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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

AMR-Wind: A Performance-Portable, High-Fidelity Flow Solver for Wind Farm Simulations

We present AMR-Wind, a verified and validated high-fidelity computational-fluid-dynamics code for wind farm flows. AMR-Wind is a block-structured, adaptive-mesh, incompressible-flow solver that enables predictive simulations of the atmospheric boundary layer and wind plants. It is a highly scalable code designed for parallel high-performance computing with a specific focus on performance portability for current and future computing architectures, including graphical processing units (GPUs). In this paper, we detail the governing equations, the numerical methods, and the turbine models. Establishing a foundation for the correctness of the code, we present the results of formal verification and validation. The verification studies, which include a novel actuator line test case, indicate that AMR-Wind is spatially and temporally second-order accurate. The validation studies demonstrate that the key physics capabilities implemented in the code, including actuator disk models, actuator line models, turbulence models, and large eddy simulation (LES) models for atmospheric boundary layers, perform well in comparison to reference data from established computational tools and theory. We conclude with a demonstration simulation of a 12-turbine wind farm operating in a turbulent atmospheric boundary layer, detailing computational performance and realistic wake interactions.

17 WIND ENERGY↗
DOE OSTI2017

Combined shape and topology optimization for minimization of maximal von Mises stress

Here, this work shows that a combined shape and topology optimization method can produce optimal 2D designs with minimal stress subject to a volume constraint. The method represents the surface explicitly and discretizes the domain into a simplicial complex which adapts both structural shape and topology. By performing repeated topology and shape optimizations and adaptive mesh updates, we can minimize the maximum von Mises stress using the p-norm stress measure with p-values as high as 30, provided that the stress is calculated with sufficient accuracy.

42 ENGINEERING↗
DOE OSTI2018

Explosion-induced ignition and combustion of acetylene clouds

We investigate the explosion-induced ignition and combustion of an acetylene cloud in a rectangular chamber (10 cm × 10 cm × 39 cm). In the experiments, a 0.2-g PETN charge was located at $x = 9.7\,\hbox {cm}$ and a soap bubble (d=5cm) filled with pure acetylene was located at $x = 27\,\hbox {cm}$ as measured relative to the end wall. Detonation of the charge created a blast wave that crushed the soap bubble—inducing mixing with the air. After 0.55 ms, the mixture ignited, forming at turbulent combustion cloud. The flow was modeled using the compressible Navier–Stokes equations assuming unity Lewis number. Arrhenius-based kinetics were used to model ignition. Adaptive mesh refinement was used to capture turbulent mixing on the grid (the MILES approach of J. Boris). Computed pressures were found to be in agreement with measured pressure histories. Finite-rate kinetics were required to capture the ignition processes over the duration of the experiment.

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

Automatic Halo Management for the Uintah GPU-Heterogeneous Asynchronous Many-Task Runtime

The Uintah computational framework is used for the parallel solution of partial differential equations on adaptive mesh refinement grids using modern supercomputers. Uintah is structured with an application layer and a separate runtime system. Uintah is based on a distributed directed acyclic graph of computational tasks, with a task scheduler that efficiently schedules and executes these tasks on both CPU cores and on-node accelerators. The runtime system identifies task dependencies, creates a task graph prior to the execution of these tasks, automatically generates MPI message tags, and automatically performs halo transfers for simulation variables. Automating halo transfers in a heterogeneous environment poses significant challenges when tasks compute within a few milliseconds, as runtime overhead affects wall time execution, or when simulation variables require large halos spanning most or all of the computational domain, as task dependencies become expensive to process. These challenges are magnified at production scale when application developers require each compute node perform thousands of different halo transfers among thousands simulation variables. The principal contribution of this work is to (1) identify and address inefficiencies that arise when mapping tasks onto the GPU in the presence of automated halo transfers, (2) implement new schemes to reduce runtime system overhead, (3) minimize application developer involvement with the runtime, and (4) show overhead reduction results from these improvements.

