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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 OSTI2013

Ertel's vorticity theorem and new flux surfaces in multi-fluid plasmas

Dedicated to Professor Harold Weitzner on the occasion of his retirement“Say to wisdom ‘you are my sister,’ and to insight ‘you are my relative.’”—Proverbs 7:4Based on an extension to plasmas of Ertel's classical vorticity theorem in fluid dynamics, it is shown that for each species in a multi-fluid plasma there can be constructed a set of nested surfaces that have this species' fluid particles confined within them. Variational formulations for the plasma evolution and its equilibrium states are developed, based on the new surfaces and all of the dynamical conservation laws associated with them. It is shown that in the general equilibrium case, the energy principle lacks a minimum and cannot be used as a stability criterion. A limit of the variational integral yields the two-fluid Hall-magnetohydrodynamic (MHD) model. A further special limit yields MHD equilibria and can be used to approximate the equilibrium state of a Hall-MHD plasma in a perturbative way.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection

This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.

Fayad, Samuel S. [University of Illinois at Urbana↗
DOE OSTI2016

Research in Theoretical High Energy Nuclear Physics at the University of Arizona

In the past decade (2004-2015) we addressed the quest for the understanding of how quark confinement works, how it can be dissolved in a limited space-time domain, and what this means: i) for the paradigm of the laws of physics of present day; and, ii) for our understanding of cosmology. The focus of our in laboratory matter formation work has been centered on the understanding of the less frequently produced hadronic particles (e.g. strange antibaryons, charmed and beauty hadrons, massive resonances, charmonium, B c ). We have developed a public analysis tool, SHARE (Statistical HAdronization with REsonances) which allows a precise model description of experimental particle yield and fluctuation data. We have developed a charm recombination model to allow for off-equilibrium rate of charmonium production. We have developed methods and techniques which allowed us to study the hadron resonance yield evolution by kinetic theory. We explored entropy, strangeness and charm as signature of QGP addressing the wide range of reaction energy for AGS, SPS, RHIC and LHC energy range. In analysis of experimental data, we obtained both statistical parameters as well as physical properties of the hadron source. The following pages present listings of our primary writing on these questions. The abstracts are included in lieu of more detailed discussion of our research accomplishments in each of the publications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

THE CONNECTION BETWEEN GALAXIES AND DARK MATTER STRUCTURES IN THE LOCAL UNIVERSE

We provide new constraints on the connection between galaxies in the local universe, identified by the Sloan Digital Sky Survey, and dark matter halos and their constituent substructures in the {Lambda}-cold dark matter model using WMAP7 cosmological parameters. Predictions for the abundance and clustering properties of dark matter halos, and the relationship between dark matter hosts and substructures, are based on a high-resolution cosmological simulation, the Bolshoi simulation. We associate galaxies with dark matter halos and subhalos using subhalo abundance matching, and perform a comprehensive analysis which investigates the underlying assumptions of this technique including (1) which halo property is most closely associated with galaxy stellar masses and luminosities, (2) how much scatter is in this relationship, and (3) how much subhalos can be stripped before their galaxies are destroyed. The models are jointly constrained by new measurements of the projected two-point galaxy clustering and the observed conditional stellar mass function of galaxies in groups. We find that an abundance matching model that associates galaxies with the peak circular velocity of their halos is in good agreement with the data, when scatter of 0.20 {+-} 0.03 dex in stellar mass at a given peak velocity is included. This confirms the theoretical expectation that the stellar mass of galaxies is tightly correlated with the potential wells of their dark matter halos before they are impacted by larger structures. The data put tight constraints on the satellite fraction of galaxies as a function of galaxy stellar mass and on the scatter between halo and galaxy properties, and rule out several alternative abundance matching models that have been considered. This will yield important constraints for galaxy formation models, and also provides encouraging indications that the galaxy-halo connection can be modeled with sufficient fidelity for future precision studies of the dark universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling

This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0

36 MATERIALS SCIENCE↗
DOE OSTI2015

An attempt to calibrate and validate a simple ductile failure model against axial-torsion experiments on Al 6061-T651

This report details a work in progress. We have attempted to calibrate and validate a Von Mises plasticity model with the Johnson-Cook failure criterion ( Johnson & Cook , 1985 ) against a set of experiments on various specimens of Al 6061-T651. As will be shown, the effort was not successful, despite considerable attention to detail. When the model was com- pared against axial-torsion experiments on tubes, it over predicted failure by 3 x in tension, and never predicted failure in torsion, even when the tube was twisted by 4 x further than the experiment. While this result is unfortunate, it is not surprising. Ductile failure is not well understood. In future work, we will explore whether more sophisticated material mod- els of plasticity and failure will improve the predictions. Selecting the appropriate advanced material model and interpreting the results of said model are not trivial exercises, so it is worthwhile to fully investigate the behavior of a simple plasticity model before moving on to an anisotropic yield surface or a similarly complicated model.

