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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Liquid Piston with Spray Cooling Near-Isothermal Compressor

The goal of this project was to prototype and characterize the performance of a liquid-piston spray-cooled gas compressor. The working principle of the compressor enables optimized high-efficiency operation over a very wide range of operating conditions, unlike conventional compressors that are optimized for a narrow range of operating conditions. The compressor technology is suitable for many applications, such as gas pipeline transport, gas storage, and commercial and residential heat pumps. Both physical testing and computational fluid dynamics (CFD) modeling of the processes using the Oak Ridge National Laboratory high-performance computing center were completed. The experimental and CFD studies focused on a near-isothermal liquid-piston compressor (LPC) that uses propylene glycol to compress CO 2 . The first prototype demonstrated isothermal operation during several sequentially executed cycles of CO 2 compression and raised the temperature of the compressed CO 2 by only 2 K, compared with approximately 6 K when the gas was compressed non-isothermally. Isothermal operation was demonstrated at CO 2 flow rates of up to 2 L/min. The second prototype was designed with two compression chambers to allow continuous flow of high-pressure CO 2 . However, the design of the valve train to direct flow between the compression chambers was not sufficient to allow demonstration of CO 2 compression. Numerical simulations of the LPC in which the compression chamber was filled with propylene glycol injected from the bottom inlet were performed using large eddy simulation (LES) with the wall-adapting local eddy-viscosity subgrid-scale model coupled with the multiphase volume of fluid (VOF) model to simulate the transient interface between gas and liquid and to capture the heat and mass transfers within the compression chamber. In this effort, the effects of boundary conditions applied to the LES-VOF calculations (i.e., no wall, an adiabatic wall, and a wall with a heat flux subscribed) on the overall pressure and temperature of the CO 2 gas as well as the transient evolution of flow and heat transfer within the compression chamber were investigated and are discussed in this report. The LES calculation with no wall showed no dynamical flow patterns, and the volume-averaged temperature of CO 2 increased from 305 to 392.7 K, whereas LES calculations with a constant wall temperature or a wall heat flux had similar increases of CO 2 temperatures. The results of the LES simulation using a wall heat flux showed different stages in the compression process and revealed dynamical formation and interaction of CO 2 gas layers and circulation flow patterns within the chamber that contributed to the overall heat transfer between the solid wall, gas, and liquid surface in the compressor. Though an industrial partnership for commercializing the compressor was not secured, the technology was attractive for an industrial partner to use in two research proposals in response to US Department of Energy funding opportunity announcements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Measurement-based performance profiles and dynamics of UDT over dedicated connections

Wide-area data transfers in high-performance computing and big data scenarios are increasingly being carried over dedicated network connections that provide high capacities at low loss rates. UDP-based transport protocols are expected to be particularly well-suited for such transfers but their performance is relatively unexplored over a wide range of connection lengths, compared to TCP over shared connections. We present extensive throughput measurements of UDP-based Data Transfer (UDT) over a suite of physical and emulated 10 Gbps connections. In sharp contrast to current UDT analytical models, these measurements indicate much more complex throughput dynamics that are sensitive to the connection modality, protocol parameters, and round-trip times. Lyapunov exponents estimated from the Poincare maps of UDT traces clearly indicate regions of instability and complex dynamics. We propose a simple model based on the ramp-up and sustainment regimes of a generic transport protocol, which qualitatively illustrates the dominant monotonicity and concavity properties of throughput profiles and relates them to Lyapunov exponents. These measurements and analytical results together enable us to comprehensively evaluate UDT performance and select parameters to achieve high throughput, and they also provide guidelines for designing effective transport protocols for dedicated connections.

