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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Active-wake mixing in atmospheric boundary layers with one-turbine arrays

This dataset includes results of high fidelity simulations of a single, offshore wind turbine under a variety of atmospheric conditions. Of primary interest is the turbine performance and wake characteristics when different turbine control strategies are applied, including when wake steering or active wake control are used. The simulations were performed with the LES code AMR-Wind (https://github.com/Exawind/amr-wind/), coupled with OpenFAST (https://github.com/OpenFAST/openfast) and the ROSCO open-source turbine controller (https://github.com/NREL/ROSCO). The turbine used in the simulations is the IEA 15MW reference turbine model.

17 WIND ENERGY↗
DOE OSTI2022

Neutronics Modeling of the pulsed plasma rocket reactor using rattlesnake

In the pulsed plasma rocket (PPR) reactor, neutron induced fission processes are utilized to implement a series of pulsed micro-explosions of very high power and eject plasma as a propellant. More specifically, projectiles (bullets) composed of moderated uranium are sent through the chamber of an unmoderated uranium barrel. By inducing rapid fission within the bullets, a plasma can be generated with appropriate delivery of neutrons. This study aims to assess the neutronics performance of the PPR reactor during normal operations using the MOOSE-based Rattlesnake code through the evaluation of the impact of the movement of the fuel bullet and the rotation of control drums on the criticality of the system. The Monte Carlo (MC) code Serpent 2 was employed to generate material-based cross sections for use in Rattlesnake and the reference neutronics solution. Cubit was used to generate the mesh for the Rattlesnake model. Parametric studies were conducted to evaluate the best approaches for cross section and mesh generation to ensure accurate results from Rattlesnake. As part of the verification process, the eigenvalue results of the system at various fuel bullet positions were obtained using Rattlesnake and compared with the reference solutions. The acceptable differences show that the Rattlesnake model with appropriate cross section and mesh generation procedure is a sufficiently accurate approximation of the continuous energy (CE) MC model for micro-sized reactors like the PPR reactor. Next, the impact of the rotation of the control drums on the system criticality was evaluated using the verified model. It was shown that the high-fidelity simulation using the deterministic code Rattlesnake can produce sufficiently accurate results for the evaluation of the reactor's neutronics performance in different phases of the normal operation with acceptable computational cost.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Utah FORGE 2-2446: Closing the Loop Between In-Situ Stress Complexity and EGS Fracture Complexity - 2024 Annual Workshop Presentation

This is a presentation on Closing the Loop Between In-Situ Stress Complexity and EGS Fracture Complexity by Lawrence Livermore National Laboratory, presented by Matteo Cusini. The video discusses the combination of high-fidelity simulations and true-triaxial block fracturing tests to explore the intricate relationship between in-situ stress and hydraulic fracture patterns. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 14, 2024.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Parallel Tensor Compression for Large-Scale Scientific Data.

As parallel computing trends towards the exascale, scientific data produced by high-fidelity simulations are growing increasingly massive. For instance, a simulation on a three-dimensional spatial grid with 512 points per dimension that tracks 64 variables per grid point for 128 time steps yields 8 TB of data. By viewing the data as a dense five way tensor, we can compute a Tucker decomposition to find inherent low-dimensional multilinear structure, achieving compression ratios of up to 10000 on real-world data sets with negligible loss in accuracy. So that we can operate on such massive data, we present the first-ever distributed memory parallel implementation for the Tucker decomposition, whose key computations correspond to parallel linear algebra operations, albeit with nonstandard data layouts. Our approach specifies a data distribution for tensors that avoids any tensor data redistribution, either locally or in parallel. We provide accompanying analysis of the computation and communication costs of the algorithms. To demonstrate the compression and accuracy of the method, we apply our approach to real-world data sets from combustion science simulations. We also provide detailed performance results, including parallel performance in both weak and strong scaling experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Development of an Efficient Meso- scale Multi-phase Flow Solver in Nuclear Applications