Concurrency↗
DOE OSTI2018

A Phase-Field Lattice-Boltzmann Study on Dendritic Growth of Al-Cu Alloy Under Convection

Effects of convection (forced and natural) on dendritic evolution of the Al-Cu alloy were investigated using a phase-field lattice-Boltzmann approach. The non-linear coupled equations were solved by applying a parallel and adaptive mesh refinement algorithm. Important physical aspects including dendritic fragmentation, splitting, and formation of solute plumes were simulated. Results showed that the dendritic growth patterns under convection exhibited remarkable difference from those without convection. The presence of flow led to variation of solute diffusion and upstream–downstream dendritic growth difference, which further influenced the development of dendritic arms and multi-dendritic competitive growth. When the convection intensity was magnified, the convection-induced anisotropy became dominated, and the growth patterns changed accordingly to accommodate the local thermodynamic variation.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Dependence of Lamellar Eutectic Growth with Convection on Boundary Conditions and Geometric Confinement: A Phase-Field Lattice-Boltzmann Study

Simulating eutectic growth with convection is challenging because of the enormous computing demand resulting from the required domain size compared with the eutectic scale. In this study, a phase-field lattice-Boltzmann approach was employed to reproduce the eutectic growth with convection (both natural and forced). To enable and accelerate large-scale simulations, a parallel-adaptive mesh refinement algorithm was developed to combine with this numerical approach. The effects of the boundary conditions and the geometric confinement on the eutectic growth with convection were systematically investigated in the Al-Cu eutectic alloy. Results showed that both boundary conditions and the geometric confinement altered the eutectic evolution by affecting the distributions of the solute and velocity ahead of the eutectic solid/liquid interface. As the first attempt to explore the two factors on the eutectic growth with convection, our thorough investigation lays a foundation for modeling the coupled convection–solute eutectic evolution.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Dendritic Growth Under Natural and Forced Convection in Al-Cu Alloys: From Equiaxed to Columnar Dendrites and from 2D to 3D Phase-Field Simulations

The interaction between convection and solute transport during solidification has significant influence on the dendritic evolution. By employing the phase-field lattice-Boltzmann approach together with the parallel and adaptive-mesh-refinement algorithm, the dendritic evolution under convection is simulated in both 2D and 3D cases. The flow-induced redistribution of the solute alters both tip velocity and the development of dendritic arms. The effect of both convection and undercooling is quantified and compared using the length ratio of the dendritic arms. The effect of convection behavior (i.e., natural and forced) and domain dimension (i.e., 2D and 3D) on dendritic growth is discussed. Results show that the convection effect is mainly dominated by the convection mode, and the melt flow in 2D can produce biased results comparing with those in 3D.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Neutronics Modeling of the High Flux Isotope Reactor using COMSOL

The High Flux Isotope Reactor located at the Oak Ridge National Laboratory is a versatile 85 MWth research reactor with cold and thermal neutron scattering, materials irradiation, isotope production, and neutron activation analysis capabilities. HFIR staff members are currently in the process of updating the thermal hydraulic and reactor transient modeling methodologies. COMSOL Multiphysics has been adopted for the thermal hydraulic analyses and has proven to be a powerful finite-element-based simulation tool for solving multiple physics-based systems of partial and ordinary differential equations. Modeling reactor transients is a challenging task because of the coupling of neutronics, heat transfer, and hydrodynamics. This paper presents a preliminary COMSOL-based neutronics study performed by creating a two-dimensional, two-group, diffusion neutronics model of HFIR to study the spatially-dependent, beginning-of-cycle fast and thermal neutron fluxes. The 238-group ENDF/B-VII neutron cross section library and NEWT, a two-dimensional, discrete-ordinates neutron transport code within the SCALE 6 code package, were used to calculate the two-group neutron cross sections required to solve the diffusion equations. The two-group diffusion equations were implemented in the COMSOL coefficient form PDE application mode and were solved via eigenvalue analysis using a direct (PARDISO) linear system solver. A COMSOL-provided adaptive mesh refinement algorithm was used to increase the number of elements in areas of largest numerical error to increase the accuracy of the solution. The flux distributions calculated by means of COMSOL/SCALE compare well with those calculated with benchmarked three-dimensional MCNP and KENO models, a necessary first step along the path to implementing two- and three-dimensional models of HFIR in COMSOL for the purpose of studying the spatial dependence of transient-induced behavior in the reactor core.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Effects of operator splitting and low Mach-number correction in turbulent mixing transition simulations

