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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.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Numerical Simulation of Illumination and Thermal Conditions at the Lunar Poles Using LOLA DTMs

We are interested in illumination conditions and the temperature distribution within the upper two meters of regolith near the lunar poles. Here, areas exist receiving almost constant illumination near areas in permanent shadow, which were identified as potential exploration sites for future missions. For our study a numerical simulation of the illumination and thermal environment for lunar near-polar regions is needed. Our study is based on high-resolution, twenty meters per pixel and 400 x 400 km large polar Digital Terrain Models (DTMs), which were derived from Lunar Orbiter Laser Altimeter (LOLA) data. Illumination conditions were simulated by synthetically illuminating the LOLA DTMs using the horizon method considering the Sun as an extended source. We model polar illumination for the central 50 x 50 km subset and use it as an input at each time-step (2 h) to evaluate the heating of the lunar surface and subsequent conduction in the sub-surface. At surface level we balance the incoming insolation with the subsurface conduction and radiation into space, whereas in the sub-surface we consider conduction with an additional constant radiogenic heat source at the bottom of our two-meter layer. Density is modeled as depth-dependent, the specific heat parameter as temperature-dependent and the thermal conductivity as depth- and temperature-dependent. We implemented a fully implicit finite-volume method in space and backward Euler scheme in time to solve the one-dimensional heat equation at each pixel in our 50 x 50 km DTM. Due to the non-linear dependencies of the parameters mentioned above, Newton's method is employed as the non-linear solver together with the Gauss-Seidel method as the iterative linear solver in each Newton iteration. The software is written in OpenCL and runs in parallel on the GPU cores, which allows for fast computation of large areas and long time scales.

Glaser, P.↗
NASA NTRS2014

Enhancement of Mutual Discovery, Search, and Access of Data for Users of NASA and GEOSS-Cataloged Data Systems

An ongoing NASA-funded Data Rods (time series) project has demonstrated the removal of a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series) for selected variables of the North American and Global Land Data Assimilation Systems (NLDAS and GLDAS, respectively) and other NASA data sets. Data rods are pre-generated or generated on-the-fly (OTF), leveraging the NASA Simple Subset Wizard (SSW), a gateway to NASA data centers. Data rods Web services are accessible through the CUAHSI Hydrologic Information System (HIS) and the Goddard Earth Sciences Data and Information Services Center (GES DISC) but are not easily discoverable by users of other non-NASA data systems. An ongoing GEOSS Water Services project aims to develop a distributed, global registry of water data, map, and modeling services cataloged using the standards and procedures of the Open Geospatial Consortium and the World Meteorological Organization. Preliminary work has shown GEOSS can be leveraged to help provide access to data rods. A new NASA-funded project is extending this early work.

data access↗
NASA NTRS2018

Progress Towards Integrating the Finite-Volume Cubed-Sphere (FV3) Dynamical Core Tangent Linear and Adjoint Models into JEDI

The Joint Effort for Data assimilation Integration (JEDI) -- led by the Joint Center for Satellite Data Assimilation (JCSDA) -- is an inter-organizational endeavor to develop a common framework for performing data assimilation. This extensive framework will ultimately provide solvers, observation operators, interpolation and model interfaces using object oriented modeling. Two partners involved in JEDI use or plan to use the Finite Volume Cubed-Sphere (FV3) dynamical core to produce weather forecasts; these are NASA's Global Modeling and Assimilation Office and NOAA's National Center for Environment Prediction. In this work we present an update on ongoing efforts to integrate the FV3 tangent linear and adjoint models into the prototype JEDI framework. We setup and run a simple cycled data assimilation experiment using 4DVAR on the cubed sphere grid and with the FV3 tangent linear and adjoint models. Development of the observation operators for JEDI is separately underway. Instead of using real observations a simplified set of simulated observations will be used. We discuss the steps required to bring the FV3 linearized model into the object oriented framework and consider what would be the computational requirements of running this configuration for an operational system. FV3 uses a small time-step to ensure that small scales are well resolved, however this presents design challenges when running 4DVAR with the adjoint. An approach to storing the FV3 model trajectory has been developed that maintains the flexibility of using automatic differentiation. We discuss how this approach is incorporated into the framework. Other important uses of adjoint models include computing observation impacts and singular vectors, we consider how these tools can be included in JEDI.