The proposed research aims at formulating a predictive high-order Lattice Boltzmann Equation for multi-phase flows relevant to nuclear energy related application - namely, saturated and sub-cooled boiling in reactors, and liquid- liquid mixing and extraction for fuel cycle separation. An efficient flow solver will be developed based on the Finite Element based Lattice Boltzmann Method (FE- LBM), accounting for phase-change heat transfer and capable of treating multiple phases over length scales from the submicron to the meter. A thermal LBM will be developed in order to handle adjustable Prandtl number, arbitrary specific heat ratio, a wide range of temperature variations, better numerical stability during liquid-vapor phase change, and full thermo-hydrodynamic consistency. Two-phase FE-LBM will be extended to liquid–liquid–gas multi-phase flows for application to high-fidelity simulations building up from the meso-scale up to the equipment sub-component scale. While several relevant applications exist, the initial applications for demonstration of the efficient methods to be developed as part of this project include numerical investigations of Critical Heat Flux (CHF) phenomena in nuclear reactor fuel bundles, and liquid-liquid mixing and interfacial area generation for liquid-liquid separations. In addition, targeted experiments will be conducted for validation of this advanced multi-phase model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Combustion energy frontier research center (CEFRC) final report (August 1, 2009 – July 31, 2016)

The Combustion Energy Frontier Research Center (CEFRC) was established to tackle the single overarching grand challenge of energy sustainability, energy security and global warming: to develop a “validated, predictive, multi-scale, combustion modeling capability to optimize the design and operation of evolving fuels in advanced engines for transportation applications,” as identified in the DOE report on “Basic Energy Needs for Clean and Efficient Combustion of 21st Century Transportation Fuels”. The challenge is particularly daunting since energy conversion efficiencies and exhaust emissions are governed by coupled chemical and transport processes at multiple length scales ranging from electron excitation to molecular rearrangements to nanoscale particulate formation to turbulent fuel/air mixing. To tackle this challenge, the CEFRC assembled a world-class team of 15 principal investigators, with the objectives to: 1) develop and test theoretical models to predict elementary reaction rates, molecule thermalization rates, chemical bond dissociation energies, and nonequilibrium transport properties using quantum chemistry calculations that account for strong electron correlation and multiple electronic potential energy surfaces; 2) develop automated kinetic mechanism generation, reduction, and error control methods for predicting alternative fuel including biofuel oxidation, fuel droplet decomposition, and NOx and particulate formation; 3) validate and improve the predictions of these models by measuring ignition delay times, species profiles, flame structures, burning limits, turbulence-kinetic coupling, and NOx and soot emissions at high-pressures and near-limit conditions, by using advanced experimental diagnostic techniques including multiple laser techniques, molecular beam sampling and synchrotron photoionization, and by conducting the measurements in high-pressure shock tubes, jet-stirred and flow reactors, flame bombs, counterflow flames, and advanced-design rapid compression ignition instruments; and 4) develop a suite of validated petascale high-fidelity simulation and modeling capabilities to understand and predict chemistry-turbulence-radiation coupling for new fuels in new regimes, including the high pressure, low-temperature combustion in advanced engine and turbine designs, and 5) establish a knowledge highway between researchers and engineers in academia, national laboratories, and industry to facilitate the dissemination and exchange of knowledge on national and international levels, and enrich the talent pool and capabilities of the next generation of combustion scientists and engineers. The technical activities of the CEFRC were conducted through three Disciplinary Working Groups – Chemistry Theory, Experiment and Mechanism, and Reacting Flows, which coordinated the Center’s research on the development of combustion chemistry of Foundation Fuels (C0–C4 hydrocarbons), Alcohols, and Biodiesel through three corresponding Mechanism Thrust Groups. Such a two-dimensional coordinated and tightly interwoven research structure has been proven to be highly effective in assuring the interplay between the developments of the fundamentals of combustion science and the utilization of the various categories of fuels. The Center has accomplished the above goals over the five year period (August 1, 2009 – July 31, 2014) with appropriated funding, followed by two additional no-cost-extension (NCE) years. The research results are documented in 230 journal publications, with six legacy review papers on the study of combustion chemistry using shock tubes, flow reactors, rapid compression machines, and flames, on uncertainty quantification, and on theoretical reaction dynamics and chemical modeling of combustion. A robust outreach program complemented these PI-led research activities, consisting of: 1) a roving post-doc program comprised of a corps of Center-appointed, co- or multi-sponsored post-doctoral fellows with rotating assignments to conduct seed projects initiated by at least two PIs, in residence with these sponsoring PIs, to rapidly pursue new and high-risk, high-payoff interdisciplinary ideas; 2) an annual summer school on combustion heavily attended (~200) by senior graduate students and practicing researchers covering advanced topics on chemical kinetics, fluid mechanics, turbulent combustion, engine combustion, new technologies, etc.; 3) a robust open web-site providing Center and community information as well as the lecture videos and notes of the summer school; and 4) widely distributed biannual newsletters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).

