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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Stability Analysis of Coupled Advection-Diffusion Models with Bulk Interface Condition

Numerical stability is of critical importance in general circulation models because it affects the design of algorithms, time to solution, and computational costs associated with the simulations, which are very expensive in practice. In this paper we extend the stability analysis for ocean-atmosphere coupling proposed in [Zhang et al., J. Sci. Comput. 84, 44(2020)] to a more realistic model that includes horizontal advection. We analyze various time-stepping strategies. We find that advection has a stabilizing effect in scenarios common to climate models when bulk interface condition and explicit flux coupling are used. We also show that our method can be used to study the stability impact of advection for other interface conditions such as Dirichlet-Neumann conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A 4DCT imaging-based breathing lung model with relative hysteresis

To reproduce realistic airway motion and airflow, the authors developed a deforming lung computational fluid dynamics (CFD) model based on four-dimensional (4D, space and time) dynamic computed tomography (CT) images. A total of 13 time points within controlled tidal volume respiration were used to account for realistic and irregular lung motion in human volunteers. Because of the irregular motion of 4DCT-based airways, we identified an optimal interpolation method for airway surface deformation during respiration, and implemented a computational solid mechanics-based moving mesh algorithm to produce smooth deforming airway mesh. In addition, we developed physiologically realistic airflow boundary conditions for both models based on multiple images and a single image. Furthermore, we examined simplified models based on one or two dynamic or static images. By comparing these simplified models with the model based on 13 dynamic images, we investigated the effects of relative hysteresis of lung structure with respect to lung volume, lung deformation, and imaging methods, i.e., dynamic vs. static scans, on CFD-predicted pressure drop. The effect of imaging method on pressure drop was 24 percentage points due to the differences in airflow distribution and airway geometry. - Highlights: • We developed a breathing human lung CFD model based on 4D-dynamic CT images. • The 4DCT-based breathing lung model is able to capture lung relative hysteresis. • A new boundary condition for lung model based on one static CT image was proposed. • The difference between lung models based on 4D and static CT images was quantified.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

MO-FG-202-01: A Fast Yet Sensitive EPID-Based Real-Time Treatment Verification System

Purpose: To create a real-time EPID-based treatment verification system which robustly detects treatment delivery and patient attenuation variations. Methods: Treatment plan DICOM files sent to the record-and-verify system are captured and utilized to predict EPID images for each planned control point using a modified GPU-based digitally reconstructed radiograph algorithm which accounts for the patient attenuation, source energy fluence, source size effects, and MLC attenuation. The DICOM and predicted images are utilized by our C++ treatment verification software which compares EPID acquired 1024×768 resolution frames acquired at ∼8.5hz from Varian Truebeam™ system. To maximize detection sensitivity, image comparisons determine (1) if radiation exists outside of the desired treatment field; (2) if radiation is lacking inside the treatment field; (3) if translations, rotations, and magnifications of the image are within tolerance. Acquisition was tested with known test fields and prior patient fields. Error detection was tested in real-time and utilizing images acquired during treatment with another system. Results: The computational time of the prediction algorithms, for a patient plan with 350 control points and 60×60×42cm^3 CT volume, is 2–3minutes on CPU and <27 seconds on GPU for 1024×768 images. The verification software requires a maximum of ∼9ms and ∼19ms for 512×384 and 1024×768 resolution images, respectively, to perform image analysis and dosimetric validations. Typical variations in geometric parameters between reference and the measured images are 0.32°for gantry rotation, 1.006 for scaling factor, and 0.67mm for translation. For excess out-of-field/missing in-field fluence, with masks extending 1mm (at isocenter) from the detected aperture edge, the average total in-field area missing EPID fluence was 1.5mm2 the out-of-field excess EPID fluence was 8mm^2, both below error tolerances. Conclusion: A real-time verification software, with EPID images prediction algorithm, was developed. The system is capable of performing verifications between frames acquisitions and identifying source(s) of any out-of-tolerance variations. This work was supported in part by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

