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Computational Fluid Dynamics Simulations of Multiphase Flow and Distributions.
Abstract not provided.
Linking basin-scale and pore-scale gas hydrate distribution patterns in diffusion-dominated marine hydrate systems: DIFFUSION-DRIVEN HYDRATE GROWTH IN SANDS
The goal of this study is to computationally determine the potential distribution patterns of diffusion-driven methane hydrate accumulations in coarse-grained marine sediments. Diffusion of dissolved methane in marine gas hydrate systems has been proposed as a potential transport mechanism through which large concentrations of hydrate can preferentially accumulate in coarse-grained sediments over geologic time. Using one-dimensional compositional reservoir simulations, we examine hydrate distribution patterns at the scale of individual sand layers (1 to 20 m thick) that are deposited between microbially active fine-grained material buried through the gas hydrate stability zone (GHSZ). We then extrapolate to two- dimensional and basin-scale three-dimensional simulations, where we model dipping sands and multilayered systems. We find that properties of a sand layer including pore size distribution, layer thickness, dip, and proximity to other layers in multilayered systems all exert control on diffusive methane fluxes toward and within a sand, which in turn impact the distribution of hydrate throughout a sand unit. In all of these simulations, we incorporate data on physical properties and sand layer geometries from the Terrebonne Basin gas hydrate system in the Gulf of Mexico. We demonstrate that diffusion can generate high hydrate saturations (upward of 90%) at the edges of thin sands at shallow depths within the GHSZ, but that it is ineffective at producing high hydrate saturations throughout thick (greater than 10 m) sands buried deep within the GHSZ. As a result, we find that hydrate in fine-grained material can preserve high hydrate saturations in nearby thin sands with burial.
Generation of feasible integer solutions on a massively parallel computer using the feasibility pump
Here, we present an approach to parallelize generation of feasible mixed integer solutions of mixed integerlinear programs in distributed memory high performance computing environments. This approachcombines a parallel framework with feasibility pump (FP) as the rounding heuristic. It runs multiple FPinstances with different starting solutions concurrently, while allowing them to share information. Ourcomputational results suggest that the improvement resulting from parallelization using our approach is statistically significant.
Design and characterization of electron beam focusing for X-ray generation in novel medical imaging architecture
A novel electron beam focusing scheme for medical X-ray sources is described in this paper. Most vacuum based medical X-ray sources today employ a tungsten filament operated in temperature limited regime, with electrostatic focusing tabs for limited range beam optics. This paper presents the electron beam optics designed for the first distributed X-ray source in the world for Computed Tomography (CT) applications. This distributed source includes 32 electron beamlets in a common vacuum chamber, with 32 circular dispenser cathodes operated in space charge limited regime, where the initial circular beam is transformed into an elliptical beam before being collected at the anode. The electron beam optics designed and validated here are at the heart of the first Inverse Geometry CT system, with potential benefits in terms of improved image quality and dramatic X-ray dose reduction for the patient.
Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework
Not Available
TU-H-CAMPUS-IeP1-05: A Framework for the Analytic Calculation of Patient-Specific Dose Distribution Due to CBCT Scan for IGRT
Purpose: To investigate the feasibility of an analytic framework to estimate patients’ absorbed dose distribution owing to daily cone-beam CT scan for image-guided radiation treatment. Methods: To compute total absorbed dose distribution, we separated the framework into primary and scattered dose calculations. Using the source parameters such as voltage, current, and bowtie filtration, for the primary dose calculation, we simulated the forward projection from the source to each voxel of an imaging object including some inhomogeneous inserts. Then we calculated the primary absorbed dose at each voxel based on the absorption probability deduced from the HU values and Beer’s law. In sequence, all voxels constructing the phantom were regarded as secondary sources to radiate scattered photons for scattered dose calculation. Details of forward projection were identical to that of the previous step. The secondary source intensities were given by using scatter-to- primary ratios provided by NIST. In addition, we compared the analytically calculated dose distribution with their Monte Carlo simulation results. Results: The suggested framework for absorbed dose estimation successfully provided the primary and secondary dose distributions of the phantom. Moreover, our analytic dose calculations and Monte Carlo calculations were well agreed each other even near the inhomogeneous inserts. Conclusion: This work indicated that our framework can be an effective monitor to estimate a patient’s exposure owing to cone-beam CT scan for image-guided radiation treatment. Therefore, we expected that the patient’s over-exposure during IGRT might be prevented by our framework.
