over the past decade, scientific machine learning has transformed the development of mathematical and computational frameworks for analyzing, modeling, and predicting complex systems. From inverse problems to numerical partial differential equations (PDEs), dynamical systems, and model reduction, these advances have pushed the boundaries of what can be simulated. Yet they have often progressed in parallel, with representation learning and algorithmic solution methods evolving largely as separate pipelines. With Latent Twins, we propose a unifying mathematical framework that creates a hidden surrogate in latent space for the underlying equations. Whereas digital twins mirror physical systems in the digital world, Latent Twins mirror mathematical systems in a learned latent space governed by operators. Through this lens, classical modeling, inversion, model reduction, and operator approximation all emerge as special cases of a single principle. We establish the fundamental approximation properties of Latent Twins for both ordinary differential equations (ODEs) and PDEs and demonstrate the framework across three representative settings: (i) canonical ODEs, capturing diverse dynamical regimes; (ii) a PDE benchmark using the shallow-water equations, contrasting Latent Twin simulations with deep operator network and forecasts with a four-dimensional variational method baseline; and (iii) a challenging real-data geopotential reanalysis dataset, reconstructing and forecasting from sparse, noisy observations. Latent Twins provide a compact, interpretable surrogate for solution operators that evaluate across arbitrary time gaps in a single-shot, while remaining compatible with scientific pipelines such as assimilation, control, and uncertainty quantification. Looking forward, this framework offers scalable, theory-grounded surrogates that bridge data-driven representation learning and classical scientific modeling across disciplines.

Latent Twins↗
DOE OSTI2023

An adjoint-based optimization method for jointly inverting heterogeneous material properties and fault slip from earthquake surface deformation data

SUMMARY Analysis of tectonic and earthquake-cycle associated deformation of the crust can provide valuable insights into the underlying deformation processes including fault slip. How those processes are expressed at the surface depends on the lateral and depth variations of rock properties. The effect of such variations is often tested by forward models based on a priori geological or geophysical information. Here, we first develop a novel technique based on an open-source finite-element computational framework to invert geodetic constraints directly for heterogeneous media properties. We focus on the elastic, coseismic problem and seek to constrain variations in shear modulus and Poisson’s ratio, proxies for the effects of lithology and/or temperature and porous flow, respectively. The corresponding nonlinear inversion is implemented using adjoint-based optimization that efficiently reduces the cost function that includes the misfit between the calculated and observed displacements and a penalty term. We then extend our theoretical and numerical framework to simultaneously infer both heterogeneous Earth’s structure and fault slip from surface deformation. Based on a range of 2-D synthetic cases, we find that both model parameters can be satisfactorily estimated for the megathrust setting-inspired test problems considered. Within limits, this is the case even in the presence of noise and if the fault geometry is not perfectly known. Our method lays the foundation for a future reassessment of the information contained in increasingly data-rich settings, for example, geodetic GNSS constraints for large earthquakes such as the 2011 Tohoku-oki M9 event, or distributed deformation along plate boundaries as constrained from InSAR.

Geochemistry & Geophysics↗
DOE OSTI2020

Engineering sensitivity and specificity of AraC-based biosensors responsive to triacetic acid lactone and orsellinic acid

Abstract We previously described the design of triacetic acid lactone (TAL) biosensor ‘AraC-TAL1’, based on the AraC regulatory protein. Although useful as a tool to screen for enhanced TAL biosynthesis, this variant shows elevated background (leaky) expression, poor sensitivity and relaxed inducer specificity, including responsiveness to orsellinic acid (OA). More sensitive biosensors specific to either TAL or OA can aid in the study and engineering of polyketide synthases that produce these and similar compounds. In this work, we employed a TetA-based dual-selection to isolate new TAL-responsive AraC variants showing reduced background expression and improved TAL sensitivity. To improve TAL specificity, OA was included as a ‘decoy’ ligand during negative selection, resulting in the isolation of a TAL biosensor that is inhibited by OA. Finally, to engineer OA-specific AraC variants, the iterative protein redesign and optimization computational framework was employed, followed by 2 rounds of directed evolution, resulting in a biosensor with 24-fold improved OA/TAL specificity, relative to AraC-TAL1.