Through long-term investments in computing, algorithms, facilities, and instrumentation, DOE is an established leader in massive-scale, high-fidelity simulations, as well as science-leading experimentation. In both cases, DOE is generating more data than it can analyze and the problem is intensifying quickly. The need for advanced algorithms that can automatically convert the abundance of data into a wealth of useful information by discovering hidden structures is well recognized. Such efforts however, are hindered by the massive volume of the data and its high velocity. Here, the challenge is developing unsupervised learning methods to discover hidden structure in high-volume, high-velocity data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

SciDAC – PSI: Plasma Surface Interactions involving Tungsten - A Summary of Project Accomplishments

Gaining physics understanding and predictive capabilities to describe the evolution of plasma facing components (PFC) requires simultaneously addressing complex and diverse physics occurring over a wide range of length and time scales, as well as integrating extensive physical processes across the plasma - surface - bulk materials boundaries. This requires development not only of detailed physics models and computational strategies at each of these scales, but computer science algorithms and methods to strongly couple them in a way that can be robustly validated through comparisons against available data and new experiments. Therefore, the objective of this project is to develop robust, high-fidelity simulation tools capable of predicting the PFC operating lifetime and the PFC impact on plasma contamination, recycling of hydrogenic species, and tritium retention in future magnetic fusion devices, with a focus on tungsten based material systems. Deploying these tools requires the development of a leadershipscale computational code, as well as a host of simulations that span the multiple scales needed to address complex physical and computational issues at the plasma - surface interface and the transition below the surface where neutron damage processes in the bulk material dominate behavior in multiple-component materials systems. Successful development will enable improved prediction of PFC performance needed to ensure magnetic fusion energy development beyond ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Physics-Informed Machine Learning for Predictive Turbulence Modeling: Towards a Complete Framework

Although increased availability of computational resources has enabled high-fidelity simulations (e.g., large eddy simulations) of turbulent flows, the Reynolds-Averaged Navier–Stokes (RANS) models are still the dominant tools in industrial applications. However, the predictive capabilities of RANS models are limited by large model-form discrepancies due to the Reynolds stress closure. Recently, a Physics-Informed Machine Learning (PIML) approach has been proposed to learn the functional form of Reynolds stress discrepancy in RANS simulations based on available data. It has been demonstrated that the learned discrepancy function can be used to improve Reynolds stresses in new flows where data are not available. However, due to a number of challenges, the improvements are only demonstrated in the Reynolds stress prediction but not in corresponding propagated quantities of interest (e.g., velocity field). In this work, we investigate the prediction performance on the velocity field by propagating the corrected Reynolds stresses in PIML approach. To enrich the input features, an integrity basis of invariants is implemented. The fully developed turbulent flow in a square duct is used as the test case. The discrepancy model is trained on flow fields from several Reynolds numbers and evaluated on a duct flow at a higher Reynolds number than any of the training cases. The predicted Reynolds stresses are propagated to velocity field via RANS equations. Numerical results show excellent predictive performances in both Reynolds stresses and their propagated velocities, demonstrating the merits of the PIML approach in predictive turbulence modeling.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Coupled Experiment and Theory to Explore the Limits of Material Strength at High Strain Rates

Critical components such as detonators in Sandia's stockpile are heterogeneous materials that rely on accurate constitutive models for each material used in the component. However, experiments to study materials at high pressure are challenging and time consumptive, therefore we turn to modeling tools to refine and predict outcomes beforehand. Efficient models balance absolute physical accuracy against approximate but computationally lightweight constitutive inputs. By using a relatively small number of high fidelity simulations and experiments we have be able to broaden the predictive power of the shock response in metals and polymers. We have tailored the analysis of these simulations to determine a size dependent material strength, which can be used as constitutive model inputs for continuum hydrodynamics codes. For the shocked Cu, MD simulations show a yield strength from RMI jet growth of approximately 450MPa that depends on the details of the free surface geometry. This value is close to the yield strength of 500MPa parameterized for an elastic-perfectly-plastic strength model from experiments at the Dynamic Compression Sector at Argonne National Lab. The same analysis applied to MD simulations of PMMA jetting resulted in no clear determination of yield strength, implying a more complex RMI process in polymeric materials. Atomistic simulations of both materials are shown to be valuable training metrics and show the need for explicit strain rate dependent strength for future improvements in strength models used in continuum codes.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Microphysics Studies of Hohlraum Dynamics (LDRD 17-ERD-060 Final Report)

