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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Predicting ecosystem metaphenome from community metagenome: A grand challenge for environmental biology

Abstract Elucidating how an organism's characteristics emerge from its DNA sequence has been one of the great triumphs of biology. This triumph has cumulated in sophisticated computational models that successfully predict how an organism's detailed phenotype emerges from its specific genotype. Inspired by that effort's vision and empowered by its methodologies, a grand challenge is described here that aims to predict the biotic characteristics of an ecosystem, its metaphenome, from nucleic acid sequences of all the species in its community, its metagenome. Meeting this challenge would integrate rapidly advancing abilities of environmental nucleic acids (eDNA and eRNA) to identify organisms, their ecological interactions, and their evolutionary relationships with advances in mechanistic models of complex ecosystems. Addressing the challenge would help integrate ecology and evolutionary biology into a more unified and successfully predictive science that can better help describe and manage ecosystems and the services they provide to humanity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Accelerated cryo-EM-guided determination of three-dimensional RNA-only structures

The discovery and design of biologically important RNA molecules is outpacing three-dimensional structural characterization. In this work, we demonstrate that cryo-electron microscopy can routinely resolve maps of RNA-only systems and that these maps enable subnanometer-resolution coordinate estimation when complemented with multidimensional chemical mapping and Rosetta DRRAFTER computational modeling. Furthermore ,this hybrid ‘Ribosolve’ pipeline detects and falsifies homologies and conformational rearrangements in 11 previously unknown 119- to 338-nucleotide protein-free RNA structures: full-length Tetrahymena ribozyme, hc16 ligase with and without substrate, full-length Vibrio cholerae and Fusobacterium nucleatum glycine riboswitch aptamers with and without glycine, Mycobacterium SAM-IV riboswitch with and without S-adenosylmethionine, and the computer-designed ATP-TTR-3 aptamer with and without AMP. Simulation benchmarks, blind challenges, compensatory mutagenesis, cross-RNA homologies and internal controls demonstrate that Ribosolve can accurately resolve the global architectures of RNA molecules but does not resolve atomic details. These tests offer guidelines for making inferences in future RNA structural studies with similarly accelerated throughput.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Metaanalysis of liana and tree functional traits

The objectives of this project were (i) to determine how tropical trees and lianas differed in terms of their functional traits, and (ii) to parameterize a computational model of tree-liana competition. We carried out a meta-analysis of tree and liana functional traits in order to achieve these goals. First, we downloaded functional trait data from the TRY database during November and December 2019. Traits of interest included leaf, wood, and root functional traits. We included only angiosperm tree and liana species that are found in tropical biomes. We then computed the species average for each trait. The results are included in “TRY_traits_metaanalysis.csv”. We also conducted a second meta-analysis focused on the hydraulic traits of tropical trees and lianas. We used Google Scholar and Web of Science to identify papers that contained hydraulic trait values. The papers that we found were all published between 1997-2019. As with our TRY-based meta-analysis, we included only angiosperm tree and liana species that are found in the tropics, and we computed species averages. The results are contained in the file “hydraulic_traits_metaanalysis.csv”. Both files are in csv format, so they can be read with any plain text editor, as well as programs like R or Excel.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2026

Validation of Simulation Models for Wall-Connected Scrape off Layer Currents during MHD Activity in the HBT-EP Tokamak

This is the final report for the DOE award DE-SC0021657, titled “Validation of Simulation Models for Wall-Connected Scrape off Layer Currents during MHD Activity in the HBT-EP Tokamak” for the period September 1, 2020 – April 30, 2025. This award is part of a broader collaborative project, which supported training of a PhD student at Columbia University under the joint supervision of Dr. Levesque and Dr. Hansen (on award DE-SC0021325). The award supported research into computational models for plasma and 3D conducting structures in the vicinity relevant to tokamak disruptions and other mode activity (eg. RWMs) using the NIMROD and PSI-Tet codes. Additional detail on progress in support of these tasks is provided below.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Uncertainty Visualization for Renewable Energy Potential

