Results for “complex fluid” — Page 50

NASA NTRS2016

Vented Chill / No-Vent Fill of Cryogenic Propellant Tanks

Architectures for extended duration missions often include an on-orbit replenishment of the space vehicle's cryogenic liquid propellants. Such a replenishment could be accomplished via a tank-to-tank transfer from a dedicated tanker or a more permanent propellant depot storage tank. Minimizing the propellant loss associated with transfer line and receiver propellant tank thermal conditioning is essential for mass savings. A new methodology for conducting tank-to-tank transfer while minimizing such losses has been demonstrated. Charge-Hold-Vent is the traditional methodology for conducting a tank-to-tank propellant transfer. A small amount of cryogenic liquid is introduced to chill the transfer line and propellant tank. As the propellant absorbs heat and undergoes a phase change, the tank internal pressure increases. The tank is then vented to relieve pressure prior to another charge of cryogenic liquid being introduced. This cycle is repeated until the transfer lines and tank are sufficiently chilled and the replenishment of the propellant tank is complete. This method suffers inefficiencies due to multiple chill and vent cycles within the transfer lines and associated feed system components. Additionally, this system requires precise measuring of cryogenic fluid delivery for each transfer, multiple valve cycling events, and other complexities associated with cycled operations. To minimize propellant loss and greatly simplify on-orbit operations, an alternate methodology has been designed and demonstrated. The Vented Chill / No Vent Fill method is a simpler, constant flow approach in which the propellant tank and transfer lines are only chilled once. The receiver tank is continuously vented as cryogenic liquid chills the transfer lines, tank mass and ullage space. Once chilled sufficiently, the receiver tank valve is closed and the tank is completely filled. Interestingly, the vent valve can be closed prior to receiver tank components reaching liquid saturation temperature. An incomplete fill results if insufficient energy is removed from the tank's thermal mass and ullage space. The key to successfully conducting the no vent fill is to assure that sufficient energy is removed from the system prior to closing the receiver tank vent valve. This paper will provide a description of the transfer methodology and test article, and will provide a discussion of test results.

Rhys, Noah O.↗

NASA NTRSDate not supplied

A Study of Parallel Scalability and Dynamic Workload Balancing in GlennICE

The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries. It utilizes user-supplied computational fluid dynamics solutions for the geometry of interest. Key developments include advancements in convergence of collection efficiency, trajectory optimization, and refinement methodology. These improvements have significantly enhanced GlennICE’s efficiency for practical engineering applications. A recent study focused on benchmarking GlennICE’s scalability in a parallel environment using static scheduling. Findings indicated a potential twofold increase in efficiency through workload balance enhancements. This paper presents an analysis of the solver’s new workload balancing improvements, incorporating shared memory and dynamic scheduling routines. Results demonstrate a highly efficient and consistent algorithm across high-performance computing clusters.

Computational Icing↗

NASA NTRSDate not supplied

A Study of Parallel Scalability and Dynamic Workload Balancing in GlennICE

The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries. It utilizes user-supplied computational fluid dynamics solutions for the geometry of interest. Key developments include advancements in convergence of collection efficiency, trajectory optimization, and refinement methodology. These improvements have significantly enhanced GlennICE’s efficiency for practical engineering applications. A recent study focused on benchmarking GlennICE’s scalability in a parallel environment using static scheduling. Findings indicated a potential twofold increase in efficiency through workload balance enhancements. This paper presents an analysis of the solver’s new workload balancing improvements, incorporating shared memory and dynamic scheduling routines. Results demonstrate a highly efficient and consistent algorithm across high-performance computing clusters.