In this paper, transition and turbulence decay with the Taylor–Green vortex have been effectively used to demonstrate emulation of high Reynolds-number ( R e ) physical dissipation through numerical convective effects of various non-oscillatory finite-volume algorithms for implicit large eddy simulation (ILES), e.g. using the Godunov-based Eulerian adaptive mesh refinement code xRAGE. The inverse-chevron shock tube experiment simulations have been also used to assess xRAGE based ILES for shock driven turbulent mixing, compared with available simulation and laboratory data. The previous assessments are extended to evaluate new directionally-unsplit high-order algorithms in xRAGE, including a correction to address the well-known issue of excessive numerical diffusion of shock-capturing (e.g., Godunov-type) schemes for low Mach numbers. The unsplit options for hydrodynamics in xRAGE are discussed in detail, followed by fundamental tests with representative shock problems. Basic issues of transition to turbulence and turbulent mixing are discussed, and results of simulations of high- R e turbulent flow and mixing in canonical test cases are reported. Finally, compared to the directional-split cases, and for each grid resolution considered, unsplit results exhibit transition to turbulence with much higher effective R e —and significantly more so with the low Mach number correction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Importance of turbulence-chemistry interactions at low temperature engine conditions

The role of turbulence-chemistry interaction in autoignition and flame stabilization is investigated for spray flames at low temperature combustion (LTC) conditions by performing high-fidelity three-dimensional computational fluid dynamics (CFD) simulations. A recently developed Tabulated Flamelet Model (TFM) is coupled with a large eddy simulation (LES) framework and validated across a range of Engine Combustion Network (ECN) ambient temperature conditions for n-dodecane fuel. High resolution grids with 0.0625 mm minimum cell size and 25 million total cell count are implemented using adaptive mesh refinement over the spray and combustion regions. Simulations with these grids and multiple LES realizations, with a 103 species n-dodecane mechanism show good agreement with experimental data for all the ambient conditions investigated. This modeling approach with the computational cost advantage of tabulated chemistry is then extended towards understanding the auto-ignition and flame stabilization at an ambient temperature of 750 K. These low temperature conditions lead to substantially higher ignition delays and flame liftoff lengths, and significantly leaner combustion compared to conventional high temperature diesel combustion. These conditions also require the simulations to span significantly larger temporal and spatial dimensions thereby increasing the computational cost. The TFM approach is able to capture autoignition and flame liftoff length at the low temperature conditions. Significant differences with respect to mixing, species formation and flame stabilization are observed under low temperature compared to conventional diesel combustion. At higher ambient temperatures, formation of formaldehyde is observed in the rich region (phi > 1) followed by the formation of OH in the stoichiometric regions. Under low temperature conditions, formaldehyde is observed to form at leaner regions followed by the onset of OH formation in significantly lean regions of the flame. Qualitative differences between species formation and transient flame development for the high and low temperature conditions are presented. The two stage ignition process is further investigated by studying the species formation in mixture fraction space by solving 1D flamelet equations for different scalar dissipation rates and homogeneous reactor assumption. Results show that scalar dissipation causes these radicals to diffuse within the mixture fraction space. As a result, this significantly enhances ignition and plays a dominant role at such low temperature conditions which cannot be captured by the homogeneous reaction assumption based model.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Numerical and experimental investigation of the flame kernel growth in a methane/air mixture near the lean flammability limit

