SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This dataset includes large-eddy simulation (LES) output from a convective atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on July 4, 2012. The dataset was used to assess the LES models for simulation of canonical convective ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.
This dataset includes large-eddy simulation (LES) output from a neutrally stratified atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on Aug. 17, 2012. The dataset was used to assess LES models for simulation of canonical neutral ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.
KIVA-hpFE is a high performance computer software for solving the physics of multi-species and multiphase turbulent reactive flow in complex geometries having immersed moving parts. The code is written in Fortran 90/95 and can be used on any computer platform with any popular complier. The code is in two versions, a serial version and a parallel version utilizing MPICH2 type Message Passing Interface (MPI or Intel MPI) for solving distributed domains. The parallel version is at least 30x faster than the serial version and much faster than our previous generation of parallel engine modeling software, by many factors. The 5th generation algorithm construction is a Galerkin type Finite Element Method (FEM) solving conservative momentum, species, and energy transport equations along with two-equation turbulent model k-ω Reynolds Averaged Navier-Stokes (RANS) model and a Vreman type dynamic Large Eddy Simulation (LES) method. The LES method is capable modeling transitional flow from laminar to fully turbulent; therefore, this LES method does not require special hybrid or blending to walls. The FEM projection method also uses a Petrov-Galerkin (P-G) stabilization along with pressure stabilization. We employ hierarchical basis sets, constructed on the fly with enrichment in areas associated with relatively larger error as determined by error estimation methods. In addition, when not using the hp-adaptive module, the code employs Lagrangian basis or shape functions. The shape functions are constructed for hexahedral, prismatic and tetrahedral elements. The software is designed to solve many types of reactive flow problems, from burners to internal combustion engines and turbines. In addition, the formulation allows for direct integration of solid bodies (conjugate heat transfer), as in heat transfer through housings, parts, cylinders. It can also easily be extended to stress modeling of solids, used in fluid structure interactions problems, solidification, porous media modeling and magneto hydrodynamics.
Aeolus is an efficient three-dimensional computational fluid dynamics code based on finite volume method developed for predicting transport and dispersion of contaminants in a complex urban area. It solves the time dependent incompressible Navier-Stokes equation on a regular Cartesian staggered grid using a fractional step method. It also solves a scalar transport equation for temperature and using the Boussinesq approximation. The model also includes a Lagrangian dispersion model for predicting the transport and dispersion of atmospheric contaminants. The model can be run in an efficient Reynolds Average Navier-Stokes (RANS) mode with a run time of several minutes, or a more detailed Large Eddy Simulation (LES) mode with run time of hours for a typical simulation. This report describes the model components, including details on the physics models used in the code, as well as several model validation efforts. Aeolus wind and dispersion predictions are compared to field data from the Joint Urban Field Trials 2003 conducted in Oklahoma City (Allwine et al 2004) including both continuous and instantaneous releases. Newly implemented Aeolus capabilities include a decay chain model and an explosive Radiological Dispersal Device (RDD) source term; these capabilities are described. Aeolus predictions using the buoyant explosive RDD source are validated against two experimental data sets: the Green Field explosive cloud rise experiments conducted in Israel (Sharon et al 2012) and the Full-Scale RDD Field Trials conducted in Canada (Green et al 2016).
This project has been focused on the experimental and numerical investigations of the water-cooled and air-cooled Reactor Cavity Cooling System (RCCS) designs. At this aim, we have leveraged an existing experimental facility at the University of Wisconsin-Madison (UW), and we have designed and built a separate effect test facility at the University of Michigan. The experimental facility at UW has underwent several upgrades, including the installation of advanced instrumentation (i.e. wire-mesh sensors) built at the University of Michigan. These provides highresolution time-resolved measurements of the void-fraction distribution in the risers of the water-cooled RCCS facility. A phenomenological model has been developed to assess the water cooled RCCS system stability and determine the root cause behind the oscillatory behavior that occurs under normal two-phase operation. Testing under various perturbations to the water-cooled RCCS facility have resulted in changes in the stability of the integral system. In particular, the effects on stability of inlet orifices, water tank volume have and system pressure been investigated. MELCOR was used as a predictive tool when performing inlet orificing tests and was able to capture the Density Wave Oscillations (DWOs) that occurred upon reaching saturation in the risers. The experimental and numerical results have then been used to provide RCCS design recommendations. The experimental facility built at the University of Michigan was aimed at the investigation of mixing in the upper plenum of the air-cooled RCCS design. The facility has been equipped with state-of-theart high-resolution instrumentation to achieve so-called CFD grade experiments, that can be used for the validation of Computational Fluid Dynanmics (CFD) models, both RANS (Reynold-Averaged) and LES (Large Eddy Simulations). The effect of risers penetration in the upper plenum has been investigated as well.