42 ENGINEERING↗
DOE OSTI2012

The Connection between Galaxies and Dark Matter Structures in the Local Universe

We provide new constraints on the connection between galaxies in the local Universe, identified by the Sloan Digital Sky Survey (SDSS), and dark matter halos and their constituent substructures in the {Lambda}CDM model using WMAP7 cosmological parameters. Predictions for the abundance and clustering properties of dark matter halos, and the relationship between dark matter hosts and substructures, are based on a high-resolution cosmological simulation, the Bolshoi simulation. We associate galaxies with dark matter halos and subhalos using subhalo abundance matching, and perform a comprehensive analysis which investigates the underlying assumptions of this technique including (a) which halo property is most closely associated with galaxy stellar masses and luminosities, (b) how much scatter is in this relationship, and (c) how much subhalos can be stripped before their galaxies are destroyed. The models are jointly constrained by new measurements of the projected two-point galaxy clustering and the observed conditional stellar mass function of galaxies in groups. We find that an abundance matching model that associates galaxies with the peak circular velocity of their halos is in good agreement with the data, when scatter of 0.20 {+-} 0.03 dex in stellar mass at a given peak velocity is included. This confirms the theoretical expectation that the stellar mass of galaxies is tightly correlated with the potential wells of their dark matter halos before they are impacted by larger structures. The data put tight constraints on the satellite fraction of galaxies as a function of galaxy stellar mass and on the scatter between halo and galaxy properties, and rule out several alternative abundance matching models that have been considered. This will yield important constraints for galaxy formation models, and also provides encouraging indications that the galaxy - halo connection can be modeled with sufficient fidelity for future precision studies of the dark Universe.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

An analytical expression for temperature in a thermodynamically consistent model with a Mie-Gruneisen equation for pressure

The objective of this paper is to develop an analytical expression for temperature in a thermodynamically consistent model with a Mie-Gruneisen equation for pressure. This expression for the temperature is obtained for both constant and variable specific heat models and can be used in common models for the material yield strength that include thermal softening due to increase in temperature. Additionally, the material parameters in the model can easily be calibrated to match known experimental data and the model has been used to determine the value of the shock pressure at the onset of melting during shock loading.

42 ENGINEERING↗
DOE OSTI2012

Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Long‐term yields in annual and perennial bioenergy crops in the Midwestern United States

Abstract Many yield predictions in perennial bioenergy species have been made based on data collected during the establishment phase of growth or a limited number of long‐term studies. Few studies compare multiple perennial crops with the dominant agricultural vegetation of the landscape over long time periods. Here, we present the results of 11 years of perennial crop management on fertile agricultural soils in central Illinois, compared with conventional row crop maize/soybean ( Zea mays L., Glycine max L.) production. We examined the long‐term productivity and drought susceptibility of Miscanthus x giganteus Greef et. Deu. ex. Hodkinson et Renvoize (miscanthus), Panicum virgatum L., Cave‐in‐Rock cultivar (switchgrass), and a native prairie mix, in contrast to annual maize/soybean agriculture. Long‐term yields for miscanthus and switchgrass failed to reach initial predictions made during the establishment phase; however, in miscanthus, the 11 th year of production shows little progressive yield loss with age, exceeding the modeled limit for the onset of age‐related decline. Harvest timing and differences in yields from hand and machine harvests in perennial crops likely contribute to overestimates of potential yields. Application of fertilizer to mature miscanthus resulted in significant increases in yield after a severe drought, though modeled effects of management and drought in miscanthus point to a more complex mechanism for yield response.

09 BIOMASS FUELS↗
DOE OSTI2023

Techno-Economic Case Study: Low-Temperature Conversion Performance Based on Isolated Anatomical Fractions of Corn Stover