Energy Scheduling-based Operating Envelopes including a Distribution System Branch Screening Algorithm
This paper presents an energy scheduling-based formulation for computing operating envelopes including a distribution branch screening algorithm, termed DBS-ES. The contribution of the paper is two-fold: firstly, it presents an innovative methodology for calculating operating envelopes using energy scheduling (baseline), and secondly, it enhances this methodology by incorporating a custom distribution branch screening algorithm (DBS-ES). The custom algorithm leverages power system knowledge to reduce both model build time and total processing time while maintaining the same scheduling results as the baseline. The effectiveness of the proposed approach is demonstrated through experiments on the IEEE13, IEEE123, and EPRI Secondary test feeders. Results highlight a 24.5% decrease in model build time and an 8.17% decrease in total processing time when using DBS-ES compared to the baseline, specifically for the IEEE123 test feeder. Additionally, the paper briefly discusses the influence of utility-controlled storage on computing operating envelopes, noting a general incre
Dosimetric comparison of lung stereotactic body radiotherapy treatment plans using averaged computed tomography and end-exhalation computed tomography images: Evaluation of the effect of different dose-calculation algorithms and prescription methods
The purpose of this article is to quantitatively evaluate differences in dose distributions calculated using various computed tomography (CT) datasets, dose-calculation algorithms, and prescription methods in stereotactic body radiotherapy (SBRT) for patients with early-stage lung cancer. Data on 29 patients with early-stage lung cancer treated with SBRT were retrospectively analyzed. Averaged CT (Ave-CT) and expiratory CT (Ex-CT) images were reconstructed for each patient using 4-dimensional CT data. Dose distributions were initially calculated using the Ave-CT images and recalculated (in the same monitor units [MUs]) by employing Ex-CT images with the same beam arrangements. The dose-volume parameters, including D{sub 95}, D{sub 90}, D{sub 50}, and D{sub 2} of the planning target volume (PTV), were compared between the 2 image sets. To explore the influence of dose-calculation algorithms and prescription methods on the differences in dose distributions evident between Ave-CT and Ex-CT images, we calculated dose distributions using the following 3 different algorithms: x-ray Voxel Monte Carlo (XVMC), Acuros XB (AXB), and the anisotropic analytical algorithm (AAA). We also used 2 different dose-prescription methods; the isocenter prescription and the PTV periphery prescription methods. All differences in PTV dose-volume parameters calculated using Ave-CT and Ex-CT data were within 3 percentage points (%pts) employing the isocenter prescription method, and within 1.5%pts using the PTV periphery prescription method, irrespective of which of the 3 algorithms (XVMC, AXB, and AAA) was employed. The frequencies of dose-volume parameters differing by >1%pt when the XVMC and AXB were used were greater than those associated with the use of the AAA, regardless of the dose-prescription method employed. All differences in PTV dose-volume parameters calculated using Ave-CT and Ex-CT data on patients who underwent lung SBRT were within 3%pts, regardless of the dose-calculation algorithm or the dose-prescription method employed.
SU-F-BRD-09: A Random Walk Model Algorithm for Proton Dose Calculation
Purpose: To develop a random walk model algorithm for calculating proton dose with balanced computation burden and accuracy. Methods: Random walk (RW) model is sometimes referred to as a density Monte Carlo (MC) simulation. In MC proton dose calculation, the use of Gaussian angular distribution of protons due to multiple Coulomb scatter (MCS) is convenient, but in RW the use of Gaussian angular distribution requires an extremely large computation and memory. Thus, our RW model adopts spatial distribution from the angular one to accelerate the computation and to decrease the memory usage. From the physics and comparison with the MC simulations, we have determined and analytically expressed those critical variables affecting the dose accuracy in our RW model. Results: Besides those variables such as MCS, stopping power, energy spectrum after energy absorption etc., which have been extensively discussed in literature, the following variables were found to be critical in our RW model: (1) inverse squared law that can significantly reduce the computation burden and memory, (2) non-Gaussian spatial distribution after MCS, and (3) the mean direction of scatters at each voxel. In comparison to MC results, taken as reference, for a water phantom irradiated by mono-energetic proton beams from 75 MeV to 221.28 MeV, the gamma test pass rate was 100% for the 2%/2mm/10% criterion. For a highly heterogeneous phantom consisting of water embedded by a 10 cm cortical bone and a 10 cm lung in the Bragg peak region of the proton beam, the gamma test pass rate was greater than 98% for the 3%/3mm/10% criterion. Conclusion: We have determined key variables in our RW model for proton dose calculation. Compared with commercial pencil beam algorithms, our RW model much improves the dose accuracy in heterogeneous regions, and is about 10 times faster than MC simulations.
Simulation study of entropy production in the one-dimensional Vlasov system
The coarse-grain averaged distribution function of the one-dimensional Vlasov system is obtained by numerical simulation. The entropy productions in cases of the random field, the linear Landau damping, and the bump-on-tail instability are computed with the coarse-grain averaged distribution function. The computed entropy production is converged with increasing length of coarse-grain average. When the distribution function differs slightly from a Maxwellian distribution, the converged value agrees with the result computed by using the definition of thermodynamic entropy. The length of the coarse-grain average to compute the coarse-grain averaged distribution function is discussed.