The goal of this project was demonstrate the feasibility and utility of modeling the effects of kinetic physics in gas-filled hohlraums using high-fidelity simulation tools. We have successfully achieved this goal. In our work, we have found that hybrid particle-in-cell simulations (Thoma et al. 2017, Higginson et al. 2019a) utilizing quasi-neutral, fluid electrons and kinetic ions are powerful tools to understand the kinetic physics of interpenetration and mix relevant to low-gas-fill (near-vacuum) hohlraums. We have benchmarked the code, Chicago, against OMEGA laser experiments designed to mimic conditions found in hohlraums; the code was able to reproduce data from x-ray imaging and Thomson Spectra resolved over both time and space. The experiments highlighted kinetic-ion effects such increased penetration depth that were only able to be resolved with a kinetic model. We have run preliminary simulations with Chicago of full-scale hohlraum experiments that include laser raytracing, equation-of-state and ionization, radiation transport, and accurate experimental geometries. Continuation of this work will predictive hohlraum models at low-gas-fill and identify the better regimes of hohlraum design with the potential for improved coupling and capsule symmetry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Plate Impact and Penetration at the Ballistic Launch Tube: Experiments

This report details the data collected from plate impact experiments performed at the Ballistics Launch Tube (BLT) in May 2019. The experiments consisted of 62 shots of copper projectiles (cylindrical and ogive) impacting 1/4", 1/2", and 3/4" aluminum plates at varying velocities. An additional 14 shots of copper cylinders on a 1" steel plate were fired at varying velocities as a Taylor anvil test. We recorded videos of the impact events and resulting fragmentation using a multi-view system of three high speed cameras. The purpose of these tests was to collect high quality data from the multi-view camera system and create digital representations of the deformed target, projectile and fragments. This data is intended to be used as validation data set for high fidelity simulation codes. This report covers the experimental setup, diagnostics, and collected data. Data processing and analysis are underway and will be discussed in a separate report.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

ECP Software Technology Capability Assessment Report

The Exascale Computing Project Software Technology (ECP ST) focus area represents the key bridge between Exascale systems and the scientists developing applications that will run on those platforms. ECP offers a unique opportunity to build a coherent set of software (often referred to as the "software stack") that will allow application developers to maximize their ability to write highly parallel applications, targeting multiple Exascale architectures with runtime environments that will provide high performance and resilience. But applications are only useful if they can provide scientific insight, and the unprecedented data produced by these applications require a complete analysis work ow that includes new technology to scalably collect, reduce, organize, curate, and analyze the data into actionable decisions. This requires approaching scientific computing in a holistic manner, encompassing the entire user workflow - from conception of a problem, setting up the problem with validated inputs, performing high-fidelity simulations, to the application of uncertainty quantification to the final analysis. The software stack plan defined here aims to address all of these needs by extending current technologies to Exascale where possible, by performing the research required to conceive of new approaches necessary to address unique problems where current approaches will not suffice, and by deploying high-quality and robust software products on the platforms developed in the Exascale systems project. The ECP ST portfolio has established a set of interdependent projects that will allow for the research, development, and delivery of a comprehensive software stack,

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Machine Learning for Turbulence Modeling

This work was conducted as part of a Harry S. Truman Fellowship Laboratory Directed Research and Development project. The goal was to use machine learning methods to provide uncertainty quantification and model improvements for Reynolds Averaged Navier Stokes (RANS) turbulence models. For applications of interest in energy, safety, and security, it is critical to be able to model turbulence accurately. Current RANS models are unreliable for many flows of engineering relevance. Machine learning provides an avenue for developing improved models based on the data generated by high fidelity simulations. In this project, machine learning methods were used to predict when current RANS models would fail. They were also used to develop improved RANS closure models. A key aim was developing a tight feedback loop between scientific domain knowledge and data driven methods. To this end, a methodology for incorporating known invariance constraints into the machine learning models was proposed and evaluated. This work demonstrated that incorporating known constraints into the data driven models provided improved performance and reduced computational cost. This research represents one of the first applications of deep learning to turbulence modeling.