Computational Icing↗

DOE OSTI2015

Analyzing the Interplay of Failures and Workload on a Leadership-Class Supercomputer

The unprecedented computational power of cur- rent supercomputers now makes possible the exploration of complex problems in many scientific fields, from genomic analysis to computational fluid dynamics. Modern machines are powerful because they are massive: they assemble millions of cores and a huge quantity of disks, cards, routers, and other components. But it is precisely the size of these machines that glooms the future of supercomputing. A system that comprises many components has a high chance to fail, and fail often. In order to make the next generation of supercomputers usable, it is imperative to use some type of fault tolerance platform to run applications on large machines. Most fault tolerance strategies can be optimized for the peculiarities of each system and boost efficacy by keeping the system productive. In this paper, we aim to understand how failure characterization can improve resilience in several layers of the software stack: applications, runtime systems, and job schedulers. We examine the Titan supercomputer, one of the fastest systems in the world. We analyze a full year of Titan in production and distill the failure patterns of the machine. By looking into Titan s log files and using the criteria of experts, we provide a detailed description of the types of failures. In addition, we inspect the job submission files and describe how the system is used. Using those two sources, we cross correlate failures in the machine to executing jobs and provide a picture of how failures affect the user experience. We believe such characterization is fundamental in developing appropriate fault tolerance solutions for Cray systems similar to Titan.

Cray↗

DOE OSTI2017

The U.S. Gas Flooding Experience: CO2 Injection Strategies and Impact on Ultimate Recovery

The Permian Basin in West Texas and southwestern New Mexico has seen 45 years of oil reserve growth through CO2 enhanced oil recovery (CO2 EOR). More than 60 CO2 EOR projects are currently active in the region’s limestone, sandstone and dolomite reservoirs. Water alternating gas (WAG) has been the development strategy of choice in the Permian for several technical and economic reasons. More recently, the technology started to get implemented in the much more porous and permeable clastic depositional systems of the onshore U.S. Gulf Coast. Continued CO2 injection (CGI), as opposed to WAG, was selected as the injection strategy to develop Gulf Coast oil fields, where CO2 injection volumes are significantly larger (up to 6 times larger) than those of the Permian. We conducted a compositional simulation based study with the objective of comparing the CO2 utilization ratios (volume of CO2 injected to produce a barrel of oil) of 4 conventional and novel CO2 injection strategies: (1) continuous gas injection (CGI), (2) water alternating gas (WAG), (3) water curtain injection (WCI), and (4) WAG and WCI combination. These injection scenarios were simulated using the GEM module from the Computer Modeling Group (CMG). GEM is an advanced general equation-of-state compositional simulator, which includes equation of state, CO2 miscible flood, CO2/brine interactions, and complex phase behavior. The simulator is set up to model three fluid phases including water, oil, and gas. Our study demonstrates how the selected field development strategy has a significant impact on the ultimate recovery of CO2-EOR projects, with GCI injection providing maximum oil recovery in absolute volume terms, but with WAG offering a more balanced technical-economical approach.

02 PETROLEUM↗

DOE OSTI2016

Artificial Neural Network Representation of Dynamic Criticality Excursion Experiment Data

