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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Perspective on Sustainable Computational Chemistry Software Development and Integration

The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a flexible forward strategy to take full advantage of existing and forthcoming computational resources. In this context, the sustainability and interoperability of computational chemistry software development are among the most pressing issues. In this perspective, we discuss software infrastructure needs and investments with an eye to fully utilize exascale resources and provide unique computational tools for next-generation science problems and scientific discoveries.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Survey of DAKOTA's V&V Capabilities in the Simulation of Residual Stresses in a Simple Composite Structure

Process-induced residual stresses occur in composite structures composed of dissimilar materials. As these residual stresses can result in fracture, their consideration when designing composite parts is necessary. However, the experimental determination of residual stresses in prototype parts can be time and cost prohibitive. Alternatively, it is possible for computational tools to predict potential residual stresses. Therefore, a process modeling methodology was developed and implemented into Sandia National Laboratories' SIERRA/SolidMechanics code. This method can be used to predict the process-induced stresses in any composite structure, regardless of material composition or geometric complexity. However, to develop confidence in these predictions, they must be rigorously validated. Specifically, sensitivity studies should be completed to define which model parameters are critical to the residual stress predictions. Then, the uncertainty associated with those critical parameters should be quantified and processed through the model to develop stress-state predictions encompassing the most important sources of physical variability. Numerous sensitivity analysis and uncertainty quantification methods exist, each offering specific strengths and weaknesses. Therefore, the objective of this study is to compare the performance of several accepted sensitivity analysis and uncertainty quantification methods during the manufacturing process simulation of a composite structure. The examined methods include simple sampling techniques as well as more sophisticated surrogate approaches. The computational costs are assessed for each of the examined methods, and the results of the study indicate that the surrogate approaches are the most computationally efficient validation methods and are ideal for future residual stress investigations.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Ab Initio Calculations Of Nuclear Reactions And Exotic Nuclei

Our ultimate goal is to develop a fundamental theory and efficient computational tools to describe dynamic processes between nuclei and to use such tools toward supporting several DOE milestones by: 1) performing predictive calculations of difficult-to-measure landmark reactions for nuclear astrophysics, such as those driving the neutrino signature of our sun; 2) improving our understanding of the structure of nuclei near the neutron drip line, which will be the focus of the DOE’s Facility for Rare Isotope Beams (FRIB) being constructed at Michigan State University; but also 3) helping to reveal the true nature of the nuclear force. Furthermore, these theoretical developments will support plasma diagnostic efforts at facilities dedicated to the development of terrestrial fusion energy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Elucidating Molecular Interactions in Glycerol Adsorption at the Metal–Water Interface with Density Functional Theory

Glycerol is an extremely versatile platform molecule for chemical and fuel production, as evidenced by successful demonstrations in electrochemical and thermochemical processes, where key catalytic chemistries occur at the solid–liquid interface. Despite the remarkable progress made in enriching the first-principles-based computational tool set to reveal and characterize solvent structures in the past decade, techniques for realistic and efficient molecular-level modeling to study aqueous-phase glycerol chemistry are still far from mature. Many aqueous-phase catalytic systems are deemed too complex for routine modeling because of their highly correlated structures at the heterogeneous solid–liquid interface. This invited feature article merges recent developments in quantum mechanical solvation models and oxygenated hydrocarbon conversion chemistry by revisiting the molecular interactions of adsorbed glycerol and its dehydrogenation intermediates at the water–metal interface. Explicit participation of water through the establishment of water–adsorbate, water–water, and water–metal interactions on Pt(111) was investigated using density functional theory. In periodic models, the adsorption favors networklike structures with adsorbates as nodal points linked by coadsorbed water molecules. We also showed that these adsorption patterns actually preserve the original bond-order-based scaling relationship framework established without the consideration of solvent. This behavior can be exploited to improve computational efficiency for future analysis of catalytic polyol conversions in the aqueous-phase environment.

