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DOE OSTI2020

A non-orthogonal variational quantum eigensolver

Variational algorithms for strongly correlated chemical and materials systems are one of the most promising applications of near-term quantum computers. We present an extension to the variational quantum eigensolver that approximates the ground state of a system by solving a generalized eigenvalue problem in a subspace spanned by a collection of parametrized quantum states. This allows for the systematic improvement of a logical wavefunction ansatz without a significant increase in circuit complexity. To minimize the circuit complexity of this approach, we propose a strategy for efficiently measuring the Hamiltonian and overlap matrix elements between states parametrized by circuits that commute with the total particle number operator. This strategy doubles the size of the state preparation circuits but not their depth, while adding a small number of additional two-qubit gates relative to standard variational quantum eigensolver. We also propose a classical Monte Carlo scheme to estimate the uncertainty in the ground state energy caused by a finite number of measurements of the matrix elements. We explain how this Monte Carlo procedure can be extended to adaptively schedule the required measurements, reducing the number of circuit executions necessary for a given accuracy. We apply these ideas to two model strongly correlated systems, a square configuration of H4 and the π-system of hexatriene (C6H8).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Constraining scale dependent growth with redshift surveys

Ongoing and future redshift surveys have the capability to measure the growth rate of large scale structure at the percent level over a broad range of redshifts, tightly constraining cosmological parameters. Beyond general relativity, however, the growth rate in the linear density perturbation regime can be not only redshift dependent but scale dependent, revealing important clues to modified gravity. We demonstrate that a fully model independent approach of binning the gravitational strength G eff (k,z) matches scalar-tensor results for the growth rate fσ 8 (k,z) to 0.02%–0.27% rms accuracy. For data of the quality of the Dark Energy Spectroscopic Instrument (DESI) we find the bin values can be constrained to 1.4%–28%. We also explore the general scalar-tensor form, constraining the amplitude and past and future scalaron mass/shape parameters. Perhaps most interesting is the strong complementarity of low redshift peculiar velocity data with DESI-like redshift space distortion measurements, enabling improvements up to a factor 6–7 on 2D joint confidence contour areas. Finally, we quantify some issues with gravity parametrizations that do not include all the key physics.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Exploiting non-linear scales in galaxy–galaxy lensing and galaxy clustering: A forecast for the dark energy survey

ABSTRACT The combination of galaxy–galaxy lensing (GGL) and galaxy clustering is a powerful probe of low-redshift matter clustering, especially if it is extended to the non-linear regime. To this end, we use an N-body and halo occupation distribution (HOD) emulator method to model the redMaGiC sample of colour-selected passive galaxies in the Dark Energy Survey (DES), adding parameters that describe central galaxy incompleteness, galaxy assembly bias, and a scale-independent multiplicative lensing bias Alens. We use this emulator to forecast cosmological constraints attainable from the GGL surface density profile ΔΣ(rp) and the projected galaxy correlation function wp, gg(rp) in the final (Year 6) DES data set over scales $r_p=0.3\!-\!30.0\, h^{-1} \, \mathrm{Mpc}$. For a $3{{\ \rm per\ cent}}$ prior on Alens we forecast precisions of $1.9{{\ \rm per\ cent}}$, $2.0{{\ \rm per\ cent}}$, and $1.9{{\ \rm per\ cent}}$ on Ωm, σ8, and $S_8 \equiv \sigma _8\Omega _m^{0.5}$, marginalized over all halo occupation distribution (HOD) parameters as well as Alens. Adding scales $r_p=0.3\!-\!3.0\, h^{-1} \, \mathrm{Mpc}$ improves the S8 precision by a factor of ∼1.6 relative to a large scale ($3.0\!-\!30.0\, h^{-1} \, \mathrm{Mpc}$) analysis, equivalent to increasing the survey area by a factor of ∼2.6. Sharpening the Alens prior to $1{{\ \rm per\ cent}}$ further improves the S8 precision to $1.1{{\ \rm per\ cent}}$, and it amplifies the gain from including non-linear scales. Our emulator achieves per cent-level accuracy similar to the projected DES statistical uncertainties, demonstrating the feasibility of a fully non-linear analysis. Obtaining precise parameter constraints from multiple galaxy types and from measurements that span linear and non-linear clustering offers many opportunities for internal cross-checks, which can diagnose systematics and demonstrate the robustness of cosmological results.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Iterated Gauss-Seidel GMRES