Existing turbulence models based on the Reynolds-averaged Navier–Stokes (RANS) equations are ill-equipped to represent transitional phenomena that are characteristic of problems such as those involved in Rayleigh-Taylor mixing layers. In this work, large eddy simulation(LES) data is used to augment and improve the predictive capability of RANS models. In-verse models are used to extract temporally varying embedded discrepancy functions from a number of LES datasets. These discrepancies are generalized into functional forms using neural networks and embedded inside the RANS solver to augment predictions. Specifically, the data-augmented models are demonstrated to provide improved predictions in Rayleigh-Taylor mixing problems at low Atwood numbers.
This Final Report documents the entire four years of the Cooperative Agreement DE-FE0011822, from October 1, 2013 through September 30, 2017, that was competitively awarded under Funding Opportunity Announcement DE-FOA-0000795 in support of the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) University Turbine Systems Research (UTSR) Program. Under the UTSR Program, DOE typically issues Funding Opportunity Announcement (FOA) solicitations every 1-2 years so that U.S. colleges/universities may propose research & development (R&D) projects in specific technical topic areas (e.g., combustion, aerodynamics/heat transfer, advanced materials) that have been identified as both scientifically and industrially relevant to the gas turbine industry. The primary objectives of this Purdue University project (in collaboration with Princeton University through a sub-award) was to develop experimental methods, kinetic models, and numerical tools to help quantify and predict the impact of exhaust gas recirculation (EGR) on NOx and CO emissions, combustion kinetics, radiation heat transfer, turbulent combustion, and combustion instabilities for natural gas and high hydrogen content (HHC) fuels by using laminar and turbulent flow reactors and gas turbine combustors operating at high temperatures and pressures. This project has obtained detailed data for improving chemical kinetic models to study EGR effects and provide insight into the effects of EGR on flame speeds and turbulent flame structure, as well as assessing the impact of EGR on emissions in a high-pressure combustion test rig. The project was divided into six primary tasks: 1) Project Management and Program Planning; 2) Flow Reactor Investigation of EGR Chemical Kinetics; 3) Combustion Bomb Investigation of EGR Effects on Flame Speed; 4) Investigation of EGR Effects on Turbulent Flame Structure in Reactor-Assisted Turbulent Slot (RATS) Burner; 5) Investigation of EGR Effects in Purdue Staged Combustion Test Rig; and 6) Development and Validation of a Large Eddy Simulation (LES) Tool for Gas Turbine Combustion and Emission Modeling using HHC Fuels. Details on the execution and results of each of these tasks are provided in the main report. To assist the reader, each task is described in a chapter-like format with its own separate listing of tables, figures, and references so that the results of each task/subtask (i.e., technical sections covering various experiments and modeling work focused on a specific area) can be quickly obtained while also getting an overall feel of the project execution. This chapter-like format also allows for acknowledgement of support from other programs relative to specific tasks. In this manner, the reader can more conveniently digest the R&D results in the various task/subtask sections that may be of most interest to the reader. In addition to this Final Report, the Purdue/Princeton team was able to publish 7 journal articles and provide 7 presentations at national scientific meetings/conferences to disseminate results. A listing of these journal articles and presentations is provided at the end of this report
Aerial Assessment of Liquid in Clouds at Oliktok (AALCO) Intensive Operation Period (IOP) began in October, 2016 and ended in October, 2017 at the ARM Mobile Facility-3 (AMF-3) at Oliktok Point, Alaska. The operations tested super-cooled liquid water sensors (SLWCs), leaf-wetness sensors, radiosondes, and a distributed temperature sensor (DTS) on tethered balloon system (TBS) platforms throughout the period. An auto-reeler system, a helikite, and a aerostat were tested. When conditions were optimal, the aerostat was preferred to the helikite and the auto-reeler. It was found the SLWCs had better transmission and sensitivity to relay information about the near-surface cloudy boundary layer than the leaf-wetness sensors. The DTS was also found to give useful information about the atmospheric column and deployment is condition-dependent. Results from the SLWCs and DTS are being compared with high resolution Large Eddy Simulations (LES) in the System for Atmospheric Modeling (SAM).