Foster, Ian↗
DOE OSTI2021

Toward the predictive discovery of ambipolarly dopable ultra-wide-band-gap semiconductors: The case of rutile GeO 2

Ultrawide-band-gap (UWBG) semiconductors are promising for fast, compact, and energy-efficient power-electronics devices. Their wider band gaps result in higher breakdown electric fields that enable high-power switching with a lower energy loss. Yet, the leading UWBG semiconductors suffer from intrinsic materials' limitations with regard to their doping asymmetry that impedes their adoption in CMOS technology. Improvements in the ambipolar doping of UWBG materials will enable a wider range of applications in power electronics as well as deep-UV optoelectronics. These advances can be accomplished through theoretical insights on the limitations of current UWBG materials coupled with the computational prediction and experimental demonstration of alternative UWBG semiconductor materials with improved doping and transport properties. As an example, we discuss the case of rutile GeO2 (r-GeO 2 ), a water-insoluble GeO 2 polytype, which is theoretically predicted to combine an ultra-wide gap with ambipolar dopability, high carrier mobilities, and a higher thermal conductivity than β-Ga 2 O 3 . The subsequent realization of single-crystalline r-GeO 2 thin films by molecular beam epitaxy provides the opportunity to realize r-GeO 2 for electronic applications. Future efforts toward the predictive discovery and design of new UWBG semiconductors include advances in first-principles theory and high-performance computing software, as well as the demonstration of controlled doping in high-quality thin films with lower dislocation densities and optimized film properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Developing And Scaling an OpenFOAM Model to Study Turbulent Flow in a HFIR Coolant Channel

Improving the understanding of how computational fluid dynamics (CFD) direct numerical simulations (DNS) of flows in the High Flux Isotope Reactor (HFIR) perform when run in parallel using the high performance computing (HPC) platform Summit at the Oak Ridge Leadership Computing Facility (OLCF) is of particular importance to boost the computational tools used to support HFIR conversion to low enriched fuel (LEU). Evaluation of scaling performance was driven by the increasing importance of graphics processing unit (GPU) usage in HPC, which is becoming the standard for modern supercomputers such as Summit. The desired results are to obtain a strong positive correlation between the computational resources dedicated to a problem and the relative speed-up of the simulation in comparison to a benchmark. This capability will allow substantially improvement in HFIR flow analytical capabilities, specifically when predicting turbulence properties at high Reynolds numbers. The study leverages previous simulation results performed with code PHASTA (finite element) on HPC platforms Cori (NERSC) and Theta (ALCF) [1] with computing options provided in the computing platform OpenFOAM (finite volume) at OLCF. Transitioning from PHASTA to OpenFOAM will (1) eliminate dependence on third-party software for mesh generation and manipulation, (2) reduce resource needs by employing modern architectures, and (3) build expertise for future modeling of HFIR-specific problems like heat transfer in involute geometry, entrance effects, flow structure in channel corners, and so on—all important issues when defining the available thermal margins in the transition to LEU. CPUs and GPUs differ significantly in their architecture and utilization, as discussed in the literature [2]. The most important differences are in the approach to computations and their memory. A single GPU contains a large quantity of cores, enabling it to perform with a much higher throughput than a CPU, but execution requires a different approach. GPU codes execute instructions using the Single-Instruction Multiple-Thread (SIMT) approach in which a single instruction is used for groups of threads called warps. A warp typically consists of 32 threads which must execute the same set of instructions, although on separate threads. Alternately, a CPU has far fewer cores that are much more flexible in their operation, excelling at quickly performing more complex serial computations. This is why GPUs have greater throughput when properly utilized. The second important difference is seen when comparing their memory spaces. Limited memory allocations and CPU–GPU communications cause a significant bottleneck in GPU-accelerated programs. Further study was required to properly take advantage of GPU resources. A comprehensive analysis of code performance and the model-specific features of turbulence constitutes the core of this work. In this study, a DNS simulation of HFIR channel turbulence was performed with the finite volume CFD code OpenFOAM v2112 and CUDA v11.0 on Red Hat Enterprise Linux v8.2. The OpenFOAM installation had AMGx integrated to enable GPU acceleration and utilizes the PETSc4FOAM library. The computational resources and the problem size were scaled on CPU and CPU + GPU architectures to gain a better understanding of the performance of a DNS problem on modern computing hardware. The study aimed to analyze the scaling of the code exclusively on CPUs and then to examine the scaling of the codes with GPU acceleration enabled. Scaling studies included CPU and GPU acceleration on a mesh of varying resolution to analyze the impact of problem size relative to computational resources. In the course of preparing the GPU configuration on Summit, mainly using the AMGX solvers, difficulties were encountered stemming from constant changes resulting from extensive ongoing development activities and the changing environment. This resulted in the inability to complete the GPU portion of the work. The code was compiled and tested, but production runs to assess acceleration were not performed because the used discretional compute time allocation expired as year-end approached. The Summit HPC platform is scheduled for decommissioning in 2024, making it unattractive for future use with Nvidia-based GPUs. Therefore, the work will be moved onto NERSC machines in FY24. An application was prepared and submitted, and sufficient node-hours were awarded to continue the research in the next calendar year. This report summarizes work performed thus far, which mostly focused on CPU OpenFOAM computing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Virtual Engineering Software Framework for Integrated Biomass Conversion Modeling