The GMRES algorithm of Saad and Schultz [SIAM J. Sci. Stat. Comput., 7 (1986), pp. 856-869] is an iterative method for approximately solving linear systems Ax = b, with initial guess x0 and residual r0 = b Ax0. The algorithm employs the Arnoldi process to generate the Krylov basis vectors (the columns of Vk ). It is well known that this process can be viewed as a QR factorization of the matrix Bk = [r0, AVk] at each iteration. Despite an O (..epsilon..)..kappa.. (Bk ) loss of orthogonality, for unit roundoff ..epsilon..and condition number ..kappa.. , the modified Gram-Schmidt formulation was shown to be backward stable in the seminal paper by Paige et al. [SIAM J. Matrix Anal.Appl., 28 (2006), pp. 264-284]. We present an iterated Gauss-Seidel formulation of the GMRES algorithm (IGS-GMRES) based on the ideas of Ruhe [Linear Algebra Appl., 52 (1983), pp. 591-601] and Swirydowicz et al. [Numer. Linear Algebra Appl., 28 (2020), pp. 1-20]. IGS-GMRES maintains orthogonality to the level O (..epsilon..)..kappa.. (Bk ) or O (..epsilon..), depending on the choice of one or two iterations; for two Gauss-Seidel iterations, the computed Krylov basis vectors remain orthogonal to working accuracy and the smallest singular value of Vk remains close to one. The resulting GMRES method is thus backward stable. We show that IGS-GMRES can be implemented with only a single synchronization point per iteration, making it relevant to large-scale parallel computing environments. We also demonstrate that, unlike MGS-GMRES, in IGS-GMRES the relative Arnoldi residual corresponding to the computed approximate solution no longer stagnates above machine precision even for highly nonnormal systems.

Arnoldi-QR↗

DOE OSTI2023

Genetic identification of avian samples recovered from solar energy installations

Renewable energy production and development will drastically affect how we meet global energy demands, while simultaneously reducing the impact of climate change. Although the possible effects of renewable energy production (mainly from solar- and wind-energy facilities) on wildlife have been explored, knowledge gaps still exist, and collecting data from wildlife remains (when negative interactions occur) at energy installations can act as a first step regarding the study of species and communities interacting with facilities. In the case of avian species, samples can be collected relatively easily (as compared to other sampling methods), but may only be able to be identified when morphological characteristics are diagnostic for a species. Therefore, many samples that appear as partial remains, or “feather spots”—known to be of avian origin but not readily assignable to species via morphology—may remain unidentified, reducing the efficiency of sample collection and the accuracy of patterns observed. To obtain data from these samples and ensure their identification and inclusion in subsequent analyses, we applied, for the first time, a DNA barcoding approach that uses mitochondrial genetic data to identify unknown avian samples collected at solar facilities to species. We also verified and compared identifications obtained by our genetic method to traditional morphological identifications using a blind test, and discuss discrepancies observed. Our results suggest that this genetic tool can be used to verify, correct, and supplement identifications made in the field and can produce data that allow accurate comparisons of avian interactions across facilities, locations, or technology types. We recommend implementing this genetic approach to ensure that unknown samples collected are efficiently identified and contribute to a better understanding of wildlife impacts at renewable energy projects.

14 SOLAR ENERGY↗

DOE OSTI2025

Stellar Mass Calibrations for Local Low-mass Galaxies

The stellar masses of galaxies are measured from integrated light via several methods—however, few of these methods were designed for low-mass (M ⋆ ≲ 10 8 M ⊙ ) “dwarf” galaxies, whose properties (e.g., stochastic star formation, low metallicity) pose unique challenges for estimating stellar masses. In this work, we quantify the precision and accuracy at which stellar masses of low-mass galaxies can be recovered using UV/optical/IR photometry. We use mock observations of 469 low-mass galaxies from a variety of models, including both semi-empirical models (GRUMPY and UniverseMachine-SAGA) and cosmological baryonic zoom-in simulations (MARVELous Dwarfs and FIRE-2), to test literature color–M ⋆ /L relations and multiwavelength spectral energy distribution (SED) mass estimators. We identify a list of “best practices” for measuring stellar masses of low-mass galaxies from integrated photometry. We find that literature color–M ⋆ /L relations are often unable to capture the bursty star formation histories (SFHs) of low-mass galaxies, and we develop an updated prescription for stellar mass based on g − r color that is better able to recover stellar masses for the bursty low-mass galaxies in our sample (with ∼0.1 dex precision). SED fitting can also precisely recover stellar masses of low-mass galaxies, but this requires thoughtful choices about the form of the assumed SFH: Parametric SFHs can underestimate stellar mass by as much as ∼0.4 dex, while nonparametric SFHs recover true stellar masses with insignificant offset (−0.03 ± 0.11 dex). Finally, we also caution that noninformative (wide) dust attenuation priors may introduce M ⋆ uncertainties of up to ∼0.6 dex.

