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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 1,135 records · Page 63

Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites

Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the “chassis” organism’s metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.

59 BASIC BIOLOGICAL SCIENCES↗

Kepler Data Analysis: Non-Gaussian Noise and Fourier Gaussian Process Analysis of Stellar Variability

We develop a statistical analysis model of Kepler stellar flux data in the presence of planet transits, non-Gaussian noise, and stellar variability. We first develop a model for the Kepler noise probability distribution in the presence of outliers, which make the noise probability distribution non-Gaussian. We develop a signal likelihood analysis based on this probability distribution, in which we model the signal as a sum of the star variability and planetary transits. We argue that these components need to be modeled together if optimal signal is to be extracted from the data. For the stellar variability model we develop an optimal Gaussian process analysis using a Fourier-based Wiener filter approach, where the power spectrum is non-parametric and learned from the data. We develop high dimensional optimization of the objective function, where we jointly optimize all the model parameters, including thousands of star variability modes, and planet transit parameters. We apply the method to Kepler-90 data and show that it gives a better match to the stellar variability than the existing methods, and robustly handles noise outliers. As a consequence, the planet radii have a higher value than what the existing methods give, including splines and celerite.

79 ASTRONOMY AND ASTROPHYSICS↗

Predictive LES Modeling and Validation of High-Pressure Turbulent Flames and Flashback in Hydrogen-enriched Gas Turbines

The overall objective of this project is to make advances in large-eddy simulation modeling of swirl-flame flashback at elevated pressure, where the reactants can be premixed or stratified, and composed of methane enriched with hydrogen. The modeling effort was conducted with a companion experimental validation effort that focused on high-speed measurements of flashback in a model swirl-flame combustor. The modeling effort focused on the development of manifold-based combustion model for flashback simulations. The main complexity in this configuration comes from the flames’ propagation through a stratified fuel-air mixture along with heat loss near the walls. For this purpose, a non-adiabatic flamelet model was developed and tested using canonical flow configurations. Further, extensive validation in premixed configurations is used to establish the model’s accuracy in capturing heat loss and flame propagation. Finally, the model was tested using the UT flame flashback experiment, and was shown to accurately capture the flame flashback process. The end products from this work are a) a full validated flameout-based approach for modeling flame flashback; b) a complete implementation of the model in the open source OpenFOAM framework, which has been made available to both industry and academia; c) an extensive suite of validation studies performed using the OpenFOAM solver. In addition, new techniques for flame modeling, subfilter modeling of scalars, and simulation of low Mach number flows were developed and tested. In the experimental validation effort, flashback was studied in a premixed and stratified swirl flame by using high-speed imaging diagnostics. Fuels tested included CH 4 enriched with H 2 , with H 2 enrichment up to 84% by volume. These flashback experiments are conducted at pressures ranging from 1 to 5 atm. A swirler-based fuel-injection system was used to create the fuel-air stratification in the radial direction. For the high-pressure measurements, an optically accessible elevated pressure chamber was used. The spatial distribution of the equivalence ratio under non-reacting conditions was characterized using planar laser-induced fluorescence with acetone as the fuel tracer. It was observed that fuel-air pockets were distributed across the mixing tube width, although in an average sense, the fuel-air mixture was radially stratified. The global behavior of upstream flame propagation was investigated for different levels of hydrogen-enrichment. For stratified hydrogen-rich flashback, the propagation path of the flame changed from the inner wall to outer wall, which was induced by the faster chemistry of stoichiometric mixtures that are frequently present near the outer wall. This behavior of hydrogen-rich flashback persists even at elevated pressures up to 5 atm, although the propagation of the flame occurs as a wide flame tongue as opposed to the acute-tipped flame structures present in the atmospheric cases. The end product of the experimental effort is greatly improved knowledge of flashback physics and a unique database that can be used for model validation.