Shan, Nannan↗
DOE OSTI2022

A scalable compressible volume of fluid solver using a stratified flow model

Here, in this paper, a Volume of Fluid method which uses a Stratified Flow model for flux calculation is proposed to solve a compressible multiphase flow in a way that has high parallel efficiency. The solver is part of an open-source computing tool to study fuel entrainment and combustion in the application of paraffin-based hybrid rocket motors. This paper focuses on the compressible Volume of Fluid solver for the liquid and gaseous phases in this environment. This solver uses perfect gas and stiffened gas models for equation of state, with the ability to easily add other models as desired. Additionally a comparison of a Riemann solver versus an AUSM+up scheme is conducted, showing improvement with the later in the solution of multiphase flows. This solver successfully reproduces shock tube cases with the expected accuracy, as well as simulating two-dimensional shear and gravity-driven flows. Results also show that the code is able to utilize a Stratified Flow model to evaluate shear flow without the need for interface reconstruction or gradient calculations. Finally, it is shown that the solver has near-ideal scaling under strong scaling tests as well as good performance in static scaling, giving improved performance over current options and implying future advancement of high-performance computing efficiency for multiphase flow solvers.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Advanced Ceramic Matrix Composites: Science and Technology of Materials, Design, Applications, Performance and Integration

Overview of NASA Transformational Tools and Technologies Project's 2700F CMC/EBC Technology Challenge J. Hurst, NASA (National Aeronautics and Space Administration, Glenn Research Center, Cleveland, OH USA Key Words: ceramic matrix composites (CMCs), SiC/SiC composites, environmental barrier coatings (EBCs), CMC modeling, simulated engine testing As advanced gas turbine engine designs continue to move toward both higher operating temperatures and increased pressures, materials capable of functioning under these extreme conditions are being sought by both government and industry. As part of its mission, the NASA's Transformational Tools and Technologies Project, under the auspices of NASA's Aeronautics Research Mission Directorate, has been pursuing a five year Technology Challenge problem. This challenge problem has sought to develop high temperature materials for turbine engines that enable a 6% reduction in fuel burn for commercial aircraft, compared to current SOA materials. Specifically this is accomplished by the development and demonstration of a ceramic matrix composite (CMC) and environmental barrier coating (EBC) system capable of sustained performance at temperature of 2700F. The NASA Glenn Research Center has continued its two decade long interest in CMC/EBCs technology by pursuing this 2700F performance goal. This ambitious effort included an array of vendors, universities and engine companies. Included in this challenge was fiber development, fiber architecture considerations, ceramic matrix composition, environmental barrier compositions and processing routes as well as test development. Evaluation of the CMC/EBC materials included a variety of testing, from coupon testing for strength and creep resistance, to testing aimed at determining material behavior under more engine-like conditions. The development and validation of thermomechanical models and computational tools for design, analysis, and life prediction, have been an important part of this effort as well. Simulated engine testing of vane subcomponents by P&W was the final step following years of development. In addition to achieving the temperature goals and enhanced durability performance of the Tech Challenge, an additional goal achieved by this work was reaching a Technology Readiness Level of 5 for the 3-D CMC/EBC system. As this five year Technical Challenge comes to a conclusion, future development interests in environmental barrier coatings and environmental modeling are discussed.

SiC/SiC composites↗
DOE OSTI2021

Learning the solution operator of parametric partial differential equations with physics-informed DeepONets

Partial differential equations (PDEs) play a central role in the mathematical analysis and modeling of complex dynamic processes across all corners of science and engineering. Their solution often requires laborious analytical or computational tools, associated with a cost that is markedly amplified when different scenarios need to be investigated, for example, corresponding to different initial or boundary conditions, different inputs, etc. In this work, we introduce physics-informed DeepONets, a deep learning framework for learning the solution operator of arbitrary PDEs, even in the absence of any paired input-output training data. We illustrate the effectiveness of the proposed framework in rapidly predicting the solution of various types of parametric PDEs up to three orders of magnitude faster compared to conventional PDE solvers, setting a previously unexplored paradigm for modeling and simulation of nonlinear and nonequilibrium processes in science and engineering.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Modeling the Effects of Ice Accretion on the Low Pressure Compressor and the Overall Turbofan Engine System Performance

The focus of this study is on utilizing a mean line compressor flow analysis code coupled to an engine system thermodynamic code, to estimate the effects of ice accretion on the low pressure compressor, and quantifying its effects on the engine system throughout a notional flight trajectory. In this paper a temperature range in which engine icing would occur was assumed. This provided a mechanism to locate potential component icing sites and allow the computational tools to add blockages due to ice accretion in a parametric fashion. Ultimately the location and level of blockage due to icing would be provided by an ice accretion code. To proceed, an engine system modeling code and a mean line compressor flow analysis code were utilized to calculate the flow conditions in the fan-core and low pressure compressor and to identify potential locations within the compressor where ice may accrete. In this study, an "additional blockage" due to the accretion of ice on the metal surfaces, has been added to the baseline aerodynamic blockage due to boundary layer, as well as the blade metal blockage. Once the potential locations of ice accretion are identified, the levels of additional blockage due to accretion were parametrically varied to estimate the effects on the low pressure compressor blade row performance operating within the engine system environment. This study includes detailed analysis of compressor and engine performance during cruise and descent operating conditions at several altitudes within the notional flight trajectory. The purpose of this effort is to develop the computer codes to provide a predictive capability to forecast the onset of engine icing events, such that they could ultimately help in the avoidance of these events.