de los Reyes, Mithi A. C. [Amherst College, MA (Un↗

DOE OSTI2023

The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae

We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multicolor PanSTARRS1 griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host–galaxy associations, redshifts, spectroscopic and/or photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z ≈ 0.5. We present relative SN rates from YSE's magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multisurvey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (~71%) SNe Ia, 339 (~23%) SNe II, and 96 (~6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Accuracy of Thermodynamic Databases for Hydroxyapatite Dissolution Constant

We report that discrepancies have been noted in the solubility constant values of calcium phosphate minerals between various databases employed in widely-used aqueous speciation calculation software programs. This can cause serious errors in the calculated speciation of waters using these software programs. The aim of the present communication was to bring to light these discrepancies. Experimental determinations of the hydroxyapatite solubility product vary by as much as ten orders of magnitude as a result of experimental challenges related to the presence of impurities in the hydroxyapatite used, incongruent dissolution and the contamination of solutions with dissolved carbon dioxide. Finally, it is suggested that the value used in the database Thermo.dat is consistent with experimental data devoid of common experimental problems, while other common databases use values that lead to a vastly overestimated solubility of hydroxyapatite.

58 GEOSCIENCES↗

DOE OSTI2021

Reliability Prediction using FMEA, FTA, and Related Techniques [Slides]

Summary: We presented a reliability analysis framework. We made point estimates of reliability using reliability block diagrams, fault trees, success trees and estimates with uncertainty using expert elicitation, Monte Carlo simulation, Bayesian analysis. Expert elicitation of failure modes and probabilities is labor-intensive, but critical. Bayesian analysis updates information from expert elicitation with data from reliability and aging tests (aging/compatibility data are needed to estimate lower-bound reliabilities at end of life). Estimation by more than one method helps insure consistency and accuracy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

A case study on the use of carbon sticky tabs for microbeam uranium isotope measurement with an emphasis on understanding the influence of subtle variations in substrate composition

In this paper we integrate laser induced breakdown spectroscopy (LIBS), large geometry secondary ion mass spectrometry (LG-SIMS), laser ablation—multi collector—inductively coupled plasma—mass spectrometry (LA-MC-ICP-MS), and trace elemental compositions obtained by bulk digestion from a suite of commercially available carbon sticky tabs to determine if subtle variations in the tab’s composition can be tied to shifts in the observed uranium isotopic composition of the IRMM-2020 standard relative to its ‘true’ value. The results suggest that, although clear differences exist in the relative abundances of carbon and hydrogen across the various carbon sticky tabs examined, as well as differences in their trace elemental and total uranium concentrations, these differences do not appear to influence the LG-SIMS or LA-MC-ICP-MS uranium isotope ratio measurements, or the appearance of the uranium mass spectrum scans. Instead, the quality (i.e., precision and accuracy) of the LG-SIMS and LA-MC-ICP-MS results are directly proportional to the observed signal intensity at the time of analysis. For the LG-SIMS measurements, there does appear to be a direct correlation between 235U1H hydride formation rates and vacuum conditions within the instrument’s analytical chamber. Furthermore, none of the carbon sticky tabs examined by LG-SIMS displayed evidence of elevated background uranium content. While a preliminary correlation was observed between the uranium isotope background signal during LA-MC-ICP-MS and the total uranium concentrations determined by isotope dilution, none of the tabs displayed a high enough analytical background to affect the isotope ratio measurements.