08 HYDROGEN↗

Design Paradigm for Modular Multilevel Converter-Based Generator Rectifier Systems

Modular Multilevel Converters (MMC) are being widely considered for medium to high voltage DC applications. Designing such converters through multi-objective optimization is of interest because such an approach allows the trade-off between competing objectives (for example mass and loss) to be explicitly and quantitatively identified. In this work, an optimization based design paradigm for MMC based generator rectifier systems is proposed. Such development typically requires detailed component design and simulation models for the electric machine and converter which are computationally expensive. As an alternative, the proposed work utilizes an electric machine metamodel, inductor metamodel, and high-speed steady-state simulation model for the MMC to facilitate multi-objective optimization minimizing system metrics of interest while satisfying system constraints. A case study is undertaken to demonstrate the functionality of the proposed design paradigm.

42 ENGINEERING↗

System Design, Analysis, and Modeling Activities Supporting the DOE Hydrogen Storage Engineering Center of Excellence (HSECoE): Final Project Report

The objective of this project was to identify optimal pathways for successful hydrogen storage system technology through modeling, analysis, and testing support. The National Renewable Energy Laboratory (1) performed vehicle simulations of various system configurations to support the overall systems engineering, (2) led the storage system energy analysis and provided the Hydrogen Storage Engineering Center of Excellence (HSECoE) with results to help guide engineering design, and (3) compiled and obtained media engineering properties for the HSECoE.

30 DIRECT ENERGY CONVERSION↗

Maven: a multimodal foundation model for supernova science

Abstract A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of this imbalance, with the number of supernovae observed photometrically outpacing the number observed spectroscopically by multiple orders of magnitude. At the same time, no data-driven models exist to understand these photometric and spectroscopic observables in a common context. Contrastive learning objectives, which have grown in popularity for aligning distinct data modalities in a shared embedding space, provide a potential solution to extract information from these modalities. We present Maven, the first foundation model for supernova science. To construct Maven, we first pre-train our model to align photometry and spectroscopy from 0.5 M synthetic supernovae using a contrastive objective. We then fine-tune the model on 4702 observed supernovae from the Zwicky transient facility. Maven reaches state-of-the-art performance on both classification and redshift estimation, despite the embeddings not being explicitly optimized for these tasks. Through ablation studies, we show that pre-training with synthetic data improves overall performance. In the upcoming era of the Vera C. Rubin observatory, Maven will serve as a valuable tool for leveraging large, unlabeled and multimodal time-domain datasets.

Zhang, Gemma (ORCID:0000000280198082)↗

Remote Off-grid Microgrid Design Support Tool (ROMDST) - An Optimal Design Support Tool for Remote, Resilient, and Reliable Microgrids Phase II - Final Report

The objective of the project is to develop an advanced optimization-based design support tool for AC or DC microgrids in remote locations, where utility grids may not be accessible. The mathematical model and the interface are being developed such that multiple design objectives and criteria/constraints can be easily enabled or disabled, to deliver a flexible tool relevant to a large, diverse user base, and to facilitate future feature developments. The new tool delivered by this project leverages the team’s extensive expertise in the development, testing, deployment, and commercialization of the state-of-the-art Distributed Energy Resources Customer Adoption Model (DER-CAM) – which is the foundation for the new tool. Phase I of the project leverages the microgrid design and decision support tool DER-CAM. Key Phase I developments include the following adaption to the DER-CAM model: a) AC or DC microgrid architectures, b) multiple economic objectives (e.g. single customer vs community vs utility perspective), c) constraints on fuel availability (e.g. total fuel available during outage periods), d) active and reactive power flow constraints for normal and N-1 contingency cases, e) component part load efficiencies (e.g. power electronic devices), and f) interactive data visualization capabilities (e.g. design topology elements). A component library including components’ default technical and economical characteristics was also developed. In Phase II, the project is focused on tool testing and validation, use for real-world microgrid designs, and outreach. Fully developing this tool will have the following impacts: 1) optimal off-grid microgrid designs that replace existing back of the envelope or non-optimal calculations, reducing capital costs and risk of microgrid deployment; 2) removing barriers to microgrid assessments by lowering microgrid soft costs, as the tool is freely usable; 3) reliable and resilient designs that reduce the cost of critical load shedding due to component outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Remote Off-grid Microgrid Design Support Tool (ROMDST) - An Optimal Design Support Tool for Remote, Resilient, and Reliable Microgrids (Phase II, Final Report)