This paper outlines time dependent considerations in applying the artificial neural network (ANN) paradigm to dynamic nuclear criticality excursion experiment data. The ANN amongst a variety of uses, can apply to function fitting of multiple input-multiple output data sets, pattern recognition, cluster analysis, and time series prediction. The ANN is a component of Machine Learning (ML) data analysis. The practical application of ML to criticality safety has been funded through the Y-12 Plant Directed Research and Demonstration (PDRD) program to support criticality safety and emergency planning efforts. The initial effort considered function fitting of criticality excursion data. Recent efforts successfully extended the ANN to criticality safety code validation. The initial ANN work applied to criticality excursions centered around multivariate function fitting of static (tabular) data taken from the French Consequences Radiologiques d'un Accident de Criticite (CRAC) and SILENE reactor campaigns. The former report (LeCorche) summarized important tabular data for the first 44 CRAC experiments. The initial ANN was trained to specific time-independent inputs, then applied to predict peak fission rate and total fissions for a wide variety of fissile solutions process conditions. This work extends that effort to apply the ANN paradigm to dynamic prediction of time-dependent criticality excursion experiment data. A dynamic criticality excursion in fissile solution represents a complex multiphysics phenomenon. Factors include among other things, coupled neutronic, multiphase fluid flow, temperature, pressure, acoustic shock, boiling heat transfer, reactivity feedback, and radiolytic gas considerations. There have been many efforts to model (over time) as coupled ordinary and (space) as partial differential equations (ODE, PDE). The ANN representation noted here does not rely on solving explicit ODE or PDEs. Rather the intent is to demonstrate that as a time series, the criticality excursion can be predicted by training specific ANN architectures to experimental input and output time series exclusively. The rationale for extending this work to the time-dependent regime is important in developing a 'criticality excursion emulator' solely on the basis of learning characteristics from experiment dynamic data. An emulator is different than a computer code simulator driven by the solution of ODEs and PDEs given select 'tuning parameters' that match outputs to experiments. A criticality excursion emulator, derived exclusively from an ANN or ML, could predict most likely excursion characteristics, trained from historic dynamic experiment data, presented with input values within the range of those experiments. The work noted shows that generic time-series ANN architectures can be satisfactorily applied to the prediction of dynamic, extremely chaotic criticality excursion power given an initial time dependent input series. In reality, an excursion emulator could be driven by user-supplied initial (t=0), time independent or constant values to generate time dependent outputs. Thus ongoing work includes developing a custom ANN such that initial dynamic conditions would be predicted. Those results then serve as inputs to a customized ANN architecture currently in development. (author)

42 ENGINEERING↗

NASA NTRS2018

Orion European Structural Test Article Propellant Tank Fill and Drain Carts

Environmental testing of the Orion European Structural Test Article (E-STA), which contains the Orion European Service Module (ESM), required that the onboard propellant tanks be filled and drained with fuel and oxidizer simulant fluids as well as pressurized and de-pressurized with an ullage gas. This conference paper will elaborate on how these objectives were fulfilled by presenting the development of derived requirements definition, initial fill and drain concepts, selection of simulant fluids, finalization of pump and pressurization design, selection of components, selection of transfer hoses and interface connections, as well as development and maintenance of budgets, schedules, reviews, construction, documentation, and test procedures. This paper also describes implementation of checkout and commissioning activities leading to successful fluid cart pumping and pressurization operations for the test campaign. The development, construction, and operation of the fluid cart pumping and pressurization systems for the Environmental testing of the Orion European Structural Test Article (E-STA), took place at the NASA Glenn Research Center's Plum Brook Station Space Environments Complex (SEC) during 2015 and 2016.

Pressurization↗

NASA NTRSDate not supplied

Coupling a Computational Fluid Dynamics Model to a Spacecraft Thermal System Model for the DraMS Instrument Thermal Analysis

The Dragonfly Mass Spectrometer (DraMS) is an instrument on the Dragonfly mission, which will spend 7 years in deep space cruise before landing and operating on the surface of Titan. Vacuum thermal analyses are required for deep space cruise, and convection analyses are required for the Titan surface operations. Model exchanges across multiple thermal teams are needed for all phases of the mission. For DraMS, Thermal Desktop® (TD) has been the main thermal analytical tool of choice due to its capability in modeling complex thermal systems with relatively low computational power and for its availability across thermal teams. However, TD does not have computational fluid dynamics (CFD) capability and struggles to accurately capture complex convective behavior. DraMS has fans operating in tandem and gas flow behaviors are not easily predicted due to its complex flow paths. CFD software, such as Fluent, can model and predict such complex flow behaviors, but CFD models are computationally expensive, and its workflow processes are not tailored towards simulating large and complex systems. Therefore, a coupled modeling approach was chosen for DraMS: A TD model was used for simulating all the conductive, radiative, and source terms, while a Fluent CFD model was added on, as needed, to the TD model to provide the convective boundary conditions using the System Coupling software. The coupling software allows the TD and Fluent models to communicate data and arrive at a co-solved and co-converged solution. Furthermore, Thermal Iso-value Exchange (TIE) method was developed to facilitate and improve the TD-Fluent data exchange process. This paper will discuss the analytical studies that were done to verify the accuracy and usability of the coupled approach and the challenges associated, which lead to the development of the TIE approach. DraMS thermal design and co-solved analysis results will also be discussed.