Holdaway, Daniel↗
NASA NTRS2018

Atmospheric Chemistry Modeling Using a Regression Forest Model

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.

Keller, Christoph A.↗
NASA NTRS2018

A Comparative Analysis of Micrometeorological Determinants of Evapotranspiration Rates Within a Heterogeneous Urban Environment

Variability in micrometeorological conditions and their influence on estimated reference evapotranspiration (RET) rates were evaluated across a heterogeneous urban environment. Micrometeorological data sets (incoming solar radiation, air temperature, relative humidity and wind speed) were collected over a one-year period at six weather stations in New York City, NY (USA). Weather stations are located at four new urban green space monitoring sites and two airports. Reference evapotranspiration (RET) rates were estimated from the micrometeorological data sets for a short reference surface at a daily time-step using the ASCE Standardized Reference Evapotranspiration Equation, a Penman-Monteith based combination equation. Nonparametric comparative statistical analyses (Kruskal-Wallis) revealed statistically significant differences (at significance level α = 0.05) in micrometeorological conditions and estimated RET rates between the six sites. On a cumulative annual basis, estimated RET varied by up to 40 percent between the sites. A new technique for adjusting weather data collected at one location (e.g. regional airports) for use at another location (e.g. interior engineered urban green spaces) was evaluated. The study highlights the importance, for accurate estimation of ET, of onsite micrometeorological data sets, but concludes that additional research is needed to more thoroughly characterize micrometeorological variability across heterogeneous urban environments, and also to evaluate the influence of non-meteorological determinants, e.g. vegetation type, soil/media type, media moisture conditions and anthropogenic heat fluxes, on urban ET.

Urban environment↗
NASA NTRS2019

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRSDate not supplied

Coupling a Particle Trajectory Capability to the US3D CFD Flow Solver

The purpose of the current research effort is to incorporate particle trajectory equations into the US3D CFD flow solver. The integrated code will allow higher-fidelity modeling of particle-fluid interactions and allow for the simulation of physical effects such as surface heating augmentation due to particle impacts. The key elements underpinning the current work are a robust tracking algorithm that can identify the encompassing mesh elements for individual particles and a point to point MPI frame-work. These developments combined with accurate property models and high-order time-stepping deliver realistic simulations on larger computational grids for a wide range of particle mass loadings. The coupled US3D-particle code will be applied to the Schiarparelli capsule and Earth-based experimental test cases.

A Sahai↗
NASA NTRSDate not supplied

A Heat and Mass Transfer Model of the Orion European Service Module Propulsion Sub-System

A tool for simulating the thermodynamics of the fluid components of the Orion European Service Module propulsion subsystem has been developed. The 1-dimensional,time-stepping method tracks energy and mass movement through the propellant and pressurant tanks during burn and coast phases of a simulated mission. The code is fast and flexible, allowing easy modifications and incorporation into other computational tools. The results from the simulation have been used to support mission planning, development of flight rules and to guide experimental testing of components and systems.

Spacecraft propulsion↗
NASA NTRSDate not supplied

RDE Nozzle Computational Design Methodology Development and Application

The Open National Combustion Code (OpenNCC) is used to simulate the aerospike exhaust nozzle region of a rotating detonation engine (RDE).The main objectives of the current study are twofold. The first goal is to validate the proposed computational methodology using the experimental data. The second goal is to demonstrate how the validated prediction tool can be used to optimize the nozzle geometry. To achieve a significant speed-up of computational time, the computational domain in divided into two parts: a combustion region and a throat-nozzle section. In the combustion region, a validated quasi-two-dimensional in-house code is utilized to generate an unsteady RDE flow field solution just upstream of the combustor throat. Subsequently, the unsteady flow data is fed into the three-dimensional throat-nozzle section as the inflow boundary condition. This facilitates the design optimization process since the unsteady inflow can be reused, and a relatively coarse mesh (i.e., larger time-step) can be used to analyze the flow fields around the nozzle. Five nozzle designs were studied and the predicted performance (i.e., thrust)compared. The optimized nozzle was found to produce 3.2% more overall thrust than a baseline nozzle design. Without the nozzle, there is a large low-pressure region at the throat exit, which significantly reduces the overall performance. This methodology is shown to be a promising approach to explore a wide variety of nozzle geometries in a relatively short amount of time.