In this paper, we present the reV (Renewable Energy Potential) Dashboard, an interactive browser-based tool for uncertainty visualization and data exploration built using customized plotly dash components. With continuing development and utilization of computational models to study the power sector there is an increasing need for data-driven visualization tools which allow scientists, researchers, and engineers to interact with their data in real-time. Our principle motivation for developing this interactive uncertainty visualization was to provide domain scientists and modelers a platform which allows them to better understand and communicate scientific findings stemming from intricate information encoded in their data that is otherwise difficult to capture by conventional analysis. The development of customized dashboard components using the React programming paradigm, combined with fully pre-processed data, allows for users to select a variety of data options to update and manipulate the visualization in a straightforward computationally efficient manner.

dashboard↗
DOE OSTI2021

Predicting Thermal Excursions During in situ Oxidative Regeneration of Packed Bed Catalytic Fast Pyrolysis Catalyst

Ex situ catalytic fast pyrolysis (CFP) uses a secondary reactor to upgrade biomass pyrolysis vapors to stabilized CFP oils with reduced oxygen content. In one configuration, the secondary reactor is operated as a packed-bed swing reactor system which allows coke-deactivated beds to be decarbonized in situ while other beds remain online for vapor upgrading. In situ decarbonization must be done carefully to avoid irreversible deactivation and/or physical degradation of catalyst pellets. Given that packed bed reactors are well known to have poor heat transfer characteristics, this is a critical issue impacting scaleability and commercial viability of the technology. To predict thermal excursions during regeneration, finite element computational models have been built to assist in scaling up oxidative decarbonization of a Pt/TiO2 CFP catalyst (0.5 mm spheres) from a bench scale packed bed with 100 g of catalyst to a pilot scale packed bed with 2 kg of catalyst and internal cooling tubes. Based on transient measurements of outlet temperature and effluent CO2 concentration, and using an assumed coke profile and activation energy, this paper demonstrates that specific combinations of effective thermal conductivity and wall heat transfer coefficient can fit bench scale oxidative regeneration data equally well. For the upscaled 2 kg bed, four bench-scale “best fit” parameter pairs give different predictions for location and magnitude of thermal excursions, with the maximum computed bed temperature gradients ranging from 30 °C cm-1 to as high as 3000 °C cm-1. The larger the fraction of heat removal by conduction through the cooling tubes, the greater the differences between the parameter pairs. The modelling results presented in this paper cast doubt on the industrial viability of the proposed combination of catalyst, bed and regeneration process, and point to the need for alternate reactor designs. However, there is considerable uncertainly in some of the key model parameters. The reliability of model predictions can be increased by adding more temperature measurements at key bed locations, testing additional variations in process conditions, performing careful bed dissections to determine the true coke profile, and perhaps most importantly, directly measuring the effective thermal conductivity of the catalyst pellets.

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

Atomistic Materials Modeling of High-Pressure Hydrogen Interactions in Ethylene Propylene Diene Monomer (EPDM) Rubber

Elastomeric rubbers serve a vital role as sealing materials in the hydrogen storage and transport infrastructure. With applications including O-rings and hose-liners, these components are exposed to pressurized hydrogen at a range of temperatures, cycling rates, and pressure extremes. Cyclic (de)pressurization is known to degrade these materials through the process of cavitation. This readily visible failure mode occurs as a fracture or rupture of the material and is due to the oversaturated gas localizing to form gas bubbles. Computational modeling in the Hydrogen Materials Compatibility Program (H-Mat), co-led by Sandia National Laboratories and Pacific Northwest National Laboratory, employs multi-scale simulation efforts to build a predictive understanding of hydrogen-induced damage in materials. Modeling efforts within the project aim to provide insight into how to formulate materials that are less sensitive to high-pressure hydrogen-induced failure. In this document, we summarize results from atomistic molecular dynamics simulations, which make predictive assessments of the effects of compositional variations in the commonly used elastomer, ethylene propylene diene monomer (EPDM).