The United States Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility held a workshop on May 2, 2019 to gather information on different weather scenarios that could be pursued for expanding the range of atmospheric processes that could be studied by the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) data product. To date, LASSO has focused on the use of LES modeling to supplement ARM’s suite of observations at the Southern Great Plains (SGP) atmospheric observatory for days that have shallow convective clouds. Input for the workshop was obtained from the community in the form of white papers that outlined additional meteorological phenomena that LASSO could pursue. Four white papers were submitted covering a wide range of phenomena of scientific interest to the DOE atmospheric modeling community and across the ARM spectrum of observation sites: maritime clouds, deep convection, clear-air turbulence, and arctic clouds. Topic experts were invited to the workshop to discuss each scenario in terms of the science drivers, feasibility of implementation (e.g., model configuration and needed observations), timing considerations, and any open issues related to the scenario that would need further investigation.
Global climate models fail to adequately represent shallow cumulus conditions. The activities at the University of Texas at Austin focus on deploying advanced statistical approaches to identify linkages between numerical models and observations using the E3SM-Single Column Model (SCM) and different ARM campaigns focused on shallow cumulus conditions at SGP. The current version of the SCM uses CLUBB to represent shallow cumulus. The parameterization represents a significant evolution from older generation parameterizations insofar as it contains a number of prognostic equations that keep track of the turbulent nature of the environment that are carried forward in time. The parameters associated with this scheme address the rate at which different prognostic quantities that determine vertical turbulent fluxes of heat and moisture decay with time. The large number of parameters gives the CLUBB scheme a lot of flexibility. The expectation of its developers is that it should represent any shallowly convective environment. The concern among developers and other theorists was not whether it could represent any one environment, but whether a single judicious choice of parameters would allow it to predict the turbulent characteristics of a range of environments. Through several exhaustive studies that made use of Bayesian statistical calibration, tens of thousands of SCM experiments, and ensembles of observations of shallow cumulus at SGP that were also modeled using Large Eddy Simulations (LES), we have only achieved partial success in using CLUBB within SCM to capture important aspects of what has been observed and modeled with LES at SGP. Even as we worked to improve observational tests and make a more scientifically focused attempt at modifying subsets of CLUBB parameters, model performance continues to be lacking in some important respects. In one calibration, we were able to achieve a near perfect match to observed properties of liquid water path, cloud radiative effects, and cloud fraction but with clouds that were trapped within a shallow layer and without the highly turbulent fluxes and vertical extent observed with ARM data and modeled with LES (Figure 1). The conclusion we are reaching is that shallow cumulus conditions are much more challenging to represent than was previously thought. This result is also prompting us to consider some aspects of the experiment design that may be limiting the performance of SCM in this case. The work has garnered the attention of Vincent Larson (CLUBB developer at Univ. Wisconsin – Milwaukee) and model developers Steven Klein and Yunyan Zhang (LLNL) with whom this work and will continue.
This report summarizes the primary results accomplished by University of Wisconsin --- Milwaukee for a DOE project on the use of Atmospheric System Research observations and large-eddy simulations to evaluate coarse-resolution models of clouds and turbulence in the atmosphere.
We have created a suite of coastally focused Large Eddy Simulation (LES), including a first ever investigation of the influence of the ocean bottom on turbulence. We have added important forcing not previously considered in LES models. We have evaluated a new more physically realistic and advanced closure against our LES database of simulations. Test cases will constrain constants in the new model and help us transition the mixing model from the deep ocean to the coastal ocean for the first time.