A BAYESIAN ESTIMATE OF THE CMB–LARGE-SCALE STRUCTURE CROSS-CORRELATION

Evidences for late-time acceleration of the universe are provided by multiple probes, such as Type Ia supernovae, the cosmic microwave background (CMB), and large-scale structure (LSS). In this work, we focus on the integrated Sachs–Wolfe (ISW) effect, i.e., secondary CMB fluctuations generated by evolving gravitational potentials due to the transition between, e.g., the matter and dark energy (DE) dominated phases. Therefore, assuming a flat universe, DE properties can be inferred from ISW detections. We present a Bayesian approach to compute the CMB–LSS cross-correlation signal. The method is based on the estimate of the likelihood for measuring a combined set consisting of a CMB temperature and galaxy contrast maps, provided that we have some information on the statistical properties of the fluctuations affecting these maps. The likelihood is estimated by a sampling algorithm, therefore avoiding the computationally demanding techniques of direct evaluation in either pixel or harmonic space. As local tracers of the matter distribution at large scales, we used the Two Micron All Sky Survey galaxy catalog and, for the CMB temperature fluctuations, the ninth-year data release of the Wilkinson Microwave Anisotropy Probe ( WMAP 9). The results show a dominance of cosmic variance over the weak recovered signal, due mainly to the shallowness of the catalog used, with systematics associated with the sampling algorithm playing a secondary role as sources of uncertainty. When combined with other complementary probes, the method presented in this paper is expected to be a useful tool to late-time acceleration studies in cosmology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, used for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Simple formalism for efficient derivatives and multi-determinant expansions in quantum Monte Carlo

We present a simple and general formalism to compute efficiently the derivatives of a multi-determinant Jastrow-Slater wave function, the local energy, the interatomic forces, and similar quantities needed in quantum Monte Carlo. Through a straightforward manipulation of matrices evaluated on the occupied and virtual orbitals, we obtain an efficiency equivalent to algorithmic differentiation in the computation of the interatomic forces and the optimization of the orbital parameters. Furthermore, for a large multi-determinant expansion, the significant computational gain afforded by a recently introduced table method is here extended to the local value of any one-body operator and to its derivatives, in both all-electron and pseudopotential calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation

The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Furthermore, our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Adaptive Circuit Learning for Quantum Metrology

Quantum sensing is an important application of emerging quantum technologies. We explore whether a hybrid system of quantum sensors and quantum circuits can surpass the classical limit of sensing. In particular, we use optimization techniques to search for encoder and decoder circuits that scalably improve sensitivity under given application and noise characteristics. Furthermore, our approach uses a variational algorithm that can learn a quantum sensing circuit based on platform-specific control capacity, noise, and signal distribution. The quantum circuit is composed of an encoder which prepares the optimal sensing state and a decoder which gives an output distribution containing information of the signal. We optimize the full circuit to maximize the Signal-to-Noise Ratio (SNR). Furthermore, this learning algorithm can be run on real hardware scalably by using the "parameter-shift" rule which enables gradient evaluation on noisy quantum circuits, avoiding the exponential cost of quantum system simulation. We demonstrate up to 13.12x SNR improvement over existing fixed protocol (GHZ), and 3.19x Classical Fisher Information (CFI) improvement over the classical limit on 15 qubits using IBM quantum computer. More notably, our algorithm overcomes the decreasing performance of existing entanglement-based protocols with increased system sizes.