This presentation covers the design and implementation of a software tool to systematically connect computational models of unit operations to simulate an integrated process of low-temperature conversion of biomass to fuel. This virtual engineering (VE) software was designed with the overarching goal of connecting unit models written in various programming languages and requiring different computational resources within a single, flexible framework. The models and features currently considered for the VE library include mechanistic models for pretreatment, enzymatic hydrolysis, and aerobic bioreaction; high-fidelity computational fluid dynamics (CFD) simulations for enzymatic hydrolysis and aerobic bioreaction; and the capability to perform techno-economic analyses (TEA) using Aspen Plus, a commercial software package. The CFD models require access to high-performance computing (HPC) resources, so in addition to handling multiple programming languages and interfaces, the VE software must also be capable of interacting with an HPC scheduler to submit, run, and post-process jobs. Using the Python programming language, a new VE software package has been developed that contains functionality to manage the input-output communication between various unit models, schedule simulations to run on NREL's HPC and analyze those results, and interface with existing TEA software workflows. A Jupyter-notebook GUI was also created to solicit user input and provide documentation. In cases where multiple models for a particular unit-operation exist, selection between models is accomplished through a simple checkbox, with the appropriate inputs and outputs being parsed and converted seamlessly in the background. Each operation makes use of a different programming language, but the flow of information from pretreatment to enzymatic hydrolysis to bioreaction is managed with an intuitive, centralized file-communication strategy. In this talk, the programming approach and implementation details of the notebook are presented for multiple possibilities of the conversion process, including a demonstration of the ability to manage HPC resources. Additionally, an example of a sensitivity study of treatment parameters governing the overall conversion outcome is shown which highlights the ease of defining new problems using the VE Notebook workflow and leads into a discussion of ongoing work to enable outer-loop optimization studies.

biofuel↗
DOE OSTI2014

Theoretical foundation for measuring the groundwater age distribution.

In this study, we use PFLOTRAN, a highly scalable, parallel, flow and reactive transport code to simulate the concentrations of 3H, 3He, CFC-11, CFC-12, CFC-113, SF6, 39Ar, 81Kr, 4He and themean groundwater age in heterogeneous fields on grids with an excess of 10 million nodes. We utilize this computational platform to simulate the concentration of multiple tracers in high-resolution, heterogeneous 2-D and 3-D domains, and calculate tracer-derived ages. Tracer-derived ages show systematic biases toward younger ages when the groundwater age distribution contains water older than the maximum tracer age. The deviation of the tracer-derived age distribution from the true groundwater age distribution increases with increasing heterogeneity of the system. However, the effect of heterogeneity is diminished as the mean travel time gets closer the tracer age limit. Age distributions in 3-D domains differ significantly from 2-D domains. 3D simulations show decreased mean age, and less variance in age distribution for identical heterogeneity statistics. High-performance computing allows for investigation of tracer and groundwater age systematics in high-resolution domains, providing a platform for understanding and utilizing environmental tracer and groundwater age information in heterogeneous 3-D systems. Groundwater environmental tracers can provide important constraints for the calibration of groundwater flow models. Direct simulation of environmental tracer concentrations in models has the additional advantage of avoiding assumptions associated with using calculated groundwater age values. This study quantifies model uncertainty reduction resulting from the addition of environmental tracer concentration data. The analysis uses a synthetic heterogeneous aquifer and the calibration of a flow and transport model using the pilot point method. Results indicate a significant reduction in the uncertainty in permeability with the addition of environmental tracer data, relative to the use of hydraulic measurements alone. Anthropogenic tracers and their decay products, such as CFC11, 3H, and 3He, provide significant constraint oninput permeability values in the model. Tracer data for 39Ar provide even more complete information on the heterogeneity of permeability and variability in the flow system than the anthropogenic tracers, leading to greater parameter uncertainty reduction.

