SEARCH · Search NASA
Results for “AMR”
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.
Whole Genome Sequencing of ISS Environmental Microorganisms Enables Description of Novel Microbial Lineages and Prediction of AMR Function
No abstract provided
Hybrid AMR-Low-Rank-Algorithm for the Boltzmann BGK Equation
Explore the source record for details and available documents.
AMR Indicator Effects on Reactive Flow Behavior
Explore the source record for details and available documents.
Comparison Study of Conventional and Adaptive Mesh Refinement in Organic Material Decomposition Models
This study compares conventional mesh refinement techniques, specifically Uniform Mesh Refinement (UMR), with a new Adaptive Mesh Refinement (AMR) method, applied to Organic Material Decomposition (OMD) models. The proposed benefit of AMR is that only areas that require refinement, based on minimizing a specific field gradient, are refined thus decreasing model wall time compared to conventional UMR methods. This work specifically focuses on comparing UMR and AMR methods on decomposing (both No-Flow and Porous-Flow material models) Polymeric Methylene Diisocyanate (PMDI) polyurethane foam. Throughout the work, the geometry increased in complexity to assess the refinement methods performance at varying levels geometric intricacy. While AMR has been shown to work well in a variety of applications, the UMR approach proved to be computationally faster, for many of the geometries and foam decomposition models, than AMR. However, it was observed that at higher levels of refinement, greater than 3 UMR, AMR begins to be computationally better. Additionally, the settings used to perform AMR greatly impact its performance, and lessons learned, in terms of OMD models, are shared. Due to physics involved in material decomposition, specifically the evolution of state variables, these problems don’t fully benefit from the advantages of AMR.
Warming is Associated With More Encoded Antimicrobial Resistance Genes and Transcriptions Within Five Drug Classes in Soil Bacteria: A Case Study and Synthesis
ABSTRACT The effect of warming on anti‐microbial resistance (AMR) genes in the environment has critical implications for public health but is little studied. We collected published soil bacterial genomes from the BV‐BRC database and tested the correlation between reported optimal growth temperature and the number of encoded AMR genes. Furthermore, we tested the relationship between temperature and AMR gene transcription in a natural ecosystem by analysing soil transcriptomes from a warming manipulation experiment in an Alaskan boreal forest. We hypothesised that there is a positive relationship between warming and AMR prevalence in gene content in bacterial genomes and transcriptomic sequences, and that this effect would vary by drug class. Regarding the bacterial genomes, we found a positive relationship between the fraction of encoded AMR genes and the reported optimal temperature of soil bacteria. The drug classes tetracycline and lincosamide/macrolide/streptogramin had the strongest positive relationship with reported optimal temperature. For the case study in a natural ecosystem, we found 61 significantly upregulated AMR gene‐associated transcripts spanning eight drug classes in warmed plots. In the Alaskan soil samples, we found that warming elicited the strongest positive effect on transcripts targeting lincosamide/streptogramin, beta‐lactam and phenicol/quinolone antibiotics. Overall, higher temperatures were linked to AMR gene prevalence.
High fidelity blade-resolved and actuator line data from a 16 turbine wind farm simulation using ExaWind
This data was generated with the ExaWind code suite (https://github.com/Exawind) as a demonstration of a large, 16 turbine wind farm simulation, calculated using two different levels of fidelity. The lower level of fidelity approach uses an actuator line approach to represent the turbines, and was simulated with AMR-Wind (https://github.com/Exawind/amr-wind/) as the background flow solver, coupled to OpenFAST (https://github.com/OpenFAST/openfast). The higher level of fidelity simulation uses a blade-resolved approach, and is done using AMR-Wind, Nalu-Wind (https://github.com/Exawind/nalu-wind), OpenFAST, and TIOGA (https://github.com/Exawind/tioga). In the blade-resolved simulation, ExaWind couples together a background flow solver, AMR-Wind, and a near-body solver, Nalu-Wind, through an overset technique from the TIOGA application. OpenFAST handles the structural dynamics of the turbine blades and towers, which informs the fluid-structure interaction of the wind turbines with the flow solvers. In the actuator line simulation, a mesh of 295M elements was used for a 5km x 5km domain, and it was simulated using 256 nodes (2048 GPU's) on the Oak Ridge Leadership Computing Facility Frontier supercomputer. For the blade-resolved simulation, 1.5B element mesh was used in the AMR-Wind background 5km x 5km domain, and 16M elements were used for each turbine in the Nalu-Wind domains, for a total of 1.7B elements. This was simulated using 384 nodes on Frontier, with each node using 56 cores for Nalu-Wind and 8 GPU cores. The data in this archive includes the turbine outputs from OpenFAST, 2D sampling planes from AMR-Wind, and full-field solution files from AMR-Wind and Nalu-Wind.
