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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 721 records · Page 40
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NASA NTRSDate not supplied

Progress Towards a Common CERES Cloud Mask Algorithm for MODIS and VIIRS: Evaluation using CALIOP Data

For over two decades The Clouds and the Earth’s Radiant Energy System (CERES) project has endeavored to produce a long-term global climate data record for detecting changes in the Earth’s radiation budget and to improve understanding of how clouds contribute to those changes. CERES incorporates cloud information derived from passive narrowband satellite imaging radiometers, and over the course of the project different instruments have contributed to this effort, namely the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS).Cloud properties have been derived from MODIS on the Aqua satellite for CERES since 2002,and VIIRS on the NOAA-20 satellite launched in 2017 will continue the cloud record once Aqua-MODIS reaches the end of its operational lifetime. However, MODIS and VIIRS have different spectral capabilities and characteristics which complicates our ability to seamlessly transition the record from MODIS to VIIRS. This study evaluates some of the recent algorithm modifications incorporated by the CERES Cloud Working Group towards developing a unified cloud mask algorithm for MODIS and VIIRS that utilizes a set of spectral bands common to both instruments. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products are used to evaluate MODIS and VIIRS regional cloud fraction estimates and identify areas where improvements can be made. The cloud mask algorithm relies on computed estimates of cloud-free top-of-atmosphere radiances to differentiate cloudy and cloud-free imager pixels. The unified cloud mask algorithm currently under development uses a reduced number of spectral bands common to both MODIS and VIIRS and so relies on the accuracy of these simulated cloud-free radiances more heavily than predecessor algorithms, i.e., the CERES Edition 4 cloud mask, which use as much spectral information as available on each imager with less consideration for cross-platform consistency. Our validation strategy using CALIOP data is also described. The CALIOP observations are critical for assessing cloud detection accuracies and for independently confirming cloud-free conditions which enables a more robust evaluation of the simulated cloud-free radiances for biases due to factors such as view angle and the spectral dependence of water vapor absorption. These effects are known to differ for the two satellite instruments. Comparisons of MODIS and VIIRS cloud fractions are presented in context with estimates from CALIOP and from the CERES Edition 4 cloud mask algorithm to gauge current progress in developing accurate and consistent MODIS and VIIRS cloud properties for CERES.

CERES↗
NASA NTRSDate not supplied

Formalization of the Bellman-Ford Algorithm for Airspace Applications

This paper describes the formal verification of one of the most well-known algorithms for finding the shortest path between all vertices in a directed graph, namely the Bellman-Ford algorithm. This formal verification, performed in the Prototype Verification System (PVS), is motivated by two applications in the aerospace domain which use the algorithm for path planning. The first is a pre-flight calculation that uses an adapted version of Bellman-Ford to find a route intended to maximize GNSS availability throughout the flight. The second is a more traditional application intended to find the shortest path between an autonomous aircraft's current position and a goal waypoint, while avoiding regions of space specified by geofences. A novel aspect of this formal verification effort is the inclusion of two distinct models of computation for the algorithm, one being a traditional serial computation, and the other being an explicitly parallel computation. The ability to use parallel computation in the Bellman-Ford algorithm is in fact why it was chosen over other traditionally more performant algorithms, especially for the GNSS application, where the size of the graph makes a purely serial computation infeasible.

formal verification↗
DOE OSTI2023

An Initial Study of the Convergence Rate of Griffin’s Pebble Bed Reactors Algorithm

This paper presents an initial study of the convergence properties of an iterative algorithm for computing the burnup distribution in a pebble bed reactor (PBR) in its equilibrium core condition. The algorithm is implemented in the Griffin code. Griffin is a reactor multiphysics analysis application jointly developed by Idaho National Laboratory (INL) and Argonne National Laboratory (ANL). Griffin’s PBR algorithm is discussed and simulation data are presented. An alternative matrix formulation of the algorithm is presented that facilitates analysis of the iterative algorithm. The dependence of the spectral radius of the iterative algorithm on operational and discretization parameters is investigated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Implementation and Comparison of Different Algorithms for on the fly Doppler Broadening in RMC Code