Zirakparvar, Alex [ORNL] (ORCID:0000000337797730)↗

DOE OSTI2025

Surrogate modeling and optimization of the leaching process in a rare earth elements recovery plant

Critical minerals (CMs) and Rare Earth Elements (REEs) play a vital role in crucial infrastructure technologies such as renewable energy generation and batteries. Recovering them from waste materials has recently been found to significantly reduce environmental impact and supply chain costs related to these materials. In this work, we investigate surrogate modeling techniques aimed to simplify the modeling, simulation, and optimization of the leaching processes involved in CM and REE recovery flowsheets. As there is currently a lack of systematic studies on this topic, we perform extensive computational testing to ascertain which surrogate models are easier to construct and offer high predictive accuracy. Further, our results suggest that sparse quadratic models balance predictive accuracy and computational efficiency. Training and using these surrogates for global optimization of the leaching process requires two orders of magnitude fewer measurements and is up to four orders of magnitude faster than optimizing the original simulation using equation-oriented optimization or derivative-free optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

A scalable framework for quantifying field-level agricultural carbon outcomes

Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N 2 O and CH 4 ) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the “System-of-Systems” solution. Such a “System-of-Systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the “System-of-Systems” solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Plasma Properties in the Earth's Magnetosheath Near the Subsolar Magnetopause: Implications for Geocoronal Density Estimates

Combined in situ ion measurements and remote sensing of energetic neutral atoms are used to determine the geocoronal Hydrogen density at large (∼10 R E ) distances from the Earth. This method for determining the geocoronal density requires global magnetospheric modeling. Observations in the Earth's subsolar magnetosheath from the Magnetospheric Multiscale mission are used to determine the accuracy of using global models to predict the geocoronal density. On average, gas dynamic and magnetohydrodynamic (MHD) models and observations are in reasonable agreement, with differences <25%. In addition, the MHD model subsolar magnetopause is about 0.5 R E sunward of the observed location. However, variations around averages are large (up to a factor of 2), indicating that global models introduce relatively large uncertainties in geocoronal density estimates. Finally, the critical ion flux in the Interstellar Boundary Explorer IBEX‐Hi energy range is often minimally affected by fluctuations of a factor of 2 in the density.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Demonstration of MOOSE-based Griffin reactor physics, code for heterogeneous lead-cooled fast reactor analysis

The MOOSE-based reactor physics code Griffin was assessed on a heterogeneous pin-resolved model of a prototype lead-cooled fast reactor assembly. This model was developed in preparation for future use in MOOSE-based multiphysics calculations for computing hot channel factors. Heterogeneous multigroup cross sections were prepared using the fast reactor multi-group cross section processing code MC{sup 2}-3 using a two-step method. Griffin simulations were performed using the DFEM-SN solver on 576 cores on Argonne's LCRC cluster. Diffusion-based acceleration methods were applied (NDA and CMFD). Reference solutions were generated with continuous energy MCNP and the hybrid MOC/finite element solver PROTEUS-MOC for code-to-code comparison. Space-angle convergence studies were conducted to observe convergence in k-eigenvalue and axial pin power distributions. The fully resolved Griffin calculation was within 68 pcm of the MCNP eigenvalue and exhibited max 1.2% relative error in the axial pin power distribution. Griffin produced nearly identical results to PROTEUS-MOC when using the same 9-group multigroup cross-section set. Griffin demonstrated favorable scaling in wall-clock time and memory usage when using diffusion-based acceleration methods. Griffin is capable of simulating the pin-resolved heterogeneous LFR assembly with good accuracy and performance, and is suitable for future use in coupled high-fidelity hot channel factor simulations. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

A meta-evaluation of the quality of reporting and execution in ecological meta-analyses

Quantitatively summarizing results from a collection of primary studies with meta-analysis can help answer ecological questions and identify knowledge gaps. The accuracy of the answers depends on the quality of the meta-analysis. We reviewed the literature assessing the quality of ecological meta-analyses to evaluate current practices and highlight areas that need improvement. From each of the 18 review papers that evaluated the quality of meta-analyses, we calculated the percentage of meta-analyses that met criteria related to specific steps taken in the meta-analysis process (i.e., execution) and the clarity with which those steps were articulated (i.e., reporting). We also re-evaluated all the meta-analyses available from Pappalardo et al. to extract new information on ten additional criteria and to assess how the meta-analyses recognized and addressed non-independence. In general, we observed better performance for criteria related to reporting than for criteria related to execution; however, there was a wide variation among criteria and meta-analyses. Meta-analyses had low compliance with regard to correcting for phylogenetic non-independence, exploring temporal trends in effect sizes, and conducting a multifactorial analysis of moderators (i.e., explanatory variables). In addition, although most meta-analyses included multiple effect sizes per study, only 66% acknowledged some type of non-independence. The types of non-independence reported were most often related to the design of the original experiment (e.g., the use of a shared control) than to other sources (e.g., phylogeny). We suggest that providing specific training and encouraging authors to follow the PRISMA EcoEvo checklist recently developed by O’Dea et al. can improve the quality of ecological meta-analyses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