42 ENGINEERING↗
DOE OSTI2017

Computing a Non-trivial Lower Bound on the Joint Entropy between Two Images

In this report, a non-trivial lower bound on the joint entropy of two non-identical images is developed, which is greater than the individual entropies of the images. The lower bound is the least joint entropy possible among all pairs of images that have the same histograms as those of the given images. New algorithms are presented to compute the joint entropy lower bound with a computation time proportional to S log S where S is the number of histogram bins of the images. This is faster than the traditional methods of computing the exact joint entropy with a computation time that is quadratic in S .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A propagation-based fault detection and discrimination method and the optimization of sensor deployment

Industrial processes can be affected by faults having a serious impact on operation when not promptly detected and diagnosed. Here in this paper, a propagation-based fault detection and discrimination(PFDD) method is proposed to develop a strategy for fault diagnosis while in the design phase of a system. The PFDD method constructs the system model using the Integrated System Fault Analysis(ISFA) technique. Based on the system model, the propagation of hardware and software faults are simulated qualitatively. Given the results of the simulation, the process by which a fault propagates can be characterized using the qualitative features of system variables including the deviation of the system variables from their expected values, the variation of the system variables over time, and the order in which each variable is influenced during the propagation of the fault. The strategy by which a fault can be detected and discriminated is defined using those features. The PFDD method supports the detection and discrimination of faults in both steady states and transient states. Based on the PFDD method, the optimization of sensor deployment in a system is discussed. A brute force algorithm is developed to examine the system’s capability at diagnosing faults and the cost of sensor deployment for all possible configurations of sensors. The optimal sensor deployment strategy can be derived accordingly. However, the brute force method is only applicable to small-scale systems due to its high computational cost. A genetic algorithm is used to optimize sensor deployment in large-scale systems. The PFDD and sensor deployment optimization methods are applied to the Experimental Breeder Reactor II (EBR-II) for verification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

On split inclusion problem and fixed point problem for multi-valued mappings

Many of the iterative schemes for solving split inclusion and fixed point problems involve step-sizes that depend on the norm of a bounded linear operator. The implementation of such algorithms are usually difficult to handle. This is because they require the computation of the operator norm. In this paper, we propose an algorithm involving a step-size selected in such a way that its implementation does not require the computation or an estimate of some spectral radius. Using our algorithm we proved strong convergence theorem for split inclusion problem and fixed point problem for multi-valued quasi-nonexpansive mappings in real Hilbert spaces. Our result generalizes some important and recent results in the literature. Some applications of our main result to game theory and variational inequality problem are also presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Multi-objective optimization of root phenotypes for nutrient capture using evolutionary algorithms

Root phenotypes are avenues to the development of crop cultivars with improved nutrient capture, which is an important goal for global agriculture. The fitness landscape of root phenotypes is highly complex and multidimensional. It is difficult to predict which combinations of traits (phene states) will create the best performing integrated phenotypes in various environments. Brute force methods to map the fitness landscape by simulating millions of phenotypes in multiple environments are computationally challenging. Evolutionary optimization algorithms may provide more efficient avenues to explore high dimensional domains such as the root phenotypic space. We coupled the three-dimensional functional–structural plant model, SimRoot, to the Borg Multi-Objective Evolutionary Algorithm (MOEA) and the evolutionary search over several generations facilitated the identification of optimal root phenotypes balancing trade-offs across nutrient uptake, biomass accumulation, and root carbon costs in environments varying in nutrient availability. Our results show that several combinations of root phenes generate optimal integrated phenotypes where performance in one objective comes at the cost of reduced performance in one or more of the remaining objectives, and such combinations differed for mobile and non-mobile nutrients and for maize (a monocot) and bean (a dicot). Functional–structural plant models can be used with multi-objective optimization to identify optimal root phenotypes under various environments, including future climate scenarios, which will be useful in developing the more resilient, efficient crops urgently needed in global agriculture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Physical formulation and numerical algorithm for simulating N immiscible incompressible fluids involving general order parameters