The objective of this project is to improve the prediction of engine knock by developing a new combustion modeling framework. Engine knock is a limiting factor to constrain the increase of fuel efficiency for spark ignition (SI) engines in most passenger cars. Efforts to increase fuel efficiency, increasing the compression ratio or downsizing, lead to the increase in the tendency of the knock occurrence. The knock is an undesired ignition of the end-gas, unburned fuel/air mixture ahead of the spark-ignited premixed flame, resulting in rapid in-cylinder pressure rises and engine damages. The combustion modeling framework developed in this project can consider turbulence-chemistry interactions during end-gas ignition, while using a reasonably detailed chemical mechanism developed for ignition and combustion reactions under engine relevant conditions, and the subtle characteristics of spark-ignited flame propagation. It is developed in the context of large eddy simulation (LES), which can capture stochastic in-cylinder processes. The developed model is incorporated into a commercial software for engine simulation, CONVERGE CFD, as a user defined function, and validated. Engine knock and knock-free experiments as well as direct numerical simulation (DNS) of end-gas ignition in homogeneous turbulence are performed to help model development and provide data sets for model validation. With further validation, the developed model is expected to advance the predictive capability for engine knock simulations and thus contribute to improving the fuel efficiency.
The presentation discusses the fundamental gaps in our understanding of ocean turbulence below ice shelves and how they were addressed via Large-Eddy Simulations (LES) specifically tailored for ice-shelf settings.
Fair-weather shallow cumuli (ShCu) play an important role in many climate-related processes. Irregular geometry of ShCu and their strong temporal and spatial variability make it challenging to observe ShCu holistically and to represent them correctly in climate models. To improve ShCu parameterizations, information on both vertically and horizontally resolved cloud properties is required. Commonly, the vertically resolved cloud properties are provided by zenith pointing lidar-radar observations with a very narrow field of view (FOV). Thus, these “pencil-beam” properties may not be representative of a larger surrounding area. Limited number of areal-averaged cloud properties, such as fractional sky cover (FSC), are offered typically by wide-FOV observations. The main goal of our project was to integrate advantages of the narrow-FOV (vertical structure of clouds) and wide-FOV (spatial arrangement of clouds) observations for an improved characterization of single-layer ShCu observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for an 18-yr period (2000-2017). There are four major accomplishments of our project, First, an updated operational cloud classification for days with ShCu has been suggested and evaluated through a detailed comparison with the manually curated records. Our classification extends successfully the latest ARM cloud type Value Added Product (VAP) based on the Active Remote Sensing of Clouds (ARSCL) cloud product by incorporating both cloud fraction (CF) provided by narrow-FOV ceilometer data and FSC from wide-FOV images offered by a Total Sky Imager (TSI). Moreover, our classification allows one to identify impact of instrumentation changes at the SGP site, namely the transition to KAZRARSCL with the updated cloud radar, on the identification of periods with single-layer ShCu. Second, a new approach that resolves cloud area distributions for a given region (up to 4x4 km 2 ) has been suggested and cloud equivalent diameters (CEDs) have been estimated for the first time. These estimations have been performed over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) using wide-FOV TSI images and cloud base height (CBH) provided by complementary narrow-FOV lidar measurements. Our simple and computationally inexpensive approach offers a previously unavailable dataset for process studies in the convective boundary layer and evaluation of ShCu parameterizations in cloud-resolving models. Third, a long-term integrated record of ShCu macrophysical properties has been developed. The developed record represents the longest available compilation of events with ShCu and includes (i) a novel visualization of the spatial variability in cloud cover both along- and across-wind directions, (ii) updated estimates of narrow-FOV CF and wide-FOV FSC, (iii) updated narrow-FOV CBH, and (iv) complementary data, such as wind speed and direction from the 915-MHz Radar Wind Profiler (RWP) data. The developed record has been used successfully to assess conventional observational estimates of cloud cover and their sensitivity to the following two factors: (i) instrument-dependent cloud detection and data merging criteria and (ii) FOV configuration. Fourth, co-variability of the ShCu macrophysical properties and environmental parameters has been analyzed for a 3-yr period (2016-2018). Our initial analysis includes diurnal changes of FSCs obtained for clouds with small, moderate and large CEDs and several environmental parameters, such as lifted condensation level (LCL) and mixed layer height (zi). Preliminary results of our analysis suggest that the horizontal extent of ShCu is controlled substantially by the sign and magnitude of difference between these two parameters (zi-LCL): the CED tends to grow with increase of this difference (zi exceeds LCL). We have initiated relationships between the ShCu and key atmospheric parameters that control both the development and evolution of ShCu using our new data product, which combines effectively the advantages of narrow-FOV data offered by zenith pointing cloud radars and lidars and wide-FOV TSI images. While the latest instrumentation at the ARM sites may address these challenging relationships in the future, we believe that the historical ARM data at the SGP site has not yet been fully utilized. Overall, our data product can be used by researchers working on a wide range of climate-related projects. These projects may include (i) a comprehensive evaluation of outputs from the Large-Eddy Simulation (LES) and single-column models for their future improvement, (ii) the representativeness of “short-period” results obtained from the previous model and observational studies and (iii) the planning of future field campaigns with focus on improved understanding of the diurnal cycle of cumulus convection.