This report summarizes analysis conducted to support a case study under the Feedstock-Conversion Interface Consortium (FCIC) focused on techno-economic analysis (TEA) modeling to quantify the yield and cost ramifications for processing isolated anatomical fractions of corn stover through a low-temperature conversion (biochemical pathway) biorefinery. It is hypothesized that different individual anatomical fractions of corn stover vary in composition and recalcitrance, such that processing each fraction on its own e.g. through dedicated campaigns may enable economic benefits relative to processing the whole stover material. To investigate this concept, we worked with FCIC researchers to obtain data on compositional analysis, as well as processing conditions and yields, for conversion of whole corn stover plus three individual anatomical fractions (cobs, husks, and stalks) across key steps of the biorefinery conversion process within FCIC research scope, namely pretreatment and enzymatic hydrolysis. After running the data through the TEA models, fractionated cobs and husks achieved higher fuel yields and lower minimum fuel selling prices than whole corn stover, but stalks fared worse than whole stover on both metrics. The most direct takeaway from this assessment is that a biorefinery would stand to benefit economically from maximizing the use of cob and husk fractions while avoiding or minimizing the use of stalks, though recognizing practical constraints with this approach in maintaining an equivalent processing capacity (fixed at 2,000 dry metric tonne per day in all cases).

09 BIOMASS FUELS↗
DOE OSTI2025

Improving the Transportability of a Deep Learning Denoising Model Using Transfer Learning Techniques

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Quinones, Louis [Sandia National Laboratories (SNL↗
DOE OSTI2012

Bioenergy crop models: Descriptions, data requirements and future challenges

Field studies that address the production of lignocellulosic biomass as a potential source of renewable energy are making available critical information for the development, validation, and use of bioenergy crop models. A literature survey revealed that 14 models have been developed and validated for herbaceous and woody bioenergy crops, and for Crassulacean acid metabolism (CAM) crops adapted to arid lands. These models simulate field-scale production of biomass for switchgrass (ALMANAC, EPIC, and Agro-BGC), miscanthus (MISCANFOR, MISCANMOD, and WIMOVAC), sugarcane (APSIM, AUSCANE, and CANEGRO), and poplar and willow (SECRETS and 3PG). Two models are adaptations of dynamic global vegetation models and simulate biomass yields of miscanthus and sugarcane as plant function types at regional scales (Agro-IBIS and LPJmL). A model of biomass production in CAM plants has been developed (EPI), but lacks the sophistication of the other models. Except for CAM plants, all the models include representations of leaf area dynamics, radiation interception and utilization, biomass production, and partitioning of biomass to roots and shoots. A few of the models are capable of simulating soil water, nutrient, and carbon cycle processes, making them especially useful for assessing environmental consequences (e.g., erosion and nutrient losses) associated with the field-scale deployment of bioenergy crops. Similar to other process-based models, simulations are challenged by computing and data management issues and an integrated framework for model testing and inter-comparison is needed. Considerable work remains concerning the development of models for unconventional bioenergy crops like CAM plants, generation and distribution of high-quality field data for model development and validation, and development of an integrated framework for efficient execution of large-scale simulations for use in planning regional to global sustainable bioenergy production systems.

Surendran Nair, Sujith↗
DOE OSTI2024

Automated Operational Forecasting of Monsoon Low Pressure Systems

Monsoon low pressure systems (LPSs) are the dominant rain-bearing weather system of South Asia, often producing extreme precipitation and hydrological disasters in a region inhabited by nearly two billion people. Despite the importance of these storms, no operational system has automatically identified and tracked LPS in real time in numerical weather prediction model output; many commonly used vortex-tracking algorithms are ill suited for monsoon LPS because of the weak winds and cold cores of these systems. Here, we describe a new system that uses optimized algorithms to identify monsoon LPS in short- to medium-range forecasts from the U.S. Global Ensemble Forecast System (GEFS) and a version of the deterministic Global Forecast System (GFS) adapted and used operationally by the Indian Institute of Tropical Meteorology (IITM). We also assess the historical performance of these models in forecasting South Asian monsoon LPS, comparing this with the performance of the Integrated Forecasting System of the ECMWF. We assess the accuracy of model predictions of LPS genesis, position, intensity, and precipitation rates for forecast lead times of 1–5 days, yielding quantitative information on model biases to guide operational forecasters and disaster managers. The system we introduce here could be extended to other low-latitude regions affected by dynamically weak, heavily precipitating atmospheric vortices that are often not included in tropical cyclone inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

13 C Metabolic Flux Analysis for Systematic Metabolic Engineering of S. cerevisiae for Overproduction of Fatty Acids

Efficient redirection of microbial metabolism into the abundant production of desired bioproducts remains non-trivial. Here, we used flux-based modeling approaches to improve yields of fatty acids in Saccharomyces cerevisiae. We combined 13 C labeling data with comprehensive genome-scale models to shed light onto microbial metabolism and improve metabolic engineering efforts. We concentrated on studying the balance of acetyl-CoA, a precursor metabolite for the biosynthesis of fatty acids. A genome-wide acetyl-CoA balance study showed ATP citrate lyase from Yarrowia lipolytica as a robust source of cytoplasmic acetyl-CoA and malate synthase as a desirable target for downregulation in terms of acetyl-CoA consumption. These genetic modifications were applied to S. cerevisiae WRY2, a strain that is capable of producing 460 mg/L of free fatty acids. With the addition of ATP citrate lyase and downregulation of malate synthase, the engineered strain produced 26% more free fatty acids. Further increases in free fatty acid production of 33% were obtained by knocking out the cytoplasmic glycerol-3-phosphate dehydrogenase, which flux analysis had shown was competing for carbon flux upstream with the carbon flux through the acetyl-CoA production pathway in the cytoplasm. In total, the genetic interventions applied in this work increased fatty acid production by ~70%.