Accurate cross sections are of great importance to Monte Carlo particle transport calculation codes which use pointwise cross sections. It is well known that cross sections have much dependence on temperature on account of Doppler effect which is particularly vital for resonance absorption cross sections. In general, pre-prepared continuous-energy libraries are used in Monte Carlo codes, which is feasible in many applications. However, in multi-physics calculations where neutronics and thermal hydraulics are coupled, cross sections at a fine interval of temperatures are desired, which brings a great challenge to computer memory. Consequently, different methods about on the fly Doppler broadening for cross sections are proposed, which can generate cross sections at the specific temperature points during neutron simulations. These methods can be roughly classified into two categories: one is to calculate the required cross sections by different algorithms, such as interpolation using a high order function expansion, multipole representation, and different integration algorithms based on Doppler broadening equation. The other, for instance, target motion sampling (TMS), does not calculate the Doppler broadening cross sections but takes into account Doppler effect by rejection technique. In this paper, several on the fly Doppler broadening algorithms based on Doppler broadening equation are implemented in the Reactor Monte Carlo code RMC, and then the accuracy and efficiency are discussed and compared. In this paper, several algorithms for on the fly Doppler broadening based on Doppler broadening equation have been implemented in RMC and the focus falls on the comparison of the Gauss Legendre quadrature and Gauss Hermite quadrature. Gauss Legendre quadrature is more accurate than Gauss Hermite quadrature but also more time consuming. Base temperature has a significant impact on Gauss Hermite quadrature while increasing base temperature to room temperature (300 K) can give satisfactory results. In addition, the proportion of overhead of Doppler broadening treatment in total computational time tends to decrease with an increase number of nuclides in fuel. For depletion calculation, both algorithms are in good agreement with reference and the relative errors of K{sub inf} are within 5 times of standard deviation. As for total computational time, the ratio between Gauss Legendre quadrature or Gauss Hermite quadrature and reference decreases and reaches 9.6 or 3.4 at last, respectively. Four important isotopes are chosen to compare the atom density and all relative errors are within 0.25%. After comparisons of different algorithms and different base temperatures, it can be found that Gauss Hermite quadrature broadening from 300 K can perform on the fly cross section Doppler broadening with least total computational time and acceptable precision. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Performance of biologically inspired algorithms tuned on TiO 2 nanoparticle benchmark system

Biologically inspired optimization algorithms (BIOA), such as particle swarm optimization (PSO) and differential evolution (DE), are often applied to a specific problem with the tacit assumptions that each algorithm will have a unique set of “ideal” tuning parameters that provide “best” performance, and that, once tuned, two different optimization algorithms ought to exhibit distinct performance. This work tests those assumptions by systematically comparing PSO and DE applied to identify stable and metastable configurations of a benchmark system, a titania dimer Ti 2 O 4 , whose energy is described by a modified Matsui-Akaogi pairwise potential. The hypotheses (H) are formally expressed as: (H1) for both PSO and DE there exists a unique, optimal set of tuning parameters that maximize its performance on a given problem; (H2) when running two different BIOAs, one algorithm must demonstrate superior performance over the other. By identifying and studying an approximate Pareto-optimal set of tuning parameters for DE and PSO, H1 is shown to be strictly false for DE, and tentatively false for PSO, while H2 is shown to be false with respect to certain statistics. Here, we further investigate the correlations between key tuning parameters and algorithm performance. Our conclusion may be implemented to further optimize and develop BIOAs applied to materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Parallel Randomized Tucker Decomposition Algorithms

The Tucker tensor decomposition is a natural extension of the singular value decomposition (SVD) to multiway data. Here, we propose to accelerate Tucker tensor decomposition algorithms by using randomization and parallelization. We present two algorithms that scale to large data and many processors, significantly reduce both computation and communication cost compared to previous deterministic and randomized approaches, and obtain nearly the same approximation errors. The key idea in our algorithms is to perform randomized sketches with Kronecker-structured random matrices, which reduces computation compared to unstructured matrices and can be implemented using a fundamental tensor computational kernel. We provide probabilistic error analysis of our algorithms and implement a new parallel algorithm for the structured randomized sketch. Our experimental results demonstrate that our combination of randomization and parallelization achieves accurate Tucker decompositions much faster than alternative approaches. We observe up to a 16X speedup over the fastest deterministic parallel implementation on 3D simulation data.