Applying Parallel Adaptive Methods with GeoFEST/PYRAMID to Simulate Earth Surface Crustal Dynamics
This viewgraph presentation reviews the use Adaptive Mesh Refinement (AMR) in simulating the Crustal Dynamics of Earth's Surface. AMR simultaneously improves solution quality, time to solution, and computer memory requirements when compared to generating/running on a globally fine mesh. The use of AMR in simulating the dynamics of the Earth's Surface is spurred by future proposed NASA missions, such as InSAR for Earth surface deformation and other measurements. These missions will require support for large-scale adaptive numerical methods using AMR to model observations. AMR was chosen because it has been successful in computation fluid dynamics for predictive simulation of complex flows around complex structures.
High temporal frequency data from a four turbine, blade-resolved wind farm simulation with ExaWind
The data was generated with ExaWind (https://github.com/Exawind) which couples AMR-Wind (https://github.com/Exawind/amr-wind/), Nalu-Wind (https://github.com/Exawind/nalu-wind), TIOGA (https://github.com/Exawind/tioga), and OpenFAST (https://github.com/OpenFAST/openfast). This is a large-scale simulation of a blade-resolved wind farm using the ExaWind software stack. ExaWind couples together a background flow solver, AMR-Wind, and a near-body solver, Nalu-Wind, through an overset technique from the TIOGA application. Another application, OpenFAST, handles the structural dynamics of the turbine blades and towers, which informs the fluid-structure interaction of the wind turbines with the flow solvers. This particular simulation includes four blade-resolved wind turbines operating in a turbulent atmospheric boundary layer. The AMR-Wind solver uses 500 million cells and is being solved on 256 AMD GPUs of the Oakridge Leadership Computing Facility Frontier supercomputer. Each turbine is assigned its own Nalu-Wind solver with over 13 million elements per turbine and solved using 448 CPU cores, for a total of 1792 CPU cores. For each node, 56 cores contain Nalu-Wind, while 8 cores correspond to AMR-Wind operations on the GPUs. Consequently, ExaWind is entirely utilizing the CPUs and the GPUs of the nodes concurrently. The data used in the visualization is full flow field data output from the simulation. It is lossy-compressed to a specific accuracy using ZFP and written to disk every 16 time-steps to enable real-time flow visualization. The flow fields are sampled at a high temporal frequency to enable real-time, 24fps visualization. The flow fields are sampled every 12 simulation time steps (every 0.04132s).
Scalable and compact magnetocaloric heat pump technology
Magnetocaloric heat pumping (MCHP) promises to be more efficient than traditional vapor compression while also eliminating the deleterious effects of gaseous refrigerants. While MCHP devices have shown the temperature spans and efficiencies needed for different heating and cooling applications, they struggle to become commercially viable due to their large size and mass, and resultant high cost. This paper evaluates a baseline MCHP device and explores methods to boost its system power density (SPD). The key components of the baseline system are the gadolinium packed-particle bed active magnetic regenerator (AMR) and a magnetic source composed of permanent magnets and high permeability magnetic steel. To enhance the SPD, the paper evaluates maximizing the AMR volume, opting for first-order magnetocaloric materials, optimizing the magnet and AMR geometry, and reducing the size of magnets and magnetic steel parts. At larger thermal powers, increasing the AMR diameter and the number of magnetic poles were evaluated. Using finite element models, solid models, and estimates of magnetocaloric material performance, thermal powers ranging from 37 W to 44 kW at a nominal 10 K temperature span were projected, and SPD was estimated to improve from 6 W/kg to 81 W/kg. Neglecting end effects, an upper limit of 114 W/g is estimated. Compared to SPD of off-the-shelf compressors with similar environment temperatures, MCHP power density using gadolinium is competitive up to roughly 200 W of cooling power. This is extended to 1 kW when using LaFeSi alloys and up to 3 kW in the limiting case. In conclusion, these results indicate that the performance and mass of MCHP can match that of compressors, which is a critical step toward cost-competitive magnetocaloric technology.