CFD↗
NASA NTRS2022

Unique Observational Constraints on the Seasonal and Longitudinal Variability of the Earth’s Planetary Albedo and Cloud Distribution Inferred From EPIC Measurements

Thorough comparison to observations is key to developing a credible climate model forecasting capability. Deep Space Climate Observatory (DSCOVR) measurements of Earth’s reflected solar and emitted thermal radiation provide a unique observational perspective that permits a more reliable model/data comparison than is possible with the otherwise available satellite data. The uniqueness is in the DSCOVR satellite’s viewing geometry, which enables continuous viewing of the Earth’s sunlit hemisphere from its Lissajous orbit around the Lagrangian L1 point. The key instrument is the Earth Polychromatic Imaging Camera (EPIC), which views the Earth’s sunlit hemisphere with 1024-by-1024-pixel imagery in 10 narrow spectral bands from 317 to 780 nm, acquiring up to 22 high spatial resolution images per day. The additional feature is that the frequency of EPIC image acquisition is nearly identical to that of the climate GCM data generation scheme where climate data for the entire globe are ‘instantaneously’ calculated at 1-h radiation time-step intervals. Implementation of the SHS (Sunlit Hemisphere Sampling) EPIC-view geometry for the in-line GCM output data sampling establishes a precise self-consistency in the space-time data sampling between EPIC observational and GCM output data generation and sampling. The remaining problem is that the GCM generated data are radiative fluxes, while the EPIC measurements are backscatter-dependent radiances. Radiance to flux conversion is a complex problem with no simple way to convert GCM radiative fluxes into spectral radiances. The more expedient approach is to convert the EPIC spectral radiances into broadband radiances by MODIS/CERES-based regression relationships and then into solar radiative fluxes using the CERES angular distribution models. Averaging over the sunlit hemisphere suppresses the meteorological weather noise, but preserves the intra-seasonal larger scale variability. Longitudinal slicing by the Earth’s rotation permits a self-consistent model/data comparison of the longitudinal model/data differences in the variability of the reflected solar radiation. Ancillary EPIC Composite data provide additional cloud property information for climate model diagnostics. Comparison of EPIC-derived seasonal and longitudinal variability of the Earth’s planetary albedo with the GISS ModelE2 results shows systematic overestimate of cloud reflectivity over the Pacific Ocean with corresponding underestimates over continental land areas.

DSCOVR↗
NASA NTRSDate not supplied

A Hybrid RANS-LES Perspective for the High Lift Common Research Model Using LAVA

An assessment of a Hybrid RANS/LES (HRLES) approach for𝐶𝐿,max prediction is presented for the NASA High-Lift Common Research Model (CRM-HL). Both the free air and the wind tunnel configuration of the CRM-HL are investigated and the results are compared to the QinetiQ wind tunnel experiments and to two other numerical approaches: Reynolds Averaged Navier-Stokes (RANS) and Wall-Modeled Large Eddy Simulations (WMLES). For the free-air configuration, HRLES was shown to address some of the known shortcomings in RANS methods and prevent inboard and outboard flow separation particularly in the region of𝐶𝐿,max and post-stall. To achieve these improvements over RANS, LES-appropriate grids and numerical discretizations are required. HRLES predicts a weak pitch break at the highest angle-of-attack due to onset of wing-root corner flow separation whereas the free-air corrected experiment values indicate an occurrence of a much stronger pitch break. The improvements of HRLES over a URANS approach has been objectively shown by computing a set of solutions with the same grid, same numerics and time-step size and comparing the solutions. It was also found that when applying HRLES to a RANS best practice grid and numerics that the HRLES method significantly under performed RANS. For the in tunnel configuration, HRLES showed good agreement with the loads, surface pressure and oil-flow photographs obtained in the experiment. HRLES was able to improve upon the RANS simulations, which showed a sharp loss of lift at the two highest angles-of-attack due to large scale inboard and outboard separation on the wing, by correctly predicting the corner flow separation and showing remarkably close agreement in the flow topologies with the experiment.