08 HYDROGEN↗
DOE OSTI2023

Modeling the Amplfied OSC Experiment at IOTA

Optical Stochastic Cooling (OSC), a beam cooling technique based on Stochastic Cooling, is in the early stages of experimental development. It uses radiation produced by the beam in an undulator magnet (the pickup) to correct the momentum deviation of particles downstream in another undulator (the kicker). The OSC mechanism was recently demonstrated at Fermilabʼs IOTA ring using a passive configuration. However, the cooling rate of OSC can be dramatically increased by first amplifying the undulator radiation before applying the corrective kick. In collaboration with the IOTA experiment, we developed a computational model of the OSC mechanism. This work presents beam‐dynamics simulations of the amplified‐OSC configuration. We implement a model of intrabeam scattering and study the effects on beam equilibrium and diffusion rate as a function of bunch charge. Finally, we investigate the phase‐space dynamics with various coupling configurations between the transverse and longitudinal planes.

Dick, A. J.↗
DOE OSTI2021

Critical analysis of velocimetry methods for particulate flows from synthetic data

Particle tracking methods that extract high-fidelity particle velocity data from high speed video of particle laden flows is a common experimental technique applied to chemical processes. These measurements are used to better understand the motion of particles and fluids in complex systems and create data against which computational models are validated. However, the methods, codes, and experimental setups all have limitations. It is imperative that practitioners verify the methods and their implementation as well as understand the limitations of experimental setups. This work focuses on quantifying the visible depth of field in a high particle concentration fluidized bed. Following a precedent set by the particle imaging velocimetry community, a particle velocity field is manufactured using a computational fluid dynamics and discrete element method simulation. Photo realistic high-speed videos are rendered based on the simulated data using the three-dimensional creation software Blender. Particle velocities are extracted from the synthetic high-speed videos using three variants of Particle Tracking Velocimetry and Optical Flow Velocimetry methodologies. Here, the tracked results are then compared to the known solution, quantifying the error associated with the assumed visible depth. The results indicate that at depth of one particle diameter, all three particle tracking codes give accurate measurements, largely within 5%. However, the error increases when the full bed video measurements are compared to the known solution at one particle diameter, i.e., mimicking a validation study. Finally, for some statistics the constant depth assumption only increases the error slightly, for others significantly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Tuning Two-Dimensional Phthalocyanine Dual Site Metal–Organic Framework Catalysts for the Oxygen Reduction Reaction

Metal-organic frameworks (MOFs) offer an interesting opportunity for catalysis, particularly for metal-nitrogen-carbon (M-N-C) motifs by providing an organized porous structural pattern and well-defined active sites for the oxygen reduction reaction (ORR), a key need for hydrogen fuel cells and related sustainable energy technologies. Here, in this work, we leverage electrochemical testing with computational models to study the electronic and structural properties in these systems and their relationship to ORR activity and stability based on dual transitional metal centers. These consists of two M1 metals with amine nodes coordinated to a single M2 metal with a phthalocyanine linker, where M1/M2 = Co, Ni, or Cu. Co-based metal centers, in particular Ni-Co, demonstrate the highest overall activity of all nine tested MOFs. Computationally, we identify the dominance of Co-sites, relative higher importance of the M2 site, and the role of layer M1 interactions on the ORR activity. Selectivity measurements indicate that M1 sites of MOFs, particularly Co, exhibits lowest (< 4%), and Ni demonstrates highest (>46%) two-electron selectivity, in good agreement with computational studies. Direct in-situ stability characterization, measuring dissolved metal ions, and calculations, using an alkaline stability metric, confirm that Co is the most stable metal in the MOF, while Cu exhibits notable instability at the M1. Overall, this study reveals how atomistic coupling of electronic and structural properties affects the ORR performance of dual site MOF catalysts and opens new avenues for tunable design and future development of these systems for practical electrochemical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Preliminary Results on Process Modeling Tools for Determining Variability in Additively Manufactured Stainless Steel 316 Parts