We present a family of physical formulations, and a numerical algorithm, based on a class of general order parameters for simulating the motion of a mixture of N (N⩾2) immiscible incompressible fluids with given densities, dynamic viscosities, and pairwise surface tensions. The N-phase formulations stem from a phase field model we developed in a recent work based on the conservations of mass/momentum, and the second law of thermodynamics. The introduction of general order parameters leads to an extremely strongly-coupled system of (N−1) phase field equations. On the other hand, the general form enables one to compute the N-phase mixing energy density coefficients in an explicit fashion in terms of the pairwise surface tensions. We show that the increased complexity in the form of the phase field equations associated with general order parameters in actuality does not cause essential computational difficulties. Our numerical algorithm reformulates the (N−1) strongly-coupled phase field equations for general order parameters into 2(N−1) Helmholtz-type equations that are completely de-coupled from one another. This leads to a computational complexity comparable to that for the simplified phase field equations associated with certain special choice of the order parameters. We demonstrate the capabilities of the method developed herein using several test problems involving multiple fluid phases and large contrasts in densities and viscosities among the multitude of fluids. In particular, by comparing simulation results with the Langmuir–de Gennes theory of floating liquid lenses we show that the method using general order parameters produces physically accurate results for multiple fluid phases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Quantum time dynamics employing the Yang-Baxter equation for circuit compression

Quantum time dynamics (QTD) is considered a promising problem for quantum supremacy on near-term quantum computers. However, QTD quantum circuits grow with increasing time simulations. This study focuses on simulating the time dynamics of one-dimensional (1D) integrable spin chains with nearest-neighbor interactions. We have proved the existence of a reflection symmetry in the quantum circuit employed for simulating the time evolution of certain classes of 1D Heisenberg model Hamiltonians by virtue of the quantum Yang-Baxter equation, and how this symmetry can be exploited to compress and produce a shallow quantum circuit. With this compression scheme, the depth of the quantum circuit becomes independent of step size and only depends on the number of spins. We show that the depth of the compressed circuit is rigorously a linear function of the system size for the studied Heisenberg model Hamiltonians in the present work. As a consequence, the number of CNOT gates in the compressed circuit only scales quadratically with the system size, which allows for the simulations of time dynamics of very large 1D spin chains. We derive the compressed circuit representations for different special cases of the Heisenberg Hamiltonian. We compare and demonstrate the effectiveness of this approach by performing simulations on quantum computers.

1-dimensional spin chains↗
DOE OSTI2016

On the Degradation of the Effectiveness of Nonlinear Diffusion Acceleration with Parallel Block Jacobi Splitting

