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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-T-36: A GPU-Accelerated Monte-Carlo Dose Calculation Platform and Its Application Toward Validating a ViewRay Beam Model

Purpose: To build a fast, accurate and easily-deployable research platform for Monte-Carlo dose calculations. We port the dose calculation engine PENELOPE to C++, and accelerate calculations using GPU acceleration. Simulations of a Co-60 beam model provided by ViewRay demonstrate the capabilities of the platform. Methods: We built software that incorporates a beam model interface, CT-phantom model, GPU-accelerated PENELOPE engine, and GUI front-end. We rewrote the PENELOPE kernel in C++ (from Fortran) and accelerated the code on a GPU. We seamlessly integrated a Co-60 beam model (obtained from ViewRay) into our platform. Simulations of various field sizes and SSDs using a homogeneous water phantom generated PDDs, dose profiles, and output factors that were compared to experiment data. Results: With GPU acceleration using a dated graphics card (Nvidia Tesla C2050), a highly accurate simulation – including 100*100*100 grid, 3×3×3 mm3 voxels, <1% uncertainty, and 4.2×4.2 cm2 field size – runs 24 times faster (20 minutes versus 8 hours) than when parallelizing on 8 threads across a new CPU (Intel i7-4770). Simulated PDDs, profiles and output ratios for the commercial system agree well with experiment data measured using radiographic film or ionization chamber. Based on our analysis, this beam model is precise enough for general applications. Conclusions: Using a beam model for a Co-60 system provided by ViewRay, we evaluate a dose calculation platform that we developed. Comparison to measurements demonstrates the promise of our software for use as a research platform for dose calculations, with applications including quality assurance and treatment plan verification.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Learning nonlinear level sets for dimensionality reduction in function approximation

We developed a Nonlinear Level-set Learning (NLL) method for dimensionality reduction in high-dimensional function approximation with small data. This work is motivated by a variety of design tasks in real-world engineering applications, where practitioners would replace their computationally intensive physical models (e.g., high-resolution fluid simulators) with fast-to-evaluate predictive machine learning models, so as to accelerate the engineering design processes. There are two major challenges in constructing such predictive models: (a) high-dimensional inputs (e.g., many independent design parameters) and (b) small training data, generated by running extremely time-consuming simulations. Thus, reducing the input dimension is critical to alleviate the over-fitting issue caused by data insufficiency. Existing methods, including sliced inverse regression and active subspace approaches, reduce the input dimension by learning a linear coordinate transformation; our main contribution is to extend the transformation approach to a nonlinear regime. Specifically, we exploit reversible networks (RevNets) to learn nonlinear level sets of a high-dimensional function and parameterize its level sets in low-dimensional spaces. A new loss function was designed to utilize samples of the target functions' gradient to encourage the transformed function to be sensitive to only a few transformed coordinates. The NLL approach is demonstrated by applying it to three 2D functions and two 20D functions for showing the improved approximation accuracy with the use of nonlinear transformation, as well as to an 8D composite material design problem for optimizing the buckling-resistance performance of composite shells of rocket inter-stages.

Zhang, Guannan↗
DOE OSTI2020

Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation

This study presents two different machine learning approaches for the modeling of hydrodynamic force on particles in a particle-laden multiphase flow. Results from particle-resolved direct numerical simulations (PR-DNS) of flow over a random array of stationary particles for eight combinations of particle Reynolds number (Re) and volume fraction (φ) are used in the development of the models. The first approach follows a two-step process. In the first flow prediction step, the perturbation flow due to a particle is obtained as an axisymmetric superposable wake using linear regression. In the second force prediction step, the force on a particle is evaluated in terms of the perturbation flow induced by all its neighbors using the generalized Faxén form of the force expression. In the second approach, the force data on all the particles from the PR-DNS simulations are used to develop an artificial neural network (ANN) model for direct prediction of force on a particle. Due to the unavoidable limitation on the number of fully resolved particles in the PR-DNS simulations, direct force prediction with the ANN model tends to over-fit the data and performs poorly in the prediction of test data. In contrast, due to the millions of grid points used in the PR-DNS simulations, accurate flow prediction is possible, which then allows accurate prediction of particle force. This hybridization of multiphase physics and machine learning is particularly important, since it blends the strength of each, and the resulting pairwise interaction extended point-particle model cannot be developed by either physics or machine learning alone.