Dominant balance-based adaptive mesh refinement for incompressible fluid flows
This work introduces a novel adaptive mesh refinement (AMR) method that utilizes dominant balance analysis (DBA) for efficient and accurate grid adaptation in computational fluid dynamics (CFD) simulations. The proposed method leverages a Gaussian mixture model (GMM) to classify grid cells into active and passive regions based on the dominant physical interactions within the equation space. By modeling truncation error probabilistically from discretized terms, the method identifies regions of high interaction where numerical accuracy is most sensitive to resolution. Unlike traditional AMR strategies, this approach does not rely on heuristic-based sensors or user-defined thresholds, providing a fully automated and problem-independent framework for AMR. Applied to the incompressible Navier-Stokes equations for steady and unsteady flow past a cylinder, the DBA-based AMR method achieves comparable accuracy to high-resolution grids while reducing computational costs by up to 70 %. The validation highlights the method’s effectiveness in capturing complex flow features while minimizing grid cells, directing computational resources toward regions with the most critical dynamics. This modular and scalable strategy is adaptable to a wide range of applications, presenting a promising tool for efficient high-fidelity simulations in CFD and other multiphysics domains.
Carrier confinement and alloy disorder exacerbate Auger–Meitner recombination in AlGaN ultraviolet light-emitting diodes
The quantum efficiency of AlGaN ultraviolet light-emitting diodes declines (droops) at increasing operating powers due to Auger–Meitner recombination (AMR). Using first-principles density-functional theory, we show that indirect AMR mediated by electron–phonon coupling and alloy disorder can induce bulk C coefficients as large as ~10 –31 cm 6 /s. Furthermore, we find that the confinement of carriers by polarization fields within quantum wells severely relaxes crystal-momentum conservation, which exacerbates the rate of AMR over radiative recombination by an order of magnitude relative to the bulk. Furthermore, this results in a striking decrease in quantum efficiency at high power. Suppressing polarization fields and jointly increasing the well width would greatly mitigate AMR and efficiency droop.
A User's Guide to AMR1D: An Instructional Adaptive Mesh Refinement Code for Unstructured Grids
This report documents the code AMR1D, which is currently posted on the World Wide Web (http://sdcd.gsfc.nasa.gov/ESS/exchange/contrib/de-fainchtein/adaptive _mesh_refinement.html). AMR1D is a one-dimensional finite element fluid-dynamics solver, capable of adaptive mesh refinement (AMR). It was written as an instructional tool for AMR on unstructured mesh codes. It is meant to illustrate the minimum requirements for AMR on more than one dimension. For that purpose, it uses the same type of data structure that would be necessary on a two-dimensional AMR code (loosely following the algorithm described by Lohner).
Highly parallel structured adaptive mesh refinement using parallel language-based approaches
Adaptive mesh refinement (AMR) calculations carried out on structured meshes play an exceedingly important role in several areas of science and engineering. A strategy for using Fortran 90 in an object-oriented fashion is presented. This permits AMR applications to be expressed in terms of familiar abstractions that are natural to the process of solving AMR hierarchies. The OpenMP features that are useful for parallel processing of AMR hierarchies in a load balanced fashion on multiprocessors is described.
Angular-spatial hp -adaptivity for radiative transfer with discontinuous Galerkin spectral element methods
Radiative transfer is important for many science and engineering applications, and numerical simulations of radiative transfer can be challenging. For instance, the radiation field is seven-dimensional – three spatial, two angular, one wavelength, and one temporal – and often features steep gradients. Therefore, memory usage is a key issue. To reduce memory, some past work has investigated the use of adaptive mesh refinement (AMR), typically for either the spatial or angular coordinate, and typically for only h -adaptivity. Here, we propose the use of AMR for the spatial and angular coordinates together, and the use of h - and p -adaptivity together as hp -AMR for the potential for further memory savings. We implemented the proposed method for several test cases in two spatial and one angular dimension, with the discontinuous Galerkin spectral element method. These test cases featured highly anisotropic angular radiation, with or without steep spatial gradients. Our primary findings from these test cases were: (1) Angular hp -adaptivity can deliver the radiation solution with the same accuracy as, and with much less computational memory than, uniform angular h - or p -refinements, or angular h -adaptivity alone. This is most obvious when the incoming radiation is highly anisotropic, in which case the savings can be orders of magnitude. (2) Full spatial-angular hp -adaptivity is more efficient in solution representation, compared to solely spatial or solely angular -adaptivity. This is most evident when steep gradients are present in both the spatial and angular distribution. These results suggest that adaptive spatial- hp angular-refinement may perform well in large-scale seven-dimensional applications.