The objective of the project is to develop an advanced optimization-based design support tool for AC or DC microgrids in remote locations, where utility grids may not be accessible. The mathematical model and the interface are being developed such that multiple design objectives and criteria/constraints can be easily enabled or disabled, to deliver a flexible tool relevant to a large, diverse user base, and to facilitate future feature developments. The new tool delivered by this project leverages the team’s extensive expertise in the development, testing, deployment, and commercialization of the state-of-the-art Distributed Energy Resources Customer Adoption Model (DER-CAM) – which is the foundation for the new tool. Phase I of the project leverages the microgrid design and decision support tool DER-CAM. Key Phase I developments include the following adaption to the DER-CAM model: a) AC or DC microgrid architectures, b) multiple economic objectives (e.g. single customer vs community vs utility perspective), c) constraints on fuel availability (e.g. total fuel available during outage periods), d) active and reactive power flow constraints for normal and N-1 contingency cases, e) component part load efficiencies (e.g. power electronic devices), and f) interactive data visualization capabilities (e.g. design topology elements). A component library including components’ default technical and economical characteristics was also developed. In Phase II, the project is focused on tool testing and validation, use for real-world microgrid designs, and outreach. Fully developing this tool will have the following impacts: 1) optimal off-grid microgrid designs that replace existing back of the envelope or non-optimal calculations, reducing capital costs and risk of microgrid deployment; 2) removing barriers to microgrid assessments by lowering microgrid soft costs, as the tool is freely usable; 3) reliable and resilient designs that reduce the cost of critical load shedding due to component outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Evaluation of CMIP6 GCMs Over the CONUS for Downscaling Studies

Despite the necessity of Global Climate Models (GCMs) sub-selection in downscaling studies, an objective approach for their selection is currently lacking. Building on the previously established concepts in GCMs evaluation frameworks, we develop a weighted averaging technique to remove the redundancy in the evaluation criteria and rank 37 GCMs from the sixth phase of the Coupled Models Intercomparison Project over the contiguous United States. GCMs are rated based on their average performance across 66 evaluation measures in the historical period (1981–2014) after each metric is weighted between zero and one, depending on its uniqueness. The robustness of the outcome is tested by repeating the process with the empirical orthogonal function analysis in which each GCM is ranked based on its sum of distances from the reference in the principal component space. The two methodologies work in contrasting ways to remove the metrics redundancy but eventually develop similar GCMs rankings. A disparity in GCMs' behavior related to their sensitivity to the size of the evaluation suite is observed, highlighting the need for comprehensive multi-variable GCMs evaluation at varying timescales for determining their skillfulness over a region. The sub-selection goal is to use a representative set of skillful models over the region of interest without substantial overlap in their future climate responses and modeling errors in representing historical climate. Additional analyses of GCMs' independence and spread in their future projections provide the necessary information to objectively select GCMs while keeping all aspects of necessity in view.

54 ENVIRONMENTAL SCIENCES↗

Deep Learning Model Segmentations on Computed Tomography 3D Reconstructions of Coffee Beans to Determine Void Ratio (U-Net) and Roast Level (LinkNet)

This project will evaluate the functionality of Object Research Dragonfly software’s user produced deep learning (DL) models on Computed Tomography (CT) scanned coffee beans from green (unroasted) through a dark roast. DL models will be expected to identify voids within the coffee beans, and identify the level of roast of the bean from the CT reconstruction. The scope of this project is intended to meet the Capstone Project requirements of University of California San Diego (UCSD) Structural Engineering master’s degree and offer useful insight on Dragonfly’s DL capability for LANL’s Non-Destructive Evaluation (NDE) CT team. The results of this project will be presented to the E-6 NDE group within LANL. Data acquisition was completed with a North Star Imaging (NSI) X-25 CT Cabinet.