Tucker decompositions↗
DOE OSTI2018

A Generalized Backward Euler algorithm for the numerical integration of a viscous breakage model

This paper discusses the formulation and the numerical performance of a fully implicit algorithm used to integrate a rate-dependent model defined within a breakage mechanics framework. For this purpose, a Generalized Backward Euler (GBE) algorithm has been implemented according to two different linearization strategies: The former is derived by a direct linearization of the constitutive equations, while the latter introduces rate effects through a consistency parameter. Additionally, the accuracy and efficiency of the GBE algorithm have been investigated by (1) performing material point analyses and (2) solving initial boundary value problems. In both cases, the overall performance of the underlying algorithm is inspected for a range of loading rates, thus simulating comminution from slow to fast dynamic problems. As the viscous response of the breakage model can be recast through a viscous nucleus function, the presented algorithm can be considered as a general framework to integrate constitutive equations relying on the overstress approach typical of Perzyna-like viscoplastic models.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Preprocessing Prediction of Advanced Algorithms for Medical Imaging

Advanced medical imaging algorithms (such as bone removal, vessel segmentation, or a lung nodule detection) can provide extremely valuable information to the radiologists, but they might sometimes be very time consuming. Being able to run the algorithms in advance can be a possible solution. However, we do not know which algorithm to run on a given dataset before it is actually used. It is possible to manually insert matching rules for preprocessing algorithms, but it requires high maintenance and does not work well in practice. This paper presents a dynamic machine learning solution for predicting which advanced visualization (AV) algorithm needs to be applied on a given series. The system gets a handful of free text DICOM tags as an input and builds a model in the clinical setting. It incorporates a Bag of Words (BOW) feature extractor and a Random Forest classifier. The approach was tested on two datasets from clinical sites which use different languages and varying scanner models. We show that even without feature extraction, sensitivity of above 90% can be reached on both of them. By using BOW feature extractor, precision and sensitivity can usually be further improved. Even on a noisy and highly unbalanced dataset, only around 100 samples were needed to reach sensitivity of above 80% and specificity of above 97%. We show how the solution can be part of a Smart Preprocessing mechanism in a viewing software. Using such a system will ultimately minimize the time to launch studies and improve radiologists reading time efficiency.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

A parallel evolutionary multiple-try metropolis Markov chain Monte Carlo algorithm for sampling spatial partitions

We develop an Evolutionary Markov Chain Monte Carlo (EMCMC) algorithm for sampling spatial partitions that lie within a large, complex, and constrained spatial state space. Our algorithm combines the advantages of evolutionary algorithms (EAs) as optimization heuristics for state space traversal and the theoretical convergence properties of Markov Chain Monte Carlo algorithms for sampling from unknown distributions. Local optimality information that is identified via a directed search by our optimization heuristic is used to adaptively update a Markov chain in a promising direction within the framework of a Multiple-Try Metropolis Markov Chain model that incorporates a generalized Metropolis-Hastings ratio. We further expand the reach of our EMCMC algorithm by harnessing the computational power afforded by massively parallel computing architecture through the integration of a parallel EA framework that guides Markov chains running in parallel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

An approach of orthogonalization within the Gram–Schmidt algorithm

In this paper we consider the variants of Gram–Schmidt such as Classical Gram–Schmidt and Modified Gram–Schmidt algorithms. It is shown that for problems of dimension more than two the round-off error of operation q{sub 1}{sup T}q{sub 2} has more propagation in both of algorithms. To cure this difficulty we will present an algorithm, namely Optimized Modified Gram–Schmidt algorithm. Numerical examples indicate the accuracy of this algorithm. We show that this method can improve the loss of orthogonality of the orthogonalization in some ill-conditioned cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On nonlinear inexact Uzawa algorithms for stabilized saddle point problems

In this paper, the nonlinear inexact Uzawa (NIU) methods for saddle point problems are studied. A further improved convergence result of the NIU algorithm for stabilized saddle point problems is presented, which results in much smaller convergence factor. This result can also be viewed as a generalization of some previous work for classical saddle point problems. Based on this result, we generalize some adaptive versions of the NIU algorithm with variable iteration parameters for classical saddle point problems to solve stabilized saddle point problems. Convergence analyses of these algorithms are presented. It shows that they converge under similar conditions as those in classical cases, which are more practical and without estimates on the extreme eigenvalues of the involved preconditioned systems. Numerical experiments are given to demonstrate the effectiveness of these algorithms over some original NIU algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Genetic algorithm optimization of nuclear criticality experiment for reduction of intermediate-energy 239 Pu nuclear data uncertainties