Gardner, William Payton↗
DOE OSTI2014

Computation Annual Report (2013)

Computation provides the intellectual electricity that helps make Lawrence Livermore National Laboratory (LLNL) a thriving and innovative research and development (R&D) institution. Our hardware, software, and discipline experts are integrated into almost every field of research at LLNL. Our in-house programs, software applications, and support initiatives help achieve mission-critical national-security objectives for the U.S. Departments of Energy, Homeland Security, and Defense, as well as other federal and state agencies. We deliver comprehensive support to a wide set of scientific disciplines that include physical sciences (materials modeling, fluid dynamics), biology (genomic sequencing), mathematics (scalable algorithms), and computer sciences (parallel processing, real-time control systems). This year, we have expanded our outreach with high performance computing (HPC) collaborations and made great strides in our capabilities related to mathematical computation, control systems, and computation infrastructure, many of them in concert with partners in industry and academia. The Computation workforce remains highly motivated and passionate about melding computational expertise with cutting-edge science and technology to enable breakthroughs. 2013 has once again proven that Computation provides the foundation and support that are essential for productivity at LLNL. Every scientific discipline and program leverages the tools and expertise provided by our staff. We remain at the forefront of HPC moving towards exascale and have developed the infrastructure and proficiency to make this an achievable goal. Our researchers repeatedly deliver outstanding research, attracting collaborations with the biggest names in academia and industry, while joining forces with other national laboratories to further push the bounds of what is scientifically possible. Computation’s mission is echoed by the President of the United States in his assertion that “our future depends on reaffirming America’s role as the world’s engine of scientific discovery and technological innovation.” Driven by this lofty goal, we employ our state-of-the-art computational capabilities to ensure LLNL’s position as a global science and technology leader.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Parallel Monte Carlo Methods for Heterogeneous Hardware Computer Systems Using GPUs and Coprocessors: Recent Development of ARCHER Code - Paper 54

Traditional methods of accelerating Monte Carlo (MC) calculations typically use CPUs as the computing unit. The parallelism is based upon multithreading on a multi-core processor, and message passing interface (MPI) across many processors and nodes. In recent years, the hardware accelerators have been introduced by the high performance computing industry as an alternative approach to achieve massive parallelism. Hardware accelerators have the advantage of high Floating-point Operations Per Second (FLOPS) and high energy efficiency. Examples include graphics processing units (GPUs) and coprocessors. Compute-intensive programs can be off-loaded to them to be concurrently executed by hundreds or thousands of active threads. Existing production MC codes, however, cannot be directly compiled or executed on the new platforms. It is therefore necessary to rewrite or redevelop the code using the hardware accelerator specific programming models and APIs. Several studies have been conducted on using GPUs to accelerate MC calculations in reactor analysis and medical physics. In this paper, we present the recent progress at Rensselaer Polytechnic Institute (RPI) in developing a new parallel MC code called ARCHER (Accelerated Radiation-transport Computations in Heterogeneous Environments) for coupled photon-electron transport simulations. The code is designed as a test bed to evaluate the efficacy of heterogeneous computing systems for low-cost MC-based radiation dosimetric calculations. The code has three variants corresponding to three parallel hardware platforms: ARCHER-CPU (multi-core CPU), ARCHER-GPU (Nvidia GPU) and ARCHER-COP (Intel Xeon Phi coprocessor). The code has been used for several applications, including CT imaging dosimetry, radiotherapy dosimetry and radiation shielding design. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2012

