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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Scalability of OpenFOAM Density-Based Solver with Runge–Kutta Temporal Discretization Scheme

Compressible density-based solvers are widely used in OpenFOAM, and the parallel scalability of these solvers is crucial for large-scale simulations. In this paper, we report our experiences with the scalability of OpenFOAM’s native rhoCentralFoam solver, and by making a small number of modifications to it, we show the degree to which the scalability of the solver can be improved. The main modification made is to replace the first-order accurate Euler scheme in rhoCentralFoam with a third-order accurate, four-stage Runge-Kutta or RK4 scheme for the time integration. The scaling test we used is the transonic flow over the ONERA M6 wing. This is a common validation test for compressible flows solvers in aerospace and other engineering applications. Numerical experiments show that our modified solver, referred to as rhoCentralRK4Foam, for the same spatial discretization, achieves as much as a 123.2% improvement in scalability over the rhoCentralFoam solver. As expected, the better time resolution of the Runge–Kutta scheme makes it more suitable for unsteady problems such as the Taylor–Green vortex decay where the new solver showed a 50% decrease in the overall time-to-solution compared to rhoCentralFoam to get to the final solution with the same numerical accuracy. Finally, the improved scalability can be traced to the improvement of the computation to communication ratio obtained by substituting the RK4 scheme in place of the Euler scheme. All numerical tests were conducted on a Cray XC40 parallel system, Theta, at Argonne National Laboratory.

Li, Sibo↗
DOE OSTI2018

Alternating Anderson–Richardson method: An efficient alternative to preconditioned Krylov methods for large, sparse linear systems

Here, we present the Alternating Anderson–Richardson (AAR) method: an efficient and scalable alternative to preconditioned Krylov solvers for the solution of large, sparse linear systems on high performance computing platforms. Specifically, we generalize the recently proposed Alternating Anderson–Jacobi (AAJ) method (Pratapa et al., 2016) to include preconditioning, discuss efficient parallel implementation, and provide serial MATLAB and parallel C/C++ implementations. In serial applications to nonsymmetric systems, we find that AAR is comparably robust to GMRES, using the same preconditioning, while often outperforming it in time to solution; and find AAR to be more robust than Bi-CGSTAB for the problems considered. In parallel applications to the Helmholtz and Poisson equations, we find that AAR shows superior strong and weak scaling to GMRES, Bi-CGSTAB, and Conjugate Gradient (CG) methods, using the same preconditioning, with consistently shorter times to solution at larger processor counts. Finally, in massively parallel applications to the Poisson equation, on up to 110,592 processors, we find that AAR shows superior strong and weak scaling to CG, with shorter minimum time to solution. We thus find that AAR offers a robust and efficient alternative to current state-of-the-art solvers, with increasing advantages as the number of processors grows.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Plasma structuring by the gradient drift instability at high latitudes and comparison with velocity shear driven processes

Using results of the in situ measurements made by the DE 2 satellite, the nature of plasma structuring at high latitudes, caused by the gradient drift instability process, is described. Using noon-midnight and dawn-dusk orbits of the DE 2 satellite, it was possible to examine the simultaneous density and electric field spectra of convecting large-scale plasma density enhancements in the polar cap known as 'patches', in directions parallel and perpendicular to their antisunward convection. The results provide evidence for the existence of at least two generic classes of instabilities operating in the high-latitude ionosphere: one driven by large-scale density gradients in a homogeneous convection field with respect to the neutrals, and the other driven by the structured convection field itself in an ambient ionosphere where density fluctuations are ubiquitous.

Basu, Sunanda↗
NASA NTRS1993

Parallel execution of a three-dimensional, chemically reacting, Navier-Stokes code on distributed-memory machines

This paper describes the parallel version of the three-dimensional, chemically reacting, computational fluid dynamics (CFD) code, SPARK. This work was performed on the Intel iPSC/860-based parallel computers. The SPARK code utilizes relatively simple explicit numerical algorithms, but models complex chemical reactions. The code solves the equations over a regular structured mesh so a simple dam decomposition is used to assign work to the individual processors. The explicit nature of the algorithm, combined with the computational intensity of the chemistry calculations, results in a very low communication-to-computation ratio when compared to typical CFD codes. The efficiency of the parallel code is examined and shown to be about 65 percent when the problem size is scaled with the number of processors. Two low-angle wall-jet injection cases are solved to demonstrate the capability of the parallel code for solving large problems efficiently.