The S-PLUS Fifth Data Release: Over 4500 Square Degrees of the Southern Sky and a Multicolor View of the Hydra and Antlia Galaxy Clusters

Abstract We present the fifth data release (DR5) of the Southern Photometric Local Universe Survey (S-PLUS), covering 4592 deg 2 across 2491 fields. Observations were conducted with the T80-South, a Brazilian robotic telescope equipped with the Javalambre 12-filter system, containing five broadband and seven narrowband filters. Data products feature FITS images and extensive catalogs containing fluxes, magnitudes, and shape parameters for over 113 million detections. In addition, several value-added catalogs are provided, offering photometric redshifts (photo- zs ), object classifications, masks, and extinction coefficients. For the first time, this release includes coverage of 110 deg 2 along the Galactic disk, facilitating new research into Galactic structure and stellar populations. The release also provides full coverage of the Hydra Supercluster and numerous other nearby clusters with improved data reduction and calibration, enhancing photo- z accuracy, which is vital for large-scale structure studies. A preliminary analysis of the Hydra and Antlia galaxy clusters up to 5 × R 200 yields an updated catalog of 1706 cluster members based on both spectroscopic and high-quality photo- zs . Our photometric data shows that both clusters have a similar proportion of galaxies with an H α excess relative to their clustercentric distance, though Hydra has a higher fraction near its center. Additionally, the spatial distribution of all objects in our sample highlights a bridge connecting both clusters. We verify that S-PLUS DR5 provides a solid foundation for future scientific investigations, ranging from solar system studies to cosmology.

Vinicius Rodrigues de Lima, Erik [Universidade de ↗

DOE OSTI2022

Photon detection probability prediction using one-dimensional generative neural network

Abstract Photon detection is important for liquid argon detectors for direct dark matter searches or neutrino property measurements. Precise simulation of photon transport is widely used to understand the probability of photon detection in liquid argon detectors. Traditional photon transport simulation, which tracks every photon using the Geant4 simulation toolkit, is a major computational challenge for kilo-tonne-scale liquid argon detectors and GeV-level energy depositions. In this work, we propose a one-dimensional generative model which efficiently generates features using an O u t e r P r o d u c t -layer. This model bypasses photon transport simulation and predicts the number of photons detected by particular photon detectors at the same level of detail as the Geant4 simulation. The application to simulating photon detection systems in kilo-tonne-scale liquid argon detectors demonstrates this novel generative model is able to reproduce Geant4 simulation with good accuracy and 20 to 50 times faster. This generative model can be used to quickly predict photon detection probability in huge liquid argon detectors like ProtoDUNE or DUNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

An Orthogonal Recursive Bisection (ORB) Based Time Advancement Algorithm for CFD-DEM Solvers

The time integration of the granular phase in coupled computational fluid dynamics (CFD) – discrete element method (DEM) simulations presents a unique computational challenge brought about by the large variations in particle collisional time scales. Particles in the dilute regions of the computational domain can be advanced with large time steps while dense regions require much smaller time increments. However, the time step size in most solvers is globally set as the limit for accuracy and stability imposed by the collisions and is typically orders of magnitude less than that required away from collisions. This work addresses this precise issue and provides a strategy to avoid the use of a global conservative small time step size for the entire set of particles.A novel time stepping algorithm for CFD-DEM solvers using a partitioning approach using orthogonal recursive bisection (ORB) that allows for variable time steps among particles is described and its computational performance is compared against baseline explicit methods, typically used in several CFD-DEM solvers. ORB has advantages of being relatively quick and easy to update incrementally and has the required heuristic behavior (i.e., it will split the region in half with a cluster on each side) when groups of particles are well separated (clustered). The algorithm presented in this work uses a local time stepping approach to resolve collisional time scales for subsets of particles that are present at the leaves of the ORB, thereby resulting in substantial reduction of computational cost. The parallel implementation of this method where a ``knapsack” algorithm is used in tandem with ORB for effective load-balancing is also presented, where a best possible partitioning is obtained based on number of particles and local time-stepping costs. The algorithm is tested against benchmark problems with varying particle distributions that include fluidized bed and riser flow scenarios. Preliminary results indicate that the approach is 2-3X faster than traditional explicit methods for problems that involve both dense and dilute regions, while maintaining the same level of accuracy.

adaptive timestepping↗