42 ENGINEERING↗
DOE OSTI2020

Lower length scale informed improvements to Bison U-Pu-Zr fuel swelling model

Due to renewed interest in metallic fuels from the U-(Pu)-Zr material system, significant improvements have recently been made to BISON's capability to simulate metallic fuel, particularly in the area of swelling. In this report, lower length scale simulations that have been conducted to inform the engineering-scale models in BISON during FY20 are described. For the high-temperature regions of the fuel that are dominated by the $\gamma$ phase of U-(Pu)-Zr, two new models for swelling have implemented in BISON. Phase-field simulations were conducted to calculate parameters for these and other BISON models that control when gaseous swelling ceases and fission gas release begins. For the low-temperature regions of the fuel dominated by the phase of uranium, microstructurally resolved simulations of pore growth and crystal deformation were performed to understand the effect of irradiation-generated crystal shape changes. Conformally meshed microstructures with pores were simulated with a crystal plasticity model and with a burnup eigenstrain model. Plastic deformation will not cause pore size to increase; burnup eigenstrain (irradiation-induced crystal shape changes) will increase the volume of existing porosity and cause it to become anisotropic in shape. Additional model development work for early-stage fission gas cluster/bubble formation in the $\gamma$ phase were performed, by coupling with defect clustering within cluster dynamics framework. Sensitivity analysis were conducted to identify the critical thermo-kinetic parameters needed for rigorous assessment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Machine learning overcomes human bias in the discovery of self-assembling peptides

Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here, in this work, we introduce a machine learning workflow—AI-expert—that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Improved Smoothing by a Tool with Ceramic Inserts

A dynamic system is analyzed by the simulation of two subsystems (for the part and the tool) that interact through the dynamic link formed by the machining process. This link corresponds to the smoothing forces, which may be expressed in terms of the state of the dynamic system. The dynamic stability and behavior of the system are expressed in terms of displacements from equilibrium.

42 ENGINEERING↗
DOE OSTI2017

Improvements to PHISICS/RELAP5-3D{sup C} Capabilities for Simulating HTGRs

Accurate, high-resolution full core simulations of prismatic high temperature reactors for burnup and transient analysis remain a challenge. The cores are geometrically large, but some features are quite small (tri-structural isotropic (TRISO) particles, burnable poison pellets). The treatment of both neutron scattering in graphite and resonance capture are complex and not adequately captured using the methods traditionally used in High Temperature Gas-cooled Reactors (HTGR) and commonly used in Light Water Reactors (LWR). For transient analysis, temperature feedback is a function of the TRISO fuel form, but full core models cannot resolve phenomena at this scale without averaging over space and energy. For burnup calculations, a second level of heterogeneity must be resolved to capture the local effects of fuel and burnable poisons, while accurately propagating their effects through and between blocks. The HTGR Methods and Simulation group has been utilizing the INL-developed PHISICS/RELAP5-3D{sup C} (P/R), code suite for participation in the Organization for Economic Cooperation and Development (OECD) Modular High Temperature Gas Cooled Reactor (MHTGR) Benchmark, funded by the US Department of Energy (DOE) under the Advanced Reactor Technologies (ART) program. The MHTGR-350 benchmark consists of several lattice, steady-state and transient exercises, and the P/R code has been successfully applied to most of the cases defined. In addition to this code-to-code verification activity, it is planned that P/R will be validated using data from several Loss of Forced Cooling (LOFC) tests performed at the High Temperature Test Reactor (HTTR) in a joint effort with Japan Atomic Energy Agency (JAEA) under the Civil Nuclear Energy Working Group (CNWG) project. These verification and validation simulations will identify code and model discrepancies, sensitivities, and establish the current state-of-the-art in HTGR simulation. The Department of Astronautical, Electrical and Energy Engineering (DIAEE), 'Sapienza' University has been involved in the development of P/R since 2014 through various graduate and PhD student projects. In the framework of this collaboration the P/R optimization for application on the OECD MHTGR-350 and HTTR models will be carried out.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2012

Computational Fluid Dynamics (CFD) and Safety Factors: Computer modeling of complex processes needs old-fashioned experiments to stay in touch with reality