Heat transfer↗

NASA NTRSDate not supplied

Coupling a Computational Fluid Dynamics (CFD) Model to a Spacecraft Thermal System Model for the DraMS Instrument Thermal Analysis

The Dragonfly Mass Spectrometer (DraMS) is an instrument on the Dragonfly mission, which will spend 7 years in deep space cruise before landing and operating on the surface of Titan. Vacuum thermal analyses are required for deep space cruise, and convection analyses are required for the Titan surface operations. Model exchanges across multiple thermal teams are needed for all phases of the mission. For DraMS, Thermal Desktop (TD) has been the main thermal analytical tool of choice due to its capability in modeling complex thermal systems with relatively low computational power and for its availability across thermal teams. However, TD does not have computational fluid dynamics (CFD) capability and struggles to accurately capture complex convective behavior. DraMS has fans operating in tandem and gas flow behaviors are not easily predicted due to its complex flow paths. CFD software, such as Fluent, can model and predict such complex flow behaviors, but CFD models are computationally expensive, and its workflow processes are not tailored towards simulating large and complex systems. Therefore, a coupled modeling approach was chosen for DraMS: A TD model was used for simulating all the conductive, radiative, and source terms, while a Fluent CFD model was added on, as needed, to the TD model to provide the convective boundary conditions using the System Coupling software. The coupling software allows the TD and Fluent models to communicate data and arrive at a co-solved and co-converged solution. Furthermore, Thermal Iso-value Exchange (TIE) method was developed to facilitate and improve the TD-Fluent data exchange process. This paper will discuss the analytical studies that were done to verify the accuracy and usability of the coupled approach and the challenges associated, which lead to the development of the TIE approach. DraMS thermal design and co-solved analysis results will also be discussed.

heat transfer↗

NASA NTRS2024

Validation & Verification of CFD Models for Cryogenic Fluid Management of Propellant Tanks in Space

This article describes the need and the strategy for CFD model development, validation, and verification for Cryogenic Fluid Management (CFM) of Propellant Tanks in Space. It describes the type of CFD models that must be developed to address the future needs of Space CFM. It also discusses the two classes of experiments currently used to validate the fidelity of the CFD models. These two experiment classes are: (a) the small-scale simulant fluid science experiments that are equipped with scientific diagnostics to elucidate the underlying two-phase fluid physics of the CFM processes; and (b) the large-scale cryogenic experiments that assess the engineering performance of the propellant tank for storage and transfer. The current status of the CFD model development and validation is briefly assessed by presenting examples of segregated two-phase flow problems that have been successfully modeled. The future model development directions for CFM situations involving more complex interpenetrating phases are also defined.