Early Evaluation of Directive-Based GPU Programming Models for Productive Exascale Computing

Graphics Processing Unit (GPU)-based parallel computer architectures have shown increased popularity as a building block for high performance computing, and possibly for future Exascale computing. However, their programming complexity remains as a major hurdle for their widespread adoption. To provide better abstractions for programming GPU architectures, researchers and vendors have proposed several directive-based GPU programming models. These directive-based models provide different levels of abstraction, and required different levels of programming effort to port and optimize applications. Understanding these differences among these new models provides valuable insights on their applicability and performance potential. In this paper, we evaluate existing directive-based models by porting thirteen application kernels from various scientific domains to use CUDA GPUs, which, in turn, allows us to identify important issues in the functionality, scalability, tunability, and debuggability of the existing models. Our evaluation shows that directive-based models can achieve reasonable performance, compared to hand-written GPU codes.

Lee, Seyong↗
DOE OSTI2012

Cielo Computational Environment Usage Model: With Mappings to ACE Requirements for the General Availability User Environment Capabilities (Release V.1.1)

Cielo is a massively parallel supercomputer funded by the DOE/NNSA Advanced Simulation and Computing (ASC) program, and operated by the Alliance for Computing at Extreme Scale (ACES), a partnership between Los Alamos National Laboratory (LANL) and Sandia National Laboratories (SNL). The primary Cielo compute platform is physically located at Los Alamos National Laboratory. This Cielo Computational Environment Usage Model documents the capabilities and the environment to be provided for the Q1 FY12 Level 2 Cielo Capability Computing (CCC) Platform Production Readiness Milestone. This document describes specific capabilities, tools, and procedures to support both local and remote users. The model is focused on the needs of the ASC user working in the secure computing environments at Lawrence Livermore National Laboratory (LLNL), Los Alamos National Laboratory, or Sandia National Laboratories, but also addresses the needs of users working in the unclassified environment. The Cielo Computational Environment Usage Model maps the provided capabilities to the tri-Lab ASC Computing Environment (ACE) Version 8.0 requirements. The ACE requirements reflect the high performance computing requirements for the Production Readiness Milestone user environment capabilities of the ASC community. A description of ACE requirements met, and those requirements that are not met, are included in each section of this document. The Cielo Computing Environment, along with the ACE mappings, has been issued and reviewed throughout the tri-Lab community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Large-Scale Data Analytics and Its Relationship to Simulation

Large-Scale Data Analytics (LSDA) problems require finding meaningful patterns in data sets that are so large as to require leading-edge processing and storage capability. LSDA problems are increasingly important for government mission work, industrial application, and scientific discovery. Effective solution of some important LSDA problems requires a computational workload that is substantially different from that associated with traditional High Performance Computing (HPC) simulations intended to help understand physical phenomena or to conduct engineering. While traditional HPC application codes exploit structural regularity and data locality to improve performance, many analytics problems lead more naturally to very fine-grained communication between unpredictable sets of processors, resulting in less regular communication patterns that do not map efficiently on to typical HPC systems. In both simulation and analytics domains, however, data movement increasingly dominates the performance, energy usage, and price of computing systems. It is therefore plausible that we could find a more synergistic technology path forward. Even though future machines may continue to be configured differently for the two domains, a more common technological roadmap between them in the form of a degree of convergence in the underlying componentry and design principles to address these common technical challenges could have substantial technical and economic benefits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Development and Validation of Southeast Asia Solar Resource Data [Slides]

Lack of access to high-quality, publicly available, time series solar data to inform decisions that will transform energy sectors in Southeast Asia is a challenge. The solution is to level the playing field by offering free, high-quality, robust solar data to inform private sector investment and policymaking. This is done by (1) leveraging deep NREL expertise in atmospheric science, solar resource assessment, high-performance computing, and cloud-based data dissemination, (2) producing and validating high spatial and temporal resolution solar resource data, (3) making data available on the USAID-funded global Renewable Energy Data Explorer platform, (4) providing capacity building for data and applications, and (5) informing future demand-driven tool development.