Computational fluid dynamics (CFD) is recognized as a powerful engineering tool. That is, CFD has advanced over the years to the point where it can now give us deep insight into the analysis of very complex processes. There is a danger, though, that an engineer can place too much confidence in a simulation. If a user is not careful, it is easy to believe that if you plug in the numbers, the answer comes out, and you are done. This assumption can lead to significant errors. As we discovered in the course of a study on behalf of the Department of Energy's Savannah River Site in South Carolina, CFD models fail to capture some of the large variations inherent in complex processes. These variations, or scatter, in experimental data emerge from physical tests and are inadequately captured or expressed by calculated mean values for a process. This anomaly between experiment and theory can lead to serious errors in engineering analysis and design unless a correction factor, or safety factor, is experimentally validated. For this study, blending times for the mixing of salt solutions in large storage tanks were the process of concern under investigation. This study focused on the blending processes needed to mix salt solutions to ensure homogeneity within waste tanks, where homogeneity is required to control radioactivity levels during subsequent processing. Two of the requirements for this task were to determine the minimum number of submerged, centrifugal pumps required to blend the salt mixtures in a full-scale tank in half a day or less, and to recommend reasonable blending times to achieve nearly homogeneous salt mixtures. A full-scale, low-flow pump with a total discharge flow rate of 500 to 800 gpm was recommended with two opposing 2.27-inch diameter nozzles. To make this recommendation, both experimental and CFD modeling were performed. Lab researchers found that, although CFD provided good estimates of an average blending time, experimental blending times varied significantly from the average.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

CFD study of natural convection mixing in a steam generator mock-up: Comparison between full geometry and porous media approaches

In CFD simulations of flow mixing in a steam generator (SG) during natural circulation, one is faced with the problem of representing the thousands of SG U-tubes. Typically simplifications are made to render the problem computationally tractable. In particular, one or a number of tubes are lumped in one volume which is treated as a single porous medium. This approach dramatically reduces the computational size of the problem and hence simulation time. In this work, we endeavor to investigate the adequacy of this approach by performing two separate simulations of flow in a mock-up with 262 U-tubes, i.e. one in which the porous media model is used for the tube bundle, and another in which the full geometry is represented. In both simulations, the Reynolds Stress (RMS) model of turbulence is used. We show that in steady state conditions, the porous media treatment yields results which are comparable to those of the full geometry representation (temperature distribution, recirculation ratio, hot plume spread, etc). Hence, the porous media approach can be extended with a good degree of confidence to the full scale SG. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

A new predictive multi-zone model for HCCI engine combustion

Here, this work introduces a new predictive multi-zone model for the description of combustion in Homogeneous Charge Compression Ignition (HCCI) engines. The model exploits the existing OpenSMOKE++ computational suite to handle detailed kinetic mechanisms, providing reliable predictions of the in-cylinder auto-ignition processes. All the elements with a significant impact on the combustion performances and emissions, like turbulence, heat and mass exchanges, crevices, residual burned gases, thermal and feed stratification are taken into account. Compared to other computational approaches, this model improves the description of mixture stratification phenomena by coupling a wall heat transfer model derived from CFD application with a proper turbulence model. Furthermore, the calibration of this multi-zone model requires only three parameters, which can be derived from a non-reactive CFD simulation: these adaptive variables depend only on the engine geometry and remain fixed across a wide range of operating conditions, allowing the prediction of auto-ignition, pressure traces and pollutants. This computational framework enables the use of detail kinetic mechanisms, as well as Rate of Production Analysis (RoPA) and Sensitivity Analysis (SA) to investigate the complex chemistry involved in the auto-ignition and the pollutants formation processes. In the final sections of the paper, these capabilities are demonstrated through the comparison with experimental data.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2018

Radiation Hydrodynamical Simulations of the First Quasars

Supermassive black holes (SMBHs) are the central engines of luminous quasars and are found in most massive galaxies today. But the recent discoveries of ULAS J1120+0641, a 2 × 10 9 ${M}_{\odot }$ black hole (BH) at z = 7.1, and ULAS J1342+0928, a 8.0 × 10 8 ${M}_{\odot }$ BH at z = 7.5, now push the era of quasar formation up to just 690 Myr after the Big Bang. Here we report new cosmological simulations of SMBHs with X-rays fully coupled to primordial chemistry and hydrodynamics which show that J1120 and J1342 can form from direct collapse black holes if their growth is fed by cold, dense accretion streams, like those thought to fuel rapid star formation in some galaxies at later epochs. Our models reproduce all of the observed properties of J1120: its mass, luminosity, and H ii region as well as star formation rates and metallicities in its host galaxy. They also reproduce the dynamical mass of the innermost 1.5 kpc of its emission region recently measured by ALMA and J-band magnitudes that are in good agreement with those found by the VISTA Hemisphere Survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Verification of bubble tracking method and DNS examinations of single- and two-phase turbulent channel flows