Wang, Zhiqing↗
DOE OSTI2016

39th Lauriston S. Taylor Lecture

Since the early days of the Manhattan Engineer District, Oak Ridge National Laboratory (ORNL) has served to advance the dosimetry models used to set protection standards for radionuclides taken into the body. Throughout the years, this effort benefited significantly from ORNL staff's active participation in national and international scientific bodies. The first such interaction was in 1946 with the National Committee on Radiation Protection (NCRP), chaired by L.S. Taylor, which led to the 1949 to 1953 series of tripartite conferences of experts from Canada, the United Kingdom, and the United States. These conferences addressed the need for standardization of dosimetry models and led to the establishment of an anatomic and physiologic model called “Standard Man,” a precursor of the reference worker defined in Publication 23 of the International Commission on Radiological Protection (ICRP). Standard Man was used in setting the maximum permissible concentrations in air and water published in NBS Handbook 52 and subsequent reports by NCRP and ICRP. K.Z. Morgan, then director of the Health Physics Division at ORNL, participated in the tripartite conferences and subsequently established ORNL as a modeling and computational resource for development of radiation protection standards. ORNL’s role expanded with participation in the work of the Medical Internal Radiation Dose (MIRD) Committee of the Society of Nuclear Medicine. Results of interactions with the MIRD Committee are evident in the radiation protection guidance for internal emitters in ICRP Publication 30. The annual limit on intake and derived air concentration values tabulated in Publication 30 were computed by an ORNL-based task group of ICRP Committee 2. A few years after the appearance of Publication 30, the Chernobyl nuclear reactor accident made clear the need to develop standard dosimetry models for pre-adult ages as members of the public. In the late 1980s, ICRP began an effort to extend its reference worker concept to a reference family and develop dosimetric models for application to intake of radionuclides by members of the public. However, the modeling approach underlying the ICRP Publication 30 computational framework was not amenable to age and gender considerations. With support of U.S. federal agencies, ORNL had begun efforts in the early 1980s to develop age- and gender-specific dosimetric models, including physiologically informed biokinetic models and age-specific dosimetric phantoms. ORNL’s models and methods became the starting point for the ICRP’s series of reports on dose coefficients for radionuclide intake by the public. Currently ICRP Committee 2 is overseeing development of a second generation of post-Chernobyl models and methods, with updates of Publications 30 and 68 soon to appear and new models for members of the public in preparation. The goal of this Lauriston S. Taylor Lecture is to chronicle advancements in the dosimetry of internal emitters with some discussion of models and methods but with due deference to decisions within scientific bodies and stimulated by radiological events.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

IDAC-Bio, A Software for Internal Dosimetry Based on the New ICRP Biokinetic Models and Specific Absorbed Fractions

Radiation dosimetry is central to virtually all radiation safety applications, optimization, and research. It relates to various individuals and population groups and to miscellaneous exposure situations—including planned, existing, and emergency situations. The International Commission on Radiological Protection (ICRP) has developed a new computational framework for internal dose estimations. Important components are more detailed and improved anatomical models and more realistic biokinetic models than before. The ICRP is currently producing new organ dose and effective dose coefficients for occupational intakes of radionuclides (OIR) and environmental intakes of radionuclides (EIR), which supersede the earlier dose coefficients in Publication 68 and the Publication 72 series, respectively. However, the ICRP only publishes dose coefficients for a single acute intake of a radionuclide and for an integration period of 50 years for intake by adults and to age 70 years for intakes by pre-adults. The new software, IDAC-Bio, performs committed absorbed dose and effective dose calculations for a selectable intake scenario, e.g., for a continuous intake or an intake during x hours per day and y days per week, and for any selected integration time. The software uses the primary data and models of the ICRP biokinetic models and numerically solves the biokinetic model and calculates the absorbed doses to organs and tissues in the ICRP reference human phantoms. The software calculates absorbed dose using the nuclear decay data in ICRP publication 107. IDAC-Bio is a further development and an important addition to the internal dosimetry program IDAC-Dose2.1. Here, the results generated by the software were validated against published ICRP dose coefficients. The potential of the software is illustrated by dose calculations for a nuclear power plant worker who had been exposed to varying levels of 60 Co and who had undergone repeated whole-body measurements, and for a hypothetical member of the public subject to future releases of 148 Gd from neutron spallation in tungsten at the European Spallation Source.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Philympics 2021: Prophage Predictions Perplex Programs

Most bacterial genomes contain integrated bacteriophages—prophages—in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms. Here, we present a library of gold-standard bacterial genome annotations that include manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms to identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f 1 score from the analysis of seven different algorithms for the prediction of prophages. We discuss caveats and concerns in this analysis and how those concerns may be mitigated.