97 MATHEMATICS AND COMPUTING↗

Geodesic Models of Quasi-periodic-oscillations as Probes of Quadratic Gravity

Future very-large-area X-ray instruments (for which the effective area is larger than >3 m{sup 2}) will be able to measure the frequencies of quasi-periodic oscillations (QPOs) observed in the X-ray flux from accreting compact objects with sub-percent precision. If correctly modeled, QPOs can provide a novel way to test the strong-field regime of gravity. By using the relativistic precession model and a modified version of the epicyclic resonance model, we develop a method to test general relativity against a generic class of theories with quadratic curvature corrections. With the instrumentation being studied for future missions such as eXTP, LOFT, or STROBE-X, a measurement of at least two QPO triplets from a stellar mass black hole can set stringent constraints on the coupling parameters of quadratic gravity.

79 ASTRONOMY AND ASTROPHYSICS↗

A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations: CESM/CAM EVALUATION BY DECISION TREES

An object-based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smooth topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small-scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales.

54 ENVIRONMENTAL SCIENCES↗

Recent Improvements in Pronghorn for Advanced Reactor Modeling

Pronghorn is a thermal-hydraulics computational tool developed using the Idaho National Laboratory's Multiphysics Object-Oriented Simulation Environment (MOOSE). It is designed to support Computational Fluid Dynamics (CFD) modeling, ranging from subchannel and porous media analysis to Reynolds Averaged Navier-Stokes (RANS) turbulence modeling. As an integral part of the MOOSE-based suite of tools, Pronghorn seamlessly couples with other MOOSE-based applications to simulate a variety of physical phenomena. This article highlights recent significant enhancements to Pronghorn's CFD modeling capabilities and demonstrates their application to advanced nuclear reactor designs. The recent improvements in Pronghorn primarily focus on modifications to its turbulence modeling capabilities, near-wall corrections and numerical schemes. In terms of turbulence modeling, the two-equation $k-\epsilon$ and $k-\omega$ SST models have been implemented and validated with both equilibrium and non-equilibrium wall treatments. Additionally, corrections for wall roughness, and curvature, and wall-channeling in pebble beds have been introduced in the near-wall modeling. These developments enable more accurate simulations of advanced nuclear reactors. Two case studies are presented in this work: a pool-type Molten Chloride Reactor and a salt-cooled Pebble-Bed High Temperature Reactor. In both cases, the previous models in Pronghorn are compared with the new implementations, demonstrating the improved accuracy achieved with the updated models.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Computational general relativistic force-free electrodynamics: I. Multi-coordinate implementation and testing

General relativistic force-free electrodynamics is one possible plasma-limit employed to analyze energetic outflows in which strong magnetic fields are dominant over all inertial phenomena. The amazing images of black hole (BH) shadows from the Galactic Center and the M87 galaxy provide a first direct glimpse into the physics of accretion flows in the most extreme environments of the universe. The efficient extraction of energy in the form of collimated outflows or jets from a rotating BH is directly linked to the topology of the surrounding magnetic field. We aim at providing a tool to numerically model the dynamics of such fields in magnetospheres around compact objects, such as BHs and neutron stars. To do so, we probe their role in the formation of high energy phenomena such as magnetar flares and the highly variable teraelectronvolt emission of some active galactic nuclei. In this work, we present numerical strategies capable of modeling fully dynamical force-free magnetospheres of compact astrophysical objects. Here, we provide implementation details and extensive testing of our implementation of general relativistic force-free electrodynamics in Cartesian and spherical coordinates using the infrastructure of the E INSTEIN T OOLKIT . The employed hyperbolic/parabolic cleaning of numerical errors with full general relativistic compatibility allows for fast advection of numerical errors in dynamical spacetimes. Such fast advection of divergence errors significantly improves the stability of the general relativistic force-free electrodynamics modeling of BH magnetospheres.