Cryogenic Fluid Management↗

NASA NTRS1997

MFLOP to GFLOP: The Impact on High Fidelity Based Computational Aeroelasticity

Aeroelasticity which involves strong coupling of fluids, structures and controls is an important element in designing an aircraft. Computational aeroelasticity using low fidelity methods such as the linear aerodynamic flow equations coupled with the modal structural equations are well advanced. Though these low fidelity approaches are computationally less intensive, they are not adequate for the analysis of modern aircraft which can experience complex flow/structure interactions. Even at moderate angles of attack supersonic aircraft can experience vortex induced aeroelastic oscillations. Near transonic speeds buffet associated structural oscillations are possible. Aircraft flying in transonic regime may experience a dip in the flutter speed. For accurate aeroelastic computations at these complex fluid/structure interaction situations, high fidelity equations such as the Navier-Stokes for fluids and the finite-elements for structures are needed. Computations using these high fidelity equations require large computational resources both in memory and speed. Current conventional supercomputers have reached their limitations both in memory and speed. As a result, parallel computers have evolved to overco me the limitations of conventional computers. This paper will address the transition that is taking place in computational aeroelasticity from conventional computers to parallel computers. The paper will address special techniques needed to take advantage of the architecture of new parallel computers. Results will be illustrated from computations made on iPSC/860 and IBM SP2 computer by using ENSAERO code that directly couples the Euler/Navier-Stokes flow equations with high resolution finite-element structural equations. Modifications required in both fluids and structural solvers in order to run efficiently on parallel computers will be discussed. Implementation of moving grids and fluid/structural interface on parallel computers will be discussed.

Guruswamy, Guru P.↗

NASA NTRSDate not supplied

Agent-based modeling of microbes in space

Space is tough on organisms. Microorganisms traveling to space experience stress from environmental features such as ionizing radiation and lack of normal microgravity; however, much remains unknown about the mechanisms by which those environmental features affect microbial physiology. Microbes experience changes in gravity not directly but rather through changes in their fluid environment, and deep-space particle radiation causes cell damage that is complex but rare. Computational modeling at the single-cell level (agent-based modeling) can allow us to probe the spatially heterogeneous processes that characterize space stresses, to gain insight into the relationships of microbial cells with their environments and with each other. Here we present two software packages for simulating microbial population dynamics in space conditions: CAMDLES and AMMPER. Microbes growing in liquid culture medium in the microgravity of an orbital space station experience a quiescent, poorly-mixed fluid environment. CAMDLES (CFD-DEM Artificial Microgravity Developments for Living Ecosystem Simulation) simultaneously simulates biological, chemical, and mechanical processes to predict microbial ecological dynamics in microgravity, and in the rotating culture vessels used to create an artificial microgravity environment in the lab. Initial results demonstrate that the growth of a cross-feeding microbial consortium, dependent on the exchange of soluble metabolites, is sensitive to the initial spatial distribution of cells, and grows differently in real versus artificial microgravity. Microbial populations exposed to deep-space radiation experience spatially and temporally heterogeneous damage from the traversal of high-energy particles. AMMPER (Agent-Based Model for Microbial Populations Exposed to Radiation) pairs a 3d model of energy deposition along a radiation particle track with a microbial population growth and damage model to predict the effects of localized radiation damage on population-level responses. It includes a user-friendly graphical interface. AMMPER results agree with experimental data indicating that indirect effects of radiation (reactive oxygen species generation, metabolic impairment) have a greater impact on microorganisms than direct effects (DNA damage).

Jessica A Lee↗

NASA NTRS2022

Agent-Based Modeling of Microbes in Space

Space is tough on organisms. Microorganisms traveling to space experience stress from environmental features such as ionizing radiation and lack of normal gravity, and much remains unknown about the mechanisms by which those environmental features affect microbial physiology. Microbes experience changes in gravity not directly but rather through changes in their fluid environment, and deep-space particle radiation causes cell damage that is complex but rare. Computational modeling at the single-cell level (agent-based modeling) can allow us to probe the spatially heterogeneous processes that characterize space stresses, to gain insight into the relationships of microbial cells with their environments and with each other. Here we present two software packages for simulating microbial population dynamics in space conditions: CAMDLES and AMMPER. Microbes growing in liquid culture medium in the microgravity of an orbital space station experience a quiescent, poorly-mixed fluid environment. CAMDLES (CFD-DEM Artificial Microgravity Developments for Living Ecosystem Simulation) simultaneously simulates biological, chemical, and mechanical processes to predict microbial ecological dynamics in microgravity, and in the rotating culture vessels used to create an artificial microgravity environment in the lab. Initial results demonstrate that the growth of a cross-feeding microbial consortium, dependent on the exchange of soluble metabolites, is sensitive to the initial spatial distribution of cells, and grows differently in real versus artificial microgravity. Microbial populations exposed to deep-space radiation experience spatially and temporally heterogeneous damage from the traversal of high-energy particles. AMMPER (Agent-Based Model for Microbial Populations Exposed to Radiation) pairs a 3d model of energy deposition along a radiation particle track with a microbial population growth and damage model to predict the effects of localized radiation damage on population-level responses. It includes a user-friendly graphical interface. AMMPER growth curves recapitulate experimental results, and allow comparison between direct effects (DNA damage) and indirect effects (reactive oxygen species generation, metabolic impairment) of radiation.