Direct numerical simulation (DNS) has been regarded as a reliable data source for the development and validation of turbulence models along with experiments. The realization of DNS usually involves a very fine mesh that should be able to resolve all relevant turbulence scales down to Kolmogorov scale [1]. As the most computationally expensive approach compared to other CFD techniques, DNS applications used to be limited to flow studies at very low Reynolds numbers. Thanks to the tremendous growth of computing power over the past decades, the simulation capability of DNS has now started overlapping with some of the most challenging engineering problems. One of those examples in nuclear engineering is the turbulent coolant flow inside reactor cores. Coupled with interface tracking methods (ITM), the simulation capability of DNS can be extended to more complicated two-phase flow regimes. Departure from nucleate boiling (DNB) is the limiting critical heat flux phenomena for the majority of accidents that are postulated to occur in pressurized water reactors (PWR) [2]. As one of the major modeling and simulation (M&S) challenges pursued by CASL, the prediction capability is being developed for the onset of DNB utilizing multiphase-CFD (M-CFD) approach. DNS (coupled with ITM) can be employed to provide closure law information for the multiphase flow modeling at CFD scale. In the presented work, research groups at NCSU and UND will focus on applying different ITM to different geometries. Higher void fraction flow analysis at reactor prototypical conditions will be performed, and novel analysis methods will be developed, implemented and verified for the challenging flow conditions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Dakota Uncertainty Quantification Methods Applied to the CFD code Nek5000

Predictive modeling and simulation of the performance of a nuclear reactor and its fuel is a challenging task because of the large number of coupled physical phenomena that must be addressed. In addition to this intrinsic complexity, model uncertainty must be accounted for in any analysis if the model will be used to facilitate design or operational decisions which may impact safety and performance. Rigorous, structured uncertainty analyses are performed by first characterizing the model's input uncertainties and, by propagating the uncertainty through the model in order to estimate the output's uncertainty. This paper presents the state of advancement of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) project to characterize the uncertainty of the CFD code Nek5000 using the Dakota package. Nek5000 is a high order spectral element computational fluid dynamics (CFD) code developed at Argonne National Laboratory for high resolution spectral-filtered large eddy simulations (LESs) and unsteady Reynolds averaged Navier-Stokes (RANS) simulations. The Dakota package developed at Sandia National Laboratory can be integrated with many scientific and engineering codes to facilitate efficient, effective uncertainty quantification (UQ) and sensitivity analyses. Dakota is designed to interface with external packages/codes such as Nek5000 through a script-driven interface called fork. Such interface provides maximum flexibility to the users for pre- and post-processing tasks preceding and following each function evaluation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Fundamental Advancements in Pre-Chamber Spark Ignition and Emissions Control for Natural Gas Engines

Argonne National Laboratory, the National Renewable Energy Laboratory, Oak Ridge National Laboratory, and Sandia National Laboratories collaborated on research to address barriers for natural gas engine efficiency. This research project focuses on early stage research focusing on pre-chamber spark-ignition (PCSI) to achieve diesel-like efficiency in medium- and heavy-duty natural gas engines by extending the lean dilution limit and/or exhaust gas recirculation (EGR) dilution limit, as well as shortening burn duration, with integrated aftertreatment. This project integrates experimental and simulation based tasks to address four key barriers to market penetration of PCSI for medium- and heavy-duty natural gas engines. These include Inadequate science base and simulation tools to describe/predict the fluid-mechanical and chemical-kinetic processes governing PCSI to enable engineers in industry to optimize designs for efficiency, noise, reliability, pollutant formation, emissions control integration, and drivability; limited ability to extend EGR and/or lean dilution limits at higher loads; increased propensity for PCSI hot-spot pre-ignition at high loads relative to spark ignition; and ineffective methane catalysts for the high engine-out unburned fuel concentrations coupled with low exhaust temperatures (<<400 degrees C) of high efficiency engines.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A detailed experimental and modeling comparison of molecular radiative heat loss in a spark-ignition engine