TTT↗
NASA NTRS2023

Fan Noise Predictions of the NASA Source Diagnostic Test Using Unsteady Simulations with LAVA Part I: Near-Field Aerodynamics and Turbulence

A sliding mesh technique within the Launch, Ascent, and Vehicle Aerodynamics (LAVA) computational framework is validated using the experimental dataset collected as part of the NASA Source Diagnostic Test (SDT) campaign. Two modeling approaches are explored: the unsteady Reynolds-Averaged Navier Stokes (URANS) with Spalart-Allmaras (SA) turbulence model closure, and a hybrid Reynolds-Averaged Navier Stokes/Large Eddy Simulation (RANS/LES) paradigm employing a Zonal Detached Eddy Simulation (ZDES) closure with enhanced shielding protection. Fan stage performance metrics, aerodynamic quantities and turbulent flow structures are analyzed in this work. Initial studies focusing on grid and time-step sensitivity are presented. Sensitivity to different variants of the SA turbulence model is analyzed, supporting the use of the baseline SA model in the production runs. Two conditions are analyzed in detail using URANS and hybrid RANS/LES (HRLES). Mean flow quantities are well-captured by both methods in the low-speed (approach) regime. While URANS misses all the upstream-propagating noise in the inlet due to the rotor-locked tones being evanescent in nature at subsonic fan tip speeds, HRLES captures this broadband component in its pressure field. At the high-speed (sideline) condition, URANS shows better agreement with the SDT data than HRLES in the interstage flow-field. In this regime, URANS captures the tonal content propagating through the inlet, since the tones are now cut-on. Both methods are suitable to capture fan stage performance metrics and mean flow quantities, but only HRLES is able to resolve the fine turbulent structures responsible for broadband noise. The results support the use of the sliding mesh technique implemented in this work for future turbomachinery applications within the LAVA solver framework.

AATT↗
NASA NTRS2023

Fan Noise Predictions of the NASA Source Diagnostic Test Using Unsteady Simulations with LAVA Part I: Near-Field Aerodynamics and Turbulence

A sliding mesh technique within the Launch, Ascent, and Vehicle Aerodynamics (LAVA) computational framework is validated using the experimental dataset collected as part of the NASA Source Diagnostic Test (SDT) campaign. Two modeling approaches are explored: the unsteady Reynolds-Averaged Navier Stokes (URANS) with Spalart-Allmaras (SA) turbulence model closure, and a hybrid Reynolds-Averaged Navier Stokes/Large Eddy Simulation (RANS/LES) paradigm employing a Zonal Detached Eddy Simulation (ZDES) closure with enhanced shielding protection. Fan stage performance metrics, aerodynamic quantities and turbulent flow structures are analyzed in this work. Initial studies focusing on grid and time-step sensitivity are presented. Sensitivity to different variants of the SA turbulence model is analyzed, supporting the use of the baseline SA model in the production runs. Two conditions are analyzed in detail using URANS and hybrid RANS/LES (HRLES). Mean flow quantities are well-captured by both methods in the low-speed (approach) regime. While URANS misses all the upstream-propagating noise in the inlet due to the rotor-locked tones being evanescent in nature at subsonic fan tip speeds, HRLES captures this broadband component in its pressure field. At the high-speed (sideline) condition, URANS shows better agreement with the SDT data than HRLES in the interstage flow-field. In this regime, URANS captures the tonal content propagating through the inlet, since the tones are now cut-on. Both methods are suitable to capture fan stage performance metrics and mean flow quantities, but only HRLES is able to resolve the fine turbulent structures responsible for broadband noise. The results support the use of the sliding mesh technique implemented in this work for future turbomachinery applications within the LAVA solver framework.