14 SOLAR ENERGY↗
DOE OSTI2022

Market Research Study: Applications of ASCR HPC Software

The Department of Energy (DOE)’s Advanced Scientific Computing Research (ASCR) program has developed hundreds of software tools for high performance computing (HPC). Although many HPC hardware advances have permeated the market, such as CPUs, GPUs, TPUs and ASICs, the software developed by DOE for different HPC applications has not necessarily been mined. The DOE is interested in making its HPC software portfolio available to the public in order to maximize the value of what has been developed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Extend an innovative HPC-Compatible Multiple Temporal-spatial Resolution Concurrent Finite Element Modeling Approach to Guide Laser Powder Bed Fusion Additive

Laser power bed fusing (PBF) additive manufacturing is a key enabling technology to manufacture highly complex and integrated automotive structures. However, the geometric complexity of PBF-AM technique also leads to highly non-uniform heating and cooling rate in the manufactured part, which may cause flaw formation and produce excessive and nonuniform residual stresses, which increase quality uncertainties and manufacture issues, leading to increases in cost and energy consumption in the form of rejected parts. In this research project, we developed an innovative Multi-Spatial-Temporal-Resolution Finite Element (MUST-FE) method and completed the corresponding high performance computation (HPC) platform-based in-house code, which enables high accuracy prediction of temperature and residual stress fields for component-scale PBF-AM manufacture in efficient computation time. The MUST-FE model is calibrated and validated with a “2D pad” AlSi10Mg experiments by matching the melt pool shape and dimension, and with a “XY-cross” AlSi10Mg experiment by matching the thermal distortion and residual stress. The innovative multi-resolution and concurrent modeling approach adopted in this code ensures accuracy and computational efficiency, which will enable energy-efficient and high-yield, low-cost manufacturing of optimized, qualifiable automotive structures and contribute towards reaching technical targets outlined in AMO’s Program Plan to develop additive manufacturing systems that deliver consistently reliable parts with predictable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Q4Q: Quantum Computation for Quantum Prediction of Materials and Molecular Properties

We aim at exploring the potential of quantum computation to solve practical problems that are currently targeted by “traditional” high performance computing (HPC). Along the way, we will develop theoretical frameworks and algorithmic strategies. We propose computational research activities that will advance the application of quantum computers to selected materials/molecular properties: (1) Band structure of solids; (2) Molecular electronic/vibronic properties; (3) Free energies and phase transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Advanced Computing Annual Report 2025 [Slides]

In Fiscal Year (FY) 2025, the National Laboratory of the Rockies (NLR) continued to advance computing as a cornerstone of energy innovation, expanding the Kestrel high-performance computing (HPC) system to 56 peak petaflops. This growth strengthened Kestrel's role as a national asset for applied energy research, enabling larger, more complex simulations and accelerating the integration of artificial intelligence (AI) methods across the laboratory's computing portfolio. In FY 2025, AI was a component of most projects running on Kestrel, underscoring its central role in modern energy science and engineering. Kestrel supported a broad and diverse set of 507 modeling and simulation projects, engaging 855 researchers across the U.S. Department of Energy's (DOE's) Office of Critical Minerals and Energy Innovation (CMEI) portfolio and other offices, as well as partners from industry, academia, and utilities. These efforts span critical materials discovery, energy systems modeling, grid modernization, advanced manufacturing, and other areas essential to strengthening U.S. energy security and competitiveness. Together, these collaborations produced 708 technical outputs, including 293 peer-reviewed publications, reflecting both the depth and impact of the science enabled by NLR's computing capabilities. This year's report highlights the growing importance and benefit of AI throughout NLR's research programs and features work by early career researchers who are helping shape the future of computing-enabled energy innovation. Explore these sections and the many project successes captured in the pages that follow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

An HPC-Based Hydrothermal Finite Element Simulator for Modeling Underground Response to Community-Scale Geothermal Energy Production