microbiology↗

DOE OSTI2016

Methods of combined bioprocessing and related microorganisms, thermophilic and/or acidophilic enzymes, and nucleic acids encoding said enzymes

A genetically modified organism comprising: at least one nucleic acid sequence and/or at least one recombinant nucleic acid isolated from Alicyclobacillus acidocaldarius and encoding a polypeptide involved in at least partially degrading, cleaving, transporting, metabolizing, or removing polysaccharides, cellulose, lignocellulose, hemicellulose, lignin, starch, sugars, sugar oligomers, carbohydrates, complex carbohydrates, chitin, heteroxylans, glycosides, xylan-, glucan-, galactan-, or mannan-decorating groups; and at least one nucleic acid sequence and/or at least one recombinant nucleic acid encoding a polypeptide involved in fermenting sugar molecules to a product. Additionally, enzymatic and/or proteinaceous extracts may be isolated from one or more genetically modified organisms. The extracts are utilized to convert biomass into a product. Further provided are methods of converting biomass into products comprising: placing the genetically modified organism and/or enzymatic extracts thereof in fluid contact with polysaccharides, cellulose, lignocellulose, hemicellulose, lignin, starch, sugars, sugar oligomers, carbohydrates, complex carbohydrates, chitin, heteroxylans, glycosides, and/or xylan-, glucan-, galactan-, or mannan-decorating groups.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Methods of combined bioprocessing and related microorganisms, thermophilic and/or acidophilic enzymes, and nucleic acids encoding said enzymes

A genetically modified organism comprising: at least one nucleic acid sequence and/or at least one recombinant nucleic acid isolated from Alicyclobacillus acidocaldarius and encoding a polypeptide involved in at least partially degrading, cleaving, transporting, metabolizing, or removing polysaccharides, cellulose, lignocellulose, hemicellulose, lignin, starch, sugars, sugar oligomers, carbohydrates, complex carbohydrates, chitin, heteroxylans, glycosides, xylan-, glucan-, galactan-, or mannan-decorating groups; and at least one nucleic acid sequence and/or at least one recombinant nucleic acid encoding a polypeptide involved in fermenting sugar molecules to a product. Additionally, enzymatic and/or proteinaceous extracts may be isolated from one or more genetically modified organisms. The extracts are utilized to convert biomass into a product. Further provided are methods of converting biomass into products comprising: placing the genetically modified organism and/or enzymatic extracts thereof in fluid contact with polysaccharides, cellulose, lignocellulose, hemicellulose, lignin, starch, sugars, sugar oligomers, carbohydrates, complex carbohydrates, chitin, heteroxylans, glycosides, and/or xylan-, glucan-, galactan-, or mannan-decorating groups.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Methods of combined bioprocessing and related microorganisms, thermophilic and/or acidophilic enzymes, and nucleic acids encoding said enzymes