79 ASTRONOMY AND ASTROPHYSICS↗

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists

Uncompetitive antagonists of the N-methyl D-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. Overall, we present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Global stellarator coil optimization with quadratic constraints and objectives

Most present stellarator designs are produced by costly two-stage optimization: the first for an optimized equilibrium, and the second for a coil design reproducing its magnetic configuration. Few proxies for coil complexity and forces exist at the equilibrium stage. Rapid initial state finding for both stages is a topic of active research. Most present convex coil optimization codes use the least square winding surface method by Merkel (NESCOIL), with recent improvements in conditioning, regularization, sparsity, and physics objectives. While elegant, the method is limited to modeling the norms of linear functions in coil current. We present QUADCOIL, a global coil optimization method that targets combinations of linear and quadratic functions of the current. It can directly constrain and/or minimize a wide range of physics objectives unavailable in NESCOIL and REGCOIL, including the Lorentz force, magnetic energy, curvature, field-current alignment, and the maximum density of a dipole array. QUADCOIL requires no initial guess and runs nearly $10$ 2 x faster than filament optimization. Integrating it in the equilibrium optimization stage can potentially exclude equilibria with difficult-to-design coils, without significantly increasing the computation time per iteration. QUADCOIL finds the exact, global minimum in a large parameter space when possible, and otherwise finds a well-performing approximate global minimum. It supports most regularization techniques developed for NESCOIL and REGCOIL. We demonstrate QUADCOIL’s effectiveness in coil topology control, minimizing non-convex penalties, and predicting filament coil complexity with three numerical examples.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Statistical Analysis of CO 2 Exposed Wells to Predict Long Term Leakage through the Development of an Integrated Neural-Genetic Algorithm

The objective of this project is to develop a computerized statistical model with the Integrated Neural-Genetic Algorithm (INGA) for predicting the probability of long-term leak of wells in CO 2 sequestration operations. This object has been accomplished by conducting research in three phases: 1) data mining of CO 2 -explosed wells, 2) INGA computer model development, and 3) evaluation of the predictive performance of the computer model with data from field tests. Data mining was conducted for 510 wells in two CO 2 sequestration projects in the Texas Gulf Coast region. They are the Hasting West field and Oyster Bayou field in the Southern Texas. Missing wellbore integrity data were estimated using an analytical and Finite Element Method (FEM) model. The INGA was first tested for performances of convergence and computing efficiency with the obtained data set of high dimension. It was concluded that the INGA can handle the gathered data set with good accuracy and reasonable computing time after a reduction of dimension with a grouping mechanism. A computerized statistical model with the INGA was then developed based on data pre-processing and grouping. Comprehensive training and testing of the model were carried out to ensure that the model is accurate and efficient enough for predicting the probability of long-term leak of wells in CO 2 sequestration operations. The Cranfield in the southern Mississippi was select as the test site. Observation wells CFU31F2 and CFU31F3 were used for pressure-testing, formation-logging, and cement-sampling. Tools run in the wells include Isolation Scanner, Slim Cement Mapping Tool (SCMT), Cased Hole Formation Dynamics Tester (CHDT), and Mechanical Sidewall Coring Tool (MSCT). Analyses of the obtained data indicate no leak of CO 2 cross the cap zone while it is evident that the well cement sheath was invaded by the CO 2 from the storage zone. This observation is consistent with the result predicted by the INGA model which indicates the well has a CO 2 leak-safe probability of 72%. This comparison implies that the developed INGA model is valid for future use in predicting well leak probability.

97 MATHEMATICS AND COMPUTING↗