Roach, Michael J.↗
DOE OSTI2019

Orbital angular momentum constraints in the variational optimization of the two-electron reduced-density matrix

The direct variational determination of the two-electron reduced-density matrix (2-RDM) corresponding to an atomic or molecular system is usually carried out in a basis of real-valued atom-centered Gaussian basis functions, under the assumption that the 2-RDM is a real-valued quantity. Furthermore, for systems that possess orbital angular momentum symmetry, the description of states with a well-defined, nonzero z projection of the orbital angular momentum requires a computational framework generalized to include either complex basis functions or a complex-valued 2-RDM. We consider a semidefinite program suitable for the direct optimization of a complex-valued 2-RDM and explore the role of orbital angular momentum constraints in systems that possess the relevant symmetries. For atomic systems, constraints on the expectation values of the square and z projection of the orbital angular momentum operator allow one to optimize 2-RDMs for multiple orbital angular momentum states. Similarly, in linear molecules, orbital angular momentum projection constraints enable the description of multiple electronic states and, moreover, the application of such constraints is essential for a qualitatively correct description of the electronic structure. For example, in the case of molecular oxygen, we demonstrate that orbital angular momentum constraints are necessary to recover the correct energy ordering of the lowest-energy singlet and triplet states near the equilibrium geometry. However, care must still be taken in the description of the dissociation limit, because the 2-RDM-based approach is not size consistent, and the size-consistency error varies dramatically, depending on the z projections of the spin and orbital angular momenta.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Transport of hot carriers in plasmonic nanostructures

Plasmonic hot carrier devices extract excited carriers from metal nanostructures before equilibration and have the potential to surpass semiconductor light absorbers. However their efficiencies have so far remained well below theoretical limits, which necessitates quantitative prediction of carrier transport and energy loss in plasmonic structures to identify and overcome bottlenecks in carrier harvesting. Here, we present a theoretical and computational framework, nonequilibrium scattering in space and energy (NESSE), to predict the spatial evolution of carrier energy distributions that combines the best features of phase-space (Boltzmann) and particle-based (Monte Carlo) methods. Within the NESSE framework, we bridge first-principles electronic structure predictions of plasmon decay and carrier collision integrals at the atomic scale, with electromagnetic field simulations at the nano- to mesoscale. Finally, we apply NESSE to predict spatially-resolved energy distributions of photoexcited carriers that impact the surface of experimentally realizable plasmonic nanostructures at length scales ranging from tens to several hundreds of nanometers, enabling first-principles design of hot carrier devices.

36 MATERIALS SCIENCE↗
DOE OSTI2019

On the Traffic Impacts of Optimally Controlled Connected and Automated Vehicles

The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework for real-time control actions aimed at optimizing energy consumption and associated benefits. Several research efforts reported in the literature to date have proposed decentralized control algorithms to coordinate CAVs in various traffic scenarios, e.g., highway on-ramps, intersections, and roundabouts. However, the impact of optimally coordinating CAVs on the performance of a transportation network has not been thoroughly analyzed yet. In this paper, we apply a decentralized optimal control framework in a transportation network and compare its performance to a baseline scenario consisting of human-driven vehicles. We show that introducing of CAVs yields radically improved roadway capacity and network performance.

Zhao, Liuhui↗
DOE OSTI2017

Optimal Power Flow of Radial Networks and Its Variations: A Sequential Convex Optimization Approach

This paper proposes a sequential convex optimization method to solve broader classes of optimal power flow (OPF) problems over radial networks. The non-convex branch power flow equation is decomposed as a second-order cone inequality and a non-convex constraint involving the difference of two convex functions. Provided with an initial solution offered by an inexact second-order cone programming relaxation model, this approach solves a sequence of convexified penalization problems, where concave terms are approximated by linear functions and updated in each iteration. It could recover a feasible power flow solution, which usually appears to be very close, if not equal, to the global optimal one. Two variations of the OPF problem, in which non-cost related objectives are optimized subject to power flow constraints and the convex relaxation is generally inexact, are elaborated in detail. One is the maximum loadability problem, which is formulated as a special OPF problem that seeks the maximal distance to the boundary of power flow insolvability. The proposed method is shown to outperform commercial nonlinear solvers in terms of robustness and efficiency. The other is the bi-objective OPF problem. A non-parametric scalarization model is suggested, and is further reformulated as an extended OPF problem by convexifying the objective function. It provides a single trade-off solution without any subjective preference. The proposed computation framework also helps retrieve the Pareto front of the bi-objective OPF via the e-constraint method or the normal boundary intersection method. This paper also discusses extensions for OPF problems over meshed networks based on the semidefinite programming relaxation method.

bi-objective optimization↗