A genetically modified organism comprising: at least one nucleic acid sequence and/or at least one recombinant nucleic acid isolated from Alicyclobacillus acidocaldarius and encoding a polypeptide involved in at least partially degrading, cleaving, transporting, metabolizing, or removing polysaccharides, cellulose, lignocellulose, hemicellulose, lignin, starch, sugars, sugar oligomers, carbohydrates, complex carbohydrates, chitin, heteroxylans, glycosides, xylan-, glucan-, galactan-, or mannan-decorating groups; and at least one nucleic acid sequence and/or at least one recombinant nucleic acid encoding a polypeptide involved in fermenting sugar molecules to a product. Additionally, enzymatic and/or proteinaceous extracts may be isolated from one or more genetically modified organisms. The extracts are utilized to convert biomass into a product. Further provided are methods of converting biomass into products comprising: placing the genetically modified organism and/or enzymatic extracts thereof in fluid contact with polysaccharides, cellulose, lignocellulose, hemicellulose, lignin, starch, sugars, sugar oligomers, carbohydrates, complex carbohydrates, chitin, heteroxylans, glycosides, and/or xylan-, glucan-, galactan-, or mannan-decorating groups.

09 BIOMASS FUELS↗

DOE OSTI2020

Methods of combined bioprocessing and related microorganisms, thermophilic and/or acidophilic enzymes, and nucleic acids encoding said enzymes

A genetically modified organism comprising: at least one nucleic acid sequence and/or at least one recombinant nucleic acid isolated from Alicyclobacillus acidocaldarius and encoding a polypeptide involved in at least partially degrading, cleaving, transporting, metabolizing, or removing polysaccharides, cellulose, lignocellulose, hemicellulose, lignin, starch, sugars, sugar oligomers, carbohydrates, complex carbohydrates, chitin, heteroxylans, glycosides, xylan-, glucan-, galactan-, or mannan-decorating groups; and at least one nucleic acid sequence and/or at least one recombinant nucleic acid encoding a polypeptide involved in fermenting sugar molecules to a product. Additionally, enzymatic and/or proteinaceous extracts may be isolated from one or more genetically modified organisms. The extracts are utilized to convert biomass into a product. Further provided are methods of converting biomass into products comprising: placing the genetically modified organism and/or enzymatic extracts thereof in fluid contact with polysaccharides, cellulose, lignocellulose, hemicellulose, lignin, starch, sugars, sugar oligomers, carbohydrates, complex carbohydrates, chitin, heteroxylans, glycosides, and/or xylan-, glucan-, galactan-, or mannan-decorating groups.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Virtual Flow Simulator

Virtual Flow Simulator (VFS) is a state-of-the-art computational fluid mechanics (CFD) package that is capable of simulating multi-physics/multi-phase flows with the most advanced turbulence models (RANS, LES) over complex terrains. The flow solver is based on the Curvilinear Immersed Boundary (CURVIB) method to handle geometrically complex and moving domains. Different modules of the VFS package can provide different simulation capabilities for specific applications ranging from the fluid-structure interaction (FSI) of solid and deformable bodies, the two-phase free surface flow solver based on the level set method for ocean waves, sediment transport models in rivers and the large-scale models of wind farms based on actuator lines and surfaces. All numerical features of VFS package have been validated with known analytical and experimental data as reported in the related journal articles. VFS package is suitable for a broad range of engineering applications within different industries. VFS has been used in different projects with applications in wind and hydrokinetic energy, offshore and near-shore ocean studies, cardiovascular and biological flows, and natural streams and river morphodynamics. Over the last decade, the development of VFS has been supported and assisted with the help of various United States companies and federal agencies that are listed in the sponsor lists. In this version, VFS-Wind contains all the necessary modeling tools for wind energy applications, including land-based and offshore wind farms. VFS is highly scalable to run on either desktop computers or high performance clusters (up to 16,000 CPUs). This released version comes with a detailed user’s manual and a set of case studies designed to facilitate the learning of the various aspects of the code in a comprehensive manner. The included documentation and support material has been elaborated in a collaboration effort with Sandia National Labs under the contract DE-EE0005482. The VFS-Wind source code is distributed under the GNU General Public License (GPL 2.0), offering to the community the flexibility to use and expand the capabilities of the package. We note that future publications including academic journals that result from the use of VFS must be cited appropriately under the GPL v.2 license agreement.

Yang, Xiaolei↗