Lean combustion has the potential to improve the thermal efficiency of spark-ignition engines, but it faces the significant challenge of increased cycle-to-cycle variation due to low mixture reactivity and unstable flame dynamics. Computational fluid dynamics (CFD) employing predictive models can guide engine design and optimize operating strategies for lean combustion. However, ignition and combustion models have rarely been validated at fuel-lean conditions, and a fundamental understanding of the early flame kernel growth process is also lacking for a successful sub-model development. Here, the present study develops a numerical simulation framework used to investigate early flame kernel growth in methane/air mixtures. A nanosecond-pulsed discharge (NPD) approach is employed to effectively decouple the flame kernel growth from the electrical discharge due to their difference in timescales, and equivalence ratios near the experimentally measured lean flammability limit (LFL) are selected to focus on challenging mixture conditions. Three numerical investigations, such as the choice of turbulence modeling, grid size, and grid control strategies, are examined to match both LFL and flame kernel structure measured from experiments. It is demonstrated that a quasi-direct numerical simulation (QDNS) with a fixed grid embedding of 10 μm can predict the LFL as φ CFD =0.61 and match the displacement speed of the kernel’s boundary marked in schlieren images. To predict the LFL and flame kernel shape, a fine grid (Δ≤12.5 μm) is needed to capture the consumption of formaldehyde (CH 2 O) in kernel’s reaction branches attached to the anode, and adaptive mesh refinement is replaced with the fixed embedding due to loss of simulation accuracy. Also, it is found that a large-eddy simulation (LES) using the Dynamic Structure model is not suitable for the NPD-induced flame kernel simulation because artificial sub-grid turbulent kinetic energy induced by shock dynamics alters the flow velocity calculation, resulting in divergence of LES from QDNS. Lastly, the simulation well matches the experimental data for the flame kernel evolution in three mixture conditions (φ = 0.7, 0.61, 0.55), showing toroidal flame kernel expansion and flame kernel growth/extinction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Computational diagnostics for flame acceleration and transition to detonation in a hydrogen/air mixture

A new computational diagnostic method for pressure-induced compressibility is proposed by projecting its local contribution to the chemical explosive mode (CEM) in the chemical explosive mode analysis (CEMA) framework. The new method is validated for the study of detonation development during the deflagration-to-detonation transition (DDT) process. The flame characteristics are identified through the quantification of individual CEM contributions of chemical reaction, diffusion, and pressure-induced compressibility. Numerical simulations are performed to investigate the DDT processes in a stoichiometric hydrogen-air mixture. A Godunov algorithm, fifth-order in space, and third-order in time are used to solve the fully compressible Navier-Stokes equations on a dynamically adapting mesh. A single-step, calibrated chemical diffusive model (CDM) described by Arrhenius kinetics is used for energy release and conservation between the fuel and the product. The new diagnostic method is first applied to onedimensional (1D) canonical flame configurations followed by two-dimensional (2D) simulations of DDT in an obstructed channel where different detonation initiation scenarios are examined using the new CEMA projection formulation. Detailed examinations of the idealized configuration of detonation initiation through shock focusing mechanism at a flame front are also studied using the new formulation. A comparison of the currently proposed CEMA projection and the original formulation by the authors suggests that including the pressure-induced compressibility is essential for the use of CEMA in DDT process. The results also show that the new formulation of CEMA projection can successively capture the detonation initiation through either a gradient mechanism or a direct initiation mechanism, and therefore can be used as an effective local analytical tool for the computational diagnostics of detonation initiation in a DDT process. It was found that detonation development is characterized by a strong contribution of chemistry role to the CEM which is pivotal to the initiation of detonation. The role of compressibility is found enhanced at the edge of the detonation front where diffusion was found to have minimal effects on detonation development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

In situ multi-tier auto-ignition detection applied to dual-fuel combustion simulations

Here we use an anomaly detection methodology that is centered on analyzing fourth-order joint moments (co-kurtosis), particularly focusing on its application in auto-ignition of combustion problems with large numbers of species. Unsupervised anomaly detection is challenging to generalize across problem types and domains. A recent technique, centered on analyzing information in the fourth-order joint moment co-kurtosis, has shown promise, especially for high-dimensional scientific data. In this work we present developments to the co-kurtosis based anomaly detection method needed to make it effective and scalable for large-scale distributed scientific data, such as those generated by massively parallel simulations. An in situ co-kurtosis algorithm is employed as the anomaly detection method for identifying ignition kernels in simulations of turbulent combustion. Here, we extend an existing methodology which identifies regions of the domain where anomalies are present, and add another tier of anomaly detection where the individual samples contributing to the anomaly are identified. We apply this algorithm on-the-fly to a variety of turbulent reacting flow problems and compare it to the widely used (but significantly more expensive) chemical explosive mode analysis (CEMA). We demonstrate the ability of the method to detect and identify the onset of low and high temperature ignition which can be used for computational steering, as chemical and combustion anomalies occur intermittently at spatio-temporal locations unknown a priori. Finally, we apply our lightweight in situ algorithm to an exascale high-fidelity simulation with a total of 2.4 Trillion degrees of freedom, performed using an adaptive mesh refinement solver. Furthermore, through a scalability analysis, we show that the relative computational cost of this in-situ anomaly detection algorithm compared to an iteration of the reacting flow solver is negligible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Surface tension models for a multi-material ALE code with AMR