Otto, John C.↗
NASA NTRS2001

Three Dimensional Atmospheric Radiative Transfer-Applications and Methods Comparison

We review applications of 3D radiative transfer in the atmosphere, emphasizing the wide spectrum of scales important to remote sensing and modeling of cloud fields, and the characteristic scales introduced into observed radiances and fluxes by the distribution of photon pathlengths at conservative and absorbing wavelengths. We define the "plane-parallel bias", which is a measure of the importance of 3D cloud structure in large-scale models, and the "independent pixel errors" that quantify the significance of 3D effects in remote sensing, and emphasize their relative magnitude and scale dependence. A variety of approaches in current use in 3D radiative transfer, and issues of speed, accuracy, and flexibility are summarized. We also describe a recently initiated "International Intercomparison of 3-Dimensional Radiation Codes", or I3RC. I3RC is a 3-phase effort that has as its goals to: (1) understand the errors and limits of 3D methods; (2) provide "baseline" cases for future 3D code development; (3) promote sharing of 3D tools; (4) derive guidelines for 3D tool selection; and (5) improve atmospheric science education in 3D radiative transfer. Selected results from Phases 1 and 2 of I3RC are discussed. These are taken from five cloud fields: a 1D field of bar clouds, a 2D radar-derived field, a 3D Landsat-derived field, a stratiform cloud from the model of C. Moeng, and a convective cloud from the model of B. Stevens. Computations have been carried out for three monochromatic wavelengths (one conservative, one absorptive, and one thermal) and two solar zenith angles (0, 60 degrees).

Cahalan, Robert F.↗
DOE OSTI2023

Scalable simulation of coupled adsorption and transport of methane in confined complex porous media with density preconditioning

The growing significance of shales and tight formations in the transition to less carbon-intensive and clean energy drives the research endeavor to understand the physics of gas flow within these systems. However, shales are composed of massively heterogeneous physical and chemical features. Most nano-sized pores connect to millimeter-scale fractures, leading to multiscale transport. These nano-scale pore throats demonstrate non-classical flow behavior, such as non-negligible slip velocities and adsorbed gas layers at the boundary. As a result, classical computational fluid dynamics models do not capture the physics. In this work, we develop a coupling scheme for the multiple-relaxation-time (MRT) lattice Boltzmann (LB) method that integrates the Peng-Robinson equation of state into a pseudo-potential interaction model to capture the physics of methane flow in irregular networks of channels that represent nano-scale porous media. We use atomistic simulations to calibrate and validate our model in slit nano-channels. We propose a preconditioning scheme to initialize the coupled transport and adsorption simulation of methane in complex porous media. The results of this implementation of LB agree with Direct Simulation Monte Carlo (DSMC) and Molecular Dynamics (MD) simulations. We then scale up the LB implementation through vectorization and indirect addressing. We parallelize it using Message Passing Interface (MPI) and OpenMP frameworks to simulate transport and adsorption in complex media with a million lattices. Additionally, we analyze the differences between coupled and transport-only simulations in two case studies and show that considering phase behavior, i.e., adsorption, can significantly change the flow behavior. This work constitutes an important step towards bridging the gap between molecular flow and system-scale behavior of complex disordered porous media.

42 ENGINEERING↗
DOE OSTI2026

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit

Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essential for reliable predictions of multiphysics interactions, but remains a grand challenge even for exascale supercomputers and advanced deep learning models. The extreme-resolution data required to represent turbulence, ranging from billions to trillions of grid points, pose prohibitive computational costs for models based on architectures like vision transformers. To address this challenge, we introduce a multiscale hierarchical Turbulence Transformer that reduces sequence length from billions to a few millions and a novel RingX sequence parallelism approach that enables scalable long-context learning. We perform scaling and science runs on the Frontier supercomputer. Our approach demonstrates excellent performance up to 1.1 EFLOPS on 32,768 AMD GPUs, with a scaling efficiency of 94\%. To our knowledge, this is the first AI model for turbulence that can capture small-scale eddies down to the dissipative range in three-dimensional turbulence at high Reynolds numbers.