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the distinct characteristics of additive manufacturing (AM) materials, stemming from their unique processing history, microstructure, and properties, pose significant challenges for the qualification and certification of nuclear components. These challenges primarily arise from component-scale variations in microstructure and properties influenced by local process conditions and geometry, which affect thermal history, melt pool dynamics, and microstructure evolution. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents preliminary results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Initial results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing the challenges inherent in process modeling of AM components and outlines a plan for future development needs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Gamma and Neutron Measurement and Modeling of Irradiated TRISO Fuel

Given the unique characteristics of the PBR fuel cycle, both gamma and neutron measurements are expected to play important roles in performing and maintaining nuclear material control and accounting for spent pebbles to safeguard the fuel cycle. Given the lack of irradiated pebbles in the US, a variety of irradiated TRISO fuel samples with wide ranges of burnups and cooling times available at ORNL were used in this work. A large number of gamma and neutron measurements have been performed on these samples to collect data to test the various detectors and to benchmark the computer models to simulate the depletion and decay of the fuel and the measurements themselves. Two neutron detectors, including a custom-made detector and the Very High-Performance Neutron Multiplicity Counting, were used to measure the neutrons emitted by these TRISO samples. Three gamma spectrometry detectors, including an HPGe and the M400 CZT detector, were used to measure gamma-ray emissions from these samples. The M400 was recently adopted by the IAEA for fresh uranium measurements, but it was tested for spent fuel measurements prior to this project. Detailed MCNP models were developed to simulate these neutron and gamma measurements. Some GADRAS models were also developed to cross check the MCNP models for the gamma measurements. It was found challenging to perform neutron measurements in the hot cell due to the high background counts. Close agreements were observed between the simulated and measured neutron count rates in both detectors’ measurements of californium calibration sources. Both the HPGe and M400 detectors were able to measure the 604 and 662 keV peaks from these samples, which are the two most important peaks used to infer fuel burnup. Although the M400 detector did not have nearly good energy resolution and did not detect some of the minor peaks as the HPGe detector, it was found to be capable of handling significantly higher dose rates than HPGe. Given the complexities in the TRISO samples (e.g., different samples sizes) and uncertainties in the alignments between the detector and the TRISO fuel inside the containers, large scatters were found between the peak area rates and the samples’ burnups. However, the 604/662 peak ratios were found to trend well with the samples’ burnups among most samples in both measured and simulated results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions

The development of effective vaccines is crucial for combating current and emerging pathogens. Despite significant advances in the field of vaccine development there remain numerous challenges including the lack of standardized data reporting and curation practices, making it difficult to determine correlates of protection from experimental and clinical studies. Significant gaps in data and knowledge integration can hinder vaccine development which relies on a comprehensive understanding of the interplay between pathogens and the host immune system. In this review, we explore the current landscape of vaccine development, highlighting the computational challenges, limitations, and opportunities associated with integrating diverse data types for leveraging artificial intelligence (AI) and machine learning (ML) techniques in vaccine design. We discuss the role of natural language processing, semantic integration, and causal inference in extracting valuable insights from published literature and unstructured data sources, as well as the computational modeling of immune responses. Furthermore, we highlight specific challenges associated with uncertainty quantification in vaccine development and emphasize the importance of establishing standardized data formats and ontologies to facilitate the integration and analysis of heterogeneous data. Through data harmonization and integration, the development of safe and effective vaccines can be accelerated to improve public health outcomes. Looking to the future, we highlight the need for collaborative efforts among researchers, data scientists, and public health experts to realize the full potential of AI-assisted vaccine design and streamline the vaccine development process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Modeling of the amplified optical stochastic cooling experiment at IOTA