MOOSE's radiation transport module Rattlesnake comprises a first order S N solver that can be accelerated with a flexible Nonlinear Diffusion Acceleration. At each NDA iteration, the streaming and collision operator of the S{sub N} equations are (approximately) inverted using a transport sweep. When executed on multiple processors parallel block Jacobi (PBJ) splitting is utilized lagging the processor sub-domain's incoming angular fluxes making the implemented method asynchronous, i.e. the convergence properties of NDA become dependent on the number of concurrent tasks and details of the partitioning. Within this work, we study the NDA convergence properties for the two-dimensional C5G7 benchmark problem PBJ with p = 1, 2, 4, 8, 12, 16, 24, 32, 48, 60, 72, 96 and 144 concurrent tasks. Rosa studied the convergence properties of PBJ splitting methods with and without transport synthetic acceleration (TSA) in slab geometry and two-dimensional Cartesian geometry, respectively. Rosa's iteration scheme lags both the incoming angular fluxes and the scattering source making it very similar to the method described here. Rosa found by means of Fourier analysis that convergence of unaccelerated PBJ can degrade for optically thin subdomains, i.e. for a large number of processors given a fixed domain size. Depending on the choice of parameters application of TSA can lead to an effective algorithm. In contrast to Rosa's work, this work focuses on PBJ splitting for NDA accelerated solution of the multigroup eigenvalue problem. Later, Zerr and Anistratov used their integral transport matrix method in conjunction with PBJ. This iteration scheme is different as it does not lag the scattering source. Nevertheless, Fourier analysis with Diamond Difference spatial discretization showed that the method degrades with increasing the overall size of the spatial domain while keeping the sub-domain size constant. In the case of an infinite medium, the observed spectral radius becomes unity. These references establish the iterative performance of PBJ for one-group fixed source problems discretized on rectangular cells, but for the Rattlesnake first order NDA method, an emphasis lies on multigroup eigenvalue problems. In this setting, the additional complication arises that the low order equations are not exactly inverted, but only to some iterative criterion. Further, as the low order problem is solved using Jacobian-Free Newton Krylov methods, a suitable preconditioner for the GMRES iterations is required. As the effectiveness of the preconditioners may decrease when more concurrent processes are used, we expect the low order solve to impact the overall effectiveness of the NDA method in parallel execution as well. Therefore, this work focuses on the question of how detrimental the asynchronicity of the high and low order iterative solutions is for the effectiveness of NDA solutions of realistic, unstructured grid, multigroup eigenvalue problems. The iterative convergence properties of Rattlesnake's NDA method for multigroup eigenvalue problems are studied for varying number of concurrent processes. Rattlesnake's NDA method is asynchronous because the high order transport sweep uses PBJ splitting and because the low order solve's preconditioners, algebraic multigrid and additive Schwarz, are asynchronous. We found that up to 144 processes the number of Picard iterations increases by a factor of 2-3 affecting but not destroying NDA's parallel performance for the C5G7 benchmark problem, a representative LWR (light water reactor) configuration. The solution of the NDA low order eigenvalue problem posed greater problems than expected for the solvers currently available in Rattlesnake mainly because of problems to effectively precondition the modified low-order problem. As a matter of fact, the low order problem becomes an advection-diffusion problem. Non-robustness was observed for the AMG preconditioned coarse mesh acceleration for more than 60 concurrent processes, which was fixed by increasing the number of permissible V-cycles per AMG call. For fine mesh acceleration AMG proved to be ineffective and we resorted to ASM. For both AMG and ASM, increasing number of linear iterations per nonlinear are observed when increasing the number of processes, but for ASM this problem appears to be worse. The results from this work also indicate that initial development efforts should be diverted from implementation of a high-order parallel sweeping algorithm to the effective and robust iterative solution of the low order NDA multigroup eigenvalue problem. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

3D Coded SUMMA: Communication-Efficient and Robust Parallel Matrix Multiplication

In this paper, we propose a novel fault-tolerant parallel matrix multiplication algorithm called 3D Coded SUMMA that achieves higher failure-tolerance than replication-based schemes for the same amount of redundancy. This work bridges the gap between recent developments in coded computing and fault-tolerance in high-performance computing (HPC). The core idea of coded computing is the same as algorithm-based fault-tolerance (ABFT), which is weaving redundancy in the computation using error-correcting codes. In particular, we show that MatDot codes, an innovative code construction for parallel matrix multiplications, can be integrated into three-dimensional SUMMA (Scalable Universal Matrix Multiplication Algorithm [30]) in a communication-avoiding manner. To tolerate any two node failures, the proposed 3D Coded SUMMA requires ~50% less redundancy than replication, while the overhead in execution time is only about 5–10%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Fast Parallel Stochastic Subspace Algorithms for Large-Scale Ambient Oscillation Monitoring

With the installation of synchrophasors widely across the power grid, measurement-based oscillation monitoring algorithms are becoming increasingly useful in identifying the real-time oscillatory modal properties in power systems. When the number of phasor measurement unit (PMU) channels grows, the computational time of many PMU data based algorithms is dominated by the computational burden in processing large-scale dense matrices. In order to overcome this limitation, this paper presents new formulations and computational strategies for speeding up an ambient oscillation monitoring algorithm, namely, stochastic subspace identification (SSI). Based on previous work, two fast singular value decomposition (SVD) approaches are first applied to the SVD evaluation within the SSI algorithm. Next, block structures are exploited so that the large-scale dense matrix computations can be processed in parallel. This helps in memory savings as well as in overall computational time. Experimental results from three sets of archived data of the western interconnection demonstrate that the new approaches can provide significant speedups while retaining modal estimation accuracy. With proposed fast parallel algorithms, the real-time oscillation monitoring of the large-scale system using hundreds of PMU measurements becomes feasible.

97 MATHEMATICS AND COMPUTING↗