Nuclear criticality experiments are conducted to investigate specific nuclear data important for safe handling and storage of fissile materials, reactor design and operation, and the validation of radiation transport codes. Incorrect or uncertain nuclear data can prohibitively impact operational safety limits, reactor licensing, and predictive simulation capability; therefore, integral measurements from criticality experiments are necessary and should be performed frequently. To maximize the impact of the integral measurements, it is important to consider experiment geometry, material selection, and component dimensions. When taking these considerations into account, the experiment design process becomes iterative and very time intensive. This work utilizes a genetic algorithm to efficiently explore potential nuclear criticality experiment designs for the Laboratory Directed Research & Development project PARADIGM (PARallel Approach of Differential and InteGral Measurements) at Los Alamos National Laboratory. In this paper, the building blocks of the genetic algorithm are discussed in detail, the genetic algorithm methodology is verified, and the genetic algorithm is used to produce three candidate experiment models for the final PARADIGM design. The three candidate models produced by the genetic algorithm consist of copper-reflected assemblies containing 14 repeating units of alumina, graphite, boron, and plutonium plates. Furthermore, in addition to the optimization results, final design considerations are also discussed for designs with a height and/or weight very close to or slightly above assembly machine operational limits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

HHL algorithm with mapping function and enhanced sampling for model predictive control in microgrids

Here, this paper presents a refined quantum Harrow Hassidim Lloyd (HHL) algorithm for microgrid control. The first novelty of the developed method is that a mapping shift function enables the original HHL algorithm to handle general linear equations with non-singular and indefinite matrix. Second, a method of Matrix Extension for Amplifying Sampling Probabilities of Intended Solution (ME-ASPI) is proposed to design the reformulated linear algebraic equations, allowing for improved sampling efficiency of the quantum tomography in the refined HHL algorithm. Then, we applied the method to solve the model predictive control (MPC) problem in nonlinear dynamical microgrids. Specifically, with the ME-ASPI method, the refined HHL algorithm can effectively obtain the intended partial optimal control inputs for MPC. The optimization of quadratic programming problem in each time step of MPC is transformed into a linear system problem, which is addressed by the proposed quantum solver through using only partial information, with the time complexity improved from $\mathscr{O}(\mathscr{N}^{2.37286})$ classically to $\mathscr{O}(\mathscr{N}^{2} log \mathscr{N}$ x $p$ log $p)$ in quantum. Numerical examples have validated the effectiveness of the refined HHL algorithm with the proposed mapping function and the ME-ASPI method. By leveraging quantum properties, the proposed method provides a hybrid quantum–classical framework for microgrid control. This generic method can also potentially tackle many other challenges in analyzing and controlling general complex engineered systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Energy characteristics of multi-chiller load distribution algorithms in a large office building

Here, this study evaluates the energy efficiency of multi-chiller systems in large office buildings, focusing on their optimization across various climate zones as defined by ASHRAE. Using EnergyPlus for simulations, the research examines five different load distribution algorithms in multi-chiller systems that range from one to ten chillers, aiming to understand their effectiveness in 15 distinct climate zones. The primary objectives of the study include identifying the energy efficiency of multi-chiller systems in each climate zone, determining the appropriate number of chillers for each zone, and evaluating the performance of the load distribution algorithms. Based on the U.S. Department of Energy’s commercial building model, the results suggest that multi-chiller systems can significantly reduce cooling energy consumption in various climates. Among the algorithms evaluated, the Sequential Uniform Part Load Ratio (SUPLR) algorithm demonstrates notable efficiency, especially in the 4A climate zone (Baltimore), where it achieves substantial energy savings. Applying the SUPLR algorithm in a multi-chiller setup with four chillers in this zone leads to an estimated 24.5 % reduction in energy usage, equivalent to 183 MW annually. The research indicates that a range of 3 to 5 chillers is typically optimal for most climate zones. In-depth analysis in the 4A climate zone highlights the importance of minimizing operation hours at low Part Load Ratios (PLR) to ensure that chillers operate at a high Coefficient of Performance (COP). This strategy underscores the potential of well-designed multi-chiller systems to reduce cooling energy demand, particularly in climates with transitional seasons. This study provides an overview of the energy-saving potential of multi-chiller systems, applicable across a variety of climatic scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

An improved iteration loop for the three dimensional quasi-static particle-in-cell algorithm: QuickPIC

We present improvements to the three-dimensional (3D) quasi-static particle-in-cell (PIC) algorithm, which is used to efficiently model short-pulse laser and particle beam–plasma interactions. In this algorithm the fields including the index of refraction created by a static particle/laser beam are calculated. These fields are then used to advance the particle/laser beam forward in time (distance). For a 3D quasi-static code, calculating the wake fields is done using a two-dimensional (2D) PIC code where the time variable is ξ=ct-z and z is the propagation direction of the particle/laser beam. When calculating the wake, the fields, particle positions and momenta are not naturally time centered so an iterative predictor corrector loop is required. In the previous iterative loop in QuickPIC (currently the only 3D quasi-static PIC code), the field equations are derived using the Lorentz gauge. Here we describe a new algorithm which uses gauge independent field equations. It is found that with this new algorithm, the results converge to the results from fully explicitly PIC codes with far fewer iterations (typically 1 iteration as compared to 2–8) for a wide range of problems. In addition, we describe a new deposition scheme for directly depositing the time derivative of the current that is needed in one of the field equations. The new deposition scheme does not require message passing for the particles inside the iteration loop, which greatly improves the speed for parallelized calculations. Comparisons of results from the new and old algorithms and to fully explicit PIC codes are also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