-omics data↗
DOE OSTI2021

$\mathcal{g}$ modes of neutron stars with hadron-to-quark crossover transitions

Here we perform the first study of the principal core g-mode oscillation in hybrid stars containing quark matter, utilizing a crossover model for the hadron-to-quark transition inspired by lattice QCD. The ensuing results are compared with our recent findings of g-mode frequencies in hybrid stars with a first-order phase transition using Gibbs constructions. We find that models using Gibbs construction yield g-mode amplitudes and the associated gravitational energy radiated that dominate over those of the chosen crossover model owing to the distinct behaviors of the equilibrium and adiabatic sound speeds in the various models. Based on our results, we conclude that were g modes to be detected in upgraded LIGO and Virgo detectors it would indicate a first-order phase transition akin to a Gibbs construction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Small Volume Fuel Testers Report

Micro-liter fuel ignition testing (μ-FIT) is based on the premise that characteristics FREI (Flames with Repetitive Extinction and Ignition, i.e. cyclically occurring combustion events within heated capillaries), are linked to fuel properties. In early FY16, proof-of-concept measurements with primary reference fuel (PRF) mixtures, i.e. blends of n-heptane and iso-octane, yielded clear evidence for the feasibility of the approach. Our experiments showed that it is critical to accurately link observed flame positions to local temperatures, which provides information on ignition, extinction and flame propagation, all of which are known to be impacted by fuel properties. In FY16, one major hurdle was uncertainty of temperature calibration, which required significant efforts for corrective action that were not included in the original scope of work. Temperature calibrations are obtained by translating a thermocouple within the capillary in absence of a flame. While measurements have good repeatability when accounting for transient and insertion effects, results from nominally identical thermocouples reveal unacceptable uncertainty (up to ±50K), which is attributed to variations in thermocouple placement and manufacturing tolerances. This issue is currently being resolved by switching to non-intrusive optical temperature measurements. Updates are expected to yield uncertainties of less than ±10K, while also eliminating transient and insertion effects. The experimental work was complemented by computational efforts where it was shown that a simplified Lagrangian zero-D model with detailed kinetics yields fuelspecific differentiation of ignition temperatures for simple fuels that are consistent with experiments. Further, a 2D transient model was implemented in OpenFOAM to investigate combustion behavior of simple fuels at elevated pressure. In an upcoming visit to LLNL, more advanced simulations using LLNL’s computational tools (e.g. zero-RK) are planned, which will yield additional numerical insights on FREI behavior of more realistic spark ignited (SI) engine fuel surrogates. As there is a lag between DOE FY16 and the time frame of the LSU subcontract, it is anticipated that deliverables outlined in the scope of work will be met by the end of the subcontract (January 2017).

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2018

235 U(n, f) Independent fission product yield and isomeric ratio calculated with the statistical Hauser–Feshbach theory

In this work, we have developed a Hauser–Feshbach fission fragment decay model, HF 3 D, which can be applied to the statistical decay of more than 500 primary fission fragment pairs (1,000 nuclides) produced by the neutron induced fission of 235 U. The HF 3 D model numerically integrates fission fragment yield and their initial excitation energy, spin and parity distributions for these 1,000 nuclides. The fission fragment yield Y ( A ) and the total kinetic energy are model inputs, and we estimate them from available experimental data for the 235 U(n th ,f) system. The model parameters in the statistical decay calculation are adjusted to reproduce some fission observables, such as the neutron emission multiplicity v - , its distribution P ( ν ), and the mass dependence v - ( A ). The calculated fission product yield (FPY) and isomeric ratio (IR) are compared with experimental data. The calculated independent FPY Y I ( A ) at the thermal energy reproduces the experimental data well, while the calculated IRs tend to be lower than the Madland–England model prediction. The HF 3 D model is extended to higher incident neutron energies up to the second-chance fission threshold. We demonstrate for the first time that most of the IRs stay constant, although the production of isomeric state changes as the incident energy increases.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