42 ENGINEERING↗
DOE OSTI2022

Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations (Abstract only)

This project directly addresses the primary goal of Area of Interest 2 in the CRADA call: to advance optimization-based integrated energy management systems in commercial and residential buildings. Pacific Northwest National Laboratory (PNNL) and its industry partner PassiveLogic aim to accomplish this by reaching three key objectives. First, to ensure a broad impact in the building controls industry, PNNL will extend its open-source library for predictive control synthesis by augmenting its capabilities with data-driven self-learning of building models and auto-calibration of predictive controllers. The effort will focus on building use cases selected in collaboration with PassiveLogic. The team will specifically address the development of methods for data-driven adaptation of building models, investigation of model architectures that best address specific building types, and automated synthesis of differentiable predictive controllers that optimize diverse objectives. Second, PNNL will collaborate with PassiveLogic to integrate the aforementioned methods with PasiveLogic’s advanced controls platform. The collaborative integration effort will inform the developments under the first objective by providing specific data on the attainable performance of model learning on resource-constrained edge computing platforms. This software integration effort will increase the technical maturity of the developed libraries by exploring the use of software integration tools and methods. Third, PNNL and PassiveLogic will work to improve the technology readiness of the developed predictive controllers by testing their performance in relevant test environments, such as high-fidelity simulation, hardware in the loop, and actual test buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

A Validated Finite Element Modeling Tool for Hydrodynamic Loading and Structural Analysis of Ocean Deployed Macroalgae Farms

The objective of this project was to develop and validate a fine-tuned, 3D computational modeling tool for high-fidelity simulations of macroalgae cultivation and harvest systems. To develop this tool, a full understanding of the geometric, material and hydrodynamic properties associated with farming kelp in exposed environments was necessary. Experiments and numerical modeling were done at increasing scales from 1) tank tests with a 1 m model, 2) to field tests with a 61 m culture line at a sheltered site, 3) to field tests with a 122 m culture line at an exposed site and to 4) a 5- line array at the exposed site with 186 m of culture length. The results of the experimental tank tests yielded a set of hydrodynamic drag-area coefficients for a 1 m aggregate based on densely grown kelp at 16 kg/m with a length up to 3 m as described in Fredriksson et al. This work provided the basis to model kelp as a 1 m aggregate based on in-situ geometric and material properties to include measurements of kelp blade length and width, number of blades, yield per m, material mass density and cantilever tests to obtain modulus of elasticity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Optimization of Membrane-based Carbon Capture using Dimensional Analysis, CFD and Process System Engineering

Carbon capture is a promising option to mitigate CO2 emissions from existing coal-fired power plants, cement and steel industries, and petrochemical complexes. Among the available technologies, membrane-based carbon capture presents the lowest energy consumption, operating costs, and carbon footprint. In addition, membrane processes have important operational flexibil-ity and response times. On the other hand, the major challenges to widespread application of this technology are related to reducing capital costs and improving membrane stability and durability.To upscale the technology into stacked flat sheet configurations, high fidelity computational fluid dynamics (CFD) that describes the separation process accurately are required. High fidelity simulations have been shown to be effective in studying the complex transport phenomena in membrane systems. In addition, obtaining high CO2 recovery percentages and product purity requires a multi-stage membrane process, where the optimal network configuration of the membrane modules must be studied in a systematic way. In order to address the design problem at process scale, we formulate a superstructure for the membrane-based carbon capture, including up to three separation stages. In the formulation of the optimization problem, we include reduced models, based on rigorous CFD simulations of the membrane modules. Numerical results indicate that the optimal design includes three membrane stages, and the capture cost is 45.4 $/t-CO2.

Pedrozo, Hector A.↗
DOE OSTI2024

Surface Pressure Fluctuations Induced by a Hypersonic Turbulent Boundary Layer on a Sharp Cone at Angle of Attack

High-fidelity simulations are performed to characterize the turbulence-induced wall pressure fluctuations on a sharp cone at a 5.5° angle-of-attack in a Mach 8 flow. Wall-resolved large-eddy simulation (LES) and wall-modeled large-eddy simulation (WMLES) results are compared to measurements at several locations on the cone body, where comparisons with high-frequency PCB sensors are good, while comparison with low-frequency kulite sensors varies depending on the location. For a given streamwise location, simulation results show significant azimuthal variation in the wall pressure fluctuation statistics. Comparisons between LES and WMLES results indicate that WMLES can accurately reproduce the low-frequency component of the autospectra quite well, while slight sensitivity is apparent in the coherence. A grid sensitivity study for WMLES further demonstrated sensitivity in the wall pressure fluctuation coherence, but not in the autospectra.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