An efficient algorithm for incompressible N-phase flows

We present an efficient algorithm within the phase field framework for simulating the motion of a mixture of N (N⩾2) immiscible incompressible fluids, with possibly very different physical properties such as densities, viscosities, and pairwise surface tensions. The algorithm employs a physical formulation for the N-phase system that honors the conservations of mass and momentum and the second law of thermodynamics. We present a method for uniquely determining the mixing energy density coefficients involved in the N-phase model based on the pairwise surface tensions among the N fluids. Our numerical algorithm has several attractive properties that make it computationally very efficient: (i) it has completely de-coupled the computations for different flow variables, and has also completely de-coupled the computations for the (N−1) phase field functions; (ii) the algorithm only requires the solution of linear algebraic systems after discretization, and no nonlinear algebraic solve is needed; (iii) for each flow variable the linear algebraic system involves only constant and time-independent coefficient matrices, which can be pre-computed during pre-processing, despite the variable density and variable viscosity of the N-phase mixture; (iv) within a time step the semi-discretized system involves only individual de-coupled Helmholtz-type (including Poisson) equations, despite the strongly-coupled phase–field system of fourth spatial order at the continuum level; (v) the algorithm is suitable for large density contrasts and large viscosity contrasts among the N fluids. Extensive numerical experiments have been presented for several problems involving multiple fluid phases, large density contrasts and large viscosity contrasts. In particular, we compare our simulations with the de Gennes theory, and demonstrate that our method produces physically accurate results for multiple fluid phases. We also demonstrate the significant and sometimes dramatic effects of the gravity, density ratios, pairwise surface tensions, and drop sizes on the N-phase configurations and dynamics. The numerical results show that the method developed herein is capable of dealing with N-phase systems with large density ratios, large viscosity ratios, and pairwise surface tensions, and that it can be a powerful tool for studying the interactions among multiple types of fluid interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

An algorithm for automatic identification of double twin variants in Mg alloys

Highlights: • An algorithm for automatic identification of {10–11}–{10–12} double twin variants is proposed. • This algorithm is applicable for the cases without knowing the primary twin orientations. • The primary twin orientations can be reconstructed using this algorithm. - Abstract: {10 1-bar 1}−{10 1-bar 2} double twins (DTs) are profuse in Mg alloys after rolling at a large reduction (>10%). In many cases, the secondary twins completely occupy the primary ones, causing difficulty in variant analysis. Here, we propose an algorithm for automatic identification of twin variants. This is applicable for the {10 1-bar 1}−{10 1-bar 2} DTs without knowing the primary twins. In addition, two kinds of stereographic projections are constructed to assist such variant selection. By this method, the variants of 67 DTs observed in a rolled AZ31 sheet are determined. Based on 14 sets of DTs with known primary twin orientations, the high efficiency and reliability of this proposed algorithm is verified. The reason for unreliable variant identification in two special cases is also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Uniformity measurements and new positioning algorithms for wavelength-shifting fiber neutron detectors

Wavelength-shifting (WLS) fiber scintillator detectors were successfully installed at two neutron powder diffractometers at the Spallation Neutron Source (SNS). But, they have the following second-order disadvantages: (i). they cannot have both high efficiency and images free of ghosting (position misassignment) concomitantly; (ii). the apparent detection efficiency and spatial resolution are not uniform. These issues are related to the diffusion of scintillation photons and the fluctuation in the number of photons (quantum noise) collected by photo-multiplier tubes (PMTs). Therefore, to mitigate these two issues, we developed two statistics-based positioning algorithms, i.e., a centroid algorithm (CEA) and a correlation algorithm (CA). Compared with the generally used maximum-photon algorithm (MPA), CEA eliminates the ghosting with only about a 10% loss in detection efficiency, and provides better uniformity in detection efficiency and intrinsic background. CA can effectively eliminate ghosting too, but the loss of efficiency at the group boundaries of PMTs is large. Our results indicate that both algorithms can reduce the influence of quantum noise on the neutron positioning.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