A New Fast Fluid Dynamics Model for Data-Center Floor Plenums
A raised-floor plenum is often used to distribute cooling airflow in data centers. Traditional Computational Fluid Dynamics (CFD) can accurately model airflow distribution in plenums, but it is expensive (computationally and financially) and is, therefore, often not accessible to the data center designer or operator. This paper presents an alternative CFD methodology based on Fast Fluid Dynamics (FFD), which is faster, more parallelizable, and simpler to program. The improvement in computational speed afforded by FFD is particularly important for multiple-iteration optimization, transient simulations, or applications for which near-real-time performance is required. Here, the potential speed improvements of FFD have been widely documented; however, it has been generally accepted that this benefit is accompanied by a modest loss of accuracy relative to traditional CFD. We show here that this is not necessarily true. Plenum airflow predictions by our FFD implementation, which utilizes a first-order upwind rather than Semi-Lagrangian advection scheme of previous research, are extremely close to those predicted by traditional CFD - provided the same turbulence model and computational grid are used consistently.
Distributed X-ray photon correlation spectroscopy data reduction using Hadoop MapReduce
Multi-speckle X-ray photon correlation spectroscopy (XPCS) is a powerful technique for characterizing the dynamic nature of complex materials over a range of time scales. XPCS has been successfully applied to study a wide range of systems. Recent developments in higher-frame-rate detectors, while aiding in the study of faster dynamical processes, creates large amounts of data that require parallel computational techniques to process in near real-time. Here, an implementation of the multi-tau and two-time autocorrelation algorithms using the Hadoop MapReduce framework for distributed computing is presented. In conclusion, the system scales well with regard to the increase in the data size, and has been serving the users of beamline 8-ID-I at the Advanced Photon Source for near real-time autocorrelations for the past five years.
Graph-based modeling and simulation of complex systems
We present graph-based modeling abstractions to represent cyber-physical dependencies arising in complex systems. Specifically, we propose an algebraic graph abstraction to capture physical connectivity in complex optimization models and a computing graph abstraction to capture communication connectivity in computing architectures. The proposed abstractions are scalable and are used as the backbone of a Julia-based software package that we call Plasmo.j1. Here, we show how the algebraic graph abstraction facilitates the implementation, analysis, and decomposition of optimization problems and we show how the computing graph abstraction facilitates the implementation of optimization and control algorithms and their simulation in virtual environments that involve distributed, centralized, and hierarchical computing architectures.
BCS-BEC crossover of the strongly interacting 6 Li − 40 K mixture
Here, we present quantum Monte Carlo calculations of the properties of a two-component mass-imbalanced Fermi gas, corresponding to the 6 Li − 40 K mixture. We compute the equation of state of the unpolarized system as a function of the scattering length with particular attention paid to the unitary limit, where the effect of the effective range of the interaction is explored. In order to investigate differences from the equal-mass case we also compute the pair-distribution function and the momentum distribution over a range of interaction strengths, which can provide information about the structure of the system. Finally, we compute the heavy-light quasiparticle spectrum for the full crossover regime. Our theoretical predictions, based on quantum Monte Carlo calculations, should inform future theoretical studies and can be tested by experiments with ultracold fermionic gases.
Constant-Overhead Fault-Tolerant Bell-Pair Distillation Using High-Rate Codes
We present a fault-tolerant Bell-pair distillation scheme achieving constant overhead through high-rate quantum low-density parity-check (qLDPC) codes. Our approach maintains a constant distillation rate equal to the code rate while requiring no additional overhead beyond the physical qubits of the code. Full circuit-level analysis demonstrates fault-tolerance for input Bell-pair infidelities below a threshold ∼10%, readily achievable with near-term capabilities. Unlike previous proposals, our scheme keeps the output Bell pairs encoded in qLDPC codes at each node, eliminating unencoding overhead and enabling direct use in distributed quantum applications through recent advances in qLDPC computation. These results establish qLDPC-based distillation as a practical route toward resource-efficient quantum networks and distributed quantum computing.
Joint Genome Institute Analysis Workflow Service (JAWS) v2.7
The U.S. Department of Energy Joint Genome Institute (JGI) has developed the JGI Analysis Workflow Service (JAWS) as a distributed framework to run computational workflows across diverse high-performance computing (HPC) and cloud environments. JAWS enhances the reusability, scalability, and robustness of scientific workflows by orchestrating data movement, code execution, and results retrieval across multiple DOE facilities. At its core, JAWS integrates the Cromwell workflow engine to run workflows expressed in the Workflow Description Language (WDL), ensuring portability and interoperability. To provide consistent runtime environments, JAWS employs container technologies such as Shifter, Apptainer, and Docker. Workflow tasks are managed via HTCondor on HPC backends, while Globus ensures secure and efficient data transfer between sites. JAWS is deployed as a multi-site workflow manager across national laboratory computing facilities, with dedicated instances supporting community projects such as the National Microbiome Data Collaborative (NMDC) and KBase. This distributed, service-oriented architecture enables users to "write once, run anywhere," providing scalable, production-quality workflow execution.