Optical Stochastic Cooling (OSC), a beam cooling technique based on Stochastic Cooling, is in the early stages of experimental development. It uses radiation produced by the beam in an undulator magnet (the pickup) to correct the momentum deviation of particles downstream in another undulator (the kicker). The OSC mechanism was recently demonstrated at Fermilab’s IOTA ring using a passive configuration. However, the cooling rate of OSC can be dramatically increased by first amplifying the undulator radiation before applying the corrective kick. In collaboration with the IOTA experiment, we developed a computational model of the OSC mechanism. This paper presents beam-dynamics simulations of the amplified-OSC configuration. We implement a model of intrabeam scattering and study the effects on beam equilibrium and diffusion rate as a function of bunch charge. Finally, we investigate the phase-space dynamics with various coupling configurations between the transverse and longitudinal planes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Model-informed, Adaptive Physical Vapor Deposition to Fabricate Hierarchical Binary-alloy Thin-films

Designing next generation thin films, tailor-made for specific applications, relies on the availability of robust processing-structure-property relationships. Traditional structure zone diagrams are limited to low-dimensional mappings, with machine-learning methods only recently attempting to relate multiple processing parameters to the final microstructure. Despite this progress, structure-processing relationships are unknown for processing conditions that vary during thin-film deposition, limiting the range of microstructures and properties achievable. In this project, we employed a phase-field computational model combined with a genetic algorithm (GA) to identify and design time-dependent processing protocols that achieve tailor-made microstructures. We simulate the physical vapor deposition of a binary-alloy thin film by employing a phase-field model, where deposition rates and diffusivities are controlled via the genetic algorithm. Our GA-guided protocols achieve targeted microstructures with lateral and vertical concentration modulations, as well as more complex, hierarchical microstructures previously not described in simple structure zone diagrams. Our algorithm provides insight to experimentalists looking for additional avenues to design novel thin-film microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Grid-based minimization at scale: Feldman-Cousins corrections for SBN

We present a computational model for the construction of Feldman-Cousins (FC) corrections frequently used in High Energy Physics (HEP) analysis. The program contains a grid-based minimization and is written in C++. Our algorithms exploit vectorization through Eigen3, yielding a single-core speed-up of 350 compared to the original implementation, and achieve MPI data parallelism by using DIY. We demonstrate the application to scale very well at High Performance Computing (HPC) sites. We use HDF5 in conjunction with HighFive to write results of the calculation to file.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

A Coarse-Grained Model of Affinity Maturation Indicates the Importance of B-Cell Receptor Avidity in Epitope Subdominance

The elicitation of broadly neutralizing antibodies (bnAbs) is a major goal in the design of vaccines against rapidly-mutating viruses. In the case of influenza, many bnAbs that target conserved epitopes on the stem of the hemagglutinin protein (HA) have been discovered. However, these antibodies are rare, are not boosted well upon reinfection, and often have low neutralization potency, compared to strain-specific antibodies directed to the HA head. Different hypotheses have been proposed to explain this phenomenon. We use a coarse-grained computational model of the germinal center reaction to investigate how B-cell receptor binding valency affects the growth and affinity maturation of competing B-cells. We find that receptors that are unable to bind antigen bivalently, and also those that do not bind antigen cooperatively, have significantly slower rates of growth, memory B-cell production, and, under certain conditions, rates of affinity maturation. The corresponding B-cells are predicted to be outcompeted by B-cells that bind bivalently and cooperatively. We use the model to explore strategies for a universal influenza vaccine, e.g., how to boost the concentrations of the slower growing cross-reactive antibodies directed to the stem. The results suggest that, upon natural reinfections subsequent to vaccination, the protectiveness of such vaccines would erode, possibly requiring regular boosts. Collectively, our results strongly support the importance of bivalent antibody binding in immunodominance, and suggest guidelines for developing a universal influenza vaccine.

60 APPLIED LIFE SCIENCES↗