Yin, Junqi [ORNL] (ORCID:0000000338435520)↗
DOE OSTI2019

MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles

Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-state operating points and road and/or track performance data. As the parameter space of control variables, design variable constraints, and objective functions increases, the cost and duration for optimal calibration become prohibitively large. In order to reduce the number of dynamometer tests required for calibrating modern engines, a large-scale simulation-driven machine learning approach is presented in this work. A parallel, fast, robust, physics-based reduced-order engine simulator is used to obtain performance and emission characteristics of engines over a wide range of control parameters under various transient driving conditions (drive cycles). We scale the simulation up to 3,906 nodes of the Theta supercomputer at the Argonne Leadership Computing Facility to generate data required to train a machine learning model. The trained model is then used to predict various engine parameters of interest, and the results are compared with those predicted by the engine simulator. Our results show that a deepneural-network-based surrogate model achieves high accuracy: Pearson product-moment correlation values larger than 0.99 and mean absolute percentage error within 1.07% for various engine parameters such as exhaust temperature, exhaust pressure, nitric oxide, and engine torque. Once trained, the deep-neural-network-based surrogate model is fast for inference: it requires about 16 mu s for predicting the engine performance and emissions for a single design configuration compared with about 0.5 s per configuration with the engine simulator. Moreover, we demonstrate that transfer learning and retraining can be leveraged to incrementally retrain the surrogate model to cope with new configurations that fall outside the training data space.

deep learning↗
DOE OSTI2021

Applying Time-Parallelization to Turbulent Flows

Parallelization of the temporal domain is explored for the solution of turbulent flows. Multigrid reduction-in-time (MGRIT) is used to advance the large-scale fluid dynamics in time sequentially on the coarsest space-time grid but propagate the information in time parallel on all other levels. The goal of this process is to accurately and efficiently resolve the coarse-scale turbulence structure and use that to drive the fine-scales of the turbulent flow. The extra forcing from nonlinear multigrid facilitates the coupling and interaction between fine and coarse scales, through which the multiscale nonlinear physics is properly captured. Adaptive mesh refinement is employed to finely resolve only the regions with strong gradients, which provides further computational efficiency. The underlying computational fluid dynamics solver is a fourth-order finite-volume scheme with the standard 4-stage Runge-Kutta method. An advanced approach is devised and implemented to enable MGRIT to solve highly turbulent flows successfully. Furthermore, the method is applied to solve a Taylor-Green vortex problem and a doubleshear-layer turbulent mixing flow. Results are promising, validating that MGRIT with the filtering approach has the potential to efficiently solve general turbulent flows.

Computational Fluid Dynamics↗
DOE OSTI2018

Numerical Prediction of Cyclic Variability in a Spark Ignition Engine Using a Parallel Large Eddy Simulation Approach

Cycle-to-cycle variability (CCV) is detrimental to IC engine operation and can lead to partial burn, misfire, and knock. Predicting CCV numerically is extremely challenging due to two key reasons. First, high-fidelity methods such as large eddy simulation (LES) are required to accurately resolve the in-cylinder turbulent flow field both spatially and temporally. Second, CCV is experienced over long timescales and hence the simulations need to be performed for hundreds of consecutive cycles. Ameen et al. (2017, "Parallel Methodology to Capture Cyclic Variability in Motored Engines,"Int. J. Engine Res., 18(4), pp. 366-377.) developed a parallel perturbation model (PPM) approach to dissociate this long time-scale problem into several shorter time-scale problems. This strategy was demonstrated for motored engine and it was shown that the mean and variance of the in-cylinder flow field was captured reasonably well by this approach. In the present study, this PPM approach is extended to simulate the CCV in a fired port-fuel injected (PFI) spark ignition (SI) engine. The predictions from this approach are also shown to be similar to the consecutive LES cycles. It is shown that the parallel approach is able to predict the coefficient of variation (COV) of the in-cylinder pressure and burn raterelated parameters with sufficient accuracy, and is also able to predict the qualitative trends in CCV with changing operating conditions. It is shown that this new approach is able to give accurate predictions of the CCV in fired engines in less than one-tenth of the time required for the conventional approach of simulating consecutive engine cycles.

Ameen, Muhsin M.↗
NASA NTRS1972

Aerodynamic stability and drag characteristics of a parallel burn/SRM ascent configuration (M equals 0.6 to 4.96)

Experimental aerodynamic investigations were conducted in the NASA/MSFC 14-inch trisonic wind tunnel during April 1972 on a 0.004-scale model of a solid rocket motor version of the space shuttle ascent configuration. The configuration consisted of a parallel burn solid rocket motor booster on an external HO centerline tank orbiter. Six component aerodynamic force and moment data were recorded over an angle of attack range from -10 deg to +10 deg at zero degrees sideslip and over a sideslip range from -10 deg to +10 deg at zero degrees angle of attack. Mach numbers ranged from 0.6 to 4.96. The purpose of the test was to determine the performance and stability characteristics of the complete ascent configuration and buildup, and to determine the effects of variations in HO tank and SRM nose shaping, orbiter incidence and position, and position of the solid rocket motors.