Radiative heat transfer has been chiefly considered negligible in internal combustion engines, except for Diesel engines where soot radiation was recognized as a significant radiative transfer source. Only more recently, detailed simulations have shown that molecular radiation can be substantial as well. In extension to this, molecular radiative heat transfer can reach detectable levels of about 5–10% of the total heat transfer in spark-ignited engines. The broadband radiative nature of the significant emitting molecules, carbon dioxide and water, makes it necessary to address whether radiative trapping plays a substantial role in either total heat loss or energy redistribution within the cylinder. An experimental setup that allows measurements of the infrared emissions at a 2-crank-angle-degree resolution was developed to determine the importance of radiative trapping by carbon dioxide and water. Measurements were conducted in the well-characterized and documented TCC-III engine at the University of Michigan. The engine operated on a stoichiometric propane/air mixture at a speed of 1,300 RPM. Large Eddy Simulations with added line-by-line photon Monte-Carlo simulations of the molecular radiation were an integral part of this study to plan and devise the measurements. Post-processing of the simulation data included an accurate representation of the experimental volume from where infrared signals are collected. Additionally, the experimental data were used for validation of the photon Monte-Carlo simulations. Joint analysis of experimental and simulated spectra allowed quantifying the significance of radiative trapping. Results show the significant role of radiative trapping when predicting radiative heat transfer in the TCC-III engine.

42 ENGINEERING↗
DOE OSTI2023

NREL CGI and PHIL Platform for Validation of Multi-Technology Energy Systems at Scale

The presentation is an update on National Renewable Energy Laboratory Flatirons Campus' capabilities around multi-megawatt grid simulation and power hardware-in-the-loop. Recent improvements in operational and dynamic range of existing 7MVA Controllable Grid Interface (CGI) are described together with improvements in site-wide automation and data acquisition systems. In later part of presentation, a second 19.9MVA CGI that is currently under development is introduced. Its features are described further explaining future site's capabilities.

acquisition↗
DOE OSTI2023

Simulation-based characterization of the variability of earthquake risk to buildings in the near-field

Recent advancements in high performance computing platforms and computational workflow for regional-scale simulations are enabling unprecedented modeling of fault-to-structure earthquake processes. Regional simulations resolving ground motions at frequencies relevant to engineered systems are becoming computationally viable and provide a new capability to improve understanding of the geographical distribution and intensity of risk to buildings and critical infrastructure. As computational capabilities advance, it is essential to move beyond illustrative single rupture realizations for scenario earthquake events towards the development of a full suite of rupture realizations that appropriately characterize the range of risk to building systems. The work described in this article investigates the application of a suite of fault rupture realizations with the objective of assessing near-fault, site-specific seismic demand variability for building structures. A representative high-performance regional-scale computational model is utilized to execute ground motion and building response simulations based on 18 kinematic rupture realizations of an M7 strike-slip scenario earthquake. The fault rupture models for the scenario earthquake are created by systematically perturbing the hypocenter location and stochastically generating rupture parameters (slip, rise time, rake angle) to represent a breadth of ground motion intensities resulting from the spatial and temporal variabilities of an earthquake rupture process. The resulting seismic demand variability for three-story (short period) and forty-story (long period) steel moment-resisting frame buildings is characterized in terms of the median and distribution of peak inter-story drift ratio for a range of near-fault sites. The full suite of 18 fault rupture realizations and approximately 280,000 nonlinear dynamic building simulations indicate that the three-story building undergoes higher median seismic demand and significantly greater variability of demand at a given site than the forty-story building, which has important implications for the level of certainty in predicting building performance during an earthquake. The simulations performed provide deeper insight into the relationship between fault rupture parameterization and building response, which is essential information for developing a representative suite of rupture realizations for specific earthquake scenarios.

58 GEOSCIENCES↗
DOE OSTI2016

A hybridized discontinuous Galerkin method on mapped deforming domains

Here in this paper we present a hybridized discontinuous Galerkin (HDG) discretization for unsteady simulations of convection-dominated flows on mapped deforming domains. Mesh deformation is achieved through an arbitrary Lagrangian-Eulerian transformation with an analytical mapping. We present details of this transformation applied to the HDG system of equations, with focus on the auxiliary gradient equation, viscous stabilization, and output calculation. We discuss conditions under which optimal unsteady output convergence rates can be attained, and we show that both HDG and discontinuous Galerkin (DG) achieve these rates in advection-dominated flows. Results for scalar advection-diffusion and the Euler equations verify the implementation of the mesh motion mapping for both discretizations, show that HDG and DG yield similar results on a given mesh, and demonstrate differences in output convergence rates depending on the choice of HDG viscous stabilization. We note that such similar results bode well for HDG, which has fewer globally-coupled degrees of freedom compared to DG. A simulation of the unsteady compressible Navier-Stokes equations demonstrates again very similar results for HDG and DG and illustrates a pitfall of using steady-state adapted meshes for accurate unsteady simulations.

42 ENGINEERING↗