Veres, Joseph P.↗
DOE OSTI2017

Decision Analyses Related to Groundwater Remediation - 17051

Traditional decision analyses of groundwater remediation scenarios frequently fail because the probability of adverse, unanticipated events occurring is often high. This is driven by many factors including that (1) the models developed to represent the flow and transport in contaminated aquifers are typically simpler than reality and do not account for all the important governing processes, (2) probability distributions are assigned to critical performance parameters (such as model inputs or characteristics of the engineered remediation systems) even though some of these parameters might be unknown or not be very well constrained. However, there is a novel decision analysis methodology and a computational tool based on Bayesian Information-Gap Decision Theory (BIG-DT) that are designed to mitigate the shortcomings of the models and probabilistic decision analyses by leveraging a non-probabilistic decision theory -- information-gap decision theory. The new decision analysis methodology considers possible models that have not been explicitly enumerated and does not require us to commit to a particular probability distribution for model and remediation-design parameters. Both the set of possible models and the set of possible probability distributions grow as the degree of uncertainty increases. The fundamental question that BIG-DT asks is 'How large can these sets be before a particular decision results in an undesirable outcome?'. The decision that allows these sets to be the largest is considered to be the best option. In this way, BIG-DT enables robust decision-support for groundwater remediation problems. Here, we apply BIG-DT to a representative groundwater remediation scenario where different options for hydraulic containment and pump and treat are being considered. BIG-DT requires many model runs and high-performance computing resources are needed when complex models are used. We demonstrate the BIG-DT analyses on a series of synthetic problems that are designed to be consistent with real-world problems such as Los Alamos National Laboratory (LANL) contamination sites. We also discuss a general framework for how the BIG-DT analyses will be carried out at the chromium groundwater contamination site at LANL. BIG-DT is implemented in Julia (a high-level, high-performance dynamic programming language for technical computing) and is part of the MADS framework (http://mads.lanl.gov and http://madsjulia.github.io/Mads.jl). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

LDRD HPC4Energy Wrapup Report - LDRD 12-ERD-074

High-performance computing and simulation has the potential to optimize production, distribution, and conversion of energy. Although a number of concepts have been discussed, a comprehensive research project to establish and quantify the effectiveness of computing and simulation at scale to core energy problems has not been conducted. We propose to perform the basic research to adapt existing high-performance computing tools and simulation approaches to two selected classes of problems common across the energy sector. The first, applying uncertainty quantification and contingency analysis techniques to energy optimization, allows us to assess the effectiveness of LLNL core competencies to problems such as grid optimization and building-system efficiency. The second, applying adaptive meshing and numerical analysis techniques to physical problems at fine scale, could allow immediate impacts in key areas such as efficient combustion and fracture and spallation. By creating an integrated project team with the necessary expertise, we can efficiently address these issues, delivering both near-term results as well as quantifying developments needed to address future energy challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2016

Parametric Modeling Investigation of a Radially-Staged Low-Emission Aviation Combustor

Aviation gas-turbine combustion demands high efficiency, wide operability and minimal trace gas emissions. Performance critical design parameters include injector geometry, combustor layout, fuel-air mixing and engine cycle conditions. The present investigation explores these factors and their impact on a radially staged low-emission aviation combustor sized for a next-generation 24,000-lbf-thrust engine. By coupling multi-fidelity computational tools, a design exploration was performed using a parameterized annular combustor sector at projected 100% takeoff power conditions. Design objectives included nitrogen oxide emission indices and overall combustor pressure loss. From the design space, an optimal configuration was selected and simulated at 7.1, 30 and 85% part-power operation, corresponding to landing-takeoff cycle idle, approach and climb segments. All results were obtained by solution of the steady-state Reynolds-averaged Navier-Stokes equations. Species concentrations were solved directly using a reduced 19-step reaction mechanism for Jet-A. Turbulence closure was obtained using a nonlinear K-epsilon model. This research demonstrates revolutionary combustor design exploration enabled by multi-fidelity physics-based simulation.

design analysis↗
NASA NTRSDate not supplied

Acoustic Testing of the Joby Aviation Propeller in the National Full-Scale Aerodynamics Complex 40- by 80-Foot Wind Tunnel