Sims, F.↗
DOE OSTI2019

Reconstruction for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures [Slides]

Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming more difficult as the detectors increase in size to reach their physics goals. In liquid argon time projection chambers (TPCs) the charged particles from neutrino interactions produce ionization electrons which drift in an electric field towards a series of collection wires, and the signal on the wires is used to reconstruct the interaction. The MicroBooNE detector currently collecting data at Fermilab has 8000 wires, and planned future experiments like DUNE will have 100 times more, which means that the time required to reconstruct an event will scale accordingly. Modernization of liquid argon TPC reconstruction code, including vectorization, parallelization and code portability to GPUs, will help to mitigate these challenges. The liquid argon TPC hit finding algorithm within the L arSoft framework used across multiple experiments has been vectorized and parallelized. This increases the speed of the algorithm on the order of ten times within a stand alone version on Intel architectures. This new version has been incorporated back into LarSoft so that it can be generally used. These methods will also be applied to other low level reconstruction of the wire signals such as the deconvolution. The applications and performance of this modernized liquid argon TPC wire reconstruction will be presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers

A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like the Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.

Sreepathi, Sarat↗
DOE OSTI2026

Commuting embeddings for parallel strategies in non-local games

Non-local games provide a versatile framework for probing quantum correlations and for benchmarking the power of entanglement. In finite dimensions, the standard method for playing several games in parallel requires a tensor product of the local Hilbert spaces, which scales additively in the number of qubits. In this work, we show that this additive cost can be reduced by exploiting algebraic embeddings. We introduce two forms of compressions. First, when a referee selects one game from a finite collection of games at random, the game quantum strategy can be implemented using a maximally entangled state of dimension equal to the largest individual game, thereby eliminating the need for repeated state preparations. Second, we establish conditions under which several games can be played simultaneously in parallel on fewer qubits than the tensor product baseline. These conditions are expressed in terms of commuting embeddings of the game algebras. Moreover, we provide a constructive framework for building such embeddings. Using tools from Lie theory, we show that aligning the various game algebras into a common Cartan decomposition enables such a qubit reduction. Beyond the theoretical contribution, our framework casts NLGs as algebraic primitives for distributed and resource-constrained quantum computations and suggested NLGs as a comparable device-independent dimension witness.

Commuting embeddings↗
NASA NTRS1995

Biomorphic architectures for autonomous Nanosat designs

Modern space tool design is the science of making a machine both massively complex while at the same time extremely robust and dependable. We propose a novel nonlinear control technique that produces capable, self-organizing, micron-scale space machines at low cost and in large numbers by parallel silicon assembly. Experiments using biomorphic architectures (with ideal space attributes) have produced a wide spectrum of survival-oriented machines that are reliably domesticated for work applications in specific environments. In particular, several one-chip satellite prototypes show interesting control properties that can be turned into numerous application-specific machines for autonomous, disposable space tasks. We believe that the real power of these architectures lies in their potential to self-assemble into larger, robust, loosely coupled structures. Assembly takes place at hierarchical space scales, with different attendant properties, allowing for inexpensive solutions to many daunting work tasks. The nature of biomorphic control, design, engineering options, and applications are discussed.

Hasslacher, Brosl↗
NASA NTRS1985

Parallel VLSI Architecture

Fermat number transformation convolutes two digital data sequences. Very-large-scale integration (VLSI) applications, such as image and radar signal processing, X-ray reconstruction, and spectrum shaping, linear convolution of two digital data sequences of arbitrary lenghts accomplished using Fermat number transform (ENT).

Truong, T. K.↗
NASA NTRS2003

Thermal Conductivity of Liquid He-4 near the Superfluid Transition in Restricted Geometries

We present measurements of the thermal conductivity near the superfluid transition of He-4 in confined geometries. The confinements we have studied include: cylindrical geometries with radii L=.5 and 1.0 microns, and parallel plates with 5 micron spacing. For L=1.0 microns, measurements at six pressures were conducted, whereas only SVP measurements have been done for other geometries. For the 1-D confinement in cylinders, the data are consistent with a universal scaling for all pressures at and above T(sub lambda). There are indications of breakdown of scaling and universality below T(sub lambda). For the 2-D confinement between parallel plates, the preliminary results indicate that the thermal conductivity is finite at the bulk superfluid transition temperature. Further analyses are needed to compare the 2-D results with those in bulk and 1-D confinement.

Liu, Yuanming↗