A number of surface tension models have been implemented in a 3D multi-physics multi-material code, ALE–AMR, which combines Arbitrary Lagrangian Eulerian (ALE) hydrodynamics with Adaptive Mesh Refinement (AMR). ALE–AMR is unique in its ability to model hot radiating plasmas, cold fragmenting solids, and most recently, the deformation of molten material. The surface tension models implemented include a diffuse interface approach with special numerical techniques to remove parasitic flow and a height function approach in conjunction with a volume-fraction interface reconstruction package. These surface tension models are benchmarked with a variety of test problems. In conclusion, based on the results, the height function approach using volume fractions was chosen to simulate droplet dynamics associated with extreme ultraviolet (EUV) lithography.

42 ENGINEERING↗
DOE OSTI2017

High performance computation of residual stress and distortion in laser welded 301L stainless sheets

Transient thermo-mechanical simulation of stainless plate laser welding process was performed by a highly efficient and accurate approach-hybrid iterative substructure and adaptive mesh method. Especially, residual stress prediction was enhanced by considering various heat effects in the numerical model. The influence of laser welding heat input on residual stress and welding distortion of stainless thin sheets were investigated by experiment and simulation. X-ray diffraction (XRD) and contour method were used to measure the surficial and internal residual stress respectively. Effect of strain hardening, annealing and melting on residual stress prediction was clarified through a parametric study. It was shown that these heat effects must be taken into account for accurate prediction of residual stresses in laser welded stainless sheets. Reasonable agreement among residual stresses by numerical method, XRD and contour method was obtained. Buckling type welding distortion was also well reproduced by the developed thermo-mechanical FEM.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Numerical analysis of soot emissions from gasoline-ethanol and gasoline-butanol blends under gasoline compression ignition conditions

In the present work, computational fluid dynamics (CFD) simulations of a single-cylinder gasoline compression ignition (GCI) engine were performed to investigate the impact of blending two biofuels, ethanol and n-butanol, with gasoline on the trade-off between combustion phasing and soot emissions under low load conditions. Here, in order to represent market gasoline (RD5-87), a four-component toluene primary reference fuel (TPRF) + ethanol (ETPRF) surrogate (with 20% ethanol by mole; E20) was formulated using a neural network based octane predictor such that the surrogate had the same ethanol content, Research Octane Number (RON) and Octane Sensitivity (S). In addition, a novel skeletal kinetic mechanism for ETPRF and TPRF + n-butanol (BTPRF) blends, incorporating polycyclic aromatic hydrocarbon (PAH) chemistry, was developed. A three-dimensional (3D) engine CFD formulation employing the skeletal mechanism, adaptive mesh refinement (AMR), finite-rate chemistry approach, and hybrid method of moments (HMOM) was adopted to capture the in-cylinder combustion phenomena and soot emissions. The engine CFD model was validated against RD5-87 experimental data for a broad range of start-of-injection (SOI) timings (-21/-27/-36/-45 crank angle degrees (CAD) after top-dead center (aTDC)), with respect to in-cylinder pressure, heat release rate, combustion phasing, and soot emissions. The closed-cycle simulation results were analyzed to elucidate the non-monotonic trend of soot emissions versus SOI timing: SOI-36 > SOI-45 > SOI-21 > SOI-27. Thereafter, the validated CFD model was employed to simulate the combustion of a gasoline-ethanol blend with 45% (by mole) ethanol (E45) and a gasoline-butanol blend with 45% (by mole) n-butanol (B45) under the same operating conditions to study the effects of fuel composition and SOI timing on combustion phasing and soot emissions. The sooting propensity followed the trend: B45 > E20 > E45 at all SOI timings. Overall, it was observed that the autoignition propensity was primarily related to fuel chemistry. On the other hand, sooting propensity showed strong coupling with both fuel chemistry and physical properties, with greater impact of fuel physical properties at advanced SOI timings.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations

CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.

Algorithmic portability↗