AATT↗
NASA NTRS2022

LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2

The Community Earth System Model version 2 (CESM2) simulates a high equilibrium climate sensitivity (ECS > 5°C) and a Last Glacial Maximum (LGM) that is substantially colder than proxy temperatures. In this study, we examine the role of cloud parameterizations in simulating the LGM cooling in CESM2. Through substituting different versions of cloud schemes in the atmosphere model, we attribute the excessive LGM cooling to the new CESM2 schemes of cloud microphysics and ice nucleation. Further exploration suggests that removing an inappropriate limiter on cloud ice number (NoNimax) and decreasing the time-step size (substepping) in cloud microphysics largely eliminate the excessive LGM cooling. NoNimax produces a more physically consistent treatment of mixed-phase clouds, which leads to an increase in cloud ice content and a weaker shortwave cloud feedback over mid-to-high latitudes and the Southern Hemisphere subtropics. Microphysical substepping further weakens the shortwave cloud feedback. Based on NoNimax and microphysical substepping, we have developed a paleoclimate-calibrated CESM2 (PaleoCalibr), which simulates well the observed twentieth century warming and spatial characteristics of key cloud and climate variables. PaleoCalibr has a lower ECS (∼4°C) and a 20% weaker aerosol-cloud interaction than CESM2. PaleoCalibr represents a physically more consistent treatment of cloud microphysics than CESM2 and is a valuable tool in climate change studies, especially when a large climate forcing is involved. Our study highlights the unique value of paleoclimate constraints in informing the cloud parameterizations and ultimately the future climate projection.

equilibrium climate sensitivity↗
DOE OSTI2024

Multiphysics Time-Integration for Turbulent Combustion at the Exascale

Turbulent reacting flow systems are often modeled with coupled time-dependent partial differential equations (PDEs). Solving such equations can easily tax the world's largest supercomputers. One pragmatic strategy for attacking such problems is to split the PDEs into components that can more easily be solved in isolation. This generic operator-splitting strategy leads to a set of ordinary differential equations (ODEs) that need to be solved as part of an "outer-loop" time-stepping approach. In many combustion applications, the ODEs to be solved can be very stiff, exhibiting timescales that span many orders of magnitude. The SUNDIALS library provides a plethora of robust time integration algorithms for solving these ODEs on exascale-capable computing hardware, yet for many complex applications (such multicomponent fuels or emissions predictions), the chemical models remain too complex to solve using reasonable resources. The Quasi-Steady State Approximation (QSSA) can be an effective tool for reducing the size and stiffness of the simulations. In this talk, I will discuss the use of the SUDIALS library of ODE solvers together with automatic code generation tools to solve complex turbulent reacting flow problems using QSSA models.

chemistry↗
DOE OSTI2024

Development of a Performance Portable Non-Equilibrium Plasma Fluid Solver on Adaptive Grids

This presentation will describe the numerical techniques, programming paradigms, verification, and performance of a non-equilibrium plasma fluid solver that can effectively utilize current and upcoming central processing and graphics processing unit (CPU+GPU) architectures. Our plasma fluid model solves the conservation equations for self-consistent electrostatic Poisson, electron and heavy species transport, and electron temperature on adaptive Cartesian grids. Our solver is written using performance portable adaptive mesh management library, AMReX (Zhang et al., JOSS, 4 (37) 1370, 2019), and can be built and run on widely available vendor specific GPU architectures (NVIDIA/AMD/Intel). We utilize a non-subcycled second order semi-implicit time-stepping method where all adaptive mesh refinement (AMR) levels are advanced with the same time step. The composite multi-level multigrid solver from within AMReX is used for each of the governing equations that are cast into a Helmholtz equation form. We have also developed a python based chemical mechanism parser framework that uses a similar format as CANTERA (Goodwin et al., Zenodo, 2018) yaml files as input. Our custom parser reads the yaml file and provides C++ files with transport and production rate functions that can be executed on both host (CPU) and device (GPU). We present verification of our solver using method of manufactured solutions that indicate formal second order accuracy with central diffusion and fifth order weighted-essentially-non-oscillatory (WENO) advection scheme. We also verify our solver with published literature on low-pressure capacitive and high-pressure streamer discharges. Our initial performance studies indicate 10X speed-up using 20 NVIDIA GPUs versus 200 CPUs for an atmospheric streamer discharge problem solved on a 512 x 1024 x 512 grid.

graphics processing units↗