The original goal of this project was to diagnose and improve CLUBB, the turbulence and cloud fraction parameterization used in DOE’s E3SM model. For this purpose, we planned to use large-eddy simulation (LES) and ARM observations from the NE Pacific Ocean and the SGP site, in coordination with ARM’s LASSO program. It was determined that for these cloud regimes, many of the turbulent ‘moment closures’ which underlie CLUBB’s mathematical formulation are inconsistent with LES, which is an appropriate benchmark for testing this. The research assistant found that these closures could be more accurately formulated using machine learning using the LES as a training dataset. However the resulting parameterization proved to quickly drift away from physical plausibility. A novel machine-learning based boundary layer parameterization called MARBLE based on matching the time evolution of a parameterized cloud-topped boundary layer to reanalysis was then developed and published.
The SPARC team has completed much of the workflow development needed for the coupled and uncoupled analyses needed to support our flight test validation work. The SPARC team has continued to make progress on development activities to support unsteady, full reentry vehicle aero analysis, which have focused on turbulence modeling, uniform mesh refinement, in-situ visualization (with the Paraview/Catalyst team), and performance analysis. The SPARC team is making solid progress towards our Q4 goal of demonstrating an improved Wall Modeled Large Eddy Simulation (WMLES) capability for standard geometries using high-order finite difference, discontinuous Galerkin, and low-dissipation finite volume methods. The SPARC team has met the minimum completion criteria for parts of the L1, including documenting performance and scaling of SPARC on Trinity, Sierra and Astra, and performing runs for the flight test validation case.
Convective storms vertically transport water vapor and condensate from Earth’s surface to the upper troposphere. Life on Earth is fundamentally linked to this transport which determines the hydrological cycle, and the intensity of severe weather responsible for the destruction of life and property. Despite advances in high-resolution modeling and better observational capabilities, the scientific community continues to be confronted with knowledge gaps about convective storms that limit our predictive capabilities. The ongoing developments in the high-resolution Energy Exascale Earth System Model (E3SM), large eddy simulations, and AI-based analytics to evaluate uncertainties are expected to provide a comprehensive framework for new scientific discovery. The model-experiment (MODEX) approach suggests that the aforementioned advancements in model development and AI-based inference techniques should be complemented by similar advancements in the experimental (observational) side so that the former does not outstrip the ability of the latter to provide meaningful constraints. What are the recent advancements in observations that will provide the necessary leap forward in improving our predictive capabilities? To address this question, we propose a new experimental paradigm called Multisensor Agile Adaptive Sampling (MAAS) that capitalizes on advancements in communications (5G), computational resources (edge/fog computing), sensor capabilities, and machine learning (ML) and AI techniques (Kollias et al., 2020). The MAAS framework allows for the collection of higher spatiotemporal resolution and quality observations of convective storms than is traditionally possible. The MAAS framework is scalable and applicable to atmospheric observatories such as those operated by the Department of Energy (DoE) Atmospheric Radiation Measurement (ARM) facility.