Coupling Noah-Multiparameterization land-surface Model with Energy Research and Forecasting Model
The Energy Research and Forecasting (ERF) model is a high-performance atmospheric model built on the AMReX adaptive mesh refinement (AMR) framework, enabling efficient simulations on heterogeneous computing platforms that combine multicore processors with hardware accelerators. To support land–atmosphere interactions within ERF’s AMR-based environment, a land-surface model must be capable of operating directly on hierarchically refined meshes. In this work, we present a methodology for coupling the Fortran-based Noah-Multiparameterization (Noah-MP) land-surface model with ERF’s C++ codebase. Rather than rewriting Noah-MP, we construct a Fortran–C interoperability layer using CodeScribe, a tool that leverages large language models (LLMs) to automate the generation of interface code. CodeScribe applies structured prompting techniques to generate bindings that support efficient data exchange and function calls between ERF and Noah-MP. The coupling framework also incorporates AMR-aware data handling strategies, allowing NoahMP to operate seamlessly within ERF’s hierarchical mesh structure. This work provides a structured approach for integrating legacy Fortran models into modern C++-based modeling systems using LLM-assisted code generation.
An object-oriented approach for parallel self adaptive mesh refinement on block structured grids
Self-adaptive mesh refinement dynamically matches the computational demands of a solver for partial differential equations to the activity in the application's domain. In this paper we present two C++ class libraries, P++ and AMR++, which significantly simplify the development of sophisticated adaptive mesh refinement codes on (massively) parallel distributed memory architectures. The development is based on our previous research in this area. The C++ class libraries provide abstractions to separate the issues of developing parallel adaptive mesh refinement applications into those of parallelism, abstracted by P++, and adaptive mesh refinement, abstracted by AMR++. P++ is a parallel array class library to permit efficient development of architecture independent codes for structured grid applications, and AMR++ provides support for self-adaptive mesh refinement on block-structured grids of rectangular non-overlapping blocks. Using these libraries, the application programmers' work is greatly simplified to primarily specifying the serial single grid application and obtaining the parallel and self-adaptive mesh refinement code with minimal effort. Initial results for simple singular perturbation problems solved by self-adaptive multilevel techniques (FAC, AFAC), being implemented on the basis of prototypes of the P++/AMR++ environment, are presented. Singular perturbation problems frequently arise in large applications, e.g. in the area of computational fluid dynamics. They usually have solutions with layers which require adaptive mesh refinement and fast basic solvers in order to be resolved efficiently.
The UAH/NSSTC Advanced Radar for Meteorological and Operational Research (ARMOR)
The past four years have seen a marked enhancement in meteorological-radar infrastructure and radar-research capability at the University of Alabama-Huntsville (UAH) and National Space Science and Technology Center (NSSTC; a collaborative center supported by UAH, NASA-MSFC and USRA). This enhancement is due in part to the development of the ARMOR C-band dual-polarimetric radar facility (amongst other mobile radar facilities also discussed in this conference). The ARMOR radar, located at Huntsville International Airport, originated as a unique collaboration between university, government and broadcast meteorologists (the very first of its kind relative to concurrent operational, research and broadcast applications of dual-polarimetry). Contributions from each of these entities resulted in the upgrade of a surplus National Weather Service WSR-74C radar to a research-grade C-band polarimetric radar. The initial upgrade of the radar took place in late 2004 with WHNT-TV purchase and installation of a SIGMET (now Vaisala) Antenna Mounted Receiver (AMR), RVP8/RCP8 radar processor/antenna controller, new radome, and a new dual-polarimetric antenna feed. The AMR enabled simultaneous transmit and receive (STSR) capability and hence collection of dual-polarimetric moments. During the initial part of the AMR upgrade the original WSR74C antenna reflector and 250 kW magnetron-transmitter were used. In early 2005, a new 350 kW magnetron transmitter was purchased from Baron Services and installed. In October of 2006 a new high performance parabolic antenna and dual-pol feed (Seavey) were installed together with a new Orbit pedestal. ARMOR Radar control and data delivery are facilitated through the use of T-1 lines that run from the airport to both NSSTC and WHNT-TV in Huntsville. Under current operating protocols radar scanning and product development are completed at NSSTC, though meteorologists at WHNT-TV can also control the radar if desired. In its default scanning configuration the radar is operated 24/7 in an STSR polarimetric rain scan mode alternating with a surveillance scan on a 5-minute cycle; scans separated by 2.5 minutes. Every 2.5 minutes the raw data arrive at NSSTC where they are corrected in real time for attenuation and differential attenuation (using a constrained ZDR/Z-PHI approach) and new products are generated (e.g., rain maps, hydrometeor identification etc.). The raw and derived products are archived at NSSTC and also redistributed locally in real time over the network within NSSTC to the National Weather Service Forecast Office in Huntsville (collocated with NSSTC) for operational use. During periods of interesting weather the radar is often operated in full, sector, or RHI volume modes from NSSTC and coincident with UAH mobile radar or NWS NEXRAD radar platforms as needed.