The advanced air mobility sector has progressed in recent years with electric vertical take-Off and landing vehicles at the forefront. In response, the NASA Advanced Air Mobility Project has initiated the Advanced Air Mobility National Campaign to partner with industry to progress this emerging market. This resulted in NASA’s Revolutionary Vertical Lift Technology project to participate to progress research efforts for software tool validation for these advanced air mobility vehicle configurations. Noise has been identified as one of the main obstacles to succeeding in this emerging market for community acceptance. To further understand electric vertical take-Off and landing vehicle noise, an isolated Joby Aviation S4 propeller was tested in the National Full-Scale Aerodynamics Complex 40- by 80-Foot Wind Tunnel to aid in providing high quality performance, loads, and acoustic data. Details of test hardware and setup are described with a focus on acoustics, which includes facility, test model, microphone locations, and data acquisition system details. Additionally, the data processing techniques and completed test matrix for acoustic points are provided. To aid in identifying potential acoustic reflections, an acoustic reflection test was performed prior to the start of the test and highlighted the presence of reflections at every microphone location. Acoustic results are provided for various flight regimes for sweeps of blade pitch, RPM, flow angle, and wind speed. These results show an optimum RPM for a specific thrust level for noise, and further reductions in RPM can increase noise levels. This dataset will be used to evaluate and improve computational tools.

Aviation Propeller↗
NASA NTRS1998

An Integrated Tool for the Coupled Thermal and Mechanical Analysis of Pyrolyzing Heatshield Materials

Materials that pyrolyze at elevated temperature have been commonly used as thermal protection materials in hypersonic flight, and advanced pyrolyzing materials for this purpose continue to be developed. Because of the large temperature gradients that can arise in thermal protection materials, significant thermal stresses can develop. Advanced applications of pyrolytic materials are calling for more complex heatshield configurations, making accurate thermal stress analysis more important, and more challenging. For non-pyrolyzing materials, many finite element codes are available and capable of performing coupled thermal-mechanical analyses. These codes do not, however, have a built-in capability to perform analyses that include pyrolysis effects. When a pyrolyzing material is heated, one or more components of the original virgin material pyrolyze and create a gas. This gas flows away from the pyrolysis zone to the surface, resulting in a reduction in surface heating. A porous residue, referred to as char, remains in place of the virgin material. While the processes involved can be complex, it has been found that a simple physical model in which virgin material reacts to form char and pyrolysis gas, will yield satisfactory analytical results. Specifically, the effects that must be modeled include: (1) Variation of thermal properties (density, specific heat, thermal conductivity) as the material composition changes; (2) Energy released or absorbed by the pyrolysis reactions; (3) Energy convected by the flow of pyrolysis gas from the interior to the surface; (4) The reduction in surface heating due to surface blowing; and (5) Chemical and mass diffusion effects at the surface between the pyrolysis gas and edge gas Computational tools for the one-dimensional thermal analysis these materials exist and have proven to be reliable design tools. The objective of the present work is to extend the analysis capabilities of pyrolyzing materials to axisymmetric configurations, and to couple thermal and mechanical analyses so that thermal stresses may be efficiently and accurately calculated.

Pronchick, Stephen W.↗
DOE OSTI2024

HIResist: a database of HIV-1 resistance to broadly neutralizing antibodies

Changing the course of the human immunodeficiency virus type I (HIV-1) pandemic is a high public health priority with approximately 39 million people currently living with HIV-1 (PLWH) and about 1.5 million new infections annually worldwide. Broadly neutralizing antibodies (bnAbs) typically target highly conserved sites on the HIV-1 envelope glycoproteins (Envs), which mediate viral entry, and block the infection of diverse HIV-1 strains. But different mechanisms of HIV-1 resistance to bnAbs prevent robust application of bnAbs for therapeutic and preventive interventions. Here we report the development of a new database that provides data and computational tools to aid the discovery of resistant features and may assist in analysis of HIV-1 resistance to bnAbs. Bioinformatic tools allow identification of specific patterns in Env sequences of resistant strains and development of strategies to elucidate the mechanisms of HIV-1 escape; comparison of resistant and sensitive HIV-1 strains for each bnAb; identification of resistance and sensitivity signatures associated with specific bnAbs or groups of bnAbs; and visualization of antibody pairs on cross-sensitivity plots. The database has been designed with a particular focus on user-friendly and interactive interface. Our database is a valuable resource for the scientific community and provides opportunities to investigate patterns of HIV-1 resistance and to develop new approaches aimed to overcome HIV-1 resistance to bnAbs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Optimization of Energy Flow through Synthetic Metabolic Modules and Regulatory Networks in a Model Photosynthetic Eukaryotic Microbe

Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methods, coupled with functional and synthetic genomics, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require a detailed systems biology understanding of photosynthetic processes and cellular metabolism. Genome-scale metabolic models (GEMs) are at the core of systems analysis of cellular processes and form a common organizational framework for analyses of data resulting from functional genomics experimental work and computational studies. Therefore, there is a clear demand for high quality photosynthetic model organisms and the appropriate computational tools that enable systems analysis of light-driven metabolism. Through research conducted we expanded the currently available repertoire of photosynthetic GEMs to include the commercially valuable model diatom Phaeoctylum tricornutum. Diatoms have a peculiar and distinct evolutionary footprint and represent a major eukaryotic lineage that is taxonomically and functionally distinct from green and red algae and vascular plants. Therefore, the true potential for light-driven metabolism aimed at biofuel production remains poorly understood at a systems level for a large subset of the global diversity of photosynthetic organisms. The metabolic capabilities of P. tricornutum were comparatively modeled with those from other photosynthetic groups in order to elucidate the occurrence of metabolic traits within and between phototrophs. Additionally, this research resulted in significant extension of the COnstraints Based Reconstruction and Analysis (COBRA) Toolbox to accommodate the crucial need for infrastructure required for ‘omics data integration and analysis in the context of genome-scale models. Therefore, the proposed research achieved two important goals. First, within the broad scope of photosynthetic organisms, we functionally compared and, as a result, identified cellular processes that require optimization in order to enable deployment as biofuel feedstock. Second, the proposed research resulted in development of key computational infrastructure, which can be further extended to other biological systems, that is currently lacking but necessary for multiple ‘omics data integration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

BISON: A Flexible Code for Advanced Simulation of the Performance of Multiple Nuclear Fuel Forms [Slides]

As fuel vendors and designers pursue the development of advanced reactors or the increase in burnup limits for existing reactors, advanced computational tools are necessary to understand the fuel performance. BISON, a fuel performance code developed primarily at Idaho National Laboratory, which is built upon the Multiphysics Object-Oriented Simulation Environment (MOOSE), provides capabilities to analyze multiple nuclear fuel forms in a wide variety of dimensions. Since its inception, BISON has been used to investigate the performance of light-water reactor fuel rods, accident tolerant fuel concepts, metallic and mixed-oxide (MOX) fuels for fast reactors, plate fuels for research reactors, and tri-structural isotropic (TRISO) fuel particles. This talk will provide a history of BISON, highlights of major development milestones, tributes to key contributors, and applications of its use to various fuel forms in one-, two-, and three-dimensions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Refining PeakDecoder Version 2

Novel computational tools for processing multidimensional mass spectrometry (MS) data are necessary to enable deeper and automated detection and quantification of metabolites in complex backgrounds. Multidimensional MS data includes measurements from liquid chromatography (LC) and ion mobility spectrometry (IM) separations, and precursor and fragment ion spectra collected in data-independent acquisition (DIA) mode. PeakDecoder is an artificial intelligence (AI)-based software that enables automated interpretation of this kind of data to identify and quantify individual metabolites in complex mixtures. The goal of this project was to improve and re-implement PeakDecoder in a better suited programming language to enable its commercialization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hierarchical deep reinforcement learning reveals a modular mechanism of cell movement

Time-lapse images of cells and tissues contain rich information about dynamic cell behaviours, which reflect the underlying processes of proliferation, differentiation and morphogenesis. However, we lack computational tools for effective inference. Here we exploit deep reinforcement learning (DRL) to infer cell–cell interactions and collective cell behaviours in tissue morphogenesis from three-dimensional (3D) time-lapse images. We use hierarchical DRL (HDRL), known for multiscale learning and data efficiency, to examine cell migrations based on images with a ubiquitous nuclear label and simple rules formulated from empirical statistics of the images. When applied to Caenorhabditis elegans embryogenesis, HDRL reveals a multiphase, modular organization of cell movement. Imaging with additional cellular markers confirms the modular organization as a novel migration mechanism, which we term sequential rosettes. Furthermore, HDRL forms a transferable model that successfully differentiates sequential rosettes-based migration from others. Our study demonstrates a powerful approach to infer the underlying biology from time-lapse imaging without prior knowledge.

59 BASIC BIOLOGICAL SCIENCES↗