Geothermal heat, as renewable energy, shows great advantage with respect to its environmental impact due to its significantly lower CO2 emissions than conventional fossil fuel. Open and closed-loop geothermal heat pumps, which utilize shallow geothermal systems, are an efficient technology for cooling and heating buildings, especially in urban areas. Integrated use of geothermal energy technologies for district heating, cooling, and thermal energy storage can be applied to optimize the subsurface for communities to provide them with multiple sustainable energy and community resilience benefits. The utilization of the subsurface resources may lead to a variation in the underground environment, which might further impact local environmental conditions. However, very few simulators can handle such a highly complex set of coupled computations on a regional or city scale. We have developed high-performance computing (HPC) based hydrothermal finite element (FE) simulator that can simulate the subsurface and its hydrothermal conditions at a scale of tens of km. The HPC simulator enables us to investigate the subsurface thermal and hydrologic response to the built underground environment (such as basements and subways) at the community scale. In this study, a coupled hydrothermal simulator is developed based on the open-source finite element library deal.II. The HPC simulator was validated by comparing the results of a benchmark case study against COMSOL Multiphysics, in which Aquifer Thermal Energy Storage (ATES) is modeled and a process of heat injection into ATES is simulated. The use of an energy pile system at the Treasure Island redevelopment site (San Francisco, CA, USA) was selected as a case study to demonstrate the HPC capability of the developed simulator. The simulator is capable of modeling multiple city-scale geothermal scenarios in a reasonable amount of time.

Sun, Xiang↗
DOE OSTI2016

Spacer Grid and Mixing Vanes Effects Study on Two-phase Flow Using Bubble Tracking Simulations

In Light Water Reactor (LWR) cores, the nuclear fuel rods are arranged together as rod bundles in a triangular or square pattern. The fuel rod bundles are supported by the spacer grids with mixing vanes. An understanding of spacer grid and mixing vanes effect is important since it has a direct impact on the turbulence structure in the flow and the heat removal efficiency from the fuel rod surface. Although a considerable amount of literature has been published on the experimental investigation of spacer grid and mixing vanes effect, the measurements were mainly focused on single-phase flow. The influence of spacer grid and mixing vanes on two-phase flow is yet to be fully understood. Some preliminary research efforts have been invested in nuclear community. For example, Wheeler et al. experimentally studied the effects of spacer grid in air-water two-phase flow. Although mixing vanes are not considered, they presented interesting results of time-averaged two-phase flow quantities at upstream and downstream locations of the spacer grid. Over the past decades, the rapid development of High-Performance Computing (HPC) is allowing for the increasingly larger-scale high-fidelity simulations. Thanks to this favorable historical trend, Direct Numerical Simulation (DNS), integrated with Interface Tracking Methods (ITM), has been emerging as a valuable tool to complement and expand our ability to understand the two-phase flow phenomenon. Meanwhile, in order to make best use of the large-scale two-phase flow simulations, advanced data analysis techniques are being developed, as they enable the extraction of detailed parameters about individual bubble behavior. The level set ITM is utilized in our DNS flow solver, which makes use of a signed distance field. The gas-liquid interface is modeled by the zero level set. The level set method can be readily used to distinguish different phases based on the sign of corresponding level set value (e.g. the sign is positive in the liquid phase while negative in the gas phase.). However, the traditional level set approach is not able to collect calculated values and associate with specific bubbles when multiple (even thousands of) bubbles present in the simulations of two-phase flow. This drawback hinders the collection of useful bubble information, which can give us valuable insights about bubbly flows. For example, how the different local fluid conditions could affect bubble interfacial forces, bubble deformation level, and eventually the bubble distribution throughout the whole domain. A bubble tracking methodology has been developed to address the drawbacks and improve the data collection efficiency of level set based two-phase flow simulations. In the present work, a single Pressurized Water Reactor (PWR) subchannel with reduced size spacer grid and mixing vanes is chosen as the computational domain. The springs and dimples of the spacer grid are not represented for simplicity in the present investigation. The bubble tracking methodology is applied to the simulations of two-phase bubbly flow through spacer grid and mixing vanes region. In the intended simulation, the liquid turbulence will be fully resolved by DNS while the two-phase behaviors are captured by level set ITM. Bubble tracking data are to be processed to investigate the spacer grid and mixing vanes effect on two-phase bubbly flow. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